1

- 1 DRAFT

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- DO NOT CITE OR QUOTE EPA/635/R

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WWW.EPA.GOV/IRIS



# TOXICOLOGICAL REVIEW

OF

METHANOL

(CAS No. 67

- 56

- 1)



## In Support of Summary Information on the

Integrated Risk Information System (IRI S)

October 200 9

NOTICE

This document is an Inter agency Science Consultation draft

. This information is distributed

solely for the purpose of pre

- dissemination peer review under applicable information quality

guidelines. It has not been formally dissemi nated by EPA. It does not represent and should not be

construed to represent any Agency determination or policy. It is being circulated for review of its

technical accuracy and science policy implications.

U.S. Environmental Protection Agency 1

Washington, DC 21

- 2 DRAFT

- DO NOT CITE OR QUOTE DISCLAIMER

This document is a preliminary draft for review purposes only. This information is 1

distributed solely for the purpose of pre

- dissemination peer review under applicable information 2

quality guidelines. It has not been formally disseminated by EPA. It does not represent and 3

should not be construed to represent any Agency determination or policy. Mention of trade 4

names or commercial products does not constitute endorsement or recommendation for use. 51

- 3 DRAFT

- DO NOT CITE OR QUOTE CONTENTS TOXICOLOGICAL REVIEW OF METHANOL

(CAS NO. 67

- 56

- 1)

1. INTRODUCTION

................................ ................................ ................................ ................. 1

- 26 1

2. CHEMICAL AND PHYS ICAL INFORM ATION

................................ ................................ . 2

- 1 2

3. TOXICOKINETICS

................................ ................................ ................................ ................ 3

- 1 3

3.1. Overview

................................ ................................ ................................ .......................... 3

- 1 4

3.2. Key Studi es

................................ ................................ ................................ ...................... 3

- 5 5

3.3. Human Variability In Methanol Metabolism

................................ ................................ . 3

- 10 6

3.4. Physiologically Based Toxicokinetic Models

................................ ................................ 3

- 15 7

3.4.1. Model Requirements for EPA Purposes

................................ ................................ . 3

- 15 8

3.4.1.1. MOA and Selection of a Dose Metric

................................ ............................ 3

- 15 9

3.4.1.2. Criteria for the Development of Methanol PBPK Models

............................. 3

- 17 10

3.4.2. Methanol PBPK Models

................................ ................................ ......................... 3

- 18 11

3.4.2.1. War d et al. (1997)

................................ ................................ ........................... 3

- 19 12

3.4.2.2. Bouchard et al. (2001)

................................ ................................ .................... 3

- 19 13

3.4.2.3. Gentry et al. (2003, 2002) and Clewell et al. (2001)

................................ ...... 3

- 20 14

3.4.3. Selected Modeling Approach

................................ ................................ ................. 3

- 21 15

3.4.3.1. Available PK Data

................................ ................................ .......................... 3

- 22 16

3.4.3.2. Model Structure

................................ ................................ .............................. 3

- 23 17

3.4.3.3. Model Parameters

................................ ................................ ........................... 3

- 25 18

3.4.4. Mouse Model Calibration and Sensitivity Analysis

................................ ............... 3

- 26 19

3.4.5. Rat Model Calibration

................................ ................................ ............................ 3

- 31 20

3.4.6. Human Model Calibration

................................ ................................ ...................... 3

- 35 21

3.4.6.1. Inhalation Route

................................ ................................ ............................. 3

- 35 22

3.4.6.2. Oral Route

................................ ................................ ................................ ...... 3

- 41 23

3.4.7. Monkey PK Data and Analysis

................................ ................................ .............. 3

- 42 24

3.4.7.1. PK Model Analysis for Monkeys

................................ ................................ ... 3

- 44 25

3.4.8. Summary and Conclusions

................................ ................................ ..................... 3

- 47 26

4. HAZARD ID ENTIFICATION

................................ ................................ ................................ 4

- 1 27

4.1. Studies in Humans

– Case Reports, Occupational and Controlled Studies

..................... 4

- 1 28

4.1.1. Case Reports

................................ ................................ ................................ ............. 4

- 1 29

4.1.2. Occupational Studies

................................ ................................ ................................ 4

- 9 30

4.1.3. Controlled Studies

................................ ................................ ................................ .. 4

- 11 31

4.2. Acut e, Subchronic and Chronic Studies and Cancer Bioassays in Animals

— Oral and 32

Inhalation

................................ ................................ ................................ .......................... 4

- 13 33

4.2.1. Oral Studies

................................ ................................ ................................ ............ 4

- 13 34

4.2.1.1. Acu te Toxicity

................................ ................................ ................................ 4

- 13 35

4.2.1.2. Subchronic Toxicity

................................ ................................ ....................... 4

- 13 36

4.2.1.3. Chronic Toxicity

................................ ................................ ............................. 4

- 14 37

4.2.2. Inhalation Studies

................................ ................................ ................................ ... 4

- 20 38

4.2.2.1. Acute Toxicity

................................ ................................ ................................ 4

- 20 39

4.2.2.2. Subchronic Toxicity

................................ ................................ ....................... 4

- 21 40

4.2.2.3. Chronic Toxicity

................................ ................................ ............................. 4

- 24 41

4.3. Reproductive AND Developmental Studies

– Oral and Inhalation

................................ . 4

- 32 42

4.3.1. Oral Studies

................................ ................................ ................................ ............ 4

- 32 43

4.3.2. Inhalation Studies

................................ ................................ ................................ ... 4

- 34 441

- 4 DRAFT

- DO NOT CITE OR QUOTE 4.3.3. Other Reproductive and Developmental Toxicit y Studies

................................ .... 4

- 46 1

4.4. Neurotoxicity

................................ ................................ ................................ .................. 4

- 50 2

4.4.1. Oral Studies

................................ ................................ ................................ ............ 4

- 51 3

4.4.2. Inhalation Studies

................................ ................................ ................................ ... 4

- 53 4

4.4.3. Studies Employing In Vitro, S.C. and I.P. Exposures

................................ ............ 4

- 60 5

4.5. Immunotoxicity

................................ ................................ ................................ .............. 4

- 64 6

4.6. Mechanistic Data and Other Studies in Support of the MOA

................................ ........ 4

- 69 7

4.6.1. Role of Methanol and Metabolites in the Developmental Toxicity of Methanol

.. 4

- 69 8

4.6.2. Role of Folate Deficiency in the Developmental Toxicity of Methanol

................ 4

- 74 9

4.6.3. Methanol

- Induced Format ion of Free Radicals, Lipid Peroxidation, and Protein 10

Modifications

................................ ................................ ................................ ............... 4

- 75 11

4.6.4. Exogenous Formate Dehydrogenase as a Means of Detoxifying the Formic Acid that 12

Results from Methanol Exposure

................................ ................................ ................ 4

- 78 13

4.6.5. Mechanistic Data Related to the Potential Carcinogenicity of Methanol

.............. 4

- 78 14

4.6.5.1. Genotoxicity

................................ ................................ ................................ ... 4

- 78 15

4.6.5.2. Lymphoma Responses Reported in ERF Life span Bioassays of Compounds 16

Related to Methanol, Including an Analogue (Ethanol), Precursors (Aspartame 17

and Methyl Tertiary Butyl Ether), and a Metabolite (For maldehyde)

............... 4

- 81 18

4.6.5.2.1. Ethanol.

................................ ................................ ................................ ...... 4

- 83 19

4.6.5.2.2. Aspartame.

................................ ................................ ................................ . 4

- 84 20

4.6.5.2.3. MTBE.

................................ ................................ ................................ ....... 4

- 89 21

4.6.5.2.4. Formaldehyde.

................................ ................................ ........................... 4

- 90 22

4.7. Synthesis of Major Noncancer Effects

................................ ................................ ........... 4

- 92 23

4.7.1. Summary of Key Studies in Methanol Toxicity

................................ ..................... 4

- 92 24

4.7.1.1. Oral

................................ ................................ ................................ ................. 4

- 95 25

4.7.1.2. Inhal ation

................................ ................................ ................................ ........ 4

- 96 26

4.8. NONCANCER MOA Information

................................ ................................ ................ 4

- 99 27

4.9. Evaluation of Carcinogenicity

................................ ................................ ...................... 4

- 102 28

4.9.1. Summary of Overall Weight

- of

- Evidence

................................ ........................... 4

- 102 29

4.9.2. Synthesis of Human, Animal, and Other Supporting Evidence

........................... 4

- 103 30

4.9.3. MOA Information

................................ ................................ ................................ 4

- 109 31

4.10. Susceptible Populations and Life Stages

................................ ................................ ... 4

- 110 32

4.10.1. Possible Childhood Su sceptibility

................................ ................................ ...... 4

- 110 33

4.10.2. Possible Gender Differences

................................ ................................ .............. 4

- 111 34

4.10.3. Genetic Susceptibility

................................ ................................ ........................ 4

- 112 35

5. DOSE

- RESPONSE ASSESSMENT AND CHARACTERIZATION

................................ .... 5

- 1 36

5.1. Inhalation RfC for Effects Other than Cancer

................................ ................................ 5

- 1 37

5.1.1. Choice of Principal Study and Critical Effect(s)

................................ ...................... 5

- 1 38

5.1.1.1. Key Inhalation Studies

................................ ................................ ..................... 5

- 1 39

5.1.1.2. Selection of Critical Effect(s)

................................ ................................ ........... 5

- 2 40

5.1.2. Methods of Analysis for the POD

— Application of PBPK and BMD Models

........ 5

- 6 41

5.1.2.1. Application of the BMD/BMDL Approach

................................ ..................... 5

- 7 42

5.1.2.2. BMD Approach Applied to Brain Weight Data in Rats

................................ ... 5

- 9 43

5.1.3. RfC Derivation

– Including Appl ication of Uncertainty Factors

........................... 5

- 13 44

5.1.3.1. Comparison Between Endpoints and BMDL Modeling Approaches

............ 5

- 13 45

5.1.3.2. Application of UFs

................................ ................................ ......................... 5

- 15 46

5.1.3.2.1. Interindividual Variation UF H

. ................................ ................................ ... 5

- 15 47

5.1.3.2.2. Animal

- to

- Human Extrapolation UF A

. ................................ ...................... 5

- 17 481

- 5 DRAFT

- DO NOT CITE OR QUOTE 5.1.3.2.3. Database UF D

. ................................ ................................ ............................ 5

- 17 1

5.1.3.2.4. Extrapolation from Subchronic to Chronic and LOAEL

- to NOAEL 2

Extrapolation UFs.

................................ ................................ ................................ ..... 5

- 18 3

5.1.4. Previous RfC Assessment

................................ ................................ ...................... 5

- 18 4

5.2. Oral RfD

................................ ................................ ................................ ......................... 5

- 18 5

5.2.1. Choice of Principal St udy and Critical Effect

– with Rationale and Justification

... 5

- 19 6

5.2.1.1. Expansion of the Oral Database by Route

- to

- Route Extrapolation

................ 5

- 20 7

5.2.2. RfD Derivation

– Including Application of UFs

................................ ..................... 5

- 21 8

5.2.1.2. Consideration of Inhalation Data

................................ ................................ .... 5

- 21 9

5.2.1.3. Sele ction of Critical Effect(s) from Inhalation Data

................................ ...... 5

- 21 10

5.2.1.4. Selection of the POD

................................ ................................ ...................... 5

- 22 11

5.2.2. RfD Derivation

– Application of UF s

................................ ................................ ...... 5

- 22 12

5.2.3. Previous RfD Assessment

................................ ................................ ...................... 5

- 22 13

5.3. Uncertanties in the Inhalation RfC and Oral RfD

................................ .......................... 5

- 23 14

5.3.1. Choice of Endpoint

................................ ................................ ................................ . 5

- 24 15

5.3.2. Choice of Dose Metric

................................ ................................ ........................... 5

- 25 16

5.3.3. Choice of Model for BMDL Derivations

................................ ............................... 5

- 26 17

5.3.4. Choice of Animal

- to

- Human Extrapolation Method

................................ ............. 5

- 27 18

5.3.5. Route

- to

- Route Extrapolation

................................ ................................ ................ 5

- 27 19

5.3.6. Statistical Uncertainty at the POD

................................ ................................ ......... 5

- 28 20

5.3.7. Choice of Bioassay

................................ ................................ ................................ . 5

- 28 21

5.3.8. Choice of Species/Gender

................................ ................................ ...................... 5

- 28 22

5.3.9. Human Population Variability

................................ ................................ ............... 5

- 28 23

5.4. 5.4. Cancer Assessment

................................ ................................ ................................ 5

- 29 24

5.4.1. Oral Exposure

................................ ................................ ................................ ......... 5

- 29 25

5.4.1.1. Choice of Study/Data

— with Rationale and Justification

............................... 5

- 29 26

5.4.1.2. Dose

- Response Data

................................ ................................ ....................... 5

- 29 27

5.4.1.3. Dose Adjustments and Extrapolation Method

................................ ................ 5

- 30 28

5.4.1.4. Oral Sl ope Factor

................................ ................................ ............................ 5

- 33 29

5.4.2. Inhalation Exposure

................................ ................................ ................................ 5

- 33 30

5.4.2.1. Choice of Study/Data

– with Rationale and Justification

................................ . 5

- 33 31

5.4.2.2. Dose

- Response Data

................................ ................................ ....................... 5

- 34 32

5.4.2.3. Dose Adjustments and Extrapolation Method

................................ ................ 5

- 35 33

5.4.2.4. IUR

................................ ................................ ................................ ................. 5

- 36 34

5.4.3. Uncertainties in Cancer Risk Assessment

................................ .............................. 5

- 36 35

5.4.3.1. Quality of Studies that are the Basi s for the PODs

................................ ........ 5

- 37 36

5.4.3.2. Interpretation of Results of the Studies that are the Basis for the PODs

........ 5

- 40 37

5.4.3.3. Consistency across Chronic Bioassays for Methanol

................................ ..... 5

- 43 38

5.4.3.4. Choice of Endpoint for POD Derivation

................................ ........................ 5

- 45 39

5.4.3.5. Choice of Species/Gender

................................ ................................ .............. 5

- 45 40

5.4.3.6. Choice of Model for POD Derivation

................................ ............................ 5

- 45 41

5.4.3.7. Choice of Dose Metric

................................ ................................ .................... 5

- 46 42

5.4.3.8. Choice of Animal

- to

- Human Extrapolation Method

................................ ...... 5

- 47 43

5.4.3.9. Human Relevance of Cancer Responses Observed in Rats and Mice

............ 5

- 48 44

6. MAJOR CONCLUSIONS IN THE CHARACTERIZAT ION OF HAZARD AND DO SE 45

RESPONSE

................................ ................................ ................................ ................................ .. 6

- 1 46

6.1. Human Hazard Potential

................................ ................................ ................................ .. 6

- 1 47

6.2. Dose Response

................................ ................................ ................................ ................. 6

- 3 481

- 6 DRAFT

- DO NOT CITE OR QUOTE 6.2.1. Noncancer/Inhalation

................................ ................................ ............................... 6

- 3 1

6.2.2. Noncancer/Oral

................................ ................................ ................................ ........ 6

- 5 2

6.2.3. Cancer/Oral and Inhalation

................................ ................................ ...................... 6

- 6 3

7. REFERENCES

................................ ................................ ................................ ........................ 7

- 1 4

APPENDIX A. SUMMARY OF EXTERNAL PEER R EVIEW AND PUBLIC COM MENTS 5

AND DISPOSITION

................................ ................................ ................................ .................. A

- 1 6

APPENDIX B. DEVELOPM ENT, CALIBRATION AND APPLICATION OF A MET HANOL 7

PBPK MODEL

................................ ................................ ................................ ............................ B

- 1 8

APPENDIX C. RFC DERI VATION OPTIONS

................................ ................................ ......... C

- 1 9

APPENDIX D. RFC DERI VATION

– COMPARISON OF DOSE M ETRICS

........................ D

- 1 10

APPENDIX E. EVALUATI ON OF THE CANCER POTENCY OF ME THANOL

................. E

- 1 111

- 7 DRAFT

- DO NOT CITE OR QUOTE LIST OF TABLES

Table 2

- 1. Relevant physical and chemical properties of methanol

................................ ........... 2

- 1 1

Table 3

- 1. Background blood methanol and formate levels in humans

................................ ... 3

- 11 2

Table 3

- 2. Human blood methanol and formate levels following methanol exposure

............. 3

- 12 3

Table 3

- 3. Monkey blood methanol and formate levels following methanol exposure

........... 3

- 13 4

Table 3

- 4. Mouse blood methanol and fo rmate levels following methanol exposure

.............. 3

- 13 5

Table 3

- 5. Rat blood methanol and formate levels following methanol exposure

................... 3

- 14 6

Table 3

- 6. Plasma formate concentrations in monkeys

................................ ............................ 3

- 15 7

Table 3

- 7. Serum folate concentrations in monkeys

................................ ................................ . 3

- 15 8

Table 3

- 8. R outes of exposure optimized in models

– optimized against blood concentration data 3

- 20 9

Table 3

- 9. Key methanol kinetic studies for model validation

................................ ................. 3

- 22 10

Table 3

- 10. Parameters used in the mouse, rat and human PBPK models

............................... 3

- 25 11

Table 3

- 11. Primate K

m s reported in the literature

................................ ................................ ... 3

- 38 12

Table 3

- 12. Parameter estimate results obtained using acslXtreme to fit all human data using 13

either saturable or first

- order metabolism

................................ ................................ . 3

- 38 14

Table 3

- 13. Compari son of LLFs for Michaelis

- Menten and first

- order metabolism

.............. 3

- 40 15

Table 3

- 14. Monkey group exposure characteristics

................................ ................................ 3

- 47 16

Table 4

– 1. Mortality rate for subjects exposed to methanol

- tainted whiskey in relation to their 17

level of acidosisa a

................................ ................................ ................................ ........ 4

- 2 18

Table 4

- 2. Incidence of carcinogenic responses in Sprague

- Dawley rats exposed to methanol in 19

drinking water for up to 2 years

................................ ................................ ................ 4

- 18 20

Table 4

- 3. Incidence of malignant lymphoma responses in Swiss mice exposed to methanol in 21

drinking water for life

................................ ................................ ................................ 4

- 20 22

Table 4

- 4. Histopathological changes in tissues of B6C3F1 mice exposed to methanol via 23

inhalation for 18 months

................................ ................................ ............................ 4

- 28 24

Table 4

- 5. Histopathological changes in lung and adrenal tissues of F344 rats exposed to 25

methanol via inhalation for 24 months

................................ ................................ ...... 4

- 3 1 26

Table 4

- 6. Reproductive and developmental toxicity in pregnant Sprague

- Dawley rats expose d to 27

methanol via inhalation during gestation

................................ ................................ .. 4

- 36 28

Table 4

- 7. Reproductive parameters in Sprague

- Dawley dams exposed to methanol during 29

pregnancy then allowed to deliver their pups

................................ ............................ 4

- 37 30

Table 4

- 8. Developmental effects in mice after methanol inhalation

................................ ........ 4

- 39 31

Table 4

- 9. Benchmark doses at two added risk levels

................................ ............................... 4

- 40 32

Table 4

- 10. Reproductive parameters in monkeys exposed via inhalation to methanol during 33

prebreeding, breeding, and pregnancy

................................ ................................ ...... 4

- 42 34

Ta ble 4

- 11. Mean serum levels of testosterone, luteinizing hormone, and corticosterone (± S.D.) 35

in male Sprague

- Dawley rats after inhalation of methanol, ethanol, n

- propanol or n

36

butanol at threshold limit values

................................ ................................ ............... 4

- 43 37

Table 4

- 12. Maternal and litter parameters when pregnant female C57BL/6J mice were injected 38

i.p. with methanol

................................ ................................ ................................ ...... 4

- 46 39

Table 4

- 13. Reported thresholds concentrations (and author

- estimated ranges) for the onset of 40

embryotoxic effects when rat and mouse conceptuses were incubated in vitro with 41

methanol, formaldehyde, and formate

................................ ................................ ....... 4

- 49 42

Table 4

- 14. Brain weights of rats exposed to methanol vapors during gestation and lactation 4

- 58 43

Table 4

- 15. Effect of methanol on Wistar rat acetylcholinesterase activities

............................ 4

- 61 44

Table 4

- 16. Effect of methanol on neutrophil functions in in vitro and in vivo studies in male 45

Wistar rats

................................ ................................ ................................ .................. 4

- 65 461

- 8 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 17. Effect of intraperitoneally injected methanol on total and differential leukocyte 1

counts and neutrophil function tests in male Wistar rats

................................ ........... 4

- 66 2

Table 4

- 18. Effect of methanol exposure on animal weight/organ weight ratios and on cell count s 3

in primary and secondary lymphoid organs of male Wistar rats.

.............................. 4

- 68 4

Table 4

- 19. The effect of methanol on serum cytokine levels in male Wistar rats

................... 4

- 69 5

Table 4

- 20. Developmental outcome on GD10 following a 6

- hour 10,000 ppm (13,104 mg/m 3 ) 6

methanol inhalation by CD

- mice or formate gavage (750 mg/kg) on GD8

.............. 4

- 70 7

Table 4

- 21. Summary of ontogeny of relevant enzymes in CD

- 1 mice and humans

................ 4

- 71 8

Table 4

- 22. Dysmorphogenic effect of methanol and formate in neurulating CD

- 1 mouse 9

embryos in culture (GD8)

................................ ................................ .......................... 4

- 72 10

Table 4

- 23. Time

- dependent effects of methanol administration on serum liver and kidney 11

function, serum ALT, AST, BUN, and creatinine in control and experimental groups 12

of male Wistar rats

................................ ................................ ................................ .... 4

- 77 13

Table 4

- 24. Effect of methanol administration on male Wistar rats on malondialdehyde 14

concentration in the lymphoid organs of experimental and control groups and the 15

effect of methanol on antiox idants in spleen

................................ ............................. 4

- 77 16

Table 4

- 25. Summary of genotoxicity studies of methanol

................................ ....................... 4

- 80 17

Table 4

- 26. Incidence of carcinogenic respons es in Sprague

- Dawley rats exposed to ethanol in 18

drinking water for up to 2 years

................................ ................................ ................ 4

- 84 19

Table 4

- 27. Incidence of lymphomas and leukemias in Sprague

- Dawley rats exposed to 20

aspartame via the diet

................................ ................................ ................................ 4

- 85 21

Table 4

- 28. Incidence of combined dysplastic hyperplasias, papillomas and carcinomas of the 22

pelvis and ureter and of malignant schwannomas in peripheral nerve in Sprague

23

Dawley rats expos ed to aspartame via the diet

................................ .......................... 4

- 86 24

Table 4

- 29. Incidence of tumors in Sprague

- Dawley rats exposed to aspartame from GD12 to 25

natural death

................................ ................................ ................................ .............. 4

- 87 26

Table 4

- 30. Comparison of the incidence of combined lymphomas and leukemias in female 27

Sprague

- Dawley rats exposed to aspartame in feed for a lifetime, either pre

- and 28

postnatally or postnatally only

................................ ................................ .................. 4

- 88 29

Table 4

- 31. Incidence of Leydig cell testicular tumors and combined lymphomas and leukemias 30

in Sprague

- Dawley rats exposed to MTBE via gavage for 104 weeks

..................... 4

- 90 31

Table 4

- 32. Incidence of hemolymphoreticular neoplasms on Sprague

- Dawley rats exposed to 32

formaldehyde in drinking water for 104 weeks

................................ ......................... 4

- 91 33

Table 4

- 33. Incidence of leukemias in breeder and offsprin g Sprague

- Dawley rats exposed to 34

formaldehyde in drinking water for 104 weeks (Test BT 7005)

............................... 4

- 92 35

Table 4

- 34. Summary of studies of methanol toxicity in experimental animals (oral)

............. 4

- 92 36

Table 4

- 35. Summary of studies of methanol toxicity in experimental animals (inhalation 37

exposure)

................................ ................................ ................................ ................... 4

- 93 38

Table 5

- 1. Summary of studies c onsidered most appropriate for use in derivation of an RfC

.... 5

- 5 39

Table 5

- 2. The EPA PBPK model estimates of methanol blood levels (AUC) in rats following 40

inhalation exposures

................................ ................................ ................................ .. 5

- 10 41

Table 5

- 3. Comparison of benchmark dose modeling results for decreased brain weight in male 42

rats at 6 weeks of age using modeled AUC of methanol as a dose metric

................ 5

- 11 43

Table 5

- 4. Summary of PODs for critical endpoints, application of UFs and conversion to HEC 44

values using BMD and PBPK modeling

................................ ................................ ... 5

- 14 45

Table 5

- 5. Summary of uncertainties in methanol noncancer risk assessment

.......................... 5

- 23 46

Table 5

- 6. Incidence data for lymphoma, lympho

- immunoblastic, and all lymphomas in male and 47

female Sprague

- Dawley rats

................................ ................................ ..................... 5

- 30 48

Table 5

- 7. BMD results and oral CSF using all lymphoma in male rats

................................ ... 5

- 33 491

- 9 DRAFT

- DO NOT CITE OR QUOTE Table 5

- 8. Incidence data for tumor responses in male and female F344 rats

........................... 5

- 34 1

Table 5

- 9. BMD results and IUR using pheochromocytoma in female rats

.............................. 5

- 36 2

Table 5

- 10. Summary of uncertainty in the methanol ca ncer risk assessment

.......................... 5

- 37 3

Table B

- 1. Parameters used in the mouse and human PBPK models

................................ ......... B

- 5 4

Table B

- 2. Primate kms reported in the literature

................................ ................................ ...... B

- 29 5

Table B

- 3. Parameter estimate results obtained using acslXtreme to fit all human data using 6

either saturable or first

- order metabolism

................................ ................................ . B

- 31 7

Table B

- 4. Comparison of LLF for Michaelis

- Menten and first

- order metabolism

................. B

- 32 8

Table B

- 5. PBPK model predicted C

max and 24

- hour AUC for mice and humans expo sed to 9

MeOH

................................ ................................ ................................ ........................ B

- 35 10

Table B

- 6. Mouse total MeOH metabolic clearance/metabolites produced following inhalation 11

exposures

................................ ................................ ................................ ................... B

- 90 12

Table B

- 7. Human total MeOH metabolic clearance/metabolites produced from inhalation 13

exposures

................................ ................................ ................................ ................... B

- 91 14

Table B

- 8. Human total MeOH metabolic clearance/metabolites produced following oral 15

exposures

................................ ................................ ................................ ................... B

- 91 16

Table B

- 9. Repeated daily oral dosing of humans with MeOH

................................ ................. B

- 93 17

Table C

- 1. EPA’s PBPK model estimates of methanol blood levels ( AUC) in rats following 18

inhalation exposures

................................ ................................ ................................ .... C

- 3 19

Table C

- 2. Comparison of BMD 1SD results for decreased brain weight in male rats at 6 weeks of 20

age using modeled AUC of methanol as a dose metric

................................ ............... C

- 4 21

Table C

- 3. Comparison of BMD 05 results for decreased brain weight in male rats at 6 weeks of 22

age using modeled AUC of methanol as a dose metric

................................ ............... C

- 9 23

Table C

- 4. EPA’s PBPK model estimates of methanol blood levels (C

max ) in rats following 24

inhalation exposures

................................ ................................ ................................ .. C

- 13 25

Table C

- 5. Comparison of BMD 1SD results for decreas ed brain weight in male rats at 8 weeks of 26

age using modeled C

max of methanol as a dose metric

................................ .............. C

- 14 27

Table C

- 6. Comparison of BMD 05 modeling results for decreased brain weight in male rats at 8 28

weeks of age using modeled C

max of methanol as a common dose metric

............... C

- 20 29

Table C

- 7. EPA’s PBPK model estimates of methanol blood levels (C

max ) in mice following 30

inhalation exposures

................................ ................................ ................................ .. C

- 24 31

Table C

- 8. Comparison of BMD modeling results for cervical rib incidence in mice using 32

modeled C

max of methanol as a common dose metric

................................ ............... C

- 25 33

Table C

- 9. Comparison of BMD modeling results for cervical rib incidence in mice using 34

modeled C

max of methanol as a common dose metric

................................ ............... C

- 33 35

Table C

- 10. Comparison of BMD modeling re sults for VDR in female monkeys using AUC 36

blood methanol as the dose metric

................................ ................................ ............ C

- 42 37

Table E

- 1. Calculation of mg/kg

- day doses

................................ ................................ .............. E

- 12 38

Table E

- 2. Incidence for neoplasms considered for dose

- response modeling

.......................... E

- 12 39

Table E

- 3. Results from multistage (1o) quantal modeling rat data using mg/kg

- day exposures 40

and default HED der ivation method

................................ ................................ .......... E

- 13 41

Table E

- 4. Results from time

- to

- tumor modeling data using mg/kg

- day exposures and default 42

HED derivation method

................................ ................................ ............................. E

- 13 43

Table E

- 5. PBPK model estimated dose

- metrics for doses

................................ ...................... E

- 14 44

Table E

- 6a. Results from time

- to

- tumor modeling of data using PBPK dose metrics

............. E

- 14 45

Table E

- 6b. HEDs from time

- to

- tumor modeling of data using PBPK dose metrics

............... E

- 14 46

Table E

- 7. Results of Multistage (1o) quantal modeling of data using PBPK dose metrics

..... E

- 15 47

Table E

- 8. Application of human PBPK model to derive HEDs from results of multistage (1o) 48

quantal modeling of data using PBPK dose metrics

................................ ................. E

- 15 491

- 10 DRAFT

- DO NOT CITE OR QUOTE Table E

- 9. Incidence for neoplasms considered for dose

- response modeling

........................... E

- 16 1

Table E

- 10. PBPK dose metrics for doses

................................ ................................ ................ E

- 16 2

Table E

- 11. Benchmark results from multistage quantal dose

- response modeling data using 3

PBPK dose

- metrics

................................ ................................ ................................ .... E

- 17 4

Table E

- 12. Application of human PBPK model to derive HECs from BMDL 10 estimates in 5

Table E

- 11 using multistage quantal modeling

................................ ......................... E

- 17 6

Table E

- 13. Incidence for malignant lymphoma (Apaja, 1980)

................................ ................ E

- 17 7

Table E

- 14. PBPK dose metrics for doses in Apaja (1980)

................................ ...................... E

- 18 8

Table E

- 15. Benchmark results from Multistage

- cancer dose

- response modeling data for 9

malignant lymphoma in Swiss Webster mice (Apaja, 1980) using PBPK dose

- metrics E

- 18 10

Table E

- 16. Application of human PBPK model to derive HEDs from BMDL 10 estimates of 11

Table E

- 15, Multistage (1o) modeling of malignant lymphoma in Swiss mice (Apaja, 12

1980) using PBPK dose metrics

................................ ................................ ................ E

- 19 13

Table E

- 17. Benchmark results for all tumor types using BMDS 2.1 multistage “background 14

dose” models and PBPK dose

- metrics

................................ ................................ ...... E

- 19 151

- 11 DRAFT

- DO NOT CITE OR QUOTE LIST OF FIGURES

Figure 3

- 1. Methanol metabolism and key metabolic enzymes in primates and rodents.

.......... 3

- 3 1

Figure 3

- 2. Folate

- dependent formate metabolism. Tetrahydrofolate (THF)

- mediated one carbon 2

metabolism is required for the synthesis of purines, thymidylate, and methionine.

... 3

- 4 3

Figure 3

- 3. Plot of fetal (amniotic) versus maternal methanol concentrations in GD20 rats.

.... 3

- 7 4

Figure 3

- 4. Schematic of the PBPK model used to describe the inhalation, oral, a nd i.v. route 5

pharmacokinetics of methanol.

................................ ................................ .................. 3

- 24 6

Figure 3

- 5. Model fits to data sets from GD6, GD7, and GD10 mice for 6

- to 7

- hour inhalation 7

exposures to 1,000

– 15,000 ppm methanol.

................................ ............................... 3

- 27 8

Figure 3

- 6. Simulation of inhalation exposures to methanol in NP mice from Perkins et al. 9

(1995a) (8

- hour exposures) and GD8 mice from Dorman et al. (1995)(6

- hour 10

exposures).

................................ ................................ ................................ ................. 3

- 28 11

Figure 3

- 7. Conceptus versus maternal blood AUC values for rats and mice plotted (A) on a log

12

linear scale, as in Figure 8 of Ward et al. (1997), and (B) on a linear

- linear scale.

.. 3

- 30 13

Figure 3

- 8. NP rat i.v. route methanol blood kinetics.

................................ .............................. 3

- 33 14

Figure 3

- 9. Model fits to data sets from inhalation exposures to 200 (triangles), 1,200 15

(diamonds), or 2,000 (squares) ppm methanol in male F

- 344 rats.

........................... 3

- 33 16

Figure 3

- 10. Model fits to datasets from oral exposures to 100 and 2,500 mg/kg methanol in 17

female Sprague

- Dawley rats.

................................ ................................ .................... 3

- 35 18

Figure 3

- 11. Urinary methanol elimination concentration (upper panel) and cumulative amount 19

(lower panel) following inhalation exposures to methanol in human volunteers.

.... 3

- 37 20

Figure 3

- 12. Data showing the visual quality of the fit using optimized first

- order or Michaelis

21

Menten kinetics to describe the metabolism of methanol in humans.

....................... 3

- 39 22

Figure 3

- 13. Inhalation exposures to methanol in human volunteers.

................................ ...... 3

- 41 23

Figure 3

- 14. Blood methanol concentration data from NP and pregnant monkeys.

................. 3

- 44 24

Figure 3

- 15. Chamber concentration profiles for monkey methanol exposures.

...................... 3

- 46 25

Figure 4

- 1. Hemolymphoreticular neoplasms in male and f emale Sprague

- Dawley rats in 26

formaldehyde

................................ ................................ ................................ ........... 4

- 110 27

Figure 5

- 1. Hill model BMD plot of decreased brain weight in male rats at 6 weeks age using 28

modeled AUC of methanol in blood as the dose metric, 1 control mean S.D.

.......... 5

- 12 29

Figure 5

- 2. PODs (in mg/m 3 ) for selected endpoints with corresponding applied UFs

............ 5

- 15 30

Figure 5

- 3. All lymphomas versus methanol metabolized (mg/day) for female and male rats

. 5

- 31 31

Figure 5

- 4. All lymphomas versus C

max (mg/L) for female and male rats.

............................... 5

- 31 32

Figure 5

- 5. All lymphomas versus AUC (hr x mg/L) for male and female rats

........................ 5

- 31 33

Figure 5

- 6. Lympho

- immunoblastic Lymphoma minus “lung

- only” response in rats of Soffrit ti et 34

al. (2002a) methanol study versus methanol metabolized (mg/day).

........................ 5

- 42 35

Figure 5

- 7. T otal amount metabolized per day (after periodicity is rea ched) in a 420 g rat

..... 5

- 47 36

Figure B

- 1. Schematic of the PBPK model used to describe the inhalation, oral, and i.v. route 37

pharmacokinetics of MeOH.

................................ ................................ ....................... B

- 3 38

Figure B

- 2. Model fits t o data sets from GD6, GD7, and GD10 mice for 7

- hour inhalation 39

exposures to 1,000

– 15,000 ppm MeOH.

................................ ................................ ..... B

- 7 40

Figure B

- 3. Simulation of inhalation exposures to MeOH in NP mice from Perkins et al

. (1 995a) 41

(8

- hour exposures) and Dorman et al

. (1995), (6

- hour exposures).

............................ B

- 8 42

Figure B

- 4. Oral exposures to MeOH in pregnant mice on GD8 (Dorman et al

. , 1995) or NP and 43

GD18.

................................ ................................ ................................ ........................ B

- 10 44

Figure B

- 5. Mouse intravenous route MeOH blood kinetics.

................................ ................... B

- 11 45

Figure B

- 6. Mouse model inhalation route sensitivity coefficients for metaboli c parameters. B

- 15 461

- 12 DRAFT

- DO NOT CITE OR QUOTE Figure B

- 7. Mouse model inhalation route sensitivity coefficients for flow rates (QCC: cardiac 1

output; QPC: alveolar ventilation), and partitioning to the body (PR) compartm ent are 2

reported for blood MeOH AUC.

................................ ................................ ............... B

- 15 3

Figure B

- 8. Mouse model sensitivity coefficients for oral exposures to MeOH.

...................... B

- 16 4

Figu re B

- 9. Mouse daily drinking water ingestion pattern (A) and resulting predicted blood 5

concentration for a 6 d/wk exposure (B).

................................ ................................ .. B

- 17 6

Figure B

- 10. NP rat i.v.

- route methanol blood kinetics

................................ ............................ B

- 19 7

Figure B

- 11. Model fits to data sets from inhalation exposures to 200 (triangles), 1,200 8

(diamonds), or 2,000 (squares) ppm MeOH in male F

- 344 rats.

.............................. B

- 20 9

Figure B

- 12. Model fits to data sets from oral exposures to 100 (squares) or 2,500 (diamonds) 10

mg/kg MeOH in female Sprague

- Dawley rat

................................ ............................ B

- 22 11

Figure B

- 13. Simulated Sprague

- Dawley rat inhalation exposures to 500, 1,000, or 2,000 ppm 12

MeOH.

................................ ................................ ................................ ....................... B

- 24 13

Figure B

- 14. Simulated rat oral exposures of Sprague

- Dawley rats to 65.9, 624.2, or 2,177 mg 14

MeOH/kg/day.

................................ ................................ ................................ ........... B

- 25 15

Figure B

- 15. Rat daily drinking water ingestion pattern (A) and resulting predicted blood 16

concentration for a 2

- day exposure (B).

................................ ................................ .... B

- 26 17

Figure B

- 16. Urinary MeOH elimination concentration (upper panel) and cumulative amount 18

(lower panel), following inhalation exposures to MeOH in human volunteers.

....... B

- 28 19

Figure B

- 17. Contr ol (nonexposed) blood methanol concentrations

................................ ......... B

- 30 20

Figure B

- 18. Data showing the visual quality of the fit using optimized first

- order or Michaelis

21

Menten kinetics to describe the metabolism of MeOH in humans.

.......................... B

- 31 22

Figure B

- 19. Inhalation exposures to MeOH in human volunteers.

................................ ......... B

- 33 23

Figure B

- 20. Predicted 24

- hour AUC (upper left), Cmax (upper right), and amount metabolized 24

(lower) for MeOH inhalation exposures in the mouse (average over a 10

- day exposure 25

at 7 hours/day) and humans (steady

- state values for a continuous exposure).

.......... B

- 36 26

Figure B

- 21. Fit of the model to i.v. data using different clearance and uptake parameter 27

optimizations.

................................ ................................ ................................ ............ B

- 41 28

Figure B

- 22. Fit of the model to oral data using di fferent clearance and uptake parameter 29

optimizations.

................................ ................................ ................................ ............ B

- 42 30

Figure B

- 23. Fit of the model to inhalation data using different clearance and uptake parameter 31

optimizations.

................................ ................................ ................................ ............ B

- 42 32

Figure B

- 24. Simulated human oral exposures to 0.1 mg MeOH/kg/

- day comparing the first few 33

days for four exposure scenarios

................................ ................................ ............... B

- 92 34

Figure C

- 1. Hi ll model, BMR of 1 Control Mean S.D.

- Decreased Brain weight in male rats at 6 35

weeks age versus AUC, F1 Generation inhalational study

................................ ......... C

- 7 36

Figure C

- 2. Hill model, BMR of 0.05 relative risk

- Decreased Brain weight in male rats at 6 37

weeks age versus AUC, F 1 Generation inhalational study.

................................ ....... C

- 12 38

Figure C

- 3. Hill model, BMR of 1 Control Mean S.D.

- Decreased Brain weight in male rats at 8 39

weeks age versus C

max , Gestation only inhalational study.

................................ ....... C

- 18 40

Figure C

- 4. Exponential4 model, BMR of 0.05 relative risk

- Decreased Brain weight in male 41

rats at 8 weeks age versus C

max , Gestation only inhalational study.

......................... C

- 23 42

Figure C

- 5. Nested Logistic Model, 0.1 Extra Risk

- Incidence of Cervical Rib in Mice versus 43

C

max Methanol, GD 6

- 15 inhalational study.

................................ ............................. C

- 32 44

Figure C

- 6. Nested Logistic Model, 0.05 Extra Risk

- Incidence of Cervical Rib in Mice versus 45

C

max Methanol, GD 6

- 15 inhalational study.

................................ ............................. C

- 39 46

Figure C

- 7. 3rd Degree Polynomial Model, BMR of 1 Control Mean S.D.

- VDR in female 47

monkeys using AUC blood methanol as the dose metric.

................................ ......... C

- 44 481

- 13 DRAFT

- DO NOT CITE OR QUOTE Figure D

- 1. Exposure

- response data for met hanol

- induced CR plus SNR malformations in mice 1

at various concentration

- time combinations.

................................ .............................. D

- 2 2

Figure D

- 2. Internal dose

- response relationships for methanol

- induced CR plus SNR 3

malformations in mice at various concentration

- time combinations for three dose 4

metrics.

................................ ................................ ................................ ....................... D

- 3 5

Figure E

- 1. Female rat survival.

................................ ................................ ............................... E

- 20 6

Figure E

- 2. Male rats survival.

................................ ................................ ................................ . E

- 20 7

Figure E

- 3. Female

– Lymphomas lympho

- immunoblastic

– Multistage Weibull Model

– 8

Approach 1.

................................ ................................ ................................ ............... E

- 21 9

Figure E

- 4. Female

– All lymphomas

– Multistage Weibull Model

– Approach 1.

................. E

- 21 10

Figure E

- 5. Male

– Hepatocellular carcinoma

– Multistage Weibull Model

– Approach 1.

.... E

- 22 11

Figure E

- 6. Male

– Lymphomas lympho

- immunoblastic

– Multistage Weibull Model

– 12

Approach 1.

................................ ................................ ................................ ............... E

- 22 13

Figure E

- 7. Male

– All lymphomas

– Multistage Wei bull Model

– Approach 1.

.................... E

- 23 14

Figure E

- 8. Female rats

– All lymphomas time

- to

- tumor model fit and Kaplan Meier curves. E

- 23 15

Figu re E

- 9. Male rats

– All lymphomas time

- to

- tumor model fit and Kaplan Meier curves.

... E

- 24 16

Figure E

- 10. Male rats

- All lymphomas; dose = amount metabolized (mg/day); 1o multistage 17

model.

................................ ................................ ................................ ........................ E

- 24 18

Figure E

- 11. Female rat survival.

................................ ................................ ............................ E

- 25 19

Figure E

- 12. Male rat survival.

................................ ................................ ................................ . E

- 25 20

Figure E

- 13. Female rats

- pheochromocytomas; dose = amount metabolized (mg/d); 3o 21

multistage model.

................................ ................................ ................................ ...... E

- 26 22

Figure E

- 14. Plots for female (top; p= 0.55) and male (bottom; p= 0.21) mice

– malignant 23

lymphoma; dose=amount metabolized (mg/d); 2o multistage model.

...................... E

- 26 241

- 14 DRAFT

- DO NOT CITE OR QUOTE LIST OF ACRONYMS

ACGIH American Conference of Governmental and Industrial Hygienists

ADH alcohol dehydrogenase

ADH1 alcohol dehydrogenase

- 1

ADH3 formaldehyde dehydrogenase

- 3

AIC Akaike Information Criterion

ALD aldehyde dehydrogenase

ALDH2 mitochondrial aldehyde dehydrogenase

- 2

ALT alanine aminotransferase

ANOVA analysis of variance

AP alkaline phosphatase

AST aspartate aminotransferase

AT P adenosine triphosphate

AT S D R Agency for Toxic Substances and Disease Registry

AUC area under the curve , representing the cumulative product of time

and concentration for a substance in the blood

β

- NAG N

- acetyl

- beta

- D

- glucosaminidase

BAA butoxyacetic acid

BAL butoxyacetaldehyde

BMC benchmark concentration

BMCL benchmark concentration, 95% lower bound

BMD benchmark dose (s)

BMD 1 SD BMD for response one standard deviation from control mean

BMDL 95% lower bound confidence limit on BMD (benchmark dose)

BMDL 1 SD BMDL for response one standard deviation from control mean

BMDS benchmark dose software

BMR benchmark response

BSO butathione sulfoximine

BUN blood urea nitrogen

BW, bw body weight

C 1 pool one carbon pool

C

max peak concentration of a substance in the blo od during the

exposure period

C

- section Cesarean section

CA chromosomal aberrations

CAR condition ed avoidance response

CASRN Chemical Abstracts Service Registry Number

CAT catalase

CERHR Center for the Evaluation of Risks to Human Reproduction

CH 3 OH methan ol

CHL Chinese hamster lung (cells)1

- 15 DRAFT

- DO NOT CITE OR QUOTE CHR contact hypersensitivity response

CI confidence interval

Cl S clearance rate

CNS central nervous system

CO 2 carbon dioxide

con

- A concanavalin

- A

CR crown

- rump length

CSF Cancer slope factor

CT computed tomography

CYP45 0 cytochrome P450

d, **δ** , Δ delta, difference, change

D 2 dopamine receptor

DA dopamine

DIPE diisopropylether

DMDC dimethyl dicarbonate

DNA deoxyribonucleic acid

DNT developmental neurotoxicity test(ing)

DOPAC dihydroxyphenyl acetic acid

DPC days past concept ion

DTH delayed

- type hypersensitivity

EFSA European Food Safety Authority

EKG electrocardiogram

EO Executive Order

EPA U.S. Environmental Protection Agency

ERF European Ramazzini Foundation

EtOH ethanol

F fractional bioavailability

F 0 parental generation

F 1 first generation

F 2 second generation

F344 Fisher 344 rat strain

FAD folic acid deficient

FAS folic acid sufficient

FD formate dehydrogenase

FP folate paire

FR folate reduced

FRACIN f raction inhaled

FS folate sufficient

FSH follicular stimulating hormon e

γ

- GT gamma glutamyl transferase1

- 16 DRAFT

- DO NOT CITE OR QUOTE g gravity

g, kg, mg, μg gram, kilogram, milligram, microgram

G6PD glucose

- 6

- phosphate dehydrogenase

GAP43 growth

- associated protein (neuronal growth cone)

GD gestation day

GFR glomerular filtration rate

GI gastrointestina l track

GLM generalized linear model

GLP good laboratory practice

GSH glutathione

H AP hazardous air pollutant

Hb hemoglobin

HCHO formaldehyde

HCOO formate

Hct hematocrit

HEC human equivalent concentration

HED human equivalent dose

HEI Health Effects Instit ute

HH hereditary hemochromatosis

5_HIAA 5

- hydroxyindolacetic acid

HMGSH S

- hydroxymethylglutathione

Hp haptoglobin

HPA hypothalamus

- pituitary

- adrenal ( axis )

HPLC high

- performance liquid chromatography

H SD B Hazardous Substances Databank

HSP70 biomarker of c ellular stress

5

- HT serotonin

IL interleukins

i.p. intraperitoneal

IPCS International Programme on Chemical Safety

IQ intelligence quotient

IRIS Integrated Risk Information System

IUR inhalation unit risk

i.v. intravenous

K 1 first order rate loss

K1C fir st order clearance of methanol from the blood to the bladder for

urinary elimination

KAI first order uptake from the intestine

KAS first order methanol oral absorption rate from stomach

KBL rate constant for urinary excretion from bladder

KIA first order u ptake from intestin e1

- 17 DRAFT

- DO NOT CITE OR QUOTE KLH keyhole limpet hemocyanin

KLL alternate first order rate constant

K

m substrate concentration at half the enzyme maximum

velocity (V

max )

K

m 2 Michaelis

- Menten rate constant for low affinity metabolic

clearance of methanol

KSI first order transfer between stomach and intestine

L, dL, mL liter, deciliter, milliliter

LD 50 median lethal dose

LDH lactate dehydrogenase

LH luteinizing hormone

LLF ( maximum ) log likelihood function

LMI leukocyte migration inhibition (assay)

LOAEL lowest

- obse rved

- adverse

- effect level

M, mM, μM m olar, millimolar, micromolar

MAA 2

- methoxyacetic acid

MCH mean corpuscular hemoglobin

MCHC mean corpuscular hemoglobin concentration

MCV mean cell volume

MeOH methanol

MLE maximum likel ihood estimate

M

- M Michaelis

- Mente n

MN micronuclei

MOA mode of action

4

- MP 4

- methylpyrazale messenger RNA

MRI magnetic resonance imaging

MTBE methyl tertiary butyl ether

MTX methotrexate

N 2 O/O 2 nitrous oxide

NAD + nicotinamide adenine dinucleotide

NADH reduced form of nicotinamide adenine d inucleotide

NBT nitroblue tetrazolium (test)

NCEA National Center for Environmental Assessment

ND not determined

NEDO New Energy Development Organization (of Japan)

NIEHS National Institute of Environmental Health Sciences

NIOSH National Inst itute of Occupational Safety and Health

NK natural killer

n mol nanomole

NOAEL no

- observed

- adverse

- effect level

NOEL no

- observed

- effect level1

- 18 DRAFT

- DO NOT CITE OR QUOTE NP nonpregnant

NR not reported

NRC National Research Council

NS not specified

NTB nitroblue tetra zolium

NTP Nation al Toxicology Program

NZW New Zealand white (rabbit)

8

- OHdG 8

- hydroxydeoxyguanosine

OR osmotic resistance

OSF oral slope factor

OU ocular uterque (each eye)

OXA oxazolone

P, p probability

PBPK physiologically based pharmacokinetic

PEG polyethylene glycol

P FC plaque

- forming cell

PK pharmacokinetic

PMN polymorphonuclear leucocytes

PND postnatal day

POD point of departure

ppb, ppb parts per billion, parts per million

PWG Pathology Working Group of the NTP of NIEHS

Q wave the initial deflection of the QRS compl ex when such deflection is

QCC cardiac output

QPC pulmonary (alveolar) ventilation scaling coefficient

QRS portion of electrocardiogram corresponding to the depolarization

of ventricular cardiac cells.

R 2 square of the correlation coefficient, a measure of the reliability

of a linear relationship.

RBC red blood cell

RfC reference concentration

RfD reference dose

RNA ribonucleic acid

ROS reactive oxygen species

S9 microsomal fraction from liver

SAP serum alkaline phosphatase

s.c. subcutaneous

SCE siste r chromatid exchange

S.D. standard deviation

S.E. standard error

SEM standard error of mean1

- 19 DRAFT

- DO NOT CITE OR QUOTE SGPT serum glutamate pyruvate transaminase

SHE Syrian hamster embryo

SOD superoxide dismutase

SOP standard operating procedure(s)

t time

T 1⁄2 , t 1⁄2 half

- life

T wave th e next deflection in the electrocardiogram after the QRS

complex; represents ventricular repolarization

TAME tertiary amyl methyl ether

TAS total antioxidant status

Tau taurine

THF tetrahydrofolate

TLV threshold limit value

TNFα tumor necrosis factor

- alpha

TNP

- LPS trinitrophenyl

- lipopolysaccharide

TRI Toxic Release Inventory

U83836E vitamin E derivative

UF (s) uncertainty factor (s)

UF A UF associated with interspecies (animal to human) extrapolation

UF D UF associated with defic iencies in the toxicity database

UF H UF associated with variation in sensitivity within the human

population

UF S UF associated with subchronic to chronic exposure

V d volume of distribution

V

max maximum enzyme velocity

V

max C maximum velocity of the high

- af finity/low

- capacity pathway

v/v volume/volume

VDR visual ly directed rea ch ing test

VitC vitamin C

VYS visceral yolk sac

WBC white blood cell

WOE weight of evidence

w/v weight/volume

χ

2 chi square1

- 20 DRAFT

- DO NOT CITE OR QUOTE FOREWORD

The purpose of this Toxicological Review is to provide scientific support and rationale

for the hazard and dose

- response assessment in IRIS pertaining to chronic exposure to methanol.

It is not intended to be a comprehensive treatise on the chemical or toxicological nature of

methanol.

The intent of Section 6, Major Conclusions in the Characterization of Hazard and Dose

Response, is to present the major conclusions reached in the derivation of the reference dose,

re ference concentration and cancer assessment, where applicable, and to characterize the overall

confidence in the quantitative and qualitative aspects of hazard and dose response by addressing

the quality of data and related uncertainties

. The discussion is intended to convey the limitations

of the assessment and to aid and guide the risk assessor in the ensuing steps of the risk

assessment process.

For other general information about this assessment or other questions relating to IRIS,

the reader is refer red to EPA’s IRIS Hotline at (202) 566

- 1676 (phone), (202) 566

- 1749 (fax), or

hotline.iris@epa.gov

.1

- 21 DRAFT

- DO NOT CITE OR QUOTE AUTHORS, CONTRIBUTOR S, AND REVIEWERS

CHEMICAL MANAGER

Jeff rey Gift , Ph.D.

National Center for Environmental A ssessment

U.S Environmental Protection Agency

Research Triangle Park, NC

AUTHORS

Stan ley Barone , Ph.D.

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Allen Davis , MSPH

National Center for Environmental As sessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Jeff rey Gift , Ph.D.

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Annette Iannucci

, M.S

. Sciences International (first draft)

2200 Wilson B oulevard

Arlington, VA

Paul Schlosser , Ph.D.

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

CONTRIBUTORS

Bruce Allen , Ph.D.

Bruce Allen Consulting

101 Corbin Hill Circle

Chapel Hill, S C

Hugh Barton , Ph.D.

National Center for Computational Toxicology

U.S. Environmental Protection Agency

Research Triangle Park, NC1

- 22 DRAFT

- DO NOT CITE OR QUOTE J. Michael Davis , Ph.D.

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Robinan Gentry

, M.S.

ENVIRON International

650 Poydras Street

New Orleans, LA

Susan Goldhaber , M.S.

Alpha

- Gamma Technologies, Inc.

3301 Benson Drive

Raleigh, NC

Mark Greenberg , Ph.D.

Senior Environmental Employee Program

U.S. Environmental Prot ection Agency

Research Triangle Park, NC

George Holdsworth , Ph.D.

Oak Ridge Institute for Science and Education (second draft)

Badger Road

Oak Ridge, TN

Angela Howard , Ph.D.

National Center for Environmental Assessment

U.S. Environmental Protection Agenc y

Research Triangle Park, NC

Connie Meacham , M.S.

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Greg Miller

Office of Policy, Economics & Innovation

U.S. Environmental Protection Agency

Washi ngton, DC

Sharon Oxe ndine , Ph.D.

Office of Policy, Economics & Innovation

U.S. Environmental Protection Agency

Washington, DC

Torka Poet , Ph.D.

Battelle, Pacific Northwest National Laboratories (Appendix B)

902 Battelle Boulevard

Richland, WA1

- 23 DRAFT

- DO NOT CITE OR QUOTE John Roger s , Ph.D.

National Health & Environmental Effects Research laboratory

U.S. Environmental Protection Agency

Research Triangle Park, NC

Reeder Sams , II , Ph.D.

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Roy Smith , Ph.D.

Air Quality Planning & Standards

U.S. Environmental Protection Agency

Research Triangle Park, NC

Frank Stack

Alpha

- Gamma Technologies, Inc.

3301 Benson Drive

Raleigh, NC

Justin TeeGuarden , Ph.D.

Battelle, Pacific Northwest Nat ional Laboratories

902 Battelle Boulevard

Richland, WA

Chad Thompson , Ph.D. , MBA

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Cynthia VanLandingham , M.S

. ENVIRON International (Appendix E)

650 Poydras S treet

New Orleans, LA

Deborah Wales

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Lutz Weber , Ph.D. , DABT

Oak Ridge Institute for Science and Education

Badger Road

Oak Ridge, TN1

- 24 DRAFT

- DO NOT CITE OR QUOTE Errol Zeiger , Ph.D.

Alpha

- Gamma Technologies, Inc.

3301 Benson Drive

Raleigh, NC

REVIEWERS

This document has been reviewed by EPA scientists, interagency reviewers from other 1

federal agencies and White House offices. 2

INTERNAL EPA REVIEWE RS

Jane Caldwell , Ph.D.

Nation al Center for Environmental Assessment

U.S Environmental Protection Agency

Washington, DC

Ila Cote , Ph.D., DABT

National Center for Environmental Assessment

U.S Environmental Protection Agency

Washington, DC

Rob ert Dewoskin Ph.D., DABT

National Center fo r Environmental Assessment

U.S Environmental Protection Agency

Research Triangle Park, NC

Joyce Donahue , Ph.D.

Office of Water

U.S. Environmental Protection Agency

Washington, DC

Marina Evans , Ph.D.

National Health and Environmental Effects Research Labo ratory

U.S Environmental Protection Agency

Research Triangle Park, NC

Lynn Flowers , Ph.D., DABT

National Center for Environmental Assessment

U.S Environmental Protection Agency

Research Triangle Park, NC

Brenda Foos

, M.S

. Office of Children’s Health Prot ection and Environmental Education

U.S Environmental Protection Agency

Washington, DC

Jennifer Jinot , Ph.D.

National Center for Environmental Assessment1

- 25 DRAFT

- DO NOT CITE OR QUOTE U.S Environmental Protection Agency

Washington, DC

Eva McLanahan , Ph.D.

National Center for Environme ntal Assessment

U.S Environmental Protection Agency

Research Triangle Park, NC

John Vandenberg , Ph.D.

National Center for Environmental Assessment

U.S Environmental Protection Agency

Research Triangle Park, NC

Debra Walsh , M.S.

National Center for Enviro nmental Assessment

U.S Environmental Protection Agency

Research Triangle Park, NC1

- 26 DRAFT

- DO NOT CITE OR QUOTE 1. INTRODUCTION

This document presents background information and justification for the Integrated Risk 1

Information System (IRIS) Summary of the hazard and dose

- response assess ment of methanol. 2

IRIS Summaries may include oral reference dose (RfD) and inhalation reference concentration 3

(RfC) values for chronic and other exposure durations, and a carcinogenicity assessment. 4

The RfD and RfC, if derived, provide quantitative infor mation for use in risk assessments 5

for health effects known or assumed to be produced through a nonlinear ( presumed threshold) 6

mode of action (MOA)

. The RfD (expressed in units of milligrams per kilogram per day 7

[ mg/kg

- day ] ) is defined as an estimate (wit h uncertainty spanning perhaps an order of 8

magnitude) of a daily exposure to the human population (including sensitive subgroups) that is 9

likely to be without an appreciable risk of deleterious effects during a lifetime. The inhalation 10

RfC (expressed in u nits of milligrams per cubic meter [ mg/m 3 ] ) is analogous to the oral RfD but 11

provides a continuous inhalation exposure estimate. The inhalation RfC considers toxic effects 12

for both the respiratory system (portal

- of

- entry) and for effects peripheral to the respiratory 13

system (extrarespiratory or systemic effects). Reference values are generally derived for chronic 14

exposures (up to a lifetime), but may also be derived for acute ( ≤ 24 hours), short

- term (>24 15

hours up to 30 days), and subchronic (>30 days up to 10% of lifetime) exposure durations, all of 16

which are derived based on an assumption of continuous exposure throughout the duration 17

specified. Unless specified otherwise, the RfD and RfC are derived for chronic exposure 18

duration. 19

The carcinogenicity assessment provides information on the carcinogenic hazard 20

potential of the substance in question

, and quantitative estimates of risk from oral and inhalation 21

exposure may be deriv ed. The information includes a weight

- of

- evidence (WOE) judgment of 22

the likelihood that the agent is a human carcinogen and the conditions under which the 23

carcinogenic effects may be expressed. Quantitative risk estimates may be derived from the 24

applicat ion of a low

- dose extrapolation procedure. If derived, the oral slope factor is a plausible 25

upper bound on the estimate of risk per mg/kg

- day of oral exposure. Similarly, an inhalation unit 26

risk (IUR) is a plausible upper bound on the estimate of risk pe r microgram per cubic meter 27

( μg/m 3 ) air breathed. 28

Development of these hazard identification and dose

- response assessments for methanol 29

has followed the general guidelines for risk assessment as set forth by the National Research 30

Council (NRC) (1983). EPA Guidelines and Risk Assessm ent Forum Technical Panel Reports 31

that may have been used in the development of this assessment include the following: Guidelines 32

for the Health Risk Assessment of Chemical Mixtures (U.S. EPA, 1986a), Guidelines for 33

Mutagenicity Risk Assessment (U.S. EPA, 1986b), Recommendations for and Documentation of 34

Biological Values for Use in Risk Assessment (U.S. EPA, 1988), Guidelines for Developmental 35

Toxicity Risk Assessment (U.S. EPA, 1991), Interim Policy for Particle Size and Limit 361

- 27 DRAFT

- DO NOT CITE OR QUOTE Concentration Issues in Inhal ation Toxicity Studies (U.S. EPA, 1994a), Methods for Derivation 1

of Inhalation Reference Concentrations and Application of Inhalation Dosimetry (U.S. EPA, 2

1994b), Use of the Benchmark Dose Approach in Health Risk Assessment (U.S. EPA, 1995), 3

Guidelines for Reproductive Toxicity Risk Assessment (U.S. EPA, 1996), Guidelines for 4

Neurotoxicity Risk Assessment (U.S. EPA, 1998), Science Policy Council Handbook : Risk 5

Characterization (U.S. EPA, 2000a), Benchmark Dose Technical Guidance Docum ent 6

(U.S. EPA, 2000b), Supplementary Guidance for Conducting Health Risk Assessment of 7

Chemical Mixtures (U.S. EPA, 2000c), A Review of the Reference Dose and Reference 8

Concentration Processes (U.S. EPA, 2002a), Guidelines for Carcinogen Risk Assessment 9

( U .S. EPA , 2005a) , Supplemental Guidance for Assessing Susceptibility from Early

- Life 10

Exposure to Carcinogens ( U.S. EPA , 2005b), Science Policy Council Handbook: Peer Review 11

(U.S. EPA, 2006a), and A Framework for Assessing Health Risks of Environmental Expos ures to 12

Children (U.S. EPA, 2006b). 13

The literature search strategy employed for this compound was based on the Chemical 14

Abstracts Service Registry Number (CASRN) and at least one common name. Any pertinent 15

scientific information submitted by the public to the IRIS Submission Desk was also considered 16

in the development of this document. The relevant literature was reviewed through January, 17

200 9

. 182

- 1 DRAFT

- DO NOT CITE OR QUOTE 2. CHEMICAL AND PHYSICA L INFORMATION

Methanol is also known as methyl alcohol, wood alcohol; Carbinol; Methylol; c olonial 1

spirit; columbian spirit; methyl hydroxide; monohydroxymethane; pyroxylic spirit; wood 2

naphtha; and wood spirit (Chemfinder

, 2002). Some relevant physical and chemical properties 3

are listed below (Hazardous Substances Data Bank [H SD B]

, 2002, Inter national Programme on 4

Chemical Safety [IPCS]

, 1997). 5

Table 2

- 1. R elevant physical and chemical properties of methanol

CASRN: 67

- 56

- 1

Empirical formula: CH 3 OH

Molecular weight: 32.04

Vapor pressure: 160 mmHg at 30 o C

Vapor Density: 1.11

Specific gravity: 0.7866 g/mL (25 o C)

Boiling point: 64.7 o C

Melting point:

- 98 o C

Water solubility: Miscible

Log octanol

- water partition

coefficient: - 0.82 to

- 0.68

Conversion factor (in air): 1 ppm = 1.31 mg/m 3 ;

1 mg/m 3

= 0.763 ppm

Methanol is a cle ar, colorless liquid that has an alcoholic odor (IPCS

, 1997). Endogenous 6

levels of methanol are present in the human body as a result of both metabolism 1 and dietary 7

sources such as fruit, fruit juices, vegetables and alcoholic beverages

, 2 and can be meas ured in 8

exhaled breath and body fluids ( Turner et al ., 2006 ; CERHR 2004; IPCS 1997). Dietary 9

exposure to methanol also occurs through the intake of some food additives. The artificial 10

sweetener aspartame and the beverage yeast inhibitor dimethyl dicarbon ate (DMDC) release 11

methanol as they are metabolized ( Stegi n k et al

., 1989 ). In general, aspartame exposure does not 12

contribute significantly to the background body burden of methanol (Butchko, 2002). Oral, 13

dermal, or inhalation exposure to methanol in th e environment, consumer products, or workplace 14

also occur. 15

Methanol is a high production volume chemical with many commercial uses and i t is a 16

basic building block for numerous chemicals. Many of its derivatives are used in the 17

1 Methanol is generated metabolically through enzymatic pathways such as the methyltransferase system (Fisher

et al., 2000).

2 Fruits and vegetables contain methanol. Further , ripe fruits and vegetables contain natural pectin, which is

degraded to methanol in the body by bacteria present in the colon (Siragusa et al., 1988). Increased levels of

methanol in blood and exhaled breath have also been observed after the consumption of ethanol (Fisher et al., 2000).2

- 2 DRAFT

- DO NOT CITE OR QUOTE construction, housing or a utomotive industries, with its use as a transportation fuel accounting 1

for

~ 20 % of its demand (Methanol Institute, 2006). Consumer products that contain methanol 2

include varnishes, shellacs, paints, windshield washer fluid, antifreeze, adhesives, de

- icers , and 3

Sterno heaters. Methanol is used to produce other chemicals and is among the highest 4

production volume chemicals in the EPA’s Toxic Release Inventory (TRI), which reported that 5

183,000 pounds of methanol was released or disposed of in the United Sta tes in 2003, making 6

methanol among the top 10 chemicals on the list en titled “On

- site and Off

- site Reported 7

Disposed of or Otherwise Released (in pounds), for F acilities in All Industries, for All 8

Chemicals” (U

. S

. EPA, 2006c). Natural gas is the primary f eedstock for methanol and as such, 9

the major portion of its production cost. Due to the high cost of natural gas in North America, 10

North America methanol production capacity is down from 9 million metric tons at the turn of 11

the century to less than 3 mill ion metric tons at the end of 2005 and is expected to be nonexistent 12

by 2010 (Methanol Institute, 2006). While production has switched to other regions of the 13

world, demand for methanol is growing steadily in almost all end uses. A large reason for the 14

i ncrease in demand is its use in the production of biodiesel, a low

- sulfur, high

- lubricity fuel 15

source that is gaining favor in nearly every major region of the world. Starting from less than 16

100,000 metric tons in 2005, global demand for methanol into bio diesel is expected to reach as 17

much as 1.5 million metric tons by the year 2010. Power generation and fuel cells could also be 18

large end users of methanol in the near future (Methanol Institute, 2006). 193

- 1 DRAFT

- DO NOT CITE OR QUOTE 3. TOXICOKINETICS

3.1. OVERVIEW

As has been noted, methano l occurs naturally in the human body as a product of 1

metabolism and through intake of fruits, vegetables, and alcoholic beverages ( Turner et al ., 2006 ; 2

CERHR 2004; IPCS 1997 ). Table 3

- 1 summarizes background blood methanol levels in healthy 3

humans which w ere found to range from 0.25

– 4.7 mg/L. One study reported a higher 4

background blood methanol level in females versus males (Batterman and Franzblau, 1997), but 5

most studies did not evaluate gender differences. Formate, a metabolite of methanol, also occu rs 6

naturally in the human body (IPCS 1997). Table 3

- 1 outlines background levels of formate in 7

human blood. In most cases, methanol and formate blood levels were measured in healthy adults 8

following restriction of methanol

- producing foods from the diet. 3 9

The absorption, excretion, and metabolism of methanol are well known and have been 10

consistently summarized in reviews such as CERHR (2004), IPCS (1997), U.S. EPA (1996), 11

Kavet and Nauss (1990 ), HEI (1987), and Tephly and McMartin (1984). Therefore, the major 12

portion of this toxicokinetics overview is based upon those reviews. 13

Studies conducted in humans and animals demonstrate rapid absorption of methanol by 14

inhalation, oral, and dermal routes of exposure. Table 3

- 2 outlines increases in human blood 15

me thanol levels following various exposure scenarios. Blood levels of methanol following 16

various exposure conditions have also been measured in monkeys, mice, and rats, and are 17

summarized in Tables 3

- 3, 3

- 4, and 3

- 5, respectively. Once absorbed, methanol p harmacokinetic 18

(PK) data and physiologically based pharmacokinetic ( PBPK ) model predictions indicate rapid 19

distribution to all organs and tissues according to water content, as an aqueous

- soluble alcohol. 20

Tissue:blood concentration ratios for methanol are predicted to be similar through different 21

exposure routes, though the kinetics will vary depending on exposure route and timing ( e.g., 22

bolus oral exposure versus longer

- term inhalation). Because smaller species generally have 23

faster respiration rates rel ative to body weight than larger species, they are predicted to have a 24

higher rate of increase of methanol concentrations in the body when exposed to the same 25

concentration in air. 26

At doses that do not saturate metabolic pathways, a small percentage of met hanol is 27

excreted directly in urine. Because of the high blood:air partition coefficient for methanol and 28

rapid metabolism in all species studied, the bulk of clearance therefore occurs by metabolism, 29

though exhalation and urinary clearance will become mo re significant when doses or exposures 30

are sufficiently high to saturate metabolism. Metabolic saturation and the corresponding 31

clearance shift have not been observed in humans and non human primates because doses used 32

3 In general, background levels among people who are on normal/non

- restricted diets will be higher than those

reported.3

- 2 DRAFT

- DO NOT CITE OR QUOTE were limited to the linear range, but the enzymes involved in primate metabolism are also 1

saturable. 2

The primary route of methanol elimination in mammals is through a series of oxidation 3

reactions that form formaldehyde, formate, and carbon dioxide ( Figure 3

- 1). As noted in 4

Figure 3

- 1, metha nol is converted to formaldehyde by alcohol dehydrogenase

- 1 (ADH1) in 5

primates and by catalase ( CAT ) and ADH1 in rodents. Although the first step of metabolism 6

occurs through different pathways in rodents and non human primates, Kavet and Nauss (1990) 7

repo rt that the reaction proceeds at similar rates (V

max = 30 and 48 mg/h/kg in rats and non human 8

primates, respectively). In addition to enzymatic clearance, methanol can react with hydroxyl 9

radicals to spontaneously yield formaldehyde (Harris et al ., 2003). Mannering et al . (1969) also 10

reported a similar rate of methanol metabolism in rats and monkeys, with 10 and 14% of a 1 g/kg 11

dose oxidized in 4 hours, respectively; the rate of oxidation by mice was about twice as fast, 25% 12

in 4 hours. In an HEI study b y Pollack and Brouwer (1996), the metabolism of methanol was 13

2 times as fast in mice versus rats

, with a V

max for elimination of 117 and 60.7 mg/h/kg, 14

respectively. Despite the faster elimination rate of methanol in mice versus rats, mice 15

consistently exh ibited higher blood methanol levels than rats when inhaling equivalent methanol 16

concentrations (See Tables 3

- 4 and 3

- 5). Possible explanations for the higher methanol 17

accumulation in mice include faster respiration (inhalation rate/body weight) and increa sed 18

fraction of absorption by the mouse (Perkins et al ., 1995 a ). Because smaller species generally 19

have faster breathing rates than larger species, humans would be expected to absorb methanol via 20

inhalation more slowly than rats or mice inhaling equivalen t concentrations. If humans eliminate 21

methanol at a comparable rate to rats and mice, then humans would also be expected to 22

accumulate less methanol than those smaller species. However, if humans eliminate methanol 23

more slowly than rats and mice, such th at the ratio of absorption to elimination stays the same, 24

then humans would be expected to accumulate methanol to the same internal concentration but to 25

take longer to reach that concentration. 26

In all species, formaldehyde is rapidly converted to formate

, with the half

- life for 27

formaldehyde being

~ 1 minute. Formaldehyde is oxidized to formate by two metabolic 28

pathways (Teng et al ., 2001). The first pathway (not shown in Figure 3

- 1) involves conversion 29

of free formaldehyde to formate by the so

- called low

- affinity pathway (affinity = 1/K

m = 30

0.002 / **μ** M) mitochondrial aldehyde dehydrogenase

- 2 (ALDH2). The second pathway 31

( Figure 3

- 1) involves a two

- enzyme system that converts glutathione

- conjugated formaldehyde 32

( S

- hydroxymethylglutathione [ HMGSH ] ) to the interm ediate S

- formylglutathione, which is 33

subsequently metabolized to formate and glutathione (GSH) by S

- formylglutathione hydrolase. 4 34

The first enzyme in this pathway, formaldehyde dehydrogenase

- 3 (ADH3), is rate limiting

, and 35

the affinity of HMGSH for ADH3 (a ffinity = 1/K

m = 0.15 / **μ** M) is about a 100

- fold higher than 36

4 Other enzymatic pathways for the oxidation of formaldehyde have been identified in other organisms, but this is

the pathway that is recognized as being present in humans (Caspi et al., 2006; http://metacyc.org )3-3 DRAFT

- DO NOT CITE OR QUOTE that of free formaldehyde for ALDH2. In addition to the requirement of GSH for ADH3 activity, 1

oxidation by ADH3 is nicotinamide adenine dinucleotide- ( NAD + -)dependent. Under normal 2

physiological c onditions NAD + levels are about two orders of magnitude higher than NADH, 3

and intracellular GSH levels (mM range) are often high enough to rapidly scavenge 4

formaldehyde (Svensson et al ., 1999; Meister and Adnerson, 1983); thus, the oxidation of 5

HMGSH is fa vorable. In addition, genetic ablation of ADH3 results in increased formaldehyde 6

toxicity (Deltour et al ., 1999). These data indicate that ADH3 is likely to be the predominant 7

enzyme responsible for formaldehyde oxidation at physiologically relevant concentrations, 8

whereas ALDHs likely contribute to formaldehyde clearance at higher concentrations (Dicker 9

and Cederbaum, 1986 ). 10

11

Primates CH 3 OH (Methanol) Rodents

Alcohol dehydrogenase

(ADH1) 

# ↓

HCHO

(Formaldehyde) 

# ↓ ( + GSH)

HMGSH

(hydroxymethyl- GSH) CAT and ADH1

Formaldehyde dehydrogenase

(ADH3)

S -formylglutathione hydrolase 

# ↓

( S -formyl glutathione) 

# ↓ (

- GSH)

HCOO (Formate) Formaldehyde dehydrogenase

(ADH3)

S -formylglutathione hydrol ase

Folate dependent pathway

(see Figure 3-2) 

# ↓

↓

CO 2 (Carbon dioxide) CAT -peroxide and

Folate -dependent pathway

(see Figure 3-2)

Fi gure 3-1. Methanol metabolism and key metabolic enzymes in primates and r odents.

Source: IPCS (1997).

Rodents convert formate to carbon dioxide (CO 2 ) through a folate-dependent enzyme 12

system and a CAT -peroxide system (Dikalova et al ., 2001). Formate can undergo adenosine 13

triphosphate- (ATP

- ) dependent addition to tetrahydrofolate (THF), which can carry either one or 14

two one-carbon groups. Formate can conjugate with THF to form N 10 -f ormyl

- THF and its 153

- 4 DRAFT

- DO NOT CITE OR QUOTE isomer N 5

- f ormyl

- THF, both of which can be converted to N 5

, N 10

- m eth en yl

- THF and 1

subsequently to other derivatives that are ultimately incorporated into DNA and proteins v ia 2

biosynthetic pathways ( Figure 3

- 2). There is also evidence that formate generates CO 2 - radicals, 3

and can be metabolized to CO 2 via CAT and via the oxidation of N 10

- f ormyl

- THF (Dikalova 4

et al ., 2001 ). 5

6

Figure 3

- 2

. Folate

- d ependent formate metabolism. Tetrahydrofolate (THF)

- mediated

one carbon metabolism is required for the synthesis of purines, thymidylate, and

methionine.

Source: Montserrat et al

. ( 2006 ).

Unlike rodents, formate metabolism in primates occurs solely throu gh a folate

- dependent 7

pathway. Black et al . (1985) reported that hepatic THF levels in monkeys are 60% of that in rats, 8

and that primates are far less efficient in clearing formate than are rats and dogs. Studies 9

involving [ 14 C]formate suggest that

~ 80% is exhaled as 14 CO 2 , 2

- 7% is excreted in the urine, and 10

~ 10% undergoes metabolic incorporation (Hanzlik et al ., 2005, and references therein). Mice 11

deficient in formyl

- THF dehydrogenase exhibit no change in LD 50 (via intraperitoneal [ i.p. ] ) for 12

methanol o r in oxidation of high dose s of formate

. T hus it has been suggested that rodents 13

efficiently clear formate via folate

- dependent pathways, peroxidation by CAT, and by an 14

unknown third pathway; conversely

, primates do not appear to exhibit such capacity and are 15

more sensitive to metabolic acidosis following methanol poisoning (Cook et al ., 2001)

. 16

Blood methanol and formate levels measured in humans under various exposure 17

scenarios are reported in Table 3

- 2. As noted in Table 3

- 2, 75

- minute to 6

- hour exposu res of 18

healthy humans to 200 parts per million (ppm) methanol vapors, the American Council of 193

- 5 DRAFT

- DO NOT CITE OR QUOTE Governmental Industrial Hygienists (ACGIH) threshold limit value (TLV) for occupational 1

exposure (ACGIH

, 2000), results in increased levels of blood methanol but not formate. A 2

limited number of monitoring studies indicate that levels of methanol in outdoor air are orders of 3

magnitude lower than the TLV (IPCS

, 1997). Table 3

- 3 indicates that exposure of monkeys to 4

600 ppm methanol vapors for 2.5 hours increased b lood methanol but not blood formate levels. 5

Normal dietary exposure to aspartame, which releases 10% methanol during metabolism, is 6

unlikely to significantly increase blood methanol or formate levels (Butchko et al ., 2002). Data 7

in Table 3

- 2 suggest that exposure to high concentrations of aspartame is unlikely to increase 8

blood formate levels; no increase in blood formate levels were observed in adults ingesting 9

“abusive doses” ( 100

– 200 mg/kg) of aspartame (Stegink et al ., 1981). Kerns et al . ( 2002) 10

stud ied the kinetics of formate in 11 methanol

- poisoned patients (mean initial methanol level of 11

57.2 mmol/L or 1

. 83 g/L) and determined an elimination half

- life of 3.4 hours for formate. 12

Kavet and Nauss (1990) estimated that a methanol dose of 11 mM or 210 m g/kg is needed to 13

saturate folate

- dependent metabolic pathways in humans. There are no data on blood methanol 14

and formate levels following methanol exposure of humans with reduced ADH activity or 15

marginal folate tissue levels, a possible concern regarding sensitive populations. As discussed in 16

greater detail in Section 3.2, a limited study in folate

- deficient monkeys demonstrated no 17

increase in blood formate levels following exposure to 900 ppm methanol vapors for 2 hours. In 18

conclusion, limited availabl e data suggest that typical occupational, environmental, and dietary 19

exposures are likely to increase baseline blood methanol but not formate levels in most humans. 20

3.2. KEY STUDIES

Some recent toxicokinetic and metabolism studies ( Burbacher et al

., 2004a

, 199 9 b; 21

Medinsky et al ., 1997; Pollack and Brouwer, 1996; Dorman et al ., 1994) provide key information 22

on interspecies differences, methanol metabolism during gestation, metabolism in the nonhuman 23

primate, and the impact of folate deficiency on the accumulatio n of formate. 24

As part of an effort to develop a physiologically based toxicokinetic model for methanol 25

distribution in pregnancy, Pollack and Brouwer (1996) conducted a large study that compared 26

toxicokinetic differences in pregnant and nonpregnant (NP) r ats and mice. Methanol disposition 5 27

was studied in Sprague

- Dawley rats and CD

- 1 mice that were exposed to 100

– 2,500 mg/kg of 28

b ody w eight pesticide

- grade methanol in saline by intravenous (i.v.) or oral routes. Exposures 29

were conducted in NP rats and mice , pregnant rats on gestation days (GD)7, GD 14, and GD 20, 30

and pregnant mice on GD 9 and GD 18. Disposition was also studied in pregnant rats and mice 31

exposed to 1,000

– 20,000 ppm methanol vapors for 8 hours. Three to five animals were 32

examined at each dose a nd exposure condition. 33

5 Methanol concentrations in whole blood and urine were determined by gas chromatography with flame ionizatio n

detection (Pollack and Kawagoe, 1991)3

- 6 DRAFT

- DO NOT CITE OR QUOTE Based on the fit of various kinetic models to methanol measurements taken from all 1

routes of exposure, the authors concluded that high exposure conditions resulted in nonlinear 2

disposition of methanol in mice and rats. 6 Both linear and nonlinear pathways were observed 3

with the relative contribution of each pathway dependent on concentration. At oral doses of 4

100

– 500 mg/kg of body weight , methanol was metabolized to formaldehyde and then formic 5

acid through the saturable nonlinear pa thway. A parallel, linear route characteristic of passive

6

diffusion accounted for an increased fraction of total elimination at higher concentrations. 7

Nearly 90% of methanol elimination occurred through the linear route at the highest oral dose of 8

2,500 mg/kg of body weight

. 9

Oral exposure resulted in rapid and essentially complete absorption of methanol. No 10

significant change in blood area under the curve (AUC) methanol was seen between NP and 11

GD 7, GD 14 and GD 20 rats exposed to single oral gavage doses of 100 and 2,500 mg/kg, nor 12

between NP and GD 9 and GD 18 mice at 2

, 500 mg/kg. The data as a whole suggested that the 13

distribution of orally and i.v. administered methanol was similar in rats versus mice and in 14

pregnant rodents versus NP rodents with the f ollowing exceptions: 15

**•** There was a statistically significant increase in the ratio of apparent volume of 16

distribution (V d ) to fractional bioavailability (F) by

~ 20% (while F decreased but not 17

significantly), between NP and GD20 rats exposed to 100 mg/kg oral ly. However, this 18

trend was not seen in rats or mice exposed to 2,500 mg/kg, and the result in rats at 19

100 mg/kg could well be a statistical artifact since both V d and F were being estimated 20

from the same data, making the model effectively over

- parameteri zed. 21

**•** There were statistically significant decreases in the fraction of methanol absorbed by the 22

fast process (resulting in a slower rise to peak blood concentrations, though the peak is 23

unchanged) and V

max for clearance between NP and GD 18 mice. No such differences 24

were observed between NP and GD 9 mice. 25

**•** The authors estimated a twofold higher V

max for methanol elimination in mice versus rats 26

following oral administration of 2,500 mg/kg methanol, suggesting that similar oral doses 27

would result in lower met hanol concentrations in the mouse versus rat. 28

Methanol penetration from maternal blood to the fetal compartment was examined in 29

GD 20 rats by microdialysis. 7 A plot of the amniotic concentration versus maternal blood 30

concentration (calculated from digitiz ation of Figure 17 of Pollack and Brouwer [ 1996 ] report) is 31

shown in Figure 3

- 3. The ratio is slightly less than 1:1 (dashed line in plot) and appears to be 32

reduced with increasing methanol concentrations, possibly due to decreased blood flow to the 33

6 A model incorporating parallel linear and nonlinear routes of methanol clearance was required to fit the data from

the highest exposure groups.

7 Microdialysis was conducted by exposing the uterus (midline incision ), selecting a single fetus in the middle of the

uterine horn and inserting a microdialysis probe through a small puncture in the uterine wall proximal to the head of

the fetus.3

- 7 DRAFT

- DO NOT CITE OR QUOTE fetal compartment. Nevertheless, this is a very minor departure from linearity, consistent with a 1

substrate such as methanol that penetrates cellular membranes readily and distributes throughout 2

total body water. 3

Figure 3

- 3. Plot of fetal (amniotic) versus maternal methanol concentrations in GD 20

rats. Da ta extracted from Figure 17 by digitization, and amniotic concentration

obtains as ("Fetal Amniotic Fluid/Maternal Blood Methanol")×("Maternal

Methanol").

Source: Pollack and Brouwer (1996)

.

Inhalation exposure resulted in less absorption in both rats and mice as concentrations of 4

methanol vapors increased, which was hypothesized to be due to decreased breathing rate and 5

decreased absorption efficiency from the upper respirato ry tract. 8 Based on blood methanol 6

concentrations measured following 8

- hour inhalation exposures to concentrations ranging from 7

1,000

– 20,000 ppm, the study authors (Pollack and Brown, 1996) concluded that methanol 8

accumulation in the mouse occurred at a t wo

- to threefold greater rate compared to the rat. They 9

speculated that faster respiration rate and more complete absorption in the nasal cavity of mice 10

may explain the higher methanol accumulation and greater sensitivity to developmental toxicity 11

(see Se ction 4.3.2). 12

8 Exposed mice spent some exposure time in an active state, characterized by a higher ventilation rate, and the

remaining time in an inactive state, with lower (~1⁄2 of active) ventilation. The inactive ventilation rate was

unchanged by methanol exposure, but the active ventilation showed a statistically significant methanol

concentr ation

- related decline. There was also some decline in the fraction of time spent in the active state, but this

too was not statistically significant.3

- 8 DRAFT

- DO NOT CITE OR QUOTE The Pollack and Brouwer (1996) study was useful for comparing effects in pregnant and 1

NP rodents exposed to high dose s, but the implication of these results for humans exposed to 2

ambient levels of methanol is not clear ( CERHR

, 2004). 3

Burbac her et al . ( 2004b, 1999a) examined toxicokinetics in M acaca fascicularis 4

monkeys prior to and during pregnancy. The study objectives were to assess the effects of 5

repeated methanol exposure on disposition kinetics, determine whether repeated methanol 6

exp osures result in formate accumulation, and examine the effects of pregnancy on methanol 7

disposition and metabolism. Reproductive and developmental toxicity associated with this study 8

were also examined and are discussed in Section s 4.3.2 and 4.4.2

. In a 2

- cohort design, 48 adult 9

females (6 animals/dose/group/cohort) were exposed to 0, 200, 600, or 1,800 ppm methanol 10

vapors (99.9% purity) for 2.5 hours/day, 7 days/week for 4 months prior to breeding and during 11

the entire breeding and gestation periods. Si x

- hour methanol clearance studies were conducted 12

prior to and during pregnancy. Burbacher et al . (2004b , 1999a ) reported that: 13

 At no point during pregnancy was there a significant change in endogenous methanol 14

blood levels, which ranged from 2.2

– 2.4 mg/L throughout. 15

 P K studies were performed initially (Study 1), after 90 days of pre

- exposure and prior to 16

mating (Study 2), between GD 66 and GD 72 (Study 3), and again between GD 126 and 17

GD 132 (Study 4). These studies were analyzed using classical PK (one

- compa rtment) 18

models. 19

 Disproportionate mean, dose

- normalized, and net blood methanol dose

- time profiles in 20

the 600 and 1,800 ppm groups suggested saturation of the metabolism

- dependent 21

pathway. Data from the 600 ppm group fit a linear model

, while data from the 1,800 ppm 22

group fit a Michaelis

- Menten model. 23

 Methanol clearance rates modestly increased between Study 1 and Study 2 (90 days prior 24

to mating). This change was attributed to enzyme induction from the subchronic 25

exposure. 26

 Blood methanol levels were mea sure d every 2 weeks throughout pregnancy, and while 27

there was measurement

- to

- measurement variation, there was no significant change or 28

trend over the course of pregnancy. There appears to be an upward trend in elimination 29

half

- life and corresponding downw ard trend in blood methanol clearance between Studies 30

2, 3, and 4

. However, the changes are not statistically significant and the time

- courses 31

for blood methanol concentration (elimination phase) appear fairly similar. 32

 Significant differences between pre

- breeding and gestational blood plasma formate levels 33

were observed but were not dose dependent ( Table 3

- 6). 34

 Significant differences in serum folate levels in periods prior to and during pregnancy 35

were not dose dependent ( Table 3

- 7). 363

- 9 DRAFT

- DO NOT CITE OR QUOTE An HEI review committ ee (Burbacher et al ., 1999a) noted that this wa s a quality study 1

using a relevant species. Although the study can be used to predict effects in adequately 2

nourished individuals, the study may not be relevant to persons who are folate deficient. 3

A series of studies by Medinsky et al . (1997) and Dorman et al . (1994) examined 4

metabolism and pharmacokinetics of [ 14 C]methanol and [ 14 C]formate in normal and folate

5

deficient cynomolgus, M

. fascicularis monkeys that were exposed to environmentally relevant 6

concen trations of [ 14 C]methanol through an endotracheal tube while anesthetized. In the first 7

stage of the study, 4 normal 12

- year

- old cynomolgus monkeys were each exposed to 10, 45, 200, 8

and 900 ppm [ 14 C]methanol vapors (>98% purity) for 2 hours. Each exposur e was separated by 9

at least 2 months. After the first stage of the study was completed, monkeys were given a folate

10

deficient diet supplemented with 1% succinylsulfathiozole (an antibacterial sulfonamide used to 11

inhibit folic acid biosynthesis from intest inal bacteria) for 6

– 8 weeks in order to obtain folate 12

concentrations of <3 ng/mL serum and <120 ng/mL erythrocytes. Folate deficiency did not alter 13

hematocrit level, red blood cell count, mean corpuscular volume, or mean corpuscular 14

hemoglobin level. The folate

- deficient mon keys were exposed to 900 ppm [ 14 C]methanol for 15

2 hours. The results of the Medinsky et al . (1997) and Dorman et al . (1994) studies showed: 16

 Dose

- dependent changes in toxicokinetics and metabolism did not occur as indicated by a 17

linear relationship between inhaled [ 14 C]methanol concentration and end

- of

- exposure 18

blood [ 14 C]methanol level, [ 14 C]methanol AUC and total amounts of exhaled 19

[ 14 C]methanol and [ 14 C]carbon dioxide. 20

 Methanol concentration had no effect on elimination half

- life (<1 hour) and percent 21

uri nary [ 14 C]methanol excretion (<0.01%) at all doses. 22

 Following exposure to 900 ppm methanol, urinary excretion or exhalation of 23

[ 14 C]methanol did not differ significantly between monkeys in the folate sufficient and 24

deficient state. There was no significan t [ 14 C] formate accumulation at any dose. 25

 Peak blood [ 14 C]formate levels were significantly higher in folate

- deficient monkeys, but 26

did not exceed endogenous blood level s reported by the authors to be between 0.1 and 27

0.2 mmol/L (4.6 to 9.2 mg/L). 28

An HEI review committee (Medinsky et al ., 1997) noted that absolute values in this study 29

cannot be extrapolated to humans because the use of an endotracheal tube in anesthetized 30

animals results in an exposure scenario that is not relevant to humans. However, th e data in this 31

study suggest that a single exposure to an environmentally relevant concentration of methanol is 32

unlikely to result in a hazardous elevation in formate levels, even in individuals with moderate 33

folate deficiency. 343

- 10 DRAFT

- DO NOT CITE OR QUOTE 3.3. HUMAN VARIABILITY IN METHANO L METABOLISM

The ability to metabolize methanol may vary among individuals as a result of genetic, 1

age, and environmental factors. Reviews by Agarwal (2001), Burnell et al . (1989), Bosron and 2

Li (1986), and Pietruszko (1980)

, discuss genetic polymorphisms for ADH. Class I ADH, the 3

primary ADH in human liver, is a hetero

- or homodimer composed of randomly associated 4

polypeptide units encoded by three separate gene loci (ADH1A, ADH1B, and ADH1C). 5

Polymorphisms have been found to occur at the ADH1B (ADH1B*2 , ADH1B*3) and ADH1C 6

(ADH1C*2) gene loci; however, no human allelic polymorphism has been found in ADH1A. 7

The ADH1B*2 phenotype is estimated to occur in

~ 15% of Caucasians of European descent, 8

85% of Asians, and < 5% of African Americans. Fifteen percent of African Americans have the 9

ADH1B*3 phenotype, while it is found in < 5% of Caucasian Europeans and Asians. To date, 10

there are two reports of polymorphisms in ADH3 ( Cichoz

- Lach et al ., 2007; Hedberg et al

., 11

2001), yet the functional consequence(s) for th ese polymorphisms remains unclear. 12

Although racial and ethnical differences in the frequency of the occurrence of ADH 13

alleles in different populations have been reported, ADH enzyme kinetics (V

max and K

m ) have not 14

been reported for methanol. There is an a bundance of information pertaining to the kinetic 15

characteristics of the ADH dimers to metabolize ethanol in vitro; however, the functional and 16

biological significance is not well understood due to the lack of data documenting metabolism 17

and disposition of methanol or ethanol in individuals of known genotype. While potentially 18

significant, the contribution of ethnic and genetic polymorphisms of ADH to the interindividual 19

variability in methanol disposition and metabolism can not be reliably quantified at t his time. 20

Because children generally have higher baseline breathing rates and are more active, they 21

may receive higher methanol doses than adults exposed to equivalent concentrations of any air 22

pollutant ( CERHR

, 2004). There is evidence that children und er 5 years of age have reduced 23

ADH activity. A study by Pikkarainen and Raiha (1967) measured liver ADH activity using 24

ethanol as a substrate and found that 2

- month

- old fetal livers have

~ 3

– 4% of adult ADH liver 25

activity. ADH activity in 4

– 5 month old fe tuses is

~ 10% of adult activity, and a n infant ’ s activity 26

is

~ 20% of adult activity. ADH continues to increase in children with age and reaches a level 27

that is within adult ranges at 5 years of age. Adults were found to have great variation in ADH 28

activi ty (1

, 625

– 6

, 530/g liver wet weight or 2

, 030

– 5

, 430 mU/100 mg soluble protein). Smith 29

et al . (1971) also compared liver ADH activity in 56 fetuses (9

– 22 weeks gestation), 37 infants 30

(premature to <1 year old), and 129 adults (>20 years old) using ethanol as a substrate. ADH 31

activity was 30% of adult activity in fetuses and 50% of adult activity in infants. There is 32

evidence that some human infants are able to efficiently eliminate methanol at high exposure 33

levels, however, possibly via CAT (Tran et al ., 20 07). 34

ADH3 exhibits little or no activity toward small alcohols, thus the previous studies 35

provide no information about the ontogeny of formaldehyde clearance. While such data on 363

- 11 DRAFT

- DO NOT CITE OR QUOTE ADH3 activity does not exist, ADH3 mRNA is abundantly expressed in the mouse fetus (Ang 1

et al ., 1996) and is detectible in human fetal tissues (third trimester), neonates and children 2

( Hines et al ., 2002; Estonius et al ., 1996). 3

As noted earlier in this s ection, folate

- dependent reactions are important in the 4

metabolism of format e. Individuals who are commonly folate deficient include those who are 5

pregnant or lactating, have gastrointestinal (GI) disorders, have nutritionally inadequate diets, are 6

alcoholics, smoke, have psychiatric disorders, have pernicious anemia, or are taki ng folic acid 7

antagonist medications such as some antiepileptic drugs ( CERHR , 2004; IPCS

, 1997). Groups 8

which are known to have increased incidence of folate deficiencies include Hispanic and African 9

American women, low

- income elderly, and mentally ill el derly ( CERHR

, 2004). A 10

polymorphism in methylene tetrahydrofolate reductase reduces folate activity and is found in 11

21% of Hispanics in California and 12% of C aucasians in the United States

. Genetic variations 12

in folic acid metabolic enzymes and folate r eceptor activity are theoretical causes of folate 13

deficiencies. 14

Table 3

- 1

. Background blood methanol and formate levels in humans

Description of human subjects Methanol (mg/L)

mean **±** S.D.

(Range) Formate (mg/L)

mean **±** S.D.

(Range) Reference

12 males on r estricted diet (no

methanol

- containing or methanol

producing foods) for 12 hr 0.570 **±** 0.305

(0.25

– 1.4) 3.8 **±** 1.1

(2.2

– 6.6)

Cook et al . (1991)

22 adults on restricted diet (no

methanol

- containing or methanol

producing foods) for 24 hr 1.8 **±** 2.6

(No range data) 11.2 **±** 9.1

(No range data) Osterloh et al

. (1996) ;

Chuwers et al . (1995)

3 males who ate a breakfast with no

aspartame

- containing cereals and no

juice 1.82 **±** 1.21

(0.57

– 3.57) 9.08 **±** 1.26

(7.31

– 10.57) Lee et al . (1992)

5 males who ate a breakfast with no

aspartame

- containing cereals and no

juice (s econd experiment) 1.93 **±** 0.93

(0.54

– 3.15) 8.78 **±** 1.82

(5.36

– 10.83) Lee et al . (1992)

Adults who drank no alcohol for 24 hr 1.8 **±** 0.7

(No range data) No data Batterman et al . (1998)

12 adults who drank n o alcohol for

24 hr 1.7 **±** 0.9

(0.4

– 4.7) No data Batterman and

Franzblau (1997)

4 adult males who fasted for 8 hr , drank

no alcohol for 24 hr , and took in no

fruits, vegetables, or juices for 18 hr No mean data

(1.4

– 2.6) No data Davoli et al . (1986)

30 fa sted adults <4

(No range data) 19.1

(No range data) Stegink et al . (1981)

24 fasted infants <3.5

(No range data) No data Stegink et al . (1983)

Source: CERHR (2004).3

- 12 DRAFT

- DO NOT CITE OR QUOTE Table 3

- 2

. Human blood methanol and formate levels following methanol exposure

Human subjects;

type of sample collected b,c Exposure

r oute Exposure

d uration or

m ethod Methanol

e xposure

c oncentration Blood

m ethanol

mean or

range

(mg/L) Blood

f ormate

mean or

range

(mg/L) Reference

Adult males and females

administered aspartame; peak

methanol level and range of

formate levels up to 24 hr after

dosing Oral 1 dose in

juice 0 19.1

Stegink et al

. (1981) 3.4 mg/kg bw a <4 No data

10 mg/kg bw a 12.7 No data

15 mg/kg bw a 21.4 No data

20 mg/kg bw a 25.8 8.4

– 22.8

Infant s administered

aspartame; peak exposure

level Oral 1 dose in

beverage 0

No data Stegink et al

. (1983) 3.4 mg/kg bw a <3.5

5 mg/kg bw a 3.0

10 mg/kg bw a 10.2

Adult males administered

aspartame; range of peak

serum methanol levels in all

subjects Oral 1 dose in

water 0

0.6

**−** 0.87

mg/kg bw a 1.4

**−** 2.6

2.4

**−** 3.6

No data Davoli et al

. (1986)

Males; postexposure samples Inhalation 75 min 0 0.570 3.8

3.6 Cook et al

. (1991) 191 ppm 1.881

Males and females;

postexposure serum levels Inhalatio n 4 hr 0 1.8 11.2 Osterloh

et al . (1996) 200 ppm 6.5 14.3

Males without exercise;

postexposure blood methanol

and plasma formate Inhalation 6 hr 0

200 ppm 1.82

6.97 9.08

8.70 Lee et al

. (1992) Males with exercise;

postexposure blood methanol

and pla sma formate Inhalation 6 hr 0 1.93 8.78

200 ppm 8.13 9.52

Females; postexposure

samples Inhalation 8 hr 0

800 ppm 1.8

30.7 No data Batterman

et al .(1998)

a Methanol doses resulting from intake of aspartame.

b Unless otherwise specified, it is assume d that whole blood was used for measurements.

c Information about dietary restrictions is included in Table 3

- 1.

Source: CERHR (2004).3

- 13 DRAFT

- DO NOT CITE OR QUOTE Table 3

- 3

. Monkey blood methanol and formate levels following methanol exposure

Strain

- s ex Exposure

r oute Exposure

d uration Methanol

e xposure

c oncentration Blood

m ethanol

mean

in mg/L Blood

f ormate

mean

in mg/L Reference

Monkey; Cynomolgus ;

female; mean blood

methanol and range of

plasma formate at 30 min

post daily exposure

during premating, mating,

and pregnancy Inhalation 2.5 hr /day,

7days/wk

during

premating,

mating, and

gestation

(348 days) 0

200 ppm

600 ppm

1,800 ppm 2.4

5

11

35 8.7

8.7

8.7

10 Burbacher et al

. ( 2004b, 1999a)

Monkey; Rhesus male;

postexposure blood level Inhalation 6 hr 200 ppm

1,200 ppm

2,0 00 ppm 3.9

37.6

64.4 5.4

– 13.2

at all doses Horton et al

. (1992)

Source: CERHR (2004).

Table 3

- 4

. Mouse blood methanol and formate levels following methanol exposure

Species/ s train/ s ex Exposure

r oute Exposure

d uration Methanol e xposure

c oncentration Bl ood

m ethanol

mean

(mg/L) Blood

f ormate

mean

(mg/L ) Reference

M ouse ; CD

- 1 ; female;

postexposure plasma

methanol and peak

formate level Inhalation 6 hr on

GD8 10,000 ppm 2,080 28.5

Dorman et al

. (1995) 10,000 ppm + 4

- MP 2,400 23

15,000 ppm 7,140 3 4.5

M ouse ; CD

- 1 ; female;

postexposure blood

methanol level Inhalation 8 hr 2,500 ppm 1,883

No data Pollack and

Brouwer (1996) ;

Perkins et al

. (1995 a ) 5,000 ppm 3,580

10,000 ppm 6,028

15,000 ppm 11,165

M ouse; CD

- 1 ; female;

mean postexposure plasma

methanol level Inhalation 7 hr/ day on

GD6

– GD 15 0 1.6

No data

Rogers et al

. (1993a) 1,000 ppm 97

2,000 ppm 537

5,000 ppm 1,650

7,500 ppm 3,178

10,000 ppm 4,204

15,000 ppm 7,330

M ouse; CD

- 1 ; female;

plasma level 1 h r

postdosing Oral

Gavage GD6

– GD 15 4,000 mg / kg bw 3,856 No data

M ouse ; CD

- 1 ; female; peak

plasma level Oral

Gavage GD8 1,500 mg/kg bw

1,500 mg/kg bw +

4

- MP 1,610

1,450 35

43 Dorman et al

. (1995)

4

- MP=4

- methylpyrazole

Source: CERHR (2004).3

- 14 DRAFT

- DO NOT CITE OR QUOTE Table 3

- 5

. R at blood methanol and formate levels following methanol exposure

Species ; strain/sex:

type of sample

collected Exposure

r oute Exposure

d uration Methanol e xposure

c oncentration Blood

m etha nol

l evel in mg/L Blood

f ormate

l evel in

mg/L Reference

Rat ; Sprague

- Dawley ;

female; postexposure

blood methanol level

on 3 days Inhalation 7 hr /day for

19 days 5,000 ppm

10,000 ppm

20,000 ppm 1,000

**−** 2,170

1,840

**−** 2,240

5,250

**−** 8,650 No data Nelson et al

. (1985)

Rat ; Sprague

- Dawley ;

female; postexposure

blood methanol level Inhalation 8 hr 1,000 ppm

5,000 ppm

10,000 ppm

15,000 ppm

20,000 ppm 83

1,047

1,656

2,667

3,916 No data Pollack and

Brouwer

(1996) ;

P erkins et al

. (1995 a )

Rat ; LongEvans ; female;

postexposure plasma

level on GD7

**−** GD 12 Inhalation 7 hr /day on

GD7

**−** GD

19 0

15,000 ppm 2.7

**−** 1.8

3,826

**−** 3,169 No data Stanton et al

. (1995)

Rat ; LongEvans ; female;

1 hr postexposure

blood level Inhalation 6 hr /day on

GD6

**−**

PND 21 4,500 ppm 555 No data

Weiss et al

. (1996) Rat ; Long

- Evans ; male

and female; 1 hr

postexposure blood

level in pups Inhalation 6 hr/ day on

PND 1

**−** PND

21 4,500 ppm 1,260 No data

Rat/Fischer

- 344 **/** male;

postexposure blood

level Inhalation 6 hr 200 p pm

1,200 ppm

2,000 ppm 3.1

26.6

79.7 5.4

**−** 13.2 at

all doses Horton et al

. (1992)

Rat ; Long

- Evans ; male;

post

- exposure serum

level Inhalation 6 hr 200 ppm

5,000 ppm

10,000 ppm 7.4

680

**−** 873

1,468 No data Cooper et al

. (1992)

Rat ; Long

- Evans ; male;

peak blood fo rmate

level Inhalation 6 hr 0 FS

0 FS

1,200 ppm

– FS

1,200 ppm

– FR

2,000 ppm

– FS

2,000 ppm

– FR No data 8.3

10.1

8.3

46

8.3

83

Lee et al

. (1994)

Rat ; Long

- Evans ; male;

peak blood methanol

and formate Oral

g avage Single dose 3,500 mg/kg bw

– FS

3,500 mg/kg bw

– FP

3,5 00 mg/kg bw

– FR

3,000 mg/kg

bw/day

– FS

3,000 mg/kg bw/day

FR

2,000 mg/kg bw/day

FS

2,000 mg/kg bw/day

FR 4,800

4,800

4,800

No data Baseline

level

382

860

9.2

718

9.2

538

FS = Folate s ufficient; FR = Folate r educed; FP = Folate p aire

Source: CERHR (2004).3

- 15 DRAFT

- DO NOT CITE OR QUOTE Table 3

- 6

. Plasma formate concentrations in monkeys

Exposure g roup Mean plasma formate level (mg/L) during each exposure period

Baseline Pre

- breeding Breeding Pregnancy

Control 8.3 7.8 10 8.3

200 ppm 7.4 8.3 9.7 7.8

600 ppm 6.9 7.8 9.2 8.7

1800 p pm 6.4 8.7 11 10

Source: Burbacher et al . (1999a).

Table 3

- 7. Serum folate concentrations in monkeys

Exposure g roup Mean serum folate level (μg/L) during each exposure period

Baseline Day 70

Pre

- pregnancy a Day 98

Pre

- pregnancy a Day 55

Pregnancy a Day 113

Pregnancy a

Control 14.4 14.0 13.4 16.0 15.6

200 ppm 11.9 13.2 12.9 15.5 13.4

600 ppm 12.5 15.4 13.4 14.8 16.4

1800 ppm 12.6 14.8 15.3 15.9 15.7

a Number of days exposed to methanol

Source: Burbacher et al . (1999a)

.

3.4. PHYSIOLOGICALLY BASE D TOXICOKINETIC MODE LS

In accordance with the needs of this human health risk assessment, particularly the 1

derivation of human health effect benchmarks from studies of the developmental effects of 2

methanol inhalation exposure in mice (Rogers et al ., 1993a) and rats (NEDO, 1987) and 3

carcinogenic effects of methanol in rats exposed via drinking water (Soffritti et al ., 2002a) and 4

inhalation (NEDO, 1987, 198 5/2008b), mouse and rat models were developed to allow for the 5

estimation of mouse and rat internal dose metrics. A human model was developed to extrapolate 6

those internal metrics to inhalation and oral exposure concentrations that would result in the 7

sam e internal dose in humans ( human equivalent concentrations [ HECs ] and human equivalent 8

doses [ HEDs ] ). The procedures used for the development, calibration and use of these models 9

are summarized in this section, with further details provided in Appendix B, “Development, 10

Calibration and Application of a Methanol PBPK Model.” 11

3.4.1. Model Requirements for EPA Purposes

3.4.1.1. MOA and Selection of a Dose Metric

Dose metrics closely associated with one or more key events that lead to the selected 12

critical effect are preferred for dose

- response analyses compared to metrics not clearly 13

correlated. For instance, internal ( e.g., blood, target tissue) measures of dose are preferred over 14

external measures of dose ( e.g., atmospheric or drinking water concentrations), especially when

, 15

as with methanol, blood methanol concentrations increase disproportionally with dose ( Rogers 163

- 16 DRAFT

- DO NOT CITE OR QUOTE et al ., 1993a)

. This is likely due to the saturable metabolism of methanol in rodents. In addition, 1

respiratory and GI absorption may vary between and within s pecies. Mode of action ( MOA ) 2

considerations can also influence whether to model the parent compound with or without its 3

metabolites for selection of the most adequate dose metric. 4

As discussed in Section 4, developmental effects following methanol exposur es have 5

been noted in both rats and mice, but are not as evident or clear in primate exposure studies 6

(Burbacher et al ., 2004a; Clary, 2003; Rogers et al ., 1993a, 1993b; Andrews et al

., 1987; Nelson 7

et al ., 1985)

, and carcinogenic effects have been observe d in a drinking water study of Sprague

8

Dawley rats (Soffritti et al ., 2002a) and an inhalation study of F344 rats (NEDO, 1985/2008b). 9

The report of the New Energy Development Organization (NEDO, 1987) of Japan, which 10

investigated developmental effects of methanol in rats, indicated that there is a potential that 11

developing rat brain weight is reduced following maternal and neonatal exposures. These 12

exposures included both in utero and postnatal exposures. The methanol PBPK models 13

developed for this asses sment do not explicitly describe these exposure routes. Mathematical 14

modeling efforts have focused on the estimation of human equivalent external exposures that 15

would lead to internal blood levels of methanol or its metabolites presumed to be associated w ith 16

developmental effects as reported in rats (NEDO, 1987) and mice (Rogers et al ., 1993a)

, and 17

carcinogenic effects as reported in rats by Soffritti et al . (2002a). 18

In a recent review of the reproductive and developmental toxicity of methanol, a panel of 19

experts concluded that methanol, not formate, is likely to be the proximate teratogen and 20

determined that blood methanol level is a useful biomarker of exposure ( CERHR , 200 4 ; Dorman 21

et al ., 1995)

. The CERHR Expert Panel based their assessment of potential methanol toxicity on 22

an assessment of circulating blood levels ( CERHR , 2004 )

. While recent in vitro evidence 23

indicates that formaldehyde is more embryotoxic than methanol and formate (Harris et al ., 200 4, 24

200 3 ), the high reactivity of formaldehyde would limit its unbound and unaltered transport as 25

free formaldehyde from maternal to fetal blood (Thrasher and Kilburn , 2001)

, and the capacity 26

for the metabolism of methanol to formaldehyde is likely lower in the fetus and neonate versus 27

adults (see discussion in Section 3.3) . Thus, even if formaldehyde is ultimately identified as the 28

proximate teratogen, methanol would likely play a prominent role, at least in terms of transport 29

to the target tissue. Further discussions of methanol metabolism, dose metric se lection

, and 30

MOA issues are covered in Sections 3.3, 4.6

, 4.8 and 4. 9

. 2. 31

It has been suggested that the lymphomas observed in Sprague

- Dawley rats following 32

methanol exposure are associated with formaldehyde because formaldehyde and other 33

compounds that me tabolize to formaldehyde have been reported to cause lymphomas in Sprague

34

Dawley rats (Soffritti et al ., 2005). Given the reactivity of formaldehyde, models that predict 35

levels of formaldehyde in the blood are difficult to validate. However, production o f 36

formaldehyde or formate following exposure to methanol can be estimated by summing the total 373

- 17 DRAFT

- DO NOT CITE OR QUOTE amount of methanol cleared by metabolic processes. 9 This metric of formaldehyde or formate 1

dose has limited value since it ignores important processes that may differ between species

, such 2

as clearance of these two metabolites, but it can be roughly be equated to the total amount of 3

metabolites produced and may be the more relevant dose metric if formaldehyde is found to be 4

the proximate toxic moiety

. Thus, both blood methanol and total metabolism metrics are 5

considered to be important components of the PBPK models. Dose metric selection and MOA 6

issue s are discussed further in Sections 3.3, 4.6 , 4.8 and 4. 9 .2

. 7

3.4.1.2. Criteria for the Development of Methanol PBPK Mode ls

The development of methanol PBPK models that would meet the needs of this 8

assessment was organized around a set of criteria that reflect: 1) the MOA (s) being considered 9

for methanol; 2) absorption, distribution, metabolism

, and elimination characteristi cs; 3) dose 10

routes necessary for interpreting toxicity studies or estimating HECs ; and 4) general parameters 11

needed for the development of predictive PK models. 12

The criteria with a brief justification are provided below: 13

1) Must simulate blood methanol conc entrations and total methanol metabolic clearance

. 14

Blood methanol is the recommended dose metric for developmental effects, but total 15

metabolic clearance may be a useful metric, particularly for cancer endpoint s. 16

2) Must be capable of simulating experimental blood methanol and total metabolic 17

clearance (mg / day) data for the inhalation route of exposure in mice and rats (a) and 18

humans (b), and the oral route in rats (c) and humans (d). T hese routes are important for 19

determining dose metrics in the most sensit ive test species under the conditions of the 20

toxicity study and in the relevant exposure routes in humans. 21

3) The model code should easily allow designation of respiration rates during inhalation 22

exposures. A standard variable in inhalation route risk asses sments is ventilation rate. 23

Blood methanol concentrations will depend strongly on ventilation rate, which varies 24

significantly between species. 25

4) Must address the potential for saturable metabolism of methanol. Saturable metabolism 26

has the potential to br ing non linearities into the exposure:tissue dose relationship. 27

5) Model complexity should be consistent with modeling needs and limitations of the 28

available data. Model should adequately describe the biological mechanisms that 29

determine the internal dose met rics (blood methanol and metabolic clearance) to assure 30

that it can be reliably used to predict those metrics in exposure conditions and scenarios 31

where data are lacking

. Compartments or processes should not be added that cannot be 32

adequately characterize d by the available data. 33

9 This assumption is more likely to be appropriate for formaldehyde than formate as formaldehyde is a dir ect

metabolite of methanol.3

- 18 DRAFT

- DO NOT CITE OR QUOTE Although the rat and mouse models are useful for the evaluation of the dose metrics 1

associated with methanol’s developmental effects and the relevant toxicity studies

, includ ing 2

gestational exposures, no pregnancy

- specific PBPK m odel exists for methanol

, and inadequate 3

data exists for the development and validation of a fetal/gestational/conceptus compartment. 4

However, EPA determined that non pregnancy models for the appropriate species and routes of 5

exposure could prove to be val uable because levels of methanol in NP , pregnant and fetal blood 6

are expected to be similar following the same oral or inhalation exposure. Pollack and Brouwer 7

(1996) determined that methanol distribution in rats and mice following repeated oral and i.v. 8

exposures up to day 20 of gestation is “virtually unaffected by pregnancy, with the possible 9

exception of the immediate perinatal period.” T he critical window for methanol induction of 10

cervical rib malformations in CD

- 1 mice has been identified as occurri ng between GD6 and GD 7 11

(Rogers and Mole, 1997; Rogers et al ., 1993b) , a developmental period roughly equivalent to 12

week 3 of human development (Chernoff and Rogers, 2004)

. Methanol blood kinetics measured 13

during and after inhalation exposure in NP and pre gnant mice on GD6

– GD 10 and GD6

– GD 15 14

(Perkins et al ., 199 6 , 1995 a ; Dorman et al ., 1995; Rogers et al

. , 1993a) are also similar. Further, 15

the available data indicate that the maternal blood:fetal partition coefficient is approximately 1 at 16

dose levels most relevant to this assessment ( Ward et al ., 1997; Horton et al ., 1992). The same 17

has been found in rat (Zorzano et al

., 1989; Guerri and Sanchis

, 1985) and sheep (Brien et al

., 18

1985 ; Cumming et al

., 1984) studies of ethanol, a structurally related chemical that also 19

penetrates cellular membranes readily and distributes throughout total body water. 20

Consequently, fetal methanol concentrations are expected to be roughly equivalent to that in the 21

mother’s blood. Thus, pharmacokinetics and blood dose metrics fo r NP mice and humans are 22

expected to provide reasonabl e approximation s of pregnancy levels and fetal exposure, 23

particularly during early gestation , that improve upon default estimations from external exposure 24

concentrations

. 25

3.4.2. Methanol PBPK Models

As has be en discussed, methanol is well absorbed by both inhalation and oral routes and 26

is readily metabolized to formaldehyde, which is rapidly converted to formate in both rodents 27

and humans. As was discussed in Section 3.1, the enzymes responsible for metaboliz ing 28

methanol are different in rodents and humans. Several rat, mouse and human PBPK models 29

which attempt to account for these species differences have been published ( Fisher et al ., 2000; 30

Ward et al ., 1997; Perkins et al ., 1995a; Horton et al ., 1992)

. In addition, a gestational model for 31

a similar water soluble compound, isopropanol, with the potential to be adapted to methanol 32

pharmacokinetics, was of interest ( Gentry et al ., 200 3 , 2002 ; Clewell et al

. , 200 1 ). Three PK 33

models ( Gentry et al

. , 2002 ; Bouch ard et al

. , 2001 ; Ward et al

. , 1997 ) were identified as 34

potentially appropriate for use in animal

- to

- human extrapolation of methanol metabolic rates and 35

blood concentrations. An additional methanol PBPK model by Fisher et al . (2000) was 363

- 19 DRAFT

- DO NOT CITE OR QUOTE considered princip ally because it ha d an important feature

– pulmonary compartmentalization 1

(see below for details)

– worth adopting in the final model. 2

3.4.2.1. Ward et al . (1997)

The PBPK model of Ward et al . (1997) describes inhalation, oral and i.v. routes of 3

exposure and is pa rameterized for both NP and pregnant mice and rats ( Table 3

- 8). The model 4

has not been parameterized for humans. 5

Respiratory uptake of methanol is described as a constant infusion into arterial blood at a 6

rate equal to the minute ventilation times the in haled concentration and includes a parameter for 7

respiratory bioavailability, which for methanol is < 100%. This simple approach is non standard 8

for volatile compounds but is expected to be appropriate for a compound like methanol, for 9

which there is little clearance from the blood via exhalation. Oral absorption is described as a 10

biphasic process, dependent on a rapid and a slow first

- order rate constant. This is conceptually 11

similar to the isopropanol model discussed below ( Gentry et al ., 2002; Clewell e t al ., 2001), 12

which also employs slow and fast absorption processes but functionally separates them into 13

stomach and duodenal compartments. 14

Methanol elimination in the Ward et al . (1997) model is primarily via saturable hepatic 15

metabolism. The parameters describing this metabolism come from the literature, primarily 16

previous work by Ward and Pollack ( 1996 ) and Pollack et al

. ( 1993)

. A first

- order elimination of 17

methanol from the kidney compartment includes a lumped metabolic term that accounts for both 18

re nal and pulmonary excretion. 19

The model adequately fits the experimental blood kinetics of methanol in rat and mice 20

and is therefore sui table for simulating blood dosimetry in the relevant test species and routes of 21

exposure (oral and i.v.). The Ward et a l . (1997) model meets criteria 1, 2a, 2c, 3, 4

, and 5. The 22

most significant limitation is the absence of parameters for the oral and inhalation routes in the 23

human. A modified version of this model that includes human parameters and a standard PBPK 24

lung compartment might be sui table for the purposes of this assessment. 25

3.4.2.2. Bouchard et al . (2001)

The Bouchard et al . (2001) model is not actually a PBPK model but is an elaborate 26

classical PK model, since the transfer rates are not determined from blood flows, v entilation, 27

partition coefficients

, and the like. The Bouchard et al . (2001) model uses a single compartment 28

for methanol: a central compartment represented by a volume of distribution where the 29

concentration is assumed to equal that in blood. The model was developed for inhalation and i.v. 30

kinetics only. Methanol is primarily eliminated via saturable metabolism. The model adequately 31

simulates blood kinetics in NP rats and humans following inhalation exposure and in NP rats 32

following i.v. exposure ; t her e is no description for oral absorption. Because methanol distributes 33

with total body water (Ward et al ., 1997; Horton et al ., 1992) , this simple model structure is 34

sufficient for predicting blood concentrations of methanol following inhalation and i.v. d osing. 353

- 20 DRAFT

- DO NOT CITE OR QUOTE The Bouchard et al . (2001) model has the advantage of simplicity, reflecting the 1

minimum number of compartments necessary for representing blood methanol pharmacokinetics. 2

Because volume of distribution can be easily and directly estimated for wat er

- soluble compounds 3

like methanol or fit directly to experimental kinetics data, concern over the scalability of this 4

parameter is absent. The model has been parameterized for a required human exposure route, 5

inhalation ( Table 3

- 8). The model meets crit eria 1, 2b, 3, 4

, and 5 described in S ection 3.4.1.2

. 6

However, the Bouchard model has specific and significant limitations. The model has neither 7

been parameterized for the mouse, a test species of concern ( Table 3

- 8)

, nor for the oral route in 8

humans. As such, the model cannot be used to conduct the necessary interspecies extrapolation. 9

Table 3

- 8

. Routes of e xposure optimized in models

– optimized against blood

concentration data

Ward et al

. Bouchard et al

.

Route Mouse Rat Human Mouse Rat Human

i. v. P/NP P/NP

-- -- NP

--

Inhalation P/NP

-- -- -- NP NP

Oral P/NP NP

-- -- -- --

P

= Pregnant

NP

= Nonpregnant

Source: Bouchard et al . (2001); Ward et al . (1997).

3.4.2.3. Gentry et al . (2003, 2002) and Clewell et al . (

2001)

The model described in these thre e papers is for isopropanol, not methanol, and therefore 10

lacks any immediately useful parameterization for the purposes of a methanol risk assessment. 11

Although the overall model structure, the description of kinetics for both parent compound and 12

primary m etabolite, gestational compartments, lactational transfer, oral and i.v. routes, etc., are 13

attractive for application to methanol, this model is not ideal. In particular, the model structure is 14

more elaborate than necessary ; because methanol partition coe fficients are near 1 for all tissues 15

except fat, there is no need to individually represent these tissues. Similarly, a fetal compartment 16

may not be necessary because methanol kinetics in the fetus (conceptus) is expected to parallel 17

maternal blood concen trations in the rodent. However, e ven if a fetal model was considered 18

necessary , other than the partition coefficient, there are insufficient data to identify conceptus 19

compartment parameters for methanol. This model would require the most modification a nd 20

parameterization to be useful for methanol risk assessment since parameters would have to be 21

estimated for all relevant species (at least mouse and humans) and for several routes of exposure. 22

Therefore the isopropanol model was not considered further. 233

- 21 DRAFT

- DO NOT CITE OR QUOTE 3.4.3. Selected Modeling Approach

As discussed earlier regarding model criteria, fetal methanol concentrations can 1

reasonably be assumed to equal maternal blood concentration. Thus, as discussed in Section 2

3.4.2.3, methanol pharmacokinetics and blood dose metric s for NP mice and humans are 3

expected to improve upon default extrapolations from external exposures as estimates of fetal 4

exposure during early gestation. The same level of confidence cannot be placed on the whole

5

body rate of metabolism, in particular a s a surrogate for formaldehyde dose. Because of 6

formaldehyde’s reactivity and the limited fetal metabolic (ADH) activity (see Sections 3.3 and 7

4. 10 .1), fetal formaldehyde concentration increases (from methanol) will probably not equal 8

maternal increases i n formaldehyde concentration. But since there is no model that explicitly 9

describes formaldehyde concentration in the adult, l et al one the fetus, the metabolism metric is 10

the closest one can come to predicting fetal formaldehyde dose. This metric is expe cted to be a 11

better predictor of formaldehyde dose than applied methanol dose or even methanol blood levels, 12

which do not account for species differences in conversion of methanol to formaldehyde. 13

Most of the published rodent kinetic models for methanol de scribe the metabolism of 14

methanol to formaldehyde as a saturable process but differ in the description of metabolism to 15

and excretion of formate (Bouchard et al ., 2001; Fisher et al ., 2000; Ward et al ., 1997)

. The 16

model of Ward et al . (1997) used one satu rable and one first

- order pathway to describe methanol 17

elimination in mice. The saturable pathway described in Ward et al . (1997) can specifically be 18

ascribed to metabolic formation of formaldehyde in the liver, while the renal first

- order 19

elimination des cribed in the model represents nonspecific clearance of methanol ( e.g., 20

metabolism, excretion, or exhalation). The model of Ward et al . (1997) does not describe 21

kinetics of formaldehyde subsequent to its formation and does not include any description of 22

f ormate. 23

Bouchard et al . (2001) employed a metabolic pathway for conversion of methanol to 24

formaldehyde and a second pathway described as urinary elimination of methanol in rats and 25

humans. They then explicitly describe two pathways of formaldehyde transfo rmation, one to 26

formate and the other to “other, unobserved formaldehyde byproducts.” Finally, formate 27

removal is described by two pathways, one to urinary elimination and one via metabolism to CO 2 28

(which is exhaled). All of these metabolic and eliminati on steps are described as first

- order 29

processes, but the explicit descriptions of formaldehyde and formate kinetics significantly 30

distinguish the model of Bouchard et al

. (2001) from that of Ward et al . (1997), which only 31

describes methanol. 32

There are two other important distinctions between the Ward et al . (1997) and Bouchard 33

et al . (2001) models. The former is currently capable of simulating blood data for all exposure 34

routes in mice but not humans, while the latter is capable of simulating human inhalat ion route 35

blood pharmacokinetics but not those in mice. The Ward et al . (1997) model has more 363

- 22 DRAFT

- DO NOT CITE OR QUOTE compartments than is necessary to adequately represent methanol disposition but has been fit to 1

PK data in pregnant and NP mice for all routes of exposure (i.v., oral, and inhalation). The Ward 2

et al . (1997) model has also been fit to i.v. and oral route PK data in rats. Based primarily on the 3

extensive amount of fitting that has already been demonstrated for this model, it was determined 4

that a modified Ward et al . (1997) model, with the addition of a lung compartment as described 5

by Fisher et al . (2000), should be used for the purposes of this assessment. See Appendix B for a 6

more complete discussion of the selected modeling approach and modeling consideration s. 7

3.4.3.1. Available PK Data

Although limited human data are available, several studies exist that contain PK and 8

metabolic data in mice, rats, and non human primates for model parameterization. Table 3

- 9 9

contains references that were used to verify the model fits as reported in Ward et al . (1997). 10

Table 3

- 9. Key methanol kinetic studies for model validation

Reference i.v. dose

(mg/kg) Inhalation

(ppm) Oral/ d ermal/

IP Species Samples Digitized

f igures a

Batterman et al ., 1998 Dermal Human Male/female Blood Fig ure 1

Batterman & Franzblau 1997 800 (8 hr ) Blood,

urine,

exhaled

Burbacher, 2004a, 2004 b 0

- 1

, 800 (2.5 hr , 4

mo ) Monkeys

Cynomolgus

Preg nant

, NP Blood

Osterloh et al.

, 1996 ;

Chuwers et al ., 1995 ;

D’Alessandro et al

., 1994 200 (4 hr ) Human Mal e/female Blood, urine Figure 1,

Osterloh

et al ., 1996

Medinsky et al., 1997;

Dorman et al

., 1 994 10

- 900 (2 hr ) Monkeys

Cynomolgus Folate

deficient Blood,

urine,

exhaled

Gonzalez

- Quevedo et al

., 2002 IP: 2 mg/kg

day, 2 wk Rat Blood

Horton et al ., 1992 100 (rats

only) 50

- 2

, 000 (6 hr ) Rat & Monkey

R hesus Blood,

urine,

exhaled Figure 7

Perkins et al ., 1996

, 1995a

, 1995 b 1,000

- 20,000 (8

hr ) M ouse and R at Blood, urine

Pollack and Brouwer, 1996 ;

Pollack et al ., 1993 100

- 2

, 500 1

, 000

- 20,000 (8

hr ) Oral:

100

- 2

, 500

mg/kg Rat: S prague

D awley, & Mouse;

CD

- 1 Pregnant, NP Blood

Rogers and Mole, 1997 ;

Rogers et al ., 1993a; 1

, 000

- 15

, 000 (7

hr , 10 d ays ) Mouse

Preg nant Blood

Sedivec et al ., 1981 78

- 231 (8 hr ) Human U rine,

b lood Figure s 2, 3,

6, 7, 8

Ward et al ., 1997; Ward and

Pollack, 1996 100, 500

( R at), 2

, 500

( M ouse) Oral:

2

, 500 mg/kg GD18 M ouse

, GD14 & GD 20 R ats Blood,

c onceptus

a data obtained from the reported figure3

- 23 DRAFT

- DO NOT CITE OR QUOTE 3.4.3.2. Model Structure

A model was developed which includes compartments for alveolar air/blood methanol 1

exchange, liver, fat, bladder (human simulations) and the rest of the body ( Figure 3

- 4). This 2

model is a revision of the model reported by Ward et al . (1997), reflecting significant 3

simplifications (removal of compartments for placenta, embryo/fetus, and extraembrionic fluid) 4

and t hree elaborations (addition of an intestine lumen compartment to the existing stomach 5

lumen compartment , use of a saturable rate of absorption from the stomach (but not intestine), 6

and addition of a bl adder compartment which impacts simulations for human urinary excretion), 7

while maintaining the ability to describe methanol blood kinetics in mice, rats, and humans. A 8

fat compartment was included because it is the only tissue with a tissue:blood partiti oning 9

coefficient appreciably different than 1 , and the liver is included because it is the primary site of 10

metabolism. A bladder compartment was also added for use in simulating human urinary 11

excretion to capture the difference in kinetics between change s in blood methanol concentration 12

and urinary methanol concentration. The model code describes inhalation, oral, and i.v. dose 13

routes

, and data exist ( Table 3

- 9) that were used to fit parameters and evaluate model predictions 14

for all three of those routes in both mice and rats. In humans, inhalation exposure data were 15

available for model calibration and validation but not oral or i.v. data. However

, oral exposures 16

were simulated in humans, assuming a continuous, zero

- order ingestion rate, thereby obviat ing 17

the need for oral uptake parameters. 18

PK data from exposure routes other than inhalation and oral were used to test or further 19

refine the parameters for methanol clearance. Monkey data were evaluated for insight into 20

primate kinetics. Data from Oste rloh et al . (1996) and Sedivec et al . (1981) were used to validate 21

the modified Ward et al . (1997) model parameterization for humans. The fact that optimized 22

human parameters were similar to those predicted in monkeys was important to the validation 23

proce ss (Bourchard et al ., 2001 )(see section 3.4.7 and Appendix B). Blood levels of methanol 24

have been reported following i.v., oral, and inhalation exposure in rats and mice and inhalation 25

exposure in non human primates and humans. 26

The metabolic clearance of m ethanol was represented in mice, rats, and humans by 27

specifying separate rate constants for the species

- specific enzymes: two saturable processes for 28

mice and rats 10 and one for humans. The requirement for two saturable processes in the mouse 29

and rat model s may reflect saturation of CAT and ADH1. Simulated methanol clearance by these 30

metabolic processes is not linked to production of formaldehyde or formate; it is simply cleared 31

from the methanol mass balance leaving the system. Metabolism of formaldehyde is not 32

explicitly simulated by the model

, and this model tracks neither formate nor formaldehyde. 33

Since the metabolic conversion of formaldehyde to formate is rapid ( < 1 minute) in all species 34

10 The need for two saturable metabolic pathways in the mouse model was confirmed through simulation and

optimization. High exposure (>2,000 ppm methanol) and low exposure (1,000 ppm methanol) blood data could not

be fit visual ly, or by more formal optimization, without the second saturable metabolic pathway.3

- 24 DRAFT

- DO NOT CITE OR QUOTE (Kavet and Nauss, 1990), the methanol clearance rate may appro ximate a formate production 1

rate, though this has not been verified. 2

3

Figure 3

- 4. Schematic of the PBPK model used to describe the inhalation, oral, and

i.v. route pharmacokinetics of methanol. KAS, first

- order oral absorption rate from

stomach ; VmA S and KMAS, Michaelis

- Menten rate constants for saturable

absorption from stomach ; KAI

, first

- order uptake from the intestine; KSI,

first

- order transfer between stomach and intestine; V

max and K

m and V

max 2 and

K

m 2, Michaelis

- Menten rate constants for high affinity/low capacity and low

affinity/high capacity metabolic clearance of methanol; KLL, alternate first

order rate constant; KBL, rate constant for urinary excretion from bladder.

Both metabolic pathways were used to describe methanol clearance in the

mouse, while a single metabolic pathway describes clearance in the human.

4

The primary purpose of this assessment is for the determination of noncancer and cancer 5

risk associated with increases in the levels of methanol or its metabolites (e.g., formate, 6

f ormaldehyde). Thus, the focus of model development is on obtaining accurate predictions of 7

increased body burdens over background. The PBPK models do not describe or account for 8

background levels of methanol, formaldehyde or formate, and background levels were subtracted 9

from the reported data before use in model fitting or validation (if not already subtracted by 10

study authors), as described below. This approach is not expected to have a significant impact on 11

PBPK model parameter estimates as background l evels of methanol and its metabolites are low 123

- 25 DRAFT

- DO NOT CITE OR QUOTE relative to exposure levels used in methanol bioassays. Further, while it is possible that 1

background levels of methanol or its metabolites contribute to background responses for some 2

adverse effects, the resu lts of dose

- response modeling of cancer endpoints using “background 3

dose” models suggest that this contribution is relatively small (see discussion in Appendix E, 4

Section E.4). 5

3.4.3.3. Model Parameters

The EPA methanol model uses a consistent set of physiological parameters obtained 6

predominantly from the open literature ( Table 3

- 10); the Ward et al . (1997) model employed a 7

number of data

- set specific parameters. 11 Parameters for blood flow, ventilation, and metabolic 8

capacity were scaled as a function of body weig ht raised to the 0.75 power, according to the 9

methods of Ramsey and Andersen (1984). 10

Table 3

- 10. Parameters used in the mouse, rat and human PBPK models

Mouse Rat

SD | F344 Human Source

Body weight (kg) 0.03 a 0.275 b 70 Measured/estimated

Tissue volum e (% body weight)

Liver 5.5 3.7 2.6

Brown et al

. 1997 Blood arterial 1.23 1.85 1.98

venous 3.68 4.43 5.93

Fat 7.0 7.0 21.4

Lung 0.73 0.50 0.8

Rest of body 72.9 73.9 58.3 Calculated c

Flows (L/hr/kg 0.75 )

Alveolar ventillation d 25.4 16.4 1 6.5 Perkins et al

. 1995a; Brown et al

. 1997;

U.S. EPA , 2004 Cardiac output 25.4 16.4 24.0

Percentage of cardiac output

Liver 25.0 25.0 22.7

Brown et al

. 1997

Fat 5.0 7.0 5.2

Rest of body 70.0 68 72.1 Calculated

Biochemical constants e 1 st

order saturable

V

max C (mg/hr/kg 0.75 ) 19 5.0 0 NA 33.1

Fitted Km (mg/L) 5.2 6.3 NA NA 23.7

V

max 2C (mg/hr/kg 0.75 ) 3.2 8.4 22.3 NA

Km2 (mg/L) 660 65 100 NA

K1C (BW 0.25 /hr) NA NA 0.0373 0.0342

11 Some data sets provided in the Ward et al., (1997) model code were corrected to be consistent with figures in the

published literature describing the experimental data.3

- 26 DRAFT

- DO NOT CITE OR QUOTE Mouse Rat

SD | F344 Human Source

KLLC (BW 0.25 /hr) f NA NA 95.7 NA

Oral absorption

Vm A S C (mg/hr/kg 0.75 ) 1830 5570 377 Mouse and rat fitted (mouse and human

KMASC assumed = rat) ; other human

values are those for ethanol from Sultatos et

al. (2004), with VmASC set so that for a

70

- kg person VmAS/KMAS = the first

order constant of Sultatos et al. KMASC (mg/kg) 620 620 620

KSI (hr

- 1 ) 2.2 7.4 3.17

KAI (hr

- 1 ) 0.33 0.0 51 3.28

K fec (hr

- 1 ) 0 0.0 29 0

Partition coefficients

Liver:Blood 1.06 1.06 0.583 h Ward et al ., 1997; Fiserova

- Bergerova and

Diaz, 1986 Fat:Blood 0.083 0.083 0.142

Blood:Air 1350 i 1350 1626 Horton et al ., 1992; Fiserova

- Bergerova and

Diaz, 1986

Body:Blood 0.66 0.66 0.805 Rodent: estimated; human: Fiserova

Bergerova and Diaz, 1986 (human "body"

assumed = muscle) Lung:Blood 1 1 1.07

KBL ( hr

- 1 ), bladder time

cons tant j NA 0.564 0.612 Fitted (human)

FRACIN (%), nhalation

fractional availability 0.665 0.2 0 0.866 k Rodent: fitted; human

Ernstgard et al ., 2005

NA

- Not applicable for that species

a Both sources of mouse data report body weights of approximately 30 g

b The midpoints of rat weights reported for each study was used and ranged from 0.22 to 0.33 kg

c The volume of the other tissues was subtracted from 91% (whole body minus a bone volume of approximately 9%) to get

the volume of the remaining tissues

d Minut e ventilation was measured and reported for much of the data from Perkins et al

. (1996) and the average alveolar

ventilation (estimated as 2/3 minute ventilation) for each exposure concentration was used in the model. When ventilation

rates were not availa ble, a mouse QPC (Alveolar Ventilation/BW 0.75 ) of 25.4 was used (average from Perkins et al ., 1995).

The QPC used to fit the human data was obtained from U.S. EPA (2004). This QPC was somewhat higher than calculated

from Brown et al

. (1997) (~13 L/hr/kg 0.7 5 )

e V

max , Km, and V

max 2, Km2 represent the two saturable metabolic clearance processes assumed to occur solely in the liver.

The V

max used in the model = V

max C (mg/kg 0.75 ·hr)  BW 0.75 . K1C is the first

- order loss from the blood for human

simulations that re presents urinary elimination. Allometric scaling for first

- order clearance processes was done as previously

described (Teeguarden et al ., 2005); The K1 used in the model= K1C / BW 0.25

f KLLC

– alternate human first

- order metabolism rate (used only when V

ma x C = V

max 2C = 0)

g Human oral simulations used a zero order dose rate equal to the mg/kg

- day dose

h Human liver:blood estimated from correlation to (measured) fat:blood, based on data from 28 other solvents

I Rat partition coefficient used for mice as done by Ward et al . (1997)

j KBL

– a first

- order rate constant for clearance from the bladder compartment, used to account for the difference between

blood kinetics and urinary excretion data as observed in humans

k For human exposures, the fractional availability was from Šedivec et al . (1981), corrected for the fact that alveolar

ventilation is 2/3 of total respiration rate

3.4.4. Mouse Model Calibration and Sensitivity Analysis

The process by which the mouse, rat, and human inhalation and oral models were 1

calibrated is discussed in more detail in Appendix B, “Development, Calibration and Application 2

of a M ethanol PBPK Model.” The calibrated mouse inhalation model predicted blood methanol 3

blood concentration time

- course agreed well with measured values in adult mice i n the critical 43

- 27 DRAFT

- DO NOT CITE OR QUOTE inhalation studies of Rogers and Mole (1997) ( Figure 3

- 5), Perkins et al . (1995a, 1995 b), and 1

Rogers et al. (1993a), as well as in NP and early gestation (GD8) mice of Dorman et al . (1995) 2

( Figure 3

- 6). Parameter values used in the calibrat ed model are given in Table 3

- 10. 3

The mouse model was also calibrated for the oral route by fitting all but one of the rate 4

constants for oral uptake of methanol to the oral

- route blood methanol kinetics of Ward et al

. 5

(1997, 1995). The best model fit to the mouse oral route blood methanol PK data was obtained 6

using a two

- compartment GI tract model , as depicted in Figure 3

- 4

. Because the oral data in rats 7

led to the conclusion that a saturable rate of uptake from the stomach lumen was necessary (see 8

secti on 3.4.5), the same equation was used for uptake in the mouse. But attempts to identify the 9

uptake saturation constant, KMASC, from the mouse data were unsuccessful; therefore KMASC 10

for the mouse was set equal to the value obtained for rats. Adjusting th e other mouse oral uptake 11

parameters gave an adequate fit to those data. This calibration allow s inhalation to oral dose

12

route extrapolations in the mouse, which can then be extrapolated to identify human oral route 13

exposures equivalent to mouse inhalatio n exposures (if equivalent human exposures exist). 14

Figure 3

- 5. Model fits to data sets from GD6, GD7, and GD10 mice for 6

- to 7

- hour

inhalation exposures to 1,000

– 15,000 ppm methanol. Maximum concentrations are

from Table 2 in Rogers et al . (1993a). The dataset for GD7 mice exposed to

10,000 ppm is from Rogers and Mole (1997) and personal communication.

Symbols are concentration **±** SEM of a minimum of N=4 mice/concentration.

Default ventilation rates ( Table 3

- 10) were us ed to simulate these data.

Source: Rogers and Mole (1997); Rogers et al. (1993a)3

- 28 DRAFT

- DO NOT CITE OR QUOTE Figure 3

- 6. Simulation of i nhalation exposures to methanol in NP mice from Perkins

et al . (1995 a ) ( 8

- hour exposures) and GD8 mice from Dorman et al . (1995 )( 6

- hour

exposure s). Data points are measured blood methanol levels and lines represent

PBPK model simulations. DigitizIt (SharIt! Inc., Greensburg, PA) was used to

digitize data from Figure 2 of Perkins et al . (1995a) and Figure 2 from Dorman

et al . (1995). Default v entilation rates ( Table 3

- 10) were used to simulate the

Dorman data. The alveolar ventilation rate for each data set from Perkins et al

. (1995a) was set equal to the measured value reported in that manuscript. For

the 2

, 500, 5

, 000, and 10,000 ppm exposur e groups, the alveolar ventilation rates

were 29, 24, and 21 (L/hours/kg 0.75 ), respectively. The cardiac output for these

simulations was set equal to the alveolar ventilation rate.

Source: Dorman et al. (1995); Perkins et al. (1995a).

The parameterizati on of methanol clearance (high

- and low

- affinity metabolic pathways) 1

was also verified by simulation of datasets describing the pharmacokinetics of methanol 2

following i.v. administration. The results of this calibration of the methanol PBPK model are 3

descr ibed in Appendix B and were generally consistent with both the available inhalation and 4

oral

- route data. Up to 20 hours postexposure , blood methanol kinetics appears similar for NP 5

and pregnant mice. However, some data suggests that clearance in GD18 mic e is slower than in 6

NP and earlier in gestation (GD10 and less), particularly beyond 20 hours postexposure (see the 7

i.v. and oral data of Ward et al

. [ 1997 ] in Appendix B). 8

Intravenous

- route blood methanol kinetic data in NP mice were only available for a 9

single i.v. dose of 2,500 mg/kg, but were available for GD18 mice following administration of a 10

broader range of doses: 100, 500, and 2,500 mg/kg. The i.v. maternal PK data in GD18 mice 11

appeared to show an unexpected dose

- dependent nonlinearity in initia l blood concentrations. 12

Before discussing the nonlinearity, it is first noted that data values used here were obtained from 13

a computational “command file” provided by Ward et al . (1997). These values appear to be 143

- 29 DRAFT

- DO NOT CITE OR QUOTE consistent with the plots in their public ation but are inconsistent with some of the values in their 1

Table 6 (Ward et al ., 1997). In particular, the initial maternal blood concentration ( i.e., the C

max ) 2

after the 2,500 mg/kg i.v. is listed as 4,250 mg/L in their command file but as 3,251 mg/L in their 3

published table. The corresponding data point in their Figure 5A is distinctly centered above 4

4,000 mg/L (digitizing yields 4,213 mg/L), and so must be 4,250 rather than 3,251 mg/L. 5

Therefore the data values listed in the command file were used in the subsequent analysis, rather 6

than those in the published table. 7

After i.v. dosing the ratio of the administered doses to the first concentrations measured 8

by Ward et al . (1997 ) ( 5

- minute time point s) were 0.588 L/kg, 0.585 L/kg, and 0.397 L/kg at 9

doses of 2,500, 500, and 100 mg/kg, respectively. The discrepancy between the first two values 10

and the third value suggests either a dose dependence in the V d or some source of experimental 11

variability. 12 It may be that V d , which is not impacted by any other PB PK parameters and is only 12

determined by the biochemical partitioning properties of methanol, is 1.5

- fold lower at 100 13

mg/kg than at the higher concentrations, while the V d at 500 and 2,500 mg/kg are exactly as 14

predicted by the PBPK model without adjustmen t. However, it was found that the PBPK model, 15

obtained with measured partition coefficients and otherwise calibrated to inhalation data, could 16

adequately fit the data at the nominal dose of 100 mg/kg without other parameter adjustment 17

simply by simulating a dose of 200 mg/kg, as shown in Figure B

- 5. The fact that the alternate 18

dose (200 mg/kg) differs by a factor of 2 from the nominal dose suggests that the data could also 19

be the result of a simple dilution error in dose preparation. If the first two of the 20

dose/concentration values were not virtually identical (0.588 and 0.585 L/kg), but instead the 21

500 mg/kg value was more intermediate between those for 2,500 and 100 mg/kg, then a regular 22

dose dependence in V d would seem more likely. However, based on these values, the U.S. EPA 23

has concluded that the apparent dose dependency is probably the result of a dosing error and 24

therefore, that dose

- dependent parameter changes ( e.g., in the partition coefficients) should not 25

be introduced in an attempt to otherwi se better fit these data. 26

F urther , the nominal “nonlinearity” between the maternal blood and conceptus shown in 27

Figure 8 of Ward et al . (1997) is the result of those data being plotted on a log

- y/linear

- x scale. 28

Replotting the data from Tables 5 and 6 (us ing the value of 4,250 mg/L from the command file as 29

the GD18 maternal C

max for the 2,500 mg/kg) shows the results to be linear, especially in the 30

low

- dose region which is of the most concern (Figure 3

- 7 ) . Therefore, the current model uses a 31

consistent se t of parameters that are not varied by dose and fit the 2,500 and 500 mg/kg i.v. data 32

adequately, al though they do not fit the 100 mg/kg i.v. data unless, as noted above, a presumed 33

i.v. dose of 200 mg/kg is employed. With that exception, both the single set of parameters used 34

herein and the assumption that maternal blood methanol is a good metric of fetal exposure are 35

well supported by the data. 36

12 It is possible that Ward et al., (1997) were unaware of that discrepancy because they plotted the results for each

dose in separate figures, and it only becomes obvious when all the data and simulations are plotted together.3

- 30 DRAFT

- DO NOT CITE OR QUOTE 10 100 1000 10000

0 1000 2000 3000 4000 5000

Maternal AUC (ug/mL-day)Conceptus AUC (ug/mL-day)

rat

mouse

y = x 

## A

0 500 1000 1500 2000 2500 3000 3500

0 1000 2000 3000 4000 5000

Maternal AUC (ug/mL-day)Conceptus AUC (ug/mL-day)

rat

mouse

y = x 

## B

Figure 3

- 7. Conceptus versus maternal blood AUC values for rats and mice plotted

(A) on a log

- linear scal e, as in Figure 8 of Ward et al . (1997), and (B) on a linear

- linear

scale. In both panels the line y = x is plotted (dashed line) for comparison. Thus the

apparent “nonlinear” relationship indicated by Ward et al . (1997) is seen to be

primarily a simpl e artifact of the choice of axes. However, as evident in panel

B, there appears to be some nonlinearity at the two highest doses in the mouse

(results of 2,500 mg/kg i.v. in GD18 mice and 15,000 ppm exposure to GD8

mice), where distribution from the dam t o the conceptus is below 1:1.

Source: Ward et al . (1997)

.

To summarize the mouse model calibration: using the single set of parameters listed for 1

the mouse in Table 3

- 10, the PBPK model has been shown to adequately fit or reproduce 2

methanol PK data from a variety of laboratories and publications, including both NP mice and 3

pregnant mice up to GD10. Two saturable metabolic pathways are thus described by the model 43

- 31 DRAFT

- DO NOT CITE OR QUOTE and supported by the data. Also, it is thereby demonstrated that a model based on NP mouse 1

phy siology adequately describes (predicts) dosimetry in the pregnant mouse dam through GD10. 2

Finally, as illustrated in Figure 3

- 7b , methanol PK in the conceptus and dam of both mice 3

(including lower doses at GD18) and rats (GD14 and GD 20) are virtually iden tical, except for 4

the very highest doses in mice. Thus the existing model appears to be adequate for predicting 5

internal methanol doses, including fetal exposures, at bioassay conditions. 6

An evaluation of the importance of selected parameters on mouse mod el estimates of 7

blood methanol AUC was performed by conducting a sensitivity analysis using the subroutines 8

within acslXtreme v2.3 (Aegis Technologies, Huntsville, Alabama). The analysis was conducted 9

by measuring the change in model output corresponding to a 1% change in a given model 10

parameter when all other parameters were held fixed. Sensitivity analyses were conducted for 11

the inhalation and oral routes. The inhalation route analysis was conducted under the exposure 12

conditions of Rogers and Mole (199 7) and Rogers et al . (1993a): 7

- hour inhalation exposures at 13

the no

- observed

- effect level (NOEL) concentration of 1,000 ppm. The oral route sensitivity 14

analysis was conducted for an oral dose of 1,000 mg/kg. 15

The parameters with the largest sensitivity coe fficients for the inhalation route at 16

1,000 ppm ( absolute values >1) were pulmonary ventilation scaling coefficient ( QPC ) and 17

maximum velocity of the high

- affinity/low

- capacity pathway (V

max C). The sensitivity 18

coefficient for QPC increases during the expo sure period as metabolism begins to saturate. 19

Following oral exposure, mouse blood methanol AUC was sensitive to the rate constants for oral 20

uptake. Blood AUC was most sensitive to the maximum and saturation rate constant s for uptake 21

from the stomach (Vm ASC and KMASC) . The sensitivity coefficient for VmASC decreased 22

during the first hours after exposure from 1 to less than 0.1 at the end of exposure. Blood 23

methanol AUC was also modestly sensitive to first

- order uptake from the intestine (KAI) , and 24

fir st

- order transfer between stomach and intestine (KSI) , the rate constants for uptake from the 25

intestine and transfer rates between compartments, respectively. For a more complete 26

description of this sensitivity analysis for the mouse methanol PBPK model s ee Appendix B. 27

3.4.5. Rat Model Calibration

The rat model was calibrated to fit data from i.v. , inhalation, and oral exposures in rats

, 28

using data provided in the command file of Ward et al . (1997) and obtained from figures in 29

Horton et al . (1992) using DigitizIt . Holding other parameters constant, the rat PBPK model was 30

initially calibrated against the entire set of i.v.

- route blood PK data ( Figure 3

- 7) by fitting 31

Michaelis

- Menten constants for one high

- affinity/low

- capacity and one low

- affinity/high

32

capacity en zyme to both the Ward et al. (1997) data for Sprague

- Dawley (SD) rats and the 33

Horton et al. (1992) data for Fischer 344 (F344) rats, assuming that any difference between the 34

two data sets (100 mg/kg data) were from experimental variability and that a singl e set of 35

parameters could be fit to data for both strains of rat. However when the resulting parameters 363

- 32 DRAFT

- DO NOT CITE OR QUOTE were then used to simulate the F344 inhalation uptake data of Horton et al. (with the fractional 1

absorption for inhalation, FRACIN, adjusted to fit tho se data), it was found that the clearance 2

rate predicted after the end of inhalation exposure was much more rapid than shown by the data. 3

More careful examination of the i.v. data then revealed that there too the clearance for F344 rats 4

was slower than fo r SD rats, and that the metabolic parameters obtained from fitting the 5

combined i.v. data best represented the SD rat data. It was concluded that the combined data set 6

indicated a true strain difference in metabolic parameters. The metabolic parameters f or SD rats 7

were then obtained by fitting only the Ward et al. (1997) i.v. data (both doses). 8

The 100 mg/kg i.v. data of Horton et al. (1992) were combined with their inhalation data 9

and a simultaneous optimization of the metabolic parameters and FRACIN f or F344 rats was 10

attempted over that data set. For this data set, however, the optimization either converged with 11

the metabolic Vmax for the high affinity (low Km) pathway at zero, or with that Km value 12

increasing to be statistically indistinguishable fro m the high Km value. Therefore the Vmax for 13

the high affinity pathway was allowed to be zero, the Km for that pathway was not estimated, 14

and only a single Vmax and low affinity (high Km) were fit to those data, with a simultaneous 15

identification of FRACIN . Since there are no inhalation data for SD rats, this value of FRACIN 16

was assumed to apply for both strains . The optimized parameters for both strains of rats are 17

given in Table 3

- 10. 18

When the model was calibrated using the available inhalation and i.v. data for F344 rats 19

(Horton et al ., 1992), a low fractional absorption of 2 0 % was optimized to best fit the data , vs. 20

66.5% for the mouse . This lower fractional absorption is consistent with values presented by 21

Perkins et al . (1995), who also found that t he fractional absorption of methanol from inhalation 22

studies was lower in rats than in mice. 23

24

25

26

273

- 33 DRAFT

- DO NOT CITE OR QUOTE 2500 mg/kg IV in rats

0 1000 2000 3000 4000 5000

0 8 16 24 32 40 48

Time (hr)Blood MeOH (mg/l)

Ward '97 data

Model fit 100 mg/kg IV in rats

0 30 60 90 120 150

0 2 4 6 8

Time (hr)Blood MeOH (mg/l)

Ward '97 data

SD rat model fit

Horton '92 data

F344 rat model fit

Figure 3

- 8. NP rat i.v. route methanol blood kinetics. Methanol (MeOH) was infused

into: female Sprague

- Dawley rats (275 g ; solid diamonds and lines ) at target

doses of 100 or 2,500 mg/kg (Ward et al ., 1997); or male F

- 344 rats (220 g ; open

triangles and dashed line ) at target doses of 100 mg/kg (Horton et al ., 1992).

Data points represent measured blood concentrations and lines r epresent PBPK

m odel simu lations.

Source: Ward et al . (1997); Horton et al . (1992).

Figure 3

- 9. Model fits to data sets from inhalation exposures to 200 (triangles), 1,200

(diamonds), or 2,000 (squares) ppm methanol in male F

- 344 rats. The model was

calibrated against all three sets of concentration data, though it converged to

parameter values that only fit the lower two data sets well. Symbols are

concentrations obtained fr o m Horton et al . (1992) using DigitizIt! Lines

represent PBPK model fits. Since the 2000 ppm data peak occurred at 7 hr, a 7

- hr simulated exposure is also shown for comparison.

Source: Horton et al . (1992).3

- 34 DRAFT

- DO NOT CITE OR QUOTE Finally, oral absorption parameters were optimized to the oral absorption data reported by 1

Ward et al . (1997)

, also using the optimization routi nes in acslXtreme v2. 5.0.6 (Aegis 2

Technologies, Huntsville, Alabama) ( Table 3

- 10: Figure 3

- 9). While the two

- compartment GI 3

model (Figure 3

- 4) allows for both slow and fast absorption modes, it was not possible to fit both 4

the 100 mg/kg data and the first several hours of the 2500 mg/kg data with that model structure 5

using linear absorption and inter

- compartment transfer rates. In particular the shorter

- time data 6

for 2500 mg/kg indicate a much slower rate of increase in blood levels than the linear

- absorp tion 7

model (top, thick line in upper panel of Figure 3

- 10), but the 100 mg/kg data (lower panel of 8

Figure 3

- 10) are indeed consistent with a linear model, showing a rapid rise to a fairly narrow 9

peak, then dropping rapidly. As long as linear rate equation s were used, the shape of the 10

absorption curve at 2500 mg/kg would mirror that at 100 mg/kg, but the data show a clear 11

difference. It was concluded that the rate of absorption must at least partly saturate at the higher 12

dose, and hence that Michaelis

- Ment en kinetics should be used. 13

Even with the addition of saturable absorprtion from the stomach, it was also found that 14

the 2500 mg/kg model simulations over

- predicted all of those data (result not shown) and it was 15

hypothesized that fecal elimination might become significant at such a high exposure level, so a 16

term for fecal elimination from the intestine compartment was added. When that fecal rate 17

constant and the saturable absorption from the stomach compartment were both used, the 18

resulting fit to the d ata (thin, dashed line in upper panel) was considerably improved with an 19

almost identical (excellent) fit to the 100 mg/kg data (saturable curve can be distinguished from 20

the linear curve just after the peak is reached in the lower panel of Figure 3

- 4). F or the purpose 21

of scaling across individuals, strains, and species, the Km for absorption from the stomach 22

(KMAS) was assumed to scale in proportion to the stomach (lumen) volume; i.e., with BW 1

. 23

The Vmax (VmAS) was assumed to scale as BW 0.75 , with the re sult that for low doses the 24

effective linear rate constant (VmAS/KMAS) scales as BW

- 0.25 , which is a standard assumption 25

for linear rates. Since the quantity on which the rate depends is the total amount in the stomach 26

(mg methanol), the resulting scaling constant for the Km, KMASC, conveniently has units of 27

mg/kg BW; i.e., the standard units for oral dosing

. 283

- 35 DRAFT

- DO NOT CITE OR QUOTE Figure 3

- 10. Model fits to datasets from oral exposures to 100 and 2,500 mg/kg

methanol in female Sprague

- Dawley rats. Symbols are concentrati on data obtained

from the command file. Lines represent PBPK model fits.

Source: Ward et al . (1997).

3.4.6. Human Model Calibration

3.4.6.1. Inhalation Route

The mouse model was scaled to humans by setting either a standard human body weight 1

(70 kg) or study

- specific body weights and using human tissue compartment volumes and blood 2

flows, and then calibrated to fit the human inhalation exposure data available from the open 3

literature, which comprised data from four publications (Ernstgard et al ., 2005; Batterman et al

. , 4

1998; Osterloh et al ., 1996; Sedivec et al ., 1981). 5

Since the human data included time

- course data for urinary elimination, a first

- order rate 6

of loss of methanol from the blood (K1) was used to provide an estimate of methanol elimination 7

to the bladder compartment in humans, and the rate of elimination from that compartment then 83

- 36 DRAFT

- DO NOT CITE OR QUOTE characterized by a second constant (KBL). Note that the total amount eliminated by this route 1

depends only on K1, while KBL affects the rate at which the material cleared from the blood 2

then appears in the urine. Inhalation

- route urinary methanol kinetic data described by Sedivec 3

et al . ( 1981 ) (Figure 3

- 1 1 ) was used in the model calibration to inform this rate constant. 4

Without use of the bladder compartment and rate constant

, the fit of the model predictions to the 5

data in Figure 3

- 1 1 is quite poor (results not shown), and a statistical test on the improvement of 6

fit obtained by introducing the additional parameter (KBL) is significant ( p < 0.0001). 7

Conversion between the PBP K

- model

- predicted rate of urinary excretion (mg/hours) or 8

cumulative urinary excretion (mg) and the urine methanol concentration data reported by the 9

authors was achieved by assuming 0.5 mL/hours/kg body weight total urinary output (Horton 10

et al

., 1992). The resulting values of K1C and KBL, shown in Table 3

- 10, differ somewhat 11

depending on whether first

- order or saturable liver metabolism is used. These are only calibrated 12

against a small dataset and should be considered an estimate. Urinary elimination is a minor 13

route of methanol clearance with little impact on blood methanol kinetics. 14

Although the high dose s used in the mouse studies clearly warrant the use of a second 15

metabolic pathway with a high K

m , the human exposure data all represent lower concen trations 16

and may not require or allow for accurate calibration of a second metabolic pathway. Horton 17

et al . (1992) employed two sets of metabolic rate constants to describe human methanol 18

disposition, similar to the description used for rats and mice, but in vitro studies using monkey 19

tissues with non methanol substrates were used as justification for this approach. Although 20

Bouchard et al . (2001) described their metabolism using Michaelis

- Menten metabolism, Starr 21

and Festa (2003) reduced that to an effect ive first

- order equation and showed adequate fits. 22

Perkins et al . (1995a) estimated a K

m of 320 **±** 1273 mg/L (mean **±** S.E.) by fitting a 23

one

- compartment model to data from a single estimated oral dose. In addition to the extremely 24

high standard error, the large standard error for the associated V

max (93 **±** 87 mg/kg/hours) 25

indicates that the set of Michaelis

- Menten constants was not uniquely identifiable using this data. 26

Other Michaelis

- Menten constants have been used to describe methanol metabolism in vario us 27

models for primates ( Table 3

- 11). 28

293

- 37 DRAFT

- DO NOT CITE OR QUOTE Sedivec et al., urine

concentration

0 1 2 3 4 5 6 7 8 9 10

0 4 8 12 16 20 24

Time (h)Conccentration (mg/L) 231 ppm saturable sim

157 ppm saturable sim

78 ppm saturable sim

231 ppm linear sim

157 linear sim

78 ppm linear sim

231 ppm data

157 ppm data

78 ppm data

Sedivec et al.,

total urinary excretion

0 0.5

1 1.5

2 2.5

3

0 4 8 12 16 20 24

Time (h)Amount (mg)

Figure 3

- 11. Urinary m ethanol elimination concentration (upper panel) and

cumulative amount (lower panel) following inhalation exposures to methanol in

human volunteers. Data points in lower panel represent est imated total urinary

methanol elimination from humans exposed to 78 (diamonds)

, 157 (triangles)

, and 231

(circles) ppm methanol for 8 hours

, and lines represent PBPK model simulations.

Source: Sedivec et al . (1981).3

- 38 DRAFT

- DO NOT CITE OR QUOTE Table 3

- 11. Primate K

m s reported in the literature

K

m (mg/L) Reference Note

320 **±** 1273 a Jacobsen et al ., 1988 Human: oral poisoning, estimated dose

716 **±** 489 a Noker et al ., 1980 Cynomolgus Monkey: 2 g/kg dose

278 Makar et al ., 1968 Rhesus Monkey: 0.05

- 1 mg/kg dose

252 **±** 116 a Eells et al

., 1983 Cynomolgus Monkey: 1 g/kg dose

33.9 Horton et al ., 1992 PBPK model: adapted from rat K

m

0.66 Fisher et al ., 2000 PBPK model, Cynomolgus Monkey:10

- 900 ppm

23.7 **±** 8.7 a ,b (This analysis.) PBPK model, human: 100

- 800 ppm

a T he values reported are mea n ± S .D.

b This K

m was optimized while varying V

max , K1C, and KBL, from all of the at

- rest human inhalation data as a part

of this project. The S.D. given for this analysis is based on the Optimize function of acslXtreme, which assumes all

data points are discrete and not from sets of data obtained over time; therefore a true S.D. would be higher. The

final value reported in Table B

- 1 (21 mg/L) was obtained by sequentially rounding and fixing these parameters,

then re

- optimizing the remaining ones.

Sourc e: Perkins et al . (1995b)

.

1

Table 3

- 12. Parameter estimate results obtained using acslXtreme to fit all human

data using either saturable or first

- order metabolism

Parameters Optimized value S.D. Correlation coefficient LLF

Michaelis

- Menten (optimize d)

- 0.994

- 24.1

K

m 23.7 8.9

V

max C 33.1 10.1

First o rder NA

- 31.0

KLLC 95.7 5.4

Note. The S.D.s are based on the Optimize function of acslXtreme v2.3, which assumes all data points are discrete

and not from sets of data obtained over time

. T herefore a tru e S.D. would be a higher value.

2

3

To estimate both Michaelis

- Menten and first

- order rates, all human data under 4

non working conditions (Batterman et al ., 1998; Osterloh et al ., 1996; Sedivec et al ., 1981) were 5

used ( Table 3

- 12). The metabol ic (first

- order or saturable) and urinary elimination constants 6

were numerically fit to the human datasets

, while holding the value for FRACIN at 0.8655 7

(estimated from the results of Sedivec et al

. [ 1981 ]) and holding the ventilation rate constant at 8

16.5 L/hours/kg 0.75 and QPC at 24 L/hours/kg 0.75 (values used by EPA [ 2000 d] for modeling the 9

inhalation

- route kinetics of vinyl chloride). Other human

- specific physiological parameters were 10

used

, as reported in Table 3

- 10. Final fitted parameters that have b een used in the saturable 113

- 39 DRAFT

- DO NOT CITE OR QUOTE model are given in Table 3

- 10. The resulting fits of two different possible parameterizations 1

(first

- order ["linear"] or optimized K

m /V

max ) are shown in Figures 3

- 11 and 3

- 12. 2

3

Batterman et al., blood concentration

(800 ppm exposures)

0 2 4 6 8 10 12 14 16 18

0 2 4 6 8

Time (h)Conccentration (mg/L) Saturable metabolsim

First-order metabolism

2 h data

1 h data

30 min data

Osterloh et al.

(200 ppm inhalation)

0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

5

0 2 4 6 8 Time (hr)Blood concentration (mg/L)

Data

Saturable metabolsim

First-order metabolism

Figure 3

- 12. Data showing the visual quality of the fit using optimized first

- order or

Michaelis

- Menten kinetics to describe the metabolism of methanol in humans. Rate

constants used for each simulation are given in Table 3

- 12.

Source: Batterman et al . (1998: top); Osterloh et al . (1996: bottom).

4

U se of a first

- order rate has the advantage of resulting in a simpler (one fewer variable) 5

model, while providing an adequate fit to the data ; however, the satur able model clearly fits 6

some of the data better. To discriminate the goodness

- of

- fit resulting of the inclusion of an 7

additional variable necessary to describe saturable metabolism versus using a single first

- order 83

- 40 DRAFT

- DO NOT CITE OR QUOTE rate, a likelihood ratio test was performed. 13 The hypothesis that one metabolic description is 1

better than another is calculated using the likelihood functions evaluated at the maximum 2

likelihood estimates. Since the parameters are optimized in the model using the maximum log 3

likelihood function (LLF), the resultant LLF is used for the statistical comparison of the models. 4

The equation states that two times the log of the likelihood ratio follows a chi square ( **χ** 2 ) 5

distribution with r degrees of freedom: 6



### ( **) (**  **) (**  **)** 

### [ **] (**  **) (**  **)** 

### [ **]** 2 model log 1 model log 2 2 model / 1 model log 2 2 **χ λ**  **λ λ**  **λ**

r **≅**

**− −**  **=**

**−** 7

The likelihood ratio test states that if the two times the difference between the maximum LLF s of 8

the two differ ent descriptions of meta bolism is greater than the χ 2 distribution then the model fit 9

has been improved (Devore, 1995; Steiner et al ., 1990)

. 10

At greater than a 99.95% confidence level, using two metabolic rate constants (K

m and 11

V

max C) is preferred over using a single rate constant ( Table 3

- 13). Forcing the model to use the 12

K

m calculated by Perkins et al . (1995b) wo uld result in model fits indistinguishable from the 13

first

- order case (results not shown). While the correlation coefficients ( Table 3

- 12) indicate that 14

V

max C

, and K

m are highly correlated, that is not unexpected, and the S.D.s ( Table B

- 3) indicate 15

that e ach is reasonably bounded. If the data were indistinguishable from a linear system, K

m in 16

particular would not be so bounded from above since the Michaels

- Menten model becomes 17

indistinguishable from a linear model as V

max C and K

m tend to infinity. Furthe r, the internal dose 18

candidate points of departure (PODs), for example the BMDL 10 for the inhalation

- induced brain

19

weight changes from NEDO (1987) with methanol blood AUC as the metric, is 90.9 mg

- hr/L, 20

which corresponds to an average blood concentration o f 3.8 mg/L. Therefore, the Michaelis

21

Menten metabolism rate equation appears to be sufficiently supported by the existing data with 22

values in a concentration range in which the nonlinearity has an impact. 23

Table 3

- 13. Comparison of LLF s for Michaelis

- Men ten and first

- order metabolism

LLF (logλ) for

M

- M LLF (logλ) for

1st order L LF

1st versus M

- M a 2

r **χ** (99%

confidence) b 2

r **χ** (99.95%

confidence) b

- 24.1

- 31.0 34.1 13.8 12.22

Note

. Models were optimized for all human datasets under non working conditi ons. M

- M: Michaelis

- Menten

a obtained using this equation: 

### ( **) (**  **)** 

### [ **]** 2 model log 1 model log 2 **λ λ** 

**− −** 

b significance level at r =1 degree of freedom

.

24

While the use of Michaelis

- Menten kinetics might allow predictions across a wide 25

exposure range (into the nonlinear region) , extrapolation above 1,000 ppm is not suggested since 26

the highest human exposure data are for 800 ppm. Extrapolation to higher concentrations is 27

13 Models are considered to be nested when the model structures are identical except for the addition of complexity,

such as the added metabolic rate. Under these conditions, the likelihood ratio can be used to compare the relative

ability of the two models to describe the data, as d escribed in "Reference Guide for Simusolv" (Steiner et al., 1990).3

- 41 DRAFT

- DO NOT CITE OR QUOTE potentially misleading since the nonlinearity in the exposure

- internal

- dose relationship for 1

humans is uncert ain above this point. The use of a BMD or internally applied uncertainty factors 2

(UFs) should place the exposure concentrations well within the linear range of the model. 3

The data from Ernstgard et al . (2005) were used to assess the use of the first

- order 4

metabolic rate constant to a dataset collected under conditions of light work. Historical measures 5

of QPC (52.6 L/hours/kg 0.75 ) and QCC (26 L/hours/kg 0.75 ) for individuals exposed under 6

conditions of 50 watts of work from that laboratory (52.6 L/hours/kg 0.75 ) ( Ernstgard, 2005 ; 7

Corley et al ., 1994 ; Johanson et al ., 1986 ) were used for the 2

- hour exposure period (Figure. 3

- 8

12). Otherwise, there were no changes in the model parameters (no fitting to these data). The 9

results are remarkably good, given the l ack of parameter adjustment to data collected in a 10

different laboratory and using different human subjects than those to which the model was 11

calibrated. 12

Ernstgard et al. (2005)

0 1 2 3 4 5 6 7 8

0 2 4 6 8 10

Time (hr)Blood concentration (mg/L)

100 ppm 200

ppm

Figure 3

- 13. Inhalation exposures to methanol in human volunteers. Data points

represent measured blood methanol concentrations from humans (4 males and

4 females) exposed to 100 ppm (open symbols) or 200 ppm (filled symbols) for 2

hours during light physical activity. Solid lines represent PBPK model

simulations with no fitting of model parameters. For the first 2 hours, a QPC

of 52.6 L/hours/kg 0.75 (Johansen et al ., 1986), and a QCC of 26 L/hours/kg 0.75

(Corley et al ., 1994) was used by the model.

Source: Ernstgard et al . (2005)

.

3.4.6.2. Oral Route

There were no methanol human data available for calibrat ion or validation of the oral 13

route for the human model. In the absence of methanol data to estimate rate constants for oral 14

uptake, h uman oral absorption parameters reported values for ethanol (Sultatos et al

. , 2004) are 153

- 42 DRAFT

- DO NOT CITE OR QUOTE set in the code, except that satu rable absorption from the stomach was retained with the KMASC 1

equal to the mouse value. The maximum rate of absorption form the stomach, VMASC, was 2

then set such that for a 70

- kg person, VMAS/KM (the effective first

- order rate constant at low 3

doses) match ed the first

- order absorption rate from Sultatos et al. (0.21 hr

- 1 ). Also, while 4

Sultatos et al. included a rate of metabolism for ethanol in the stomach, the corresponding fecal 5

elimination rate was set to zero here, effectively assuming 100% absorption of methanol for 6

humans

. H owever, h uman oral dosimetry was described as zero

- order uptake, in which 7

continuous infusion at a constant rate into the stomach equal to the daily dose/24 hours was 8

assumed and human internal doses were computed at steady state. Since absorption is 100% for 9

the human model, at steady state the net rate of absorption must equal the rate of infusion to the 10

stomach, irrespective of the other parameter values. (Changes in the absorption constants simply 11

cause the amount of methanol in each GI compartment at steady state to change until the net rate 12

of absorption from the somtach and intestine equals the rate of infusion.) Thus the human 13

absorption constants were set to what is considered a reasonable estimate, given the lack of 14

hum an oral PK data, but the simulations are conducted in a way that makes the result insensitive 15

to their values; having human values set does allow for simulations of non

- constant infusion, 16

should such be desired . Since the AUC was computed for a continuous oral exposure, its value 17

is just 24 hours times the steady

- state blood concentration at a given oral uptake rate. 18

3.4.7. Monkey PK Data and Analysis

In order to estimate internal doses (blood AUCs) for the monkey health

- effects study of 19

Burbacher et al . (1999b) and further elucidate the potential differences in methanol 20

pharmacokinetics between NP and pregnant individuals (2nd and 3rd trimester), a focused 21

reanalysis of the data of Burbacher et al . (1999a) was performed . Individual blood concentration 22

measuremen ts prior to and following exposure are shown in scatter plots in Appendix B of 23

Burbacher et al . (1999a). More specifically, the monkeys in the study were exposed for 2.5 24

hours/day

, with the methanol concentration raised to approximately the target concent ration for 25

the first 2 hours of each exposure and the last 30 minutes providing a chamber "wash

- out" 26

period, when the exposure chamber concentration was allowed to drop to 0 . Blood samples 27

were taken and analyzed for methanol concentration at 30 minutes, 1, 2, 3, 4, and 6 hours after 28

removal from the chamber (or 1, 1.5, 2.5, 3.5, 4.5, and 6.5 hours after the end of active 29

exposure). These data were analyzed to compare the PK in NP versus pregnant animals, and 30

fitted with a simple PK model to estimate 24

- hour blood AUC values for each exposure level. 31

Dr. Burbacher graciously provided the original data, which were used in this analysis. 32

Two cohorts of monkeys were examined, but the data (plots) did not indicate a systematic 33

difference between the two, so t he data from the two cohorts were combined. The data from the 34

scatter plots of Burbacher et al . (1999a) for the NP (pre

- pregnancy), first pregnancy (2nd 35

trimester), and second pregnancy (3rd trimester) studies are compared in Figure 3

- 13, along with 363

- 43 DRAFT

- DO NOT CITE OR QUOTE model simulations (explained below). Since the pregnancy time point s were from animals that 1

had been previously exposed for 87 days plus the duration of pregnancy to that time point , the 2

pre

- exposed NP animals were used for comparison, rather than naïve animal s, with the 3

expectation that effects due to changes in enzyme expression ( i.e., induction) from the 4

sub chronic exposure would not be a distinguishing factor. Note that each exposure group 5

included a pre

- exposure baseline or background measurement, also sh own. To aid in 6

distinguishing the data visually, the NP data are plotted at times 5 min utes prior to the actual 7

blood draws and the 3rd trimester at 5 min utes after each blood draw. 8

Overall there appears to be no significant or systematic difference amon g the NP and 9

pregnant groups. The solid lines are model simulations calibrated to only the 2nd trimester data 10

(details below), but they just as adequately represent average concentrations for the NP and 3rd 11

trimester data. Likewise, a PK model calibrate d to the NP PK data adequately predicted the 12

maternal methanol concentrations in the pregnant monkeys (results not shown). Since any 13

maternal:fetal methanol differences are expected to be similar in experimental animals and 14

humans (with the maternal:fetal ratio being close to one due to methanol's high aqueous 15

solubility and relatively limited metabolism by the fetus), the predicted levels for the 2nd 16

trimester maternal blood are used in place of measured or predicted fetal concentrations. 17

18

19

20

21

22

23

24

25

26

27

28

29

30

313

- 44 DRAFT

- DO NOT CITE OR QUOTE 1800 ppm

0.0 16.0 32.0 48.0 64.0

-200 -100 0 100 200 300 400 Time (min)Blood MeOH (mg/L)

600 ppm

0.0 6.0 12.0 18.0 24.0

-200 -100 0 100 200 300 400 Time (min)Blood MeOH (mg/L) Pre-pregnancy

2ndTrimester

3rdTrimester

Simulation

200 ppm

0.0 2.0 4.0 6.0 8.0 10.0

-200 -100 0 100 200 300 400 Time (min)Blood MeOH (mg/L)

Figure 3

- 14. Blood methanol concentration data from NP and pregnant monkeys. NP

and 3rd trimester data are plotted, respectively, at 5 min utes before and after

actual collection times to facilitate comparison. Solid line is from simple PK

model, fit to 2nd trimester data only.

Source: Burbacher et al . (1999a; Figure B

- 4).

3.4.7.1. PK Model Analysis for Monkeys

To analyze and integrate the PK data of Burbacher et al . (1999a), the one

- compartment 1

model for Michaelis

- Menten kinetics used by Burbacher et al . (19 99a, 1999 b) was extended by 2

the addition of a chamber compartment to capture the kinetics of concentration change in the 3

exposure chamber, as shown in Figure 3

- 14. The data in Figure 3

- 14 (digitized from Figure 5 of 4

Burbacher et al ., 1999a, 1999 b) show an exponential rise to and fall from the approximate target 5

concentration during the exposure period. The use of a single

- compartment model for the 63

- 45 DRAFT

- DO NOT CITE OR QUOTE chamber allows this dynamic to be captured, so that the full concentration

- time course is used in 1

simulating the monkey internal concentration rather than an approximate step function ( i.e. rather 2

than assuming an instantaneous rise and fall). The pair of equations representing the time

- course 3

in the chamber and monkey are as follows (bolded parameters are fit t o data): 4

Chamber: dC ch /dt = [(C CM ·S

– C ch )·F ch

– R inh ]/V ch 5

Monkey: dC mk /dt = [R inh

– V

max ·C mk /(K

m + C mk )]/(V mk ·BW) 6

with R inh = C ch ·R C ·(1000·BW) 0.74 ·F and C

net = C mk + C bg

. 7

d: delta, change 8

C ch : instantaneous chamber concentration (mg/L) 9

t: time (h o u r) 10

C CM : chamber in

- flow methanol concentration (mg/L), which was set to the concentrations 11

corresponding to those reported in Table 2 of Burbacher et al . (1999a), using the 12

"Breeding" column for the NP (87 days pre

- exposed; values in Table 3

- 14) 13

S: exposu re switch, set to 1 when exposure is on (first 2 hours) and 0 when off 14

F ch : chamber air

- flow, 25,200 L/hours, as specified by Burbacher et al . (1999a, 1999 b) 15

R inh : net rate of methanol inhalation by the monkeys (mg/hr) 16

V ch (1,220 L): chamber volume, initial ly set to 1,380 L ("accessible volume" stated by 17

Burbacher et al

. [1999a, 1999b] ), but allowed to vary below that value to account for 18

volume taken by equipment, monkey, and to allow for imperfect mixing 19

C mk : instantaneous inhalation

- induced monkey blood m ethanol concentration (mg/L); this is 20

added to the measured background/endogenous concentration before comparison to data 21

V

max ( 39.3 mg/hr ): fitted ( non scaled) Michaelis

- Menten maximum elimination rate 22

K

m (14.6 mg/L) : fitted ( non scaled) Michaelis

- Menten saturation constant 23

V mk (0.75 L/kg) : fitted volume of distribution for monkey 24

BW: monkey body weight (kg); for NP monkeys set to group average values in data of 25

Burbacher et al . (1999a, 1999 b; personal communication) 26

R

c : allometric scaling factor for total monkey respiration (0.12 L/hours/g 0.74

= 27

2 mL/minute/g 0.74 ), as used by Burbacher et al

. ( 1999a, 1999 b ) (note that scaling is to BW 28

in g, not kg) 29

F: fractional absorption of inhaled methanol, set to 0.6 (60%), the (rounded) value measured 30

in humans by Sedi vec et al . (1981); F and V mk cannot be uniquely identified, given the 31

model structure, so F was set to the (approximate) human value to obtain a realistic 32

estimate of V mk 33

C

net : net blood concentration, equal to sum of the inhalation

- induced concentration ( C mk ) and 34

the background blood level (C bg ) (mg/L) 35

C bg : background (endogenous) methanol concentration, set to the pre

- exposure group

36

specific mean from the data of Burbacher et al . (1999a, 1999 b; personal communication) 373

- 46 DRAFT

- DO NOT CITE OR QUOTE Chamber concentration profile

0 200 400 600 800 1000 1200 1400 1600 1800 2000

0 0.5 1 1.5 2

Time (hr)Concentration (ppm)

200 ppm 600 ppm 1800 ppm

Figure 3

- 15. Chamber concentration profiles for monkey methanol exposures. Lines

are model simulations. Indicated concentrations are target concentrations;

measured concentrations differed slightly (see Table 3

- 14).

Source: Burbacher et al . (1999a).

The model was specifically fit to th e 2nd trimester monkey data, assuming that the 1

parameters were the same for all the exposure groups and concentrations. While the discussion 2

above and data show little difference between the NP and two pregnancy groups, the 2nd 3

trimester group was presume d to be most representative of the average internal dosimetry over 4

the entire pregnancy. Further, the results of Mooney and Miller (2001) show that developmental 5

effects on the monkey brain stem following ethanol exposure are essentially identical for 6

mon keys exposed only during early pregnancy versus full

- term, indicating that early pregnancy 7

is a primary window of vulnerability. 8

Model simulation results are the lines shown in Figures 3

- 13 and 3

- 14. The model 9

provides a good fit to the monkey blood and chamber air concentration data. While the chamber 10

volume was treated as a fitted parameter, which was not done by Burbacher et al . (1999a), the 11

chamber concentration data support this estimate. The model does an adequate job of fitting the 12

data for all exposure groups without group

- specific parameters. In particular, the data for all 13

exposure levels can be adequately fit using a single value for the volume of distribution (V mk ) as 14

well as each of the metabolic parameters. While one may be able to show statistically distinct 15

parameters for different groups or exposure levels (by fitting the model separately to each), as 16

was done by Burbacher et al . (1999a), it is unlikely that such differences are biologically 17

significant, given the fairly large number o f data points and the large variability evident in the 18

blood concentration data. Thus, the single set of parameters listed with the parameter 19

descriptions above will be used to estimate internal blood concentrations for the dose

- response 203

- 47 DRAFT

- DO NOT CITE OR QUOTE analysis. The ch amber concentrations for “pregnancy” exposures recorded by Burbacher et al

. 1

(1999a ; Table 2) and average body weights for each exposure group at the 2nd trimester time 2

point were used along with the model to calculate 24

- hour blood methanol AUCs ( Table 3

- 1 4). 3

Table 3

- 14. Monkey group exposure characteristics

Exposure concentration (ppm) a Group average BW (kg) b 24

- hr blood methanol AUC

(mg

- hr/L) c

206 3.46 6.73

610 4.08 28.28

1,822 3.83 138.11

a From Burbacher et al . (1999a, 1999 b), Table 2, "pregnancy " exposure.

b From Burbacher, original data (personal communication).

c Calculated using the two

- compartment PK model as described above.

3.4.8. Summary and Conclusions

Mouse, rat, and human versions of a methanol PBPK model have been developed and 4

calibrated to d ata available in the open literature. The model simplifies the structure used by 5

Ward et al . (1997), while adding specific refinements such as a standard lung compartment 6

employed by Fisher et al . (2000) and a two

- compartment GI tract. 7

Although the endpo ints of concern are developmental effects which occur during in utero 8

and (to a lesser extent) lactational exposure, no pregnancy

- specific PBPK model exists for 9

methanol and inadequate data exists for the development and validation of a 10

fetal/gestational/c onceptus compartment. The fact that the unique physiology of pregnancy and 11

the fetus/conceptus are not represented in a methanol model would be important if methanol 12

pharmacokinetics differed significantly during pregnancy or if the observed partitioning of 13

methanol into the fetus/conceptus versus the mother showed a concentration ratio significantly 14

greater than or less than 1. Methanol pharmacokinetics during GD 6

– GD 10 in the mouse are not 15

different from NP mice (Pollack and Brouwer, 1996)

, and the mater nal blood:fetus/conceptus 16

partition coefficient is reported to be near 1 (Ward et al ., 1997; Horton et al ., 1992). At GD18 in 17

the mouse, maternal blood levels are only modestly different from those in NP animals (see 18

F igures B

- 4 and B

- 5 [Appendix B] for e xamples), and in general the PBPK model simulations 19

for the NP animal match the pregnancy data as well as the non pregnancy data. Likewise, 20

maternal blood kinetics in monkeys differs little from those in NP animals (see Section 3.4.7 for 21

details). Further , in both mice and monkeys, to the extent that late

- pregnancy blood levels differ 22

from NP for a given exposure, they are higher; i.e., the difference between model predictions and 23

actual concentrations is in the same direction. These data support the assu mption that the ratio of 24

actual target

- tissue methanol concentration to (predicted) NP maternal blood concentrations will 25

be about the same across species, and hence

, that using NP maternal blood levels in place of fetal 26

concen trations will not lead to a systematic error when extrapolating risks. 273

- 48 DRAFT

- DO NOT CITE OR QUOTE The findings in the mouse (similar blood methanol kinetics between NP and pregnant 1

animals prior to GD18 and a maternal blood:fetal partition coefficient close to 1 ) are assumed to 2

be applicable to the rat. How ever, the critical gestational window for the reduced brain weight 3

effect observed in the NEDO (1987) rat study is broader than for the mouse cervical rib effect. 4

In addition, NEDO (1987) rats were exposed not only to methanol gestationally but also 5

lacta tionally and via inhalation after parturition. The additional routes of exposure presented to 6

the pups in this study present uncertainties and suggest that average blood levels in pups might 7

be greater than those of the dam. 8

Methanol is transported direc tly from the maternal circulation to fetal circulation via the 9

placenta, but transfer via lactation involves distribution to the breast tissue, then milk, then 10

uptake from the pup’s GI tract. Therefore blood or target

- tissue levels in the breast

- feeding 11

i nfant or pup are likely to differ more from maternal levels than do fetal levels. In addition , the 12

health

- effects data indicate that most of the effects of concern are due to fetal exposure, with a 13

relatively small influence due to postbirth exposures. F urther , it would be extremely difficult to 14

distinguish the contribution of postbirth exposure from pre birth exposure to a given effect in a 15

way that would allow the risk to be estimated from estimates of both exposure levels, even if one 16

had a lactation/c hild PBPK model that allowed for prediction of blood (or target

- tissue) levels in 17

the offspring. Finally, one would still expect the target

- tissue concentrations in the offspring to 18

be closely related to maternal blood levels (which depend on ambient expo sure and determine 19

the amount delivered through breast milk), with the relationship between maternal levels and 20

those in the offspring being similar across species. Further, as discussed to a greater extent in 21

Section s 5.1.2 and 5.3, it is likely that the difference in blood levels between rat pups and dams 22

would be similar to the difference between mothers and human offspring. Therefore, it is 23

assumed that the potential differences between pup and dam blood methanol levels do not have a 24

significant impac t on this risk assessment and the estimation of HECs. The use of the full 25

intrahuman UF of 10 is also expected to account for PK differences between children and adults. 26

Therefore, the development of a lactation/child PBPK model appears not to be necessar y, 27

given the minimal change that is likely to result in risk extrapolations, and use of ( NP ) maternal 28

blood levels as a measure of risk in the offspring is considered preferable over use of default 29

extrapolation methods. In particular, the existing human data allow for predictions of maternal 30

blood levels, which depend strongly on the rate of maternal methanol clearance. Since bottle

- fed 31

infants do not receive methanol from the ir mother s , they are expected to have lower or, at most, 32

similar overall exposu res for a given ambient concentra tion than the breast

- fed infant, so that use 33

of maternal blood levels for risk estimation should also be adequately protective for that group. 34

The model fits to the mouse oral

- route methanol kinetic data

, using a consisten t set of 35

parameters ( Figure B

- 4 in Appendix B)

, are fairly good for doses of 1,500 mg/kg but 36

underpredict blood levels by 30% or more after a dose of 2,500 mg/kg. In particular, the oral 37

mouse model consistently underpredicts the amount of blood methanol reported in two studies 383

- 49 DRAFT

- DO NOT CITE OR QUOTE (Ward et al ., 1997, 1995). Ward et al . (1997) utilized a different V

max for each oral absorption 1

dataset; the GD18 and the GD8 data from Dorman et al . (1995) were both fit using a V

max of 2

~80 mg/kg/hours (body weights were not list ed; the model assumed that GD8 and GD18 mice 3

were both 30 g; Ward et al

. [ 1997 ] did not scale by body weight). Additionally, lower partition 4

coefficients for placenta (1.63 versus 3.28) and embryonic fluid (0.0037 versus 0.77) were used 5

for GD8 and GD18. The current refined model adequately fits the oral PK data using a single set 6

of parameters that is not varied by dose or source of data. 7

The rat models were able to adequately predict the limited inhalation, oral and i.v. 8

datasets available. Low

- dose ex posures were emphasized in model optimization due to their 9

greater relevance to risk assessment. Based on a rat inhalation exposure to 500 ppm, the H EC 10

would be 281 ppm (by applying an AUC of 201.3 [ Figure B

- 12] to E quation 1 of Appendix B). 11

The final mo use, rat, and human methanol PBPK models fit multiple datasets for 12

inhalation, oral

, and i.v., from multiple research groups using consistent parameters that are 13

representative of each species but are not varied within species or by dose or source of data. 14

Also, a simple PK model calibrated to NP monkey data, which were shown to be essentially 15

indistinguishable from pregnant monkey PK data, was used to estimate blood methanol AUC 16

values (internal doses) in that species. In Section 5, the models and these results are used to 17

estimate chronic human exposure concentrations from internal dose metrics. 184

- 1 DRAFT

- DO NOT CITE OR QUOTE 4. HAZARD IDENTIFICATIO N

4.1. STUDIES I N HUMANS

– CASE REPORTS, OCCUPA TIONAL AND

CONTROLLED STUDIES

4.1.1. Case Reports

An extensive library of case reports has documented the consequences of acute 1

accidental/intentional methanol poisoning. Nearly all have involved ingestion, but a few have 2

involved percutaneous and/or inhalation exposure. As many of the case reports demonstrate, the 3

association of Parkinson

- like symptoms wi th methanol poisoning is related to the observation 4

that lesions in the putamen are a common feature both in Parkinson’s disease and methanol 5

overexposure

. These lesions are commonly identified using computed tomography (CT) or by 6

Magnetic Resonance Imagi ng (MRI). Other areas of the brain ( e.g., the cerebrum, cerebellum, 7

and corpus callosum) also have been shown to be adversely affected by methanol overexposure

. 8

Various therapeutic procedures ( e.g., ethanol infusion, sodium bicarbonate or folic acid 9

admi nistration, and hemodialysis) have been used in many of these methanol overexposure s

, and 10

the reader is referred to the specific case reports for details in this regard. The reader also is 11

referred to Kraut and Kurtz (2008) and Barceloux et al . (2002) for a more in

- depth discussion of 12

the treatments in relation to clinical features of methanol toxicity. A brief discussion of the 13

terms cited in case report literature follows

. 14

Basal ganglia, a group of interconnected subcortical nuclei in each cerebral hemi sphere, 15

refers to various structures in the grey matter of the brain that are intimately involved, for 16

example, in coordinating motor function, maintaining ocular and respiratory function, and 17

consciousness. The connectivity within the basal ganglia invol ves both excitatory and inhibitory 18

neurotransmi t ters such as dopamine ( associated with Parkinson’s disease when production is 19

deficient ). 20

The structures comprising the basal ganglia include but are not limited to : the putamen 21

and the globus pallidus (toget her termed the lentiform nuclei), the pontine tegmentum, and the 22

caudate nuclei. Dystonia or involuntary muscle contraction can result from lesions in the 23

putamina; if there are concomitant lesions in the globus pallidus, Parkinsonism can result (Bhatia 24

a nd Marsden, 1994). Bhatia and Marsden (1994) have discussed the various behavioral and 25

motor consequences of focal lesions of the basal ganglia from 240 case

- study reports. Lesions in 26

the subcortical white matter adjacent to the basal ganglia often occur as well ( Airas et al ., 2008; 27

Rubinstein et al ., 1995; Bhatia and Marsden, 1994). In the case reports of Patankar et al . (1999), 28

it was noted that the severity and extent of necrosis in the lenticular nuclei do not necessarily 29

correlate with clinical outc ome. 304

- 2 DRAFT

- DO NOT CITE OR QUOTE In one of the earliest reviews of methanol overexposure , Bennett et al . (1953) described a 1

mass accidental poisoning when 323 persons, ranging in age from 10 to 78 years , in Atlanta

, 2

Georgia, consumed “whisky” adulterated with as much as 35

– 40% metha nol. In all, 41 people 3

died. Of the 323 individuals , 115 were determined to be acidotic with symptoms (visual 4

impairment, headache [ affecting

~ 62% ] , dizziness [ affecting

~ 30% ] , nausea, abdominal pain and 5

others) beginning around 24 hours postexposure

. V isual impairment was mostly characterized 6

by blurred or indistinct vision; some who were not acidotic experienced transient visual 7

disturbances. The cardiovascular parameters were unremarkable. The importance of acidosis to 8

outcome is shown in Table 4

- 1. Among the key pathological features were cerebral edema, lung 9

congestion, gastritis, pancreatic necrosis, fatty liver, epicardial hemorrhages, and congestion of 10

abdominal viscera. 11

In another early investigation of methanol poisoning (involving 320 indi viduals), Benton 12

and Calhoun (1952) reported on methanol’s visual disturbances

. 13

Table 4

– 1. Mortality rate for subjects exposed to methanol

- tainted whiskey in relation

to their level of acidosisa a

Subjects Number Percent deaths

All patients 323 6.2

Acido tic (CO 2 < 20 mEq) 115 19

Acidotic (CO 2 < 10 mEq) 30 50

a These data do not include those who died outside the hospital or who were moribund on arrival.

Source: Bennett et al . (1953).

Riegel and Wolf (1966), in a case report involving a 60

- year

- old woman who ingested 14

methanol, noted that nausea and dizziness occurred within 30 minutes of ingestion. She 15

subsequently passed out and remained unconscious for 3 days. Upon awakening she had 16

paralysis of the vocal cords and was clinically blind in one eye after 4 months. Some aspects of 17

Parkinson

- like symptoms were evident. There was a pronounced hypokinesia with a mask

- like 18

face resembling a severe state of Parkinson’s disease. The patient had difficulty walking and 19

could only make right turns with difficult y. There was no memory loss. 20

Treatment of a 13

- year

- old girl who ingested an unspecified amount of a windshield

21

washer solution containing 60% methanol was described by Guggenheim et al . (1971). She 22

displayed profound acidosis; her vital signs, once she was treated for acidosis, were normal by 23

36 hours after hospital admission. During the ensuing 6 months after discharge from the 24

hospital, visual acuity (20/400 , both eyes ) worsened

, and she experienced muscle tremors, arm 25

pain, and difficulty in walking. A regimen of levadopa treatment greatly improved her ability to 26

function normally. 274

- 3 DRAFT

- DO NOT CITE OR QUOTE Ley and Gali (1983) also noted symptoms that are Parkinson like following methanol 1

intoxication

. In this case report respiratory support was needed; the woman was in a co ma. 2

Once stabilized, she exhibited symptoms similar to those noted in other case study reports

, such 3

as blurred vision, movement difficulty, and tremors. Computerized Axial Tomography scan 4

findings highlighted the central nervous system (CNS) as an impor tant site for methanol 5

poisoning. 6

Rubinstein et al . (1995) presented evidence that a methanol blood level of 36 mg/dL 7

( 360 m g/L) is associated with a suite of CNS and ocular deficits that led to a 36

- year

- old man 8

(who subsequently died) becom ing comatose. CT scans at 1

– 2 days following ingestion were 9

normal. However, MRI scans at day 4 revealed lesions in the putamen and peripheral white 10

matter of the cerebral and cerebellar hemispheres. Bilateral cerebellar cortical lesions had been 11

reported in an earli er case of methanol poisoning by Chen et al . (1991). 12

Finkelstein and Vardi (2002) reported that long

- term inhalation exposure of a woman 13

scientist to methanol without acute intoxication resulted in a suite of delayed neurotoxic 14

symptoms ( e.g., hand tremor, dystonia, bradykinesia, and other decrements in body movement). 15

Despite treatment with levadopa, an increase in the frequency and severity of effects occurred. 16

Exposure to bromine fumes was concomitant with exposure to methanol. 17

Hantson et al . (1997a) f ound, in four cases, that MRI and brain CT scans were important 18

tools in revealing specific brain lesions ( e.g., in the putamina and white matter). The first subject 19

was a 57

- year

- old woman who complained of blurred vision, diplopia, and weakness 24 hours 20

after ingesting 250 mL of a methanolic antifreeze solution. Upon hospital admission she was 21

comatose and in severe metabolic acidosis. An MRI scan at 9 days indicated abnormal 22

hyperintense foci in the putamina (decreased in size by day 23) and subtle le sions (no change by 23

day 23) in the white matter. Upon her discharge, bilateral deficits in visual acuity and color 24

discrimination persisted. 25

Similar deficits (metabolic acidosis, visual acuity, and color discrimination) were seen in 26

a man who ingested 300 mL of 75% methanol solution. His blood methanol level was 27

163 mg/dL (1,630 mg/L)

. An MRI administered 24 hours after hospital admission revealed 28

abnormal hyperintense foci in the putamina

, with less intense lesions in the white matter. Like 29

the first s ubject, a subsequent MRI indicated the foci decreased in size over time , but visual 30

impairments persisted. 31

The third individual, a male, ingested an unspecified amount of a methanolic solution. 32

His blood methanol level was 1,290 mg/dL (12,900 mg/L) , and h e was in a coma upon hospital 33

admission. An MRI revealed lesions in the putamina and occipital subcortical white matter. A 34

follow

- up CT scan was performed after 1 year and showed regression of the putaminal lesions 354

- 4 DRAFT

- DO NOT CITE OR QUOTE but no change in the occipital lesions. Upon his discharge, severe visual impairment remained 1

but no extrapyramidal signs were observed. 2

The last case was a man who became comatose 12 hours after ingesting 100 mL 3

methanol. His blood methanol level at th at time was 60 mg/dL (600 mg/L)

. An MRI revealed 4

lesions in the putamina; at 3 weeks these lesions were observed to have decreased in size. Upon 5

his discharge, the neurological signs had improved but optic neuropathy (in visual evoked 6

potential) was observed. 7

In a separate publication , Hantson et al . (1997b) reported a case of a 26

- year

- old woman 8

who had ingested 250

– 500 mL methanol during the 38th week of pregnancy. H er initial blood 9

methanol level was 230 mg/dL (2,300 mg/L) (formate was 33.6 mg/dL or 336 mg/L )

, yet only a 10

mild metabolic acid osis was indicated. No distress to the fetus was observed upon gynecologic 11

examination. Six days after therapy was initiated (methanol was not present in blood)

, she gave 12

birth. No further complications with either the mother or newborn were noted. 13

The re have been several case reports involving infant or toddler exposure s to methanol 14

( De Brabander et al ., 2005; Wu et al ., 1995; Brent et al ., 1991; Kahn and Blum, 1979). The 15

report by Wu et al . (1995) involved a 5

- week

- old infant with moderate metabolic acidosis and a 16

serum methan ol level of 1,148 mg/ dL (11,480 mg/L) , a level that is ordinarily fatal. However, 17

this infant exhibited no toxic signs and survived without any apparent permanent problems. De 18

Brabander et al . (2005) report ed the case of a 3

- ye ar

- old boy who ingested an unknown amount 19

of pure methanol; at 3 hours after ingestion, the blood methanol level was almost 30 mg/dL (300 20

mg/L)

. Ethanol infusion as a therapeutic measure was not well tolerated; at 8 hours after 21

ingestion, fomepizole was a dministered

, and blood methanol levels stabilized below 20 mg/dL 22

(200 mg/L) , a level above which is considered to be toxic by the American Academy of Clinical 23

Toxicology (Barceloux et al ., 2002). Neither metabolic acidosis nor visual impairment was 24

observ ed in this individual. Hantson et al . (1997a), in their review, touted the efficacy of 25

fomepizole over ethanol in the treatment of methanol poisoning 26

Bilateral putaminal lesions

, suggestive of non hemorrhagic necrosis in the brain of a man 27

who accidentally ingested methanol

, were reported by Arora et al . (2005). A pproximately 28

10 hours after MRI examination, he developed blurred vision and motor dysfunction. After 29

5 months, visual deficits persisted along with extrapyramidal symptoms. Persistent visual 30

d ysfunction was also reported in another methanol poisoning case (Arora et al ., 2007); the vision 31

problems developed

~ 46 hours subsequent to the incident. 32

Vara

- Castrodeza et al . (2007) applied diffusion

- weighted MRI on a methanol

- induced 33

comatose woman. Diffusion

- weighted MRI provides an image contrast distinct from standard 34

imaging in that contrast is dependent on the molecular motion of water (Schaefer et al ., 2000). 35

The neuroradiological findings were suggestive of bilateral putaminal hemorrhagic necr osis, 364

- 5 DRAFT

- DO NOT CITE OR QUOTE cerebral and intraventricular hemorrhage, diffuse cerebral edema, and cerebellar necrosis. 1

Diffusion

- weighted MRI allows for differentiation of restricted diffusion which is indicative of 2

nonviable tissue. In this case, treatment for acidosis (bloo d methanol levels had risen to 3

1

, 000 mg/L) was unsuccessful and the patient died. 4

Emergency treatment was unable to save the life of a 38

- year

- old man who presented 5

with abdominal pain and convulsions after methanol intoxication (Henderson and Bruba ch er, 6

2002)

. A review of a head CT scan performed before the individual went into respiratory arrest 7

revealed bilateral globus pallidus ischemia. 8

Discrete lesions of the putamen, cerebral white matter, and corpus callosum were 9

observed upon MRI (8 days post ing estion) in a man exposed to methanol (blood level 370 10

mg/L) complaining of vision loss (Keles et al ., 2007). Standard treatments corrected the acidosis 11

(pH 6.8)

, and at 1

- month follow

- up

, his cognitive function improved but blindness and bilateral 12

optic a trophy were described as permanent. The follow

- up MRI showed persistent putaminal 13

lesions with cortical involvement. 14

Fontenot and Pelak (2002) described a case of a woman who presented with persistent 15

blurred vision and a worsening mental status 36 hours after ingestion of an unspecified amount 16

of methanol. The initial CT scan revealed mild cerebral edema. The blood methanol level at this 17

time was 86 mg/dL (860 mg/L)

. A repeat CT scan 48 hours after presentation showed 18

hypodensities in the putamen and peripheral white matter. One month after discharge, cognitive 19

function improved

, and the patient experienced only a mild lower

- extremity tremor. 20

Putaminal necrosis and edema of the deep white matter (the corpus callosum was not 21

affected) was found upon MR I examination of a 50

- year

- old woman who apparently ingested an 22

unknown amount of what was believed to be pure laboratory methanol (Kuteifan et al ., 1998). 23

Her blood methanol level was 39.7 mM (1 27 mg/dL ; 1, 272 mg/L ) upon hospital admission and 24

dropped to 102 mg/dL (1,020 mg/L) at 10 hours and to 71 mg/dL (710 mg/L) at 34 hours. The 25

woman, a chronic alcoholic, was in a vegetative state when found and di d not improved over the 26

course of a year. 27

MRI and CT scans performed on a 51

- year

- old man with generali zed seizures who had a 28

blood methanol level of 95 mM (304 mg/dL ; 3,04 4 mg/L ) revealed bilateral hemorrhagic 29

necrosis of the putamen and caudate nuclei (Gaul et al ., 1995 ). In addition, there was extensive 30

subcortical necrosis and bilateral necrosis of the pontine tegmentum and optic nerve. The patient 31

died several hours after the scans were performed. 32

The relation of methanol overexposure to brain hemorrhage was a focus of the report by 33

Phang et al . (1988) , which followed the treatment of 7 individuals, 5 of whom died within 34

72 hours after hospital admission. In two of the deceased individuals, CT scans and autopsy 35

revealed putaminal hemorrhagic necrosis. The investigators postulated that the association of 364

- 6 DRAFT

- DO NOT CITE OR QUOTE methanol with hemorrhagic necrosis may be compl icated by the use of heparin during 1

hemodialysis treatment for acidosis 2

Treatment of two men who had drunk a solution containing 58% methanol and presented 3

with impaired vision, coma, and seizures was discussed in a case report by Bessell

- Browne and 4

Byneve lt (2007). A CT scan on one individual revealed bilateral putaminal and cerebral lesions. 5

Blood methanol levels were 21 mg/L. This individual, despite standard treatments, never 6

regained consciousness. The second individual, upon MRI, showed scattered hemorrhage at the 7

grey

- white interface of the cerebral hemispheres. 8

There have been two case reports ( Adanir et al ., 2005; Downie et al ., 1992) that involved 9

percutaneous and inhalation exposure. Use of a methanol

- containing emollient by a woman with 10

chr onic pain led to vision loss, hyperventilation and finally, coma (Adanir et al ., 2005). 11

Subsequent to standard treatment followed by hospital discharge, some visual impairment and 12

CNS decrements remained. The methanol blood threshold for ocular damage an d acidosis 13

appeared to be

~ 20 mg/L. Dutkiewicz et al . (1980) have determined the skin absorption rate to 14

be 0.192 mg/cm 2 /minute. In the case report of Aufderheide et al . (1993)

, two firefighters were 15

transiently exposed to methanol by inhalation and the percutaneous route. Both only complained 16

of a mild headache and had blood methanol levels of 23 and 16 mg/dL (230 an d 160 mg/L)

, 17

respectively. 18

Bebarta et al . (2006) conducted a prospective observational study of seven men who had 19

purposefully inhaled a me thanol

- containing product. Four had a blood methanol level upon 20

hospital presentation of >24 mg/dL (240 mg/L) ; the mean formic acid level was 71 μg/dL. One 21

individual had a blood methanol level of 86 mg/dL (860 mg/L) and a blood formic acid level of 22

250 μg/mL upon hospital admission. This latter individual was treated with fomepizole. No 23

patient had an abnormal ophthalmologic examination. All seven stabilized quickly and acidosis 24

was normalized in 4 hours. 25

Numerous other case reports documenting putami nal necrosis/hemorrhage and/or 26

blindness have been reported ( Blanco et al ., 2006; Feany et al ., 2001; Hsu et al ., 1997; Pelletier 27

et al ., 1992; Chen et al ., 1991). 28

Hovda et al . (2005) presented a combined prospective and retrospective case series study 29

of 51 individuals in Norway (39 males and 12 females, many of whom were alcoholics) who 30

were hospitalized after consuming tainted spirits containing 20% methanol and 80% ethanol. In 31

general, serum methanol concentrations were highest among those most severel y affected. The 32

poor outcome was closely correlated with the degree of metabolic acidosis. It was noted by the 33

investigators that the concomitant consumption of ethanol prevented more serious sequelae in 34

2/5 individuals who presented with detec table etha nol levels and were not acidotic despite 2 35

having the highest blood methanol levels. However, others with detec table levels of ethanol 364

- 7 DRAFT

- DO NOT CITE OR QUOTE along with severe metabolic acidosis (two of whom died) presumably had sub therapeutic levels 1

of ethanol in their system. 2

In a later report, Hovda et al . (2007) focused on formate kinetics in a 63

- year

- old male 3

who died 6 days after being admitted to the hospital with headache, vomiting, reduced vision, 4

and dizziness. The investigators speculated that the prolonged metaboli c acidosis observed (T 1/2 5

for formic acid was 77 hours before dialysis, compared to a typical normal range of 2.5

– 6

12 hours) may have been related to retarded formate elimination. 7

Hovda and colleagues (Hunderi et al ., 2006) found a strong correlation betwee n blood 8

methanol concentration and the osmolal gap (R 2

= 0.92) among 17 patients undergoing dialysis 9

after consuming methanol

- contaminated spirits. They concluded that the osmolal gap could be 10

taken as a priori indication of methanol poisoning and be used to guide initiation and duration of 11

dialysis. As they indicated

, many hours of dialysis could be safely dispensed with. The osmolal 12

gap pertains to the effect that methanol (and other alcohols) has on the depression of the freezing 13

point of blood in the presence of normal solutes. Braden et al . (1993) demonstrated in case 14

studies that the disappearance of the osmolal gap correlates with the correction of acidosis; they 15

cautioned that methanol and ethanol should not be assumed to be the main factors in c ausing 16

osmolal gap as glycerol and acetone and its metabolites can as well. A more detailed discussion 17

of the anion and osmolal gap has been provided by Henderson and Brubacher (2002 ). 18

Hassanian

- Moghaddam et al . (2007) compiled data on the prognostic fact or relating to 19

outcome in methanol

- poisoning cases in Iran. They examined 25 patients, 12 of whom died ; 3 20

of the survivors were rendered blind. There was a significant difference in mean pH of the first 21

arterial blood gas measurements of those who subse quently died compared with survivors. It 22

was concluded that poor prognosis was associated with pH <7, coma upon admission, and 23

>24

- hour s delay from intake to admission. 24

The use of blood methanol levels as predictors of outcome is generally not recommended 25

(Barceloux et al ., 2002). These investigators cited differences in sampling time, ingestion of 26

ethanol

, and levels of toxic ( e.g., formic acid) metabolites among the complicating factors. As 27

an illustration, the case report by Prabhakaran et al . (1993) cites two women who ingested a 28

methanol solution (photocopying diluent) at about the same time, were admitted to the hospital 29

about the same time (25

– 26 hours after ingestion) and had identical plasma methanol 30

concentrations (83 mg/dL ; 830 mg/L ) upon admis sion, but different outcomes. Patient #1 was in 31

metabolic acidosis and had an uns table conscious state even after treatment. Upon discharge at 32

day 6, there were no apparent sequelae. Patient #2 had severe metabolic acidosis, fixed and 33

dilated pupils, an d no brain stem reflexes. This patient died at day 3 even though therapeutic 34

measures had been administered. 354

- 8 DRAFT

- DO NOT CITE OR QUOTE In a discussion of 3 fatal methanol

- overexposure cases, Andresen et al . (2008) found 1

antemortem blood methanol levels of 540 and 740 mg/dL (5,400 and 7,400 mg/L) in 2 2

individuals. At autopsy brain stem blood levels were 738 and 1,008 mg/dL (7,380 and 3

10,080 mg/L) , respectively. These brain levels were much higher than blood levels postmortem. 4

Autopsy revealed brain and pulmonary edema in all thre e individuals; in the two who had the 5

longer survival times, there was hemorrhagic necrosis of the putamen and hemorrhages of the 6

tissue surrounding the optic nerve. In their study of 26 chronic users of methylated spirits, 7

Meyer et al . (2000) found that the best predictor of death or a poor outcome in chronic abusers 8

was a pH <7.0; there was no correlation between blood methanol levels and outcome. Mahieu 9

et al . (1989) considered a latency period before treatment exceeding 10 hours and a blood 10

formate le vel >50 mg/dL (500 mg/L) as predictive of possible permanent sequelae. Liu et al

. 11

(1998) in their examination of medical records of 50 patients treated for methanol poisoning over 12

a 10

- year period found that 1) deceased patients had a higher mean blood me thanol level than 13

survivors ; and 2) initial arterial pH levels <7.0 ( i.e.

, severe metabolic acidosis)

. C oma or seizure 14

was also associated with higher mortality upon hospital admission. 15

Numerous case s of methanol poisoning ha ve been documented in a varie ty of countries. 16

In Tunisia, 16 cases of methanol poisoning were discussed by Brahmi et al . (2007). Irreversible 17

blindness occurred in two individuals

, with others reporting CNS symptoms, GI effects, visual 18

disturbances, and acidosis. Putaminal necrosis was also described in case reports from Iran 19

(Sefidbakht et al ., 2007). Of 634 forensic autopsies carried out in Turkey during 1992

– 2003, 18 20

appeared to be related to methanol poisoning (Azmak, 2006). Brain edema and focal necrosis of 21

the optic nerve we re among various sequelae noted. Dethlefs and colleagues ( Naraqi et al

., 22

1979; Dethlefs and Naraqi, 1978) described permanent ocular damage in 8/24 males who 23

ingested methanol in Papua New Guinea. 24

In summary, most cases of accidental/intentional methanol poisoning reveal a common 25

set of symptoms, many of which are likely to be presented upon hospital admission. These 26

include: 27

**•** blurred vision and bilateral or unilateral blindness 28

**•** convulsions

, tremors , and coma 29

**•** nausea

, headache , and dizziness 30

**•** abdominal pain 31

**•** diminished motor skills 32

**•** acidosis 33

**•** dyspnea 34

**•** behavioral and/or emotional deficits 35

**•** speech impediments 364

- 9 DRAFT

- DO NOT CITE OR QUOTE Acute symptoms generally are nausea, dizziness, and headache. In the case reports cited 1

above, the onset of symptom sets as well as their severity varies dep ending upon how much 2

methanol was ingested, whether or not and when appropriate treatment was administered, and 3

individual variability. A longer time between exposure and treatment, with few exceptions, 4

results in more severe outcomes ( e.g., convulsions, coma, blindness, and death). The diminution 5

of some acute and/or delayed symptoms may reflect concomitant ingestion of ethanol or how 6

quickly therapeutic measures (one of which includes ethanol infusion) were administered in the 7

hospital setting. 8

Those individuals who are in a metabolic acidotic state ( e.g., pH <7.0) are typically the 9

individuals who manifest the more severe symptoms. Many case reports stress that, unlike blood 10

pH levels <7.0, blood levels of methanol are not particularly good predicto rs of health outcome. 11

According to a publication of the American Academy of Clinical Toxicology (Barceloux et al

., 12

2002), “the degree of acidosis at presentation most consistently correlates with severity and 13

outcome.” 14

As the case reports demonstrate, tho se individuals who present with more severe 15

symptoms ( e.g., coma, seizures, severe acidosis) generally exhibit higher mortality (even after 16

treatment) than those without such symptoms. In survivors of poisoning, persistence or 17

permanence of vision decreme nts and particularly blindness often have been observed 18

Correlation of symptomatology with blood levels of methanol has been shown to vary 19

appreciably between individuals. Blood methanol levels in the case reports involving ingestion 20

ranged from values o f 30 to over 1,000 mg/dL (300 to over 10,000 mg/L) . The lowest value 21

(20 mg/dL ; 200 mg/L ) reported (Ada n ir et al

. , 2005) involved a case of percutaneous absorption 22

(with perhaps associated inhalation exposure) that led to vision and CNS deficits after hos pital 23

discharge. In one case report (Rubinstein et al ., 1995) involving ingestion, coma and subsequent 24

death were associated with an initial blood methanol level of 36 mg/dL (360 mg/L)

. 25

Upon MRI and CT scans, the more seriously affected individuals typica lly have focal 26

necrosis in both brain white matter and more commonly, in the putamen. Bilateral hemorrhagic 27

and nonhemorrhagic necrosis of the putamen is considered by many radiologists as the most 28

well

- known sequelae of methanol overexposure

. 29

4.1.2. Occupatio nal Studies

Occupational health studies have been carried out to investigate the potential effects of 30

chronic exposure to lower levels of methanol than those seen in acute poisoning cases such as 31

those described above. For example, Frederick et al . (1984) conducted a health hazard 32

evaluation on behalf of the National Institute for Occupational Safety and Health (NIOSH) to 334

- 10 DRAFT

- DO NOT CITE OR QUOTE determine if vapor from duplicating fluid (which contains 99% methanol) used in mimeograph 1

duplicating machines caused adverse health ef fects in exposed persons. A group of 84 teacher’s 2

aides were selected for study, 66 of whom responded with a completed medical questionnaire. A 3

group of 297 teachers (who were not exposed to methanol vapors to the same extent as the 4

teacher’s aides) comp leted questionnaires as a control group. A 15

- minute breathing zone 5

sample was taken from 21 duplicators, 15 of which were greater than the NIOSH

- recommended 6

short term ceiling concentration of 800 ppm (1048 mg/m 3 ). The highest breathing zone 7

concentrati ons were in the vicinity of duplicators for which no exhaust ventilation had been 8

provided (3,080 ppm [4,036 mg/m 3 ] was the highest value recorded). Upon comparison of the 9

self

- described symptoms of the 66 teacher’s aides with those of 66 age

- matched teac hers chosen 10

from the 297 who responded, the number of symptoms potentially related to methanol were 11

significantly higher in the teacher’s aides. These included blurred vision (22.7 versus 1.5%), 12

headache (34.8 versus 18.1%), dizziness (30.3 versus 1.5%), and nausea (18 versus 6%). By 13

contrast, symptoms that are not usually associated with methanol exposure (painful urination, 14

diarrhea, poor appetite, and jaundice) were similar in incidence among the groups. 15

To further investigate these disparities, NIOS H physicians (not involved in the study) 16

defined a hypothetical case of methanol toxicity by any of the following four symptom 17

aggregations: 1) visual changes ; 2) one acute symptom (headache, dizziness, numbness, 18

giddiness, nausea or vomiting) combined wit h one chronic symptom ( un usual fatigue , muscle 19

weakness, trouble sleeping, irritability, or poor memory) ; 3) two acute symptoms ; or 4) three 20

chronic symptoms. By these criteria

, 45% of the teacher’s aides were classified as being 21

adversely affected by met hanol exposure compared to 24% of teachers ( p < 0.025). Those 22

teacher’s aides and teachers who spent a greater amount of time using the duplicators were 23

affected at a higher rate than those who used the machines for a lower percentage of their work 24

day. 25

T anner (1992) reviewed the occupational and environmental causes of Parkinsonism, 26

spotlighting the potential etiological significance of manganese, carbon monoxide, repeated head 27

trauma (such as suffered by boxers), and exposure to solvents. Among the latt er, Tanner (1992) 28

discussed the effects of methanol and n

- hexane on the nervous system. Acute methanol 29

intoxication resulted in inebriation

, followed within hours by GI pain, delirium, and coma. 30

Tanner (1992) pinpointed the formation of formic acid, with consequent inhibition of 31

cytochrome oxidase, impaired mitochondrial function, and decreased ATP formation as relevant 32

biochemical and physiological changes for methanol exposure . Nervous system injury usually 33

includes blindness, Parkinson

- like symptoms, dystonia, and cognitive impairment, with injury to 34

putaminal neurons most likely underlying the neurological responses. 354

- 11 DRAFT

- DO NOT CITE OR QUOTE Kawai et al . (1991) carried out a biomarker study in which 33 occupational ly exposed 1

workers in a factory making methanol fuel were expo sed to concentrations of methanol of up to 2

3,577 ppm (4,687 mg/m 3 ), as measured by personal samplers of breathing zone air. Breathing 3

zone exposure samples were correlated with the concentrations of methanol in urine at the end of 4

the shift in 38 exposed individuals and 30 controls (r = 0.82). Eleven of 22 individuals who 5

experienced high exposure to methanol (geometric mean of 459 ppm [601 mg/m 3 ]) complained 6

of dimmed vision during work while 32% of this group of workers experienced nasal irritation. 7

Th ese incidences were statistically significant compared to those of persons who worked in low

8

exposure conditions (geometric mean of 31 ppm [41 mg/m 3 ]). One 38

- year

- old female worker 9

who had worked at the factory for only 4 months reported that her visual acuity had undergone a 10

gradual impairment. She also displayed a delayed light reflex. 11

Lorente et al . (2000) carried out a case control study of 100 mothers whose babies had 12

been born with cleft palates. Since all of the mothers had worked during the firs t trimester, 13

Lorente et al . (2000) examined the occupational information for each subject in comparison to 14

751 mothers whose babies were healthy. Industrial hygienists analyzed the work histories of all 15

subjects to determine what, if any, chemicals the af fected mothers may have been exposed to 16

during pregnancy. Multivariate analysis was used to calculate odds ratios, with adjustments 17

made for center of recruitment, maternal age, urbanization, socioeconomic status, and country of 18

origin. Occupations with positive outcomes for cleft palate in the progeny were hairdressing 19

(OR = 5.1, with a 95% confidence interval [ CI ] of 1.0

– 26) and housekeeping (OR = 2.8, with a 20

95% CI of 1.1

– 7.2). Odds ratios for cleft palate only and cleft lip with or without cleft pala te 21

were calculated for 96 chemicals. There seemed to be no consistent pattern of association for 22

any chemical or group of chemicals with these impairments, and possible exposure to methanol 23

was negative for both outcomes. 24

4.1.3. Controlled Studies

Two controlled studies have evaluated humans for neurobehavioral function following 25

exposure to

~ 200 ppm (262 mg/m 3 ) methanol vapors in a controlled setting. The occupational 26

TLV established by the American Conference of Governmental Industrial Hygienists (ACGIH, 27

2000) is 200 ppm (262 mg/m 3 ). In a pilot study by Cook et al . (1991), 12 healthy young men 28

(22

– 32 years of age) served as their own controls and were tested for neurobehavioral function 29

following a random acute exposure to air or 191 ppm (250 mg/m 3 ) methanol v apors for 30

75 minutes. The majority of results in a battery of neurobehavioral endpoints were negative. 31

However, statistical significance was obtained for results in the P

- 200 and N1

- P2 component of 32

event

- related potentials (brain wave patterns following light flashes and sounds), the Sternberg 33

memory task, and subjective evaluations of concentration and fatigue. As noted by the Cook et 344

- 12 DRAFT

- DO NOT CITE OR QUOTE al. (1991) , effects were mild and within normal ranges. Cook et al . (1991) acknowledged 1

limitations in their study desi gn

, such as small sample size, exposure to only one concentration 2

for a single duration time, and difficulties in masking the methanol odor from experimental 3

personnel and study subjects. 4

In a randomized double

- blind study, neurobehavioral testing was cond ucted o n 15 men 5

and 11 women (healthy, aged 26

– 51 years) following exposure to 200 ppm (262 mg/m 3 ) 6

methanol or water vapors for 4 hours (Chuwers et al ., 1995) ; s ubjects served as their own 7

controls in this study. Exposure resulted in elevated blood and ur ine methanol levels (up to peak 8

levels of 6.5 mg/L and 0.9 mg/L, respectively) but not formate concentrations. The majority of 9

study results were negative. No significant findings were noted for visual, neurophysiological, 10

or neurobehavioral tests except for slight effects ( p < 0.05) on P

- 3 00 amplitude (brain waves 11

following exposure to sensory stimuli) and Symbol Digit testing (ability to process information 12

and psychomotor skills). Neurobehavioral performance was minimally affected by methanol 13

exposure at this level. L imitation s noted by Chuwers et al. (1995) are that studies of alcohol’s 14

affect on P

- 300 amplitude suggest that this endpoint may be biased by unknown factors and 15

some experimenters and subjects correctly guessed if methanol was used. 16

Alth ough the slight changes in P

- 200 and P

- 300 amplitude noted in both the Chuwers 17

et al . (1995) and Cook et al . (1991) studies may be an indication of moderate alterations in 18

cognitive function, the r esults of the se studies are generally consistent and sugges t that the 19

exposure concentrations employed were below the threshold for substantial neurological effects. 20

This is consistent with the data from acute poisoning events which have pointed to a serum 21

methanol threshold of 200 mg/L for the instigation of aci dosis, visual impairment, and CNS 22

deficits. 23

Mann et al . (2002) studied the effects of methanol exposure on human respiratory 24

epithelium as manif est ed by local irritation, ciliary function, and immunological factors. Twelve 25

healthy men (average age 26.8 ye ars) were exposed to 20 and 200 ppm (26.2 and 262 mg/m 3

, 26

respectively) methanol for 4 hours at each concentration ; e xposures were separated by 1

- week 27

intervals. The 20 ppm (26.2 mg/m 3 ) concentration was considered to be the control exposure 28

since previous studies had demonstrated that subjects can detect methanol concentrations of 29

20 ppm (26.2 mg/m 3 ) and greater. Following each single exposure, subclinical inflammation was 30

assessed by measuring concentrations of interleukins (IL

- 8, IL

- 1β, and IL

- 6) and pr ostaglandin 31

E2 in nasal secretions. Mucociliary clearance was evaluated by conducting a saccharin transport 32

time test and measuring ciliary beat frequency. Interleukin and prostaglandin data were 33

evaluated by a 1

- tailed Wilcoxon test

, and ciliary functio n data were assessed by a 2

- tailed 34

Wilcoxon test. Exposure to 200 (262 mg/m 3 ) versus 20 ppm (26.2 mg/m 3 ) methanol resulted in a 35

statistically

- significant increase in IL

- 1β (median of 21.4 versus 8.3 pg/mL) and IL

- 8 (median of 364

- 13 DRAFT

- DO NOT CITE OR QUOTE 424 versus 356 pg/mL). There were no significant effects on IL

- 6 and prostaglandin E2 1

concentration, ciliary function, or on the self

- reported incidence of subjective symp toms of 2

irritation. The authors concluded that exposure to 200 ppm (262 mg/m 3 ) methanol resulted in a 3

subclinical inflammatory response. 4

In summary, adult human subjects acutely exposed to 200 ppm (262 mg/m 3 ) methanol 5

have experienced slight neurological (Chuwers et al ., 1995) and immunological effects 6

( increased subclinical biomarkers for inflammation) with no self

- reported symptoms of irritation 7

(Mann et al ., 2002). These exposure levels were associated with peak methanol blood levels of 8

6.5 mg/L (Chuw ers et al ., 1995), which is approximately three fold higher than background 9

methanol blood levels reported for adult human subjects on methanol

- restrictive diets 10

( Table 3

- 1). Nasal irritation effects have been reported by adult workers exposed to 459 ppm 11

( 601 mg/m 3 ) methanol (Kawai et al ., 1991) . Frank effects such as blurred vision

, bilateral or 12

unilateral blindness, coma, convulsions/tremors, nausea, headache, abdominal pain, diminished 13

motor skills, acidosis, and dyspnea begin to occur as blood levels a pproach 200 mg methanol/L, 14

while 800 mg/L appears to be the threshold for lethality. Data for subchronic, chronic or in utero 15

human exposures are very limited and inconclusive. 16

4.2. ACUTE, SUBCHRONIC AND CHRONIC STUDIES AND CANCER BIOASSAYS IN

ANIMALS

— ORAL AND INHALATION

A number of studies in animals have investigated the acute, subchronic, and chronic 17

toxicity of methanol. Most are via the inhalation route. Presented below are summaries of these 18

investigations. 19

4.2.1. Oral Studies

4.2.1.1. Acute Toxicity

Although there ar e few studies that have examined the short

- term toxic effects of 20

methanol via the oral route, a number of median lethal dose (LD 50 ) values have been published 21

for the compound. As listed in Lewis (1992), these include 5,628 mg/kg in rats, 7,300 mg/kg in 22

m ice, and 7,000 mg/kg in monkeys. 23

4.2.1.2. Subchronic Toxicity

An oral repeat dose study was conducted by the EPA (1986c) in rats. Sprague

- Dawley 24

rats (30/sex/dose) were gavaged with 0, 100, 500, or 2,500 mg/kg

- day of methanol. Six weeks 25

after dosing, 10 rats/sex/ dose group were subjected to interim sacrifice

, while the remaining rats 26

continued on the dosing regimen until the final sacrifice (90 days). This study generated data on 27

weekly body weights and food consumption, clinical signs of toxicity, ophthalmologic 28

evaluations, mortality, blood and urine chemistry (from a comprehensive set of hematology, 294

- 14 DRAFT

- DO NOT CITE OR QUOTE serum chemistry

, and urinalysis tests), and gross and microscopic evaluations for all test animals. 1

Complete histopathologic examinations of over 30 organ tissues were done on the control and 2

high

- dose rats. Histopathologic examinations of livers, hearts, and kidneys and all gross lesions 3

seen at necropsy were done on low

- dose and mid

- dose rats. There were no differences between 4

dosed animals and controls in body weight gain, food consumption, or upon gross or microscopic 5

evaluations. Elevated levels ( p ≤ 0.05 in males) of serum alanine transaminase (ALT) 14 and 6

serum alkaline phosphatase (SAP), and increased ( but not statistically significant ) liver weights 7

in both male and female rats suggest possible treatment

- related effects in rats bolus dosed with 8

2,5 00 mg methanol/kg

- day despite the absence of supportive histopathologic lesions in the liver. 9

Brain weights of high

- dose group (2,500 mg/kg

- day) males and females were significantly less 10

than those of the control group at terminal sacrifice. Based on the se findings, 500 mg/kg

- day of 11

methanol is considered a n NOEL from this rat study. 12

4.2.1.3. Chronic Toxicity

A report by Soffritti et al . (2002a) summarized a European Ramazzini Foundation (ERF) 13

chronic duration experimental study of methanol 15 in which the compound was provided to 14

100 Sprague

- Dawley rats/sex/group ad libitum in drinking water at concentrations of 0, 500, 15

5,000, and 20,000 ppm (v/v). The animals were 8 weeks old at the beginning of the study. In 16

general, ERF does not randomly assign animals to treat ment groups, but assigns all animals from 17

a given litter to the same treatment group (Bucher, 2002). All rats were exposed for up to 18

104 weeks, then maintained until they died naturally. Rats were housed in groups of 5 in 19

Makrolon cages (41 × 25 × 15 cm) in a room that was maintained at 23 ± 2°C and 50

– 60% 20

relative humidity. The in

- life portion of the experiment ended at 153 weeks with the death of the 21

last animal. Mean daily drinking water, food consumption, and body weights were monitored 22

weekly for t he first 13 weeks, every 2 weeks thereafter for 104 weeks, then every 8 weeks until 23

the end of the experiment. Clinical signs were monitored 3 times/day, and the occurrence of 24

gross changes was evaluated every 2 weeks. All rats were necropsied at death t hen underwent 25

histopathologic examination of organs and tissues. 16 26

14 Also known as serum glutamate pyruvate transaminase (SGPT)

15 Soffritti et al. (2002a) report that methanol was obtained from J.T. Baker, Deventer, Holland, purity grade 99.8%.

16 Histopa thology was performed on the following organs and tissues: skin and subcutaneous tissue, brain, pituitary

gland, Zymbal glands, parotid glands, submaxillary glands, Harderian glands, cranium (with oral and nasal cavities

and external and internal ear ducts ) (5 sections of head), tongue, thyroid and parathyroid, pharynx, larynx, thymus

and mediastinal lymph nodes, trachea, lung and mainstem bronchi, heart, diaphragm, liver, spleen, pancreas,

kidneys, adrenal glands, esophagus, stomach (fore and glan dular), i ntestine (four levels), urinary bladder, prostate,

gonads, interscapular fat pad, subcutaneous and mesenteric lymph nodes, and any other organs or tissues with

pathologic lesions.4

- 15 DRAFT

- DO NOT CITE OR QUOTE Soffritti et al. (2002a) reported no substantial dose

- related differences in survival, but no 1

data were provided. Using individual animal data available from the ERF website, 17 Cruzan 2

(2009) reports that male rats treated with methanol generally survived better than controls, with 3

50% survival occurring at day 629, 686, 639 and 701 in the 0, 500, 5

, 000, and 20, 000 mg/L 4

groups, respectively. There were no significant differences in survival between female control 5

and treatment groups

, with 50% survival occurring at day 717, 691, 678 and 708 in the 0, 500, 6

5

, 000, and 20, 000 mg/L groups, respectively. Body weight and water and food consumption 7

were monitored in the study, but the data were no t documented in the published report. 8

However, based on data available from the ERF website , average doses of 0, 53.2, 524, and 9

1,780 mg/kg

- day in males and 0, 66.0, 624.1, and 2,177 mg/kg

- day in females could be 10

calculated (see Appendix E) from drinking water concentrations of 0, 500, 5,000, and 11

20,000 ppm. 12

Soffritti et al. (2002a) reported that water consumption in high

- dose females was reduced 13

compared to controls between 8 and 56 weeks and that the mean body weight in high

- dose males 14

tended to be highe r than that of control males. Overall, there was no pattern of compound

15

related clinical signs of toxicity

, and the available data d id not provide any indication that the 16

control group was not concurrent with the treated group (Cruzan, 2009)

. Soffritti e t al. (2002a) 17

further reported that there were no compound

- related signs of gross pathology or histopathologic 18

lesions indicative of noncancer toxicological effects in response to methanol. 19

Soffritti et al . (2002a) reported a number of oncogenic responses to methanol ( Table 4

- 2), 20

principally hemolymphoreticular neoplasms, the majority of which were reported to be lympho

21

immunoblastic lymphomas. In ERF bioassays, including this methanol study, 22

hemolymphoreticular neoplasms are generally divided into specif ic histological types 23

(lymphoblastic lymphoma, lymphoblastic leukemia, lymphocytic lymphoma, lympho

24

immunoblastic lymphoma, myeloid leukemia, histocytic sarcoma, and monocytic leukemia) for 25

identification purposes. According to Soffritti et al. (2007), th e overall incidence of 26

hemolymphoreticular tumors ( lymphomas/leukemias ) in ERF studies is 13.3% (range, 4.0

– 27

25.0%) in female historical controls (2,274 rats) and 20.6% (range, 8.0

– 30.9%) in male historical 28

controls (2,265 rats). The high

- dose responses

, s hown in Table 4

- 2

, of 28% and 40% for females 29

and males, respectively, are above their corresponding historical ranges. 18 30

The National Toxicology Program ( NTP ) does not routinely subdivide lymphomas into 31

specific histological types as was done by the ERF. In 2004, a Pathology Working Group 32

(PWG) of National Institute of Environmental Health Sciences ( NIEHS ) performed a limited 33

review of about 75 slides provided by ERF as representi ve of lesions in Sprague

- Dawley rats 34

17 http://w ww.ramazzini.it/fondazione/foundation.asp

. 18 While historical control data can be informative, for reasonably well

- conducted studies, it should not take

precedence over concurrent controls or appropriate statistical dose

- response trend tests.4

- 16 DRAFT

- DO NOT CITE OR QUOTE associated with aspartame exposure ( EF SA, 2006 ; Hailey, 2004 ). The primary objective of this 1

review was to “provide a second opinion for this set of lesions by a group of pathologists 2

experienced in Toxicologic Pathology.” 19 Eleven of the slides reviewed by the PWG were 3

related to lymphomas

, a nd three of these had been classified by ERF as lympho

- immunoblastic

. 4

T he PWG concluded that “The diagnoses of lymphatic and histocytic neoplasms in the cases 5

reviewed were generally confirmed” ( Hailey , 2004 ) . In particular, the PWG accepted the more 6

spe cific diagnoses of ERF when the lesions were considered to be consistent with a neoplasm of 7

lymphocytic, histocytic, monocytic, and/or myeloid origin. The PWG noted, however, that while 8

lymphoblastic lymphomas, lymphocytic lymphomas, lympho

- immunoblastic lymphomas

, and 9

lymphoblastic leukemias as malignant lymphomas can be combined, myeloid leukemias, 10

histocytic sarcomas

, and monocytic leukemia should be treated as separate malignancies and not 11

combined with the other lymphomas since they are of different c ellular origin (Hailey, 2004)

. 12

McConnell et al. (1986) and Cruzan (2009) have also noted that myeloid leukemia, histocytic 13

sarcoma, and monocytic leukemia are of a different cell line and are not typically combined with 14

other lymphomas for statistical sig nificance or dose

- response modeling. Consistent with these 15

judgments

, EPA has not included the myeloid leukemia, histocytic sarcoma, and monocytic 16

leukemia in combination with lymphoblastic lymphoma, lymphoblastic leukemia, lymphocytic 17

lymphoma, and lymph o

- immunoblastic lymphoma in its consideration of tumorgenic responses 18

reported by ERF (see Section 5.4.1 ; Table 5

- 6 ). Thus, EPA’s analysis of this tumorogenic 19

response differs from the lymphoreticular tumor response shown in Table 4

- 2 and reported by 20

Soff riti et al . ( 2002a). As described in Section 5.4.1.1, EPA’s reassessment indicates a 21

significant increase in tumor response at the two highest doses for males and across all doses for 22

females (Fisher’s exact, p < 0.05), as well as a significant dose

- respo nse trend (Cochran 23

Armitage trend test; p < 0.05). 24

Schoeb et al. (2009) have suggest ed that the interpretation of lesions in ERF studies, 25

including the Soffritti et al

. (2002a) methanol study , may have been confounded by a respiratory 26

infection referred to as Mycoplasma pulmonis (M. pulmonis ) disease and that lesions of this 27

disease were interpreted as lymphoma

. They note d that l ympho

- immunoblastic lymphoma is not 28

listed as a lymphoma type in rats in available reference sources and that the cellular morpho logy 29

of the lung l ympho

- immunoblastic lymphomas reported by ERF for aspartame (Soffritti et al., 30

2005) and MTBE (Belpoggi et al., 1999) studies are more consistent with M. pulmonis disease

. 31

As noted above, an NIEHS PWG (Hailey, 2004) has confirmed the ERF diagnosis of the several 32

lymphomas, including three lymphomas from the lung, thymus and medullary lymph node and 33

mesenteric lymph node that were characterized by ERF as “ lympho

- immunoblastic

. ” Hailey 34

19 This rev iew was not considered a “peer review” of the pathology data from this study. As noted by Hailey (2004),

“a peer review would necessitate a review of the study data by a second party, and selection and examination of

lesions based upon that data review.”4

- 17 DRAFT

- DO NOT CITE OR QUOTE (2004) reports that the PWG “ accepted their [ERF’s] mo re specific diagnosis if the lesion was 1

considered to be consistent with a neoplasm of lymphocytic, histocytic, monocytic, and/or 2

myeloid origin .” T he concerns of Schoeb et al. (2009) regarding the possibility of infection 3

confounding the interpretation o f lung lesions in the ERF study are not unfounded

. Chronic 4

inflammatory changes are apparently a common finding in ERF studies (Caldwell et al., 2008)

, 5

probably caused by the ERF bioassay design that does not employ specific pathogen

- free (SPF) 6

rats (EFSA , 2006) and allows the rats to live out their “natural life span” in the absence of 7

disease barriers (e.g., fully enclosed cages). However, an infection of the ERF colony with 8

M

. pulmonis has not been confirmed (Caldwell et al., 2008) and, without confirm ation, cannot be 9

used to discount an existing dose

- related trend (U

. S

. EPA, 2005)

. Further , e ven if the rats of the 10

ERF methanol study were suffering from a respiratory infection that confounded the 11

interpretation of lung lesions

, 60 % of reported lymphoma incidences involved other organ 12

systems , and the dose

- response for lymphomas in other organ systems is not remarkably 13

different than for all lymphomas (see analysis in Section 5.4.3.2 )

. 14

Another cancer response

, reported by Soffritti et al . ( 2002a)

, that i s considered to be 15

potentially related to methanol exposure was an increase in rare hepatocellular carcinomas in 16

male rats. Although the increase was not statistically increased compared to concurrent controls, 17

EPA has analyzed historical data for this tu mor type in this species and determined that the 18

incidence in all dose groups was significantly elevated relative to historical controls (Fisher’s 19

exact p < 0.05 for all doses and p < 0.01 for the high

- dose group). The historical control group 20

(n

= 407) u sed was the combined control groups from ERF studies for which individual animal 21

pathology data have been made available via the ERF website 20 and include data for methanol, 22

formaldehyde, aspartame, MTBE, and TAME. 23

As noted in Table 4

- 2, increased incidence s of c arcinomas of the ear ducts and 24

osteosarcomas of the head were reported for both female and male rats, with a statistically 25

significant increase in only the high

- dose male ear duct c arcinomas

. E ar duct carcinomas are a 26

rare finding in Charles River r ats and NTP historical databases of Sprague

- Dawley rats (Cruzan, 27

2009)

. I n the ir limited review of pathology slides from the ERF aspartame bioassay (Soffritti 28

et al ., 200 6, 200 5 ), NTP pathologists interpreted a majority of such head pathologies, including 29

in the ear duct, as being hyperplastic in nature, not carcinogenic ( EFSA, 2006; Hailey , 2004). 30

Soffritti et al . (2002a) also noted an increased incidence of testicular hyperplasia in high

- dose 31

males and uterine sarcomas in high

- dose females compared to c ontrols

. H owever, these 32

increases were not statistically significant and were within historical control ranges for this 33

species and strain ( NTP, 2007, 1999; Haseman et al ., 1998). The group

- specific total number of 34

malignant tumors was also shown to incr ease with dose in both sexes of rats

. 35

20 http://www.ramazzini.it/fondazione/foundation.asp

.4

- 18 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 2. Incidence of carcinogenic responses in Sprague

- Dawley rats exposed to

methanol in drinking water for up to 2 years

Tissues / a ffected sites Dose (mg/kg

- day)

Males Females

0 53.2 524 1780 0 66.0 624.1 217 7

Ear duct (carcinomas) 9/100 13/100 17/100 24/100 b 9/100 8/100 16/100 19/100

Head (osteosarcomas) 6/100 6/100 13/100 11/100 1/100 4/100 3/100 6/100

Hemol ymphoreticular tumors 28/100 35/100 36/100 40/100 13/100 24/100 24/100 28/100 a

Liver (hepatocarcin omas) 0/100 2/100 2/100 3/100 0/100 0/100 1/100 0/100

Testis (interstitial cell adenomas) 12/100 9/100 13/100 17/100

Total malignant tumors 50/100 55/100 64/100 70/100 b 43/100 48/100 48/100 63/100 b

a p < 0.05 using the χ 2 test.

b p < 0.01 using the χ 2 test.

Source: Soffritti et al . (2002a)

.

Apaja (1980) performed dermal and drinking water chronic bioassays in which male and 1

female Eppley Swiss Webster mice ( 25/sex/dose group; 8 weeks old at study init i ation) were 2

exposed 6 days per week until natur al death to various concentrations of malonaldehyde and 3

methanol. The stated purpose of the study was to determine the carcinogenicity of 4

malonaldehyde , a product of oxidative lipid deterioration in rancid beef and other food products 5

in advanced stages o f degradation. However, d ue to its instability

, malonaldehyde was obtained 6

from the more stable malonaldehyde bis(dimethyacetal), which was hydrolyzed to 7

malonaldehyde and methanol in dilute aqueous solutions in the presence of a strong mineral acid. 8

In the drinking water portion of this study

, mice were exposed to 3 different concentrations of 9

the malonaldehyde/methanol solution and three different control solutions of methanol alone

, 10

0. 222 %, 0.444% and 0. 889 % methanol in drinking water (222, 444 and 88 9 ppm, assuming a 11

density of 1 g/ml)

, corresponding to the stoichiometric amount of methanol liberated by 12

hydrolysis of the acetal in the three test solutions. The methanol was described as Mallinckrodt 13

analytical grade. No unexposed control groups were included in these studies. However, the 14

author provided pathology data from historical records of untreated Swiss mice of the Eppley 15

colony used in two separate chronic studies, one involving 100 untreated males and 1 00 16

untreated females (Toth et al., 197 7) and the other involving 100 untreated females 17

histopathological analy zed by Apaja ( Apaja, 1980)

. 18

Mice in the Apaja (1980) study were housed five/plastic cage and fed Wayne Lab

- Blox 19

pelleted diet. Water was available ad libitum throughout life

. Liqui d consumption per animal 20

was measured 3 times / week. The methanol dose in the dermal study (females only) was 21.3 mg 21

( 5 32 mg/kg

- day using an average weight of 0.04 kg as approximated from Figure 4 of the study)

, 22

three times/week. The methanol doses in th e drinking water study were reported as 22.6, 40.8 234

- 19 DRAFT

- DO NOT CITE OR QUOTE and 84.5 mg/day (560, 1000 and 2100 mg/kg

- day using an average weight of 0.04 kg as 1

approximated from Figures 14

- 16 of the study) for females, and 24.6, 43.5 and 82.7 mg/day 2

(550

, 970 , and 1800 mg/kg

- day u sing an average weight of 0.04 5 kg as approximated from 3

Figures 14

- 16 of the study) for males , 6 days/week

. The animals were checked daily and body 4

weights were monitored weekly. The in

- life portion of the experiment ended at 1 20 weeks with 5

the death of the last animal. Like the Soffritti et al. (2002a) study, test animals were sacrificed 6

and necropsied when moribund. 21 7

The authors reported that survival of the methanol exposed females of the drinking water 8

study was lower than untreated historical contro ls ( p < 0.05), but no significant differences in 9

survival was noted for male s

. An increase in liver parenchymal cell necrosis was reported in the 10

male and female high

- dose groups, with the incidence in females (8%) being significant 11

(p < 0.01) relative to untr e ated historical controls. Incidence of acute pancreatitis was higher in 12

high

- dose males ( p <0.001), but did not appear to be dose

- related in females, increasing at the 13

mid

- ( p <0.0001) and low

- dose s ( p <0.01) when compared to historical controls but not 14

appearing at all in the high

- dose females

. Significant increases relative to untreated historical 15

controls were noted in a myloidosis of the spleen , nephropathy and pneumonia, but the increases 16

did not appear to be dose related. 17

The author reported incidences of malignant lymphoma in females of 15%, 16%, 36%, 18

and 40% for 5 32 mg/kg

- day (dermal), 56 0, 1

, 000, and 2

, 100 mg/kg

- day ( drinking water ), 19

respectively. Males from the drinking water study had incidences of malignant lymphoma of 4, 20

24, and 16% fo r 550, 970, and 1

, 800 mg/kg

- day

. The lymphomas were classified according to 21

Rappaport’s classification ( Rappapo r t, 1966 ) , but location of the lymphoma (organ system) was 22

not reported

. The distributions of lymphomas according to subclasses reported by the author are 23

shown in Table 4

- 3 for historical untreated and methanol exposed mice in the drinking water 24

studies

. The author indicates that the incidences in both males and females were “within the 25

normal range of occurrence of malignant lymphomas in Epple y Swiss mice

, ” but provides no 26

references or supporting data for this statement and reports elsewhere that the response in high

27

dose females and mid

- dose males were significantly different from unexposed mice from 28

“historical data of untreated controls (Ta ble 9)” of Toth et al. (1977) ( p < 0.05)

. Though not 29

statistically significant (Fishers exact p

= 0.06), t he malignant lymphoma response in the mid

30

dose females was also more than double that of untreated controls from another study (18/100) 31

for which the histopathology was also performed by Apaja (Apaja, 1980)

. 32

21 The following tisues were fixed in 10% formalin (pH 7.5), embedded in paraffin, sectioned, stained routinely with

hematoxylineosin (sp ecial stains used as needed) and histologically evaluated: skin, lungs, liver spleen, pancreas,

kidneys, adrenal glands, esophagus, stomach, small and large intestines, rectum, urinary bladder, uterus and ovaries

or testes, prostate glands and tumors or ot her gross pathological lesions

.4

- 20 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 3. Incidence of malignant lymphoma responses in Swiss mice exposed to

methanol in drinking water for life

Malignant Lymphoma Dose (mg/kg

- day)

Males (%) Females (%)

0 a

n=100 550

n=25 970

n=25 1800

n=24 0 a

n=100 0 b

n=100 560

n=25 1000

n=25 2100

n=25

Lymphocytic, well diff. 8 4 12

Lymphocytic moderately diff. 4 3 4 4

Lymphocytic, poorly diff. 4 8.3 7 4 4 4

Mixed cell type 4 4 4 8

Histocytic type 4 4 4.2 12 12 8

Unclassified 8 4.2 8 4

Total 8 4 24 c 17 20 18 16 36 d 40 c

a Toth et al. (1977); b Hinderer et al. (1979)

c p < 0.05 as reported by author compared with Toth et al. (1977); Female response is also significant (p < 0.05;

Fishers exact test) versus un treated controls from Hinderer et al. (1979) and combined controls from both studies.

d p = 0.06 by Fishers exact test versus untreated controls from Hinderer et al. (1979) and combined controls from

both studies.

4.2.2. Inhalation Studies

4.2.2.1. Acute Toxicity

Lewis ( 1992) reported a 4

- hour median lethal concentration (LC 50 ) for methanol in rats of 1

64,000 ppm (83,867 mg/m 3 ). 2

Japan’s NEDO sponsored a series of toxicological tests on monkeys ( M. fascicularis ), 3

rats, and mice

, using inhalation exposure. 22 A short

- term expo sure study evaluated monkeys (sex 4

unspecified) exposed to 3

, 000 ppm (3

, 931 mg/m 3 ), 21 hours/day for 20 days ( 1 animal), 5

5,000 ppm (6,552 mg/m 3 ) for 5 days ( 1 animal), 5,000 ppm (6,552 mg/m 3 ) for 14 days ( 2 6

animals), and 7,000 and 10,000 ppm (9,173 and 13,1 04 mg/m 3 , respectively) for up to 6 days ( 1 7

animal at each exposure level) (NEDO, 1987 , unpublished report ). Most of the experimental 8

findings were discussed descriptively in the report, without specifying the extent of change for 9

any of the effects in co mparison to seven concurrent controls. However, the available data 10

indicate that clinical signs of toxicity were apparent in animals exposed to 5

, 000 ppm (all 11

exposure durations) or higher concentrations of methanol . These included reduced movement, 12

crou ching, weak knees, involuntary movements of hands, dyspnea, and vomiting. In the 13

22 In their bioassays, NEDO (1987) used inbred rats of the F344 or Sprague

- Dawley strain, inbred mice of the

B6C3F1 strain and wild

- caught M. fascicularis monkeys imported from Indonesia. The possibility of disease among

wil d

- caught animals is a concern, but NEDO (1987) state that the monkeys were initially quarantined for 9 weeks

and measures were taken throughout the studies against the transmission of pathogens for infectious diseases. The

authors indicated that “no infec tious disease was observed in monkeys” and that “subjects were healthy throughout

the experiment.”4

- 21 DRAFT

- DO NOT CITE OR QUOTE discussion section of the summary report, the authors state d that there was a sharp increase in the 1

blood levels of methanol and formic acid in monkey exposed to > 3,000 ppm ( 3,931 mg/m 3 ) 2

methanol. They reported that methanol and formic acid concentrations in the blood of monkeys 3

exposed to 3,000 ppm or less were 80 mg/L and 30 mg/L, respectively. 23 In contrast, monkeys 4

exposed to 5,000 ppm or higher concentrations of methanol had blood methanol and formic acid 5

concentrations of 5,250 mg/L and 1,210 mg/L, respectively. Monkeys exposed to 7,000 ppm and 6

10,000 ppm became critically ill and had to be sacrificed prematurely. Food intake was said to 7

be little affected at 3,000 ppm , but those exposed to 5,000 ppm or more showed a marked 8

reduction

. Clinically, the monkeys exposed to 5,000 ppm or more exhibited reduced movement, 9

weak knees, and involuntary movement of upper extremities , eventually losing consciousness 10

and dying. 11

Th ere were no significant changes in growth

, with the exception of animal s exposed to 12

the highest concentration, wh ere body weight was reduced by 13%. There were few compound

13

related changes in hematological or clinical chemistry effects, although animals e xposed to 7,000 14

and 10,000 ppm showed an increase in white blood cells. A marked change in blood pH values 15

at the 7,000 ppm and 10,000 ppm levels (values not reported) was attributed to acidosis due to 16

accumulation of formic acid. A range of histopatholo gic changes to the CNS was apparently 17

related to treatment. Severity of the effects was increased with exposure concentration. Lesions 18

included characteristic degeneration of the bilateral putamen, caudate nucleus, and claustrum, 19

with associated edema in the cerebral white matter. Necrosis of the basal gangli a was noted 20

following exposure to 5,000 ppm for 5 days ( 1 animal) and 14 days ( 1 animal). Exposure to 21

3

, 000 ppm was considered to be close to the threshold for these necrotic effects

, as the monkeys 22

exposed at this level experienced little more than minimal fibrosis of responsive stellate cells of 23

the thalamus, hypothalamus and basal ganglion. The authors reported that no clinical or 24

histopathological effects of the visual system were apparent, but t hat exposure to 3,000 ppm 25

(3,931 mg/m 3 ) or more caused dose

- dependent fatty degeneration of the live r

, and exposure to 26

5,000 ppm (6,552 mg/m 3 ) or more caused vacuolar degeneration of the kidneys, centered on the 27

proximal uniferous tubules. 28

4.2.2.2. Subchronic Toxic ity

A number of experimental studies have examined the effects of subchronic exposure to 29

methanol via inhalation. For example, Sayers et al . (1944) employed a protocol in which 2 male 30

dogs were repeatedly exposed ( 8 times daily for 3 minutes/exposure) to 10,000 ppm 31

(13, 104 mg/m 3 ) methanol for 100 days. One of the dogs was observed for a further 5 days 32

before sacrifice; the other dog was observed for 41 days postexposure . There were no clinical 33

23 Note that Burbacher et al. (2004b, 1999a) measured blood levels of methanol and formic acid in control monkeys

of 2.4 mg/L and 8.7 mg/L, respectively (see Table 3

- 3).4

- 22 DRAFT

- DO NOT CITE OR QUOTE signs of toxicity, and both gained weight during the study pe riod. Blood samples were drawn on 1

a regular basis to monitor hematological parameters

, but few if any compound

- related changes 2

were observed . Ophthalmoscopic examination showed no incipient anomalies at any point 3

during the study period. Median blood co ncentrations of methanol were 65 mg/L (range 0

– 280 4

mg/L) for one dog

, and 140 mg/L (70

– 320 mg/L) for the other. 5

White et al . (1983) exposed 4 male Sprague

- Dawley rats/group, 6 hours/day, 5 days/week 6

to 0, 200, 2,000, or 10,000 ppm (0, 262, 2,621, and 13, 104 mg/m 3 ) methanol for periods of 1, 2, 7

4, and 6 weeks. Additional groups of 6

- week

- exposure animals were granted a 6

- week 8

postexposure recovery period prior to sacrifice. The lungs were excised intact and lavaged 9

6 times with known volumes of physiolog ical saline. The lavage supernatant was then assayed 10

for lactate dehydrogenase (LDH) and N

- acetyl

- β

- D

- glucosamidase ( β

- NAG) activities. Other 11

parameters monitored in relation to methanol exposure included absolute and relative lung 12

weights, lung DNA cont ent, protein, acid RNase and acid protease, pulmonary surfactant, 13

number of free cells in lavage/unit lung weight, surface protein, LDH, and β

- NAG. As discussed 14

by the authors , none of the monitored parameters showed significant changes in response to 15

met hanol exposure. 16

Andrews et al . (1987) carried out a study of methanol inhalation in 5 Sprague

- Dawley 17

rats/sex/group and 3 M. fascicularis monkeys/sex/group, 6 hours/day, 5 days/week, to 0, 500, 18

2,000, or, 5,000 ppm (0, 660, 2,620, and 6,552 mg/m 3 ) methano l for 4 weeks. Clinical signs 19

were monitored twice daily

, and all animals were given a physical examination once a week. 20

Body weights were monitored weekly

, and animals received an ophthalmoscopic examination 21

before the start of the experiment and at ter m. Animals were sacrificed at term by 22

exsanguination following i.v. barbiturate administration. A gross necropsy was performed, 23

weights of the major organs were recorded, and tissues and organs taken for histopathologic 24

examination. As described by the authors, all animals survived to term with no clinical signs of 25

toxicity among the monkeys and only a few signs of irritation to the eyes and nose among the 26

rats. In the latter case, instances of mucoid nasal discharges appeared to be dose related. There 27

were no differences in body weight gain among the groups of either rats or monkeys, and overall, 28

absolute and relative organ weights were similar to controls. The only exception to this was a 29

decrease in the absolute adrenal weight of female high

- concent ration monkeys and an increase in 30

the relative spleen weight of mid

- concentration female rats. These changes were not considered 31

by the authors to have biological significance. For both rats and monkeys, there were no 32

compound

- related changes in gross pa thology, histopathology, or ophthal m oscopy. These data 33

suggest a NOAEL of 5,000 ppm (6,600 mg/m 3 ) for Sprague

- Dawley rats and monkeys under the 34

conditions of the experiment. 354

- 23 DRAFT

- DO NOT CITE OR QUOTE Two studies by Poon et al . (1995, 1994) examined the effects of methanol on Sprag ue

1

Dawley rats when inhaled for 4 weeks. The effects of methanol were evaluated in comparison to 2

those of toluene and toluene/methanol mixtures (Poon et al ., 1994), and to gasoline and 3

gasoline/methanol mixtures (Poon et al ., 1995). In the first case (Po on et al., 1994)

, 10 Sprague

4

Dawley rats/sex/group were exposed via inhalation, 6 hours/day, 5 days/week to 0, 300, or 5

3,000 ppm (0, 393, 3,930 mg/m 3 ) methanol for 4 weeks. Clinical signs were monitored daily, 6

and food consumption and body weight gain wer e monitored weekly. Blood was taken at term 7

for hematological and clinical chemistry determinations. Weights of the major organs were 8

recorded at necropsy

, and histopathologic examinations were carried out. A 10,000 × g liver 9

supernatant was prepared fr om each animal to measure aniline hydroxylase, aminoantipyrine N

10

demethylase, and ethoxyresorufin

- O

- deethylase activities. For the most part, the responses to 11

methanol alone in this experiment were unremarkable. All animals survived to term

, and there 12

we re no clinical signs of toxicity among the groups. Body weight gain and food consumption 13

did not differ from controls

, and there were no compound

- related effects in hematological or 14

clinical chemistry parameters or in hepatic mixed function oxidase activi ties. However, the 15

authors described a reduction in the size of thyroid follicles that was more obvious in female than 16

male rats. The authors considered this effect to possibly have been compound related, although 17

the incidence of this feature for the 0, 300, and 3,000 ppm

- receiving females was 0/6, 2/6, and 18

2/6, respectively. 19

The second experimental report by Poon et al. (1995) involved the exposure of 20

15 Sprague

- Dawley rats/sex/group, 6 hours/day, 5 days/week for 4 weeks to 0 or 2,500 ppm (0 21

and 3

, 276 m g/m 3 ) to methanol as part of a study on the toxicological interactions of methanol 22

and gasoline. Many of the toxicological parameters examined were the same as those described 23

in Poon et al . (1994) study . However, in this study urinalysis featured the de termination of 24

ascorbic and hippuric acids. Additionally, at term

, the lungs and tracheae were excised and 25

aspirated with buffer to yield bronchoalveolar lavage fluid that was analyzed for ascorbic acid, 26

protein, and the activities of gamma

- glutamyl trans ferase ( γ

- GT), AP and LDH. Few if any of 27

the monitored parameters showed any differences between controls and those animals exposed to 28

methanol alone. However, two male rats had collapsed right eyes, and there was a reduction in 29

relative spleen weight in females exposed to methanol. Histopathologic changes in methanol

30

receiving animals included mild panlobular vacuolation of the liver in females and some mild 31

changes to the upper respiratory tract, including mucous cell metaplasia. The incidence of the 32

latter effect, though higher, was not significantly different than controls in rats exposed to 33

2,500 ppm (3,267 mg/m 3 ) methanol. However, there were also signs of an increased severity of 34

the effect in the presence of the solvent. No histopathologic chan ges were seen in the lungs or 35

lower respiratory tract of rats exposed to methanol alone. 364

- 24 DRAFT

- DO NOT CITE OR QUOTE 4.2.2.3. Chronic Toxicity

Information on the chronic toxicity of methanol has come from NEDO (1987

, 1

unpublished report ) which includes the results of experiments on 1) monkeys exposed for up to 3 2

years, 2) rats a nd mice exposed for 12 months, 3) m ice exposed for 18 months, and 4) rats 3

exposed for 2 years. 4

In the monkeys, 8 animals (sex unspecified) were exposed to 10, 100, or 1,000 ppm (13, 5

131, and 1,310 mg/m 3 ) methanol, 21 hou rs/day, for 7 months (2 animals), 19 months, 6

(3 animals)

, or 29 months (3 animals). There was no indication in the NEDO (1987) report that 7

this study employed a concurrent control group. One of the 3 animals receiving 100 ppm 8

methanol and scheduled for s acrifice at 29 months was terminated at 26 months. Clinical signs 9

were monitored twice daily, body weight changes and food consumption were monitored weekly, 10

and all animal s were given a general examination under anesthetic once a month. Blood was 11

collec ted for hematological and clinical chemistry tests at term, and all animals were subject to a 12

histopathologic examination of the major organs and tissues. 13

While there were no clinical signs of toxicity in the low

- concentration animals, there was 14

some evi dence of nasal exudate in monkeys in the mid

- concentration group. High

- concentration 15

(1,000 ppm) animals also displayed this response and were observed to scratch themselves over 16

their whole body and crouch for long periods. F ood and water intake, body t emperature

, and 17

body weight changes were the same among the groups. NEDO (1987) reported that there was no 18

abnormality in the retina of any monkey. When animals were examined with an 19

electrocardiogram, there were no abnormalities in the control or 10 ppm groups. However, in the 20

100 ppm group, one monkey showed a negative change in the T wave. All 3 monkeys exposed 21

to 1,000 ppm (1,310 mg/m 3 ) displayed this feature

, as well as a positive change in the Q wave. 22

This effect was described as a slight myocardi al disorder and suggests that 10 ppm (13.1 mg/m 3 ) 23

is a NOAEL for chronic myocardial effects of methanol and mild respiratory irritation. There 24

were no compound

- related effects on hematological parameters. However, 1 monkey in the 25

100 ppm (131 mg/m 3 ) grou p had greater than normal amounts of total protein, neutral lipids, 26

total and free cholesterol, and glucose, and displayed greater activities of ALT and aspartate 27

transaminase (AST). The authors expressed doubts that these effects were related to methanol 28

exposure and speculated that the animal suffered from liver disease. 24 29

Histopathologically, no degeneration of the optical nerve, cerebral cortex, muscles, lungs, 30

trachea, tongue, alimentary canal, stomach, small intestine, large intestine, thyroid gland

, 31

pancreas, spleen, heart, aorta, urinary bladder, ovary or uterus were reported (neuropathological 32

findings are discussed below S ection 4.4 .2

. Most of the internal organs showed no compound

- 33

24 Ordinarily, the potential for liver disease in test animals would be remote, but may be a possibility in this case

given that these monkeys were captured in the wild.4

- 25 DRAFT

- DO NOT CITE OR QUOTE related histopathologic lesions. However, there were signs of i ncipient fibrosis and round cell 1

infiltration of the liver in monkeys exposed to 1,000 ppm (1,310 mg/m 3 ) for 29 months. NEDO 2

( 1987) indicated that this fibrosis occurred in 2/3 monkeys of the 1

, 000 ppm group to a “strictly 3

limited extent.” They also qual itatively reported a dose

- dependent increase in “fat granules” in 4

liver cells “centered mainly around the central veins” at all doses, but did not provide any 5

response data. The authors state that 1,000 ppm ( 1, 310 mg/m 3 ) represents a chronic lowest

6

observ ed

- adverse

- effect level (LOAEL) for hepatic effects of inhaled methanol, suggesting that 7

the no effect level would be 100 ppm (131 mg/m 3 ). However, this is a tenuous determination 8

given the lack of information on the pathological progression and significa nce of the appearance 9

of liver cell fat granules at exposures below 1,000 ppm and the lack detail ( e.g., time of sacrifice) 10

for the control group. 11

D ose

- dependent changes were observed in the kidney ; NEDO (1987) described the 12

appearance of Sudan

- positive granules in the renal tubular epithelium at 100 ppm (131 mg/m 3 ) 13

and 1,000 (1,310 mg/m 3 ) and hyalinization of the glomerulus and penetration of round cells into 14

the renal tubule stroma of monkeys exposed to methanol at 1,000 (1,310 mg/m 3 ). The former 15

effec t was more marked at the higher concentration and was thought by the authors to be 16

compound

- related. This would indicate a no effect level at 10 ppm (13.1 mg/m 3 ) for the chronic 17

renal effects of methanol. The authors observed atrophy of the tracheal epit helium in four 18

monkeys. However, the incidence of these effects was unrelated to dose and therefore, could not 19

be unequivocally ascribed to an effect of the solvent. No other hist opathologic abnormalities 20

were related to the effects of methanol. C onfide nce in these determinations is considerably 21

weakened by concern over whether a concurrent control group was used in the chronic study. 25 22

NEDO (1987) describes a 12

- month inhalation study in which 20 F344 rats/sex/group 23

were exposed to 0, 10, 100, or 1,000 p pm (0, 13.1, 131, and 1,310 mg/m 3 ) methanol, 24

approximately 20 hours/day, for a year. Clinical signs of toxicity were monitored daily ; body 25

weights and food consumption were recorded weekly for the first 13 weeks, then monthly. 26

Blood samples were drawn a t term to measure hematological and clinical chemistry parameters. 27

Weights of the major organs were monitored at term, and a histopathologic examination was 28

carried out on all major organs and tissues. Survival was high among the groups; one high

29

concent ration female died on day 337 and one low

- concentration male died on day 340. As 30

described by the authors, a number of procedural anomalies arose during this study. For 31

example, male controls in two cages lost weight because of an interruption to the wat er supply. 32

Another problem was that the brand of feed was changed during the study. Fluctuations in some 33

clinical chemistry and hematological parameters were recorded

. T he authors considered the 34

fluctuations to be minor and within the normal range. Likew ise, a number of histopathologic 35

25 All control group responses were reported in a single table in the sectio n of the NEDO (1987) report that describes

the acute monkey study, with no indication as to when the control group was sacrificed.4

- 26 DRAFT

- DO NOT CITE OR QUOTE changes were observed, which, in every case, were considered to be unrelated to exposure level 1

or due to aging. 2

A companion experiment featured the exposure of 30 B6C3 F1 mice/sex/group for 1 year 3

to the same concentrations as the F344 rats (NEDO, 1987). Broadly speaking, the same suite of 4

toxicological parameters was monitored as described above, with the addition of urinalysis. 5

10 mice/sex/group were sacrificed at 6 months to provide interim data on the parameters under 6

in vestigation. A slight atrophy in the external lacrimal gland was observed in both sexes and was 7

significant in the 1,000 ppm male group compared with controls. An apparently dose

- related 8

increase in moderate fatty degeneration of hepatocytes was observed in males (1/20, 4/20, 6/20 9

and 8/20 in the 0, 10, 100, and 1,000 ppm dose groups, respectively) which was significantly 10

increased over controls at the 1

, 000 ppm dose. However the incidence of moderate to severe 11

fatty degeneration was observed in untreate d animals maintained outside of the chamber. In 12

addition, there was a clear correlation between fatty degeneration and body weight (a change 13

which was not associated with treatment at 12 months) ; heavier animals tended to have more 14

severe cases of fatty d egeneration. The possibility of renal deficits due to methanol exposure 15

was suggested by the appearance of protein in the urine. However, this effect was also seen in 16

controls and did not display a dose

- response effect. Therefore, it is unlikely to be a consequence 17

of exposure to methanol. NEDO (1987) reported other histopathologic and biochemical ( e.g., 18

urinalysis and hematology) findings that do not appear to be related to treatment, including a 19

number of what were considered to be spontaneous tumors i n both control and exposure groups. 20

NEDO (1987

, 1985/2008a , unpublished reports ) 26 exposed 52 male and 53 female 21

B6C3 F1 mice/group for 18 months at the same concentrations of methanol (0, 10, 100 and 22

1,000 ppm) and with a similar experimental protocol to th at described in the 12

- month studies. 27 23

The fact that the duration of this study was only 18 months and not the more typical 2 year s 24

limits its ability to detect carcinogenic responses with relatively long latency periods. Animals 25

were sacrificed at the end of the 18

- month exposure period. NEDO (1985/2008a) reported that 26

“there was no microbiological contamination that may have influenced the result of the study” 27

and that the study included an assessment of general conditions, body weight change, food 28

co nsumption rate, laboratory tests (urinalysis, hematological, and plasma biochemistry) and 29

pathological tests (pathological autopsy, 28 organ weight check and histopathology 29 ). As stated 30

26 This study is described in a summary report (NEDO, 1987) and a more detailed, eight volume translation of the

original chro nic mouse study report (NEDO, 1985/2008a).

27 The authors reported that “[t]he levels of methanol turned out to be ~4 ppm in low level exposure group (10 ppm)

for ~11 weeks from week 43 of exposure due to the analyzer malfunction” and that “the average dura tion of

methanol exposure was 19.1 hours/day for both male and female mice.”

28 Autopsy was performed on all cases to look for gross lesions in each organ.4

- 27 DRAFT

- DO NOT CITE OR QUOTE in the summary report (NEDO, 1987), a few animals showed clinical signs of toxicity, but the 1

incidence of these responses was not related to dose. Likewise, there were no compound

- related 2

changes in body weight increase, food consumption, 30 urinalysis, hematology, or clinical 3

chemistry parameters. High

- concentration males ha d lower testis weights compared to control 4

males. Significant differences were detected for both absolute and relative testis weights. One 5

animal in the high

- dose group had severely atrophied testis weights

, approximately 25% of that 6

of the others in the dose group. Exclusion of this animal in the analysis still resulted in a 7

significant difference in absolute testis weight compared to controls but resulted in no difference 8

in relative testis weight. High

- concentration females had higher absolute kidney and spleen 9

weights compared to controls, but there was no significant difference in these organ weights 10

relative to body weight. At necropsy, there were signs of swelling in spleen, preputial glands, 11

and uterus in some animals. Some animals developed no des in the liver and lung although, 12

according to the authors, none of these changes were treatment

- related. NEDO (1985/2008a) 13

reported that all non neoplastic changes were “nonspecific and naturally occurring changes that 14

are often experienced by 18

- month o ld B6C3 F1 mice” and that fatty degeneration of liver that 15

was suspected to occur dose

- dependently in the 12

- month NEDO (1987) study was not observed 16

in this study. Similarly, though the study found various neoplastic changes across dose groups, 17

there was no compound

- related formation of tumors in any organ or tissue. 18

EPA reviewed the cancer findings documented in a recent translation of the original 19

NEDO report on this chronic mouse study ( NEDO

, 1985/ 2008a) to identify possible compound

20

related effects. H yperplastic and neoplastic histopathological findings have been tabulated and 21

are as shown in Table 4

- 4

. 22

29 Complete histopathological examinations were performed for the control group and high

- dose (1,000 ppm)

groups. Only histopathological examinations of the liver were performed on the low

- and medium

- level exposure

groups because no chemical

- related changes were found in the high

- level exposure group and because liver changes

were noted in the 12

- month mouse study (NEDO, 1987).

30 NEDO (1985/2008a) reports sporadic reductions in food consumption of the 1,000 ppm group, but no associated

weight loss or abnormal test results.4

- 28 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 4 . Histopathological changes in tissues of B6C3 F1 mice exposed to methanol

via inhalation for 18 months

Tissues/ t umor type Exposure concentrati on (ppm)

0 10 100 1,000 0 10 100 1,000

Number of animals affected/number examined

Males Females

Lung

Adenomatosis 0/52 0/3 0/3 0/52 0/53 0/0 0/5 1/53

Pulmonary adenoma 4/52 0/3 0/3 7/52 3/53 0/0 0/0 2/53

Liver

Hepatocellular adenoma 3/52 2/52 2/ 52 4/52 1/53 1/52 1/53 4/53

Hepatocellular carcinoma 2/52 4/52 0/52 1/52 3/53 0/52 3/53 2/53

Neoplastic nodule 16/52 13/52 16/52 20/52 1/53 0/52 0/53 1/53

Source: NEDO (1985/2008a).

There is no clear evidence for treatment

- related carcinogenic effect s in the mice in this 1

study. However, the fact that the study duration was limited to 18 months rather than the 2

traditional 2

- year bioassay makes it difficult to draw a definitive conclusion, particularly 3

regarding pulmonary adenomas, which were marginall y increased in high

- dose male mice of this 4

study and were also increased in male rats of the NEDO chronic rat study (NEDO, 1987

, 5

1985/2008b , unpublished reports ). In this study, the lack of adenomatosis in control or treated 6

male mice supports the conclus ion of the authors that the observed tumors were probably 7

unrelated to methanol exposure. There was no apparent relationship to treatment in any 8

neoplastic findings in the liver. Of relevance to the findings of treatment

- related lymphomas and 9

leukemias i n Sprague

- Dawley rats receiving methanol in drinking water in the Soffritti et al

. 10

(2002a) study, few lymphomas and leukemias were identified in the NEDO (1987

, 1985/2008 b ) 11

study reports, with no sign of a dose

- related trend. 12

Another study reported in (NE DO (1987

, 1985/2008b) 31 was a 24

- month carcinogenicity 13

bioassay in which 52 F344 rats/sex/group were kept in whole body inhalation chambers 14

containing 0, 10, 100, or 1,000 ppm (0, 13.1, 131, and 1,310 mg/m 3 ) methanol vapor. Animals 15

were maintained in the e xposure chambers for approximately 19.5 hours/day for a total of 733

– 16

736 days (males) and 740

– 743 days (females). Animals were monitored once a day for clinical 17

signs of toxicity, body weights were recorded once a week, and food consumption was measured 18

w eekly in a 24

- animal subset from each group. Urinalysis was carried out on the day prior to 19

sacrifice for each animal, the samples being monitored for pH, protein, glucose, ketones, 20

bilirubin, occult blood, and urobilinogen. Routine clinical chemistry an d hematological 21

31 This study is described in a summary report (NEDO, 1987) and a more detailed, e ight

- volume translation of the

original chronic rat study report (NEDO, 1985/2008b).4

- 29 DRAFT

- DO NOT CITE OR QUOTE measurements were carried out and all animals were subject to necropsy at term, with a 1

comprehensive histopathological examination of tissues and organs. 32 2

There was some fluctuation in survival rates among the groups in the rat study, thoug h 3

apparently unrelated to exposure concentration. 33 In all groups, at least 60% of the animals 4

survived to term. A number of toxicological responses were described by the authors, including 5

atrophy of the testis, cataract formation, exophthalmia, small e ye ball, alopecia, and paralysis of 6

the hind leg. However, according to the authors, the incidence of these effects were unrelated to 7

dose and more likely represented effects of aging. NEDO (1985/2000b) reported a mild, 8

nonsignificant (4%) body weight sup pression among 1,000 ppm females between 51 and 9

72 weeks, but that body weight gain was largely similar among the groups for the duration of the 10

experiment. Food consumption was significantly lower than controls in high

- concentration male 11

rats during the day 210

– 365 time interval, but no corresponding weight loss was observed. 12

Among hematological parameters, mid

- and high

- concentration females had a significantly 13

( p > 0.05) higher differential leukocyte count than controls, but dose dependency was not 14

obs erved. Serum total cholesterol, triglyceride, free fatty acid, and phospholipid concentrations 15

were significantly ( p > 0.05) lower in high

- concentration females compared to controls. 16

Likewise, serum sodium concentrations were significantly ( p > 0.05) lowe r in mid

- and high

17

concentration males compared to controls. High

- concentration females had significantly lower 18

( p > 0.05) serum concentrations of inorganic phosphorus but significantly ( p > 0.05) higher 19

c oncentrations of potassium compared to controls. Glucose levels were elevated in the urine of 20

high

- concentration male rats relative to controls

, and female rats had lower pH values and higher 21

bilirubin levels in mid

- and high

- concentration groups relative to controls. In general, NEDO 22

(1987

, 1985/2008b) reported that these variations in urinary, hematology, and clinical chemistry 23

parameters were not related to chemical exposure. 24

NEDO (1987) reported that there was little change in absolute or relative weights of the 25

major organs or tissues. When the ani mals were examined grossly at necropsy, there were some 26

signs of swelling in the pituitary and thyroid, but these effects were judged to be unrelated to 27

treatment. The most predominant effect was the dose

- dependent formation of nodes in the lung 28

of males ( 2/52, 4/52, 5/52, and 10/52 [ p < 0.01] for control, low

- , mid

- , and high

- concentration 29

groups, respectively). Histopathologic examination pointed to a possible association of these 30

nodes with the appearance of pulmonary adenoma (1/52, 5/52

, 2/52, and 6/52 for control, low

- , 31

mid

- and high

- concentration groups, respectively) and a single pulmonary adenocarcinoma in the 32

32 Complete histopathological examinations were performed on the cases killed on schedule (week 104) among the

control and high

- exposure groups, and the cases that were fou nd dead/ killed in extremis of all the groups. Because

effects were observed in male and female kidneys, male lungs as well as female adrenal glands of the high

- level

exposure group, these organs were histopathologically examined in the low

- and mid

- exposu re groups.

33 Survival at the time of exposure termination (24 months) was 69%, 65%, 81%, and 65% for males and 60%, 63%,

60% and 67% for females of the control, low

- , mid

- and high

- exposure groups, respectively.4

- 30 DRAFT

- DO NOT CITE OR QUOTE high

- dose group (1/52). Other examples of tumor formation that were increased in high

1

concentration animals versus controls included an incr eased incidence of pituitary adenomas in 2

high

- concentration males (17/52 compared to 12/52 controls), hyperplastic change in the testis in 3

high

- concentration males (10/52 compared to 4/52 controls)

, and chromaffinoma 4

(pheochromocytomas) 34 in the adrenals of high

- concentration females (7/52 compared to 2/52 5

controls). Individually, these changes did not achieve statistical significance, and in general, the 6

authors concluded that few if any of the observed changes were effects of methanol. 7

EPA reviewed the ca ncer findings of this study that are documented in a recent translation 8

of the original NEDO (1985/2008 b ) report to identify possible compound

- related effects. High

9

dose incidences of pituitary adenomas (17/52; 33%) and hyperplastic change in testes (10/5 2; 10

19%) mentioned above were within historical incidences for this rat strain. 35 However, the 11

observed incidence rate for pulmonary adenoma/adenocarcinoma in high

- dose males of 13.5% 12

(7/52) was significantly elevated (Fisher’s exact test p < 0.05) over the concurrent control rate of 13

2% (1/52) and historical control rates of 2.5% ± 2.6% (n

= 1054) and 3.84% ± 2.94% ( n = 1199) 14

reported by NTP for the pre

- 1995 control F344 male rats fed NIH

- 07 diet (NTP, 1999) and post

15

1994 control F344 male rats fed NTP

- 2000 diet (NTP, 2007), respectively. Also, the incidence of 16

pulmonary adenoma/adenocarcinoma in male rats exhibited a dose

- response trend (Cochrane

17

Armitage p < 0.05). While the observed incidence rate for pheochromocytomas in high

- dose 18

females of 13.7% (7/51 ) was not significantly elevated over the concurrent control rate of 4% 19

(2/50), it was significantly elevated (Fisher’s exact test p < 0.05) over NTP historical control 20

rates for total (benign, complex and malignant) pheochromocytomas of 2.5% ± 2.6% ( n = 1 054) 21

and 3.84% ± 2.94% (n

= 1199) reported by NTP for pre

- 1995 control F344 female rats fed NIH

22

07 diet (NTP, 1999) and post

- 1994 control F344 female rats fed NTP

- 2000 diet (NTP, 2007), 23

respectively. 36 Also, the incidence of pheochromocytomas in female ra ts exhibited a dose

24

response trend (Cochrane

- Armitage p < 0.05). The histopathological incidences for pulmonary 25

and adrenal effects reported by NEDO (1987

, 1985/2008b) are shown in Table 4

- 5

. 26

34 There were some differences in nomenclatu re used in the NEDO (1985/2008b) report translation versus those

used in the older summary report (NEDO, 1987). For example, it is probable that the adrenal chromaffinoma

referred to in NEDO (1987) are the same lesions as the pheochromocytoma referred to in NEDO (1985/2008b).

35 NTP reports high incidences in historical control male F344 rats of pituitary gland adenomas, ranging from 45.4%

± 20.19% (NTP, 2007) to 63.4% ± 18.3% (NTP, 1999). While control incidences for testicular hyperplasia are not

reporte d, historical incidences of testicular ademoma ranged from 70.1% ±11.2% (NTP, 1999) to 86.32% ±9.34%

(NTP, 2007) in this rat strain.

36 NEDO (1987, 1985/2008b) does not categorize reported chromoffinoma (pheochromocytomas) as benign,

complex or malignant. The historical rates for complex and malignant tumors are much lower, ranging from 0.1%

to 0.7 % for female F344 rats (NTP, 2007; NTP, 1999; Haseman et al., 1998).4

- 31 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 5 . Histopathological changes in lung and adrenal tissu es of F344 rats exposed

to methanol via inhalation for 24 months

Tissues/

t umor type Exposure concentration (ppm)

0 10 100 1000 0 10 100 1000

Number of animals affected/number examined

Males Females

Lung

Pulmonary adenoma 1/52 5/50 2/52 6/52 2/52 0 /19 0/20 0/52

Pulmonary adenocarcinoma 0/52 0/50 0/52 1/52 0/52 0/19 0/20 0/52

Combined pulmonary

adenoma/adenocarcinoma 1/52 5/50 2/52 7/52 a ,b 2/52 0/19 0/20 0/52

Adenomatosis 4/52 1/50 5/52 4/52 3/52 2/19 1/20 1/52

Epithelial swelling 3/52 2/50 1/52 1/52 0/52 0/19 0/20 0/52

Adrenal glands

Pheochromocytoma 7/52 2/16 2/10 4/51 2/50 3/51 2/49 7/51 b ,c

Medullary hyperplasia 0/52 0/16 0/10 2/51 2/50 3/51 7/49 2/51

a p < 0.05 over concurrent controls using the Fisher’s Exact test.

b p < 0.05 for Cochrane

- Armitage test of overall dose

- response trend.

c p < 0.05 over NTP historical controls for total (benign, complex and malignant) pheochromocytomas using the

Fisher’s Exact test

Source: NEDO (1987, 1985/2008b).

In contrast to the conclusions of the NEDO ( 1987) summary report that there were no 1

compound

- related changes in F344 rats exposed to methanol via inhalation, EPA identifies 2

potential treatment

- related changes in the lungs of male rats and the adrenal medulla of female 3

rats in the more detailed trans lation of the original report (NEDO

, 1985/2008b). The NEDO 4

(1987) summary report did not report the statistical ly significant combined pulmonary adenoma 5

and adenocarcinoma finding in the high

- dose group of male rats

. Table 6 (page 146) of the 6

NEDO ( 1987 ) summary reports only “Tumural changes occurring at a rate of over 5%.” The 7

lung response of the male rats as shown in Table 4

- 5 suggests a proliferative change in cells of 8

the alveolar epithelium involving a progression towards adenoma and adenocarcinoma that 9

appears to be more pronounced with increasing methanol exposure and considerably elevated 10

over historical controls. Similarly, for female rats, the observed increase in medullary 11

hyperplasia in the 100 ppm dose group

, in conjunction with a higher in cidence of 12

pheochromocytoma in the adrenal gland is suggestive of a methanol

- induced progressive change 13

leading to a carcinogenic response. 144

- 32 DRAFT

- DO NOT CITE OR QUOTE 4.3. REPRODUCTIVE AND DEVELOPMENTAL STUDIE S

– ORAL AND INHALATION

Many studies have been conducted to investigate the repro ductive and developmental 1

toxicity of methanol. The purpose of these studies was principally to determine if methanol has 2

a similar toxicology profile to another widely studied teratogen, ethanol. 3

4.3.1. Oral Studies

Three studies were identified that investiga ted the reproductive and developmental effects 4

of methanol in rodents via the oral route ( Fu et al ., 1996; Sakanashi et al ., 1996; Rogers et al

., 5

1993a ) . Two of these studies also investigated the influence of folic acid

- deficient (FAD) diets on 6

the effect s of methanol exposures ( Fu et al ., 1996; Sakanashi et al ., 1996). 7

Rogers et al . (1993a) conducted a developmental toxicity study in which methanol in 8

water was administered to pregnant female CD

- 1 mice via gavage on GD6

– GD 15. Eight test 9

animals receive d 4 g/kg

- day methanol given in 2 daily doses of 2g/kg ; 4 controls received 10

distilled water. By analogy to the protocol of an inhalation study of methanol that was described 11

in the same report, it is assumed that dams were sacrificed on GD17, at which poin t implantation 12

sites, live and dead fetuses, resorptions/litter

, and the incidences of external and skeletal 13

anomalies and malformations were determined. In the brief summary of the findings provided 14

by the authors, it appears that cleft palate (43.5% per litter versus 0% in controls) and 15

exencephaly (29% per litter versus 0% in controls) were the prominent external defects 16

following maternal methanol exposure by gavage. Likewise, an increase in totally resorbed 17

litters and a decrease in the number of liv e fetuses per litter were evident. However, it is possible 18

that these effects may have been caused or exacerbated by the high bolus dosing regimen 19

employed. It is also possible that effects were not observed due to the limited study size. The 20

small numb er of animals in the control group relative to the test group limits the power of this 21

study to detect treatment

- related responses. 22

Sakanashi et al . (1996) tested the influence of dietary folic acid intake on various 23

reproductive and developmental effects observed in CD

- 1 mice exposed to methanol. Starting 24

5 weeks prior to breeding and continuing for the remainder of the study, female CD

- 1 mice were 25

fed folic acid free diets supplemented with 400 (low), 600 (marginal) or 1,200 (sufficient) nmol 26

folic acid/kg. After 5 weeks on their respective diets, females were bred with CD

- 1 male mice. 27

On GD6

– GD 15, pregnant mice in each of the diet groups were given twice

- daily gavage doses of 28

2.0 or 2.5 g/kg

- day methanol (total dosage of 4.0 or 5.0 g/kg

- day). On GD18, mice were 29

weighed and killed

, and the liver, kidneys and gravid uteri removed and weighed. Maternal liver 30

and plasma folate levels were measured

, and implantation sites, live and dead fetuses, and 31

resorptions were counted. Fetuses were weighed individual ly and examined for cleft palate and 32

exencephaly. One third of the fetuses in each litter were examined for skeletal morphology. 334

- 33 DRAFT

- DO NOT CITE OR QUOTE They observed an approximate 50% reduction in liver and plasma folate levels in the mice fed 1

low versus sufficient folic acid diets in both the methanol exposed and unexposed groups. 2

Similar to Rogers et al . (1993a)

, Sakanashi et al. (1996) observed that an oral dose of 4

– 5 g/kg

3

day methanol during GD6

– GD 15 resulted in an increase in cleft palate in mice fed sufficient 4

folic ac id diets, as well as an increase in resorptions and a decrease in live fetuses per litter. They 5

did not observe an increase in exencephaly in the FAS group at these doses, and the authors 6

suggest that this may be due to diet and the source of CD

- 1 mice di ffering between the two 7

studies. 8

In the case of the animals fed the folate deficient diet, there was a 50% reduction in 9

maternal liver folate concentration and a threefold increase in the percentage of litters affected by 10

cleft palate (86.2% versus 34.5% in mice fed sufficient folic acid) and a 10

- fold increase in the 11

percentage of litters affected by exencephaly (34.5% versus 3.4% in mice fed sufficient folic 12

acid) at the 5 g/kg methanol dose. Sakanashi et al. (1996) speculate that the increased methanol 13

effect from the FAD diet could have been due to an increase in tissue formate levels (not 14

measured) or to a critical reduction in conceptus folate concentration following the methanol 15

exposure. Plasma and liver folate levels at GD18 within each dietary g roup were not 16

significantly different between exposed versus unexposed mice. However, these measurements 17

were taken 3 days after methanol exposure. Dorman et al . (1995) observed a transient decrease 18

in maternal red blood cells (RBCs) and conceptus folate levels within 2 hours following 19

inhalation exposure to 15,000 ppm met h anol on GD8. Thus, it is possible that short

- term 20

reductions in available folate during GD6

– GD 15 may have affected fetal development. 21

Fu et al . (1996) also tested the influence of die tary folic acid intake on reproductive and 22

developmental effects observed in CD

- 1 mice exposed to methanol. This study was performed 23

by the same laboratory and used a similar study design and dosing regimen as Sakanashi et al

. 24

(1996), but exposed the preg nant mice to only the higher 2.5 g/kg

- day methanol (total dosage of 25

5.0 g/kg

- day) on GD6

– GD 10. Like Sakanashi et al . (1996), Fu et al . (1996) measured maternal 26

liver and plasma folate levels on GD18 and observed similar, significant reductions in these 27

le vels for the FAD versus FAS mice. However, Fu et al . (1996) also measured fetal liver folate 28

levels at GD18. This measurement does not address the question of whether methanol exposure 29

caused short

- term reductions in fetal liver folate because it was tak e n 8 days after the GD6

– 30

GD 10 exposure period. However, it did provide evidence regarding the extent to which a 31

maternal FAD diet can impact fetal liver folate levels in this species and strain. Significantly, the 32

maternal FAD diet had a greater impact on fetal liver folate than maternal liver folate levels. 33

Relative to the FAS groups, f etal liver folate levels in the FAD groups were reduced 2.7

- fold for 34

mice not exposed to methanol (1.86 ± 0.15 nmol/g in the FAD group versus 5.04 ± 0.22 nmol/g 35

in the FAS group ) and 3.5

- fold for mice exposed to methanol (1.69 ± 0.12 nmol/g in the FAD 364

- 34 DRAFT

- DO NOT CITE OR QUOTE group versus 5.89 ± 0.39 nmol/g in the FAS group ) . M aternal folate levels in the FAD groups 1

were only reduced two fold both for mice not exposed (4.65 ± 0.37 versus 9.54 ± 0.5 0 nmol/g) 2

and exposed (4.55 ± 0.19 versus 9.26 ± 0.42 nmol/g). Another key finding of the Fu et al . (1996) 3

study is that methanol exposure during GD6

– GD 10 appeared to have similar fetotoxic effects, 4

including cleft palate, exencephaly, resorptions

, and de crease in live fetuses, as the same level of 5

methanol exposure administered during GD6

– GD 15 (Sakanashi et al ., 1996; Rogers et al

., 6

1993a). This is consistent with the hypothesis made by Rogers et al . (1993b) that the critical 7

period for methanol

- induced cleft palate and exencephaly in CD

- 1 mice is within GD6

– GD 10. 8

As in the studies of Sakanashi et al . (1996) and Rogers et al . (1993a), Fu et al . (1996) reported a 9

higher incidence of cleft palate than exencephaly. 10

4.3.2. Inhalation Studies

Nelson et al

. (1985) e xposed 15 pregnant Sprague

- Dawley rats/group to 0, 5,000, 10,000, 11

or 20,000 ppm (0, 6,552, 13,104, and 26,209 mg/m 3 ) methanol (99.1% purity) for 7 hours/day. 12

Exposures were conducted on GD1

– GD 19 in the two lower concentration groups and GD7

– 13

GD 15 in the hi ghest concentration group, apparently on separate days. Two groups of 15 control 14

rats were exposed to air only. Day 1 blood methanol levels measured 5 minutes after the 15

termination of exposure in NP rats that had received the same concentrations of metha nol as 16

those animals in the main part of the experiment were 1.00 ± 0.21, 2.24 ± 0.20, and 8.65 ± 0.40 17

mg/mL for those exposed to 5

, 000, 10,000 and 20,000 ppm methanol, respectively. Evidence of 18

maternal toxicity included a slightly unsteady gait in the 2 0,000 ppm group during the first few 19

days of exposure. Maternal bodyweight gain and food intake were unaffected by methanol. 20

Dams were sacrificed on GD20

, and 13

– 30 litters/group were evaluated. No effect was observed 21

on the number of corpora lutea or im plantations or the percentage of dead or resorbed fetuses. 22

Statistical evaluations included analysis of variance ( ANOVA ) for body weight effect, Kruskal

23

Wallis test for endpoint s such as litter size and viability and Fisher’s exact test for 24

malformations. Fetal body weight was significantly reduced at concentrations of 10,000 and 25

20,000 ppm by 7% and 12

– 16%, respectively, compared to controls. An increased number of 26

litters with skeletal and visceral malformations w ere observed at **≥** 10,000 ppm

, with statis tical 27

significance obtained at 20,000 ppm. Numbers of litters with visceral malformations were 0/15, 28

5/15, and 10/15 and with skeletal malformations were 0/15, 2/15, and 14/15 at 0, 10,000, and 29

20,000 ppm, respectively. Visceral malformations included e xencephaly and encephaloceles. 30

The most frequently observed skeletal malformations were rudimentary and extra cervical ribs. 31

The developmental and maternal NOAELs for this study were identified as 5,000 ppm (6

, 552 32

mg/m 3 ) and 10,000 ppm (13,104 mg/m 3 ), re spectively. 334

- 35 DRAFT

- DO NOT CITE OR QUOTE NEDO (1987) sponsored a teratology study in Sprague

- Dawley rats that included an 1

evaluation of postnatal effects in addition to standard prenatal endpoint s . Thirty

- six pregnant 2

females/group were exposed to 0, 200, 1,000, or 5,000 ppm (0, 262, 1,310, and 6,552 mg/m 3 ) 3

methanol vapors (reagent grade) on GD7

– GD 17 for 22.7 hours/day. Statistical significance of 4

results was evaluated by t

- test, Mann

- Whitney U test, Fisher’s exact test, and/or Armitage’s χ 2 5

test. 6

Contrary to the Nelson et al . (1985) report of a 10,000 ppm NOAEL for this rat strain, in 7

the prenatal portion of the NEDO (1987) study, reduced body weight gain and food and water 8

intake during the first 7 days of exposure were reported for dams in the 5

, 000 ppm group. 9

However, it was not specified if these results were statistically significant. One dam in the 10

5,000 ppm group died on GD19, and one dam was sacrificed on GD18 in moribund condition. 11

On GD20, 19

– 24 dams/group were sacrificed to eva luate the incidence of reproductive deficits 12

and such developmental parameters as fetal viability, weight, sex, and the occurrence of 13

malformations. As summarized in Table 4

- 6 , adverse reproductive and fetal effects were limited 14

to the 5,000 ppm group and included an increase in late

- term resorptions, decreased live fetuses, 15

reduced fetal weight, and increased frequency of litters with fetal malformations, variations, and 16

delayed ossifications. Malformations or variations included defects in ventricular s eptum, 17

thymus, vertebrae, and ribs. 18

Postnatal effects of methanol inhalation were evaluated in the remaining 12 dams/group 19

that were permitted to deliver and nurse their litters. Effects were only observed in the 20

5,000 ppm group

, and included a 1

- day prol ongation of the gestation period and reduced post

21

implantation survival, number of live pups/litter, and survival on PND4 ( Table 4

- 7 ). When the 22

delay in parturition was considered, methanol treatment had no effect on attainment of 23

developmental milestones such as eyelid opening, auricle development, incisor eruption, testes 24

descent, or vaginal opening. There were no adverse body weight effects in offspring from 25

methanol treated groups. The weights of some organs (brain, thyroid, thymus, and testes) were 26

reduced in 8

- week

- old offspring exposed to 5000 ppm methanol during prenatal development. 27

28

29

30

314

- 36 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 6 . Reproductive and developmental toxicity in pregnant Sprague

- Dawley rats

exposed to methanol via inhalation during gestation

Effect Exposure concentra tion (ppm)

0 200 1,000 5,000

Reproductive effects

Number of pregnant females examined 19 24 22 20

Number of corpora lutea 17.0 ± 2.6 17.2 ± 2.7 16.4 ± 1.9 16.5 ± 2.4

Number of implantations 15.7 ± 1.6 15.0 ± 3.0 15.5 ± 1.2 14.5 ± 3.3

Number of resor ptions 0.79 ± 0.85 0.71 ± 1.23 0.95 ± 0.65 1.67 ± 2.03

Number of live fetuses 14.95 ± 1.61 14.25 ± 3.54 14.55 ± 1.1 12.86 ± 4.04 a

Sex ratio (M/F) 144/140 177/165 164/156 134/136

Fetal weight (male) 3.70 ± 0.24 3.88 ± 0.23 3.82 ± 0.29 3.02 ± 0.27 c

Fetal weight (female) 3.51 ± 0.19 3.60 ± 0.25 3.60 ± 0.30 2.83 ± 0.26 c

Total resorption rate (%) 11.2 ± 9.0 15.6 ± 21.3 10.6 ± 8.4 23.3 ± 22.7 a

Soft tissue malformations

Number of fetuses examined 136 165 154 131

Abnormality at base of right subclavian 0.7 ± 2.87 (1) 0 0 0

Excessive left subclavian 0 0 0 3.5 ± 9.08 (3)

Ventricular septal defect 0 0.6 ± 2.96 (1) 0 47.6 ± 36.51 (16) b

Residual thymus 2.9 ± 5.91 (4) 2.4 ± 5.44 (4) 2.6 ± 5.73 (4) 53.3 ± 28.6 (20) b

Serpengious urinary tract 43.0 ± 24.64 (18) 3 5.2 ± 31.62 (19) 41.8 ± 38.45 (15) 22.1 ± 22.91 (13)

Skeletal abnormalities

Number of fetuses examined 148 177 165 138

Atresia of foramen costotransversarium 23.5 ± 5.47 (3) 7.7 ± 1.3 (8) 3.5 ± 8.88 (4) 45.2 ± 25.18 (20) b

Patency of foramen costotransv ersium 0 0 0.6 ± 2.67 (1) 13.7 ± 20.58 (7)

Cleft sternum 0 0 0 5.6 ± 14.14 (3)

Split sternum 0 0 0 7.0 ± 14.01 (5)

Bifurcated vertebral center 0.8 ± 3.28 (1) 1.6 ± 5.61 (2) 3.0 ± 8.16 (3) 14.5 ± 16.69 (11) b

Cervical rib 0 0 0 65.2 ± 25.95 (19) b

Excess ive sublingual neuropore 0 0 0 49.9 ± 27.31 (19)

Curved scapula 0 0 0 0.7 ± 3.19 (1)

Waved rib 0 0 0 6.1 ± 11.84 (5)

Abnormal formation of lumbar

vertebrae 0 0 0 0.7 ± 3.19 (1)

a p < 0.05, b p < 0.01, c p < 0.001, as calculated by the authors.

Values are means ± S.D. Values in parentheses are the numbers of litters.

Source: NEDO (1987).

1

2

3

44

- 37 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 7 . Reproductive parameters in Sprague

- Dawley dams exposed to methanol

during pregnancy then allowed to deliver their pups

Effect Exposure concentration (ppm )

0 200 1,000 5,000

Number of dams 12 12 12 12

Duration of gestation (days) 21.9 ± 0.3 21.9 ± 0.3 21.9 ± 0.3 22.6 ± 0.5 c

Number of implantations 15.8 ± 1.6 14.8 ± 1.2 15.3 ± 1.3 14.6 ± 1.1 a

Number of pups 15.2 ± 1.6 14.4 ± 1.3 14.5 ± 1.4 13.1 ± 2.2 a

Number of live pups 15.2 ± 1.6 14.1 ± 1.4 14.3 ± 1.4 12.6 ± 2.5 b

Number of live pups on PND4 15.0 ± 1.7 (2) 13.8 ± 1.5 (3) 14.2 ± 1.6 (1) 10.3 ± 2.8 (9) c

Sex ratio (M/F) 88/94 87/85 103/70 a 75/81

Postimplantation embryo

survival rate 96.3 ± 4.2 94.9 ± 5.1 93.6 ± 6.1 86.2 ±16.2 a

a p < 0.05, b p < 0.01, c p < 0.001, as calculated by the authors.

Values are means ± S.D. Values in parentheses are the numbers of litters.

Source: NEDO (1987).

1

NEDO (1987) contains an account of a two

- generation reproductive s tudy that evaluated 2

the effects of pre

- and postnatal methanol (reagent grade) exposure (20 hours/day) on 3

reproductive and other organ systems of Sprague

- Dawley rats. The F 0 generation (30 males and 4

30 females per exposure group) 37 was exposed to 0, 10, 10 0, and 1

, 000 ppm (0, 13.1, 131, and 5

1,310 mg/m 3 ) from 8 weeks old to the end of mating (males) or to the end of lactation period 6

(females). The F 1 generation was exposed to the same concentrations from birth to the end of 7

mating (males) or to weaning of F 2 pups 21 days after delivery (females). Males and females of 8

the F 2 generation were exposed from birth to 21 days old (one animal/sex/litter was exposed to 9

8 weeks of age). NEDO (1987) noted reduced brain, pituitary, and thymus weights, and early 10

testic ular descent in the offspring of F 0 and F 1 rats exposed to 1

, 000 ppm methanol. The early 11

testicular descent is believed to be an indication of earlier fetal development as indicated by the 12

fact that it was correlated with increased pup body weight. Howev er, no histopathologic effects 13

of methanol were observed. As discussed in the report, NEDO (1987) sought to confirm the 14

possible compound

- related effect of methanol on the brain by carrying out an additional study in 15

which Sprague

- Dawley rats were exposed to 0, 500, 1,000, and 2,000 ppm (0, 655, 1,310, and 16

2,620 mg/m 3 ) methanol from the first day of gestation through the F 1 generation (see 17

S ection 4.4.2)

. 18

37 A second control group of 30 animals / sex was maintained in a separate room to “confirm th at environmental conditions inside

the chambers were not unaccep table to the animals.”4

- 38 DRAFT

- DO NOT CITE OR QUOTE Rogers et al . (1993a) evaluated development toxicity in pregnant female CD

- 1 mice 1

exposed to air or 1,000, 2,000, 5,000, 7,500, 10,000, or 15,000 ppm (0, 1,310, 2,620, 6,552, 2

9,894, 13,104, and 19,656 mg/m 3 ) methanol vapors ( **≥** 99.9% purity) in a chamber for 3

7 hours/day on GD6

– GD 15 in a 3

- block design experiment. The numbers of mice exposed at 4

each dose were 114, 40, 80, 79, 30, 30, and 44, respectively. During chamber exposures to air or 5

methanol, the mice had access to water but not food. In order to determine the effects of the 6

chamber exposure conditions, an additional 88 control mice were not handl ed and remained in 7

their cages; 30 control mice were not handled but were food deprived for 7 hours/day on GD6

– 8

GD 15. Effects in dams and litters were statistically analyzed using the General Linear Models 9

procedure and multiple t

- test of least squares mea ns for continuous variables and the Fisher’s 10

exact test for dichotomous variables. An analysis of plasma methanol levels in 3 pregnant 11

mice/block/treatment group on GD6, GD 10, and GD 15 revealed a dose

- related increase in 12

plasma methanol concentration that did not seem to reach saturation levels

, and methanol plasma 13

levels were not affected by gestation stage or number of previous exposure days. Across all 14

3 days, the mean plasma methanol concentrations in pregnant mice were approximately 97, 537, 15

1,650, 3 ,178, 4,204, and 7,330 μg/mL in the 1,000, 2,000, 5,000, 7,500, 10,000, and 15,000 ppm 16

exposure groups, respectively. 17

The dams exposed to air or methanol in chambers gained significantly less weight than 18

control dams that remained in cages and were not han dled. There were no methanol

- related 19

reductions in maternal body weight gain or overt signs of toxicity. Dams were sacrificed on 20

GD17 for a comparison of developmental toxicity in methanol

- treated groups versus the chamber 21

air

- exposed control group. Fet uses in all exposure groups were weighed, assessed for viability, 22

and examined for external malformations. Fetuses in the control, 1,000, 2,000, 5,000, and 23

15,000 ppm groups were also examined for skeletal and visceral defects. Incidence of 24

developmental effects is listed in Table 4

- 8 . A statistically significant increase in cervical 25

ribs/litter was observed at concentrations of 2

, 000, 5,000, and 15,000 ppm. At doses of 26

**≥** 5 ,000 ppm the incidences of cleft palates/litter and exencephaly/litter were increa sed with 27

statistical significance achieved at all concentrations with the exception of exencephaly which 28

increased but not significantly at 7,500 ppm. 38 A significant reduction in live pups/litter was 29

noted at **≥** 7 ,500 ppm

, with a significant increase in ful ly resorbed litters occurring at 30

**≥** 1 0,000 ppm. Fetal weight was significantly reduced at **≥ 1**  0,000 ppm. Rogers et al. (1993a) 31

identified a developmental NOAEL and LOAEL of 1,000 ppm and 2,000 ppm, respectively. 32

They also provide BMD maximum likelihood estimates ( benchmark concentration [ BM C ] ; 33

referred to by the authors as MLE) and estimates of the lower 95% confidence limit on the BM C 34

38 Due to the serious nature of this response and the relative lack of a response in controls, all incidence of

exenceaphaly reported in this study at 5,000 ppm or highe r are considered biologically significant.4

- 39 DRAFT

- DO NOT CITE OR QUOTE ( benchmark concentration, 95% lower bound [ BM C L ] ; referred to as benchmark dose [ BMD ] by 1

Rogers et al . (1993a) for 5% and 1% added risk, by applying a log

- logistic dose

- response model 2

to the mean percent/litter dat a for cleft palate, exencephaly and resorption. The BM C 05 and 3

BM C L 05 values for added risk estimated by Rogers et al . (1993a) are listed in Table 4

- 9 . From 4

this analysis, the most sensitive indicator of developmental toxicity was an increase in the 5

propo rtion of fetuses per litter with cervical rib anomalies. The most sensitive BM C L and BM C 6

from this effect for 5% added risk w ere 305 ppm (400 mg/m 3 ) and 824 ppm (1

, 080 mg/m 3 ), 7

respectively. 39 8

Table 4

- 8 . Developmental effects in mice after methanol inhalati on

Endpoint Exposure concentration (ppm)

0 1,000 2,000 5,000 7,500 10,000 15,000

No. l ive pups/litter 9.9 9.5 12.0 9.2 8.6 b 7.3 c 2.2 c

No. fully resorbed litters 0 0 0 0 3 5 a 14 c

Fetus weight (g) 1.20 1.19 1.15 1.15 1.17 1.04 c 0.70 c

Cleft palate/ litt er ( %) 0.21 0.65 0.17 8.8 b 46.6 c 52.7 c 48.3 c

Exencephaly/litter (%) 0 0 0.88 6.9 a 6.8 27.4 c 43.3 c

Anomalies

Cervical ribs/litter (%) 28 33.6 49.6 b 74.4 c ND ND 60.0 a

Sternebral defects/litter (%) 6.4 7.9 3.5 20.2 c ND ND 100 c

Xiphoid defects/litter (%) 6.4 3.8 4.1 10.9 ND ND 73.3 c

Vertebral arch defects/litter (%) 0.3 ND ND 1.5 ND ND 33.3 c

Extra lumbar ribs/litter (%) 8.7 2.5 9.6 15.6 ND ND 40.0 c

Ossifications (values are means of litter means)

Sternal 5.96 5.99 5.94 5.81 ND ND 5.07 c

Caudal 5.93 6.2 6 5.71 a 5.42 ND ND 3.20 a

Metacarpal 7.96 7.92 7.96 7.93 ND ND 7.60 b

Proximal phalanges 7.02 7.04 7.04 6.12 ND ND 3.33 c

Metatarsals 9.87 9.90 9.87 9.82 ND ND 8.13 c

Proximal phalanges 7.18 7.69 6.91 5.47 ND ND 0 c

Distal phalanges 9.64 9.59 9.57 8.46 b ND ND 4.27 c

Supraoccipital score+ 1.40 1.65 1.57 1.48 ND ND 3.20 c

ND = Not determined. + = on a scale of 1

– 4, where 1 is fully ossified and 4 is unossified.

Statistical significance: a p < 0.05, b p < 0.01, c p < 0.001 , as calculated by the authors.

Source: Rogers et al . (1993a).

39 The BMD analysis of the data described in Section 5 was performed similarly using, among others, a similar

nested logistic model. However, the Rogers et al. (1993a) analysis was performed using added risk and ex ternal

exposure concentrations, whereas the analyses in Section 5 used extra risk and internal dose metrics that were then

converted to human equivalent exposure concentrations.4

- 40 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 9 . Benchmark doses at two added risk levels

Endpoint BM C 05 (ppm) BM C L 05 (ppm) BM C 01 (ppm) BM C L 01 (ppm)

Cleft Palate (CP) 4,314 3,398 2,717 1,798

Exencephaly (EX) 5,169 3,760 2,122 784

CP and EX 3,713 3,142 2,381 1,816

R esorptions (RES) 5,650 4,865 3,749 2,949

CP, EX, and RES 3,667 3,078 2,484 1,915

Cervical ribs 824 305 302 58

Source: Rogers et al . (1993a).

Rogers and Mole (1997) investigated the critical period of sensitivity to the 1

developmental toxicity of inhale d methanol in the CD

- 1 mouse by exposing 12

– 17 pregnant 2

females to 0 or 10,000 ppm (0 and 13,104 mg/m 3 ), 7 hours/day on 2 consecutive days during 3

GD6

– GD 13, or to a single exposure to the same methanol concentration during GD5

– GD 9. 4

Another group of mice re ceived a single 7

- hour exposure to methanol at 10,000 ppm. The latter 5

animals were sacrificed at various time intervals up to 28 hours after exposure. Blood samples 6

were taken from these animals to measure the concentration of methanol in the serum. Ser um 7

methanol concentrations peaked at

~ 4 mg/mL 8 hours after the onset of exposure. Methanol 8

concentrations in serum had declined to pre

- exposure levels after 24 hours. All mice in the main 9

body of the experiment were sacrificed on GD17, and their uteri r emoved. The live, dead, and 10

resorbed fetuses were counted, and all live fetuses were weighed, examined externally for cleft 11

palate, and then preserved. Skeletal abnormalities were determined after the carcasses had been 12

cleaned and eviscerated. Cleft pa late, exencephaly, and skeletal defects were observed in the 13

fetuses of exposed dams. For example, cleft palate was observed following 2

- day exposures to 14

methanol on GD6

– GD 7 through GD 11

– GD 12. These effects also were apparent in mice 15

receiving a single e xposure to methanol on GD5

– GD 9. This effect peaked when the dams were 16

exposed on GD7. Exencephaly showed a similar pattern of development in response to methanol 17

exposure. However, the data indicated that cleft palate and exencephaly might be competing 18

m alformations, since only one fetus displayed both features. Skeletal malformations included 19

exoccipital anomalies, atlas and axis defects, the appearance of an extra rudimentary rib on 20

cervical vertebra No.7, and supernumerary lumbar ribs. In each case, the maximum time point 21

for the induction of these defects appeared to be when the dams were exposed to methanol on or 22

near GD7. When dams were exposed to methanol on GD5, there was also an increased 23

incidence of fetuses with 25 presacral vertebrae (26 is normal). However, an increased incidence 24

of fetuses with 27 presacral vertebrae was evident when dams were exposed on GD7. These 25

results indicate that gastrulation and early organogenesis is a period of increased embryonic 26

sensitivity to methanol. 274

- 41 DRAFT

- DO NOT CITE OR QUOTE Burbac her et al . (199 9 a, 199 9 b) carried out toxicokinetic and reproductive/developmental 1

studies of methanol in M. fascicularis monkeys that were published by the Health Effects 2

Institute (HEI) in a two

- part monograph. Some of the data were subsequently publish ed in the 3

open scientific literature (Burbacher et al ., 2004a, 2004 b). The experimental protocol featured 4

exposure to 2 cohorts of 12 monkeys/group to low exposure levels (relative to the previously 5

discussed rodent studies) of 0, 200, 600, or 1,800 ppm ( 0, 262, 786, and 2

, 359 mg/m 3 ) methanol 6

vapors (99.9% purity), 2.5 hours/day, 7 days/week, during a premating period and mating period 7

(

**−** 180 days combined) and throughout the entire gestation period (

**−** 168 days). The monkeys 8

were 5.5

– 13 years old and were a mixture of feral

- born and colony

- bred animals. The study 9

included an evaluation of maternal reproductive performance and tests to asses s infant postnatal 10

growth and newborn health, reflexes, behavior, and development of visual, sensorimotor, 11

cognitive, and social behavioral function (see S ection 4.4.2 for a review of the developmental 12

neurotoxicity findings from this study) . Blood methan ol levels, clearance, and the appearance of 13

formate were also examined and are discussed in Section 3.2. 14

With regard to reproductive parameters, there was a statistically significant decrease 15

( p = 0.03) in length of pregnancy in all treatment groups, as sh own in Table 4

- 10 . Maternal 16

menstrual cycles, conception rate, and live birth index were all unaffected by exposure. There 17

were also no signs of an effect on maternal weight gain or clinical toxicity among the dams. The 18

decrease in pregnancy length was largely due to complications of pregnancy requiring Cesarean 19

section (C

- section) deliveries in the methanol exposure groups. The C

- section deliveries were 20

performed in response to signs of difficulty in the pregnancy and thus may serve as supporting 21

evide nce of reproductive dysfunction in the methanol

- exposed females. 22

While pregnancy duration was virtually the same in all exposure groups, there were some 23

indications of increased pregnancy duress only in methanol

- expose d monkeys . C

- sections were 24

done in 2 monkeys from the 200 ppm group and 2 from the 600 ppm group due to vaginal 25

bleeding, presumed, but not verified, to be from placental detachment. 40 A monkey in the 26

1

, 800 ppm group also received a C

- section after experiencing nonproductive labor for 3 nigh ts. 27

In addition, s igns of prematurity were observed in 1 infant from the 1,800 ppm group that was 28

born after a 150

- day gestation period. The authors speculated that the shortened gestation length 29

could be due to a direct effect of methanol on the fetal hypothalamus

- pituitary

- adrenal (HPA) 30

axis or an indirect effect of methanol on the maternal uterine environment. Other fetal 31

parameters such as crown

- rump length and head circumference were unchanged among the 32

groups. Infant growth and tooth eruption wer e unaffected by prenatal methanol exposure. 33

40 Burbacher et al. (2004a, 2004b) note, however, that in studies of pregnancy complication in alcohol

- exposed

human subjects, an increased incidence of uterine bleeding and abrutio placenta has been reported

.4

- 42 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 10 . Reproductive parameters in monkeys exposed via inhalation to methanol

during prebreeding, breeding, and pregnancy

Exposure (ppm) Conception rate W eight gain (kg) Pregnancy duration (days) a Live born d elivery rate

0 9/11 1.67 ± 0.07 168 ± 2 8/9

200 9/12 1.27 ± 0.14 160 ± 2 b 9/9

600 9/11 1.78 ± 0.25 162 ± 2 b 8/9

1,800 10/12 1.54 ± 0.20 162 ± 2 b 9/10

Values are means ± SE.;

a Live

- born offspring only ; b p < 0.05, as calculated by the authors.

Source: Burbacher et al . (2004a).

In later life, 2 females out of the total of 9 offspring in the 1,800 ppm group experienced 1

a wasting syndrome at 12 and 17 months of age. Food intake was normal and no cause of the 2

syndrome could be determined in tests for viru ses, hematology, blood chemistry, and liver, 3

kidney, thyroid, and pancreas function. Necropsies revealed gastroenteritis and severe 4

malnourishment. No infectious agent or other pathogenic factor could be identified. Thus, it 5

appears that a highly signif icant toxicological effect on postnatal growth can be attributed to 6

prenatal methanol exposure at 1,800 ppm (2,300 mg/m 3 ). 7

In summary, the Burbacher et al

. (2004a, 2004 b, 1999b) studies suggest that methanol 8

exposure can cause reproductive effects, manifes ted as a shortened mean gestational period due 9

to pregnancy complications that precipitated delivery via a C

- section, and developmental 10

neurobehavioral effects which may be related to the shortened gestational period (see 11

S ection 4.4.2) . The low exposure of 200 ppm may signify a LOAEL for reproductive effects. 12

However, the decrease in gestational length was marginally significant and largely the result of 13

human intervention (C

- section) for reasons (presumably pregnancy complications) that were not 14

objecti vely confirmed with clinical procedures ( e.g., placental ultrasound) . Also, this effect did 15

not appear to be dose related, the greatest gestational period decrease having occurred at the 16

lowest (200 ppm) exposure level. Thus, a clear NOAEL or LOAEL canno t be determined from 17

this study. 18

In a study of the testicular effects of methanol, Cameron et al . (1984) exposed 5 male 19

Sprague

- Dawley rats/group to methanol vapor, 8 hours/day, 5 days/week for 1, 2, 4, and 6 weeks 20

at 0, 200, 2,000, or 10,000 ppm (0, 262, 2,620, and 13,104 mg/m 3 ). The authors examined the 21

possible effects of methanol on testicular function by measuring blood levels of testosterone, 22

luteinizing hormone (LH), and follicular stimulating hormone (FSH) using radioimmunoassay. 23

When the authors tabulated their results as a percentage of the control value for each duration 24

series, the most significant changes were in blood testosterone levels of animals exposed to 25

200 ppm methanol, the lowest concentration evaluated. At this exposure level, anima ls exposed 26

for 6 weeks had testosterone levels that were 32% of those seen in controls. However, higher 274

- 43 DRAFT

- DO NOT CITE OR QUOTE concentrations of methanol were associated with testosterone levels that were closer to those of 1

controls. However, the lack of a clear dose

- response is not necessarily an indication that the 2

effect is not related to methanol. The higher concentrations of methanol could be causing other 3

effects ( e.g., liver toxicity) which can influence the results. Male rats exposed to 10,000 ppm 4

methanol for 6 weeks displayed blood levels of LH that were about 3 times higher (mean ± S.D.) 5

than those exposed to air (311 ± 107% versus 100 ± 23%). In discussing their results, the authors 6

placed the greater emphasis on the fact that an exposure level equal to the ACGIH TLV 7

(200 ppm) had caused a significant depression in testosterone formation in male rats. 8

A follow

- up study report by the same research group (Cameron et al ., 1985) described the 9

exposure of 5 male Sprague

- Dawley rats/group, 6 hours/day for either 1 day or 1 week, to 10

methanol, ethanol, n

- propanol, or n

- butanol at their respective TLVs. Groups of animals were 11

sacrificed immediately after exposure or after an 18

- hour recovery period, and the levels of 12

testosterone, LH, and corticosterone measured in serum. As shown in Table 4

- 1 1 , the data were 13

consistent with the ability of these aliphatic alcohols to cause a transient reduction in the 14

formation of testosterone. Except in the case of n

- butanol, rapid recovery from these deficits can 15

be inferred from the 18

- hour postexposure data. 16

Table 4

- 11. Mean serum levels of testosterone, luteinizing hormone, and

corticosterone (± S.D.) in male Sprague

- Dawley rats after inhalation of methanol,

ethanol, n

- propanol or n

- butanol at threshold limit values

Testosterone (as a percentage of control)

Condition TLV

(ppm) Single

- day exposure One

- week exposure

End of exposure 18 hr

postexposure End of exposure 18 hr

postexposure

Control 100 ± 17 100 ± 20 100 ± 26 100 ± 17

Methanol 200 41 ± 16 a 98 ± 18 81 ± 22 82 ± 27

Ethano l 1,000 64 ± 12 a 86 ± 16 88 ± 14 101 ± 13

n

- Propanol 200 58 ± 15 a 81 ± 13 106 ± 28 89 ± 17

n

- Butanol 50 37 ± 8 a 52 ± 22 a 73 ± 34 83 ± 18

Luteinizing hormone

Control 100 ± 30 100 ± 35 100 ± 28 100 ± 36

Methanol 200 86 ± 32 110 ± 40 78 ± 13 70 ± 14

Et hanol 1,000 110 ± 22 119 ± 54 62 ± 26 81 ± 17

n

- Propanol 200 117 ± 59 119 ± 83 68 ± 22 96 ± 28

n

- Butanol 50 124 ± 37 115 ± 28 78 ± 26 98 ± 234

- 44 DRAFT

- DO NOT CITE OR QUOTE Testosterone (as a percentage of control)

Condition TLV

(ppm) Single

- day exposure One

- week exposure

End of exposure 18 hr

postexposure End of exposure 18 hr

postexposure

Corticosterone

Control 100 ± 20 ND 100 ± 21 ND

Methanol 200 115 ± 18 ND 74 ± 26 ND

Ethanol 1,000 111 ± 32 N D 60 ± 25 ND

n

- Propanol 200 112 ± 21 ND 79 ± 14 ND

n

- Butanol 50 143 ± 11 a ND 85 ± 26 ND

ND = No data.;

a p < 0.05, as calculated by the authors.

Source: Cameron et al. (1985).

1

In a series of studies that are relevant to the reproductive toxicity of m ethanol in males, 2

Lee et al . (1991) exposed 8

- week

- old male Sprague

- Dawley rats (9

– 10/group) to 0 or 200 ppm 3

(0 and 262 mg/m 3 ) methanol, 8 hours/day, 5 days/week, for 1, 2, 4, or 6 weeks to measure the 4

possible treatment effects on testosterone production. Study results were evaluated by one factor 5

ANOVA followed by Student’s t

- test. In the treated rats, there was no effect on serum 6

testosterone levels, gross structure of reproductive organs, or weight of testes and seminal 7

vesicles. Lee et al . (1991) al so studied the in vitro effect of methanol on testosterone production 8

from isolated testes, but saw no effect on testosterone formation either with or without the 9

addition of human chorionic gonadotropin hormone. 10

In a third experiment from the same report, Lee et al . (1991) examined testicular 11

histopathology to determine if methanol exposure produced lesions indicative of changing 12

testosterone levels ; t he effects of age and folate status were also assessed. This is relevant to the 13

potential toxicity of met hanol because folate is the coenzyme of tetrahydrofolate synthetase, an 14

enzyme that is rate limiting in the removal of formate. Folate deficiency would be expected to 15

cause potentially toxic levels of methanol, formaldehyde

, and formate to be retained. T he same 16

authors examined the relevance of folate levels, and by implication, the overall status of formate 17

formation and clearance in mediating the testicular functions of Long

- Evans rats. Groups of 18

4

- week

- old male Long

- Evans rats were given diets contain ing either adequate or reduced folate 19

levels plus 1% succinylsulfathiazole, an antibiotic that, among other activities, 41 would tend to 20

reduce the folate body burden

. At least 9 rats/dietary group/dose were exposed to 0, 50, 200, or 21

800 ppm (0, 66, 262, a nd 1,048 mg/m 3 ) methanol vapors starting at 7 months of age while 8

– 22

12 rats/dietary group/dose were exposed to 0 or 800 ppm methanol vapors at 15 months of age. 23

41 Succinylsulfathiazone antibiotic may have a direct impact on the effects being measured, the extent of which was

not addre ssed by the authors of this study.4

- 45 DRAFT

- DO NOT CITE OR QUOTE The methanol exposures were conducted continuously for 20 hours/day for 13 weeks. Without 1

pro viding details, the study authors reported that visual toxicity and acidosis developed in rats 2

fed the low folate diet and exposed to methanol. No methanol

- related testicular lesions or 3

changes in testes or body weight occurred in rats that were fed eithe r the folate sufficient or 4

deficient diets and were 10 months old at the end of treatment. Likewise, no methanol

- lesions 5

were observed in 18

- month

- old rats that were fed diets with adequate folate. However, the 6

incidence but not severity of age

- related t esticular lesions was increased in the 18

- month

- old rats 7

fed folate

- deficient diets. Subcapsular vacuoles in germinal epithelium were noted in 3/12 8

control rats and 8 /13 rats in the 800 ppm group. One rat in the 800 ppm group had atrophied 9

seminiferous tu bules and another had Leydig cell hyperplasia. These effects, as well as the 10

transient decrease in testosterone levels observed by Cameron et al . (198 5 , 198 4 ), could be the 11

result of chemically

- related strain on the rat system as it attempts to maintain ho rmone 12

homeostasis. 13

Dorman et al . (1995) conducted a series of in vitro and in vivo studies of developmental 14

toxicity in ICR BR (CD

- 1) mice associated with methanol and formate exposure. The studies 15

used HPLC grade methanol and appropriate controls. PK an d developmental toxicity parameters 16

were measured in mice exposed to a 6

- hour methanol inhalation (10,000 or 15,000 ppm), 17

methanol gavage (1.5 g/kg) or sodium formate (750 mg/kg by gavage) on GD8. In the in vivo 18

inhalation study, 12

– 14 dams/group were exp osed to 10,000 ppm methanol for 6 hours on 19

GD8, 42 with and without the administration of fomepizole (4

- methylpyrazole) to inhibit the 20

metabolism of methanol by ADH1. Dams were sacrificed on GD10

, and folate levels in 21

maternal RBC and conceptus ( decidual sw elling) were measured, as well as fetal neural tube 22

patency (an early indicator of methanol

- induced dysmorphogenic response). The effects 23

observed included a transient decrease in maternal RBC and conceptus folate levels within 24

2 hours following exposure and a significant ( p < 0.05) increase in the incidence of fetuses with 25

open neural tubes (9.65% in treated versus 0 in control). These responses were not observed 26

following sodium formate administration, despite peak formate levels in plasma and decidual 27

swellings being similar to those observed following the 6

- hour methanol inhalation of 28

15,000 ppm. This suggests that these methanol

- induced effects are not related to the 29

accumulation of formate. As this study provides information relevant to the identif ication of the 30

proximate teratogen associated with developmental toxicity in rodents, it is discussed more 31

extensively in Section 4.6 .1. 32

42 Dorman et al. (1985) state that GD8 was chosen because it encompasses the period of murine neurulation and the

time of greatest vulnerability to methanol

- induced neural tube defects.4

- 46 DRAFT

- DO NOT CITE OR QUOTE 4.3.3. Other Reproductive and Developmental Toxicity Studies

Additional information relevant to the possible effects o f methanol on reproductive and 1

developmental parameters has been provided by experimental studies that have exposed 2

experimental animals to methanol during pregnancy via i.p. injections (Rogers et al ., 2004). 3

Relevant to the developmental impacts of the c hemical, a number of studies also have examined 4

the effects of methanol when included in whole

- embryo culture (Hansen et al ., 2005; Harris 5

et al ., 2003; Andrews et al ., 199 8, 1995

, 199 3 ). 6

Pregnant female C57BL/6J mice received 2 i.p. injections of methanol on GD7 (Rogers 7

et al ., 2004). The injections were given 4 hours apart to provide a total dosage of 0, 3.4, and 4.9 8

g/kg. Animals were sacrificed on GD17 and the litters were examined for live, dead, and 9

resorbed fetuses. Rogers et al . (2004) monitored fetal weight and examined the fetuses for 10

external abnormalities and skeletal malformations. Methanol

- related deficits in maternal and 11

litter parameters observed by Rogers et al . (2004) are summarized in Table 4

- 1 2

. 12

Table 4

- 1 2 . Maternal and litter paramet ers when pregnant female C57BL/6J mice

were injected i.p. with methanol

Parameter Methanol dose (g/kg)

0 3.4 4.9

No. pregnant at term 43 13 24

Wt gain GD7

– GD 8 (g) 0.33 ± 0.10 0.37 ± 0.15

- 0.24 ± 0.14 a

Wt gain GD7

– GD 10 (g) 1.63 ± 0.18 2.20 ± 0.20 1.50 ± 0.20

Live fetuses/litter 7.5 ± 0.30 6.3 ± 0.5 a 3.7 ± 0.4 a

Resorbed fetuses/litter 0.4 ± 0.1 1.3 ± 0.4 a 4.4 ± 0.4 a

Dead fetuses/litter 0.1 ± 0.1 0 0.1 ± 0.1

Fetal weight (g) 0.83 ± 0.02 0.82 ± 0.03 0.70 ± 0.02 a

Values are means ± SEM.

a p < 0.05, as calculated by the authors.

Source: Rogers et al . (2004).

Rogers et al . (2004) used a number of sophisticated imaging techniques, such as confocal 13

laser scanning and fluorescence microscopy, to examine the morphology of fetuses excised at 14

GD7, GD 8, and GD 9 . They identified a number of external craniofacial abnormalities, the 15

incidence of which was, in all cases, significantly increased in the high

- dose group compared to 16

controls. For some responses, such as microanophthalmia and malformed maxilla, the inc idence 17

was also significantly increased in animals receiving the lower dose. Fifteen compound

- related 18

skeletal malformations were tabulated in the report. In most cases, a dose

- response effect was 19

evident, resulting in statistically significant incidences in affected fetuses and litters, when 20

compared to controls. Apparent effects of methanol on the embryonic forebrain included a 214

- 47 DRAFT

- DO NOT CITE OR QUOTE narrowing of the anterior neural plate, missing optical vesicles, and holoprosencephaly (failure of 1

the embryonic forebrain to divide). The authors noted that there was no sign of incipient cleft 2

palate or exencephaly, as had been observed in CD

- 1 mice exposed to methanol via the oral and 3

inhalation routes (Rogers et al ., 1993a). 4

In order to collect additional information on cell proliferation and histological changes in 5

methanol

- treated fetuses, Degitz et al . (2004a) used an identical experimental protocol to that of 6

Rogers et al . (2004) by administering 0, 3.4, or 4.9 g methanol/kg in distilled water i.p. (split 7

doses, 4 hours a part) to C57BL/6J mice on GD7. Embryos were collected at various times on 8

GD8 and GD 10. Embryos from dams exposed to 4.9 g/kg and examined on GD8 exhibited 9

reductions in the anterior mesenchyme, the mesenchyme subjacent to the mesencephalon and the 10

base of the prosencephalon (embryonic forebrain), and in the forebrain epithelium. The optic 11

pits were often lacking ; where present their epithelium was thin and there were fewer neural 12

crest cells in the mid

- and hindbrain regions. 13

At GD9, there was extensiv e cell death in areas populated by the neural crest, including 14

the forming cranial ganglia. Dose

- related abnormalities in the development of the cranial nerves 15

and ganglia were seen on GD7. In accordance with an arbitrary dichotomous scale devised by 16

the authors, scores for ganglia V, VIII, and IX were significantly (not otherwise specified) 17

reduced at all dose levels, and ganglia VII and X were reduced only at the highest dose. At the 18

highest dose (4.9 g/kg)

, the brain and face were poorly developed and the brachial arches were 19

reduced in size or virtually absent. Flow cytometry of the head regions of the embryos from the 20

highest dose at GD8 did not show an effect on the proportion of cells in S

- phase. 21

Cell growth and development were compared in C57BL/ 6J and CD

- 1 mouse embryos 22

cultured in methanol (Degitz, et al ., 2004b). GD8 embryos, with 5

– 7 somites, were cultured in 23

0, 1, 2, 3, 4, or 6 mg methanol/mL for 24 hours and evaluated for morphological development. 24

Cell death was increased in both strains in a developmental stage

- and region

- specific manner at 25

4 and 6 mg/mL after 8 hours of exposure. The proportions of cranial region cells in S

- phase 26

were significantly ( p < 0.05) decreased at 6 mg/mL following 8

- and 18

- hour exposures to 27

methanol. After 24 hours of exposure, C57BL/6J embryos had significantly ( p < 0.05) decreased 28

total protein at 4 and 6 mg/kg. Significant ( p < 0.05) developmental effects were seen at 3, 4, 29

and 6 mg/kg, with eye dysmorphology being the most sensitive endpoint . CD

- 1 embryos had 30

significantly decreased total protein at 3, 4, and 6 mg/kg, but developmental effects were seen 31

only at 6 mg/kg. It was concluded that the C57BL/6J embryos were more severely affected by 32

methanol in culture than the CD

- 1 embryos. 33

Andrews et al . (1993 ) carried out a comparative study of the developmental toxicity of 34

methanol in whole Sprague

- Dawley rat or CD

- 1 mouse embryos. Nine

- day rat embryos were 35

explanted and cultured in rat serum containing 0, 2, 4, 8, 12, or 16 mg/mL methanol for 24 hours 364

- 48 DRAFT

- DO NOT CITE OR QUOTE then transferred to rat serum alone for a further 24 hours. Eight

- day mouse embryos were 1

cultured in 0, 2, 4, 6, or 8 mg/mL methanol in culture medium for 24 hours. At the end of the 2

culture period, embryos were examined for growth, development and dysmorphog enesis. For 3

the rats, doses of 8 mg/mL and above resulted in a concentration

- related decrease in somite 4

number, head length, and developmental score. Some lethality was seen in embryos incubated at 5

12 mg/mL methanol. For the mouse embryos, incubation co ncentrations of 4 mg/mL methanol 6

and above resulted in a significant decrease in developmental score and crown

- rump length. The 7

high concentration (8 mg/mL) was associated with embryo lethality. These data suggest that 8

mouse embryos are more sensitive th an rat embryos to the developmental effects of methanol. 9

Using a similar experimental system to examine the developmental toxicity of formate and 10

formic acid in comparison to methanol, Andrews et al . (1995) showed that the formates are 11

embryotoxic at doses that are four times lower than equimolar doses of methanol. Andrews et al

. 12

(1998) showed that exposure to combinations of methanol and formate was less embryotoxic 13

than would be expected based on simple toxicity additivity, suggesting that the embryotoxi city 14

observed following low

- level exposure to methanol is mechanistically different from that 15

observed following exposure to formate. 16

A study by Hansen et al . (2005) determined the comparative toxicity of methanol and its 17

metabolites, formaldehyde and sodi um formate, in GD8 mouse (CD

- 1) and GD10 rat (Sprague

18

Dawley) conceptuses. Incubation of whole embryos was for 24 hours in chemical

- containing 19

media (mouse: 4

– 12 mg/mL methanol

, 1

– 6 μg/mL formaldehyde, 0.5

– 4 mg/mL sodium formate; 20

rat: 8

– 20 mg/mL, 1

– 8 μg/m L, 0.5

– 8 mg/mL). Subsequently, the visceral yolk sac (VYS) was 21

removed and frozen for future protein and DNA determination. The embryos were examined 22

morphologically to determine growth and developmental parameters such as viability, flexure 23

and rotation , crown

- rump length, and neuropore closure. In other experiments, the chemicals 24

were injected directly into the amniotic space. For each response, Table 4

- 1 3 provides a 25

comparison of the concentrations or amounts of methanol, formaldehyde, and formate th at 26

resulted in statistically significant changes in developmental abnormalities compared to controls. 27

For a first approximation, these concentrations or amounts may be taken as threshold

28

dose ranges for the specific responses under the operative experiment al conditions. The data 29

show consistently lower threshold values for the effects of formaldehyde compared to those of 30

formate and methanol. The mouse embryos were more sensitive towards methanol toxicity than 31

rat embryos, consistent with in vivo findings , whereas the difference in sensitivity disappeared 32

when formaldehyde was administered. Hansen et al . (2005) hypothesized that, while the MOA 33

for the initiation of the organogenic defects is unknown, the relatively low threshold levels of 34

formaldehyde for most measured effects suggest formaldehyde involvement in the embryotoxic 35

effects of methanol. By contrast, formate, the putative toxicant for the acute effects of methanol 364

- 49 DRAFT

- DO NOT CITE OR QUOTE poisoning (acidosis, neurological deficits), did not appear to reproduce the meth anol

- induced 1

teratogenicity in these whole embryo culture experiments. 2

Table 4

- 1 3 . Reported thresholds concentrations (and author

- estimated ranges) for the

onset of embryotoxic effects when rat and mouse conceptuses were incubated in vitro

with methanol, f ormaldehyde, and formate

Parameter Mouse Rat

Methanol Formaldehyde Formate Methanol Formaldehyde Formate

In vitro incubation (mg/mL)

Viability (%) 8.0 0.004 NS 16.0 0.006 2.0

Normal rotation (%) 4.0 0.003 0.5 8.0 0.003 4.0

CR a length No change No ch ange No change No change No change No change

Neural tube closure (%) 8.0 0.001 2.0 12.0 No change No change

Reduced e mbryo protein 8.0 0.003 4.0 8.0 0.004 2.0

Reduced VYS b protein 10.0 0.004 4.0 12.0 0.004 NR

Reduced e mbryo DNA 8.0 0.003 No change 12.0 0.003 NR

Reduced VYS DNA 4.0 0.001 0.5 12.0 0.003 NR

Microinjection (author

- estimated dose ranges in μg)

Viability (%) 46

– 89 0.003

– 0.5 1.01

– 1.5 46

– 89 1.01

– 1.5 1.51

– 4.0

Normal rotation (%) 1

– 45 0.003

– 0.5 0.03

– 0.5 46

– 89 1.01

– 1.5 0.51

– 1.0

CR a length No change No change No change No change No change No change

Neural tube closure (%) 1

– 45 0.003

– 0.5 1.01

– 1.5 No change No change 1.01

– 1.5

Reduced e mbryo protein 1

– 45 0.501

– 1.0 No change No change 1.51

– 2.0 0.51

– 1.0

Reduced VYS b protein 135

– 178 1.01

– 1.5 No ch ange No change No change 1.01

– 1.5

Reduced e mbryo DNA 46

– 89 0.501

– 1.0 No change No change No change 0.51

– 1.0

Reduced VYS b DNA 1

– 45 0.003

– 0.5 0.03

– 0.5 No change No change 0.51

– 1.0

a CR = crown

- rump length,

b VYS = visceral yolk sac.

NR = not reported

Sour ce: Hansen et al . (2005); Harris et al . (2004) (adapted).

Harris et al . (2003) provided biochemical evidence consistent with the concept that 3

formaldehyde might be the ultimate embryotoxicant of methanol by measuring the activities of 4

enzymes that are in volved in methanol metabolism in mouse (CD

- 1) and rat (Sprague

- Dawley ) 5

whole embryos at different stages of development. Specific activities of the enzymes ADH1, 6

ADH3, and CAT, were determined in rat and mouse conceptuses during the organogenesis period 7

o f 8

– 25 somites. Activities were measured in heads, hearts, trunks, and VYS from early

- and 8

late

- stage mouse and rat embryos. While CAT activities were similar between rat and mouse 9

embryos, mouse ADH1 activities in the VYS were significantly lower throug hout organogenesis 10

when compared to the rat VYS or embryos of either species. ADH1 activities of heads, hearts, 11

and trunks from mouse embryos were significantly lower than those from rats at the 7

– 12 somite 124

- 50 DRAFT

- DO NOT CITE OR QUOTE stage. However, these interspecies differences were not evident in embryos of 20

– 22 somites. 1

ADH3 activities were lower in mouse versus rat VYS

, irrespective of the stage of development. 2

However, while ADH3 activities in mouse embryos were markedly lower than those of rats in 3

the early stages of devel opment, the levels of activity were similar to at the 14

– 16 somite stage 4

and beyond. A lower capacity to transform formaldehyde to formate might explain the increased 5

susceptibility of mouse versus rat embryos to the toxic effects of methanol. The hypoth esis that 6

formaldehyde is the ultimate embryotoxicant of methanol is supported by the demonstration of 7

diminished ADH3 activity in mouse versus rat embryos and by the demonstration by Hansen 8

et al . (2005) that formaldehyde has a far greater embryotoxicity than either formate or methanol 9

itself. 10

That formate can induce similar developmental lesions in whole rat and mouse 11

conceptuses was demonstrated by Andrews et al . (1995)

, who evaluated the developmental 12

effects of sodium formate and formic acid in rodent whole embryo cultures in vitro. Day 9 rat 13

(Sprague

- Dawley) embryos were cultured for 24 or 48 hours and day 8 mouse (CD

- 1) cultures 14

were incubated for 24 hours. As tabulated by the authors, embryos of either species showed 15

trends towards increasing letha lity and incidence of abnormalities with exposure concentration. 16

Among the anomalies observed were open anterior and posterior neuropores, plus rotational 17

defects, tail anomalies, enlarged pericardium, and delayed heart development. 18

4.4. NEUROTOXICITY

A subst antial body of information exists on the toxicological consequences to humans 19

who consume or are exposed to large amounts of methanol. As discussed in Section 4.1, 20

neurological consequences of acute methanol intoxication in humans include Parkinson

- like 21

r esponses, visual impairment, confusion, headache, and numerous subjective symptoms. The 22

occurrence of these symptoms has been shown to be associated with necrosis of the putamen 23

when neuroimaging techniques have been applied (Salzman, 2006). Such profoun d changes 24

have been linked to tissue acidosis that arises when methanol is metabolized to formaldehyde 25

and formic acid through the actions of ADH1 and ADH3. However, the well

- documented impact 26

of the substantial amounts of formate that are formed when hum ans and animals are exposed to 27

large amounts of methanol may obscure the potentially harmful effects that may arise when 28

humans and animals exposed to smaller amounts. H uman acute exposure studies ( Chuwers et al

., 29

1995; Cook et al ., 1991 ) ( See S ection 4.1. 3 ) at TLV levels of 200 ppm would indicate that some 30

measures of neurological function ( e.g., sensory evoked potentials, memory testing and 31

psychomotor testing) were impa i red in the absence of measureable formate production. 324

- 51 DRAFT

- DO NOT CITE OR QUOTE 4.4.1. Oral Studies

Two rodent stu dies investigated the neurological effects of developmental methanol 1

exposure via the oral route (Aziz et al ., 2002; Infurna and Weiss, 1986). One of these studies also 2

investigated the influence of FAD diets on the effects of methanol exposures (Aziz et a l ., 2002). 3

In the first, Infurna and Weiss (1986) exposed 10 pregnant female Long

- Evans rats/dose to 2% 4

methanol (purity not specified) in drinking water on either GD15

– GD 17 or GD17

– GD 19. Daily 5

methanol intake was calculated at 2,500 mg/kg

- day by the stu dy authors. Dams were allowed to 6

litter and nurse their pups. Data were analyzed by ANOVA with the litter as the statistical unit. 7

Results of the study were equivalent for both exposure periods. Treatment had no effect on 8

gestational length or maternal bodyweight. Methanol had no effect on maternal behavior as 9

assessed by the time it took dams to retrieve pups after they were returned to the cage following 10

weighing. Litter size, pup birth weight, pup postnatal weight gain, postnatal mortality, and day 11

of eye opening did not differ from controls in the methanol treated groups. Two neurobehavioral 12

tests were conducted in offspring. Suckling ability was tested in 3

– 5 pups/treatment group on 13

PND1. An increase in the mean latency for nipple attachment wa s observed in pups from the 14

methanol treatment group

, but the percentage of pups that successfully attached to nipples did 15

not differ significantly between treatment groups. Homing behavior, the ability to detect home 16

nesting material within a cage contai ning one square of shavings from the pup’s home cage and 17

four squares of clean shavings, was evaluated in 8 pups/group on PND10. Pups from both of the 18

methanol exposure groups took about twice as long to locate the home material and took less 19

direct paths than the control pups. Group

- specific values differed significantly from controls. 20

This study suggests that developmental toxicity can occur at this drinking water dose without 21

readily apparent signs of maternal toxicity. 22

Aziz et al . (2002) investigated the role of developmental deficiency in folic acid and 23

methanol

- induced developmental neurotoxicity in PND45 rat pups. Wistar albino female rats 24

(80/group) were fed FAD 43 and FAS diets separately. Following 14

– 16 weeks on the diets, liver 25

folate levels w ere estimated and females exhibiting a significantly low folic acid level were 26

mated. Throughout their lactation period, dams of both the FAD and the FAS group were given 27

0, 1, 2, or 4% v/v methanol via drinking water, equivalent to approximately 480, 960 and 28

1

, 920 mg/kg

- day. 44 Pups were exposed to methanol via lactation from PND1

– PND21. Litter size 29

was culled to 8 with equal male/female ratios maintained as much as possible. Liver folate 30

levels were determined at PND21 and neurobehavioral parameters (mot or performance using the 31

43 Along with the FAD diet, 1% succin ylsulphathiazole was also given to inhibit folic acid biosynthesis from

intestinal bacteria.

44 Assuming that Wistar rat drinking water consumption is 60 mL/kg

- day (Rogers et al., 2002), 1% methanol in

drinking water would be equivalent to 1% x 0.8 g/mL x 6 0 mL/kg

- day = 0.48 g/kg

- day = 480 mg/kg

- day.4

- 52 DRAFT

- DO NOT CITE OR QUOTE spontaneous locomotor activity test and cognitive performance using the conditioned avoidance 1

response [CAR] test)

, and neurochemical parameters (dopaminergic and cholinergic receptor 2

binding and dopamine levels) were measured at P ND45. The expression of growth

- associated 3

protein (GAP 43), a neuro

- specific protein in the hippocampus that is primarily localized in 4

growth cone membranes and is expressed during developmental regenerative neurite outgrowth, 5

was examined using immunohis tochemistry and western blot analysis. 6

A loss in body weight gain was observed at PND7, PND 14, and PND 21 in animals 7

exposed to 2% (11, 15 and 19% weight gain reduction ) and 4% (17, 24 and 29% weight gain 8

reduction ) methanol in the FAD group and only at 4% (9, 14 and 17% weight gain reduction ) 9

methanol in the FAS group. No significant differences in food and water intake were observed 10

among the different treatment groups. Liver folate levels in the FAD group were decreased by 11

63% in rats prior to mating an d 67% in pups on PND21. 12

Based on reports of Parkinson

- like symptoms in survivors of severe methanol poisoning 13

(see Section 4.1), Aziz et al . (2002) hypothesized that methanol may cause a depletion in 14

dopamine levels and degeneration of the dopaminergic n igrostriatal pathway. 45 Consistent with 15

this hypothesis, they found dopamine levels were significantly decreased (32% and 51%) in the 16

striatum of rats in the FAD group treated with 2% and 4% methanol, respectively. In the FAS 17

group, a significant decrease (32%) was observed in the 4% methanol

- exposed group. 18

Methanol treatment at 2% and 4% was associated with significant increases in activity, in 19

the form of distance traveled in a spontaneous locomotor activity test, in the FAS group (13 % 20

and 39%, respecti vely) and mo re notably, in the FAD group (33 % and 66%, respectively) when 21

compared to their respective controls. Aziz et al . (2002) suggest that these alterations in 22

locomotor activity may be caused by a significant alteration in dopamine receptors and 23

di sruption in neurotransmitter availability. Dopamine receptor (D 2) binding in the hippocampus 24

of the FAD group was significantly increased (34%) at 1% methanol, but was significantly 25

decreased at 2% and 4% methanol exposure by 20% and 42%, respectively. I n the FAS group, 26

D 2 binding was significantly increased by 22% and 54% in the 2% and 4% methanol

- exposed 27

groups. 28

At PND45, the CAR in FAD rats exposed to 2% and 4% methanol was significantly 29

decreased by 48% and 52%, respectively, relative to nonexposed controls. In the FAS group, the 30

CAR was only significantly decreased in the 4% methanol

- exposed animals and only by 22% as 31

compared to their respective controls. Aziz et al . (2002) suggest that the impairment in CAR of 32

the methanol

- exposed FAD pups may b e due to alterations in the number of cholinergic 33

(muscarinic) receptor proteins in the hippocampal region of the brain. Muscarinic receptor 34

45 The nigrostriatal pathway is one of four major dopamine pathways in the brain that are particularly involved in the

production of movement. Loss of dopamine neurons in the substantia nigra is one of the pathol ogical features of

Parkinson's disease (Kim et al., 2003),4

- 53 DRAFT

- DO NOT CITE OR QUOTE binding was significantly increased in the 2% (20%) and 4% (42%) methanol

- exposed group in 1

FAD animals, while FAS group animals had a significant increase in cholinergic binding only in 2

the 4% methanol exposed group (21%). High concentrations of methanol may saturate the 3

body’s ability to remove toxic metabolites, including formaldehyde and formate, and this may be 4

e xacerbated in FAD pups having a low store of folate. 5

Immunohistochemistry showed an increase in the expression of GAP

- 43 protein in the 6

dentate granular and pyramidal cells of the hippocampus in 2% and 4% methanol

- exposed 7

animals in the FAD group. The FA S group showed increased expression only in the 4% 8

methanol

- exposed group. The Western blot analysis also confirmed a higher expression of 9

GAP

- 43 in the 2% and 4% methanol

- exposed FAD group rats. Aziz et al . (2002) suggested that 10

up

- regulation of GAP

- 43 in the hippocampal region may be associated with axonal growth or 11

protection of the nervous system from methanol toxicity. 12

The Aziz et al . (2002) study provide s evidence that hepatic tetrahydrofolate is an 13

important contributing factor in methanol

- induced developmental neurotoxicity in rodents. The 14

immature blood

- brain barrier and inefficient drug

- metabolizing enzyme system make the 15

developing brain a particularly sensitive target organ to the effects of methanol exposure. 16

4.4.2. Inhalation Studies

A review by Ca rson et al . (1981) has summarized a number of older reports of studies on 17

the toxicological consequences of methanol exposure. In one example relevant to neurotoxicity, 18

the review cites a research report of Chen

- Tsi (1959) who exposed 10 albino rats/group (sex and 19

strain unstated) to 1.77 and 50 mg/m 3 (1.44 and 40.7 ppm) methanol vapor, 12 hours/day, for 20

3 months. Deformation of dendrites, especially the dendrites of pyramidal cells, in the cerebral 21

cortex was included in the description of histopathologi cal changes observed in adult animals 22

following exposure to 50 mg/m 3 (40.7 ppm) methanol vapor. One out of ten animals exposed to 23

the lower methanol concentration also displayed this feature. 24

Information on the neurotoxicity of methanol inhalation exposur e in adult monkeys 25

( M

. fascicularis ) has come from NEDO (1987) which describes the results of a number o f 26

experiments . The study included an acute study, a chronic study monkeys, and a repeated 27

exposure experiment (of variable duration depending upon expos ure level)

, followed by recovery 28

period (1

– 6 m on ths)

, and an experiment looking at chronic formaldehyde exposure (1 or 5 ppm), 29

a combustion product of methanol

. This last experiment was apparently only a pilot and included 30

only one monkey per exposure con dition. 31

In the chronic experiment 8 monkey s were included per exposure level (control, 10, 100, 32

1,000 ppm or 13, 131, and 1,310 mg/m 3 , respectively

, for 21 hours/day ); however, animals were 33

serially sacrificed at 3 time points : 7 months , 19 months , or >2 6 months . This design reduced 344

- 54 DRAFT

- DO NOT CITE OR QUOTE the number of monkeys at each exposure level to 2 subjects at 7 m o n ths and 3 subjects at the 1

subsequent time points (see S ection 4.2.2). One of the 3 animals receiving 100 ppm methanol 2

and scheduled for sacrifice at 29 months w as terminated at 26 months. 3

Histopathologically, no overt degeneration of the retina, optical nerve, cerebral cortex, or 4

other potential target organs (liver and kidney) was reported in the chronic experiment. 5

Regarding the peripheral nervous system, 1/ 3 monkeys exposed to 100 ppm (131 mg/m 3 ) and 2/3 6

exposed to 1,000 ppm (1,310 mg/m 3 ) for 29 months showed slight but clear changes in the 7

peroneal nerves. There was limited evidence of CNS degeneration inside the nucleus of the 8

thalamus of the brain at exp osure to 100 ppm (131 mg/m 3 ) or 1,000 ppm (1,310 mg/m 3 ) for 9

7 months or longer . Abnormal appearance of stellate cells (presumed astroglia) within the 10

cerebral white matter was also observed in a high proportion (7/8 in both mid

- and high

- exposure 11

groups) o f monkeys exposed to 100 ppm and 1,000 ppm for 7 months or more. All monkeys that 12

had degeneration of the inside nucleus of the thalamus also had degeneration of the cereb ral 13

white matter. According to NEDO (1987) , the stellate cell response was transient and “not 14

characteristic of degeneration.” The authors also note d that the stellate cell response was “nearly 15

absent in normal monkeys in the control group” and “in the groups exposed to a large quantity of 16

methanol or for a long time their presence tended to become permanent, so a relation to the long 17

term over which the methanol was inhaled is suspected.” However, all control group responses 18

are reported in a single table in the section of the NEDO (1987) report that describes the acute 19

monkey study, wit h no indication as to when the control group was sacrificed. 20

In the recovery experiment

, monkeys were exposed to 1,000, 2,000, 3,000, or 5,000 ppm 21

methanol

, followed by recovery periods of various duration

. Monkeys exposed to 3,000 ppm for 22

20 days followe d by a 6

- month recovery period experienced relatively severe fibrosis of 23

responsive stellate cells and lucidation of the medullary sheath. However, resolution of some of 24

the glial responses was noted in the longer duration at lower exposure levels

, with n o effects 25

observed on the cerebral white matter in monkeys exposed for 7 months to 1,000 ppm methanol 26

followed by a 6

- month recovery period. In general, the results from the recovery experiment 27

corroborated results observed in the chronic experiment. NEDO (1987) interpreted the lack of 28

glial effects after a 6

- month recovery as an indication of a transient effect. The authors failed to 29

recognize that glial responses to neural damage do not necessarily persist following resolution of 30

neurodegeneration (Aschne r and Kimmelberg, 1996). 31

The limited information available from the NEDO (1987) summary report suggests that 32

10 0 ppm (131 mg/m 3 ) may be an effect level following continuous, chronic exposure to 33

methanol. However, t he current report does not indicate a robu st dose response for the 34

neurodegenerative changes in the thalamus and glial changes in the white matter. The number of 35

animals at each exposure level for each serial sacrifice also limits statistical power 36

( 2

– 3 monkeys/time point/ exposure level). Confid ence in this study is also weakened by the lack 37

of documentation for a concurrent control group. 384

- 55 DRAFT

- DO NOT CITE OR QUOTE Weiss et al . (1996) exposed 4 cohorts of pregnant Long

- Evans rats (10

– 12 dams/ 1

treatment group/cohort) to 0 or 4,500 ppm (0 and 5,897 mg/m 3 ) methanol vapor (hi gh

2

performance liquid chromatography [ HPLC ] grade), 6 hours/day

, from GD6 to PND21. Pups 3

were exposed together with the dams during the postnatal period. Average blood methanol levels 4

in pups on PND7 and PND 14 were about twice the level observed in dams. However, methanol 5

exposure had no effect on maternal gestational weight gain, litter size, or postnatal pup weight 6

gain up to PND18 46 . Neurobehavioral tests were conducted in neonatal and adult offspring; the 7

data generated from those tests were evaluate d by repeated measures ANOVA. Three 8

neurobehavioral tests conducted in 13

– 26 neonates/group included a suckling test, conditioned 9

olfactory aversion test, and motor activity test. In contrast to earlier test results reported by 10

Infurna and Weiss (1986), methanol exposure had no effect on suckling and olfactory aversion 11

tests conducted on PND5 and PND 10, respectively. Results of motor activity tests in the 12

methanol group were inconsistent, with decreased activity on PND18 and increased activity on 13

PND25. Tests that measured motor function, operant behavior, and cognitive function were 14

conducted in 8

– 13 adult offspring/group. Some small performance differences were observed 15

between control and treated adult rats in the fixed wheel running test only when fi ndings were 16

evaluated separately by sex and cohort. The test requires the adult rats to run in a wheel and 17

rotate it a certain amount of times in order to receive a food reward. A stochastic spatial 18

discrimination test examined the rats’ ability to learn patterns of sequential responses. Methanol 19

exposure had no effect on their ability to learn the first pattern of sequential responses, but 20

methanol

- treated rats did not perform as well on the reversal test. The result indicated possible 21

subtle cognitive deficits as a result of methanol exposure. A morphological examination of 22

offspring brains conducted on PND1 and PND 21 indicated that methanol exposure had no effect 23

on neuronal migration, numbers of apoptotic cells in the cortex or germinal zones, or 24

my elination. However

, neural cell adhesion molecule (NCAM) 140 and NCAM 180 gene 25

expression in treated rats was reduced on PND4 but not 15 months after the last exposure. 26

NCAMs are glycoproteins required for neuron migration, axonal outgrowth, and establis hing 27

mature neuronal function patterns. 28

Stanton et al . (1995) exposed 6

– 7 pregnant female Long

- Evans rats/group to 0 or 29

15,000 ppm (0 and 19,656 mg/m 3 ) methanol vapors ( **≥** 99.9% purity) for 7 hours/day on GD7

– 30

GD 19. Mean serum methanol levels at the end of the 1st, 4th, 8th, and 12th days of exposure 31

were, 3,836, 3,764, 3,563, and 3,169 μg/mL

, respectively

. As calculated by authors, dams 32

received an estimated methanol dose of 6,100 mg/kg

- day. A lower body weight on the first 33

46 The fact that this level of exposure caused effects in the Sprague

- Dawley rats of the NEDO (1987) study but did

not cause a readily apparent maternal effect in Long

- Evans rats of this study could be due to diffences in strain

susceptibility.4

- 56 DRAFT

- DO NOT CITE OR QUOTE 2 days of exposure was the only maternal effect; there was no increase in postimplantation loss. 1

Dams were allowed to deliver and nurse litters. Parameters evaluated in pups included mortality, 2

growth, pubertal development, and neurobehavioral function. Examinations of pups revealed 3

that two pups from the same methanol

- exposed litter were missing one eye; aberrant visual ly 4

evoked potentials were observed in those pups. A modest but significant reduction in body 5

weight gain on PND1, PND 21, and PND 35 was noted in pups from the methanol group. For 6

example, by PND35, male pups of dams exposed to methanol had a mean body weight of 7

129 grams versus 139 grams in controls ( p < 0.01). However, postnatal mortality was unaffected 8

by exposure to methanol. The study authors did not consider a 1 .7

- day delay in vaginal opening 9

in the methanol group to be an adverse effect. Preputial separation was not affected by prenatal 10

methanol exposure. Neurobehavioral status was evaluated using 8 different tests on specific 11

days up to PND160. Tests include d motor activity on PND13

– PND 21, PND 30, and PND 60, 12

olfactory learning and retention on PND18 and PND 25, behavioral thermoregulation on 13

PND20

- 21, T

- maze delayed alternation learning on PND23

– PND 24, acoustic startle reflex on 14

PND24, reflex modification audio metry on PND61

– PND 63, passive avoidance on PND73, and 15

visual evoked potentials on PND160. A single pup/sex/litter was examined in most tests

, and 16

some animals were subjected to multiple tests. The statistical significance of neurobehavioral 17

testing was a ssessed by one

- way ANOVA

, using the litter as the statistical unit. Results of the 18

neurobehavioral testing indicated that methanol exposure had no effect on the sensory, motor, or 19

cognitive function of offspring under the conditions of the experiment. Ho wever, given the 20

comparative ly small number of animals tested for each response, it is uncertain whether the 21

statistical design had sufficient power to detect small compound

- related changes. 22

NEDO (1987 , unpublished report ) sponsored a teratology study that included an 23

evaluation of postnatal effects in addition to standard prenatal endpoint s in Sprague

- Dawley rats

. 24

Thirty

- six pregnant females/group were exposed to 0, 200, 1,000, or 5,000 ppm (0, 262, 1,310, 25

and 6,552 mg/m 3 ) methanol vapors (reagent grade) on GD7

– GD 17 for 22.7 hours/day. Statistical 26

significance of results was evaluated by t

- test, Mann

- Whitney U test, Fisher’s exact test, and/or 27

Armitage’s χ 2 test. 28

Postnatal effects of methanol inhalation were evaluated in the remaining 12 dams/group 29

that we re permitted to deliver and nurse their litters. Effects were only observed in the 30

5,000 ppm. There were no adverse effects on offspring body weight from methanol exposure. 31

However, the weights of some organs (brain, thyroid, thymus, and testes) were red uced in 32

8

- week

- old offspring following prenatal

- only exposure to 5

, 000 ppm methanol. An unspecified 33

number of offspring were subjected to neurobehavioral testing or necropsy, but results were 34

incompletely reported. 354

- 57 DRAFT

- DO NOT CITE OR QUOTE NEDO (1987 , unpublished report ) also cont ains an account of a two

- generation 1

reproductive study that evaluated the effects of pre

- and postnatal methanol (reagent grade) 2

exposure (20 hours/day) on reproductive and other organ systems of Sprague

- Dawley rats and in 3

particular the brain. The F 0 gen eration (30 males and 30 females per exposure group) 47 was 4

exposed to 0, 10, 100, and 1

, 000 ppm (0, 13.1, 131, and 1,310 mg/m 3 ) from 8 weeks old to the 5

end of mating (males) or to the end of lactation period (females). The F 1 generation was exposed 6

to the same concentrations from birth to the end of mating (males) or to weaning of F 2 pups 7

21 days after delivery (females). Males and females of the F 2 generation were exposed from 8

birth to 21 days old ( 1 animal/sex/litter was exposed to 8 weeks of age). NEDO (1987) noted 9

reduced brain, pituitary, and thymus weights, in the offspring of F 0 and F 1 rats exposed to 10

1,000 ppm methanol. As discussed in the report, NEDO (1987) sought to confirm the possible 11

compound

- related effect of methanol on the brain by carryi ng out an additional study in which 12

Sprague

- Dawley rats were exposed to 0, 500, 1,000, and 2,000 ppm (0, 655, 1,310, and 13

2,620 mg/m 3 ) methanol from the first day of gestation through the F 1 generation. Brain weights 14

were measured in 10

– 14 offspring/sex/gr oup at 3, 6, and 8 weeks of age. As illustrated in 15

Table 4

- 1 4 , brain weights were significantly reduced in 3

- week

- old males and females exposed 16

to **≥** 1,000 ppm. At 6 and 8 weeks of age, brain weights were significantly reduced in males 17

exposed to **≥** 1,000 ppm and females exposed to 2,000 ppm. Due to the toxicological 18

significance of this postnatal effect and the fact that it has not been measured or r eported 19

elsewhere in the peer

- reviewed methanol literature, the brain weight changes observed by NEDO 20

(1987) following gestational and postnatal exposures and following gestation

- only exposure (in 21

the teratology study discussed above) are evaluated quantit atively and discussed in more detail in 22

Section 5 of this review. 23

47 A second control group of 30 animals / sex was maintained in a separate room to “confirm that environmental conditions inside

the chambers were not unaccep table to the animals.”4

- 58 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 1 4 . Brain weights of rats exposed to methanol vapors during gestation and

lactation

Offspring

age Sex Brain weight (g) (% control) at each exposure level

0 ppm 200 ppm 500 ppm 1,0 00 ppm 2,000 ppm 5,000 ppm

3 wk Male

Female 1.45 ± 0.06

1.41 ± 0.06 --

-- 1.46 ± 0.08

(101%)

1.41 ± 0.07

(100%) 1.39 ± 0.05 c

(96%)

1.33 ± 0.07 d

(94%) 1.27 ± 0.06 e

(88%)

1.26 ± 0.09 e

(89%) --

--

6 wk Male

Female 1.78 ± 0.07

1.68 ± 0.08 --

-- 1.74 ± 0.09

(98%)

1.71 ± 0.08

(102%) 1.69 ± 0.06 d

(95%)

1.62 ± 0.07

(96%) 1.52 ± 0.07 e

(85%)

1.55 ± 0.05 e

(92%) --

--

8 wk a Male

Female 1.99 ± 0.06

1.85 ± 0.05 --

-- 1.98 ± 0.09

(99%)

1.83 ± 0.07

(99%) 1.88 ± 0.08 d

(94%)

1.80 ± 0.08

(97%) 1.74 ± 0.05 e

(87%)

1.67 ± 0.06 e

(90%) --

--

8 wk b Male

Female 2.00 ± 0.05

1.86 ± 0.08 2.01 ± 0.08

(100%)

1.91 ± 0.06

(103%) --

-- 1.99 ± 0.07

(100%)

1.90 ± 0.08

(102%) --

-- 1.81 ± 0.16 d

(91%)

1.76 ± 1.09

(95%)

a Exposed throughout gestation and F 1 generation.

b Expo sed on gestational days 7

- 17 only.

c p < 0.05, d p < 0.01, e p < 0.001, as calculated by the authors.

Values are means ± S.D.

Source: NEDO (1987).

Burbacher et al

. ( 1999a,1999b) c arried out toxicokinetic

, reproductive

, developmental 1

and postnatal neurologi cal and neurobehavioral studies of methanol in M. fascicularis monkeys 2

that were published by HEI in a two

- part monograph

. Some of the data were subsequently 3

published in the open scientific literature (Burbacher et al ., 2004a, 2004b). The experimental 4

p rotocol featured exposure to 2 cohorts of 12 monkeys/group to low

- exposure levels (relative to 5

the previously discussed rodent studies) of 0, 200, 600, or 1,800 ppm (0, 262, 786, and 2,359 6

mg/m 3 ) methanol vapors (99.9% purity), 2.5 hours/day, 7 days/week, during a premating period 7

and mating period (

**−** 180 days combined) and throughout the entire gestation period (

**−** 168 days). 8

The monkeys were 5.5

– 13 years old and were a mixture of feral

- born and colony

- bred animals. 9

The outcome study included an evaluation of maternal reproductive performance (discussed in 10

Section 4.3.2) and tests to assess infant postnatal growth and newborn health, neurological 11

outcomes included reflexes, behavior, and development of visual, sensorimotor, cognitive, and 12

social behavioral fun ction. Blood methanol levels, clearance, and the appearance of formate 13

were also examined and are discussed in Section 3.2. The effects observed were in the absence of 14

appreciable increases in maternal blood formate levels. 154

- 59 DRAFT

- DO NOT CITE OR QUOTE Neurobehavioral function was as sessed in 8

– 9 infants/group during the first 9 months of 1

life (Burbacher et al ., 2004a, 1999b). Although results in 7/9 tests were negative, 2 effects were 2

possibly related to methanol exposure. The Visually Directed Reaching (VDR) test is a measure 3

of s ensorimotor development and assesse d the infants’ ability to grasp for a brightly colored 4

object containing an applesauce

- covered nipple. Beginning at 2 weeks after birth, infants were 5

tested 5 times/day, 4 days/week. Performance on th is test

, measured a s age from birth at 6

achievement of test criterion (successful object retrieval on 8/10 consecutive trials over 2 testing 7

sessions)

, was reduced in all treated male infants. The times (days after birth) to achieve the 8

criteria for the VDR test were 23.7 ± 4.8 (n = 3), 32.4 ± 4.1 (n = 5), 42.7 ± 8.0 (n

= 3), and 40.5 ± 9

12.5 (n = 2) days for males and 34.2 ± 1.8 (n = 5), 33.0 ± 2.9 (n = 4), 27.6 ± 2.7 (n = 5), and 40.0 10

± 4.0 (n = 7) days for females in the control to 1,800 ppm groups, respectively. Statistic al 11

significance was obtained in the 1,800 ppm group when males and females were evaluated 12

together ( p = 0.04) and in the 600 ppm ( p = 0.007) for males only. However, there wa s no 13

significant difference between responses and/or variances among the dose lev els for males and 14

females combined ( p = 0.244), for males only ( p = 0.321) and for males only, excluding the high

15

dose group ( p = 0.182). Yet there was a significant dose

- response trend for females only 16

( p = 0.0265). The extent to which VDR delays were d ue to a direct effect of methanol on 17

neurological development or a secondary effect due to the methanol

- induced decrease in length 18

of pregnancy and subsequent prematurity is not clear. Studies of reaching behavior have shown 19

that early motor development i n pre

- term human infants without major developmental disorders 20

differs from that of full

- term infants (Fallang et al ., 2003). Clinical studies have indicated that 21

the quality of reaching and grasping behavior in pre

- term infants is generally less than tha t in 22

full

- term infants (Fallang et al ., 2003; Plantinga et al ., 1997). For this reason, measures of 23

human infant development generally involve adjustment of a child’s “test age” if he or she had a 24

gestational age of fewer than 38 weeks, often by subtracti ng weeks premature from the age 25

measured from birth (Wilson and Cradock, 2004). When this type of adjustment is made to the 26

Burbacher et al . (2004a, 1999b) VDR data, the dose

- response trend for males only becomes 27

worse ( p = 0.448) and the dose

- response tr end for the females only is improved ( p = 0.009), 28

though the variance in the data could not be modeled adequately. Thus, only the unadjusted 29

VDR response for females only exhibited a dose response that could be adequately modeled for 30

the purposes of this assessment (see Appendix C). 31

At 190

– 210 days of age, the Fagan Test of infant intelligence was conducted. The 32

paradigm makes use of the infant’s proclivity to direct more visual attention to novel stimuli 33

rather than familiar stimuli. The test measures the time infants spend looking at familiar versus 34

novel items. Deficits in the Fagan task can qualitatively predict deficits in intelligence quotient 35

(IQ) measurements assessed in children at later ages (Fagan and Singer, 1983). Control monkey 364

- 60 DRAFT

- DO NOT CITE OR QUOTE infants in the Burbacher et al . (2004a, 1999b) study spent more than 62% ± 4% (mean for both 1

cohorts) of their time looking at novel versus familiar monkey faces, while none of the treated 2

monkeys displayed a preference for the novel faces (59% ± 2%, 54% ± 2% and 59 % ± 2% in 3

200, 600 and 1,800 ppm groups, respectively). Unlike the VDR results discussed previously, 4

results of this test did not appear to be gender specific and were neither statistically significant 5

(ANOVA p = 0.38) nor related to exposure concentrati on. The findings indicated a cohort effect 6

which appeared to reduce the statistical power of this analysis. The authors’ exploratory analysis 7

of differences in outcomes between the 2 cohorts indicated an effect of exposure in the second 8

cohort and not the first cohort due to higher mean performance in controls of cohort 2 (70% + 9

5% versus 55% ± 4% for cohort 1 ) . In addition, this latter finding could reflect the inherent 10

constraints of this endpoint . If the control group performs at the 60% level and the m ost 11

impaired subjects perform at approximately the 50% chance level (worse than chance 12

performance would not be expected), the range over which a concentration

- response relationship 13

can be expressed is limited. Because of the longer latency between assess ment and birth, these 14

results would not be confounded with the postulated methanol

- induced decrease in gestation 15

length of the exposed groups of this study. Negative results were obtained for the remaining 16

seven tests that evaluated early reflexes, gross motor development, spatial and concept learning 17

and memory, and social behavior. Infant growth and tooth eruption were unaffected by methanol 18

exposure. 19

4.4.3. S tudies Employing In Vitro, S .C. and I .P. Exposures

There is some experimental evidence that the presenc e of methanol can affect the activity 20

of acetylcholinesterase (Tsakiris, et al ., 2006). Although these experiments were carried out on 21

erythrocyte membranes in vitro, the apparent compound

- related changes may have implications 22

for possible impacts of meth anol and/or its metabolites on acetylcholinesterase at other centers, 23

such as the brain. Tsakiris et al . (2006) prepared erythrocyte ghosts from blood samples of 24

healthy human volunteers by repeated freez ing

- thawing. The ghosts were incubated for 1 hour at 25

37 o C in 0, 0.07, 0.14, 0.6 or 0.8 mmol/L methanol and the specific activities of 26

acetylcholinesterase monitored. Respective values (in change of optical density units/minute

- mg 27

protein) were 3.11 ± 0.15, 2.90 ± 0.10, 2.41 ± 0.10 ( p < 0.05), 2.05 ± 0.11 ( p < 0.01), and 1.81 ± 28

0.09 ( p < 0.001). More recently, Simintzi et al . (2007) carried out an in vitro experiment to 29

investigate the effects of aspartame metabolites, including methanol, on 1) a pure preparation of 30

acetylcholinesterase

, and 2) the same ac tivity in homogenates of frontal cortex prepared from the 31

brains of (both sexes of) Wistar rats. The activities were measured after incubations with 0, 0.14, 32

0.60, or 0.8 mmoles/L (0, 4.5, 19.2, and 25.6 mg/L) methanol, and with methanol mixed with the 33

ot her components of aspartame metabolism, phenylalanine and aspartic acid. After incubation at 344

- 61 DRAFT

- DO NOT CITE OR QUOTE 37 o C for 1 hour, the activity of acetylcholinesterase was measured spectrophotometrically. As 1

shown in Table 4

- 1 5 , the activities of the acetylcholinesterase pre parations were reduced dose 2

dependently after incubation in methanol. Similar results were also obtained with the other 3

aspartame metabolites, aspartic acid

, and phenylalanine, both individually or as a mixture with 4

methanol. While the implications of thi s result to the acute neurotoxicity of methanol are 5

uncertain, the authors speculated that methanol may bring about these changes through either 6

interactions with the lipids of rat frontal cortex or perturbation of proteinaceous components. 7

Table 4

- 1 5 . Eff ect of methanol on Wistar rat acetylcholinesterase activities

Methanol concentration

(mmol/L) Acetylcholinesterase activity (ΔOD/min

- mg)

Frontal cortex Pure enzyme

Control 0.269 ± 0.010 1.23 ± 0.04

0.14 0.234 ± 0.007 a 1.18 ± 0.06

0.60 0.223 ± 0.009 b 1 .05 ± 0.04 b

0.80 0.204 ± 0.008 b 0.98 ± 0.05 b

Values are means ± S.D. for four experiments. The average value of each experiment was derived from three

determinations of each enzyme activity.

a p < 0.01.

b p < 0.001.

Source: Simintzi et al . (2007).

In ano ther experiment of relevance to neurotoxicity, the impact of repeat methanol 8

exposure on amino acid and neurotransmitter expression in the retina, optic nerve, and brain was 9

examined by Gonzalez

- Quevedo et al . ( 2002). The goal of the study was to determin e whether a 10

sustained increase in formate levels, at concentrations below those known to produce toxic 11

effects from acute exposures, can induce biochemical changes in the retina, optical nerve

, or 12

certain regions of the brain. Male Sprague

- Dawley rats (5

– 7/group; 100

– 150 g) were divided 13

into 6 groups and treated for 4 weeks according to the following plan

. Four groups of animals 14

received tap water ad libitum as drinking water for 1 week. During the second week, groups 1 15

and 2 (control and methanol respec tively) received saline subcutaneously, (s.c.) and groups 3 and 16

4 (methotrexate 48 [ MTX ] and methotrexate

- methanol [ MTX

- methanol ] , respectively) received 17

MTX s.c. (0.2 mg/kg

- day). During the 3rd week, MTX was reduced to 0.1 mg/kg and 20% 18

methanol (2g/kg

- day ) was given i.p. to groups 2 (methanol) and 4 (MTX

- methanol). Groups 1 19

(control) and 3 (MTX) received equivalent volumes of saline administered i.p. The treatment 20

was continued until the end of the fourth week. Groups 5, (taurine 49 [ Tau ] ) and 6, (Tau

- MTX

- 21

48 Methotrexate depletes folate stores (resulting in an increase in the formate levels of methanol exposed animals) by

interfering with tetrahydrofolate(THF) regeneration (Dorman et al., 1994).

49 Taurine plays and important role in the CNS, especially in the retina and optical nerve, an d was administered here

to explore its possible protective effect (Gonzalez

- Quevedo et al., 2002)4

- 62 DRAFT

- DO NOT CITE OR QUOTE methanol) received 2% Tau in their drinking water ad libitum during the first 4 weeks

, after 1

which they were treated in the same manner as groups 1 and 4, respectively. Weights were 2

documented weekly on all animals. Blood for formate and amino acid deter minations and 3

biopsy samples of retina, optic nerve, hippocampus, and posterior cortex of each animal were 4

collected at the end of the experiment. Formate levels were not affected by Tau alone or MTX 5

alone. While methanol alone increased blood formate le vels, MTX

- methanol

, and Tau

- MTX

6

methanol produced a threefold increase in blood formate levels as compared to controls and a 7

twofold increase as compared to methanol alone. The amino acids aspartate, glutamate, 8

asparagine, serine, histidine, glutamine, th reonine, glycine, arginine, alanine, hypotaurine, 9

gamma

- aminobutyric acid (which is also a neurotransmitter), and tyrosine were measured in 10

blood, brain, and retinal regions. 11

None of the amino acids measured were altered in the blood of methanol

- , MTX

- , or 12

MTX

- methanol

- treated animals. Tau was increased in the blood of animals treated with taurine 13

in the drinking water (Tau and Tau

- MTX

- methanol) and histidine was increased in the Tau group 14

but not in the Tau

- MTX

- methanol group. 15

The levels of aspartate, Tau , glutamine, and glutamate were found to be altered by 16

treatment in various areas of the brain. Aspartate was increased in the optic nerve of animals 17

treated with MTX

- methanol and Tau

- MTX

- methanol, indicating a possible relation to formate 18

accumulation. The authors note that L

- a spartate is a major excitatory am i no acid in the brain and 19

that increased levels of excitatory amino acids can trigger neuronal cell damage and death (Albin 20

and Greenamyre, 1992). Aspartate, g lutamine and Tau were found to be incr eased with respect 21

to controls in the hippocampus of the three groups receiving methanol. Glutamate was 22

significantly increased in the hippocampus in the methanol and the Tau

- MTX

- methanol groups 23

with respect to controls, but no statistically significant d ifference was found in the MTX

24

methanol group when compared to controls, methanol alone, or the Tau

- MTX

- methanol groups. 25

The authors suggest that increased levels of a spartate and g lutamine in the hippocampus could 26

provide an explanation for some of the C NS symptoms observed in methanol poisonings on the 27

basis of their observed impact on cerebral arteries (Huang et al ., 1994). The fact that these 28

increases resulted primarily from methanol without MTX is significant in that it indicates 29

methanol can cause excitotoxic effects without formate mediation. The treatments used did not 30

produce any significant changes in amino acid levels in the posterior cortex. 31

The neurotransmitters serotonin (5

- HT) and dopamine (DA) and their respective 32

metabolites

, 5

- hydroxyin dolacetic acid (5

- HIAA) and dihydroxyphenylacetic acid (DOPAC)

, 33

were measured in the brain regions described. The levels of these monoamines were not affected 34

by formate accumulation

, as the only increases were observed for 5

- HT and 5

- HIAA following 35

metha nol

- only exposure. 5

- HT was increased in the retina and hippocampus of methanol

- only 364

- 63 DRAFT

- DO NOT CITE OR QUOTE treated animals, and the metabolite 5

- HIAA was increased in the hippocampus of methanol

- only 1

treated animals; DA and DOPAC levels were not altered by the treatments in an y of the areas 2

measured. The posterior cortex did not show any changes in monoamine levels for any treatment 3

group. 4

Rajamani et al . (2006) examined several oxidative stress parameters in male Wistar rats 5

following methotrexate

- induced folate deficiency. Animals ( 6 /group) were divided into 3 e 6

groups : saline controls, methotrexate (MTX) controls

, and MTX

- methanol treated animals. 7

Animals in the MTX

- only group were treated with 0.2 mg/kg

- day MTX s.c. injection for 7 days 8

and following confirmation of folate deficiency, received either saline for MTX control and 9

saline controls or a single dose of 3 g/kg methanol (20% w/v in saline) i.p. on day 8. On the 9th 10

day, all animals were sacrificed and blood and tissue samples were collected. The optic nerve, 11

retin a, and brain were collected and the brain was dissected into the following regions: cerebral 12

cortex, cerebellum, mid

- brain, pons medulla, hippocampus and hypothalamus. Each region was 13

homogenized, then centrifuged at 300 × g for 15 minutes and the superna tant was examined for 14

indicators of oxidative stress including the free radical scavengers superoxide dismutase (SOD), 15

CAT, glutathione peroxidase, and reduced GSH levels. The levels of protein thiols, protein 16

carbonyls, and amount of lipid peroxidation w ere also measured. Compared to controls the 17

levels of SOD, CAT, GSH peroxidase, oxidized GSH, protein carbonyls and lipid peroxidation 18

were elevated in all of the brain regions where it was measured, with greater increases observed 19

in the MTX

- methanol tre ated animals than in the MTX alone group. The level of GSH and 20

protein thiols was decreased in all regions of the brain

, with a greater decrease observed in the 21

MTX

- methanol

- treated animals than MTX

- treated animals. In addition, expression of HSP70, a 22

bi omarker of cellular stress, was increased in the hippocampus. Overall, these results suggest that 23

methanol treatment of folate

- deficient rats results in increased oxidative stress in the brain, retina 24

and optic nerve. 25

To determine the effects of methanol i ntoxication on the HPA axis, a combination of 26

oxidative stress, immune and neurobehavioral parameters were observed (Parthasarathy et al

., 27

2006a). Adult male Wistar albino rats (6 animals/group) were treated with either 0 or 2.37g/kg

28

day methanol i.p. for 1, 15 or 30 days. Oxidative stress parameters examined included SOD, 29

CAT, GSH peroxidase, GSH, and ascorbic acid (Vitamin C). Plasma corticosterone levels were 30

measured, and lipid peroxidation was measured in the hypothalamus and the adrenal gland. An 31

assay for DNA fragmentation was conducted in tissue from the hypothalamus, the adrenal gland 32

and the spleen. Immune function tests conducted included the footpad thickness test for delayed 33

type hypersensitivity (DTH), a leukocyte migration inhibition assa y, the hemagglutination assay 34

(measuring antibody titer), the neutrophil adherence test, phagocytosis index, and a nitroblue 35

tetrazolium (NBT) reduction and adherence assay used to measure the killing ability of 364

- 64 DRAFT

- DO NOT CITE OR QUOTE polymorphonuclear leukocytes (PMNs). The op en field behavior test was used to measure 1

general locomotor and explorative activity during methanol treatment in the 30

- day treatment 2

group, with tests conducted on days 1, 4, 8, 12, 16, 20, 24, and 28. All enzymatic (SOD, CAT, 3

and GSH peroxidase) and n on enzymatic antioxidants (GSH and Vit amin C) were significantly 4

increased in the 1

- day methanol

- exposed group as compared to controls. However, with 5

increasing time of treatment, all of the measured parameters were significantly decreased when 6

compared wi th control animals. Lipid peroxidation was significantly increased in both the 7

hypothalamus and the adrenal gland at 1, 15, and 30 days

, with the 30

- day treated animals also 8

significantly increased when compared to the 15

- day methanol

- treated animals. 9

Leu kocyte migration and antibody titer were both significantly increased over controls 10

for all time points, while footpad thickness was significantly decreased in 15

- and 30

- day treated 11

animals. Neutrophil adherence was significantly decreased after 1 and 30 days of exposure. A 12

significant decrease in the NBT reduction and adherence was found when comparing PMNs from 13

the 30

- day treated animals with cells from the 15

- day methanol

- treated group. 14

The open field behavior tests showed a significant decrease in amb ulation from the 4th 15

day on and significant decreases in rearing and grooming from the 20th day on. A significant 16

increase was observed in immobilization from the 8th day on and in fecal bolus from the 24th 17

day on in methanol

- exposed animals. 18

While cortic osterone levels were significantly increased following 1 or 15 days of 19

methanol treatment, they were significantly decreased after 30 days of treatment, as compared to 20

controls. Following 30 days of methanol treatment, DNA from the hypothalamus, the adren al 21

gland

, and the spleen showed significant fragmentation. The authors conclude that exposure to 22

methanol

- induced oxidative stress, disturbs HPA

- axis function, altering corticosterone levels and 23

producing effects in several nonspecific and specific immune responses. 24

4.5. IMMUNOTOXICITY

Parthasarathy et al . (2005a) provided data on the impact of methanol on neutrophil 25

function in an experiment in which 6 male Wistar rats/group were given a single i.p. exposure of 26

2

, 370 mg/kg methanol mixed 1:1 in saline. Anothe r group of 6 animals provided blood samples 27

that were incubated with methanol in vitro at a methanol concentration equal to that observed in 28

the in vivo

- treated animals 30 and 60 minutes postexposure . Total and differential leukocyte 29

counts were measured from these groups in comparison to in vivo and in vitro controls. 30

Neutrophil adhesion was determined by comparing the neutrophil index in the untreated blood 31

samples to those that had been passed down a nylon fiber column. The cells’ phagocytic ability 32

w as evaluated by their ability to take up heat

- killed Candida albicans . In another experiment, 33

neutrophils were assessed for their killing potential by measuring their ability to take up then 344

- 65 DRAFT

- DO NOT CITE OR QUOTE convert NBT to formazan crystals. 50 One hundred neutrophils/slid e s were counted for their total 1

and relative percent formazan

- positive cells. 2

The blood methanol concentrations 30 and 60 minutes after dosing were 2,356 ± 162 and 3

2,233 ± 146 mg/L, respectively. The mean of these values was taken as the target concentrat ion 4

for the in vitro methanol incubation. In the in vitro studies, there were no differences in total and 5

differential leukocyte counts, suggesting that no lysis of the cells had occurred at this methanol 6

concentration. This finding contrasts with the ma rked difference in total leukocytes observed as 7

a result of methanol incubation in vivo, in which, at 60 minutes after exposure, 16,000 ± 1,516 8

cells/mm 3 were observed versus 23,350 ± 941 in controls ( p < 0.001). Some differences in 9

neutrophil function we re observed in blood samples treated with methanol in vitro and in vivo. 10

These differences are illustrated for the 60

- minute postexposure samples in Table 4

- 1 6

. 11

Table 4

- 1 6 . Effect of methanol on neutrophil functions in in vitro and in vivo studies in

male Wistar rats

Parameter In vitro studies (60 minutes) In vivo studies (60 minutes)

Control Methanol Control Methanol

Phagocytic index (%) 89.8 ± 3.07 81.6 ± 2.2 a 66.0 ± 4.8 84.0 ± 7.0 b

Avidity index 4.53 ± 0.6 4.47 ± 0.7 2.4 ± 0.1 3.4 ± 0.3 a

NBT reducti on (%) 31.6 ± 4.6 48.6 ± 4.3 b 4.6 ± 1.2 27.0 ± 4.6 b

Adherence (%) 50.2 ± 5.1 39.8 ± 2.4 a 49.0 ± 4.8 34.6 ± 4.0 b

Values are means ± S.D. for six animals.

a p < 0.01.

b p < 0.001.

Source: Parthasarathy et al . (2005a).

Parthasarathy et al . (2005a) observed differences in the neutrophil functions of cells 12

exposed to methanol in vitro versus in vivo, most notably in the phagocytic index that was 13

reduced in vitro but significantly increased in vivo. However, functions such as adherence and 14

NBT reduction showed consistency in the in vitro and in vivo responses. The authors noted that, 15

by and large, the in vivo effects of methanol on neutrophil function were more marked than those 16

in cells exposed in vitro. 17

Another study by Parthasarathy et al . (2005b) also expo sed 6 male Wistar rats/group i.p. 18

to methanol at approximately 1/4 the LD 50 (2.4 g/kg). The goal was to further monitor possible 19

methanol

- induced alterations in the activity of isolated neutrophils and other immunological 20

parameters. The exposure protoco l featured daily injections of methanol for up to 30 days in the 21

presence or absence of sheep RBCs. Blood samples were assessed for total and differential 22

leukocytes

, and isolated neutrophils were monitored for changes in phagocytic and avidity 23

50 Absence of NBT reduction indicates a defect in some of the metabolic pathways involved in intracellular

microbial killing.4

- 66 DRAFT

- DO NOT CITE OR QUOTE indices, N BT reduction, and adherence. In the latter test, blood samples were incubated on a 1

nylon fiber column, then eluted from the column and rechecked for total and differential 2

leukocytes. Phagocytosis was monitored by incubating isolated buffy coats from the blood 3

samples with heat

- killed C. albicans . NBT reduction capacity examined the conversion of the 4

dye to formazan crystals within the cytoplasm. The relative percentage of formazan

- positive 5

cells in each blood specimen gave a measure of methanol’s capac ity to bring about cell death. 6

As tabulated by the authors, there was a dose

- dependent reduction in lymphoid organ weights 7

(spleen, thymus, and lymph node) in rats exposed to methanol for 15 and 30 days via i.p. 8

injection, irrespective of the presence of sheep RBCs. Methanol also appeared to result in a 9

reduction in the total or differential neutrophil count. These and potentially related changes to 10

neutrophil function are shown in Table 4

- 1 7

. 11

Table 4

- 1 7 . Effect of intraperitoneally injected methanol on total and differential

leukocyte counts and neutrophil function tests in male Wistar rats

Parameter Without sheep red blood cell treatment With sheep red blood cell treatment

Control 15

- day

methanol 30

- day

methanol Control 15

- day

methanol 30

- day

methano l

Organ weights (mg)

Spleen 1223 ± 54 910 ± 63 a 696 ± 83 a

, b 1381 ± 27 1032 ± 39 a 839 ± 35 a

, b

Thymus 232 ± 12 171 ± 7 a 121 ± 10 a

, b 260 ± 9 172 ± 10 a 130 ± 24 a

, b

Lymph node 32 ± 2 24 ± 3 a 16 ± 2 a

, b 39 ± 2 28 ± 1 a 23 ± 1 a

, b

Leukocyte counts

Total leukoc ytes 23,367 ± 946 16,592 ± 1219 a 13,283 ±

2553 a

, b 18,633 ± 2057 16,675 ± 1908 14,067 ± 930 a

, b

% neutrophils 24 ± 8 21 ± 3 16 ± 3 a 8 ± 3 23 ± 4 a 15 ± 5 a

, b

% Lymphocytes 71 ± 7 76 ± 3 79 ± 5 89 ± 4 78.5 ± 4 a 82 ± 6

Neutrophil function tests

Phagocytic in dex

(%) 91.0 ± 2.0 80.0 ± 4.0 a 79.0 ± 2.0 a 87.0 ± 4.0 68.0 ± 3.0 a 63.0 ± 4.0 a

Avidity index 2.6 ± 0.3 3.2± 0.5 a 3.2 ± 0.1 a 4.1 ± 0.1 2.6 ± 0.3 a 2.1 ± 0.3 a

NBT reduction (%) 6.3 ± 2.0 18.2 ± 2.0 a 15.0 ± 1.0 a

, b 32.0 ± 3.3 22.0 ± 3.0 a 19.0 ± 2.4 a

Adherence (%) 49.0 ± 5.0 44.0 ± 5.0 29.5 ± 5.0 a

, b 78.0 ± 9.2 52.0 ± 9.0 a 30.0 ± 4.3 a

, b

Values are means ± S.D. (n = 6).

a p < 0.05 from respective control.

b p < 0.05 between 15

- and 30

- day treatment groups.

Source: Parthasarathy et al . (2005b).

The study provided data that showed altered neutrophil functions following repeated 12

daily exposures of rats to methanol for periods up to 30 days. This finding is indicative of a 13

possible effect of methanol on the immunocompetence of an exposed host. 144

- 67 DRAFT

- DO NOT CITE OR QUOTE Parthasarathy et al . (2 006a) reported on additional immune system indicators as part of a 1

study to determine the effects of methanol intoxication on the HPA axis. As described in 2

Section 4.4. 3 . immune function tests conducted included the footpad thickness test for DTH, a 3

leuko cyte migration inhibition assay, the hemagglutination assay (measuring antibody titer), the 4

neutrophil adherence test, phagocytosis index, and a NBT reduction and adherence assay used to 5

measure the killing ability of PMNs. 6

Leukocyte migration and antibody titer were both significantly increased over controls 7

for all time points, while footpad thickness was significantly deceased in 15

- and 30

- day treated 8

animals. Neutrophil adherence was significantly decreased after 1 and 30 days of exposure. A 9

signific ant decrease in the NBT reduction and adherence was found when comparing PMNs from 10

the 30

- day treated animals with cells from the 15

- day methanol

- treated group. 11

Parthasarathy et al . (2007) reported the effects of methanol on a number of specific 12

immune fun ctions. As before, 6 male Wistar rats/group were treated with 2,370 mg/kg methanol 13

in a 1:1 mixture in saline administered intraperitoneally for 15 or 30 days. Animals 14

scheduled/designated for termination on day 15 were immunized intraperitoneally with 5 × 10 9 15

sheep RBCs on the 10th day. Animals scheduled for day 30 termination were immunized on the 16

25th day. Controls were animals that were not exposed to methanol but immunized with sheep 17

RBCs as described above. Blood samples were obtained from all an imals at sacrifice and 18

lymphoid organs including the adrenals, spleen, thymus, lymph nodes, and bone marrow were 19

removed. Cell suspensions were counted and adjusted to 1 × 10 8 cells/mL. Cell

- mediated 20

immune responses were assessed using a footpad thickne ss assay and a leucocyte migration 21

inhibition (LMI) test, while humoral immune responses were determined by a hemagglutination 22

assay, and by monitoring cell counts in spleen, thymus, lymph nodes, femoral bone marrow, and 23

in splenic lymphocyte subsets. Pla sma levels of corticosterone were measured along with levels 24

of such cytokines as TNF

- α, IFN

- γ, IL

- 2, and IL

- 4. DNA damage in splenocytes and thymocytes 25

was also monitored using the Comet assay. 26

Table 4

- 1 8 shows decreases in the animal weight/organ weight ratios for spleen, thymus, 27

lymph nodes and adrenal gland as a result of methan ol exposure. However, the splenocyte, 28

thymocyte, lymph node, and bone marrow cell counts were time

- dependently lower in methanol

29

treated animals. 304

- 68 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 1 8 . Effect of methanol exposure on animal weight/ organ weight ratios and on

cell counts in primary a nd secondary lymphoid organs of male Wistar rats

.

Organ Immunized

Control 15 days 30 days

Animal weight/ o rgan weight ratio

Spleen 3.88 ± 0.55 2.85 ± 0.36 a 2.58 ± 0.45 a

Thymus 1.35 ± 0.29 0.61 ± 0.06 a 0.63 ±0.04 a

Lymph node 0.10 ± 0.01 0.08 ± 0.01 a 0. 06 ± 0.02 a

Adrenal 0.14 ± 0.01 0.15 ± 0.01 0.12 ± 0.01 a, b

Cell counts

Splenocytes (× 10 8 ) 5.08 ± 0.06 3.65 ±0.07 a 3.71 ± 0.06 a

Thymocytes (× 10 8 ) 2.66 ± 0.09 1.95 ± 0.03 a 1.86 ± 0.09 a

Lymph node (× 10 7 ) 3.03 ± 0.04 2.77 ± 0.07 a 2.20 ± 0.06 a, b

Bone marrow (× 10 7 ) 4.67 ± 0.03 3.04 ± 0.09 a 2.11 ± 0.05 a,b

Values are means ± six animals.

a p < 0.05 versus control groups.

b p < 0.05 versus 15

- day treated group.

Source: Parthasarathy et al . (2007).

Parthasarathy et al . (2007) also documented their results on the cell

- mediated and 1

humoral immunity induced by methanol. Leucocyte migration was significantly increased 2

compared to control animals, an LMI of 0.82 ± 0.06 being reported in rats exposed to methanol 3

for 30 days. This compares to an LMI of 0.73 ± 0 .02 in rats exposed for 15 days and 0.41 ± 0.10 4

in controls. By contrast, footpad thickness and antibody titer were decreased significantly in 5

methanol

- exposed animals compared to controls (18.32 ± 1.08, 19.73 ± 1.24, and 26.24 ± 1.68% 6

for footpad thickne ss; and 6.66 ± 1.21, 6.83 ± 0.40, and 10.83 ± 0.40 for antibody titer in 30

- day, 7

15

- day exposed rats

, and controls, respectively). 8

Parthasarathy et al . (2007) also provided data in a histogram that showed a significant 9

decrease in the absolute numbers of P an T cells, CD4, macrophage, major histocompatibility 10

complex (MHC) class II molecule expressing cells, and B cells of the methanol

- treated group 11

compared to controls. The numbers of CD8 cells were unaffected. Additionally, as illustrated in 12

the report, D NA single strand breakage was increased in immunized splenocytes and thymocytes 13

exposed to methanol versus controls. Although some fluctuations were seen in corticosterone 14

levels, the apparently statistically significant change versus controls in 15

- day e xposed rats was 15

offset by a decrease in 30

- day exposed animals. Parthasarathy et al . (2007) also tabulated the 16

impacts of methanol exposure on cytokine levels ; t hese values are shown in Table 4

- 1 9

. 174

- 69 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 1 9 . The effect of methanol on serum cytokine leve ls in male Wistar rats

Cytokines (pg/mL) Immunized

Control 15 days 30 days

IL

- 2 1810 ± 63.2 1303.3 ± 57.1 a 1088.3 ± 68.8 a, b

IL

- 4 44.8 ± 2.0 74.0 ± 5.1 a 78.8 ± 4.4 a

TNF

- α 975 ± 32.7 578.3 ± 42.6 a 585 ± 45 a

IFN

- γ 1380 ± 55.1 961 6 ± 72.7 a 950 ± 59.6 a

Values are means ± six animals.

a p < 0.05 versus control groups.

b p < 0.05 versus 15

- day treated group.

Source: Parthasarathy et al . (2007).

Drawing on the results of DNA single strand breakage in this experiment, the authors 1

speculated that methanol

- ind uced apoptosis could suppress specific immune functions such as 2

those examined in this research report. Methanol appeared to suppress both humoral and cell

3

mediated immune responses in exposed Wistar rats. 4

4.6. MECHANISTIC DATA AND OTHER STUDIES IN SUP PORT OF THE MOA

While the role of the methanol metabolite, formate, in inducing the toxic consequences of 5

acute exposure to methanol, including ocular toxicity and metabolic acidosis, is well established 6

in humans (see Section 4.1), there is controversy over the p ossible roles of the parent compound, 7

metabolites, and folate deficiency (potentially associated with methanol metabolism) in the 8

developmental neurotoxicity of methanol. Experiments that have attempted to address these 9

issues are reviewed in the followin g paragraphs. 10

4.6.1. Role of Methanol and Metabolites in the Developmental Toxicity of Methanol

Dorman et al . (1995) conducted a series of in vitro and in vivo studies that provide 11

information for identifying the proximate teratogen associated with developmenta l toxicity in 12

CD

- 1 mice. The studies used CD

- 1 ICR BR (CD

- 1) mice, HPLC grade methanol, and 13

appropriate controls. PK and developmental toxicity parameters were measured in mice exposed 14

to sodium formate (750 mg/kg by gavage), a 6

- hour methanol inhalation (10,000 or 15,000 ppm), 15

or methanol gavage (1.5 g/kg). In the in vivo inhalation study, 12

– 14 dams/ group were exposed 16

to 10,000 ppm methanol for 6 hours on GD8, 51 with and without the administration of 17

fomepizole (4

- methylpyrazole) to inhibit the metabol ism of methanol by ADH1. Dams were 18

sacrificed on GD10

, and fetuses were examined for neural tube patency. As shown in 19

Table 4

- 20 , the incidence of fetuses with open neural tubes was significantly increased in the 20

51 Dorman et al . (1995) state that GD8 was chosen because it encompasses the period of murine neurulation and the

time of greatest vulnerability to methanol

- induced neural tube defects.4

- 70 DRAFT

- DO NOT CITE OR QUOTE methanol group (9.65% in treated versus 0 in control) and numerically but not significantly 1

increased in the group treated with methanol and fomepizole (7.21% in treated versus 0 in 2

controls). These data should not be interpreted to suggest that a decrease in methanol 3

metabolism is protective. As discussed in Section 3.1, rodents metabolize methanol via both 4

ADH1 and CAT. This fact and the Dorman et al . (1995) observation that maternal formate levels 5

in blood and decidual swellings (swelling of the uterine lining) did not differ in dams exposed to 6

methanol alone or methanol and fomepizole suggest that the role of ADH1 relative to CAT and 7

non enzymatic methanol clearance is not of great significance in adult rodents. 8

Table 4

- 20 . Developmental outcome on GD10 following a 6

- hour 10,000 ppm

(13,104 mg/m 3 ) methanol inhalation by CD

- mice or formate gavage (750 mg/kg) on

GD8

Treatment No. of litters Open neural tubes (%) Head length (mm) Body length (mm)

Air 14 2.29 ± 1.01 3.15 ± 0.03 5.89 ± 0.07

Air/fomepizole 14 2.69 ± 1.19 3.20 ± 0.05 5.95 ± 0.09

Methanol 12 9.65 ± 3.13 a 3.05 ± 0.07 5.69 ± 0.13

Methanol/fomepizole 12 7.21 ± 2.65 3.01 ± 0.05 5.61 ± 0.11

Water 10 0 3.01 ± 0.07 5.64 ± 0.11

Formate 14 2.02 ± 1.08 2.91 ± 0.08 5.49 ± 0.12

Values are means ± S.D.

a p < 0.05, as calculated by the autho rs.

Source: Dorman et al . (1995) (adapted).

The data in Table 4

- 20 suggest that the formate metabolite is not responsible for the 9

observed increase in open neural tubes in CD

- 1 mice following methanol exposure. Formate 10

administered by gavage (750 mg/kg) did not increase this effect despite the fact that this formate 11

dose produced the same toxicokinetic profile as a 6

- hour exposure to 10,000 ppm methanol 12

vapors (1.05 mM formate in maternal blood and 2.0 mmol formate/kg in decidual swellings). 13

However, th e data are consistent with the hypotheses that the formaldehyde metabolite of 14

methanol may play a role. Both CAT and ADH1 activity are immature at days past conception 15

(DPC)8 ( Table 4

- 2 1 ). If fetal ADH1 is more mature than fetal CAT, it is conceivable tha t the 16

decrease in the open neural tube response observed for methanol combined with fomepizole 17

( Table 4

- 20 ) may be due to fomepizole having a greater effect on the metabolism of fetal 18

methanol to formaldehyde than is observed in adult rats

. Unfortunately, the toxicity studies were 19

carried out during a period of development where ADH1 expression and activity are just starting 20

to develop ( Table 4

- 2 1 ) ; therefore, it is uncertain whether any ADH1 was present in the fetus to 21

be inhibited by fomepizole. 224

- 71 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 2 1 . Summary of ontogeny of relevant enzymes in CD

- 1 mice and humans

CD

- 1 Mouse Human

Days Past Conception (DPC) Trimesters

6.5 7.5 8.5 9.5 1 2 3

Somites (8

– 12) (13

– 20) (21

– 29)

CAT

mRNA

activity a

embryo

VYS 1

10 10

15 20

20 N/A N/A N/A

ADH1

mRNA

activity

embryo

VYS **− −** 

320

240 **−**

460

280 +

450

290 + + +

ADH3

mRNA

activity

embryo

VYS + +

300

500 +

490

500 +

550

550 **− −**  +

a Activity of CAT and ADH1 are expressed as nmol/minute/mg and pmol/minute/mg, respectively.

Source: Harris et al . (2003).

Dorman et al . (1995) provide additional support for their hypothesis that methanol’s 1

developmental effects in CD

- 1 mice are not caused by form ate in an in vitro study involving the 2

incubation of GD8 whole CD

- 1 mouse embryos with increasing concentrations of methanol or 3

formate. Developmental anomalies were observed on GD9

, including cephalic dysraphism, 4

asymmetry and hypoplasia of the prosencep halon, reductions of brachial arches I and II, 5

scoliosis, vesicles on the walls of the mesencephalon, and hydropericardium ( Table 4

- 2 2 ). The 6

concentrations of methanol used for embryo incubation (0

– 375 mM) were chosen to be broadly 7

equivalent to the peak methanol levels in plasma that have been observed (approximately 8

100 mM) after a single 6

- hour inhalation exposure to 10,000 ppm (13,104 mg/m 3 ). As discussed 9

above, these exposure conditions induced an increased incidence of open neural tubes on GD10 10

embr yos when pregnant female CD

- 1 mice were exposed on GD8. ( Table 4

- 20 ). Embryonic 11

lesions such as cephalic dysraphism, prosencephalic lesions, and brachial arch hypoplasia were 12

observed with 250 mM (8,000 mg/L) methanol and 40 mM (1,840 mg/L) formate. The study 13

authors noted that a formate concentration of 40 mM (1,840 mg/L) greatly exceeds blood 14

formate levels in mice inhaling 15,000 ppm methanol (0.75 mM = 35 mg/L), a teratogenic dose. 154

- 72 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 2 2 . Dysmorphogenic effect of methanol and formate in neurulati ng CD

- 1

mouse embryos in culture (GD8)

Treatment Concentration

(mM) Live e mbryos Cephalic dysraphism Prosencephalic lesions

Brachial

arch

hypoplasia Total No.

abnormal Severe Moderate Total Hypoplasia Asymmetry Total

Vehicle 20 3 0 2 2 2 0 2 0

Methanol 62 13 1 0 0 0 1 0 1 0

125 14 5 1 0 2 2 2 4 1

187 13 7 2 4 6 3 1 4 1

250 15 7 2 5 7 7 a 1 8 6 a

375 12 7 6 a 5 11 a 9 a 1 10 a 8 a

Formate 4 12 2 0 0 0 2 0 2 1

8 13 5 1 5 6 4 2 6 0

12 9 5 0 5 5 1 2 3 0

20 16 7 2 5 7 2 1 3 1

40 16 14 a 10 a 4 14 a 3 5 a 8 13 a

a p < 0.05, as calculated by the authors.

Source: Dorman et al . (1995) (adapted).

As discussed in Section 4.3.3, a series of studies by Harris et al . (200 4 , 200 3 ) also 1

provide evidence as to the moieties that may be responsible for metha nol

- induced developmental 2

toxicity. Harris et al . (2004) have shown that among methanol and its metabolites, viability of 3

cultured rodent embryos is most affected by formate. In contrast, teratogenic endpoint s (of 4

interest to this risk assessment) in cul tured rodent embryos are more sensitive to methanol and 5

formaldehyde than formate. D ata from these studies indicate that developmental toxicity may be 6

more related to formaldehyde than methanol, as formaldehyde

- induced teratogenicity occurs at 7

several ord ers of magnitude lower than methanol ( Table 4

- 1 4 ) (Hansen et al ., 2005; Harris et al

., 8

2004). It should also be noted that CAT, ADH1, and ADH3 activities are present in both the rat 9

embryo and VYS at stages as early as 6

– 12 somites (Harris et al ., 2003); thus

, it is presumable 10

that in these ex vivo studies methanol is metabolized to formaldehyde and formaldehyde is 11

subsequently metabolized to S

- formylglutathione. 12

Studies involving GSH also lend support that formaldehyde may be a key proximal 13

teratogen. In hibition of GSH synthesis with butathione sulfoximine (BSO) has little effect on 14

developmental toxicity endpoint s , yet treatment with BSO and methanol or formaldehyde 15

increases developmental toxicity (Harris et al ., 2004). Among the enzymes involved in me thanol 16

clearance, only ADH3

- mediated metabolism of formaldehyde is GSH dependent. This 17

hypothesis that ADH3

- mediated metabolism of formaldehyde is important for the amelioration 18

of methanol’s developmental toxicity is also supported by the diminished ADH3 activity in the 19

mouse versus rat embryos, which is consistent with the greater sensitivity of the mouse to 204

- 73 DRAFT

- DO NOT CITE OR QUOTE methanol developmental toxicity (Harris et al ., 2003 ) ( Section 4.3.3). Similarly reasonable 1

explanations for this greater mouse sensitivity are not readily apparent for the two MOAs 2

described below that attribute methanol toxicity to methanol metabolism per se

, either through 3

the depletion of folate (Section 4.6 .2) or the generation of reactive oxidant species (Section 4

4.6 .3). Mouse livers actually have considerably higher hepatic tetrahydrofolate and total folate 5

than rat or monkey liver. Harris et al . (2003) and Johlin et al

. ( 1987 ) have shown that CAT 6

activity in the embryo and VYS of rats and mice appear similar.. 7

Without positive identification of the actual moiety responsible for methanol

- induced 8

teratogenicity , MOA remains unclear. If the moiety is methanol, then it is possible that 9

generation of NADH during methanol oxidation creates an imbalance in other enzymatic 10

reactions. Studies have s hown that ethanol intake leads to a > 100

- fold increase in cellular 11

NADH, presumably due to ADH1

- mediated reduction of the cofactor NAD + to NADH 12

(Cronholm, 1987; Smith and Newman, 1959). This is of potential importance because, for 13

example, ethanol intake has been shown to increase the in vivo and in vitro enzymatic reduction 14

of other endogenous compounds ( e.g., serotonin) in humans (Svensson et al ., 1999; Davis et al

., 15

1967). In rodents, CAT

- mediated methanol metabolism may obviate this effect ; i n humans, 16

however, methanol is primarily metabolized by ADH1. 17

If the teratogenic moiety of methanol is formaldehyde, then reactivity with protein 18

sulfhydryls and non protein sulfhydryls ( e.g., GSH) or DNA protein cross

- links may be involved. 19

Metabolic roles ascribed to ADH3, particularly regulation of S

- nitrosothiol biology (Foster and 20

Stamler, 2004), could also be involved in the MOA. Recently, Staab et al . (2008) have shown 21

that formaldehyde alters other ADH3

- mediated reactions through cofactor recycling and that 22

formaldehyde alters levels of cellular S

- nitrosothiol, which plays a key role in cellular signaling 23

and many cellular functions and pathways (Hess et al ., 2005). 24

Studies such as those by Harris et al . (200 4, 200 3 ) and Dorman et al . (1995

, 1994) 25

suggest th at formate is not the metabolite responsible for methanol’s teratogenic effects. The 26

former researchers suggest that formaldehyde is the proximate teratogen, and provide evidence 27

in support of that hypothesis. However, questions remain. Researchers in t his area have not yet 28

reported using a sufficient array of enzyme inhibitors to conclusively identify formaldehyde as 29

the proximate teratogen. Studies involving other inhibitors or toxicity studies carried out in 30

genetically engineered mice, while not dev oid of confounders, might further inform regarding 31

the methanol MOA for developmental toxicity. Even if formaldehyde is ultimately identified as 32

the proximate teratogen, methanol would likely play a prominent role, at least in terms of 33

transport to the ta rget tissue. T he high reactivity of formaldehyde would limit its unbound and 34

unaltered transport as free formaldehyde from maternal to fetal blood (Thrasher and Kilburn

, 354

- 74 DRAFT

- DO NOT CITE OR QUOTE 2001)

, and, as has been discussed, the capacity for the metabolism of methanol to for maldehyde 1

is likely lower in the fetus and neonate versus adults (Section 3.3)

. 2

4.6.2. Role of Folate Deficiency in the Developmental Toxicity of Methanol

As discussed in Sections 3.1 and 4.1, humans and other primates are susceptible to the 3

effects of methano l exposure associated with formate accumulation because they have lower 4

levels of hepatic tetrahydrofolate

- dependent enzymes that help in formate oxidation. 5

Tetrahydofolate

- dependent enzymes and critical pathways that depend on folate, such as purine 6

and pyrimidine synthesis, may also play a role in the developmental toxicity of methanol. 7

Studies of rats and mice fed folate

- deficient diets have identified adverse effects on reproductive 8

performance, implantation, fetal growth and developmental defects, and the inhibition of folate 9

cellular transport has been associated with several developmental abnormalities, ranging from 10

neural tube defects to neurocristopathies such as cleft

- lip and cleft

- palate, cardiacseptal defects, 11

and eye defects (Antony, 2007). F olate deficiency has been shown to exacerbate some aspects of 12

the developmental toxicity of methanol in mice ( see discussion of Fu et al ., 1996

, and Sakanashi 13

et al ., 1996

, in Section 4.3.1 ) and rats ( see discussion of Aziz et al ., 2002

, in Section 4.4.1 ). 14

The studies in mice focused on the influence of FAD on the reproductive and skeletal 15

malformation effects of methanol. Sakanashi et al . (1996) showed that dams exposed to 16

5 g/kg

- day methanol on GD6

– GD 15 experienced a three fold increase in the percent age of litters 17

affected by cleft palate and a 10

- fold increase in the percentage of litters affected by exencephaly 18

when fed a FAD (resulting in a 50% decrease in liver folate) versus a FAS diet. They speculate d 19

that the increased methanol effect from FAD diet could have been due to an increase in tissue 20

formate or a critical reduction in conceptus folate concentration immediately following the 21

methanol exposure. The latter appears more likely, given the high levels of formate needed to 22

cause embryotoxici ty (Section 4.3.3) and the decrease in conceptus folate that is observed within 23

2 hours of GD8 methanol exposure (Dorman et al ., 1995). Fu et al . (1996) confirmed the 24

findings of Sakanashi et al . (1996) and also determined that the maternal FAD diet had a much 25

greater impact on fetal liver folate than maternal liver folate levels. 26

The rat study of Aziz et al . (2002) focused on the influence of FAD on the developmental 27

neurotoxicity of methanol. Experiments by Aziz et al . (2002) involving Wistar rat dams and 28

pups exposed to methanol during lactation provide evidence that methanol exposure during this 29

postnatal period affects the developing brain. These effects (increased spontaneous locomotor 30

activity, decreased conditioned avoidance response, disturbanc es in dopaminergic and 31

cholinergic receptors and increased expression of GAP

- 43 in the hippocampal region) were more 32

pronounced in FAD as compared to FAS rats. This suggests that folic acid may play a role in 33

methanol

- induced neurotoxicity. These results do not implicate any particular proximate 344

- 75 DRAFT

- DO NOT CITE OR QUOTE teratogen

, as folate deficiency can increase levels of both methanol, formaldehyde and formate 1

(Medinsky et al ., 1997). Further, folic acid is used in a number of critical pathways such as 2

purine and pyrimidine s ynthesis. Thus, alterations in available folic acid, particularly to the 3

conceptus, could have significant impacts on the developing fetus apart from the influence it is 4

presumed to have on formate removal. 5

4.6.3. Methanol

- Induced Formation of Free Radicals, Lip id Peroxidation, and Protein

Modifications

Oxidative stress in mother and offspring has been suggested to be part of the teratogenic 6

mechanism of a related alcohol, ethanol. Certain reproductive and developmental effects ( e.g., 7

resorptions and malformatio n rates) observed in Sprague

- Dawley rats following ethanol 8

exposure were reported to be ameliorated by antioxidant (Vitamin E) treatment (Wentzel et al

., 9

2006; Wentzel and Eriksson, 2006). A number of studies have examined markers of oxidative 10

stress asso ciated with methanol exposure. 11

Skrzydlewska et al . (2005) provided inferential evidence for the effects of methanol on 12

free radical formation, lipid peroxidation, and protein modifications, by studying the protective 13

effects of N

- acetyl cysteine and the V i tamin E derivative, U83836E, in the liver of male Wistar 14

rats exposed to the compound via gavage. Forty

- two rats/group received a single oral gavage 15

dose of either saline or 50% methanol. This provided a dose of approximately 6

, 000 mg/kg, as 16

calculated b y the authors. Other groups of rats received the same concentration of methanol, but 17

were also injected intraperitoneally with either N

- acetylcysteine or U

- 83836E. N

- acetylcysteine 18

and U

- 83836E controls were also included in the study design. Animals in each group were 19

sacrificed after 6, 14, and 24 hours or after 2, 5, or 7 days. Livers were rapidly excised for 20

electron spin resonance (ESR) analysis, and 10,000 × g supernatants were used to measure GSH, 21

malondialdehyde, a range of protein parameters, i ncluding free amino and sulfhydryl groups, 22

protein carbonyls, tryptophan, tyrosine, and bityrosine, and the activity of cathepsin B. 23

Skrzydlewska et al . (2005) provided data that showed an increase in an ESR signal at 24

g = 2.003 in livers harvested 6 and 12 hours after methanol exposure. The signal, thought to be 25

indicative of free radical formation, was opposed by N

- acetylcysteine and U83836E. Other 26

compound

- related changes included : 1) a significant decrease in GSH levels that was most 27

evident in rats sa crificed 12 and 24 hours after exposure ; 2) increased concentrations in the lipid 28

peroxidation product, malondialdehyde (by a maximum of 44% in the livers of animals 29

sacrificed 2 days after exposure) ; 3) increased specific concentrations of protein carbony l groups 30

and bityrosine ; but 4) reductions in the specific level of tryptophan. Given the ability of N

31

acetylcysteine and U83836E to oppose these changes, at least in part, the authors speculated that 32

a number of potentially harmful changes may have occur red as a result of methanol exposure. 334

- 76 DRAFT

- DO NOT CITE OR QUOTE These include free radical formation, lipid peroxidation, and disturbances in protein structure. 1

However, it is unclear whether or not the metabolites of methanol, formaldehyde

, and/or formate, 2

were involved in any o f these changes. 3

Rajamani et al . (2006) examined several oxidative stress parameters in male Wistar rats 4

following methotrexate

- induced folate deficiency. Compared to controls

, the levels of free 5

radical scavengers SOD, CAT, GSH peroxidase, oxidized GSH, protein carbonyls, and lipid 6

peroxidation were elevated in several regions of the brain, with greater increases observed in the 7

MTX

- methanol

- treated animals than in the MTX

- alone group. The level of GSH and protein 8

thiols was decreased in all regions of t he brain

, with a greater decrease observed in the MTX

9

methanol

- treated animals than MTX

- treated animals. 10

Dudka (2006) measured the total antioxidant status (TAS) in the brain of male Wistar rats 11

exposed to a single oral gavage dose of methanol at 3 g/kg. The animals were kept in a nitrous 12

oxide atmosphere (N 2 O/O 2 ) throughout the experiment to reduce intrinsic folate levels

, and 13

various levels of ethanol and/or fomepizole (as ADH antidotes) were administered i.p. after 14

4 hours. Animals were sacrificed afte r 16 hours, the brains homogenized, and the TAS 15

determined spectrophotometrically. As illustrated graphically by the author, methanol 16

administration reduced TAS in brain irrespective of the presence of ADH antidotes. The author 17

speculated that, while mos t methanol is metabolized in the liver, some may also reach the brain. 18

Metabolism to formate might then alter the NADH/NAD + ratio resulting in an increase in 19

xanthine oxidase activity and the formation of the superoxide anion. 20

Parthasarathy et al . ( 2006b) investigated the extent of methanol

- induced oxidative stress 21

in rat lymphoid organs. Six male Wistar rats/group received 2

, 370 mg/kg methanol (mixed 1:1 22

with saline) injected i.p. for 1, 15 or 30 days. A control group received a daily i.p. injection of 23

s aline for 30 days. At term, lymphoid organs such as the spleen, thymus, lymph nodes, and bone 24

marrow were excised, perfused with saline, then homogenized to obtain supernatants in which 25

such indices of lipid peroxidation as malondialdehyde, and the activi ties of CAT, SOD, and GSH 26

peroxidase were measured. Parthasarathy et al . ( 2006b) also measured the concentrations of 27

GSH and ascorbic acid ( non enzymatic antioxidants) and the serum concentrations of a number of 28

indicators of liver and kidney function, suc h as ALT, AST, blood urea nitrogen (BUN), and 29

creatinine. 30

Table 4

- 2 3 shows the time

- dependent changes in serum liver and kidney function 31

indicators that resulted from methanol administration. Treatment with methanol for increasing 32

durations resulted in in creased serum ALT and AST activities and the concentrations of BUN and 33

creatinine. 344

- 77 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 2 3 . Time

- dependent effect s of methanol administration on serum liver and

kidney function

, serum ALT, AST, BUN, and creatinine in control and experimental

groups of m ale Wistar rats

Parameters Methanol administration (2

, 370 mg/kg)

Control Single dose 15 days 30 days

ALT (μmoles/min

- mg) 29.0 ± 2.5 31.4 ± 3.3 53.1 ± 2.3 a 60.4 ± 2.8 a

AST (μmoles/min

- mg) 5.8 ± 0.4 6.4 ± 0.3 9.0 ± 1.2 a 13.7 ± 1.2 a

BUN (mg/L) 301 ± 36 3 32 ± 29 436 ± 35 a 513 ± 32 a

Creatinine (mg/L) 4

. 6 ± 0.3 4

. 8 ± 0.3 5

. 6 ± 0.2 a 7

. 0 ± 0.4 a

Values are means ± S.D. of 6 animals.

a p < 0.05 versus controls.

Source: Parthasarathy et al . (2006b) (adapted).

Table 4

- 2 4 . Effect of methanol administration on ma le Wistar rats on malondialdehyde

concentration in the lymphoid organs of experimental and control groups and the

effect of methanol on antioxidants in spleen

Parameters Methanol administration (2

, 370 mg/kg)

Control Single dose 15 days 30 days

Malondial dehyde in lymphoid organs

Spleen 2.62 ± 0.19 4.14 ± 0.25 a 7.22 ± 0.31 a 9.72 ± 0.52 a

Thymus 3.58 ± 0.35 5.76 ± 0.36 a 9.23 ± 0.57 a 11.6 ± 0.33 a

Lymph nodes 3.15 ± 0.25 5.08 ± 0.24 a 8.77 ± 0.57 a 9.17 ± 0.67 a

Bone marrow 3.14 ± 0.33 4.47 ± 0.18 a 7.20 ± 0.4 2 a 9.75 ± 0.56 a

Antioxidant levels in spleen

SOD ( u nits/mg protein) 2.40 ± 0.16 4.06 ± 0.19 a 1.76 ± 0.09 a 1.00 ± 0.07 a

CAT (μmoles H 2 O 2

consumed/min

- mg protein 35.8 ± 2.77 52.5 ± 3.86 a 19.1 ± 1.55 a 10.8 ± 1.10 a

GPx (μg GSH

consumed/min

- mg protein) 11.2 ± 0.60 20.0 ± 1.0 a 7.07 ± 0.83 a 5.18 ± 0.45 a

GSH (μg/mg protein) 2.11 ± 0.11 3.75 ± 0.15 a 1.66 ± 0.09 a 0.89 ± 0.04 a

Vit C (μg/mg protein) 0.45 ± 0.04 0.73 ± 0.05 a 0.34 ± 0.18 a 0.11 ± 0.03 a

Values are means ± S.D. of six animals.

a p < 0.05, versus cont rols.

Source: Parthasarathy et al . (2006b) (adapted).

Table 4

- 2 4 gives the concentration of malondialdehyde in the lymphoid organs of control 1

and experimental groups, and, as an example of all tissue sites examined, the levels of enzymatic 2

and nonenzymat ic antioxidants in spleen. The results show that malondialdehyde concentrations 3

were time

- dependently increased at each tissue site and that, in spleen as an example of all the 4

lymphoid tissues examined, increasing methanol administration resulted in lowe r levels of all 5

antioxidants examined compared to controls. Parthasarathy et al . ( 2006b) concluded that 64

- 78 DRAFT

- DO NOT CITE OR QUOTE exposure to methanol may cause oxidative stress by altering the oxidant/antioxidant balance in 1

lymphoid organs in the rat. 2

4.6.4. Exogenous Formate Dehydrogen ase as a Means of Detoxifying the Formic Acid that

Results from Methanol Exposure

In companion reports, Muthuvel et al . (2006a, 2006 b) used 6 male Wistar rats/group to 3

test the ability of exogenously

- administered formate dehydrogenase (FD) to reduce the se rum 4

levels of formate that were formed when 3 g/kg methanol was administered i.p. to rats in saline. 5

In the first experiment, purified FD (from Candida boitinii ) was administered by i.v. conjugated 6

to the N

- hydroxysuccinimidyl ester of monomethoxy polyeth ylene glycol propionic acid 7

(PEG

- FD) (Muthuvel et al ., 2006a). In the second, rats were administered FD

- loaded 8

erythrocytes (Muthuvel et al ., 2006b). In the former case, some groups of rats were made folate 9

deficient by means of a folate

- depleted diet; i n the latter, folate deficiency was brought about by 10

i.p. administration of methotrexate. In some groups, the rats received an infusion of an 11

equimolar mixture of carbonate and bicarbonate (each at 0.33 mol/L) to correct the formate

12

induced acidosis. As illustrated by the authors, methanol

- exposed rats receiving a folate

13

deficient diet showed significantly higher levels of serum formate than those receiving a folate

14

sufficient diet. However, administration of native or PEG

- FD reduced serum formate in 15

met hanol

- receiving folate

- deficient rats to levels seen in animals receiving methanol and the 16

folate

- sufficient diet. 17

In the second report, Muthuvel et al . (2006b) carried out some preliminary experiments to 18

show that hematological parameters of normal

, recon stituted but unloaded, and reconstituted and 19

FD

- loaded erythrocytes

, were similar. In addition, they showed that formate levels of serum 20

were reduced in vitro in the presence of FD

- loaded erythrocytes. Expressing blood formate 21

concentration in mmol/L at the 1

- hour time point after carbonate/bicarbonate and enzyme

- loaded 22

erythrocyte infusion via the tail vein, the concentration was reduced from 10.63 ± 1.3 (mean ± 23

S.D.) in methanol and methotrexate

- receiving controls to 5.83 ± 0.97 (n

= 6). This differenc e 24

was statistically significant at the p < 0.05 level. However, FD

- loaded erythrocytes were less 25

efficient at removing formate in the absence of carbonate/bicarbonate. Effective elimination of 26

formate appears to require an optimum pH for the FD activity in the enzyme

- loaded 27

erythrocytes. 28

4.6.5. Mechanistic Data Related to the Potential Carcinogenicity of Methanol

4.6.5.1. Genotoxicity

The genotoxicity/mutagenicity of methanol has not been extensively studied, but the 29

results of those studies that have thus far have been mostly negative. For example, in a survey of 304

- 79 DRAFT

- DO NOT CITE OR QUOTE the capacity of 71 drinking water contaminants to induce gene reversion in the Ames test, 1

Simmon et al . (1977) listed methanol as one of 45 chemicals that gave negative results with 2

Salmonella typhimurium strai ns TA 98, 100, 1535, 1537, and 1538, irrespective of the presence 3

or absence of metabolic activation (an S9 microsomal fraction). This result was confirmed by 4

DeFlora et al . (1984) and in NEDO (1987) for the same strains of Salmonella. DeFlora et al

. 5

(19 84) also found methanol to be negative for induction of DNA repair in E. coli strains WP2, 6

WP2 ( uvrA - , polA

- ), and CM871 ( uvrA - , recA - , lexA

- ), again irrespectively of the presence or 7

absence of S9. 8

Abbondandolo et al . (1980) used a ade6

- 60/rad10

- 198,h

- st rain of Schizosaccharomyces 9

pombe (P1 strain) to determine the capacity of methanol and other solvents to induce forward 10

mutations. Negative results were obtained for methanol, irrespective of metabolic activation 11

status. In other genotoxicity/mutagenici ty studies of methanol using fungi, Griffiths (1981) 12

reported methanol to be negative for the induction of aneuploidy in Neurospora crassa . By 13

contrast, weakly positive results for the compound were obtained by Crebelli et al . (1989) for the 14

induction of chromosomal malsegregation in the diploid strain P1 of Aspergillus nidulans

. 15

In an extensive review of the capacity of a wide range of compounds to induce 16

transformation in mammalian cell lines, Heidelberger et al . (1983) reported methanol to be 17

negative i n Syrian hamster embryo (SHE) cells. It also did not enhance the transformation of 18

SHE cells by Simian adenovirus. However, McGregor et al . (1985) reported in an abstract that a 19

statistically significant increase in forward mutations in the mouse lymphom a L5178Y tk + /tk

- cell 20

line occurred at a concentration of 7.9 mg/mL methanol in the presence of S9. 21

The capacity of methanol to bring about genetic changes in human cell lines was 22

examined by Ohno et al . (2005)

, who developed a system in which the chemical activation of the 23

p53R2 gene was assessed by the incorporation of a p53R2

- dependent luciferase reporter gene 24

into two human cell lines, MCF

- 7 and HepG2. Methanol, among 80 chemicals tested in this 25

system, gave negative results. NEDO (1987) used Chinese hamster lung (CHL) cells to monitor 26

methanol’s capacity to induce 1) forward mutations to az a guanine , 6

- thioguanine, and ouabain 27

resistance, and 2) chromosomal aberrations (CA) though with negative results throughout. 28

However, methanol did display some cap acity to induce sister chromatid exchanges (SCE) in 29

CHL cells, since the incidence of these lesions at the highest concentration (28.5 mg/mL) was 30

significantly greater than in controls (9.41 ± 0.416 versus 6.42 ± 0.227 [mean ± SE per 100 31

cells]). 32

In an in vivo experiment examining the genotoxicity/mutagenicity of methanol, Campbell 33

et al . (1991) exposed 10 male C57BL/6J mice/group to 0, 800, or 4

, 000 ppm (0, 1

, 048, and 34

5

, 242 mg/m 3 ) methanol, 6 hours/day, for 5 days. At sacrifice, blood cells were examined for the 35

formation of micronuclei (MN). Excised lung cells for SCE, CA and MN, and excised testicular 364

- 80 DRAFT

- DO NOT CITE OR QUOTE germ cells were examined for evidence of synaptonemal damage, in each case with negative 1

results. 2

There was no evidence of methanol

- induced formation of M N in the blood of fetuses or 3

pregnant CD

- mice when the latter were gavaged twice daily with 2

, 500 mg/kg methanol on 4

GD6

– GD 10 (Fu et al ., 1996). The presence of marginal or adequate amounts of folic acid in the 5

diet of the dams did not affect MN formation. NEDO (1987) carried out an in vivo MN test in 6

6 male SPF mice/group who received a single gavage dose of 1

, 050, 2

, 110, 4

, 210, and 8

, 410 7

mg/kg methanol. Twenty

- four hours later, 1,000 cells were counted for MN in bone marrow 8

smears. No compound

- related e ffects on MN incidence were observed. Table 4

- 2 5 provides a 9

summary of the genotoxicity/mutagenicity studies of methanol. 10

Table 4

- 2 5 . Summary of genotoxicity studies of methanol

Test s ystem Cell/strain Result Reference Comments

In vitro tests

Gene rever sion/

S. typhimurium TA98; TA100; TA1535,

TA1537

, TA1538 - (+S9);

- (

- S9) Simmon et al . (1977)

TA98; TA100; TA1535,

TA1537

, TA1538 - (+S9);

- (

- S9) De Flora et al . (1984)

TA98; TA100; TA1535,

TA1537

, TA1538 - (+S9);

- (

- S9) NEDO (1987)

DNA r ep air/ E. coli WP2, WP2 (uvrA

-

, polA

- ), CM871 (uvrA

- , recA

-

, lexA

- ) - (+S9),

- (

- S9) DeFlora et al . (1984)

Forward m utations/

S. pombe P1

( ade6

- 60/rad10

- 198,h

- )

- (+S10),

- (

- S10) Abbondandolo et al

. (1980) Molecular activation

used a 10,000 × g (S10)

super natant from liver

of induced Swiss mice

Aneuploidy/

N. crassa (arg

- 1, ad

- 3A, ad

- 3B, nic

2, tol, C/c, D/d, E/e) - (S9 status not

reported) Griffiths (1981)

Chromosomal

malsegregation/

A. nidulans P1 (diploid) + (S9 status not

reported) Crebelli et al . ( 1989)

Forward

mutations/ Mouse

lymphoma cells L5178Y tk + /tk

-

+ (+S9), ND (

- S9) McGregor et al

. (1985) Results reported in an

abstract

Forward

mutations/ Chinese

hamster lung cells to azaguanine, 6

- thioguanine and ouabain

resistance - (

- S9), ND (+S9) NEDO (1987)

Chromosomal

aberrations/ Chinese

hamster lung cells - (

- S9), ND (+S9) NEDO (1987)

Sister chromatid

exchanges/ Chinese

hamster lung cells + (

- S9), ND (+S9) NEDO (1987)4

- 81 DRAFT

- DO NOT CITE OR QUOTE Test s ystem Cell/strain Result Reference Comments

Genetic activation/

h uman cell lines MCF

- 7 and HepG2

containing a p53R2

- dep endent luciferase

reporter gene - (

- S9), ND (+S9) Ohno et al . (2005)

Cell transformation/

Syrian hamster

embryo cells with/without

transformation by Simian

adenovirus - (

- S9), ND (+S9)

- (

- S9), ND (+S9) Heidelberger et al

. (1983) Review

In vi vo tests

Mouse/ MN

formation C57BL/6J

(Blood cells) – Campbell et al

. (1991) Molecular activation

not applicable

C57BL/6J

(Lung cells) – Campbell et al

. (1991) Molecular activation

not applicable

Mouse/ SCEs C57BL/6J

(Lung cells) – Campbell et al

. (1991) Molecu lar activation

not applicable

Mice/ CA C57BL/6J

(Lung cells) – Campbell et al

. (1991) Molecular activation

not applicable

Mouse/ s ynaptonem

al damage C57BL/6J

(Testicular germ cells) – Campbell et al

. (1991) Molecular activation

not applicable

Mouse/ MN

for mation CD

- 1

(Blood cells) – Fu et al . (1996) Molecular activation

not applicable

SPF

(Bone marrow cells) – NEDO (1987) Molecular activation

not applicable

ND = not determined.

4.6.5.2. Lymphoma Responses Reported in ERF Life span Bioassays of Compounds

Related to Methanol, Including an Analogue (Ethanol), Precursors (Aspartame and Methyl

Tertiary Butyl Ether), and a Metabolite (Formaldehyde)

The ERF or the European Foundation of Oncology and Environmental Sciences have 1

conducted nearly 400 experimental bioassay s on over 200 compounds/agents

, using some 2

148,000 animals over nearly 4 decades. Of the over 200 compounds tested by ERF, 52 8 have 3

been associated with an increased incidence of hemolymphoreticular tumors in Sprague

- Dawley 4

rats, suggesting that it may be a rare and potentially species/strain

- specific finding. These eight 5

chemicals are : methanol, formaldehyde, aspartame, MTBE, DIPE, TAME, mancozeb

, and 6

toluene. Methanol, formaldehyde, aspartame, and MTBE share a common metabolite, 7

formaldehyde, and DIPE, TAME, methanol and MTBE are all gasoline

- oxygenate additives 8

(Caldwell et al ., 2008). 9

With the exception of a positive study for malignant lymphomas in Swiss Webster mice 10

exposed to methanol (Apaja, 1989), l ymphoma responses have not been reported by other 11

institutions performing long

- term testing of these chemicals in various strains of rats

, including 12

52 While ERF has tested over 200 chemicals in 398 long

- term ERF bioassays, only 112 of their bioassays ha ve been

published to date (Caldwell, et al., 2008). The extent to which the unpublished studies are documented varies.4

- 82 DRAFT

- DO NOT CITE OR QUOTE formaldehyde inhalation studies in F344 (Kerns et al ., 1983 ; Kamata et al., 1987 ) 53 and Sprague

1

Dawley ( Albert et al., 1982; Sellakumar et al., 1985 ) rats , f ormaldehyde oral studies in Wistar 2

rats (Til et al ., 1989; Tobe et al ., 1989), toluene oral studies in F344 rats (NTP, 1990), MTBE 3

inhalation studies in F344 rats (Chun et al ., 1992)

, aspartame oral studies in Wistar ( Ishii et al., 4

1981 ) and Sprague

- Dawley ( Molinary, 1984 ) rats, and methanol inhalation studies in F344 rats 5

(NEDO, 1987, 1985/2008b ). Several differences in study design may contribute to the 6

differences in responses observed across institutions, particularly study duration and test animal 7

str ain. Fischer

- 344 rats have a high background of mononuclear cell leukemia (20% in control 8

females ) 54 and a very low background rate of ‘‘lymphoma’’ (0% in control females) at 104 weeks 9

( NTP, 2006)

. In contrast, Sprague

- Dawley rats from NTP studies exhibit a low background rate 10

of “leukemias” (0.8% in control females) and a higher background rate of “lymphomas” ( 1.08% 11

in control females ) at 104 weeks (NTP, 2006). Similarly, Chandra et al. ( 1992 ) report a 12

background level of 1.6% for malignant lymphocytic l ymphomas in female control Sprague

13

Dawley rats for 17 2

- year carcinogenicity studies. 14

In lifetime studies of Sprague

- Dawley rats at ERF, the overall incidence of 15

lymphomas/leukemias has been reported to be 13.3% (range, 4.0

– 25.0%) in female historical 16

con trols (2,274 rats) and 20.6% (range, 8.0

– 30.9%) in male historical controls (2,265 rats) 17

(Soffritti et al., 2007)

. The difference in background rates reported by ERF versus other labs for 18

this tumor type could be due to differences in study duration , diff erences in tumor classification 19

systems, and/ or misdiagnoses due to confounding effects (see discussion in Section 4.2.1.3 ). A 20

high background incidence can increase the difficulty of detecting chemically related responses 21

(Melnick et al., 2007) , and the background rate reported by ERF for this tumor type is considered 22

to be high relative to other tumor types and relative to the background rate for this tumor type in 23

Sprague

- Dawley rats from other laboratories (EFSA, 2006; Cruzan, 2009)

. 55 However, it is i n a 24

range that can be considered reasonable for studies that employ a large number of animals 25

( Leakey et al.

, 2003; Caldwell et al., 2009)

. 26

Thus, w ith respect to the identification of hemolymphoreticular carcinogenic responses, 27

life span studies of Spragu e

- Dawley rats performed by ERF may be more sensitive than the 28

2

- year studies of Fischer 344 (F344) strain of rats used by NTP (1990) and NEDO ( 1987, 29

53 Though Kerns et al. (1983) did not report a positive response for lymphoma, a s urvival

- adjusted analys i s of the

data from this study i ndicates a statistically significant trend in female rat mononuclear cell leukemia (p = 0.0056)

and a nearly significant increase in female mouse lymphoma (p = 0.06 ). In the Kamata et al., ( 1987 ) study, only a

small percentage of the original 32 rats/group survived to the end of the study (28 months) due largely to interim

sacrifices (5/group) at 12, 18 and 24 months.

54 Due to this and other health concerns, NTP transitioned to the use of Wistar rats in 2008, and more recently has

adopted Sprague

- Dawley rat s as the rat model for NTP studies due to the reproductive capability and size of Wistar

rats (http://ntp.niehs.nih.gov/go/29502).

55 Cruzan (2009) reports that the incidences of total cancers derived from bloodforming cells, designated as

hemolymphoreticul ar tumors by Ramazzini pathologists, is consistently about four times higher than the incidences

of such tumors in SD rats recorded in the Charles River Laboratory historical database (CRL database).4

- 83 DRAFT

- DO NOT CITE OR QUOTE 1985/2008b). The results of ERF studies of the carcinogenic potential of methanol, MTBE, and 1

formaldehyde a nd related chemicals, ethanol and aspartame, are summarized in this section. 2

4.6.5.2.1. Ethanol

. In a study that was reported in the same article that described the 3

carcinogenic responses of Sprague

- Dawley rats to methanol, Soffritti et al . (2002a) exposed 4

110 Sp rague

- Dawley rats/sex/group to ethanol in drinking water at concentrations of 0 or 10% 5

(v/v) beginning at 39 weeks of age and ending at natural death, and including a single breeding 6

cycle. Various numbers of the offspring (30 male controls, 39 female con trols, 49 exposed 7

males, and 55 exposed females) were exposed to ethanol in drinking water at the same 8

concentrations as their parents. The experiment concluded with the death of the last offspring at 9

179 weeks of age. Animals were examined for the same toxicological parameters as those 10

described for methanol

, and organs and tissues were grossly and histopathologically examined at 11

necropsy. Soffritti et al . (2002a) reported that food and drinking water intake were lower in 12

exposed animals compared to con trols but that body weight changes were similar among the 13

groups. 56 There were no compound

- related clinical signs of toxicity and no differences in 14

survival rates among the groups. While there were apparently no non oncogenic pathological 15

changes evident on gross inspection or histopathologic examination, a number of benign and 16

malignant tumors were considered by the authors to be compound

- related. Compared to 17

controls, these included increased incidences of : 1) total malignant tumors in male and female 18

b reeders (145/220 versus 99/220) and offspring (49/69 versus 54/104) ; 2) total malignant tumors 19

per 100 animals in female breeders (130 versus 60.9) and offspring (164.1 versus 96.4) ; 3) 20

carcinomas of the head and neck, especially to the oral cavity, lips a nd tongue in male and 21

female breeders (27/220 versus 5/220) and offspring (26/69 versus 5/104) ; 4) squamous cell 22

carcinomas of the forestomach in male and female breeders (5/220 versus 0/220) and offspring 23

(2/69 versus 0/104) ; 5) interstitial cell adenomas of the testis in male breeders (23/110 versus 24

9/110) and offspring (4/30 versus 4/49) ; 6) Sertoli

- Leydig cell tumors in ovaries of female 25

offspring (3/39 versus 1/55) ; 7) adenocarcinomas of the uterus in female breeders (9/110 versus 26

2/110) and offspring (8/39 versus 6/55) ; 8) pheochromoblastomas in male and female breeders 27

(13/220 versus 4/220) and offspring (4/69 versus 2/104) ; and 9) osteosarcomas in male and 28

female breeders ([for the head] 14/220 versus 4/220) and offspring ([for the head] 10/69 versus 29

7/104). Notably, Soffritti et al . (2002a) did not observe increases in any of the lymphoma 30

responses reported in their methanol bioassay. Incidence data for these responses and their 31

statistical significance compared to controls are shown in Table 4

- 2 6

.

56 Test animals were likely receiving calories from ethan ol exposure.4

- 84 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 2 6 . Incidence of carcinogenic responses in Sprague

- Dawley rats exposed to

ethanol in drinking water for up to 2 years

Tissues/ a ffected s ites Concentration in percent (v/v)

Male (breeders) Female (breeders) Male (offspring) Female (offspring)

0 10 0 10 0 10 0 10

Total malignant tumors 51/110 66/110 48/110 79/110 b 23/49 23/30 a 31/55 26/39

Oral cavity (carcinomas) 3/110 15/110 b 2/110 12/110 2/49 10/30 b 3/55 16/39 b

Forestomach (squamous

cell carcinomas) 0/110 2/110 0/110 3/110 0/49 1/30 0/55 1/39

Testis (interstitial cell

adenomas) 9/110 23/110 d 4/49 4/30

Sertoli

- Leydig cell tumors

(ovary) 1/110 2/110 0/55 3/39

Uterus (adenocarcinomas) 2/110 9/110 c 6/55 8/39

Head (osteosarcomas) 0/110 8/110 4/110 6/110 4/49 6/30 3/55 4/39

Adre nal gland

(pheochromoblastomas) 3/110 9/110 1/110 4/110 1/49 4/30 1/55 0/39

Total malignant tumors per

100 animals 61.8 89.1 b 60.9 130 b 61.2 136.7 b 96.4 164.1 b

a p < 0.05 using the χ 2 test, as calculated by the authors.

b p < 0.01 using the χ 2 test, as calculated by the authors.

c p < 0.05 using Fisher’s exact test, as calculated by the reviewers.

d p < 0.01 using Fisher’s exact test, as calculated by the reviewers.

Sou rce: Soffritti et al . (2002a).

4.6.5.2.2. Aspartam e. Soffritti et al . (2006, 2005) reported the results of a cancer bioassay on 1

the artificial sweetener aspartame. The study has potential relevance to the carcinogenicity of 2

methanol because aspartame has been sh own to be metabolized to aspartic acid, phenylalanine, 3

and methanol in the GI tract prior to absorption into systemic circulation

. In t he study , aspartame 4

(>98% purity) was given to 100 or 150 Sprague

- Dawley rats/sex/group in feed at dietary 5

concentration s of 0, 80, 400, 2,000, 10,000, 50,000, and 100,000 ppm. The authors reported 6

these concentrations to be equivalent to approximate daily doses of 0, 4, 20, 100, 500, 2,500, and 7

5,000 mg/kg

- day, respectively, under the conditions of the study. A nimals wer e maintained until 8

their “ natural death, ” with the in

- life phase of the experiment concluding with the death of the 9

last animal at 151 weeks. All animals were monitored for body weight, food and water 10

consumption. At death, animals were examined grossly and given a complete histopathological 11

examination. 12

Soffritti et al . (2006, 2005) reported that there were no differences among the groups in 13

mean body weight, survival, or daily water consumption. However, there appeared to be a dose

14

related reduction in food consumption in both male and female rats. 154

- 85 DRAFT

- DO NOT CITE OR QUOTE The principal histopathological finding was an increase d incidence of lymphomas and 1

leukemias in female rats, a response reported by the authors to be statistically significant 2

compared to concurrently expose d controls ( Table 4

- 2 7 ) and greater than the range of overall 3

incidence of lymphomas and leukemias in historical controls at the ERF (13.4% [ range, 7.0

– 4

18.4] in females and 21.8% [ range, 8.0

– 30.9] in males). Among the hemolymphoreticular 5

neoplasms observe d, the most frequent type observed was lympho

- immunoblastic lymphoma. 6

T he authors concluded that aspartame causes a “dose

- related statistically significant increase in 7

lymphomas and leukemias in females at dose levels very near those to which humans can be 8

exposed.” The y postulated that an increase in the incidence of lymphomas and leukemias could 9

be associated with the formation of either methanol or formaldehyde. Other potentially 10

compound

- related effects of aspartame were (1) an increase in combined dy splastic hyperplasias, 11

papillomas, and carcinomas of the renal pelvis and ureter, and (2) a n increasing trend in the 12

formation of malignant schwannomas in peripheral nerves ( Table 4

- 2 8 ). 13

T he European Commission asked the European Food Safety Authority (EFS A) to assess 14

the study and review all ERF findings related to aspartame . An E FS A review panel assessed the 15

study and considered additional unpublished data provided to it by the ERF . In their report, 16

E FS A (2006) concluded that the Soffritti et al. (2005, 2006) study had flaws that brought into 17

question the reported findings. The review panel noted the high background of chronic 18

inflammatory changes in the lung and other vital organs. These background inflammatory 19

changes were thought to contribute signi ficant uncertainty to the interpretations of the study. In 20

fact, the review panel concluded that most of the documented changes, in particular, the apparent 21

compound

- related increase in lymphomas and leukemias, may have been incidental findings and, 22

there fore, unrelated to aspartame. 23

Table 4

- 2 7 . Incidence of lymphomas and leukemias in Sprague

- Dawley rats exposed to

aspartame via the diet

Group ppm in feed Dose (mg/kg

- day) Lymphomas/leukemias (incidence and %)

Male Female

I 0 0 31/150 (21) 13/150 (9)

II 80 4 23/150 (15) 22/150 (15)

III 400 20 25/150 (17) 30/150 b (20)

IV 2,000 100 33/150 (22) 28/150 a (19)

V 10,000 500 15/100 (15) 19/100 a (19)

VI 50,000 2,500 20/100 (20) 25/100 b (25)

VII 100,000 5,000 29/100 (29) 25/100 b (25)

a p < 0.05 using the po ly

- k test.

b p < 0.01 using the poly

- k test.

Source: Soffritti et al . (2006, 2005).4

- 86 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 2 8 . Incidence of combined dysplastic hyperplasias, papillomas and carcinomas

of the pelvis and ureter and of malignant schwannomas in peripheral nerve in

Sprague

- D awley rats exposed to aspartame via the diet

Group ppm in feed Dose

(mg/kg

- day) Incidence and %

Combined hyperplasias,

papillomas and carcinomas

of the pelvis and ureter Peripheral nerve malignant

schwannomas

Male Female Male Female

I 0 0 1/150 ( 0.7) 2/150 c (1.3) 1/150 c (0.7) 0/150 (0)

II 80 4 3/149 (2) 6/150 (4) 1/150 (0.7) 2/150 (1.3)

III 400 20 5/149 (3) 9/150 a (6) 3/150 (2) 0/150 (0)

IV 2,000 100 5/150 (3) 10/150 a (7) 2/150 (1.3) 3/150 (2)

V 10,000 500 3/100 (3) 10/100 b (10) 2/100 (2) 1/10 0 (1)

VI 50,000 2,500 3/100 (3) 10/99 b (10) 3/100 (3) 1/100 (1)

VII 100,000 5,000 4/100 (4) 15/100 b (15) 4/100 (4) 2/100 (2)

a p < 0.05 using the poly

- k test.

b p < 0.01 using the poly

- k test.

c p < 0.05 using the Cochran

- Armitage test for trend.

So urce: Soffritti et al . (2006).

In their conclusions, the EFSA review panel took note of negative results of 2

- year 1

carcinogenic studies of aspartame (Ishii et et al., 1981; Ishii, 1981; NTP, 2003) and of the 2

findings of a recent epidemiological study carried ou t by the US National Cancer Institute (NCI, 3

2006). 4

In an effort to further clarify these issues, Soffritti et al . (2007) reported the results of 5

another lifetime study of aspartame in which 95 controls and 70 Sprague

- Dawley rats/sex/group 6

were exposed, fir st in utero, then via the diet, to aspartame at concentrations of 0, 400, or 7

2

, 000 ppm (mg/kg) of feed. The authors assumed an average food consumption of 20 g / day and 8

an average body weight (males and females) of 400 g, thereby deriving average target do ses of 0, 9

40, and 200 mg/k g

- day . Soffritti et al . (2007) began administering the aspartame

- supplemented 10

feed to the dams on GD12; and offspring receiv ed feed containing aspartame at the appropriate 11

concentration from weaning until natural death. Animals w ere observed three times daily 12

Monday

– Friday, and twice daily on Saturdays, Sundays, and holidays. This regimen was both to 13

monitor clinical signs and to reduce the possibility of decedents undergoing autolysis before 14

discovery. As described by the autho rs, all deceased animals were refrigerated, then necropsied 15

no more than 19 hours after discovery. 16

Food and drinking water consumption was monitored once/day. Beginning at 6 weeks of 17

age, individual body weights were recorded once a week for 13 weeks, the n every 2 weeks until 18

natural death. All animals were examined grossly every 2 weeks. After necropsy , tissues and 19

organs were sampled for histopathological processing and microscopic examination ( includ ing 204

- 87 DRAFT

- DO NOT CITE OR QUOTE skin and subcutaneous tissue, mammary gland, bra in, pituitary, Zymbal’s gland, salivary gland, 1

Harderian gland, cranium, tongue, thyroid, parathyroid, pharynx, larynx, thymus and mediastinal 2

lymph nodes, trachea, lung and main stem bronchi, heart, diaphragm, liver, spleen, pancreas, 3

kidney, adrenal glan d, esophagus, stomach, intestine, urinary bladder, prostate, vagina, gonads, 4

interscapular brown fat pads, subcutaneous and mesenteric lymph nodes )

, as were all 5

pathological lesions identified on gross necropsy. 6

There were no differences in food and wate r consumption or in body weights among the 7

dose groups . A s illustrated graphically by the authors, there was little change in overall survival 8

rates. Discussion of the histopathological findings focused exclusively on the cancer outcomes. 9

The incidence of total malignant tumors was increased significantly in high

- dose males 10

compared to controls ( p < 0.01). The slight increase in the incidence of total malignant tumors in 11

females was not statistically significant ( Table 4

- 2 9 ). With regard to the inciden ce of type

- or 12

site

- specific neoplasms, Soffritti et al . (2007) reported statistically significant increases 13

(calculated using the Cox regression model) in combined lymphomas and leukemias in both 14

sexes of Sprague

- Dawley rats. In males, the most frequentl y observed hist i otypes were 15

lymphoblastic lymphomas involving the lung and mediastinal peripheral nodes, while in females, 16

the most commonly observed lesions were lymphocytic lymphomas and lympho

- immunoblastic 17

lymphomas involving the thymus, spleen, lung a nd peripheral nodes. There was also an increase 18

in the incidence of mammary gland carcinomas in female Sprague

- Dawley rats. The incidences 19

of total malignant, mammary, and lymphocytic and leukocytic tumors, in comparison to 20

concurrent and the range of hi storical controls for combined lymphomas and leukemias and 21

mammary gland tumors observed at the ERF, are shown in Table 4

- 2 9

. 22

Table 4

- 2 9 . Incidence of tumors in Sprague

- Dawley rats exposed to aspartame from

GD12 to natural death

Dose

(mg/kg

- day) Malignant tumors Lymphomas/leukemias Mammary carcinomas

Tumor

- bearing

animals (percent) Tumors/100

animals Tumor

- bearing animals

(percent) Tumor

- bearing animals

(percent)

Males

0 23/95 (24.2) 27.4 9/95 (9.5) 0/95 (0)

20 18/70 (25.7) 27.1 11/70 (15.7) 0/70 (0)

100 28/70 (40.0) a 44.3 12/70 (17.1) b 2/70 (2.9)

Historical controls ND ND 8

– 31% NR

Females

0 42/95 (44.2) 50.5 12/95 (12.6) 5/95 (5.3)

20 31/70 (44.3) 62.9 12/70 (17.1) 5/70 (7.1)

100 37/70 (52.9) 85.7 22/70 (33.4) a 11/70 (15.7) b

Historical controls ND ND 7

- 18% 4

– 14%4

- 88 DRAFT

- DO NOT CITE OR QUOTE Dose

(mg/kg

- day) Malignant tumors Lymphomas/leukemias Mammary carcinomas

Tumor

- bearing

animals (percent) Tumors/100

animals Tumor

- bearing animals

(percent) Tumor

- bearing animals

(percent)

a p < 0.01 versus concurrent controls, as calculated by the authors using the Cox regression model.

b p < 0.05 versus concurrent controls, as calculated by the authors using the Cox regression model.

ND = no data. ; NR = not reported.

Sour ce: Soffritti et al . (2007).

With regard to target organs and tissues susceptible to aspartame carcinogenicity, Soffritti 1

et al . (2007) drew attention to the similar outcome of these results to those reported by Soffritti 2

et al . (2006, 2005). The author s suggested that the increased incidence in combined lymphomas 3

and leukemias in female Sprague

- Dawley rats as compared to the earlier study was likely due to 4

the earlier exposure to aspartame experienced by the animals in the Soffritti et al. (2007) study 5

(prenatal and postnatal versus postnatal only). The authors provided a direct comparison of the 6

incidence of lymphomas/leukemias between the studies, as summarized in Table 4

- 30

. 7

Table 4

- 30 . Comparison of the incidence of combined lymphomas and leukemias in

female Sprague

- Dawley rats exposed to aspartame in feed for a lifetime

, either pre

and postnatally or postnatally only

Dose (mg/kg

- day) Percent with lymphomas/leukemias

Pre

- and postnatal exposure a Postnatal exposure only b

0 12.6 8.7

20 17.1 20.0

1 00 31.4 18.7

Source: a Soffritti et al . (2007); b Soffritti et al . (2006, 2005).

An EFSA (2009) review of the Sofftitti et al. (2007) study notes that the ratio between the 8

incidence in the low

- dose group and the incidence in the concurrent control in T able 4

- 30 is 9

considerably lower in the animals exposed prenatally (1.4:1) compared to those exposed 10

postnatally (2.3:1). The ratio in the groups exposed to 100 mg/kg bw/day relative to the 11

respective concurrent controls is only slightly higher in animals exposed prenatally (2.5:1) 12

compared to those exposed postnatally (2.2:1). EFSA also notes that the incidence of 13

lymphomas and leukemias in aspartame

- receiving males of the Soffritti et al. (2007) study was 14

within the range of historical controls for these responses in Sprague

- Dawley rats at the ERF. 15

For example, t he percent incidence of combined lymphomas and leukemias in males exposed 16

pre

- and postnatally to 100 mg/kg

- day aspartame (17.1%) was within the range of historical 17

controls for this response (8

– 31%, with an overall mean of 20.6%). Soffritti et al. (2007) 18

acknowledge this fact, but reason that comparisons of potentially compound

- associated 19

incidences of tumor formation to incidences in concurrent controls provide a more scientifically 204

- 89 DRAFT

- DO NOT CITE OR QUOTE valid indic ator of the tumorigenic impact of a chemical under investigation than comparisons to 1

historical control data. 57 Furthermore, the incidence of combined lymphomas and leukemias in 2

the female rats in the high

- dose group is well above the historical control range. Therefore, 3

Soffritti et al. (2007) concluded that their second experiment confirmed the carcinogenic 4

potential of aspartame in Sprague

- Dawley rats observed in Soffritti et al . (2006, 2005). The high 5

and variable incidence of this tumor type in ERF controls remains a concern. However, t he 6

results provide support for studies suggesting similar effects from methanol (Soffritti et al

., 7

2002a) since methanol is one of the degradation products of aspartame and appears to have 8

carcinogenic potential at s ome of the same target organs and tissues. 9

4.6.5.2.3. MTBE

. In an experiment that also may be relevant to the carcinogenicity of 10

methanol, scientists at the ERF carried out a cancer bioassay on MTBE, in which the compound 11

was administered to 60 Sprague

- Dawley rats /sex/group by gavage in olive oil at 0, 250, and 12

1

, 000 mg/kg

- day, 4 days/week, for 104 weeks (Belpoggi et al ., 1995). D oses adjusted to daily 13

dose were 0, 143, and 571 mg/kg

- day. This experiment and its findings may relate to the 14

carcinogenicity of metha nol, since methanol is one of several metabolites of MTBE (AT SD R, 15

1997). At the end of the exposure period, the animals were allowed to live out their “ natural ” 16

life, the last animal dying 166 weeks after the start of the experiment (at 174 weeks of age). 17

Mean daily feed and drinking water consumption were determined weekly for the first 18

13 weeks of the experiment, then every 2 weeks until 112 weeks of age. Individual body weights 19

were measured according to the same protocol, then every 8 weeks until the end of the 20

experiment. All animals were examined for gross lesions weekly for the first 13 weeks, then 21

every 2 weeks until term. All animals were examined grossly at death, then histopathologically 22

examined for a full suite of organs and tissues

. 23

As desc ribed by the authors, there were no differences among the groups in body weight 24

and clinical signs of toxicity. Survival was dose

- dependently reduced in female rats after 25

16 weeks of exposure

. P aradoxically, survival was improved in high

- dose males compa red to 26

controls after 80 weeks. Although there were no noncarcinogenic effects of MTBE reported , a 27

number of benign and malignant tumors were identified, including tumor types that were not 28

observed in the ERF methanol study such as an increased incidence of testicular Leydig tumors 29

in high

- dose males and as determined by the authors, as well as a dose

- related statistically 30

significant increase in lymphomas and leukemias in females. The incidences of these tumors 31

compared to the initial number of animals exposed and compared to those at risk at the time of 32

the first observed tumor formation are shown in Table 4

- 3 1

. 33

57 There are also potential problems with the use of historical control information from a colony that has been

maintained for over three decades. Population sensitivity can and does change over time.4

- 90 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 3 1 . Incidence of Leydig cell testicular tumors and combined lymphomas and

leukemias in Sprague

- Dawley rats exposed to MTBE via gavage f or 104 weeks

Duration

adjusted d ose Leydig cell tumors Combined lymphomas and leukemias

Number of males Number of males Number of females

Affected Initial At

risk c Affected Initial At

R isk d Affected Initial At

R isk e

0 2 60 26 10 60 59 2 60 58

143 2 60 25 9 60 59 6 b 60 51

571 11 a 60 32 7 60 58 12 b 60 47

a p < 0.05 using prevalence analysis

b p < 0.01 using a log

- ranked test.

c Alive male rats at 96 weeks of age, when first Leydig cell tumor was observed.

d Alive male rats at 32 weeks of age, when fi rst leukemia was observed.

e Alive female rats at 56 weeks of age, when first leukemia was observed.

Source: Belpoggi et al . (1995).

The possible contribution of the metabolite methanol to the reported responses cannot be 1

quantified

. It is also possibl e that the parent compound and/or one or more of MTBE’s other 2

metabolites ( e.g., tertiary butanol or formaldehyde) may be etiologically linked to the formation 3

of the identified neoplasms (Blancato et al ., 2007). 4

4.6.5.2.4. Formaldehyde

. Scientists at the ERF have carried out two long

- term experiments on 5

the potential carcinogenicity of formaldehyde , which is itself a metabolite of methanol

, 6

aspartame and MTBE . While the tumorigenic effects at the portal

- of

- entry (such as in the oral 7

cavity and GI tract, for oral studies) may lack relevance to the possible effects of metabolite s 8

formed in situ following methanol exposure

, systemic neoplasms such as lymphomas and 9

leukemias have been described for formaldehyde as well (Soffritti et al ., 2002b, 1989). This 10

suggests t hat formaldehyde metabolized from methanol , aspartame and MTBE may be 11

etiologically important in the formation of lymphomas and leukemias in animals exposed to 12

the se compound s

. 13

In the first formaldehyde study (designated BT 7001 ; Soffritti et al., 1989 )

, 50 Sprague

14

Dawley rats/sex/group (starting at 7 weeks of age) were exposed to formaldehyde in drinking 15

water at concentrations of 10, 50, 500, 1,000, and 1,500 mg/L for 104 weeks. Another 50 16

Sprague

- Dawley rats/sex received methanol in drinking water at 1 5 mg/L, and 100 rats/sex 17

received water only, as controls. Body weight and water and food consumption were monitored 18

weekly for the first 13 weeks, then every 2 weeks thereafter. All animals were allowed to live 19

out their “ natural ” life, at which point t hey were subjected to necropsy and a complete 20

histopathological examination. 21

The final results of the BT 7001 Soffritti et al

. (1989) experiment were reported by 22

Soffriti et al. ( 2002b). Water consumption was reduced compared to controls in high

- dose ma les 234

- 91 DRAFT

- DO NOT CITE OR QUOTE and in females at the three highest doses. However, there appeared to be no evidence of 1

compound

- related body weight changes, clinical signs of toxicity among the groups

, no r 2

non oncogenic histopathological effects of formaldehyde. The authors noted s tatistically 3

significant increases in the incidence of tumor

- bearing males at 1500 ppm (p<0.01) and in total 4

malignant tumors in females at 100, 1000 and 1500 ppm (p<0.01) and in males at 500 ppm 5

(p<0.05) and 1500 ppm (p<0.01). They reported statistically significant increases in malignant 6

mammary tumors in females at 100 ppm (p<0.01) and 1500 ppm (p<0.05) , and in testicular 7

interstitial cell adenomas in the 1000 ppm males (p<0.05) . They also noted sporadic incidences 8

in the treatment groups only (primari ly at the highest dose) of leiomyosarcomas of the stomach 9

and intestine considered to be very rare for the ERF rat colony

. As for methanol and the other 10

compounds discussed in this section, they reported increases in the number of 11

hemolymphoreticular tumo rs for both sexes

. The incidence of hemolymphoreticular neoplasms 12

among the dose groups is shown in Table 4

- 3 2

. 13

Table 4

- 3 2 . Incidence of hemolymphoreticular neoplasms on Sprague

- Dawley rats

exposed to formaldehyde in drinking water for 104 weeks

Concentra tion in drinking water (mg/L) Males Females

0 8/100 7/100

0 (15 mg/L methanol) 10/50 5/50

10 4/50 5/50

50 10/50 7/50

100 13/50 b 8/50

500 12/50 a 7/50

1,000 11/50 a 11/50 a

1,500 23/50 b 10/50 b

a p < 0.05 using the χ 2 test; b p < 0.01 using the χ 2 test.

Source: Soffritti et al. (2002b).

14

Soffritti et al . (1989) also described the results of another experiment (BT 7005) in which 15

approximately 20 Sprague

- Dawley rats/sex/group were exposed to either regular drinking water 16

or 2

, 500 mg/L formaldehyde

, beginning at 25 weeks of age for 104 weeks . These animals were 17

allowed to mate and approximately 40

– 60 of the F 0 pups were likewise exposed to 0 or 18

2,500 ppm formaldehyde in drinking water (after weaning) for 104 weeks. As before, parents 19

and progeny lived out their normal life span but then were subjected to a complete 20

histopathological examination. Incidence of leukemias in exposed breeders and offspring is 21

shown in Table 4

- 3 3

. T he authors considered t h is data to indicate a “slight” increase in 22

leukemias in breeders at 2,500 ppm, but the changes did not achieve statistical significance. 23

244

- 92 DRAFT

- DO NOT CITE OR QUOTE Table 4

- 3 3 . Incidence of leukemias in breeder and offspring Sprague

- Dawley rats

exposed to formaldehyde in drinking w ater for 104 weeks (Test BT 7005)

Concentration (ppm) Incidence of leukemias

Breeder Offspring

Males Females Males Females

0 0/20 1/20 3/59 3/49

2

, 500 2/18 2/18 4/36 0/37

Source: Soffritti et al . (1989).

4.7. SYNTHESIS OF MAJOR N ONCANCER EFFECTS

4.7.1. Summar y of Key Studies in Methanol Toxicity

A substantial body of information exists on the toxicological consequences to humans 1

who consume or are acutely exposed to large amounts of methanol. Neurological and 2

immunological effects have been noted in adult hum an subjects acutely exposed to as low as 3

200 ppm (262 mg/m 3 ) methanol ( Mann et al ., 2002; Chuwers et al ., 1995). Nasal irritation 4

effects have been reported by adult workers exposed to 459 ppm (601 mg/m 3 ) methanol. Frank 5

effects such as blurred vision an d bilateral or unilateral blindness, coma, convulsions/tremors, 6

nausea, headache, abdominal pain, diminished motor skills, acidosis

, and dyspnea begin to occur 7

as blood levels approach 200 mg methanol/L, and 800 mg/L appears to be the threshold for 8

lethali ty. Data for subchronic, chronic or in utero human exposures are very limited. 9

Determinations regarding longer term effects of methanol are based primarily on animal studies. 10

An end

- point

- by

- end

- point survey of the primary effects of methanol in experi mental 11

animals is given in the following paragraphs. Tabular summaries of the principal toxicological 12

studies that have examined the impacts of methanol when experimental animals were exposed to 13

methanol via the oral or inhalation routes are provided in T ables 4

- 34 and 4

- 35

. Most studies 14

focused on developmental and reproductive effects. A large number of the available studies were 15

performed by routes of exposure ( e.g., i.p.) that are less relevant to the assessment. The data are 16

summarized separate sec tions that address oral exposure (Section 4.7 .1.1) and inhalation 17

exposure (Section 4.7 .1.2). 18

Table 4

- 34 . Summary of studies of methanol toxicity in experimental animals (oral)

Species, strain,

number/sex Dose/duration NOAEL

(mg/kg

- day) LOAEL

(mg/kg

- day) E ffect Reference

Rat

Sprague

- Dawley

30/sex/group 0, 100, 500, and

2,500 mg/kg

- day for

13 w k 500 2,500 Reduction of brain

weights, increase in the

serum activity of ALT

and AP. Increased liver

weights U.S. EPA

(1986c)4

- 93 DRAFT

- DO NOT CITE OR QUOTE Species, strain,

number/sex Dose/duration NOAEL

(mg/kg

- day) LOAEL

(mg/kg

- day) E ffect Reference

Rat

Sprague

- Dawley

100/sex/group 0, 50 0, 5,000, or

20,000 ppm (v/v) in

drinking water, for

104 w k . Doses were

approx. 0, 46.6, 466,

and 1,872 mg/kg

- day

(male) and 0, 52.9,

529, and 2101 mg/kg

day (female) 466

– 529 1

, 872

– 2

, 101 Increased incidence of

ear duct a carcinomas,

lymphoreticular

tumors, and total

malignant tumors. No

noncancer effects Soffritti et al

. (2002a)

Mouse

Swiss 560, 1000 and 2100

mg/kg/d ( female )

and 550, 970, and

1800 mg/kg/d ( male )

, 6 d ays /wk for life ND 1,800

- 2,100 Increased incidence of

liver parenchymal cell

necrosis and malignant

lymphomas Apaja (1980)

Reproductive/developmental toxicity studies

Rat

Long

- Evans

10 pregnant

females/group 0 and 2,500 mg/kg

day on either GD15

–

GD 17 or GD17

–

GD 19. NA 2,500 Neurobehavioral

deficits (such as

homing behavior,

suckling ability In furna and

Weiss (1986)

Mouse

CD

- 1

8 pregnant

females and 4

controls 4 g/kg

- day in 2 daily

doses on GD6

- GD 15 NA 4,000 Increased incidence of

totally resorbed litters,

cleft palate and

exencephaly. A

decrease in the number

of live fetuses/litter Rogers, et al

. (1993a)

NA = Not applicable; ND = Not determined; M = male, F =female.

a In an NTP evaluation of pathology slides from another bioassay from this laboratory in which a similar ear duct

carcinoma finding was reported (Soffritti et al ., 2006, 2005), NTP pa thologists interpreted a majority of these ear

duct responses as being hyperplastic, not carcinogenic, in nature (EFSA, 2006; Hailey , 2004).

Table 4

- 35 . Summary of studies of methanol toxicity in experimental animals

(inhalation exposure)

Species, strain

,

number/sex Dose/duration NOAEL

(ppm) LOAEL

(ppm) Effect Reference

Monkey

M. fascicularis

, 1 or 2

animals/group 0, 3

, 000, 5,000,

7,000, or 10,000

ppm, 21 h r /day, for

up to 14 days ND ND Clinical signs of toxicity, CNS

changes, including degeneration

of t he bilateral putamen, caudate

nucleus, and claustrum. Edema

of cerebral white matter. NEDO (1987)

Dog (2) 10,000 ppm for 3

min, 8 times/ day for

100 days NA NA None Sayers et al

. (1944)

Rat

Sprague

- Dawley

5 males/ group 0, 200, 2000, or

10,000 ppm, 8

h r

/ day, 5 days/ wk for

up to 6 w k NA 200 Transient reduction in plasma

testosterone levels Cameron et al

. (1984)4

- 94 DRAFT

- DO NOT CITE OR QUOTE Species, strain

,

number/sex Dose/duration NOAEL

(ppm) LOAEL

(ppm) Effect Reference

Rat

Sprague

- Dawley

5 males/ group 0, or 200 ppm,

6 h r /day, for either 1

or 7 days NA 200 Transient reduction in plasma

testosterone levels Camero n et al

. (1985)

Rat

Sprague

- Dawley

5/sex/group 0, 500, 2,000, or

5,000 ppm,

5 days/w k for 4 w k 5,000 NA No compound

- related effects

Andrews et al

. (1987) Monkey

M. fascicularis

3/sex/group 0, 500, 2,000, or

5,000 ppm,

5 days/w k for 4 w k 5,000 NA No compo und

- related effects

Rat

Sprague

- Dawley

10/sex/group 0, 300, or 3,000 ppm,

6 h r / d ay, 5 days/ wk

for 4 w k NA 300 Reduction in size of thyroid

follicles Poon et al

. (1994)

Rat

Sprague

- Dawley

15/sex/group 0 or 2,500 ppm, 6

h r

/day, 5 days/w k for

4 wk NA 2,500 Reduction of relative spleen

weight in females,

histopathologic changes to the

liver, irritation of the upper

respiratory tract Poon et al

. (1995)

Monkey

M. fascicularis

2 or 3 animals/

group/time point 0, 10, 100, or

1,000 ppm, 21 h r /day

for either 7, 19, or 29

mo ND

ND ND

ND Limited fibrosis of the liver

Possible myocardial and renal

effects

NEDO (1987) Rat

F344

20/sex/group 0, 10, 100, or

1,000 ppm,

20 h r /day, for 12 mo NA NA No compound

- related effects

Mouse

B6C3 F 1 30/sex/group 0, 10, 100, or

1 000 ppm, 20 h r /day,

for 12 mo NA NA No clear

- cut compound

- related

effects

Mouse

B6C3 F 1

52

– 53/sex/group 0, 10, 100, or

1,000 ppm,

20 h r /day, for 12 mo 100 1,000 Increase in kidney weight,

decrease in testis and spleen

weights

Rat

F344

52/sex/group 0, 10 , 100, or

1,000 ppm,

~ 20 h r /day for 2 yr 100 1,000 Fluctuations in a number of

urinalysis, hematology, and

clinical chemistry parameters.

Development of pulmonary

adenoma/adenocarcinoma

(males), pheochromocytomas

(females)

Reproductive/developmental toxi city studies

Rat

Sprague

- Dawley

15 / pregnant

females/group 0, 5,000, 10,000, or

20,000 ppm, 7 h r /day

on either GD1

– GD 19

or GD 7

– GD 15. 5,000 10,000 Reduced fetal body weight,

increased incidence of visceral

and skeletal abnormalities,

including rudimentary a nd extra

cervical ribs Nelson et al

. (1985)

Rat

Sprague

- Dawley

36 / pregnant

females/group 0, 200, 1,000, or

5000 ppm,

22.7 h r /day, on GD7

–

GD 17 1,000 5,000 Late

- term resorptions, reduced

fetal viability, increased

frequency of fetal

malformations, variation s and

delayed ossifications. NEDO (1987)4

- 95 DRAFT

- DO NOT CITE OR QUOTE Species, strain

,

number/sex Dose/duration NOAEL

(ppm) LOAEL

(ppm) Effect Reference

Rat

Sprague

- Dawley

F 1 and F 2 generations of a

two

- generation

study 0, 10, 100, or

1000 ppm, 20 h r /day;

F 1 - b irth to end of

mating (M) or

weaning (F); F2

-

b irth to 8 wks 100 1,000 Reduced weight of brain,

pituitary , and thymus at 8, 16

and 24 w k postnatal in F 1 and at

8 w k in F 2

Rat

Sprague

- Dawley

Follow

- up study of

brain weights in F 1 generation of 10

– 14/sex/group in F 1 generation 0, 500, 1,000, and

2,000 ppm; GD0

through F 1 generation 500 1,000 Reduced brain weight at 3 wk

and 6 wk (males only). Reduced

brain and cerebrum weight at 8

w k (males only)

Mouse

CD

- 1

30

– 114 pregnant

females/group 0, 1,000, 2,000,

5,000, 7,500, 10,000,

or 15,000 ppm,

7 h r /day on GD6

–

GD 15. 1,000 2,000 Increased incidence of extra

ce rvical ribs, cleft palate,

exencephaly; reduced fetal

weight and pup survival,

Delayed ossification Rogers et al

. (1993a)

Mouse

CD

- 1

12

– 17 pregnant

females/group 0 and 10,000 ppm on

two consecutive days

during GD6

– GD 13

or on a single day

during GD5

– GD 9 NA 10,000 Cleft palate, exencephaly,

skeletal malformations Rogers and

Mole (1997)

Rat

Long

- Evans

6

– 7 pregnant

females/group 0 or 15,000 ppm,

7 h r /day on GD7

–

GD 19 NA 15,000 Reduced pup weight Stanton et al

. (1995)

Rat

Long

- Evans

10

– 12 pregnant

females/grou p 0 or 4,500 ppm from

GD10 to PND21. NA 4,500 Subtle cognitive deficits Weiss et al

. (1996)

Monkey

M. fascicularis

12 m onkeys/group 0, 200, 600, or

1800 ppm, 2.5

h r

/day, 7 days/ wk,

during premating,

mating and gestation N D N D a Shortened period of gestati on;

may be related to exposure (no

dose

- response),

neurotoxicological deficits

including reduced performance

in the VDR test; may be related

to premature births. Burbacher

et al . ( 20004a,

2004b, 1999a,

1999 b )

ND = Not determined due to study limitations such as small number of animals /time point/ exposure level

NA = Not applicable.

a Gestation resulted in a shorter period of gestation in dams exposed to as low as 200 ppm (263 mg/m 3 ). However,

because of uncertainties associated with these results, incl uding clinical intervention and the lack of a dose

response, EPA was not able to identify a definitive NOAEL or LOAEL from this stud y.

4.7.1.1. Oral

There have been very few subchronic, chronic, or in utero experimental studies of oral 1

methanol toxicity. In one such experiment, an EPA

- sponsored 90

- day gavage study in Sprague

2

Dawley rats suggested a possible effect of the compound on the liver (U.S. EPA, 1986c). In the 3

absence of gross or histopathologic evidence of toxicity, fluctuations on some clinical chemist ry 44

- 96 DRAFT

- DO NOT CITE OR QUOTE markers of liver biochemistry and increases in liver weights at the highest administered dose 1

(2,500 mg/kg

- day) justify the selection of the mid

- dose level (500 mg/kg

- day) as a NOAEL for 2

this effect under the operative experimental conditions. That the bolus effect may have been 3

important in the induction of those few effects that were apparent in the subchronic study is 4

suggested by the outcome of lifetime drinking water study of methanol that was carried out in 5

Sprague

- Dawley rats by Soffritti et al

. (2002a). According to the authors, no noncancer 6

toxicological effects of methanol were observed at drinking water concentrations of up to 7

20,000 ppm (v/v). Based on default assumptions on drinking water consumption and body 8

weight gain assumptions, the h igh concentration was equivalent to a dose of 1,780 mg/kg

- day in 9

males and 2,177 mg/kg

- day in females. In the stated absence of any changes to parameters 10

reflective of liver toxicity in the Soffritti et al . (2002a) study, the slight impacts to the liver 11

o bserved in the subchronic study at 2,500 mg/kg

- day suggest the latter dose to be a minimal 12

LOAEL. Logically, the true but unknown threshold would at the high end of the range from 500 13

(the default NOAEL) to 2,500 mg/kg

- day for liver toxicity via oral gava ge. 14

Two studies have pointed to the likelihood that oral exposure to methanol is associated 15

with developmental neurotoxicity or developmental deficits. When Infurna and Weiss (1986) 16

exposed pregnant Long

- Evans rats to 2% methanol in drinking water (provid ing a dose of 17

approximately 2,500 mg/kg

- day)

, they observed no reproductive or developmental sequelae 18

other than from 2 tests within a battery of fetal behavioral tests (deficits in suckling ability and 19

homing behavior). In the oral section of the Rogers et al . (1993a) study, such teratological 20

effects as cleft palate and exencephaly and skeletal malformations were observed in fetuses of 21

pregnant female mice exposed to daily gavage doses of 4,000 mg/kg methanol during GD6

– 22

GD 15. Likewise, an increase in to tally resorbed litters and a decrease in the number of live 23

fetuses/litter appear likely to have been an effect of the compound. Similar skeletal 24

malformations were observed by Rogers and Mole (1997), Rogers et al . (1993a), and Nelson 25

et al . (1985) follow ing inhalation exposure. 26

4.7.1.2. Inhalation

Some clinical signs, gross pathology, and histopathological effects of methanol have been 27

seen in experimental animals including adult nonhuman primates exposed to methanol vapor. 28

Results from an unpublished study (NEDO , 1987) of M. fascicularis monkeys

, chronically 29

exposed to concentrations as low as 10 ppm for up to 29 months

, resulted in histopathological 30

effects in the liver, kidney, brain and peripheral nervous system. These results were generally 31

reported as subt l e and do not support a robust dose response over the range of exposure levels 32

used. Confide n ce in the methanol

- induced findings of effects in adult nonhuman primates is 33

limited because this stud y utilized a small number (2

– 3) of animals /dose level/time of sacrifice 34

and inadequately report ing of results ( i.e., lack of clear documentation of a concurrent control 354

- 97 DRAFT

- DO NOT CITE OR QUOTE group). In addition, the monkeys used in this study were all wild

- caught. All of these concer n s 1

limit the study’s utility in derivation of an RfC. 2

A number of studies have examined the potential toxicity of methanol to the male 3

reproductive system (Lee et al ., 1991; Cameron et al ., 1985, 1984). The data from Cameron 4

et al . (1985, 1984) showed a transient but not necessarily dose

- related decrease in serum 5

testosterone levels of male Sprague

- Dawley rats. Lee et al . (1991) reported the appearance of 6

testicular lesions in 18

- month

- old male Long

- Evans rats that were exposed to methanol for 7

13 weeks and maintained on folate

- deficient diets. Taken togeth er, the Lee et al . (1991) and 8

Cameron et al . (1985, 1984) study results could indicate chemically

- related strain on the rat 9

system as it attempts to maintain hormone homeostasis. However, the available data are 10

insufficient to definitively characterize me thanol as a toxicant to the male reproductive system. 11

When Sprague

- Dawley rats were exposed to methanol, 6 hours/day for 4 weeks, there 12

were some signs of irritation to the eyes and nose. Mild changes to the upper respiratory tract 13

were also described in Sprague

- Dawley rats that were exposed for 4 weeks to up to 300 ppm 14

methanol (Poon et al ., 1995). Other possible effects of methanol in rats included a reduction in 15

size of thyroid follicles (Poon et al ., 1994), panlobular vacuolation of the liver, and a decrease in 16

spleen weight (Poon et al ., 1995). NEDO (1987) reported dose

- related increases in moderate 17

fatty degeneration in hepatocytes of male mice exposed via inhalation for 12 months, but this 18

finding was not observed in the NEDO (1987) 18

- month mous e inhalation study. Nodes 19

reported in the liver of mice from the 18

- month study may have been precancerous, but the 20

18

- month study duration was not of sufficient duration to make a determination. 21

One of t he most definitive and quantifiable toxicological impacts of methanol when 22

administered to experimental animals via inhalation is related to the induction of developmental 23

abnormalities in fetuses exposed to the compound in utero. Developmental effects have been 24

demonstrated in a number of species, incl uding monkeys, but particularly rats and mice. Most 25

developmental teratological effects appear to be more severe in the latter species. For example, 26

in the study of Rogers et al . (1993a) in which pregnant female CD

- 1 mice were exposed to 27

methanol vapors on GD6

– GD 15 at a range of concentrations, reproductive and fetal effects 28

included an increase in the number of resorbed litters, a reduction in the number of live pups, and 29

increased incidence of exencephaly, cleft palate, and the number of cervical ribs. While the 30

biological significance of the cervical rib effect has been the subject of much debate (See 31

discussion of Chernoff and Rogers [ 2004 ] in Section 5), it appears to be the most sensitive 32

indicator of developmental toxicity from this study, with a N OAEL of 1

, 000 ppm (1,310 mg/m 3 ). 33

In rats, however, the most sensitive developmental effect

, as reported in the NEDO (1987) two

34

generation inhalation studies

, was a postnatal reduction in brain weight at 3, 6 and 8 weeks 35

postnatally , which was significantl y lower than controls when pups and their dams were exposed 364

- 98 DRAFT

- DO NOT CITE OR QUOTE to 1,000 ppm (1,310 mg/m 3 ) during gestation and throughout lactation. The NOAEL reported in 1

this study was 500 ppm (655 mg/m 3 ). 2

Rogers and Mole (1997) addressed the question of which period of ge station was most 3

critical for the adverse developmental effects of methanol in CD

- 1 rats. Such malformations and 4

anomalies as cleft palate, exencephaly, and a range of skeletal defects, appeared to be induced 5

with a greater incidence when the dams were ex posed on or around GD6. These findings were 6

taken to indicate that methanol is most toxic to embryos during gastrulation and in the early 7

stages of organogenesis. However, NEDO (1987) gestation

- only and two

- generation studies 8

showed that significant redu ctions in brain weight were observed at a lower exposure levels 9

when pups and their dams were exposed during lactation as well as gestation, indicating that 10

exposure during the later stages of organogenesis, including postnatal development, can 11

significant ly contribute to the severity of the effects in this late

- developing organ system. 12

In comparing the toxicity (NOAELs and LOAELs) for the onset of developmental effects 13

in mice and rats exposed in utero, there is suggestive evidence from the above studies that mice 14

may be more susceptible to methanol than rats. Supporting evidence for this proposition has 15

come from in vitro studies in which rat and mouse embryos were exposed to methanol in culture 16

(Andrews et al ., 1993). Further evidence for species

- by

- sp ecies variations in the susceptibility of 17

experimental animals to methanol during organogenesis has come from experiments on monkeys 18

(Burbacher et al

., 2004a, 2004 b, 1999a, 1999 b). In these studies, exposure of monkeys to 19

methanol during premating, mating, and throughout gestation resulted in a shorter period of 20

gestation in dams exposed to as low as 200 ppm (263 mg/m 3 ). The shortened gestation period 21

was largely the result of C

- sections performed in the methanol

- exposure groups “in response to 22

signs of po ssible difficulty in the maintenance of pregnancy,” including vaginal bleeding. 23

Though statistically significant, the finding of a shortened gestation length may be of limited 24

biological significance. G estational age, birth weight and infant size observa tions in all exposure 25

groups were within normal ranges for M. fascicularis monkeys

, and vaginal bleeding 1

– 4 days 26

prior to delivery of a healthy infant does not necessarily imply a risk to the fetus (as cited in 27

CERHR , 2004) . An ultrasound examination c ou ld have substantiated fetal or placental problems 28

arising from presumptive pregnancy duress ( s ee S ection 4.3.2) . As discussed in Section 4. 4 .2, 29

there is also evidence from this study that methanol caused neurobehavioral effects in exposed 30

monkey infants t hat may be related to the gestational exposure . However, the data are not 31

conclusive, and a dose

- response trend is not robust. T here is insufficient evidence to determine if 32

the primate fetus is more or less sensitive than rodents to methanol teratogenesis

. Several other 33

uncertainties contributed to decreased confidence in the use of this primate in quantitative 34

estimates of risk. These included : a mixture of wild

- and colony

- derived monkey mothers used 35

in the study; the use of a cohort design necessitate d by the complexity of this study also 364

- 99 DRAFT

- DO NOT CITE OR QUOTE seemingly resulted in limitations in power to detect effects ( e.g., Fagan test results for controls); 1

and no apparent adjustment in statistical analysis for results from the neurobehavioral battery of 2

tests employed l eading to concern about inflation of type 1 error. Because of the uncertainties 3

associated with these results, including the fact that the decrease in gestational length was not 4

exacerbated with increasing methanol exposure, EPA was not able to identify a definitive 5

NOAEL or LOAEL from this study

. This study does support the weight of evidence for 6

developmental neur o toxicity in the hazard characterization of low

- level methanol exposure

. 7

Weiss et al

. (1996) and Stanton et al

. (1995) evaluated the developme ntal and 8

developmental neurotoxicological effects of methanol exposure on pregnant female Long

- Evans 9

rats and their progeny. In the former study, exposure of dams to 15,000 ppm (19,656 mg/m 3 ), 10

7 hours/day on GD7

– GD 19 resulted in reduced weight gain in pup s, but produced little other 11

evidence of adverse developmental effects. The authors subjected the pups to a number of 12

neurobehavioral tests that gave little if any indication of compound

- related changes. This study

, 13

while using high exposure levels

, was limited in its power to detect effects due to the small 14

number of animals used

. In the Weiss et al . (1996) study, exposure of pregnant female Long

15

Evans rats to 0 or 4,500 (0 and 5

, 897 mg/m 3 ) methanol from GD6 to PND21 likewise provided 16

fluctuating and in consistent results in a number of neurobehavioral tests that did not necessarily 17

indicate any compound

- related impacts. The finding of this study indicated subtle cognitive 18

defects not on the learning of an operant task but in the reversal learning. This study also 19

reported exposure

- related changes in neurodevelopmental markers of NCAMs on PND4

. 20

NCAMs are a family of glycoproteins that is needed for migration, axonal outgrowth, and 21

establishment of the pattern for mature neuronal function

. 22

T ak ing all of t hese findings into consideration reinforce s the conclusion that the most 23

appropriate endpoint s for use in the derivation of an RfC for methanol are associated with 24

developmental neurotoxicity and developmental toxicity. Among an array of findings indicat ing 25

developmental neurotoxicity and developmental malformations and anomalies that have been 26

observed in the fetuses and pups of exposed dams, an increase in the incidence of cervical ribs of 27

gestationally exposed mice (Rogers et al ., 1993a) and a decrease in the brain weights of 28

gestationally and lactationally exposed rats (NEDO, 1987) appear to be the most robust and most 29

sensitive effects

. 30

4.8. NONCANCER MOA INFORMATION

A review by Jacobsen and McMartin (1986) has provided a comprehensive summary of 31

the mec hanism by which methanol brings about its acute toxic effects. Overwhelmingly, the 32

evidence points to methanol poisoning being a consequence of formate accumulation. This 33

compound is formed from formaldehyde under the action of ADH3. Formaldehyde itself is 344

- 100 DRAFT

- DO NOT CITE OR QUOTE formed from methanol under the action of ADH1. Evidence for the involvement of formate 1

comes from the delay in the onset of harmful symptoms, detection of formate in the blood 2

stream, and the profound acidosis that develops 12

– 24 hours after exposure to methanol. 3

Treatments for methanol poisoning include the i.v. administration of buffer to correct the 4

acidosis, hemodialysis to remove methanol from the blood stream, and i.v. administration of 5

either ethanol or fomepizole to inhibit the activity of ADH1 . Therapies to increase endogenous 6

levels of folate may enhance the activity of THF synthetase, an enzyme that catalyzes the 7

oxidation of formate to CO 2

. Jacobsen and McMartin (1986) have drawn attention to the 8

accumulation of lactate in advanced stages of severe methanol poisoning, a possible consequence 9

of formate inhibition of mitochondrial respiration and tissue hypoxia. The additional decrease in 10

blood pH is likely to enhance the nonionic diffusion of formic acid across cell membranes, with 11

resultin g CNS

- depression, hypotension, and further lactate production. 12

Jacobsen and McMartin (1986) summarized a body of evidence that also points to the 13

formate

- related acidosis as the etiologically important factor in ocular damage. The hypothesis 14

suggests that ocular toxicity is due to the inhibition of cytochrome oxidase in the optic nerve by 15

formate. This would cause inhibition of ATP formation and consequent disruption of optic nerve 16

function. 17

While it is well established that the toxic consequences of acut e methanol poisoning arise 18

from the action of formate, there is less certainty on how the toxicological impacts of longer

19

term exposure to lower levels of methanol are brought about. For example, since developmental 20

effects in experimental animals appear to be significant adverse effects associated with in utero 21

methanol exposure, it is important to determine potential MOAs for how these specific effects 22

are brought about. 23

As described in Section 4.6 .1, data from experiments carried out by Dorman et al . (1 995)

, 24

formate is not the probable proximate teratogen in pregnant CD

- 1 mice exposed to high 25

concentrations of methanol vapor. Th is conclusion is based on the fact that there appeared to be 26

little, if any, accumulation of formate in the blood of methanol

- e xposed mice, and exencephaly 27

did not occur until formate levels were grossly elevated. Another line of argument is based on 28

the observation that treatment of pregnant mice with a high oral dose of formate did not induce 29

neural tube closure defects at medi a concentrations comparable to those observed in uterine 30

decidual swelling after maternal exposure to methanol. Lastly, methanol

- but not formate

31

induced neural tube closure defects in mouse embryos in vitro at media concentrations 32

comparable to the leve ls of methanol detected in blood after a teratogenic exposure. 33

Harris et. al ( Hansen et al ., 2005; Harris et al ., 200 4, 200 3 ) carried out a series of 34

physiological and biochemical experiments on mouse and rat embryos exposed to methanol, 35

formaldehyde and f ormate, concluding that the etiologically important substance for embryo 364

- 101 DRAFT

- DO NOT CITE OR QUOTE dysmorphogenesis and embryolethality was likely to be formaldehyde rather than the parent 1

compound or formate. Specific activities for enzymes involved in methanol metabolism were 2

de termined in rat and mouse embryos during the organogenesis period of 8

– 25 somites (Harris 3

et al ., 2003). The experiment was based on the concept that differences in the metabolism of 4

methanol to formaldehyde and formic acid by the enzymes ADH1, ADH3, and CAT may 5

contribute to hypothesized differences in species sensitivity that were apparent in toxicological 6

studies. A key finding was that the activity of ADH3 (converting formaldehyde to formate) was 7

lower in mouse VYS than th at of rats throughout organo genesis, consistent with the greater 8

sensitivity of the mouse to the developmental effects of methanol exposure. Another study 9

(Harris et al ., 2004) which showed that the inhibition of GSH synthesis increases the 10

developmental toxicity of methanol also le nds support to this hypothesis because ADH3

- 11

mediated metabolism of formaldehyde is the only enzyme involved in methanol clearance that is 12

GSH

- dependent. These finding s provide inferential evidence for the proposition that 13

formaldehyde may be the ultimate teratogen through diminished ADH3 activity. This concept is 14

further supported by the demonstration that the LOAELs for the embryotoxic effects of 15

formaldehyde in rat and mouse embryos were much lower than those for formate and methanol 16

(Hansen et al ., 200 5). Taking findings from both sets of experiments together, Harris et. al. 17

(Hansen et al ., 2005; Harris et al ., 200 4, 200 3 ) concluded that the demonstrable lower capacity of 18

mouse embryos to transform formaldehyde to formate (by ADH3) could explain the in creased 19

susceptibility of mouse versus rat embryos to the toxic effects of methanol. 20

While studies such as those by Harris et al . (200 4, 200 3 ) and Dorman et al . (1995

, 1994) 21

strongly suggest that formate is not the metabolite responsible for methanol’s te ratogenic effects, 22

there are still questions regarding the relative involvement of methanol versus formaldehyde. In 23

v i tro evidence suggests that formaldehyde is the more embryotoxic moiety, but methanol would 24

likely play a prominent role, at least in ter ms of transport to the target tissue. The high reactivity 25

of formaldehyde would limit its unbound and unaltered transport as free formaldehyde from 26

maternal to fetal blood (Thrasher and Kilburn , 2001)

, and the capacity for the metabolism of 27

methanol to fo rmaldehyde is likely lower in the fetus and neonate versus adults (see discussion 28

in Section 3.3) 29

In humans, metabolism of methanol occurs primarily through ADH1, whereas in rodents 30

methanol clearance involves primarily CAT, as well as ADH1. There are no known studies that 31

compare enzyme activities of human ADH1 and rodent CAT. Assuming that relative expression 32

and activity of ADH1 is comparable across species, rodents are expected to clear methanol more 33

rapidly than humans due to involvement of CAT. In fact, even among rodents the metabolism of 34

methanol may be quite different, as one study has demonstrated that the rate of methanol 35

oxidation in mice is twice the rate in rats, as well as nonhuman primates (Mannering et al

., 364

- 102 DRAFT

- DO NOT CITE OR QUOTE 1969). Despite a faster rate o f methanol metabolism, mice have consistently shown higher blood 1

methanol levels than rats following exposure to equivalent concentrations (Tables 3

- 4 and 3

- 5). 2

A faster respiration rate and increased fraction of absorption by mice is thought to be the re ason 3

for the higher blood methanol levels compared to rats (Perkins et al ., 1995 a ). Using the exposure 4

conditions of Horton et al . (1992) for rats, when the respiration rate scaling coefficient (QPC) 5

was increased from the rat value of 16.4 to the mouse v alue of 25.4 while holding all other 6

parameters constant, peak blood concentrations were predicted by the PBPK model to increase 7

by 1.4

- fold at 200 ppm and 1.8

- fold at 2,000 ppm (where metabolism is becoming saturated). 8

Because smaller species generally have faster breathing rates than larger species (in the PBPK 9

model, the respiration rate/BW is 3 times slower in humans versus rats and almost 10 times 10

slower versus mice), humans would be expected to accumulate less methanol than rats or mice 11

inhaling equ ivalent concentrations and given the same metabolism rate. However, Horton et al

. 12

(1992) measured a blood concentration in rats exposed to 200 ppm methanol of about 3.7 mg/L 13

after 6 hours of exposure while Sedevic et al . (1981) measured around 5.5 mg/L in human 14

volunteers after 6 hours of exposure to 231 ppm. Correcting for the higher exposure, human 15

blood concentrations would be around 4.8 mg/L if exposed at 200 ppm. Simulations with the 16

mouse model predict a blood level of 5.7 mg/L after 6 hours of exp osure to 200 ppm, only 20% 17

higher than this interpolated human value. Thus the slower inhalation rate in humans is offset by 18

the slower metabolic rate, leading to equivalent blood concentrations. (If the same rate of 19

metabolism/BW as mice is used, human blood concentrations are predicted to decrease by 20

approximately five fold.). These differences are considered in Section 5 for the characterization 21

of human and rodent PBPK models used for the derivation of human equivalent concentrations 22

(HECs). 23

4.9. EVALUATIO N OF CARCINOGENICITY

4.9.1. Summary of Overall Weight

- of

- Evidence

Under the current Guidelines for Carcinogen Risk Assessment (U.S. EPA 2005a, 2005b), 24

methanol is likely to be carcinogenic to humans by all routes of exposure based on dose

25

dependent trends in mul tiple tumors in both sexes of two strains of rats, by inhalation and oral 26

routes of exposure and increases in malignant lymphoma in both sexes of Swiss mice by oral 27

exposure

. Specifically, EPA’s analysis of the Soffritti et al . (2002a) lifespan study of S prague

28

Dawley rats exposed to methanol in drinking water for 104 weeks indicates a statistically 29

significant increase in the incidence of lymphoma 58 in lung and other organs at the two highest 30

58 Combining lymphoblastic lymphomas, lym phocytic lymphomas, lympho

- immunoblastic lymphomas and /or

lymphoblastic leukemias as mali gnant lymphomas but excluding myeloid leukemias, histocytic sarcomas and

monocytic leukemia as tumors of different origin (Cruzan, 2009; Hailey, 2004; McConnell et al. , 1986).4

- 103 DRAFT

- DO NOT CITE OR QUOTE doses for males and across all doses for females (Fisher’s exact

, p < 0.05) and a statistically 1

significant increase in relatively rare hepatocellular carcinomas in males compared to historical 2

controls (n=407) 59 (Fisher’s exact p < 0.05 for all doses and p < 0.01 for the high

- dose group). 3

S tatistically significant in creases in the incidence of malignant lymphomas relative to historical 4

controls ( Fisher’s exact, p < 0.05) have also been observed in another rodent species, Swiss 5

mice, following similar mg/kg

- day exposures to methanol in drinking water for life (Apaja, 6

1 980). The only available chronic inhalation stud ies of methanol ( NEDO

, 1985/ 2008a, 2008b ) 7

reported slight but statistically significant tumor responses in F344 rats at 24 months , and no 8

evidence of carcinogenicity in B6C3 F 1 mice at 18 month s

. EPA’s analy sis of the NEDO 9

( 1985/2008b ) inhalation study of F344 rats indicates a dose

- response trend (Cochrane

- Armitage 10

p < 0.05) and an increased incidence over concurrent controls at the high dose (Fisher’s exact 11

p < 0.05) of pulmonary adenomas/adenocarcinomas in male rats. This analysis also indicates a 12

statistically significant dose

- response trend (Cochrane

- Armitage p < 0.05) and a statistically 13

significant increased incidence over NTP historical controls at the high

- dose (Fisher’s exact 14

p < 0.05) of pheochromoc ytomas in female rats. 15

This WOE conclusion is supported by the results of other studies performed by ERF that 16

have show n tumorigenic responses similar to that of methanol in male and female Sprague

17

Dawley rats exposed to formaldehyde (via drinking water), a metabolite of methanol, and to 18

aspartame (via feed) and MTBE (via olive oil gavage ), substances that hydrolyze to release 19

methanol and formaldehyde. Confidence in the designation of methanol as a likely human 20

carcinogen is strengthened by the fact that methanol is metabolized to formaldehyde , a chemical 21

that has been classified as carcinogenic to humans (group 1) (IARC, 2004). As discussed below 22

and in Section 5 .4.3 , there are uncertainties in the interpretation of these findings. All of the key 23

studie s have design and reporting limitations. EPA has reanalyzed the reported data from both 24

the ERF (Soffritti et al., 2002a) and NEDO ( 1987, 1985/ 2008a, 2008b ) studies. In reassessing 25

the ERF study data, EPA decided to combine only those lymphomas considere d to have 26

originated from the same cell type. In the case of the NEDO data, the significance of the tumor 27

findings were incompletely reported in the original NEDO (1987) summary. Hence, EPA used 28

translations of the original, detailed Japanese study repor ts provided by NEDO and the Methanol 29

Institute (NEDO, 1985/ 2008a, 2008 b ) and reanalyzed the individual animal data. 30

4.9.2. Synthesis of Human, Animal, a nd Other Supporting Evidence

E vidence of the carcinogenic potential of methanol aris e s from drinking water st ud ies in 31

Sprague

- Dawley rats (Soffritti et al ., 2002a) and in Eppley Swiss Webster mice (Apaja, 1980), 32

59 Obtained by combining control data from ERF studies of methanol, formaldehyde, aspartame, MTBE, and

TAME. available from the ERF website at http://www.ramazzini.it/fondazione/foun dation.asp ).4

- 104 DRAFT

- DO NOT CITE OR QUOTE and an inhalation study in F344 rats (NEDO, 1985/ 2008b ), with no information available in 1

humans. As is described in Section 4.2.1.3 ( Table 4

- 2), Soffri tti et al . (2002a) reported a number 2

of tumors in methanol

- exposed Sprague

- Dawley rats. EPA reanalyzed the tumor findings from 3

this study using individual animal pathology available from the ERF website (see Section 4

5.4.1.1)

. 60 As indicated above, the inc rease in a relatively rare hepatocellular carcinoma in males 5

compared to historical controls (Fisher’s exact p < 0.05 for all doses and p < 0.01 for the high

6

dose group) is potentially related to methanol dosing. A significant increase in the incidence of 7

ear duct carcinoma was also reported by Soffritti et al . (2002a). However, the high incidence for 8

this tumor in controls of the Soffritti et al. (2002a) study relative to other studies of Sprague

9

Dawley rats (Cruzan, 2009) and the results of an NTP evalu ation of pathology slides from 10

another bioassay ( EFSA, 2006; Hailey , 2004) raise questions about the ear duct pathological 11

determinations of Soffritti et al . (2002a)

. 61 12

As is described in Section 4.2.1.3 ( Table 4

- 3 ), Apaja (1980) found an increase in 13

mali gnant lymphomas in mid

- dose (p = 0.06) and high

- dose (p < 0.05) female and mid

- dose 14

(p < 0.05) male Eppley Swiss Webster mice exposed for life via drinking water

. The lack of a 15

concurrent unexposed control data limit the confidence that can be placed on t he relevance of the 16

increased lymphoma responses noted in this study. However, while controls were not 17

concurrent, they were from proximate (within 3 years) generations of the same mouse colony, 18

lymphomas were evaluated via the same classification cr i teri a and, in the case of the Hinderer 19

( 1979 ) controls, the histopathological analysis was performed by the same author (Apaja, 1980). 20

In addition, this is a late developing tumor, as noted by the author, suggesting the possibility of a 21

higher tumor response in the females of all exposure groups had their survival not been 22

significantly lower than untreated historical controls (see dose

- response analysis in Appendix E). 23

Further, additional support for these study results comes from the fact that, as described above, 24

Soffritti et al. (2002a) subsequently reported an increase in lymphomas following similar levels 25

of mg/kg

- day methanol drinking water doses to Sprague

- Dawley rats, another rodent species 26

with a high spontaneous lymphoma rate. 27

Chronic inhalation bioa ssays have been conducted in monkeys, mice, and F344 rats 28

(NEDO, 1987, 1985/2008a; 1985/2008b). No exposure

- related carcinogenic responses were 29

observed in the monkey or mouse studies. As is described in Section 4.2.2.3, individual tumor 30

responses from t he rat study were not significantly increased over concurrent controls, but the 31

60 ERF provided the EPA with the detailed, individual animal data via reports available through their web portal

( http://www.ramazzini.it/fondazione/foundation.asp ). This allo wed the EPA to combine lymphomas of similar

histopathological origins and confirm the tumor incidences reported in the Soffriti et al. (2002a) paper.

61 In an NTP evaluation of pathology slides from another bioassay from this laboratory in which a similar ear duct

carcinoma finding was reported (Soffritti et al., 2006, 2005), NTP pathologists interpreted a majority of these ear

duct responses as being hyperplastic, not carcinogenic, in nature (EFSA, 2006; Hailey, 2004).4

- 105 DRAFT

- DO NOT CITE OR QUOTE response in the high

- dose (1,000 ppm) group for pulmonary adenomas/adenocarcinomas in male 1

rats was increased over concurrent controls (Fisher’s exact p < 0.05)

, and the dose

- r esponse for 2

both pulmonary adenomas/adenocarcinomas in male rats and pheochromocytomas in female rats 3

represent increasing trends (Cochran

- Armitage trend test p < 05). Further, the high

- dose 4

responses for both of these tumor types were elevated ( p < 0.05) over historical control 5

incidences within their respective sex and strain. As can be seen from Table 4

- 5 , the severity and 6

combined incidence of effects reported in the alveolar epithelium of male rat lungs (epithethial 7

swelling, adenomatosis, pulmonary adenoma and pulmonary adenocarcinoma) and the adrenal 8

glands of female rats (hyperplasia and pheochromocytoma) were increased over controls and 9

lower exposure groups. This pathology and the appearance of a rare adeno carcinoma in the 10

high

- dose group are su ggestive of a progressive effect associated with methanol exposure. The 11

increased pheochromocytoma response in female rats is considered to be potentially treatment 12

related because this is a historically rare tumor type for female F344 rats ( NTP, 2007, 19 99; 13

Haseman et al ., 1998) 62 and because, when viewed in conjunction with the increased medullary 14

hyperplasia observed in the mid

- exposure (100 ppm) group females, it is indicative of a 15

proliferative change with increasing methanol exposure. 16

Additional suppo rt for the designation of methanol as a likely carcinogen is provided by 17

the fact that methanol is metabolized to formaldehyde , which has been associated with increased 18

incidences of lymphoma and leukemia in humans (IARC, 2004) . Furthermore, lymphomas 19

sim ilar to those noted in Sprague

- Dawley rats following exposure to methanol in drinking water 20

and following a similar dose

- response pattern were noted in a bioassay for formaldehyde in 21

drinking water conducted by the same laboratory (Soffritti et al ., 2002b, 1989 ) (Section 4. 9.3 ). 22

Th ese shared endpoint s suggests that the carcinogenic effects of methanol may result from its 23

conversion to formaldehyde, though the moiety and MOA responsible for methanol

- associated 24

tumor formation have not been identified

. 25

Signi ficant increases in the incidence of lymphoreticular tumors have also been reported 26

for other chemicals that convert in the body to methanol and/or formaldehyde including 27

aspartame (Soffritti et al ., 2007, 2006, 2005) and MTBE (Belpoggi et al ., 1997, 1995) . In 28

contrast, no such tumors have been reported in a similar study conducted with a structurally 29

similar alcohol, ethanol (Soffritti et al ., 2002a). In addition, epidemiological studies have 30

associated formaldehyde exposure with increases in the inciden ce of related 31

lymphohematopoietic tumors. While lymphomas are a rare finding in chronic laboratory 32

bioassays, NCI (Hauptmann et al ., 2003) and NIOSH (Pinkerton et al ., 2004) have reported 33

increased lymphohematopoietic cancer risk, principally leukemia, in humans from occupational 34

62 Haseman et al . (1998) report rates for spontaneous pheochromocytomas in 2

- year NTP bioassays of 5.7%

(benign) and 0.3% (malignant) in male F344 rats and 0.3% (benign) and 0.1% (malignant) in female (n

= 1517)

F344 rats.4

- 106 DRAFT

- DO NOT CITE OR QUOTE exposure to formaldehyde

. 63 Th e similarities in tumor response across these chemicals, as well 1

as a similar shape in the dose

- response curve, supports the hypothesis that the common 2

carcinogenic moiety for these compounds is the g eneration or presence of formaldehyde. The 3

dose

- response analysis discussed in Section 5 provides additional evidence supporting a role for 4

the formaldehyde metabolite of methanol. When “total metabolites in blood” predicted by a 5

PBPK model was used as t he dose metric, model fit to the dose

- response data was significantly 6

improved. 7

As discussed in Section 4.6 .5.2, there are challenges relative to the interpretation of the 8

observed lymphoreticular tumors because there is no indication that ERF used specifi c pathogen

9

free (SPF) rats (Schoeb et al., 2009)

, and the protocol for the studies conducted by the ERF 10

( Soffritti et al ., 2002c) is different from 2

- year bioassays conducted by NTP and NEDO. A 11

distinct characteristic of the protocol for long

- term bioassa ys conducted by the ERF is to 12

maintain animals until spontaneous death, rather than sacrificing them at the end of exposure at 13

104 weeks. This difference in protocol may have a n impact on the tumors observed compared to 14

a 2

- year bioassay (Melnick et al

., 2007). The ERF methanol and ethanol stud ies (Soffritti et al., 15

2002a) , as well as the aspartame studies (Soffritti et al., 200 7, 200 6 ) described in Section 4.6 .5.2, 16

employed a large number of animals (100 or more per dose group) compared to a typical ( e.g

., 17

NTP) cancer bioassay. In addition, the Sprague

- Dawley rats used by ERF appear to have 18

increased sensitivity to certain lymphoma responses relative to F344 rats that have been typically 19

used in NTP studies (Caldwell et al., 2008)

. 64 According to Soffrit ti et al. ( 200 7, 200 6 ), the 20

overall incidence of lymphomas/leukemias in ERF studies is 13.3% (range, 4.0

– 25.0%) in 21

female historical controls (2,274 rats) and 20.6% (range, 8.0

– 30.9%) in male historical controls 22

(2,265 rats). This background rate is consi der e d to be high relative to other tumor types and 23

relative to the background rate for this tumor type in Sprague

- Dawley rats from other 24

laboratories ( Cruzan, 2009; EFSA, 2006 )

, 65 However, it is in a range that can be considered 25

reasonable for studies that employ a large number of animals ( Caldwell et al., 2009 ; Leakey et 26

al.

, 2003 )

. These characteristics of ERF studies ( i.e., lifetime observation, large number of 27

animals, and test strain sensitive to endpoint but with a relatively low control background ra te 28

and mortality) may give them the sensitivity needed to detect a chemical ly related lymphoma 29

response. 30

63 IARC (2004 ) concluded that there was sufficient epidemiological evide nce that formaldehyde causes

nasopharyngeal cancer in humans but, also, that there was strong evidence for a causal association between

formaldehyde and the development of leukemia in humans

.

64 F344 rats have a high mortality rate due to late

- developing mo nonuclear cell leukemia, but the lymphoblastic and

immunoblastic lymphomas reported in the Sprague

- Dawley rat by ERF following methanol, MTBE, formaldehyde

and aspartame administration are rarely diagnosed in the F344 rat (Caldwell et al

., 2008 ).

65 Cruzan (2009) reports that the incidences of total cancers derived from bloodforming cells, designated as

hemolymphoreticular tumors by Ramazzini pathologists, is consistently about four times higher than the incidences

of such tumors in SD rats recorded in the Charles River Laboratory historical database (CRL database).4

- 107 DRAFT

- DO NOT CITE OR QUOTE O ther aspects of ERF studies may impede their ability to reliably detect a chemical ly 1

related response (EFSA, 200 9, 200 6 ). C hronic inflammatory resp onses have been reported in 2

test animals of some ERF studies (EFSA, 200 9, 2006 ), which may be the result of infections in 3

test animals resulting from a bioassay design that does not employ SPF rats (Schoeb et al., 2009) 4

and allows the rats to live out thei r “natural life span ” in the absence of disease barriers ( e.g., 5

fully enclosed cages). In fact, t he ERF has acknowledged that the primary cause of spontaneous 6

death in their rats is respiratory infection (Caldwell et al ., 2008; Ramazzini Foundation, 2006 ; 7

Soffritti et al., 2006 ). Cruzan (2009) has suggested that respiratory infection s in test animals of 8

the Soffritti et al. (2002a) methanol study were not specific to older rats, as finding s of lung 9

pathology were reported as often in rats dying prior to 1 8 months as in rats dying at or after 10

24 months. 66 11

In their review s of the recently published ERF stud ies on aspartame (Soffritti et al., 200 7, 12

200 6 ) , the European Food Safety Authority (EFSA) have suggested that the increased incidence 13

of lymphomas/leuke mias reported in treated rats was related to chronic respiratory disease in the 14

rat colony (EFSA, 200 9, 2006 ) , which they suggest was caused by a M ycoplas ma pulmonis 15

( M

. pulmonis ) infection

. EFSA felt that the increased incidence of these tumors was unrel ated to 16

aspartame, given the high background incidence of chronic inflammatory changes such as 17

bronchopneumonia in the lungs of treated and untreated rats

, and the concern that such tumors 18

might arise as a result of abundant lymphoid hyperplasia in the lun gs of rats suffering from 19

chronic respiratory disease. The scientific evidence to support the EFSA opinion that 20

lymphomas/leukemias can result from chronic infection is limited ( Schoeb et al., 2009; Caldwell 21

et al., 2008)

. Epithelial hyperplasias and lym phoid accumulations are commonly found in the 22

larynx and trachea of rats infected with M. pulmonis , but induction of lymphoma has not been 23

noted (Everitt and Richter, 1990; Lindsey et al., 1985). Further, the lung

, not the larynx or 24

trachea

, has been repo rted as the site of respiratory tract hemolymphoreticular tumors in ERF 25

studies of MTBE (Belpoggi et al., 1998, 1995) and methanol (Soffritti et al., 2002a). 67 In their 26

review of the molecular biology and pathogenicity of M. pulmonis , Razin et al. ( 1998 ) n ote that 27

further study is needed before any conclusion can be reached regarding a relationship between 28

M. pulmonis and neoplasia

. In addition, if the increased incidence o f lymphoreticular tumors in 29

the ERF methanol study was strictly the consequence of a n incipient respiratory infection in the 30

ERF rat colony

, one would expect this to be a common finding across ERF studies. However, a s 31

discussed in Section 4.6 .5.2

, of the 200 compounds tested by ERF, only 8 , which includes 32

66 The infection rate did not have a significant impact on survival, however. The 2

- year survival rate was 40

– 50% in

the ERF methanol bioassay (see Appendix E, Figures E

- 1 and E

- 2), which is abov e the average 2

- year NTP study

survival rate of 41.5% for Sprague

- Dawley rats (Caldwell et al., 2008).

67 ERF provided EPA with the detailed, individual animal data for the Soffriti et al. (2000a) via reports available

through their web portal ( http://www.ramazzini.it/fondazione/foundation.asp ).4

- 108 DRAFT

- DO NOT CITE OR QUOTE methanol, have been associated w ith an increased incidence of hemolymphoreticular tumors. 1

Further, t he chemicals for which hemolymphoreticular tumors have been reported have chemical 2

characteristics or physical properties in common

, 68 consistent with the hypothesis that the 3

increased res ponse is chemical

- related. 4

While evidence for a causal association between respiratory infections and lymphomas is 5

limited, there is evidence that respiratory infections may have confounded the interpretation of 6

lung lesions in the ERF studies . Schoeb et al. ( 2009 ) state that lymphomas illustrated in two 7

ERF studies ( Figure 10 of Soffritti et al., 2005 and Figures 1

- 5 of Belpoggi et al., 1999 ) do not 8

demonstrate the lymphoma type, cellular morphology, and organ distribution typical of 9

lymphoma in rats, b ut are consistent with “ lymphocyte and plasma cell accumulation in the lung 10

that is characteristic of M. pulmonis disease

. ” They suggest that , because M. pulmonis disease 11

can be exacerbated by chemical treatment, a plausible alternative explanation for the dose

- related 12

response reported in the MTBE, aspartame and methanol ERF bioassays is that the studies were 13

confounded by M. pulmonis disease and that lesions of the disease were interpreted as 14

lymphoma. However

, several ERF lymphoma diagnoses in multiple r at organ systems , including 15

the lung , have been confirmed by an independent panel of six NIEHS pathologists ( Hailey

, 16

2004) . Further

, 60% of the lymphoma incidences reported in the ERF methanol study involved 17

organ systems other than the lungs (Schoeb et a l., 2009)

. T he incidence of “lung

- only” 18

lymphomas is evenly distributed across the control and dose groups of the methanol study such 19

that removing “lung

- only” lympho

- immunoblastic lymphomas from consideration (i.e., using 20

only lymphomas from other organ systems) does not significantly alter the dose

- resp onse for this 21

lesion (see Section 5.4.3.2)

. 22

Based on the NEDO (1987) summary report

, IPCS (1997) concluded that “n o evidence of 23

carcinogenicity was found in either species [F344 rats and B6C3 F 1 mice].” This determination 24

was made based on Fisher ’s exact test results which indicated that the reported h igh

- dose 25

pulmonary adenoma response in male rats and the high

- dose pheochromocytoma response in 26

female rats were not statistically significant

. However, IP CS did not have translations of the 27

original NEDO mouse and rat chronic studies ( NEDO, 1985/2008a; 1985/2008b ) , which 28

provided additional detail for EPA’s analysis and reported combine d lung adenoma and 29

adenocarcinoma results for high

- dose male rats

. In a ddition, IPCS did not consider trend test 30

results or historical tumor data for F344 rats , both of which indicate a positive result for lung 31

adenoma/adenocarcinoma (males) and pheochromocytomas (females) from the NEDO rat study. 32

68 Methanol, formaldehyde, aspartame, and MTBE, have common metabolites (e.g.

, formaldehyde) ; DIPE, TAME,

methanol, and MTBE are all gasoline

- oxygenat e additives.4

- 109 DRAFT

- DO NOT CITE OR QUOTE 4.9.3. MOA Information

As discussed in Section 4.6 .5.1, the results of genotoxicity/mutagenicity studies have 1

been largely negative, irrespective of the presence or absence of metabolic activation (an S9 2

microsomal fraction). Studies that investigate the MOA for methanol, particularly with respect 3

to its developmental effects, have been discussed extensively in Sections 4.6

. and 4. 8 . Studies 4

such as those by Harris et al . (200 4, 200 3 ) suggest that formaldehyde is the proximate teratogen 5

and provide evidence in support of that hypothesis. It is reasonable to hypothesize that the 6

highly reactive molecule, formaldehyde, has a role in the carcinogenicity of methanol

, given the 7

ability of formaldehyde to bind to proteins and DNA, induce DNA

- protein cross

- links, and 8

possibly participate in react ions leading to free radical formation and the formation of lipid 9

peroxidation products. As discussed in Section 4.6

. 3 , evidence of oxidative st r ess following 10

methanol exposure has been reported in several organ systems. Studies of Wistar rats suggest 11

th at methanol exposure can cause the production of free radical formation, lipid peroxidation, 12

and protein modifications in the liver (Skrzydlewska et al ., 2005) and brain (Rajamani et al

., 13

2006), and adversely impact the oxidant/antioxidant balance in the brain ( Dudka, 2006 ) and 14

lymphoid organs ( Parthasarathy et al

., 2006b). 15

As discussed in Section 4.6 .5.2, ERF studies of a number of compounds that have 16

formaldehyde as a metabolic product have been reported to cause lymphomas in 17

Sprague

- Dawley rats. As des cribed in Section 4.6 .5.2.4, the ERF has conducted a formaldehyde 18

drinking water study (Soffritti et al ., 2002b

, 1989) that is comparable in its design to the 19

methanol drinking water study of Soffritti et al . (2002a). The mg/kg

- day doses of metabolized 20

me thanol in Sprague

- Dawley rats from the ERF methanol study estimated from the PBPK model 21

described in Section 3.4 and mg/kg

- day doses of formaldehyde reported in the ERF 22

formaldehyde study were plotted together versus the hemolymphoreticular neoplasm incide nces 23

in their respective studies ( Figure 4

- 1). Separate linear models were fit to the male and female 24

rat data from these studies. The model fits shown in Figure 4

- 1 demonstrate that when 25

metabolized methanol is used as the dose metric for the methanol study data, the dose

- response 26

data from these two studies can be adequately fit by two separate linear dose

- response functions 27

for the combined male (R 2 = 0.6722) and combined female (R 2 = 0.779) responses. Even if it is 28

true that formaldehyde is the comm on moiety responsible for these tumors, one would not expect 29

this approach to result in perfect dose

- response alignment because the metabolized methanol 30

estimate is not an accurate representation of formaldehyde distribution

, and formaldehyde from 31

methanol administration would not be expected to distribute the same as orally administered 32

formaldehyde. However, the similarities in the dose

- response data for male and female rats from 33

these studies are consistent with the hypothesis that formaldehyde is key t o methanol’s 34

carcinogenic MOA

. 354

- 110 DRAFT

- DO NOT CITE OR QUOTE y = 0.0258Ln(x) + 0.0889

R 2 = 0.7592 y = 0.0492Ln(x) + 0.0932

R 2 = 0.6832

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

1.00 10.00 100.00 1000.00

mg formaldehyde/kg/d or mg metabolized methanol/kg/dIncidence of Hemolymphoreticular Neoplasms

Sofritti et al. (2002b), formaldehyde - males Sofritti et al. (2002a), methanol - males

Soffritti et al. (2002a), methanol - females Soffritti et al. (2002b), formaldehyde - females

Figure 4

- 1. H emolymphoreticular neoplasm s in male and female S prague

- Dawley rats

in formaldehyde and methanol drinking water studies versus mg formaldehyde/kg/ d ay

or mg metabolized methanol/kg/ d ay (predicted by EPA PBP K model)

.

Source: Soffritti et al . (2002b)

.

As discussed above, methanol is metabolized to formaldehyde, which is deemed to be 1

carcinogenic to humans (IARC, 2004) by both the oral and inhalation routes

, and there are 2

readily apparent similarities between the dose

- response data from oral studies of rats exposed to 3

formaldehyde and methanol. In addition, the dose

- response model fit for the lymphoma 4

response observed in the Soffritti et al . (2002a) study is improved when predicted total 5

metabolites is used a s the dose

- metric (Section 5.4.1.2). However, the database of information 6

available concerning methanol’s carcinogenic MOA is limited, and the extent to which the parent 7

or a metabolite such as formaldehyde is responsible for the carcinogenic effects obse rved in the 8

studies conducted by Soffritti et al . (2002a) or NEDO (1987

, 1985/2008b) is not clear. 9

4.10. SUSCEPTIBLE POPULATI ONS AND LIFE STAGES

4.10.1. Possible Childhood Susceptibility

Studies in animals have identified the fetus as being more sensitive than adults to the 10

toxic effects of methanol; the greatest susceptibility occurs during gastrulation and early 11

organogenesis ( CERHR , 2004). Table 4

- 2 1 summarizes some of the data regarding the relative 12

ontogeny of CAT, ADH1, and ADH3 in humans and mice. Human fetus es have limited ability to 13

metabolize methanol as ADH1 activity in 2

- month

- old and 4

– 5 month

- old fetuses is 3

– 4% and 144

- 111 DRAFT

- DO NOT CITE OR QUOTE 10% of adult activity, respectively (Pikkarainen and Raiha, 1967). ADH1 activity in 9

– 22 week 1

old fetal livers was found to be 30% of adul t activity (Smith et al ., 1971). Likewise, ADH1 2

activity is

~ 20

– 50% of adult activity during infancy (Smith et al ., 1971; Pikkarainen and Raiha, 3

1967). Activity continues to increase until reaching adult levels at 5 years of age (Pikkarainen 4

and Raiha, 1 967). However, no difference between blood methanol levels in 1

- year

- old infants 5

and adults was observed following ingesting the same doses of aspartame, which releases 10% 6

methanol by weight during metabolism (Stegink et al ., 1983). Given that the expos ure was 7

aspartame as opposed to methanol, it is difficult to draw any conclusions from this study vis

- à

8

vis ontogeny data and potential influences of age differences in aspartame disposition. With 9

regard to inhalation exposure, increased breathing rates r elative to adults may result in higher 10

blood methanol levels in children compared to adults ( CERHR , 2004). It is also possible that 11

metabolic variations resulting in increased methanol blood levels in pregnant women could 12

increase the fetus’ risk from exp osure to methanol. In all, unresolved issues regarding the 13

identification of the toxic moiety increase the uncertainty with regards to the extent and 14

pathologic basis for early life susceptibility to methanol exposure. 15

The prevalence of folic acid deficie ncy has decreased since the United States and Canada 16

introduced a mandatory folic acid food fortification program in November 1998. However, 17

folate deficiency is still a concern among pregnant and lactating women

, and factors such as 18

smoking, a poor quali ty diet, alcohol intake, and folic antagonist medications can enhance 19

deficiency ( CERHR , 2004). Folate deficiency could affect a pregnant woman’s ability to clear 20

formate, which has also been demonstrated to produce developmental toxicity in rodent in in 21

vitro studies at high

- dose s (Dorman et al ., 1995). It is not known if folate

- deficient humans have 22

higher levels of blood formate than individuals with adequate folate levels. A limited study in 23

folate

- deficient monkeys demonstrated no formate accumulati on following an endotracheal 24

exposure of anesthetized monkeys to 900 ppm methanol for 2 hours (Dorman et al ., 1994). The 25

situation is obscured by the fact that folic acid deficiency during pregnancy by itself is thought to 26

contribute to the development of severe congenital malformations (Pitkin, 2007). 27

4.10.2. Possible Gender Differences

There is limited information on potential differences in susceptibility to the toxic effects 28

of methanol according to gender. However, one study reported a higher background bloo d 29

methanol level in human females versus males (Batterman and Franzblau, 1997). In rodents, 30

fetuses exposed in utero were found to be the most sensitive subpopulation. One study suggested 31

a possible increased sensitivity of male versus female rat fetuses and pups. When rats were 32

exposed to methanol pre

- and postnatally, 6

- and 8

- week

- old male progeny had significantly 33

lower brain weights at 1

, 000 ppm, compared to those in females that demonstrated the same 344

- 112 DRAFT

- DO NOT CITE OR QUOTE effect only at 2

, 000 ppm (NEDO, 1987). In gener al, there is little evidence for substantial 1

disparity in the level or degree of toxic response to methanol in male versus female experimental 2

animals or humans. However, it is possible that the compound

- related deficits in fetal brain 3

weight that were ev ident in the pups of F 1 generation Sprague

- Dawley rats exposed to methanol 4

in the NEDO (1987) study may reflect a threshold neurotoxicological response to methanol. It is 5

currently unknown whether higher levels of exposure would result in brain sequelae c omparable 6

to those observed in acutely exposed humans. 7

4.10.3. Genetic Susceptibility

Polymorphisms in enzymes involved in methanol metabolism may affect the sensitivity 8

of some individuals to methanol. For example, as discussed in Chapter 3, data summarized in 9

reviews by Agarwal (2001), Burnell et al . (1989), Bosron and Li (1986), and Pietruszko (1980) 10

discuss genetic polymorphisms for ADH. Class I ADH, the primary ADH in human liver, is a 11

dimer composed of randomly associated polypeptide units encoded by three genetic loci 12

(ADH1A, ADH1B, and ADH1C). Polymorphisms are observed at the ADH1B (ADH1B*2, 13

ADH1B*3) and ADH1C (ADH1C*2) loci. The ADH1B*2 phenotype is estimated to occur in 14

~ 15% of Caucasians of European descent, 85% of Asians, and less that 5% of Africa n 15

Americans. Fifteen percent of African Americans have the ADH1B*3 phenotype, while it is 16

found in less than 5% of Caucasian Europeans and Asians. The only reported polymorphisms in 17

ADH3 occur in the promoter region, one of which reduces the transcriptio nal activity in vitro 18

nearly two fold (Hedberg et al

., 2001)

. While polymorphisms in ADH3 are described in more 19

than one report ( Cichoz

- Lach et al ., 2007; Hedberg et al ., 2001), the functional consequence(s) 20

for these polymorphisms remains unclear. 215

- 1 DRAFT

— DO NOT CITE OR QUOTE 5. DOSE

- RESPONSE ASSESSMENT AND CHARACTERIZATION

5.1. INHALATION R f C FOR EFFECTS OTHER THAN CANCER 69

In general, the RfC is an estimate of a daily exposure of the human population (including 1

susceptible subgroups) that is likely to be without an appreciable risk of adverse health effects 2

over a lifetime. It is derived from a POD, generally the statistical lower confidence limit on the 3

BM C L or BMDL , with uncertainty/variability factors applied to reflect limitations of the data 4

used. The inhal ation RfC considers toxic effects for both the respiratory system (portal

- of

- entry) 5

effects and systems peripheral to the respiratory system (extra

- respiratory or systemic effects). It 6

is generally expressed in mg/m 3

. EPA performed an IRIS assessment of methanol in 1991 and 7

determined that the database was inadequate for derivation of an RfC. While some limitations 8

still exist in the database (see Sections 5.1.3.2 and 5.3), the experimental toxicity database has 9

expanded and newer methods and models have been developed to analyze the results. In this 10

update, the PBPK model

, described in Section 3.4

, was developed by EPA and is used to estimate 11

HECs and HEDs from inhalation study data for the derivation of both the RfC and RfD. In both 12

cases, the use of a PBPK model replaces part of the UF adjustments traditionally used for 13

species

- to

- species extrapolation. 14

Additionally, this assessment uses the BMD method in its derivation of the POD. 70 The 15

suitability of these methods to derive a POD is dependent on the nature of the toxicity database 16

for a specific chemical. Details of the BMD analyses are found in Appendix C. The use of the 17

BMD approach for determining the POD improves the assessment by including consideration of 18

shape of the dose

- response curve, ind ependence from experimental doses, and estimation of the 19

uncertainty pertaining to the calculated dose response. However, the methanol database still has 20

limitations and uncertainties associated with it

, i n particular, those uncertainties associated with 21

human variability , animal

- to

- human differences

, and limitations in the database influence 22

derivation of the RfC. 23

5.1.1. Choice of Principal Study and Critical Effect(s)

5.1.1.1. Key Inhalation Studies

While a substantial body of information exists on the toxicological con sequences to 24

humans exposed to large amounts of methanol, no human studies exist that would allow for 25

69 The RfC discussion precedes the RfD discussion in this assessment because the inhalation database ultimately

serves as the basis for the RfD. The RfD development would be difficult to follow without prior discussion of

inhalation database a nd PK mode ls used for the route

- to

- route extrapolation.

70 Use of BMD methods involves fitting mathematical models to dose

- response data and using the results to select a

POD that is associated with a predetermined benchmark response (BMR), such as a 10% in crease in the incidence of

a particular lesion or a 10% decrease in body weight gain (see Section 5.1.2.2).5

- 2 DRAFT

— DO NOT CITE OR QUOTE quantification of subchronic, chronic, or in utero effects of methanol exposure. Table 4

- 35 1

summarizes available experimental animal inhalation studies o f methanol. Several of these 2

studies, including the monkey chronic (NEDO, 1987) and developmental (Burbacher et al

., 3

2004a, 2004b, 1999a, 1999 b) studies, the male rat reproductive studies ( Lee et al ., 1991; 4

Cameron et al ., 1985, 1984)

, and the 4

- week rat studies (Poon et al ., 1994), are lacking in key 5

attributes ( e.g., documented dose response, documented controls

, and duration of exposure) 6

necessary for their direct use in the quantification of a chronic RfC. These studies will be 7

considered in this chap ter for their contributions to the overall RfC uncertainty. Several 8

inhalation reproductive or developmental studies were adequately documented and are of 9

appropriate size and design for quantification and derivation of an RfC. These studies are 10

consider ed for use in the derivation of an RfC and are summarized below. 11

5.1.1.2. Selection of Critical Effect(s)

Developmental effects have been assessed in a number of toxicological studies of 12

monkeys, rats, and mice. The findings of Rogers and Mole (1997) indicate that methanol is toxic 13

to mouse embryos in the early stages of organogenesis, on or around GD7. In the study of 14

Rogers et al . (1993a)

, in which pregnant female CD

- 1 mice were exposed to methanol vapors 15

(1

, 000, 2

, 000, and 5

, 000 ppm) on GD6

– GD 15, reproductive a nd fetal effects included an 16

increase in the number of resorbed litters, a reduction in the number of live pups, and increased 17

incidences of exencephaly, cleft palate, and the number of cervical ribs. They reported a 18

NOAEL for cervical rib malformations a t 1

, 000 ppm (1

, 310 mg/m 3 ) and a LOAEL of 2

, 000 ppm 19

(2

, 620 mg/m 3 , 49.6% per litter versus 28.0% per litter in the control group). Increased incidence 20

of cervical rib s was also observed in the rat organogenesis study (NEDO, 1987) in the 5

, 000 ppm 21

dose group (65.2% per litter versus 0% in the control group), indicating that the endpoint is 22

significant across species. 23

The biological significance of the cervical rib endpoint within the regulatory arena has 24

been the subject of much debate (Chernoff and Rogers, 2 004). Previous studies have classified 25

this endpoint as either a malformation (birth defect of major importance) or a variation 26

(morphological alternation of minor significance). There is evidence that incidence of 27

supernumerary ribs (including cervical ribs) is not just the addition of extraneous, single ribs but 28

rather is related to a general alteration in the development and architecture of the axial skeleton 29

as a whole. In CD

- 1 mice exposed during gestation to various types of stress, food and water 30

deprivation, and the herbicide dinoseb, supernumerary ribs were consistently associated with 31

increases in length of the 13th rib (Branch et al ., 1996). This relationship was present in all fetal 32

ages examined in the study. The authors conclude d that thes e findings are consistent with 33

supernumerary ribs being one manifestation of a basic alteration in the differentiation of the 34

thoraco

- lumbar border of the axial skeleton. The biological significance of this endpoint is 35

further strengthened by the associat ion of supernumerary ribs with adverse health effects in 365

- 3 DRAFT

— DO NOT CITE OR QUOTE humans. The most common effect produced by the presence of cervical ribs is thoracic outlet 1

disease ( Nguyen et al ., 1997; Fernandez Noda et al ., 1996; Henderson, 1914). Thoracic outlet 2

disease is c haracterized by numbness and/or pain in the shoulder, arm, or hands. Vascular effects 3

associated with this syndrome include cerebral and distal embolism (Bearn et al ., 1993; Connell 4

et al ., 1980; Short, 1975), while neurological symptoms include extreme p ain, migraine, and 5

symptoms similar to Parkinson’s (Evans, 1999; Saxton et al ., 1999 ; Fernandez Noda et al., 1996 ). 6

Schumacher et al . (1992) observed 242 rib anomalies in 218 children with tumors (21.8%) and 7

11 (5.5%) in children without malignancy, a st atistically significant (p < 0.001) difference that 8

indicates a strong association between the presence of cervical rib s and childhood cancers. 9

Specific cancers associated with statistically significant increases in anomalous ribs included 10

leukemia, brain , tumor, neuroblastoma, soft tissue sarcoma, and Wilm’s tumor. 11

A number of rat studies have confirmed the toxicity of methanol to embryos during 12

organogenesis ( Weiss et al ., 1996; NEDO, 1987; Nelson et al ., 1985) . NEDO (1987) reported 13

reduced brain, pitui tary, and thymus weights in F 1 and F 2 generation Sprague

- Dawley rats at 14

1

, 000 ppm methanol. In a follow

- up study of the F 1 generation brain weight effects, NEDO 15

(1987) reported decreased brain, cerebellum, and cerebrum weights in F 1 males exposed at 16

1

, 000 ppm methanol from GD 0 through the F 1 generation. The exposure levels used in these 17

studies are difficult to interpret because dams were exposed prior to gestation, and dams and 18

pups were exposed during gestation and lactation. However, it is clear that postnatal exposure 19

increases the severity of brain weight reduction. In another experiment in which NEDO (1987) 20

exposed rats only during organogenesis (GD7

– GD 17)

, the observed decreases in brain weights in 21

offspring at 8 weeks of age were less severe than in the studies for which exposure was 22

continued postnatally. This finding is not unexpected

, given that the brain undergoes tremendous 23

growth beginning early in gestation and continuing in the postnatal period. Rats are considered 24

altricial ( i.e., born at relatively underdeveloped stages)

, and many of their neurogenic events 25

occur postnatally (Clancy et al ., 2007). Brain effects from postnatal exposure are also relevant to 26

humans given that, in humans, gross measures of brain growth increase for at leas t 2

– 3 years 27

after birth, with the growth rate peaking approximately 4 months after birth (Rice and Barone, 28

2000). 29

A change in brain weight is considered to be a biologically significant effect ( U.S. EPA, 30

1998). This is true regardless of changes in body weight because brain weight is generally 31

protected during malnutrition or weight loss, unlike many other organs or tissues 32

( U.S. EPA, 1998). Thus, change in absolute brain weight is an appropriate measure of effects on 33

this critical organ system. Decreas es in brain weight have been associated with simultaneous 34

deficits in neurobehavioral and cognitive parameters in animals exposed during gestation to 35

various solvents

, including toluene and ethanol ( Gibson, 2000; Coleman et al ., 1999; Hass, 365

- 4 DRAFT

— DO NOT CITE OR QUOTE 1995). NEDO (1 987) reports that brain, cerebellum, and cerebrum weights decrease in a dose

1

dependant manner in male rats exposed to methanol throughout gestation and the F 1 generation. 2

Developmental neurobehavioral effects associated with methanol inhalation exposure 3

have been investigated in monkeys. Burbacher et al . ( 2004a, 2004b, 1999a, 1999 b) exposed M. 4

fascicularis monkeys to 0, 262, 786, and 2

, 359 mg/m 3 methanol

, 2.5 h ou rs/day, 7 days/week 5

during premating/mating and throughout gestation (approximately 168 days)

. In these studies, 6

exposure of monkeys to up to 1

, 800 ppm (2

, 359 mg/m 3 ) methanol during premating, mating, and 7

throughout gestation resulted in no changes in reproductive parameters other than a shorter 8

period of gestation in all exposure groups that did not appear to be dose related. The shortened 9

gestation period was largely the result of C

- sections performed in the methanol exposure groups 10

“in response to signs of possible difficulty in the maintenance of pregnancy,” including vaginal 11

bleeding. As di scussed in Section 4.7 .1

. 2 , though statistically significant, the shortened gestation 12

finding may be of limited biological significance given questions concerning its relation to the 13

methanol exposure. Developmental parameters

, such as fetal crown

- rump le ngth and head 14

circumference

, were unaffected, but there appeared to be neurotoxicological deficits in methanol

15

exposed pups. VDR was significantly reduced in the 786 mg/m 3 group for males and the 2

, 359 16

mg/m 3 group for both sexes. However, a dose

- response trend for this endpoint was only 17

exhibited for females. In fact, this is the only effect reported in the Burbacher et al

. (2004a, 18

2004b, 1999a, 1999b) studies for which a significant dose

- response trend is evident. As 19

discussed in Section 4. 4 .2, confide nce may have been increased by statistical analyses to adjust 20

for multiple testing ( CERHR , 2004). Yet it is worth noting that the dose

- response trend for VDR 21

in females remained significant with ( p = 0.009) and without ( p = 0.0265) an adjustment for the 22

s hortened gestational periods

, and it is a measure of functional deficits in sensorimotor 23

development that is consistent with early developmental CNS effects (brain weight changes 24

discussed above) that have been observed in rats. 25

Another test, the Fagan t est of infan t intelligence, indicated small but not significant 26

deficits of performance (time spent looking a t novel faces versus familiar faces) in treated 27

monkeys . Although not statistically significant and not quantifiable, the results of this test are 28

also important when considered in conjunction with the brain weight changes noted in the NEDO 29

(1987) rat study. As discussed in Section 4.7 .1.2, the monkey data are not conclusive

, and there 30

is insufficient evidence to determine if the primate fetus is m ore or less sensitive than rodents to 31

methanol teratogenesis. Taken together, however, the NEDO (1987) rat study and the Burbacher 32

et al

. (2004a, 2004b, 1999a, 1999b) monkey study suggest that prenatal exposure to methanol 33

can result in adverse effects on developmental neurology pathology and function, which can be 34

exacerbated by continued postnatal exposure. 35

A number of studies described in Section 4.3.2 and summarized in Section 4.7 .1.2 have 36

examined the potential toxicity of methanol to the male reprod uctive system (Lee et al ., 1991; 375

- 5 DRAFT

— DO NOT CITE OR QUOTE Cameron et al ., 1985, 1984). Some of the observed effects, including a transient decrease in 1

testosterone levels, could be the result of chemical ly related strain on the rat system as it attempts 2

to maintain hormone homeos tasis. However, the data are insufficient to definitively characterize 3

methanol as a toxicant to the male reproductive system. 4

The studies considered for use in the derivation of an RfC are summarized in Table 5

- 1. 5

As discussed in Sections 5.1.3.1 and 5. 3, there is uncertainty associated with the selection of an 6

effect endpoint from the methanol database for use in the derivation of an RfC. Taking into 7

account the limitations of the studies available for quantification purposes, d e creased brain 8

weight at 6 weeks in male Sprague

- Dawley rats exposed throughout gestation and the postnatal 9

period (NEDO, 1987) was chosen as the critical effect for the purposes of this dose

- response 10

assessment as it can be reliably quantified and represents both a sensitive org an system and a key 11

period of development . RfC derivations utilizing alternative endpoint s ( e.g., cervical rib effects 12

in mice and delayed sensorimotor development in monkeys) and alternative methods ( e.g., use of 13

different BMRLs ) are summarized in Append ix C and in Section 5.1.3.1

. 14

Table 5

- 1. Summary of studies considered most appropriate for use in derivation of an

RfC

Reference Species

(strain) Sex Number/

dose group Exposure

Duration Critical Effect NOAEL

(ppm) LOAEL

(ppm)

NEDO (1987)

Two

- generation

s tudy Rat

Sprague

Dawley M,F Not specified

-

F 1 and F 2 generation F 1 - Birth to end

of mating (M)

or weaning (F);

F 2 - b irth to 8

wk Reduced weight of

brain, pituitary, and

thymus at 8, 16

, and

24 w k postnatal in F 1

and at 8 w k in F 2 100 1

, 000

NEDO (19 87)

Follow

- up study

of F 1 generation 10

– 14/ sex/

gr ou p

- F 1 generation GD0 through

F 1 generation Reduced brain weight

at 3 wk and 6 wk

(males only).

Reduced brain and

cerebrum weight at 8

w k (males only) 500 1

, 000

NEDO (1987)

Teratology study Rat

Sprague

Dawley M,F 10

– 12/sex/

group GD7

– GD 17 Reduced brain,

pituitary, thyroid

, thymus, and testis

weights at 8 w k

postnatal. 1

, 000 5

, 000

Nelson et al

. ( 1985 ) Rat

Sprague

Dawley F 15 pregnant

dams/ group GD1

– GD 19 or

GD 7

– GD 15 Reduced fetal body

weight, i ncreased

incidence of visceral

and skeletal

abnormalities,

including rudimentary

and extra cervical ribs 5

, 000 10

, 0005

- 6 DRAFT

— DO NOT CITE OR QUOTE Reference Species

(strain) Sex Number/

dose group Exposure

Duration Critical Effect NOAEL

(ppm) LOAEL

(ppm)

Rogers et al

. (1993a) Mouse

CD

- 1 F 30

– 114

pregnant dams/

gr ou p GD6

- 15 Increased incidence

of extra cervical ribs,

cleft palate,

exencep haly; reduced

fetal weight and pup

survival, d elayed

ossification 1

, 000 2

, 000

Burbacher et al

. ( 20004a, 2004b,

1999a, 1999 b) M.

fascicularis 12 pregnant

monkeys/group 2.5 hr /day,

7 days/wk,

during

pre mating,

mating and

gestation Shortened period of

gest ation; may be

related to exposure

(no dose response),

neurotox

. deficits

including reduced

performance in the

VDR test - - b

a Animals were dosed 20

– 21 h r /day. NS = Not Specified

b Gestational exposure resulted in a shorter period of gestation in dams expose d to as low as 200 ppm (263 mg/m 3 ).

However, because of uncertainties associated with these results, including clinical intervention and the lack of a

dose

- response, EPA was not able to identify a definitive NOAEL or LOAEL from this study.

5.1.2. Methods of Ana lysis for the P OD

— Application of PBPK and BMD Models

Potential PODs for the RfC derivation

, described here and in Appendix C

, have been 1

calculated via the use of monkey, rat and mouse PBPK models

, described in Section 3.4. First, 2

the doses used in an exp erimental bioassay were converted to an internal dose metric that is most 3

appropriate for the endpoint being assessed. The PBPK models are capable of calculating 4

several measures of dose for methanol, including the following: 5

 C

max – The peak concentration of methanol in the blood during the exposure period; 6

 AUC

– Area under the curve, which represents the cumulative product of 7

concentration and time for methanol in the blood; and 8

 Total metabolism

– The production of metabolites of methanol, namely formalde hyde 9

and formate. 10

As described in Section 3.4, t he focus of model development is on obtaining accurate 11

predictions of increased body burden s over background

. The PBPK models do not describe or 12

account for background levels of methanol, formaldehyde or for mate

. 13

Although there remains uncertainty surrounding the identification of the proximate 14

teratogen of importance (methanol, formaldehyde, or formate), the dose metric chosen for 15

derivation of an RfC was based on blood methanol levels. This decision was p rimarily based on 16

evidence that the toxic moiety is not likely to be the formate metabolite of methanol ( CERHR

, 17

2004) and evidence that levels of the formaldehyde metabolite following methanol maternal 18

and/or neonate exposure would be much lower in the fet us and neonate than in adults. While 19

recent in vitro evidence indicates that formaldehyde is more embryotoxic than methanol and 20

formate, the high reactivity of formaldehyde would limit its unbound and unaltered transport as 215

- 7 DRAFT

— DO NOT CITE OR QUOTE free formaldehyde from maternal to fetal blood (Thrasher and Kilburn , 2001)

, and the capacity 1

for the metabolism of methanol to formaldehyde is likely lower in the fetus and neonate versus 2

adults (see discussion in Section 3.3). Thus, even if formaldehyde is identified as the proximate 3

teratogen, methanol would likely play a prominent role, at least in terms of transport to the target 4

tissue. Further discussions of methanol metabolism, dose metric selection

, and MOA issues are 5

covered in Sections 3.3, 4.6 , 4.8 and 4. 9

. 2

. 6

A BMDL was th en derived in terms of the internal dose metric utilized. Finally, the 7

BMDL values were converted to HECs via the use of a PBPK model parameterized for humans. 8

The next section describes the rationale for and application of the benchmark modeling 9

method ology for the RfC derivation. 10

5.1.2.1. Application of the BMD/BMDL Approach

Several developments over the past few years impact the derivation of the RfC: 1) EPA 11

has developed draft BMD assessment methods (U.S. EPA, 2000b, 1995) and supporting software 12

(Appendix C) to improve upon the previous NOAEL/LOAEL approach ; 2) MOA studies have 13

been carried out that can give more insight into methanol toxicity ; and 3) EPA has refined PBPK 14

models for methanol on the basis of the work of Ward et al . (1997) (see Section 3.4

. for 15

description of the EPA model). The EPA PBPK model provide s estimates of HECs from rodent 16

exposures that are supported by pharmacokinetic information available for rodents and humans

. 17

The following sections describe how the BMD/BMDL approach, along with the EPA PBPK 18

model, is used to obtain a POD for use in the derivation of an RfC for methanol in accordance 19

with current draft BMD technical guidance (U.S. EPA, 2000b). 20

The BMD approach attempts to fit models to the dose

- response data for a given endpoint

. 21

It has the advantage of taking more of the dose

- response data into account when determining the 22

POD

, as well as estimating the dose for which an effect may have a specific probability of 23

occurring. The BMD approach also accounts , in part, for the qualit y of the study ( e.g., study 24

size) by estimating a BMDL, the 95% lower bound confidence limit on the BMD. The BMDL is 25

closer to the BMD (higher) for large studies and further away from the BMD (lower) for small 26

studies. Because the BMDL approach will acco unt , in part, for a study’s power, dose spacing

, 27

and the steepness of the dose

- response curve

, it is generally preferred over the NOAEL 28

approach

. 29

When possible, all experimental data points are included in this assessment to ensure 30

adequate fit of a BMD mo del and derivation of a BMDL. A summary of the POD values 31

determined by BMD analysis for the critical endpoint (as well as other considered endpoint s ) 32

( see Appendix C for modeling results), application of UFs , and conversion to HECs using the 33

BMD and PBPK approach

, is included in Section 5.1.3.1. 345

- 8 DRAFT

— DO NOT CITE OR QUOTE Use of the BMD approach has uncertainty associated with it. An element of the BMD 1

approach is the use of several models to determine which best fits the data. 71 In the absence of 2

an established MOA or a theoretica l basis for why one model should be used over another, model 3

selection is based on best fit to the experimental data selection. Model fit was determined by 4

statistics (AIC and χ 2 residuals of individual dose groups) and visual inspection recommended by 5

EP A (2000b) 72 6

The PBPK model developed by EPA for methanol (described in Section 3.4) was applied 7

for the estimation of methanol blood levels in the exposed dams (NEDO, 1987). When using 8

PBPK models

, it is very important to determine what estimate of interna l dose ( i.e., dose metric) 9

can serve as the most appropriate dose metric for the health effects under consideration. 10

The results of NEDO (1987), shown in Table 4

- 8 , indicate that there is not a cumulative 11

effect of ongoing exposure on brain

- weight decrem ents in rats exposed postnatally; i.e., the dose 12

response in terms of percent of control is about the same at 3 weeks postnatal as at 8 weeks 13

postnatal in rats exposed throughout gestation and the F 1 generation. However, there does 14

appear to be a greater brain

- weight effect in rats exposed postnatally versus rats exposed only 15

during organogenesis (GD7

– GD 17). In male rats exposed during organogenesis only, there is no 16

statistically significant decrease in brain weight at 8 week s after birth at the 1

, 000 p pm exposure 17

level. Conversely, in male rats exposed to the same level of methanol throughout gestation and 18

the F 1 generation, there was an approximately 5% decrease in brain weights (statistically 19

significant at the p < 0.01 level). The fact that male ra ts exposed to 5

, 000 ppm methanol only 20

during organogenesis experienced a decrease in brain weight of 10% at 8 weeks postnatal 21

indicates that postnatal exposure is not necessary for the observation of persistent postnatal 22

effects. However, the fact that th is decrease was less than the 13% decrease observed in male 23

rats exposed to 2

, 000 ppm methanol throughout gestation and the 8 week postnatal period 24

indicates that both exposure concentration and duration are important components of the ability 25

of methanol to cause this effect. The extent to which the observation of the increased effect is 26

due to a cumulative effect in rats exposed postnatally versus recovery in rats for which exposure 27

was discontinued at birth is not clear. 28

The fact that brain weight is susceptible to both the level and duration of exposure 29

suggest s that a dose metric that incorporates a time component would be the most appropriate 30

metric to use. For these reasons, and because it is more typically used in internal

- dose

- based 31

71 USEPA’s BMDS 2.1 was used for this assessment as it provides data management tools for run ning multiple

models on the same dose

- response data set. At this time, BMDS offers over 30 different models that are appropriate

for the analysis of dichotomous, continuous

, nested dichotomous and time

- dependent toxicolog ical data. Results

from all models include a reiteration of the model formula and model ru n options chosen by the user, goodness

- of

- fit

information, the BMD, and the estimate of the lower

- bound confidence limit on the BMD (BMDL).

72 Akaike’s Information Criterion (AIC) (Akaike, 1973) is used for model selection and is defined as

- 2L + 2P where

L is the log

- likelihood at the maximum likelihood estimates for the parameters and P is the number of model degrees

of freedom.5

- 9 DRAFT

— DO NOT CITE OR QUOTE assessments and better reflects total exposure within a given day, daily AUC (measured for 1

22 hours exposure/day) was chosen as the most appropriate dose metric for modeling the effects 2

of methanol exposure on brain weights in rats exposed throughout gestation and con tinuing into 3

the F 1 generation. 4

Application of the EPA methanol PBPK model ( described in Section 3.4 ) to the NEDO 5

(1987) study

, in which developing rats were exposed during gestation and the postnatal period

, 6

presents complications that need to be discusse d. The neonatal rats in this study were exposed to 7

methanol g estationally before parturition as well as lactationally and inhalationally after 8

parturition. The PBPK model developed by EPA only estimates internal dose metrics for 9

methanol exposure in NP a dult mice and rats. Experimental data indicate that inhalation

- route 10

blood methanol kinetics in NP mice and pregnant mice on GD6

– GD 10 are similar (Dorman 11

et al ., 1995; Perkins et al ., 1995a, 1995 b; Rogers et al ., 1993a, 1993 b). In addition, experimental 12

d ata indicate that the maternal blood:fetal partition coefficient for mice is approximately 1 (see 13

Section 3.4.1.2). Assuming that these findings apply for rats, the data indicate that PBPK 14

estimate s of PK and blood dose metrics for NP rats are better pred ictors of fetal exposure during 15

gestation than would be obtained from default extrapolations from external exposure 16

concentrations

. However, as is discussed to a greater extent in Section 5.3, the additional routes 17

of exposure presented to the pups in thi s study (lactation and inhalation) present uncertainties 18

that suggest th e average blood levels in pups in the NEDO (1987) report might be greater than 19

those of the dam. The assumption made in this assessment is that, if such differences exist 20

between huma n mothers and their offspring, they are not expected to be significantly greater than 21

that which has been postulated for rats. Thus, the PBPK model

- estimated adult blood methanol 22

level is considered to be an appropriate dose metric for the purpose of this analysis and HEC 23

derivation. 24

5.1.2.2. BMD Approach Applied to Brain Weight Data in Rats

The NEDO (1987) study reported decreases in brain weights in developing rats exposed 25

during gestation only (GD7

– GD 17) or during gestation and the postnatal period, up to 8 wee ks. 26

Because of the biological significance of decreases in brain weight as an endpoint in the 27

developing rat and because this endpoint was not evaluated in other peer

- reviewed studies, BMD 28

analysis was performed using these data. For the purposes of deri ving an RfC for methanol from 29

developmental endpoint s using the BMD method and rat data, decreases in brain weight at 30

6 weeks of age in the more sensitive gender, male s

, exposed throughout gestation and continuing 31

into the F 1 generation (both through lacta tion and inhalation routes) were utilized. Decreases in 32

brain weight at 6 weeks (gestation and postnatal exposure), rather than those seen at 3 and 8 33

weeks, were chosen as the basis for the RfC derivation because they resulted in lower estimated 34

BMDs and BMDLs. Decreased brain weights in male rats at 8 weeks age after gestation

- only 355

- 10 DRAFT

— DO NOT CITE OR QUOTE exposure were not utilized because they were less severe at the same dose level (1

, 000 ppm ) 1

compared to gestation and postnatal exposure. 2

The first step in the current BMD ana lysis is to convert the inhalation doses, given 3

as ppm values from the studies, to an internal dose metric using the EPA PBPK model (see 4

Section 3.4). For decreased brain weight in male rats, AUC of methanol in blood (hr × mg/L) is 5

chosen as the appropria te internal dose metric for the reasons discussed in Section 5.1.2.1. 6

Predicted AUC values for methanol in the blood of rats are summarized in Table 5

- 2. These 7

AUC values are then used as the dose metric for the BMD analysis of decreased brain weight in 8

male rats. 73 The full d etails of this analysis are reported in Appendix C. More details concerning 9

the PBPK modeling were presented in Section 3.4. 10

Table 5

- 2. The EPA PBPK model estimates of methanol blood levels (AUC) in rats

following inhalation exposur es

Exposure l evel (ppm) Methanol in b lood AUC (hr × mg/L) a in Rats

500 79.2

1,000 226.7

2,000 967.8

a AUC values were obtained by simulating 22 h r /day exposures for 5 days and calculated for the last 24 h ou r s of that

period.

The c urrent draft BMD techn ical guidance (U.S. EPA, 2000b) suggests that

, in the 11

absence of knowledge as to what level of response to consider adverse, a change in the mean 12

equal to one S.D. from the control mean can be used as a BMR for continuous endpoint s

. 13

However, it has been su ggested that other BMRs, such as 5% change relative to estimated 14

control mean, are also appropriate when performing BMD analyses on fetal weight change as a 15

developmental endpoint (Kavlock et al ., 1995). Therefore, both a one S.D. change from the 16

control mean and a 5% change relative to estimated control mean were considered (see Appendix 17

C for RfC derivations using alternative BMRs). For this endpoint , a one S.D. change from the 18

control mean returned the lowest BMDL estimates and was considered the most sui table BMR 19

for use in the RfC derivation. All models were fit using restrictions and option settings 20

suggested in the draft EPA BMD technical guidance document (U.S. EPA, 2000b). 21

A summary of the results most relevant to the development of a POD using the BMD 22

approach (BMD, BMDL, and model fit statistics) for decreased brain weight at 6 weeks in male 23

rats exposed to methanol throughout gestation and continuing into the F 1 generation is provided 24

in Table 5

- 3. BMDL values in Table 5

- 3 represent the 95% l ower

- bound confidence limit on the 25

AUC estimated to result in a mean that is one S.D. from the control mean. There is a 2.5

- fold 26

73 All BMD assessments in this review were performed using BMDS version 2.1

. A copy of the BMDS can be

obtained at: <http://www.epa .gov/ncea/bmds.htm>.5

- 11 DRAFT

— DO NOT CITE OR QUOTE range of BMDL estimates from adequately fitting models, indicating considerable model 1

dependence. In addition, the fit of the Hill and more complex Exponential models is better than 2

the other models in the dose region of interest as indicated by a lower scaled residual at the dose 3

group closest to the BMD (0.09 versus

- 0.67 or

- 0.77) and visual inspection. In accordance with 4

dr aft EPA BMD Technical Guidance (EPA, 2000b), the BMDL from the Hill model (bolded), is 5

selected as the most approriate basis for an RfC derivation because it results in the lowest BMDL 6

from among a broad range of BMDLs and provides a superior fit in the lo w dose region nearest 7

the BMD. The Hill model dose

- response curve for decreased brain weight in male rats is 8

presented in Figure 5

- 1, with response plotted against the chosen internal dose metric of AUC of 9

methanol in rats. The BMDL 1 SD was determined to b e 90.9 hr × mg/L using the 95% lower 10

confidence limit of the dose

- response curve expressed in terms of the AUC for methanol in 11

blood. 12

Table 5

- 3. Comparison of benchmark dose modeling results for decreased brain weight

in male rats at 6 weeks of age using m odeled AUC of methanol as a dose metric

Model BMD 1SD (AUC,

hr × mg/L ) a BMDL 1SD (AUC,

hr × mg/L ) a p

- value AIC c Scaled r esidual d

Linear 278.30 225.30 0.5376

- 203.84

- 0.77

2 nd degree p olynomial 278.30 225.30 0.5376

- 203.84

- 0.77

3rd degree p olynomial 278 .30 225.30 0.5376

- 203.84

- 0.77

Power 278.30 225.30 0.5376

- 203.84

- 0.77

Hill b 170.57 90.93 0.8366

- 203.04 0.09

Exponential 2 260.94 209.10 0.612

- 204.10

- 0.67

Exponential 3 260.94 209.10 0.612

- 204. 10

- 0.67

Exponential 4 172.08 96.93 0.8205

- 203. 10 0 .09

Exponential 5 172.08 96.93 0.8205

- 203. 10 0.09

a T he BMDL is the 95% lower confidence limit on the AUC estimated to decrease brain weight by 1 control mean

S.D. using BMDS model options and restrictions suggested by EPA BMD technical guidance (U.S. EP A, 2000b)

. b In accordance with draft EPA BMD Technical Guidance (EPA, 2000), the BMDL from the Hill model (bolded) is

chosen for us in an RfC derivation because it is the lowest of a broad range of BMDL estimates from adequately

fitting models and because the Hill model provides good fit in the dose region of inter e st as indicated by a relatively

low scaled residual at the dose group closest to the BMD (0.09 versus

- 0.67 or

- 0.77)

.

c AIC = Akaike Information Criterion =

- 2L + 2P, where L is the log

- likeliho od at the maximum likelihood

estimates for the parameters, and P is the number of modeled degrees of freedom (usually the number of parameters

estimated)

. d χ

2 d residual (measure of how model

- predicted responses deviate from the actual data) for the dose group closest to

the BMD scaled by an estimate of its S.D

. Provides a comparative measure of model fit near the BMD. Residuals

that exceed 2.0 in absolute v alue should cause one to question model fit in this region.

Source: NEDO (1987).5

- 12 DRAFT

— DO NOT CITE OR QUOTE 1.45 1.5 1.55 1.6 1.65 1.7 1.75 1.8 1.85

0 200 400 600 800 1000Mean Response

dose Hill Model with 0.95 Confidence Level

09:05 08/24 2009 BMD BMDL Hill

Figure 5

- 1. Hill model BMD plot of decreased brain weight in male rats at 6 weeks age

using modeled AUC of methanol in blood as the dose metric, 1 control mean S.D.

1

Once the BMDL 1 SD was obtained in units of hr × mg/L, it was used to derive a chronic 2

RfC. The first step is to calculate the HEC using the PBPK model described in Appendix B. An 3

algebraic equation is provided (Equation 1 of Appendix B) that describes the rel ationship 4

between predicted methanol AUC and the human equivalent inhalation exposure concentration 5

(HEC) in ppm. 6

BMDLHEC (ppm) = 0.0224*BMDL1 SD +(1334*BMDL1 SD )/(794+ BMDL1 SD ) 7

BMDL HEC (ppm) = 0.0224* 90.9 +(1334* 90.9 )/(794+ 90.9 ) = 139 ppm 8

Next, because Rf Cs are typically expressed in units of mg/m 3 , the HEC value in ppm was 9

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m 3 : 10

HEC (mg/m 3 ) = 1.31 × 139 ppm = 182 mg/m 3 115

- 13 DRAFT

— DO NOT CITE OR QUOTE 5.1.3. RfC Derivation

– Including Application of Uncertainty Fact ors

5.1.3.1. Comparison Between Endpoint s and BMDL M odeling A pproaches

A summary of the PODs for the various developmental endpoint s and BMD modeling 1

approaches considered for the derivation of an RfC, along with the UFs applied 74 and the 2

conversion to an HEC, are presented in Table 5

- 4 and graphically compared in Figure 5

- 2 (see 3

Appendix C for details). Information is presented that compares the use of different endpoint s 4

( i.e., cervical rib, decreased brain weight, and increased latency of VDR ) and different meth ods 5

( i.e., different BMR levels) for estimating the POD. These comparisons are presented to inform 6

the analysis of uncertainty surrounding these choices. Each approach considered for the 7

determination of the POD has strengths and limitations, but when co nsidered together for 8

comparative purposes they allow for a more informed determination for the POD for the 9

methanol RfC. 10

A 10% extra risk BMR is adequate for m ost traditional bioassays using 50 animals per 11

dose group. A smaller BMR of 5% extra risk can s ometimes be justified for developmental 12

studies ( e.g., Rogers et al

., 1993a) , because they generally involve a larger number of subjects. 13

Reference values estimated for cervical rib incidence in mice using C

max as the dose metric were 14

13.6 and 10.4 mg/m 3 using BMDL 10 and BMDL 05 PODs, respectively (see Appendix D for 15

discussion of choice of C

max as the appropriate dose metric for incidence of cervical rib in mice). 16

The reference value estimated for alterations in sensorimotor development and performance as 17

measured by the VDR test in female monkeys using AUC as the dose metric was 1.7 mg/m 3 18

using the BMDL SD as the POD. As discussed in Section 4.4.2, confidence in this endpoint is 19

reduced by a marginal dose

- response trend in one sex (females) and a limited sample size

. 20

Although the VDR test demonstrates that prenatal and continuing postnatal exposure to methanol 21

can result in neurotoxicity

, the use of such statistically borderline results is not warranted in the 22

derivation of the RfC

, given the availability of better dose

- response data in other species. 23

Decreases in brain weight at 6 weeks of age in male rats exposed during gestation and 24

throughout the F 1 generation using AUC as the dose metric yield the reference values of 1.8 and 25

2.4 mg/m 3 for BMRs of one S.D. from the control mean and 5% change relative to control mean, 26

respectively. Because decreases in brain weight in male rats at 6 weeks postbirth resulted in a 27

clear dose response and returned RfC estimates lower than or approximate to the other endpo int s 28

considered, it was chosen as the critical endpoint . One S.D. from the control mean was chosen 29

as the appropriate level of response (BMR) for the calculation of the RfC because it is the 30

standard recommended by EPA’s draft technical guidance ( U.S. EPA , 2000b) and yields a lower 31

BMDL than 5% relative deviance for this data set . Thus, the RfC is: 32

74 The rationale for the selection of these UFs is discussed later in Section 5.1.3

.5

- 14 DRAFT

— DO NOT CITE OR QUOTE RfC = POD HEC ÷ UF = 182 mg/m 3 ÷ 100 = 2 mg/m 3 (rounded to one significant figure) 1

Table 5

- 4. Summary of PODs for critical endpoint s , application of UFs and c onversion

to HEC values using BMD and PBPK modeling

Rogers et al . (1993a) Burbacher et al

. ( 1999a, 1999 b) NEDO (1987)

BMDL 10 m ouse

cervical rib C

max BMDL 05 mouse

cervical rib C

max BMDL 1 SD

f emale m onkey

VDR a AUC BMDL 05

r at b rain w t. b

AUC BMDL 1 SD

r at b rain w t. b

AUC

BMDL 94.3 mg/L 44.7 mg/L 81.7 hr×mg/L 123.9 hr×mg/L 90.9 hr×mg/L

HEC (mg/m 3 ) c 1360 1036 165 240 182

UF H d 10 10 10 10 10

UF A e 3 3 3 3 3

UF D 3 3 3 3 3

UF S 1 1 1 1 1

UF L 1 1 1 1 1

UF TOTAL 100 100 10 0 100 100

RfC (mg/m 3 ) 13.6 10.4 1. 7 2.4 1.8

a VDR = test of sensorimotor development as measured by age from birth at achievement of test criterion for

grasping a brightly colored object.

b Brain weight at 6 weeks postbirth , multiple routes of exposure (whole gestation, lactation, inhal ation)

c The PBPK model used for this HEC estimate is described in Appendix B. An algebraic equation (Equation 1 of

Appendix B) describes the relationship between predicted methanol AUC and the human equivalent inhalation

exposure concentration (HEC) in pp m. This equation can also be used to estimate model predictions for HECs

from C

max values because C

max values and AUC values were estimated at steady

- state for constant 24 hours

exposures ( i.e., AUC = 24 x C

max ). The ppm HEC estimate is then converted t o mg/m 3 by multiplying by 1.31.

d The rationale for the selection of these UFs is discussed in Section 5.1.3 below.

e These uncertainty factor (UF) acronyms are defined in Sections 5.1.2.1.1 to 5.1.2.1.4

.5

- 15 DRAFT

— DO NOT CITE OR QUOTE 1 10 100 1000 10000

Rogers et al., 1993a; GD

6-15 mouse study;

cervical rib;

BMDL(HEC)10 Rogers et al., 1993a; GD

6-15 mouse study;

cervical rib BMDL(HEC)05 Burbacher et al., 1999a,b;

w hole gestation monkey

study; visually directed

reaching; BMDL(HEC)1SD NEDO, 1987; 1-gen repro

study; 6 w eek male brain

w eight; BMDL(HEC)05 NEDO, 1987; 1-gen repro

study; 6 w eek male brain

w eight; BMDL(HEC)1SDConcentration (mg/m3)

POD Interspecies Extrapolation Inter

- individual Variation Database RfC 1 10 100 1000 10000

Rogers et al., 1993a; GD

6-15 mouse study;

cervical rib;

BMDL(HEC)10 Rogers et al., 1993a; GD

6-15 mouse study;

cervical rib BMDL(HEC)05 Burbacher et al., 1999a,b;

w hole gestation monkey

study; visually directed

reaching; BMDL(HEC)1SD NEDO, 1987; 1-gen repro

study; 6 w eek male brain

w eight; BMDL(HEC)05 NEDO, 1987; 1-gen repro

study; 6 w eek male brain

w eight; BMDL(HEC)1SDConcentration (mg/m3)

POD Interspecies Extrapolation Inter

- individual Variation Database RfC Interspecies Extrapolation individual Variation Database

Figure 5

- 2. P ODs (in mg/m 3 ) for selected endpoi nt s with corresponding applied UFs

(chosen RfC value is circled)

5.1.3.2. Application of UF s

UFs are applied to the POD, identified from the rodent data, to account for recognized 1

uncertainties in extrapolation from experimental conditions to the assumed human s cenario ( i.e., 2

chronic exposure over a lifetime). A composite UF of 100

- fold (10

- fold for interindividual 3

variation, 3

- fold for residual toxicodynamic differences associated with animal

- to

- human 4

extrapolation, and 3

- fold for database uncertainty) was appl ied to the POD for the derivation of 5

the RfC, as described below. 6

5.1.3.2.1. Interindividual Variation UF H

. A factor of 10 was applied to account for variation in 7

sensitivity within the human population (UF H ). The UF of 10 is commonly considered to be 8

appropriate i n the absence of convincing data to the contrary. The data from which to determine 9

the potential extent of variation in how humans respond to chronic exposure to methanol are 10

limited

, given the complex nature of the developmental endpoint employed and unc ertainties 11

surrounding the importance of metabolism to the observed teratogenic effects. Susceptibility to 12

methanol is likely to involve intrinsic and extrinsic factors. Some factors may include alteration 13

of the body burden of methanol or its metabolite s, sensitization of an individual to methanol 145

- 16 DRAFT

— DO NOT CITE OR QUOTE effects, or augmentation of underlying conditions or changes in processes that share common 1

features with methanol effects. Additionally, inherent differences in an individual’s genetic 2

make

- up, diet, gender, age, or disease state may affect the pharmacokinetics and 3

pharmacodynamics of methanol, influencing susceptibility intrinsically. Co

- exposure to a 4

pollutant that alters metabolism or clearance or that adds to background levels of metabolites 5

may also affe ct the pharmacokinetics and pharmacodynamics of methanol

, influencing 6

susceptibility extrinsically (see Section 4.9 ). The determination of the UF for human variation is 7

supported by several types of information

, including information concerning background levels 8

of methanol in humans, variation in pharmacokinetics revealed through human studies and from 9

PBPK modeling, variation of methanol metabolism in human tissues, and information on 10

physiologic factors (including gender and age)

, or acquired factors (i ncluding diet and 11

environment) that may affect methanol exposure and toxicity. 12

In using the AUC of methanol in blood as the dose metric for derivation of health 13

benchmarks for methanol, the assumption is made that concentrations of methanol in blood over 14

time are related to its toxicity

, either through the actions of the parent or it subsequent 15

metabolism. However, the formation of methanol’s metabolites has been shown in humans to be 16

carried out by enzymes that are inducible, highly variable in activity, polymorphic, and to also be 17

involved in the metabolism of other drugs and environmental pollutants. Hence, differences in 18

the metabolism of methanol that are specific for target tissue, gender, age, route of 19

administration, and prior exposure to other en vironmental chemicals may give a different pattern 20

of methanol toxicity if metabolism is required for that toxicity. Eighty

- five percent of Asians 21

carry an atypical phenotype of ADH that may affect their ability to metabolize methanol 22

(Agarwal

, 2001; Bosr on and Li

, 1986 ; Pietruszko

, 1980). Also, polymorphisms in ADH3 23

occurring in the promoter region reduce the transcriptional activity in vitro nearly two fold, 24

although no studies have reported differences in ADH3 enzyme activity in humans (Hedberg 25

et al

., 2001). 26

Although data on the specific potential for increased susceptibility to methanol are 27

lacking, there is information on PK and pharmacodynamic factors suggesting that children may 28

have differential susceptibility to methanol toxicity (see Section 4. 10 .1). Thus, there is 29

uncertainty in children’s responses to methanol that should be taken into consideration for 30

derivation of the UF for human variation that is not available from either measured human data 31

or PBPK modeling analyses. The enzyme primarily responsible for metabolism of methanol in 32

humans, ADH, has been reported to be reduced in activity in newborns. Differences in 33

pharmacokinetics include potentially greater pollutant intake due to greater ventilation rates, 34

activity

, and greater intake of liquids in children. In terms of differences in susceptibility to 35

methanol due to pharmacodynamic considerations, the substantial anatomical, physiologic, and 36

biochemical changes that occur during infancy, childhood, and puberty suggest that there are 375

- 17 DRAFT

— DO NOT CITE OR QUOTE de velopmental periods in which the endocrine, reproductive, immune, audiovisual, nervous, and 1

other organ systems may be especially sensitive. 2

There are some limited data from short

- term exposure studies in humans and animal 3

experiments that suggest differe ntial susceptibility to methanol on the basis of gender. Gender 4

can provide not only different potential targets for methanol toxicity but also differences in 5

methanol pharmacokinetics and pharmacodynamics. NEDO (1987) reported that in rats exposed 6

to me thanol pre

- and postnatally, 6

- and 8

- week

- old male progeny had significantly lower brain 7

weights at 1

, 000 ppm, whereas females only showed decreases at 2

, 000 ppm. In general, gender

8

related differences in distribution and clearance of methanol may result from the greater muscle 9

mass, larger body size, decreased body fat, and increased volumes of distribution in males 10

compared to females. 11

5.1.3.2.2. Animal

- to

- Human Extrapolation U F

A

. A factor of 3 was applied to account for 12

uncertainties in extrapolating from r ode nt s to humans. Application of a full UF of 10 would 13

depend on two areas of uncertainty : toxicokinetic and toxicodynamic uncertainty. In this 14

assessment, the toxicokinetic component is largely addressed by the determination of a HEC 15

through the use of PBP K modeling. Given the chosen dose metric (AUC for methanol blood), 16

uncertainties in the PBPK modeling of methanol are not expected to be greater for one species 17

than another. The analysis of parameter uncertainty for the PBPK modeling performed for 18

human , mouse, and rat data gave similar results as to how well the model fit the available data. 19

Thus, the human and rodent PBPK model performed similarly using this dose metric for 20

comparisons between species. As discussed in Section 5.3 below, uncertainty d oes exist 21

regarding the relation of maternal blood levels estimated by the model to fetal and neonatal 22

blood levels that would be obtained under the (gestational, postnatal and lactational) exposure 23

scenario employed in the critical study. However, at env ironmentally relevant exposure levels, it 24

is assumed that the ratio of the difference in blood concentrations between a human infant and 25

mother would be similar to and not significantly greater than the difference between a rat dam 26

and its fetus. Key para meters and factors which determine the ratio of fetal or neonatal human 27

versus mother methanol blood levels either do not change significantly with age (partition 28

coefficients, relative blood flows) or scale in a way that is common across species 29

(allometr ically). For this reason and because EPA has confidence in the ability of the PBPK 30

model to accurately predict adult blood levels of methanol, the PK uncertainty is reduced and a 31

value of 1 was applied. Rodent

- to

- human pharmacodynamic uncertainty is cove red by a factor 32

of 3 , as is the practice for deriving RfCs (U.S. EPA, 1994b). Therefore, a factor of 3 is used for 33

interspecies uncertainty. 34

5.1.3.2.3. Database UF D

. A database UF of 3 was applied to account for deficiencies in the 35

toxicity database. The database for methanol toxicity is quite extensive: there are chronic and 365

- 18 DRAFT

— DO NOT CITE OR QUOTE developmental toxicity studies in rats, mice, and monkeys, a two

- generation reproductive 1

toxicity study in rats, and neurotoxicity and immunotoxicity studies. However, there is 2

uncertainty regarding which test species is most relevant to humans. In addition, limitations of 3

the developmental toxicity database employed in this assessment include gaps in testing and 4

imperfect study design, reporting, and analyses. Developmental studies were c onducted at levels 5

inducing maternal toxicity, a full developmental neurotoxicity test (DNT) in rodents has not been 6

performed and is warranted given the critical effect of decreased brain weight , there are no 7

chronic oral studies in mice, and chronic and developmental studies in monkeys were generally 8

inadequate for quantification purposes

, for reasons discussed in Section 5.1.1.1. Problems of 9

interpretation of developmental and reproductive studies also arise given the dose spacing 10

between lowest and nex t highest level. For these reasons, an UF of 3 was applied to account for 11

deficiencies in the database. 12

5.1.3.2.4. Extrapolation from Subchronic to Chronic and LOAEL

- to NOAEL 13

Extrapolation UF s. A UF was not necessary to account for extrapolation from less than chr onic 14

results because developmental toxicity (cervical rib and decreased brain weight) was used as the 15

critical effect. The developmental period is recognized as a susceptible lifestage where exposure 16

during certain time windows is more relevant to the ind uction of developmental effects than 17

lifetime exposure (U.S. EPA, 1991)

. 18

A UF for LOAEL

- to

- NOAEL extrapolation was not applied because BMD analysis was 19

used to determine the POD, and this factor was addressed as one of the considerations in 20

selecting the B MR . In this case, a BMR of one S.D. from the control mean in the critical effect 21

was selected based on the assumption that it represents a minimum biologically significant 22

change

. 23

5.1.4. Previous RfC Assessment

The health effects data for methanol were assessed for the IRIS database in 1991 and 24

were determined to be inadequate for derivation of an RfC. 25

5.2. ORAL R f D

In general, the RfD is an estimate of a daily exposure to the human population (including 26

susceptible subgroups) that is likely to be without an appreciab le risk of adverse health effects 27

over a lifetime. It is derived from a POD, generally the statistical lower confidence limit on the 28

BMDL, with uncertainty/variability factors applied to reflect limitations of the data used. The 29

RfD is expressed in terms of mg/kg

- day of exposure to an agent and is derived by a similar 30

methodology as is the RfC. Ideally, studies with the greatest duration of exposure and conducted 31

via the oral route of exposure give the most confidence for derivation of an RfD. For metha nol, 32

the oral database is currently more limited than the inhalation database. With the development of 335

- 19 DRAFT

— DO NOT CITE OR QUOTE PBPK models for methanol, the inhalation database has been used to help bridge data gaps in the 1

oral database to deriv e an RfD. 2

5.2.1. Choice of Principal Stud y and Critical Effect

– with Rationale and Justification

No studies have been reported in which humans have been exposed subchronically or 3

chronically to methanol by the oral route of exposure and thus

, would be sui table for derivation 4

of an oral RfD. Data exist regarding effects from oral exposure in experimental animals, but 5

they are more limited than data from the inhalation route of exposure (see Sections 4.2 , 4.3

, and 6

4. 4 ). 7

Only 2 oral studies of 90

- days duration or longer in animals have been reported ( Soffritti 8

et al ., 2002a; U.S. EPA, 1986 c ) for methanol

. EPA (1986 c ) reported that there were no 9

differences in body weight gain, food consumption, or gross or microscopic evaluations in 10

Sprague

- Dawley rats gavaged with 100, 500, or 2

, 500 mg/kg

- day versus control animals. Liver 11

weights in both male and female rats were increased, although not significantly, at the 2

, 500 12

mg/kg

- day dose level, suggesting a treatment

- related response despite the absence of 13

histopathologic lesions in the liver. Brain weights of high

- dose group males and females were 14

significantly less than control animals at terminal (90 days) sacrifice. The data were not reported 15

in adequate detail for dose

- response modeling and BMD estimation. Based primarily on the 16

qualitative findings p resented in this study, the 500 mg/kg

- day dose was deemed to be a 17

NOAEL

. 75 18

The only lifetime oral study available was conducted by Soffiritti et al . (2002a) in 19

Sprague

- Dawley rats exposed to 0, 500, 5

, 000, 20,000 ppm (v/v) methanol, provided ad libitum 20

in drinking water. Based on default, time

- weighted average body weight estimates for Sprague

21

Dawley rats (U.S. EPA, 1988) , average daily doses of 0, 46.6, 466, and 1

, 872 mg/kg

- day for 22

males and 0, 52.9, 529, 2

, 101 mg/kg

- day for females were reported by the s tudy authors. All rats 23

were exposed for up to 104 weeks, and then maintained until natural death. The authors report 24

no substantial changes in survival nor was there any pattern of compound

- related clinical signs 25

of toxicity. The authors did not report noncancer lesions

, and there were no reported compound

26

related signs of gross pathology or histopathologic lesions indicative of noncancer toxicological 27

effects in response to methanol. 28

Five oral studies investigated the reproductive and developmental effe cts of methanol in 29

rodents ( Aziz et al ., 2002; Fu et al ., 1996; Sakanashi et al ., 1996; Rogers et al ., 1993a; Infurna 30

and Weiss, 1986), including three studies that investigated the influence of FAD diets on the 31

effects of methanol exposures ( Aziz et al

., 2002; Fu et al ., 1996; Sakanashi et al ., 1996 )

. Infurna 32

and Weiss (1986) exposed pregnant Long

- Evans rats to 2

, 500 mg/kg

- day in drinking water on 33

either GD15

– GD 17 or GD 17

– GD 19. Litter size, pup birth weight, pup postnatal weight gain, 34

75 EPA (1986c) did not report d etails required for a BMD analysis such as standard deviations for mean responses.5

- 20 DRAFT

— DO NOT CITE OR QUOTE postnatal mortality, and day of eye opening were not different in treated animals versus controls. 1

Mean latency for nipple attachment and homing behavior (ability to detect home nesting 2

material) were different in both methanol treated groups. These differences were signifi cantly 3

different from controls. Rogers et al . (1993a) exposed pregnant CD

- 1 mice via gavage to 4 g/kg

4

day methanol, given in 2 equal daily doses. Incidence of cleft palate and exencephaly was 5

increased following maternal exposure to methanol. Also, an i ncrease in totally resorbed litters 6

and a decrease in the number of live fetuses per litter were observed. 7

Aziz et al . (2002), Fu et al

. (1996)

, and Sakanashi et al

. (1996) investigated the role of 8

folic acid in methanol

- induced developmental neurotoxicit y. Like Rogers et al . (1993a), the 9

former 2 studies observed that an oral gavage dose of 4

– 5 g/kg

- day during GD6

– GD 15 or GD6

– 10

GD 10 resulted in an increase in cleft palate in mice fed sufficient folic acid diets, as well as an 11

increase in resorptions and a decrease in live fetuses per litter. Fu et al . (1996) also observed an 12

increase in exencephaly in the FAS group. Both studies found that an approximate ly 50% 13

reduction in maternal liver folate concentration resulted in an increase in the percentage of l itters 14

affected by cleft palate (as much as threefold ) and an increase in the percentage of litters affected 15

by exencephaly (as much as 10

- fold). Aziz et al . (2002) exposed rat dams throughout their 16

lactation period to 0, 1, 2, or 4% v/v methanol via the drinking water, equivalent to 17

approximately 480, 960 and 1

, 920 mg/kg

- day. 76 Pups were exposed to methanol via lactation 18

from PND1

– PND21. Methanol treatment at 2% and 4% was associated with significant 19

increases in activity (measured as distance traveled i n a spontaneous locomotor activity test ) in 20

the FAS group (13 and 39%, respectively) and most notably, in the FAD group (33 and 66%, 21

respectively) when compared to their respective controls. At PND45, the CAR in FAD rats 22

exposed to 2% and 4% methanol was significantly decreased by 48% and 52%, respectively, 23

relative to non exposed controls. In the FAS group, the CAR was only significantly decreased in 24

the 4% methanol

- exposed animals and only by 22% as compared to their respective controls. 25

5.2.1.1. Expansion of t he Oral Database by Route

- to

- Route Extrapolation

Given the oral database limitations, including the limited reporting of noncancer findings 26

in the subchronic (U.S. EPA, 1986 c ) and chronic studies (Soffritti et al ., 2002a) of rats and the 27

high

- dose levels u sed in the two rodent developmental studies, EPA has derived an RfD by using 28

relevant inhalation data and route

- to

- route extrapolation with the aid of the EPA PBPK model 29

(see Sections 3.4 and 5.1). Several other factors support use of route

- to

- route extra polation for 30

methanol. The limited data for oral administration indicate similar effects as reported via 31

inhalation exposure ( e.g., the brain and fetal skeletal system are targets of toxicity). Methanol 32

has been shown to be rapid ly and well

- absorbed by both the oral and inhalation routes of 33

76 Assuming that Wistar rat drinking wa ter consumption is 60 mL/kg

- day (Rogers et al., 2002), 1% methanol in

drinking water would be equivalent to 1% x 0.8 g/mL x 60 mL/kg

- day = 0.48 g/kg

- day = 480 mg/kg

- day.5

- 21 DRAFT

— DO NOT CITE OR QUOTE exposure (CERHR, 2004; Kavet and Nauss, 1990). Once absorbed, methanol distributes rapidly 1

to all organs and tissues according to water content , regardless of route of exposure. 2

As with the species

- to

- species extrap olation used in the development of the RfC, the dose 3

metric used for species

- to

- species and route

- to

- route extrapolation of inhalation data to oral data 4

is the AUC of methanol in blood. Simulations for human oral methanol exposure were 5

conducted using the model parameters as previously described for human inhalation exposures, 6

with human oral kinetic/absorption parameters from S u ltatos et al . (2004) ( i.e., KAS = 0.2, 7

KSI

= 3.17, and KAI

= 3.28 )

. Human oral exposures were assumed to occur during six drinki ng 8

episodes during the day, at times 0, 3, 5, 8, 11, and 15 h ours from the first ingestion of the day. 9

For example, if first ingestion occurred at 7 am, these would be at 7 am, 10 am, 12 noon, 3 pm, 6 10

pm, and 10 pm. Each ingestion event was treated as oc curring over 3 minute s , during which the 11

corresponding fraction of the daily dose was infused into the stomach lumen compartment. The 12

fraction of the total ingested methanol simulated at each of these times was 25%, 10%, 25%, 13

10%, 25%, and 5%, respectivel y. Six days of exposure were simulated to allow for any 14

accumulation (visual inspection of plots showed this to be finished by the 2nd or 3rd day), and 15

the results for the last 24 h ours were used. Dividing the exposure into more and smaller episodes 16

woul d decrease the estimated peak concentration but have little effect on AUC. This dose metric 17

was used for dose

- response modeling to derive the POD, expressed as a BMDL. The BMDL 18

was then back

- calculated using the EPA PBPK model to obtain an equivalent ora l drinking water 19

dose in terms of mg/kg

- day. 20

5.2.2. RfD Derivation

– Including Application of UFs

5.2.1.2. Consideration of Inhalation Data

Inhalation studies considered for derivation of the RfC are used to supplement the oral 21

database using the route

- to

- route ext rapolation, as previously described. BMD approaches we re 22

applied to the existing inhalation database

, and the EPA PBPK model was used for species

- to

23

species extrapolations. The rationale and approach for determining the RfC is described above 24

(Section 5. 1), and the data used to support the derivation of the RfC were extrapolated using the 25

EPA PBPK model to provide an oral equivalent POD. 26

5.2.1.3. Selection of Critical Effect(s) from Inhalation Data

Methanol

- induced effects on the brain in rats (weight decrease) an d fetal axial skeletal 27

system in mice (cervical rib s and cleft palate) were consistently observed at lower levels, than 28

other targets, in the oral and inhalation databases

. Analysis of inhalation developmental toxicity 29

studies shows lower BMDLs for decrea sed male brain weight in rats exposed throughout 30

gestation and the F 1 generation (NEDO, 1987) than BMDLs associated with the fetal axial 31

skeletal system in mice

. Therefore, as was noted in Section 5.1.3.1, the BMDL for decreases in 325

- 22 DRAFT

— DO NOT CITE OR QUOTE brain weight in male ra ts is chosen to serve as the basis for the route

- to

- route extrapolation and 1

calculation of the RfD. 2

5.2.1.4. Selection of the POD

The BMDL chosen for the RfC is used to determine the POD for the RfD. This value is 3

based on a developmental toxicity dataset that in cludes in utero and postnatal exposures and is 4

below the range of estimates for other developmental datasets consisting of exposure only 5

throughout organogenesis. The neonatal brain is the target organ chosen for derivation of the 6

RfC. The BMDL for the R fC (AUC of 90.9 hr × mg/L methanol in blood) is converted using the 7

EPA model to a human equivalent oral exposure of 38.5 mg/kg

- day. 77 8

5.2.2. RfD Derivation

– Application of UFs

In an approach consistent with the RfC derivation, UFs are applied to the oral POD of 9

3 8.5 mg/kg

- day to address interspecies extrapolation, intraspecies variability, and database 10

uncertainties for the RfD. Because the same dataset, endpoint

, and PBPK model used to derive 11

the RfC were also used to calculate the oral POD, t he total UF of 100 is applied to the BMDL of 12

38.5 mg/kg

- day to yield an RfD of 1.12 mg/kg

- day for methanol. 13

RfD = 38.5 mg/kg

- day ÷ 100 = 0.4 mg/kg

- day (rounded to one significant figure) 14

5.2.3. Previous RfD Assessment

The previous IRIS assessment for methanol included an RfD of 0. 5 mg/kg

- day that was 15

derived from a EPA (1986 c ) subchronic oral study in which Sprague

- Dawley rats (30/sex/dose) 16

were gavaged daily with 0, 100, 500, or 2

, 500 mg/kg

- day of methanol. There were no 17

differences between dosed animals and controls in body weig ht gain, food consumption, gross or 18

microscopic evaluations. Elevated levels of SGPT, serum alkaline phosphatase ( SAP ) , and 19

increased but not statistically significant liver weights in both male and female rats suggest 20

possible treatment

- related effects i n rats dosed with 2

, 500 mg methanol/kg

- day

, despite the 21

absence of supportive histopathologic lesions in the liver. Brain weights of both high

- dose group 22

males and females were significantly less than those of the control group

. Based on these 23

findings, 5 00 mg/kg

- day of methanol wa s considered a NOAEL in this rat study

. Application of 24

a 1

, 000

- fold UF (interspecies extrapolation, susceptible human subpopulations, and subchronic 25

to chronic extrapolation) yielded an RfD of 0.5 mg/kg

- day. 26

77 The PBPK model used for this HEC estimate is described in Appendix B. An algebraic equation is provided

(Equation 2) that describes the relationship between predicted methanol AUC and the HE D in mg/kg

- day.5

- 23 DRAFT

— DO NOT CITE OR QUOTE 5.3. UNCERTANTIES IN TH E INHALATION R f C AND ORAL R f D

The following is a more extensive discussion of the uncertainties associated with the RfC 1

and RfD for methanol beyond that which is addressed quantitatively in Sections 5.1.2, 5.1.3, and 2

5.2.2. A summary of these uncertaintie s is presented in Table 5

- 5. 3

Table 5

- 5. Summary of uncertainties in methanol non cancer risk assessment

Consideration Potential Impact Decision Justification

Choice of endpoint Use of other endpoint

could ↑ RfC by up to

~ 5

- fold (see Table 5

- 4

and Section 5.3 .1 ) RfC is based on the

most sensitive and

quantifiable endpoint

, decreased brain weight

in male rats exposed

pre

- and postnatally Chosen endpoint is considered the most

relevant due to its biolog ical significance,

and consistency across a developmental

and a subchronic study in rats and with the

observation of other developmental

neurotoxicities reported in monkeys.

Choice of dose metric Alternatives could ↑

or ↓ RfC/D ( e.g., use

of C

max increase d RfC

by ~20%) AUC for methanol in

arterial blood AUC was selected as the most appropriate

dose metric because it incorporates time

(brain weight is sensitive to both the level

and duration of exposure) and better

reflects exposure within a given day.

Cho ice of model for

BMDL derivation Use of a linear model

could ↑ RfC by ~2.5

-

fold (see Table 5

- 3) Hill model used Hill model gave lowest of a broad range of

BMDL estimates from adequate models

and provides good fit in low dose region.

Choice of animal

- to

human extrapolation

method Alternatives could ↑

or ↓ RfC/ D ( e.g., use

of standard dosimetry

assumption would ↑

RfC by

~ 2

- fold ; see

Section 5.3 .4 ) A PBPK model was

used to extrapolate

animal to human

concentrations Use of a PBPK model reduced uncertainty

associated with the animal to human

extrapolation. AUC bloo d levels of

methanol is an appropriate dose metric and

a peer

- reviewed PBPK model that

estimates this metric was verified by EPA

using established (U.S. EPA, 2006a)

methods and procedures

Statistical uncertainty

at POD (sampling

variability due to

bioass ay size) POD would be ~ 90 %

higher if BMD were

used A BMDL was used as

the POD Lower bound is 95% CI of administered

exposure

Choice of bioassay Alternatives could ↑

RfC/D NEDO (1987) Alternative bioassays were available, but

the chosen bioassay was adequately

conducted and reported and resulted in the

most sensitive and reliable BMDL for

derivation of the RfC.

Choice of

species/gender RfC would be ↑ or ↓

if based on another

species/gender RfC is based on the

most sensitive and

quantifiable endpoint

( ↓ brain weight) in the

most sensitive species

and gender adequately

evaluated (male rats). Choice of female rats would have resulted

in a higher RfC/ D. Effects in mice also

yield higher RfCs. Qualitative evidence

from NEDO (1987) and Burbacher et al

. ( 2004a, 2004b ) suggest that monkeys may

be a more sensitive species, but data are not

as reliabl e for quantification.

Human population

variab ility RfC c ould ↓ or ↑ if

a nother value of the

UF was used 10

- fold uncertainty

factor applied to derive

the RfC/ Rf D values 10

- fold UF is applied because of limited

data on human variability or potential

susceptible subpopulations, particularly

pregnant mothers and th eir neonates.5

- 24 DRAFT

— DO NOT CITE OR QUOTE 5.3.1. Choice of Endpoint

The impact of endpoint selection on the derivation of the RfC and RfD was discussed in 1

Section s 5.1.3.1 and 5.2.2.2

. Potential RfC values considered ranged from 1. 7 to 13.6 mg/m 3

, 2

depending on whether neurobehavioral fu nction in male monkeys, brain weight decrease in male 3

rats

, or cervical rib s incidence in mice was chosen as the critical effect for derivation of the POD, 4

with the former endpoint representing the lower end of the RfC range. The use of other 5

endpoint s , p articularly pre

- term births identified in the Burbacher et al . ( 2004a, 2004b, 1999a, 6

1999 b) monkey study, would potentially result in lower reference values, but significant 7

uncertainties associated with those studies preclude their use as the basis for an RfC. 8

Burbacher et al

. (2004a, 2004b, 1999a, 1999b) exposed M. fascicularis monkeys to 0, 9

262, 786, and 2

, 359 mg/m 3 methanol 2.5 h ou r s /day, 7 days/week during premating/mating and 10

throughout gestation (approximately 168 days). They observed a slight but s tatistically 11

significant gestation period shortening in all exposure groups that was largely due to C

- sections 12

performed in the methanol exposure groups “in response to signs of possible difficulty in the 13

maintenance of pregnancy,” including vaginal bleedi ng. As discussed in Section s 4.3.2 and 14

5.1.1.2, there are questions concerning this effect and its relationship to methanol exposure. An 15

ultrasound was not done to confirm the existence of real fetal or placental problems. 16

Neurobehavioral function was a ssessed in infants during the first 9 months of life. Two tests out 17

of nine, returned positive results possibly related to methanol exposure. VDR performance was 18

reduced in all treated male infants, and was significantly reduced in the 2

, 359 mg/m 3 group for 19

both sexes and the 786 mg/m 3 group for males. However, an overall dose

- response trend for this 20

endpoint was only observed in females. As discussed in Section 4.4 .2, confidence in this 21

endpoint may have been increased by statistical analyses to adjust for multiple testing (CERHR, 22

2004), but it is a measure of functional deficits in sensorimotor development that is consistent 23

with early developmental CNS effects (brain weight changes discussed above) that have been 24

observed in rats. T he Fagan test of i nfant intelligence indicated small but not significant deficits 25

of performance (time spent looking a novel faces versus familiar faces) in treated infants. 26

Although these results indicate that prenatal and continuing postnatal exposure to methanol can 27

res ult in neurotoxicity to the offspring, especially when considered in conjunction with the gross 28

morphological effects noted in NEDO (1987), the use of such statistically borderline results is 29

not warranted in the derivation of the RfC

, given the availabili ty of better dose

- response data in 30

other species. 31

NEDO (1987) also examined the chronic neurotoxicity of methanol in M. fascicularis 32

monkeys exposed to 13.1, 131, or 1

, 310 mg/m 3 for up to 29 months. Multiple effects were noted 33

at 131 mg/ m 3 , including sli ght myocardial effects (negative changes in the T wave on an EKG), 34

degeneration of the inside nucleus of the thalamus, and abnormal pathology within the cerebral 35

white tissue in the brain. The results support the identification of 13.1 mg/m 3 as the NOAEL for 365

- 25 DRAFT

— DO NOT CITE OR QUOTE neurotoxic effects in monkeys exposed chronically to inhaled methanol. However, as discussed 1

in Section 4.2.2.3, there exists significant uncertainty in the interpretation of these results and 2

their utility in deriving an RfC for methanol. These unc ertainties include lack of appropriate 3

control group data, limited nature of the reporting of the neurotoxic effects observed, and use of 4

wild

- caught monkeys in the study. Thus, while the NEDO (1987) study suggests that monkeys 5

may be a more sensitive spe cies to the neurotoxic effects of chronic methanol exposure than 6

rodents, the substantial deficits in the reporting of data preclude the quantification of data from 7

this study for the derivation of an RfC. 8

The increased incidence of c ervical rib s was ident ified as a biologically significant, 9

potential co

- critical effect based on the findings of Rogers et al . (1993a). Mice were exposed to 10

1

, 000, 2

, 000, or 5

, 000 ppm

, and incidence of cervical rib s was statistically increased at 11

2

, 000 ppm. However, given tha t the reference values for the increased incidence of cervical rib s 12

are estimated to be approximately five times higher than the reference values calculated using 13

decreases in brain weight in male rats (NEDO, 1987), decreased brain weight was chosen as the 14

basis for the derivation of the RfC. 15

5.3.2. Choice of Dose Metric

A recent review of the reproductive and developmental toxicity of methanol by a panel of 16

experts concluded that methanol, not its metabolite formate, is likely to be the proximate 17

teratogen and that blood methanol level is a useful biomarker of exposure (CERHR, 2002; 18

Dorman et al ., 1995)

. The CERHR Expert Panel based their assessment of potential methanol 19

toxicity on an assessment of circulating blood levels (CERHR, 2002 )

. In contrast to the 20

co nclusions of the NTP

- CERHR panel, in vitro data from Harris et al . (2004, 2003) suggest that 21

the etiologically important substance for embryo dysmorphogenesis and embryolethality was 22

likely to be formaldehyde rather than the parent compound or formate. Al though there remains 23

uncertainty surrounding the identification of the proximate teratogen of importance (methanol, 24

formaldehyde, or formate), the dose metric chosen for derivation of an RfC was based on blood 25

methanol levels. This decision was primarily based on evidence that the toxic moiety is not 26

likely to be the formate metabolite of methanol (CERHR, 2004), and evidence that levels of the 27

formaldehyde metabolite following methanol maternal and/or neonate exposure would be lower 28

in the fetus and neonat e than in adults. While recent in vitro evidence indicates that 29

formaldehyde is more embryotoxic than methanol and formate, the high reactivity of 30

formaldehyde would limit its unbound and unaltered transport as free formaldehyde from 31

maternal to fetal blo od (Thrasher and Kilburn , 2001) (see discussion in Section 3.3). Thus, even 32

if formaldehyde is ultimately identified as the proximate teratogen, methanol would likely play a 33

prominent role, at least in terms of transport to the target tissue. Further dis cussions of methanol 34

metabolism, dose metric selection

, and MOA issues are in Sections 3.3, 4.6

, 4.8 and 4. 9 .2

. 355

- 26 DRAFT

— DO NOT CITE OR QUOTE There exists some concern in using the F 1 generation NEDO (1987) rat study as the basis 1

from which to derive the RfC. This concern mainly ari ses from issues related to the low 2

confidence that the PBPK model is accurately predicting dose metrics for neonates exposed 3

through multiple and simultaneous routes. The PBPK model was structured to predict internal 4

dose metrics for adult NP animals and was optimized using adult metabolic and physiological 5

parameters. Young animals have very different metabolic and physiological profiles than adults 6

(enzyme activities, respiration rates, etc

. ). This fact, coupled with multiple routes of exposure, 7

make i t likely that the PBPK did not accurately predict the internal dose metrics for the offspring. 8

Stern et al . (1996) reported that when rat pups and dams were exposed together during lactation 9

to 4

, 500 ppm methanol in air, methanol blood levels in pups from GD6

– PND21 were 10

approximately 2.25 times greater than those of dams. This discrepancy persisted until PND48

, 11

when postnatal exposure continued to PND52. It is logical to assume that similar differences in 12

blood methanol levels would also be observed in t he NEDO (1987) F 1 study, as the exposure 13

scenario is similar to that of Stern et al . (1996). D ifferences between pup and dam blood 14

methanol levels might be expected to be slightly greater than twofold in the NEDO (1987) F 1 15

study as the exposure was contin uous (versus 6 hours/day in the Stern et al

. [199 6 ] paper) and 16

lasted for a longer duration (

~ 64 days versus 37). Under a similar scenario, human newborns 17

may experience higher blood levels than the ir mother s as a result of breast feeding. As has been 18

di scussed in Chapter 3, children have a limited capacity to metabolize methanol via ADH ; 19

however, there is some evidence that human infants are able to efficiently eliminate methanol at 20

high

- exposure levels, possibly via CAT (Tran et al ., 2007). At environm entally relevant 21

exposure levels, it is assumed that the ratio of the difference in blood concentrations between 22

infant and mother would not be significantly greater than the twofold difference that has been 23

observed in rats. 78 For this reason and because EPA has confidence in the ability of the PBPK 24

model to accurately predict adult blood levels of methanol, the maternal blood methanol levels 25

for the estimation of HECs from the NEDO (1987) study were used as the dose metric

. 26

5.3.3. Choic e of Model for BMDL Deriv ations

The Hill model adequately fit the dataset (goodness

- of

- f

it p

- value = 0. 84 ). Data points 27

were well predicted near the BMD (scaled residual = 0. 09 ) ( see Figure 5

- 1). There is a 2.5

- fold 28

range of BMDL estimates from adequately fitting models, indic ating considerable model 29

dependence. The BMDL from the Hill model was selected , in accordance with EPA BMD 30

Technical Guidance (EPA, 2000b), because it results in the lowest BMDL from among a broad 31

range of BMDLs and provides a superior fit in the low dos e region nearest the BMD. 32

78 Key parameters and factors which determine the ratio of fetal or neonatal human versus mother methanol blood

levels either do not c hange significantly with age (partition coefficients, relative blood flows) or scale in a way that

is common across species (allometrically).5

- 27 DRAFT

— DO NOT CITE OR QUOTE 5.3.4. Choice of Animal

- to

- Hu man Extrapolation Method

A PBPK model developed by the EPA, adapted from Ward et al . (1997), was used to 1

extrapolate animal

- to

- human concentrations. An AUC blood level of methanol ( 90.9 hr x mg/L) 2

associat ed with a one S.D. change from the control mean for brain weights in rats was estimated 3

using the rat PBPK model. Then the human PBPK model was used to convert back to a human 4

equivalent exposure concentration or a BM C L HEC/1 SD of 182 mg/m 3 . If no PBPK mo dels were 5

available, a BMCL HEC/1 SD of 424 mg/m 3 would have been derived by adjusting the 556.5 mg/m 3 6

BM C L 1 SD for external exposure concentration for duration and the animal

- to

- human standard 7

adjustment factor for systemic effects (the ratio of animal and h uman blood:air partition 8

coefficients). This value is approximately 2

- fold higher than the value derived using the PBPK 9

model. However, as discussed above, use of PBPK

- estimated maternal blood methanol levels 10

for the estimation of HECs allows for the use of data

- derived extrapolations rather than standard 11

methods for extrapolations from external exposure levels

. 12

As discussed in Section 3.4, t he PBPK models do not describe or account for background 13

levels of methanol, formaldehyde or formate, and backgroun d levels were subtracted from the 14

reported data before use in model fitting or validation (if not already subtracted by study 15

authors), as described below. This approach was taken because the relationship between 16

background doses and background responses i s not known, because the primary purpose of this 17

assessment is for the determination of noncancer and cancer risk associated with increases in the 18

levels of methanol or its metabolites (e.g., formate, formaldehyde) over background, and because 19

the subtract ion of background levels is not expected to have a significant impact on PBPK model 20

parameter estimates as background levels of methanol and its metabolites are low relative to 21

exposure levels used in methanol bioassays. 22

5.3.5. Route

- to

- R oute Extrapolation

To estimate an oral dose POD for decrease in brain weight in rats, a route

- to

- route 23

extrapolation was performed on the inhalation exposure POD used to derive the RfC. One way 24

to characterize the uncertainty associated with this approach is to compare risk l evels (BMDL 25

values) using the dose metric, AUC methanol, for developmental decreases in brain weight 26

derived from 1) an existing oral subchronic study and 2) from a model estimating this metric 27

from an existing inhalation subchronic study. There are curre ntly no oral developmental studies 28

investigating decreases in brain weight available to compare to the risk values estimated using 29

the second procedure. However, the fact that the oral BMDL of 38.5 mg/kg

- day estimated in this 30

assessment from the NEDO (198 7) inhalat i on study of neonate rats via a PBPK model is lower 31

than the NOAEL of 500 mg/kg

- day identified in EPA (1986 c ) methanol study of adult rats is 32

consistent with other studies which suggest that fetal/neonatal organisms are a sensitive 33

subpopulation. 345

- 28 DRAFT

— DO NOT CITE OR QUOTE 5.3.6. Statistical Uncertainty at the POD

There is uncertainty in the selection of the BMR level. For decreased brain weight in 1

rats, no established standard exists , so a BMR of one S.D. change from the control mean was 2

used. Parameter uncertainty can be as sessed through CI s

. Each description of parameter 3

uncertainty assumes that the underlying model and associated assumptions are valid. For the 4

Hill model applied to the data for decrease d brain weight in rats, there is a degree of uncertainty 5

at the one S .D. level (the POD for derivation of the RfC), with the 95% one

- sided lower 6

confidence limit (BMDL) being

~ 50 % below the maximum likelihood estimate of the BMD. 7

5.3.7. Choice of Bioassay

The NEDO (1987) study was used for development of the RfC and RfD because i t 8

resulted in the lowest BMDL. It was also a well

- designed study, conducted in a relevant species 9

with an adequate number of animals per dose group, and with examination of appropriate 10

developmental toxicological endpoint s . Developmental (Burbacher et al

., 2004a, 2004b, 1999a, 11

1999 b) and chronic studies (NEDO, 1987) of methanol have been performed in monkeys. As 12

discussed above in Section 5.3.1 and other sections of this assessment

, while the monkey may be 13

a sensitive species for use in the determination of human risk, reporting deficits and study 14

uncertainties preclude their use in the derivation of an RfC. 15

5.3.8. Choice of Species/Gender

The RfC and RfD w ere based on decreased brain weight at 6 weeks postbirth in male rats 16

(the gender most sensitive to this effect) (NEDO, 1987). This decrease in brain weight also 17

occurs in female rats; however, if the decreased brain weight in female rats had been used , higher 18

RfC and RfD values would have been derived (approximately 66% higher than the male derived 19

value s ). 20

5.3.9. Human Population Variability

The extent of interindividual variation of methanol metabolism in humans has not been 21

well characterized. As discussed in Section 4.9 , there are a number of issues that may lead to 22

sensitive human subpopulations. Potentia lly sensitive subpopulations would include individuals 23

with polymorphisms in the enzymes involved in the metabolism of methanol and individuals 24

with significant folate deficiencies. Sensitive lifestages would include children and neonates, as 25

they have in creased respiration rates compared to adults, which may increase their methanol 26

blood levels compared to adults. Also, children have been shown to have decreased ADH 27

activity relative to adults, thus decreasing their ability to metabolize and eliminate me thanol. As 28

demonstrated by these examples, there exists considerable uncertainty pertaining to human 295

- 29 DRAFT

— DO NOT CITE OR QUOTE population variability in methanol metabolism, which provides justification for the 10

- fold 1

intraspecies UF used to derive the RfC and RfD. 2

5.4. 5.4. CANCER ASSESSMENT

5.4.1. Oral Exposure

5.4.1.1. Choice of Study/Data

— with Rationale and Justification

No human data exist that would allow for quantification of the cancer risk of chronic 3

methanol exposure. Table 4

- 34 summarizes the available ex perimental animal oral exposure 4

studies of methanol. The Soffritti et al . (2002a) and Apaja (1980) oral stud ies report effects that 5

show a statistically significant increase in incidence of cancer endpoint s in the treated groups 6

versus the control group ( pair

- wise comparison). As detail ed in Section 4.2.1.3, Soffritti et al

. 7

(2002a) exposed S prague

- Dawley rats via drinking water to 500

– 20,000 ppm methanol for 8

104 weeks. Exposure ended at 104 weeks

, but the animals were not euthanized and were 9

followed un til their natural death. Increased lymphoma responses in multiple organs of male and 10

female rats were the only carcinogenic effects reported in the Soffritti et al. (2002a) methanol 11

drinking water study that are considered dose related and quantifiable. Hepatocellular 12

carcinomas observed in male rats are considered potentially dose related (relative to historical 13

controls) but are not quantifiable due to the lack of a statistically significant dose

- response trend

. 14

Significant increases reported for head and ear duct carcinomas in male rats were not used 15

because NTP pathologists interpreted a majority of these ear duct responses as being 16

hyperplastic, not carcinogenic, in nature ( EFSA, 2006 ; Hailey , 2004 ). Apaja (1980) observed 17

significant increases in ma lignant lymphomas relative to untreated, historical controls in Swiss 18

mice exposed to methanol in drinking water for life. Due to the lack of a concurrent control, the 19

Apaja (1980) study was not considered adequate for derivation of an oral slope factor, but a 20

quantitative analysis of the dose

- response data from this study is included in Appendix E for 21

comparison purposes. 22

5.4.1.2. Dose

- R

esponse D ata

The tumor incidence data selected for modeling were the lympho

- immunoblastic 23

lymphomas and the combined lympho

- im munoblastic , lymphoblastic and lymphocytic 24

lymphomas in both male and female rats of the Soffritti et al (2002a) study

. These lymphomas 25

were combined at the recommendation of NTP pathologists due to their similar histological 26

origin. The incidence of his tiocytic sarcomas and myeloid leukemias was not significantly 27

increased in either sex

, and t he data for these tumors was not combined with the lymphoblastic 28

lymphomas because they are of a different cell line and the combination is not typically 29

evaluated either for statistical significance or dose

- response modeling ( Hailey , 2004; McConnell, 30

et al ., 1986). Table 5

- 6 gives the lymphoma incidence data from the study which differs slightly 315

- 30 DRAFT

— DO NOT CITE OR QUOTE from the data reported in Soffritti et al

. ( 2002a ) in the incidence of lympho

- immunoblastic 1

lymphomas in the male 5

, 000 ppm group. 79 2

Table 5

- 6 . Incidence data for lymphoma, lympho

- immunoblastic , and all lymphomas

in male and female Sprague

- Dawley rats

Dose (ppm) Dose

(mg/kg

- day) Number of a nimals

e xamined Lymphoma l ympho

i mm unoblastic All l ymphomas

c ombined

Female r ats

0 0 100 9 9

500 66.0 100 17 19 a

5

, 000 624.1 100 19 a 20 a

20

, 000 2

, 177 100 21 a 22 b

Male r ats

0 0 100 16 17

500 53.2 100 24 27

5

, 000 524 100 2 8 a 29 a

20

, 000 1

, 780 99 37 b 38 b

Statistically significant by Fisher’s Exact test : a p < 0.05, b p < 0.01

Source: Soffritti et al . (2002a) and ERF web portal ( http://www.ramazzini.it/fondazione/foundation.asp ).

5.4.1.3. Dose Adjustments and Extrapolation Meth od

As with the extrapolations used in the development of the RfC and RfD, the PBPK model 3

was used for species

- to

- species extrapolation of the doses to be used in the cancer dose

- response 4

analysis. Three dose metrics were considered for use in the dose

- res ponse analysis: total 5

metabolized methanol ; maximum blood concentration of the parent (C

max ) ; and area under the 6

blood concentration time curve (AUC) for the parent. Internal dose estimates (above 7

background) corresponding to the administered dose s from t he animal bioassay were determined 8

for each of these metrics with the PBPK model (see Appendix E, Table E

- 5). To help inform the 9

selection of the most appropriate dose metric, dose

- response analyses were performed using 10

these PBPK model results to assess which dose metric best corresponded to the observed 11

incidence data in Table 5

- 6 (see Appendix E, Tables E

- 6 and E

- 7). Figures 5

- 3, 5

- 4, and 5

- 5 12

show the fit of the multistage model to the all lymphoma incidence data for female and males, 13

using each dose m etric as the dose input. 14

79 EPA obtained detailed, individual animal data via an interagency agreement with NIEHS which supported the

develo pment of reports made available through the Ramazzini Foundation (ERF) web portal

( http://www.ramazzini.it/fondazione/foundation.asp ). This allowed EPA to combine lymphomas of similar

hist opathological origin and confirm the tumor incidences reported in the Soffriti et al. (2000a) paper.5

- 31 DRAFT

— DO NOT CITE OR QUOTE Figure 5

- 3. All lymphomas versus methanol metabolized (mg/day) for female and male

rats

Figure 5

- 4. All lymphomas ver sus C

max (mg/L) for female and male rats.

Figure 5

- 5. All lymphomas versus AUC ( hr x mg/L ) for male and female rats

1 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

0 20000 40000 60000 80000 100000Fraction Affected

dose Multistage Cancer Model

22:44 07/23 2009 Multistage Cancer

Male rats ( **p** = 0.15) 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

0 50 100 150 200Fraction Affected

dose Multistage Cancer Model

22:51 07/23 2009 Multistage Cancer

Male rats ( **p** = 0.2)

0.05 0.1 0.15 0.2 0.25 0.3

0 20 40 60 80 100 120 140Fraction Affected

dose Multistage Cancer Model

22:46 07/23 2009 Multistage Cancer

Female rats ( **p** = 0.1 8 )

0.05 0.1 0.15 0.2 0.25 0.3

0 20000 40000 60000 80000 100000Fraction Affected

dose Multistage Cancer Model

22:41 07/23 2009 Multistage Cancer

Female rats ( **p** = 0.07 5 ) 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

0 500 1000 1500 2000 2500 3000 3500 4000 4500Fraction Affected

dose Multistage Cancer Model

22:38 07/23 2009 Multistage Cancer

Male rats ( **p** = 0.1 5 )

0.05 0.1 0.15 0.2 0.25 0.3

0 1000 2000 3000 4000 5000Fraction Affected

dose Multistage Cancer Model

22:35 07/23 2009 Multistage Cancer

Female rats ( **p** = 0.07 7 )5

- 32 DRAFT

— DO NOT CITE OR QUOTE The dose

- response modeling suggests that total metabol ized methanol is a better dose 1

metric than the parent compound metrics as indicated by improved model fit to responses 2

reported by Soffritti et al. (2002a) for both lympho

- immunoblastic lymphoma (see Appendix E, 3

Table E

- 7) and all lymphoma (see Figures 5

- 3 to 5

- 5 and Appendix E, Table E

- 7), and also for 4

malignant lymphoma responses reported by Apaja (1989) (see Appendix E, Table E

- 17). Chi

5

square p values for the total metabolite dose metric ranged from 0.18 to 0.55 and were 6

consistently higher than for th e other dose metrics. This could be an indication of the importance 7

of metabolite formation, which is likely to be more rapid at low doses, to the carcinogenic 8

response. The total metabolized methanol dose metric was selected as the dose metric for use i n 9

the dose

- response assessment to derive the POD because it provided the best fit to the response 10

data. The estimated BMDL for the total methanol metabolized dose metric was then back

11

calculated using the EPA PBPK model to obtain a human equivalent oral d rinking water dose in 12

terms of mg/kg

- day (see Appendix E, Table E

- 8). 13

Multistage and multistage Weibull time

- to

- tumor models were applied to the lymphoma 14

data obtained from ERF for the Sofffritti et al . (2002a) drinking water study and considered for 15

deter mining the POD to be used in the derivation of the oral cancer slope factor (see Appendix E, 16

Table 3

- 8). Appendix E gives the details and justification for the various approaches used. As 17

described in Appendix E, time

- to

- tumor modeling and multistage qua ntal modeling gave similar 18

results

, and the tumor responses modeled did not exhibit significant time dependence on dose. 19

T he EPA multistage cancer model fit the response data adequately and was used to derive the 20

oral cancer slope factor ( CSF ) (see Append ix E, Table s E

- 7

, E

- 8, and Figure E

- 10 ). 21

BMDs and BMDLs were estimated for the combined lymphomas in male and female rats. 22

The BMR selected was the standard value of 10% extra risk recommended for dichotomous 23

models (U.S. EPA, 2000 b )

. 80 The 95% one

- sided lower confidence limit defined the BMDL. 24

The dose terms in the fitting were set equal to the estimated total metabolized doses derived 25

using the PBPK model for methanol for each of the administered doses in the bioassay. 26

Application of the multistage mod el to the incidence data for all lymphomas in male rats 27

( Table 5

- 6 ) resulted in the BMD and BMDL 10 values presented in Table 5

- 7

. The results for the 28

male rat were used in the derivation of the CSF because the female data for this endpoint yielded 29

slightl y higher values (see Appendix E, Tables E

- 7 and E

- 8). Assuming that metabolized 30

methanol distributes in the body according to body weight to the 3⁄4 power, the male rat mg

- day 31

BMDL 10 was converted to a human mg

- day BMDL 10

. 81 The human PBPK model (Appendix B ) 32

was then used to convert this human mg

- day value for total methanol metabolized back to a 33

80 The use of lower BMR values was determined not to have a significant impact on the CSF derivation.

81 Male rat mg/day was converted to human mg/day by mu ltiplying by (BW human ) 3⁄4 /(BW

rat ) 3⁄4 =(70 kg) 3⁄4 /(0.33 kg ) 3⁄4

=

55.65

- 33 DRAFT

— DO NOT CITE OR QUOTE human equivalent methanol oral dose HED(BMDL 10 ) of 5 1.5 mg/kg

- day for lymphomas in the 1

male rat (see Appendix E, Table E

- 8). 82 2

Table 5

- 7 . BMD results and oral CSF using all lymphoma in male rats

Amount metabolized (mg /d ) Human equivalent

BMDL 10 (m g/kg

- day ) Oral CSF

(m g/kg

- day ) - 1 BMD 10 (mg /d ) Rat BMDL 10 (mg/d) Estimated h uman

BMDL 10 (mg/d)

10 1.7 6 3.9 3, 553 51.5 1. 9 E

- 0 3

Source: Soffritti et al

. ( 2002a).

In the case of methanol, there is no information to inform the MOA for carcinogenicity. 3

As recommended in the Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), “when 4

the weight of evidence evaluation of all available data is insufficient to establ ish the MOA for a 5

tumor site and when scientifically plausible based on the available data, linear extrapolation is 6

used as a default approach.” Accordingly, for the derivation of a quantitative estimate of cancer 7

risk for ingested methanol, a linear extr apolation was performed to determine the CSF. 8

5.4.1.4. Oral Slope Factor

The oral slope factor was derived based on a linear extrapolation from this POD 9

(BMDL 10/HED of 55.4 mg/kg

- day for lymphomas in the male rat) to the estimated background 10

response level: 11

0.1/ HED(BMDL 10 ) = 0.1/ 5 1.5 mg/kg

- day = 2 E

- 0 3 (mg/kg

- day)

- 1 12

(rounded to one significant figure)

5.4.2. Inhalation Exposure

5.4.2.1. Choice of Study /Data

–

with Rationale and Justification

No human data exist that would allow for quantification of the cancer risk associated with 13

chronic methanol exposure. Table 4

- 35 summarizes the available experimental animal inhalation 14

exposure studies of methanol. The NEDO ( 1987, 1985/2008b) 24

- month rat study is the only 15

inhalation bioassay available that reports an increase in incidence of any cancer endpoint s (see 16

Section 4.2.2.3) . This NEDO (1987, 1985/2008b) study was of high quality and was based on 17

standard OECD guidelines (OECD, 2007)

. F344 rats were exposed for 104 weeks to air 18

concentration s of 0, 10, 100 and 1,000 ppm methanol. Rats were sacrificed and necropsied at the 19

82 The following algebraic equation is provided in Appendix B (Equation 4) to describe the relationship between

predicted human mg

- day methanol cleared and the human equivalent oral dose (HED) in mg/kg

- day:

HED = (3.3* 3,553 mg/d) /(19

, 282.9

– 3, 553 mg/d ) + (0.014287* 3, 553 mg

- d ) = 51.5 mg/kg

- d5

- 34 DRAFT

— DO NOT CITE OR QUOTE end of the 104

- week exposure period. The NEDO (1987, 1985/2008b) study reports increased 1

pulmonary adenomas / adenocarcinomas and pheochromocytomas in high

- dose (1

, 000 ppm) male 2

and female rats, respectively. T he combined incidence of pulmonary adenomas and 3

adenocarcinomas was significantly increased in the high

- dose males (see Table s 4

- 4 and 5

- 8 )

, 4

and both tumor type s w ere considerably elevated at the high

- dose over historical control 5

incidences within their resp ective sex and str ain (see discussion in Section 4.2.2.3). As shown in 6

Table 4

- 5 , the severity and combined incidence of po tential precursor effects in the alveolar 7

epithelium of male rat lungs (epithethial swelling, adenomatosis, pulmonary adenoma

, and 8

p ulmonary adenocarcinoma) and the adrenal glands of female rats (hyperplasia and 9

pheochromocytoma ) were increased in the higher exposure groups compared with the controls 10

and lower exposure groups. While the incidence of male rat pulmonary adenomas was als o high 11

in the lowest (10 ppm) exposure group, the appearance of a rare adeno carcinoma in the high

12

dose group is suggestive of a progressive effect associated with methanol exposure. While the 13

increased phe o chromocytoma response in female rats is not stati stically increased over controls, 14

it is considered to be potentially treatment related because this is a historically rare tumor type 15

for female F344 rats ( NTP, 2007, 1999; Haseman et al ., 1998)

, 83 and when viewed in conjunction 16

with the increased medullary hyperplasia observed in the mid

- exposure (100 ppm) group 17

females, it is suggestive of a proliferative change with increasing methanol exposure. 18

5.4.2.2. Dose

- Response Data

The tumor incidence data selected for modeling are the NEDO (1987

, 1985/2008b) 19

reported inci dences of adenoma/adenocarcinoma in male rats and pheochromocytoma in female 20

rats. These data are presented in Table 5

- 8

. 21

Table 5

- 8 . Incidence data for tumor responses in male and female F344 rats

Dose (ppm) Number of animals affected/number examined

P heochromocytoma Pulmonary a denoma/adenocarcinoma

Female r ats

0 2/50 2/52

10 3/51 0/19

100 2/49 0/20

1000 7/51 a ,b 0/52

Male r ats

0 7/52 1/52

10 2/16 5/50

100 2/10 2/52

1000 4/51 7/52 b ,c

83 Haseman et al., (1998) report rates for spontaneous pheochromocytomas in 2

- year NTP bioassays of 5 .7 %

(benign) and 0 .3% (malignant) in male F344 rats and 0.3 % (benign) and 0.1% (malignant) in female (n=1517) F344

rats.5

- 35 DRAFT

— DO NOT CITE OR QUOTE Dose (ppm) Number of animals affected/number examined

a p < 0.05 over NTP historical controls for total (benign, complex and malignant) pheochromocytomas using the

Fisher’s Exact test

b p < 0.05 for Cochrane

- Armitage test of overall dose

- response trend.

c p < 0.05 over concurrent controls using the Fisher’s Exact test.

Source: NEDO (1987, 1985/2008b)

.

5.4.2.3. Dose Adjustmen ts and Extrapolation Method

As with the extrapolations used in the development of the RfC and RfD, the PBPK model 1

was used for species

- to

- species extrapolation of the doses to be used in the cancer dose

- response 2

analysis. Three dose metrics were considere d for use in the dose

- response analysis: total 3

metabolized methanol, maximum blood concentration of the parent (C

max )

, and area under the 4

blood concentration time curve (AUC) for the parent. Each of the dose metrics corresponding to 5

the administered dose from the animal bioassay was determined with the PBPK model (see 6

Appendix E, Table E

- 10). To help inform the selection of the most appropriate dose metric, 7

dose

- response analyses were performed using these PBPK model results to assess which dose 8

metric be st corresponded to the observed incidence data in Table 5

- 8 (see Table E

- 11)

. All of 9

the dose metrics resulted in similar fit to the incidence data for both endpoint s , with the total 10

metabolites metric providing a slightly improved fit to the female pheoc hromocytoma response 11

data. 12

Unlike the oral data discussed in Section 5.4.1.2, dose

- response modeling of the 13

inhalation data from NEDO (1987, 1985/2008b) does not suggest the use of any one dose metric 14

over the other. However, since the pheochromocytoma re sponse likely involves systemically 15

distributed, metabolized methanol, and to be consistent with the oral CSF, analysis the total 16

methanol metabolized dose metric is selected as the dose metric for use in the dose

- response 17

assessment to derive the inhalati on POD. The estimated BMDL for the methanol metabolized 18

dose metric was then back

- calculated using the EPA PBPK model to obtain a human equivalent 19

air exposure concentration in terms of mg/m3 (see Table E

- 13). 20

The EPA multistage model was applied to the d ata in Table 5

- 8 obtained from the NEDO 21

(1987

, 1985/2008b) inhalation study and considered for determining POD to be used in the 22

derivation of the inhalation cancer unit risk ( Table E

- 11). Appendix E gives the details and 23

justification for the various app roaches used. As described in Appendix E, time

- to

- tumor and 24

quantal modeling gave similar results

, and the tumor responses modeled did not exhibit 25

significant time dependence on dose. T he EPA multistage cancer model fit the response data 26

adequately and w as used to derive the IUR (Table s E

- 11

, E

- 12, and Figure E

- 13 ). 27

BMDs and BMDLs were estimated for tumor responses in male and female rats as shown 28

in Table 5

- 8

. The BMR selected was the standard value of 10% extra risk recommended for 295

- 36 DRAFT

— DO NOT CITE OR QUOTE quantal models (U.S. EPA, 2000 b )

. 84 The 95% one

- sided lower confidence limit defined the 1

BMDL. The dose terms in the fitting were set equal to the estimated total metabolized doses 2

derived using the PBPK model for methanol for each of the administered doses in the bioassay. 3

Application of the multistage model to the incidence data for pheochromocytomas in 4

female rats ( Table 5

- 8 ) resulted in the BMD and BMDL 10 values presented in Table 5

- 9

. The 5

results for the female rat were used because the female data for pheochromocytoma yielded 6

slightly lower BMDL values (Tables E

- 11 and E

- 13). Assuming that metabolized methanol 7

distributes in the body according to body weight to the 3⁄4 power, the female rat mg

- day BMDL 10 8

was converted to a human mg

- day BMDL 10

. 85 The human PBPK model (Ap pendix B) was then 9

used to convert this human mg

- day value for total methanol metabolized back to a human 10

equivalent methanol inhalation concentration

, HEC( BM C L 10 )

, of 81.9 mg/m 3 or 81

, 9 00 μg/m 3 11

for pheochromocytomas in the female rat (see Appendix E, Tabl e E

- 13). 86 12

Table 5

- 9 . BMD results and IUR using pheochromocytoma in female rats

Amount metabolized (mg /day )

Human equivalent

BMCL 10 (mg/m 3 ) IUR

(μg/m 3 ) - 1 BMD 10 (mg /day ) BMDL 10 (mg / d ay ) Estimated h uman

BMDL 10 (mg / d ay )

29.5 14.6 971 81.9 1. 2 E

- 06

Source: NEDO (1987, 1985/2008b).

5.4.2.4. IUR

The IUR in terms of (μg/m 3 )

- 1 was then derived based on a linear extrapolation from this 13

POD to the estimated background response level: 14

0.1/ HEC(BMCL 10 ) = 0.1/( 81,9 00 μg/m 3 ) = 1E

- 06 (μg/m 3 )

- 1 15

(rounded to one significant figure)

5.4.3. Uncertainties in Cancer Risk Assessment

The following is a discussion of the uncertainties associated with the cancer potency 16

estimate for methanol beyond that which can be addressed with the quantitative approach 17

applied. A summary of these uncertainties is presented in Table 5

- 1 0

. 18

84 The use of lower BMR values was determined not to have a significant impact on the IUR derivation.

85 Female rat mg/day is converted to human mg/day by multiplying by (BW human ) 3⁄4 /(BW

rat ) 3⁄4 = ( 70 kg) 3⁄4 /(0.26 kg ) 3⁄4

=

66.5

86 The following algebraic equation is provided in Appendix B (Equation 3) to describe the relationship between

predicted human mg

- day methanol cleared and the human equivalent inhalation concentration (HEC) in mg/m 3 :

HED = ( 14.69 x 971 mg/d) /(19282.9

- 971 mg/d ) + (0.0 63554 x 971 mg/d ) = 81.9 mg/ m 35

- 37 DRAFT

— DO NOT CITE OR QUOTE Table 5

- 1 0 . Summary of uncertainty in the methanol cancer risk assessment

Consideration Potential impact Decision Justification

Quality of the

studies relied

upon for the

determination of

the PODs Key chro nic studies

not always well

reported; could lead

to ↑ or ↓ of risks Utilize re

- analyses of

the Soffritti et al.

(2002a) and NEDO

(1985/ 2008a, 2008b )

chronic studies Consideration of all available information resulted

in the determination that the Soffritt i et al. (2002a)

and NEDO (1985/ 2008a, 2008b ) chronic studies

are adequate (see discussion of individual studies

in Sections 4.2.1.3 and 4.2.2.3 and summaries in

Sections 4. 9 .1 and 4. 9 .2).

Interpretation of

results from

study relied upon

for the

determina tion of

the POD Differences in tumor

classification can

lead to over or

underestimate of risk Derive POD based on

incidence of combined

lymphomas as

suggested by NTP

pathologists; Assume

proper classification of

lung and adrenal

tumors by NEDO Both NTP an d EFSA recommend that only

lymphomas of the same cellular origin be

combined for dose

- response analysis. With

respect to lung and adrenal tumors, e xamination of

concurrent alveolar and adrenal noncancer

hyperplastic endpoint s supports a proliferative

chan ge in these organ systems consistent with the

appearance of carcinogenic responses.

Consistency of

results across

chronic studies If effects not relevant

to humans, risk is

overestimated. Derive PODs based on

Soffritti et al. (2002a)

and NEDO

(1985/2008b ). Though t issue concordance across species, strains

and routes of exposure is not assumed , lymphomas

have been observed in more than one species by

oral route . Also there is evidence that the observed

lymphomas are relevant to humans (see

discussions in S ection 4. 9

. concerning human

studies of methanol metabolite formaldehyde)

.

Choice of

endpoint for

POD d eriv ation Route

- to

- route

extrapolation from

Soffriti et al. (2002a)

study would ↑

inhalation POD by

about 4

- fold Derive oral CSF from

lymphoma data and

inhalation CSF from

adrenal effects

. Oral POD was based on the only tumor type from

Soffritti et al. (2002a) drinking water study

significantly increased (all lymphomas); Inhalation

POD based on most sensitive tumor response from

NEDO (1985/2008b) study, increased

pheochromocytoma in female rats.

Choice of

species/ g ender CSF and IUR would

be ↓ if based on

another gender CSF and IUR are

based on the most

sensitive and relia bly

quantifiable

species/gender Choice of female rat lymphoma and male rat

adenoma/adenocarcinoma would have resulted in

lower CSF and IUR values, respectively. Use of

the Apaja (1989) mouse data would have resulted

in a higher , but less reliable CSF due to study

problems , including a lack of concurrent controls

Choice of m odel

for POD

d erivation Use of other models

could ↑ or ↓ POD,

but not significantly Derive cancer potency

factor based on

multistage model. Use of the multistage model is consistent with

EPA guidance (U.S. EPA, 2005a). The m ultistage

model provides adequate fit to the data , whic h is

not improved by a time

- to

- tumor modeling.

Choice of

animal

- to

- human

extrapolation

method Traditional method

could ↑ the

HEC( BM C L 10 )

estimate by 4

- fold

. A PBPK model was

used to extrapolate

animal

– to

- human

concentrations. Use of a PBPK model reduces uncertainty

associated with the animal to human extrapolation.

Total metabolites normalized by body weight is an

appropriate dose metric and a verified PBPK

model exists that estimates this metric.

5.4.3.1. Quality of Studies that are the Basis for the PODs

T he protocols used at the laboratories that have performed cancer bioassays of methanol, 1

particularly those of the ERF, differ from the more commonly used ( e.g., NTP) protocols. The 2

unique features of the ERF study design and their implications to a methan ol cancer risk 35

- 38 DRAFT

— DO NOT CITE OR QUOTE assessment are discussed in Section 4. 9 .2. Separate from these experimental design issues are 1

considerations relative to the quality of the cancer bioassays and any associate uncertainties. 2

The number of animals per dose group in ERF stud ies is often higher than the 50 animals 3

per sex per dose group typically used in EPA and NTP studies, increasing the statistical power of 4

the ERF cancer bioassay. However, ERF sometimes shares controls across concurrent studies 5

( Cruzan, 2009; Belpoggi et al., 1995 )

. In contrast, EPA requires (U.S. EPA, 1998) and NTP 6

generally uses (Melnick et al., 2007) concurrent, matched controls for each carcinogen bioassay. 7

The use shared controls does not necessarily compromise a study, but the use of a concurrent, 8

m atched control is generally preferred as a means of further avoiding confounding factors and 9

increasing the reliability of a study regarding the interpretation of findings in treated animals. 10

The published report of the methanol bioassay (Soffritti et al

., 2002a) indicates that the 11

experiment was performed according to good laboratory practice ( GLP ) and standard operating 12

procedures ( SOP ) of the ERF. Further, an independent review of ERF (Huff, 2002) suggest s that 13

quality control procedures associated with GLP were in place. However, q uestions have been 14

raised about the quality of studies at the ERF by European Food Safety Authority (EFSA, 2006) 15

in regards to t he aspartame bioassays conducted by the ERF (Soffritti et al ., 2006); by extension 16

this EFSA repo rt has raised issues for consideration in regards to methanol. EFSA (2006) has 17

suggested that an inspection by the Italian GLP compliance monitoring authority ( Ministry of 18

Health ) necessary to confirm GLP had not been conducted. 87 The EFSA (2006) report a lso 19

identifies specific deviations from OECD guidelines (OECD, 2007) , including a lack of a 20

complete analysis of the test substance, no clear information on the stability of the substance, a 21

lack of clinical observations or macroscopic changes, a lack of h ematological assays, a lack of 22

serology ( e.g., to confirm the presence of infection) and limited histopathology reports. While 23

these details may be recorded internally by the ERF as part of their standard protocol, because 24

there is no documentation of the se details available for consideration, there remains some 25

uncertainty regarding the level at which they were performed. There is limited evidence, 26

however, that these factors had a significant impact on the adequacy of the study for assessing 27

carcinogeni c potential (see Section s 4.2.1.3

, 4. 9 .2 and 5.4.3.2 ). 28

EFSA (2006) also expresses concern over the possibility of compromised pathological 29

diagnosis in the ERF aspartame study (Soffritti et al ., 2006) due to extensive autolysis. ERF 30

performs pathological examinations on “dying animals undergoing necropsy” (Soffritti et al

., 31

2002a). This creates difficulties in pathological examinations associated with cell autolysis that 32

can occur when pathology slides are prepared after natural death. The NTP ( Hailey , 2 004) 33

commented on the increased prevalence of autolysis in slides from the ERF (Soffritti et al ., 2006) 34

aspartame study. EPA conducted a detailed analysis of the individual animal tissue data obtained 35

87 Since the publication of the EFSA (2006) report, the EPA has confirmed through communication with the ERF

laboratory (Knowles, 2008) that ERF is currently in the process of obtainin g this certification.5

- 39 DRAFT

— DO NOT CITE OR QUOTE from ERF for their chronic methanol, MTBE, formaldehyd e

, and aspartame studies

, and 1

determined that autolysis and other causes of tissue loss did not substantially impact tissue 2

denominators. For most of the tissues evaluated there were more than 96 (individual animal) 3

samples available for microscopic evalu ation

, and for all sites and dose groups, denominators 4

were larger than for routine NTP bioassays ( i.e., >50). Thus, missing tissues does not appear to 5

have been a serious problem in the methanol study. While this analysis does not completely rule 6

out th e possibility that pathology slides and diagnoses were impacted by autolysis, it does 7

indicate that this possibility would be offset by the large group size (response denominator) 8

employed. Further, even if autolysis was a confounding factor, its presence would not negate 9

positive cancer findings as autolysis would tend to decrease, not increase, the power to observe 10

an effect. 11

There were no differences in survival among the methanol dose groups of the Soffritti et 12

al. (2002a) study. However, Cruzan (2 009) has suggested that “While the survival at 104 weeks 13

was within the normal, but widespread, range for Sprague

- Dawley rats, there was significant 14

early mortality among all groups, including the controls” and that “the control group from an 15

inhalation st udy (Cruzan et al., 1998) had much better survival through 104 weeks than seen in 16

the RF methanol study.” Yet, according to Table 12 of the Cruzan (2009) article, 104

- week 17

survival in male (~40%) and female (~50%) control rats of the Cruzan et al. (1998) study was 18

not discernibly different from the 104 week survival of male (~40%) and female (~50%) control 19

rats of the Soffritti et al. (2002a) methanol study (see Appendix E, Figures E

- 1 and E

- 2). 20

Survival of male and female Sprague

- Dawley rats in the Soffri tti et al. (2002a) study at 21

104 weeks was greater than 40% in all but the female 5

, 000 ppm group. Further, at the NTP 22

(NTP, 2006), the 104

- week survival of 353 control female Sprague

- Dawley rats was 41.5% 23

(range of 28.3%

– 51% in 7 studies) using NTP’s new diet and corn oil gavage, indicating that 24

survival in the Soffritti et al. (2002a) methanol study was not low. 25

Studies such as the Soffritti et al. (2002a) methanol study that allow test animals to live a 26

full life span can be difficult to interpret due t o the need to distinguish between age

- related and 27

chemical

- related effects. Full life

- span studies may have advantages. Huff et al. (2009) note that 28

“studies truncated after 2 years of exposure do not allow sufficient latency periods for late

29

developing tumors, such as the 80% of all human cancers that occur after 60 years of age.” 30

Several recent publications have noted deficiencies with the 2

- year study design used at the NTP 31

and have recommended extending the duration of rodent studies to increase the sensitivity of 32

their bioassays (Huff et al., 2009; Huff et al., 2007; Maronpot et al., 2004; Bucher et al., 2002). 33

While arguments have been documented related to the possible confounding influence of 34

infection and autolysis on the results obtained from t he ERF, available evidence does not indicate 35

that these factors significantly influenced the observed lymphoma/leukemia response in the 36

methanol or other bioassays conducted at ERF. In addition, for the purposes of this assessment 375

- 40 DRAFT

— DO NOT CITE OR QUOTE and at the request of th e EPA, the ERF and NEDO have provided additional study details beyond 1

that which is normally available from published journal articles, including quality assurance 2

reports and individual animal data. Based on a review of this information, consideration of the 3

issues

, and absent additional data to the contrary, EPA has determined that both studies were 4

sufficient for use in the assessment of risk from methanol exposure. 5

5.4.3.2. Interpretation of Results of the Studies that are the Basis for the PODs

There are a nu mber of uncertainties regarding the interpretation of both the lymphoma 6

response in male Sprague

- Dawley rats (Soffritti et al

. , 2002a) that forms the basis of the oral 7

CSF and the pheochromocytoma response in F344 rats (NEDO, 198 5/ 200 8 b) that forms the bas is 8

for the inhalation CSF. 9

There is also a wide range in the background incidence of hemolymphoreticular tumors 10

reported in control groups of ERF studies. Between 1984 and 1997, incidence rates of 11

hemolymphoreticular neoplasms in control rats at ERF incre ased by 38% among male rats and 12

decreased by 12% among female rats (Caldwell et al ., 2008). Soffritti et al . (200 7, 2006 ) reports 13

that among 2,265 untreated males and 2,274 untreated females the average incidence of 14

lymphomas and leukemias is 20.6% (range , 8.0

- 30.9%) in males and 13.3% (range, 4.0

- 25%) in 15

females. Caldwell et al . (2008) noted that for the incidences of these lesions for the ERF colony 16

are relatively low and s table across studies. EFSA (2006) and Cruzan (2009) consider it to be 17

high relati ve to other tumor types and relative to the background rate for this tumor type in 18

Sprague

- Dawley rats from other laboratories (see Section 4.9.2 for further discussion). 19

In the ERF bioassays, including methanol, hemolymphoreticular neoplasms were divided 20

into specific histological types (lymphoblastic lymphoma, lymphoblastic leukemia, lymphocytic 21

lymphoma, lympho

- immunoblastic lymphoma, myeloid leukemia, histocytic sarcoma, and 22

monocytic leukemia) for identification purposes. Upon examining slides from th e aspartame 23

study conducted by the ERF, a PWG of the NTP at the NIEHS (Hailey , 2004 ) found that “The 24

diagnoses of lymphatic and histocytic neoplasms in the cases reviewed were generally 25

confirmed. The NTP does not routinely subdivide lymphomas into specif ic histological types as 26

was done by the ERF, however the PWG accepted their more specific diagnosis if the lesion was 27

considered to be consistent with a neoplasm of lymphocytic, histocytic, monocytic, and/or 28

myeloid origin.” The NTP PWG also noted that w hile lymphoblastic lymphomas, lymphocytic 29

lymphomas, lympho

- immunoblastic lymphomas and lymphoblastic leukemias as malignant 30

lymphomas can be combined, myeloid leukemias, histocytic sarcomas and monocytic leukemia 31

should be treated as separate malignancies and not combined with the other lymphomas for 32

statistical evaluation since they are of different cellular origin (Hailey, 2004) . Other researchers 33

have also noted this distinction between myeloid leukemias and histiocytic sarcomas and other 34

lymphomas (Mc Connell et al ., 1986). To decrease the uncertainty in the combination of tumors 35

relied upon for dose

- response modeling, the current dose

- response modeling conducted for 365

- 41 DRAFT

— DO NOT CITE OR QUOTE methanol did not include myeloid leukemia, histocytic sarcoma, monocytic leukemia, alo ne or in 1

combination with lymphoblastic lymphoma, lymphoblastic leukemia, lymphocytic lymphoma, 2

and lympho

- immunoblastic lymphoma. 3

As expressed by EFSA (2006) and others (Cruzan, 2009 ; Schoeb et al., 2009 ), there is 4

concern that the lympho

- immunoblastic ly mphoma response in the ERF aspartame study 5

(Soffritti et al ., 200 6, 200 5 ) was caused by or confused with sequalae of a M. pulmonis infection. 6

I nfection of the ERF colony with M. pulmonis has not been confirmed (Caldwell et al ., 2008) 7

and, as discussed in S ection 4.9.2, a link between M. pulmonis infection and induction of 8

lymphoma in rats has not been established in the literature. As noted in S ection 4.9.2, there is 9

evidence suggesting that respiratory infections may have confounded the interpretation of lung 10

lesions in ERF studies. Lymphoma illustrations in 2 ERF studies (Figure 10 of Soffritti et al. 11

[ 2005 ] and Figures 1

- 5 of Belpoggi et al. [ 1999 ] ), suggest that ERF MTBE and aspartame 12

bioassays may have been confounded by a respiratory infection such as M. pulmonis and that 13

lesions associated with this infection may have been interpreted as lymphoma (Schoeb et al., 14

2009)

. However, o ther ERF lymphoma diagnoses in multiple rat organ systems, including the 15

lung , have been confirmed by an independent work ing group panel of six NIEHS pathologists 16

( Hailey , 2004)

. In addition , the incidence of “lung

- only” lympho

- immunoblastic lymphomas 17

was evenly distributed across the control and 0, 500 5000, 20,000 ppm dose groups for male (9, 18

10, 14 and 13) and female ( 3, 5, 6 and 7 ) rats of the Soffritti et al. (2002a) methanol study 19

(Schoeb et al., 2009) . Consequently, removing “lung

- only” lympho

- immunoblastic lymphomas 20

from consideration and using only lymphomas from organ systems not likely to be confounded 21

by a respi ratory infection (i.e., subtracting the lung

- only incidence from lympho

- immunoblastic 22

lymphomas reported in Table 5

- 6 ) still results in significant dose

- response curves for this lesion 23

with p values of 0.20 and 0.42 for males and females, respectively ( see Figure 5

- 6 ). The 24

BMDL 10 estimates of 99 (males) and 110 (females) mg metabolized methanol/day a re about 50% 25

higher than metabolized methanol BMDL 10 estimates for this endpoint when “lung

- only” 26

responses are included (Appendix E, Table E

- 7). 27

28

295

- 42 DRAFT

— DO NOT CITE OR QUOTE 1

Figure 5

- 6

. Lympho

- i mmunoblastic Lymphoma minus “lung

- only” response in rats of

Soffritti et al. (2002a) methanol study versus methanol metabolized (mg/day)

.

For increases in 2 other tumor types (ear duct and head/oral cavity tumors) rep orted in the 2

ERF methanol study ( Table 4

- 2), an independent review of the 75 pathology slides from the ERF 3

aspartame study has suggested differences in interpretation. After reviewing these slides, the 4

NTP PWG noted that “about half” of hyperplastic and n eoplastic lesions in the ear duct or oral 5

cavity were more severely classified by ERF study pathologists, compared with diagnosis from 6

the PWG (EFSA, 2006). Though a similar review has not been conducted of the Soffritti et al

. 7

(2002a) ERF methanol bioass ay results, there is uncertainty regarding the ERF interpretation of 8

these lesions. For this reason, these lesions were not considered in the derivation of the oral 9

CSF. 10

Another uncertainty that confounds the interpretation of some ERF studies is the 11

pos sibility of litter effects in ERF test groups. Bucher (2002) has reported that ERF does not 12

randomize the assign ment of animals to treatment groups , but generally “assigns all animals 13

from a given litter to the same treatment group, recording which litter each animal came from.” 14

This approach would make it more difficult to distinguish the relative contributions of the 15

chemical and genetics to the effect of concern. In response to an EPA query regarding this matter 16

(Knowles, 2008) , ERF has stated that “ th e assignment of test animals to dose groups will var y in 17

ERF studies according to the experimental protocol and aim s of the research ” and “ i n the case of 18

experiments in which exposure begins at 6

- 8 weeks of age (for example BT960, methanol), 19

randomization is performed so as to have no more than one female and one male from each litter 20

in each experimental group. ” For other experiments in which exposure begins during prenatal 21

life, 88 “ randomization is performed on the breeders, ” but the offspring are not ran domized across 22

dose groups in order to “.. simulate as much as possible the human situation in which all 23

descendents are part of a population .” 24

88 For some chemicals such as vinyl chloride (Maltoni and Cotti, 1988) and aspartame (Soffriti et al., 2006), ERF

has started exposure as early as in utero

, This early exposure study design can markedly increase the sensitivity of a

ca ncer bioassay (Maltoni and Cotti, 1988; Soffriti et al., 2006; Melnick et al., 2007). 0.05 0.1 0.15 0.2 0.25 0.3 0.35

0 50 100 150 200Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

08:35 09/04 2009 BMD BMDL Multistage Cancer

Linear extrapolation

0.05 0.1 0.15 0.2

0 50 100 150 200Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

08:40 09/04 2009 BMD BMDL Multistage Cancer

Linear extrapolation

Males (p= 0.20) Females (p= 0.42)5

- 43 DRAFT

— DO NOT CITE OR QUOTE There is uncertainty regard ing the pheochromocytoma response observed in the NEDO 1

(1985/2008b) study with respec t to both its relation to exposure and to its pathology. As 2

discussed in Section 4.2.2.3, the incidence of pheochromocytomas in female rats exhibited a 3

dose

- response trend (Cochrane

- Armitage p < 0.05) . While t he incidence of 13.7% (7/51) in the 4

high

- dose group was significantly elevated ( p < 0.05) over NTP historical control s, it was not 5

significantly elevated over the concurrent control rate of 4% (2/50) . Much of the uncertainty is 6

inherent in difficulties associated with the characterization of this le sion. A ccording to NTP 7

(2000)

, the primary criterion used to distinguish pheochromocytomas from nonneoplastic adrenal 8

medullary hyperplasia

, the presence of mild

- to

- moderate compression of the adjacent tissue

, can 9

be a difficult determination. Further, w hile NEDO (1985/2008b) reported adrenal effects as 10

“hyperplasia of medullary cells” and “pheochromocytoma

, ” NTP pathologists categorize 11

pheochromocytomas into three types : benign, complex and malignant (NTP, 2007, 1999 ). This 12

is an important distinction a s complex and malignant pheochromocytomas are a rare tumor type

, 13

occurring spontaneously in female F344 rats at rates ranging from 0.1% to 0.7% (NTP, 2007, 14

1999; Haseman et al ., 1998) and with cell proliferation activity that is much higher than benign 15

phe ochromocytomas (Pace et al ., 2002). Thus, severity of the pheochromocytoma response 16

reported by NEDO (1985 / 2008b) is uncertain, potentially ranging from mischaracterized 17

hyperplasia to highly proliferative and potentially metastatic malignancies. Finall y , the NEDO 18

study was a two

- year study

, and these lesions, which include diffuse hyperplasia, nodular 19

hyperplasia, and pheochromocytoma, progress with age. Thus, it is possible that a life

- span 20

study would have detected a more severe carcinogenic response ( e.g., progression of the mid

21

dose group hyperplastic responses as reported in Table 4

- 5 ). 22

5.4.3.3. Consistency a cross Chronic Bioassays for Methanol

The observation of a lymphoma response in rats (Soffritti et al., 2002a) and mice (Apaja, 23

1980), along with rep orted associations between lymphomas and human exposure to methanol ’s 24

metabolite

, formaldehyde (see Section 4.9)

, contributes significantly to the cancer weight

- of

25

evidence determination. This was the only carcinogenic response that was observed in more t han 26

one animal bioassay. However, t issue concordance across species, strains and routes of exposure 27

is not assumed , and a lack of such concordance does not negate the validity of individual 28

findings nor the potential relevance of such findings to humans

. 29

Besides the Soffritti et al . (2002a) drinking water study of rats (2 years) reported by ERF, 30

the only other chronic studies available are the Apaja (1980) lifespan drinking water study study 31

in Swiss mice which did not include a concurrent untreated contr ol group, and those reported in 32

the Japanese NEDO ( 1987, 1985/ 2008a, 2008b ) study of monkeys (7

– 29 months), mice 33

(18 months)

, and F344 rats (2 years). None of the NEDO studies involved lifetime evaluations

, 34

and only the rat study can be said to cover a si gnificant portion of the test species life span

. The 35

only organ system that exhibited an increase in effects in both inhalation and drinking water 365

- 44 DRAFT

— DO NOT CITE OR QUOTE studies was the testes. High

- dose rats of the Soffritti et al . (2002a) methanol study exhibited an 1

increase in testicular interstitial cell adenomas

, and the incidence of testicular hyperplasia was 2

increased in high

- dose rats of the NEDO ( 1987, 1985/2008b ) study. However, neither effect was 3

statistically increased over controls

, and both effects were well with in their historical control 4

ranges. An increase in lymphoma s w as the only carcinogenic effect reported in the Soffritti et al

. 5

(2002a) and Apaja (1980) drinking water stud ies that is considered dose related and quantifiable, 6

and male pulmonary adenoma/ade nocarcinoma and female pheochromocytoma were the only 7

carcinogenic effects from the NEDO ( 1987, 1985/2008b) inhalation study that are considered 8

exposure related and quantifiable. 9

As discussed in Section 4. 9 .2, there are several differences between the NED O (1987

, 10

1985/2008b) and Soffritti et al . (2002a) studies conducted in rats which limit their direct 11

comparison and may explain some of the differences in response. These include route of 12

exposure, lifetime evaluation period, and strain of species used. Pulmonary adenomas observed 13

in the NEDO inhalation study could be portal

- of

- entry effects associated with the inhalation 14

route of exposure. Differences in systemic effects observed in the two studies such as the 15

pheochromocytoma response in the NEDO ( 1987 , 1985/2008b) study and the lymphoma 16

responses observed in the Soffritti et al

. ( 2002a) study are systemic responses and differences 17

would not be expected based on route of exposure. D ifferences in the evaluation period between 18

the two studies may have co ntributed to the lack of endpoint concordance

. In the Soffritti et al. 19

(2002a) study, the animals were administered methanol for 104 weeks and then followed until 20

the completion of their natural lifetime. The average life span for these animals was 94 an d 96 21

weeks for males and females, respectively, with animals living as long as 153 weeks (female). 22

However, this does not explain the difference in lymphoma response between the studies as 23

m any of the lympho

- immunoblastic lymphomas (most common type obser ved) were observed 24

prior to 104 weeks (control

– 5/9; 500 ppm

– 7/17; 5

, 000 ppm

– 13/19; 20

, 000 ppm

– 11/21). The 25

NEDO (1987

, 1985/2008b) study was conducted in F344 rats versus the Sprague

- Dawley rats 26

used in the Soffritti et al. (2002a) ERF study

. More importantly, t he background rates of selected 27

types of lymphomas and leukemias are very different between the two strains of rats. In the 28

F344 rat, there is a high background rate of mononuclear cell leukemia, while there is a much 29

lower background rate o f this leukemia type in the Sprague

- Dawley rat (Caldwell et al ., 2008). 30

The types of lymphoma reported in the Sprague

- Dawley rat by Soffritti et al. (2002a) following 31

methanol administration are rarely diagnosed in the F344 rat. Thus, the strain differen ce 32

between the two studies is a likely explanation for the fact that lymphomas were only increased 33

in the Soffritti et al. (2002a) rat study. NTP ( 2007, 1999) reports do not suggest a significant 34

difference between F344 and Sprague

- Dawley rats with respec t to pulmonary adenomas and 35

pheochromocytomas. Another possible explanation for this difference includes a different 36

profile of circulating metabolites associated with oral first

- pass liver metabolism. 375

- 45 DRAFT

— DO NOT CITE OR QUOTE 5.4.3.4. Choice of Endpoint for POD Derivation

Keeping in min d the aforementioned uncertainties associated with the interpretation of 1

the Soffritti et al. (2002a) and NEDO (1985/2008b) study results

, t he choice of tumors to use for 2

the derivation of the oral and inhalation CSFs was made on the basis of the appearanc e of a dose

3

related increase in response rates for the selected tumor categori es. Analysis of lymphomas used 4

a pair

- wise statistical comparison (Fisher’s Exact test) and a trend test (Cochran Armitage trend 5

test) to determine whether the slope of that tre nd was statistically significantly greater than 0

. 6

The Fisher’s Exact result for the male rat high

- dose group and the results of the Cochran 7

Armitage Trend Test w ere both p < 0.01. The decision not to include the liver tumors in the 8

dose

- response analysi s was made based on a lack of dose response according to pair

- wise and a 9

trend test results versus concurrent controls This is not to say that the slight increase in the 10

incidence of this tumor type observed in all dose groups of male rats was not relate d to methanol 11

exposure (this is a relatively rare Sprague

- Dawley rat tumor), only that the increase was not 12

statistically significant and did not contribute significantly to the overall tumor response. 13

As discussed in Section s 4.2.2.3 and 4. 9 .2, the high

- d ose incidences for pulmonary 14

adenomas/adenocarcinomas were increased over concurrent controls ( p < 0.05). While the high

15

dose incidences of pheochromocytomas in the NEDO (1985 / 2008b) study were not statistically 16

increased over concurrent controls, the do se

- response for both tumor types represent s increasing 17

trends (Cochran Armitage trend test; p < 0.05)

, and in both cases, the high

- dose response 18

incidences were considerably elevated over historical control incidences ( p < 0.05) within their 19

respective sex and strain. Further, both tumor responses are accompanied by proliferative 20

changes ( e.g., hyperplastic responses) in their respective cell types that suggest tumor 21

progression. 22

5.4.3.5. Choice of Species/ Gender

The oral CSF was based on male rat lymphomas rath er than female rat lymphomas. The 23

inhalation IUR was based on female rat pheochromocytomas rather than male rat 24

adenoma/carcinomas. In both cases, the gender that exhibited the steeper dose response and the 25

higher risk estimate was chosen. 26

Both the CSF and IUR were based on rat studies. Use of the Apaja (1989) mouse data 27

would have resulted in a 5

- fold higher, but less reliable oral CSF due to a high level of 28

uncertainty associated with the Apaja (1989) study, which contained limited experimental detail 29

and did not include a concurrent control group (see Section 4.2.1.3). 30

5.4.3.6. Cho ice of Model for POD Derivation

The multistage

- cancer model contained in EPA’s BMDS version 2.1 was used to derive 31

both the CSF and IUR estimates for methanol. When no biologicall y

- based cancer model exists 32

and evidence for a nonlinear cancer MOA is lacking , as is the case for methanol, the preference 335

- 46 DRAFT

— DO NOT CITE OR QUOTE within the EPA’s IRIS program is for the use of a multistage model . The re is uncertainty 1

associated with whether the multistage mod el is the most appropriate choice, but in the absence 2

of a biologically based model, dose

- response modeling is largely a curve

- fitting exercise, and the 3

multistage model is sufficiently flexible for most cancer bioassay data. In the case of the oral 4

CSF, individual animal response data was obtained from the authors of the princip a l study 5

( Sofffritti et al

. , 2002a ) and a multistage

- Weibull time

- to

- tumor model was applied to determine 6

whether the lifespan study design of the study had an appreciable impact o n the dose

- response 7

analysis. As described in Appendix E, time

- to

- tumor modeling and multistage quantal modeling 8

gave similar results

, and the tumor responses modeled did not exhibit significant time 9

dependence on dose. 10

11

5.4.3.7. Choice of Dose Metric

The to tal methanol metabolized was selected over AUC or the C

max as the most 12

appropriate dose metric for derivation of the oral cancer slope factor and inhalation unit risk 13

primarily because it provided the best fit to response data, particularly lymphoma incide nce from 14

Soffritti et al. (2002a) (see Figures 5

- 3 through 5

- 5 and Table E

- 7 of Appendix E ) and Apaja 15

(1989) (see Table E

- 17 of Appendix E)

. Also, lymphomas and respiratory effects have been 16

observed in studies conducted with formaldehyde, and lymphomas h ave been observed in 17

chronic bioassays conducted with other compounds that convert to formaldehyde ( i.e., MTBE 18

and aspartame). As discussed in Section 4. 9 .3, metabolites of methanol, particularly 19

formaldehyde, may play a role in the MOA. 20

In considering th e dose

- response relationship for methanol

- induced carcinogenesis, a key 21

factor is the saturation of metabolism since metabolic transformation to formaldehyde and 22

generation of oxidative stress are considered likely candidates in the mode of action. Cruzan 23

(2009) indicates that saturation occurs at dose of “600 mg/kg,” but saturation depends on the 24

dose rate , not the total administered dose. For example, if 600 mg/kg is given in a single bolus, 25

the internal concentration immediately following that bolus co uld well be high enough to 26

saturate metabolism, while the same total dose ingested over the course of a day might not. 27

To aid in interpretation of the Soffretti et al. (2002a) bioassay, water ingestion in rats was 28

assumed to shift between nocturnal (high activity) and diurnal (low activity) periods, each lasting 29

12 h ours . Rats were assumed to consume 20% of their daily water ingestion during the diurnal 30

period and 80% during the nocturnal period. Ingestion in each period was assumed to occur in 31

“bouts” which were treated as periods of continuous (zero

- order) infusion to the stomach. 32

During the nocturnal period each bout was assumed to last 45 min utes , followed by 45 min utes 33

without ingestion (overall period is 1.5 h ours ) and during the diurnal period th e bout was 34

assumed to last on 3 h ours followed by 2.5 h ours without ingestion (overall period is 3 h ours ). 355

- 47 DRAFT

— DO NOT CITE OR QUOTE Given this exposure pattern, the total amount metabolized per day (after periodicity is 1

reached) in a 420 g rat (average weight in Soffretti et al. , 2002a) was estimated using the PBPK 2

model, with the results shown in F igure 5

- 7 . The amount increases almost linearly with 3

exposure until ~ 400 mg/kg/d, but continues to increase above that point, becoming almost 4

completely saturated by 2,000 mg/kg/d. This pattern occurs in part because of the circadian 5

ingestion pattern. The more rapid ingestion rate during the dark cycle leads to the highest 6

internal concentrations and hence the initial metabolic saturation during that part of the day. But 7

the lower ingestion (light) period, internal concentrations drop, allowing for an exposure range 8

(400

- 1600 mg/kg/d) where nocturnal metabolism is saturated but diurnal metabolism is not. 9

10

Figure 5

- 7. T otal amount metabolized per day (after periodicity is rea ched) in a 420 g

rat

While

, based on this exposure

- dose pattern

, one might expect a similar exposure

11

response relationship, this pattern does not include detoxification mechanisms. If such 12

mechanisms also saturate, then it is possible for the slower increase in total metabolism above 13

400 mg/kg/d to result in a significant increase in effect, though full metabolic saturation at 14

~2

, 000 mg/kg/d would still be expected to result in a maximal effect at that exposure level. 15

5.4.3.8. Choice of Animal

- to

- Human Extrapolation Meth od

A PBPK model was used to extrapolate animal

- to

- human concentrations. The estimated 16

methanol metabolized for each dose administered to the animals in NEDO (1985/2008b) and 17

Soffritti et al . (2002a) were determined using the animal PBPK model

, and then t he BMDL 10 18

determined by the methods described previously (Section 5.4.2.1). Assuming that metabolized 19

methanol distributes in the body according to body weight to the 3⁄4 power, the rat mg

- day 20

BMDL 10 was converted to a human mg

- day BMDL 10

. The human PBPK m odel (Appendix B) 215

- 48 DRAFT

— DO NOT CITE OR QUOTE was then used to convert this human mg

- day values for total methanol metabolized back to a 1

human equivalent methanol oral dose HED(BMDL 10 ) of 51.5 mg/kg

- day for lymphomas in the 2

male rat, and a human equivalent methanol inhalation concent ration HEC( BM C L 10 ) of 3

81.9 mg/m 3 for pheochromocytomas in the female rat. If traditional dosimetry assumptions are 4

used, the HED(BMDL 10 ) and HEC( BM C L 10 ) estimates would have been approximately 4

- fold 5

higher than the value derived using the PBPK model. 6

As discussed in Sections 3.4 and 5.3.4, t he PBPK models do not describe or account for 7

background levels of methanol, formaldehyde or formate, and background levels were subtracted 8

from the reported data before use in model fitting or validation (if not alrea dy subtracted by 9

study authors), as described below. This approach was taken because the relationship between 10

background doses and background responses is not known, because the primary purpose of this 11

assessment is for the determination of noncancer and c ancer risk associated with increases in the 12

levels of methanol or its metabolites (e.g., formate, formaldehyde) over background, and because 13

the subtraction of background levels is not expected to have a significant impact on PBPK model 14

parameter estimates as background levels of methanol and its metabolites are low relative to 15

exposure levels used in methanol bioassays. Further, while it is possible that background levels 16

of methanol or its metabolites contribute to background responses for some adverse e ffects, the 17

results of dose

- response modeling of cancer endpoints using “background dose” models suggest 18

that this contribution is relatively small (see discussion in Appendix E, Section E.4). 19

20

5.4.3.9. Human Relevance of Cancer Responses Observed in Rats and Mice

As discussed in Sections 4.9 .2 , there is human evidence for the association of lymphomas 21

with a metabolite of methanol, formaldehyde. However, t here is no information available in the 22

literature regarding the observation of cancer in humans following chr onic administration of 23

methanol. The only observations in animals were noted in the chronic studies of methanol 24

conducted by Apaja (1980), Soffritti et al . (2002a) and NEDO (1985/2008b) and there is 25

uncertainty associated with the interpretation of the tu mor responses reported in these studies

. 26

As a consequence, the overall WOE, while convincing, is not strong

. 276

- 1 DRAFT

- DO NOT CITE OR QUOTE 6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF HAZARD AND DOSE

RESPONSE

6.1. HUMAN HAZARD POTENTI AL

Methanol is the smallest member of the family of al iphatic alcohols. Also known as 1

methyl alcohol or wood alcohol, among other synonyms, it is a colorless, very volatile, and 2

flammable liquid that is widely used as a solvent in many commercial and consumer products. It 3

is freely miscible with water and o ther short

- chain aliphatic alcohols but has little tendency to 4

distribute into lipophilic media. Methanol can be formed in the mammalian organism as a 5

metabolic byproduct and can be ingested with foodstuffs, such as fruits or vegetables. A 6

potential for h uman exposure exists today in the form of the artificial sweetener, aspartame, 7

which is a methyl ester of the dipeptide aspartyl

- phenylalanine. Methanol is the major anti

8

freeze constituent of windshield washer fluid. Its use as a fuel additive for intern al combustion 9

engines is, as yet, limited by its corrosive properties. 10

Because of its very low oil:water partition coefficient

, methanol is taken up efficiently by 11

the lung or the intestinal tract and distributes freely in body water without any tendency t o 12

accumulate in fatty tissues. It can be metabolized completely to CO 2 , but may also, as a regular 13

byproduct of metabolism, enter the C 1

- pool and become incorporated into biomolecules. Animal 14

studies indicate that blood methanol levels increase with the b reathing rate and that metabolism 15

becomes saturated at high exposure levels. Because of its volatility it can also be excreted 16

unchanged via urine or exhaled air. 17

The acute toxicity in laboratory animals in response to high levels of exposure results 18

fro m CNS depression. NEDO (1987) reported that methanol blood levels around 5

, 000 mg/L 19

were necessary to cause clinical signs and CNS changes in monkeys. In humans, however, acute 20

toxicity is caused by metabolic acidosis that appears to affect predominantly the nervous system

, 21

with potentially lasting effects such as blindness, Parkinson

- like symptoms, and cognitive 22

impairment. These effects can be observed in humans when blood methanol levels exceed 23

200 mg/L. The species differences in toxicity from acute exposures appear to be the result of a 24

limited ability of humans to metabolize formic acid. 25

Despite the existence of many case reports on acute human exposures, the knowledge 26

base for long

- term, low

- level exposure of humans to methanol is limited. The c urrent TLV for 27

methanol is 200 ppm (262 mg/m 3 ) (ACGIH, 2000). Controlled experiments with human 28

volunteers indicate that only minor neurobehavioral changes occur following 4

- hour exposure to 29

this concentration. A limited study on self

- reported health eff ects in 66 persons exposed to 30

methanol at levels that came close to or exceeded the NIOSH short

- term ceiling of 800 ppm 31

(1048 mg/m 3 ), in comparison with an age

- matched group of 66 less or not exposed persons, 32

suggested a statistically significant increase in the incidence of CNS

- related symptoms, such as 33

dizziness, nausea, headache, and blurred vision (Frederick et al ., 1984). Impaired vision and 346

- 2 DRAFT

— DO NOT CITE OR QUOTE nasal irritation were observed in a study of 33 methanol

- exposed workers (Kawai et al ., 1991). 1

None of the case reports or human studies have investigated cancer as a potential outcome of 2

methanol exposure. 3

A number of reproductive, developmental, subchronic and chronic exposure duration 4

studies have been conducted in mice, rats, and monkeys. This summary will fo cus primarily on 5

reproductive and developmental toxicity, and cancer as the main endpoint s of concern. Sections 6

4.7 , 5.1.1 and 5.2.1 contain more extensive summaries that consider the dose

- related effects that 7

have been observed in other organ systems fol lowing subchronic or chronic exposure. 8

Although there is no evidence in humans, methanol has shown to be a reproductive and 9

developmental toxicant in several animal studies. No studies have been reported in which 10

humans have been exposed subchronically or chronically to methanol by the oral route of 11

exposure, and thus would be sui table for derivation of an oral RfD. Data exist regarding effects 12

from oral exposure in experimental animals, but they are more limited than data from the 13

inhalation route of e xposure (see Sections 4.2 , 4.3

, and 4. 4 ). Two oral studies in rats (Soffritti 14

et al ., 2002a; U.S. EPA, 1986 ), on e oral study in mice (Apaja, 1980 ) and several inhalation 15

studies in monkeys, rats and mice (NEDO, 1987, 1985/ 2008a, 2008b ) of 90

- days duration or 16

longer have been reported. While some noncancer effects of methanol exposure were noted in 17

these studies, principally in the liver and brain, they were either not quantifiable due to study 18

limitations or occurred at high doses relative to reproductive /developmental effects. As 19

discussed below, the results of inhalation reproductive/developmental toxicity studies in rats 20

(NEDO, 1 987), mice (Rogers et al ., 1993a)

, and monkeys (Burbacher et al

., 2004a, 2004b, 21

1999a, 1999 b ) are the princip al consideratio ns for both the RfD and RfC values derived in this 22

assessment. 23

A larger number of studies have used the inhalation route to assess the potential of 24

reproductive or developmental toxicity of methanol in mice, rats, and monkeys

, with 25

concentrations ranging from 200 to 20,000 ppm (blood levels reaching as high as 8.65 mg/mL). 26

To sum up the findings, rat dams survived even the highest doses without gross signs of toxicity, 27

but their offspring were severely affected (Nelson et al ., 1985). Two more inhalation studies, 28

Rogers et al . (1993a, 1993 b) and Rogers and Mole (1997)

, confirmed that methanol causes 29

exencephaly and cleft palate in mice, the most sensitive days being GD6 and GD 7 ( i.e., early 30

organogenesis )

. These severe malformations were observed at exposu re concentrations of 31

5,000 ppm or above. Nelson et al . (1985) and Rogers et al . (1993a) also observed an increased 32

occurrence of ossification disturbances and skeletal anomalies at methanol concentrations 33

≥ 2 ,000 ppm, of which cervical ribs in mouse fetus es is considered the critical effect for toxicity 34

value derivation in this review. A study conducted in pregnant cynomolgus monkeys that were 35

exposed to 200

– 600 ppm methanol for 2.5 hours/day throughout premating, mating, and 36

gestation showed no signs of maternal or fetal toxicity. The potential compound

- related effects 376

- 3 DRAFT

— DO NOT CITE OR QUOTE noted were a shortening of the gestation period by less than 5% and developmental neurotoxicity, 1

particularly delayed sensorimotor development (Burbacher et al

., 2004a, 2004b, 1999a, 1999b ). 2

While all of the above studies were conducted with exposure durations of 7 hours/day or 3

less, NEDO (1987) conducted a series of developmental/reproductive studies in rats that used 4

exposure times of 20 hours/day or more at concentrations between 500 and 5

, 000 ppm. A two

5

generation study by these researchers that exposed the dams throughout pregnancy and the pups 6

through 8 weeks of age, demonstrated dose

- dependent reductions in brain weights that forms the 7

basis for the RfC derived in this review. 8

Carci nogenic effects following methanol exposure were observed in a chronic drinking 9

water study in Eppley Swiss Webster mice (Apaja, 1980) and two chronic rat studies : a drinking 10

water study of Sprague

- Dawley rats (Soffritti et al ., 2002a) and an inhalation st udy of F344 rats 11

(NEDO, 1985/2008b). Following administration via drinking water , both Apaja (1980) and 12

Soffritti et al. (2002a) observed positive dose

- response trends for increases in the incidence of 13

lymphomas in both test animal genders. Soffritti et al . (2002a) characterized the lymphomas in 14

their study as lymphoreticular , principally lympho

- immunoblastic

. EPA re

- analyzed the 15

lymphoma data from the Soffritti et al. (2002a) study for quantification purposes, combining 16

only tumors of the same cell type origin. There was a slight increase in hepatocellular 17

carcinomas in male rats of all exposure groups of this study that was not statistically elevated 18

over controls in any group, but potentially this tumor is related to methanol exposure given the 19

low hi storical background rate for this tumor in this rat strain. As discussed in Section 5.4.1.1, 20

the other tumor increases reported by Soffritti et al . (2002a) are not quantifiable or were 21

considered hyperplastic rather than carcinogenic following a review by NTP pathologists (EFSA, 22

2006; Hailey, 2004)

. N o tumor responses were significantly increased over controls in the 23

chronic inhalation bioassays performed by NEDO (1987) in monkeys, and mice, but the high

24

dose incidences for pulmonary adenomas/adenocarcino mas in male rats was elevated over 25

concurrent controls and pheochromocytomas in female rats were significantly elevated over 26

historical control incidences for these tumor types within their respective sex and strain. The 27

dose response for both of these tu mor types represents increasing trends (Cochran Armitage 28

trend test; p < 0.05). Further, both tumor responses are accompanied by proliferative changes 29

( e.g., hyperplastic responses) in their respective cell types. 30

6.2. DOSE RESPONSE

6.2.1. Noncancer/Inhalation

Clear ly defined toxic endpoints at moderate exposure levels have been observed only in 31

reproductive and developmental toxicity studies. Three endpoints from developmental toxicity 32

studies were considered for derivation of the RfC: formation of cervical ribs in CD

- 1 mice 33

exposed to methanol during organogenesis (Rogers et al ., 1993a), deficits in sensorimotor 346

- 4 DRAFT

— DO NOT CITE OR QUOTE development as measured by VDR tests administered to monkeys exposed to methanol 1

(Burbacher et al

., 2004a, 2004 b, 1999a, 1999 b )

, and reduced brain weights in rats exposed to 2

methanol from early gestation through 8 weeks of postnatal life (NEDO, 1987). For the purpose 3

of comparability and to better illustrate methodological uncertainty, reference values were 4

derived for all of these endpoint s using a BMD mod eling approach which evaluated several 5

models and various measures of risk. In the present review

, mostly because of a paucity of 6

adequate long

- term or developmental oral studies and the existence of several inhalation studies 7

that examined sensitive subp opulations (pregnant mothers, developing fetuses and neonates) in 8

various species, it was decided to use the critical effect from an inhalation study to derive an 9

RfD. Thus, the criteria and rationales on which the RfC assessment is based also form the ba sis 10

for the RfD derivation. 11

The Rogers et al . (1993a) inhalation study is a multidose developmental study that was 12

considered for use in the derivation of a reference value. The exposure concentrations in this 13

study were 0, 1

, 000, 2

, 000, and 5,000 ppm adm inistered for 7 hours/day on GD7

– GD 17. The 14

BMD evaluation, based on the nested log

- logistic model of BMDS version 2.1, produced 15

BM D / BM D L values in terms of internal peak blood methanol (C

max ). PBPK modeling was used 16

to convert the internal animal dose met rics to HECs, and a UF of 100 was applied to yield RfCs 17

of 10.4 mg/m 3 and 13.6 mg/m 3 for 5 and 10% extra risk, respectively. 18

Reproductive and developmental neurobehavioral effects observed in monkeys following 19

methanol inhalation exposure (Burbacher et al

. , 2004a, 2004b, 1999a, 1999b ) were also 20

considered for use in the derivation of a reference value. M. fascicularis monkeys were exposed 21

to 0, 262, 786, and 2

, 359 mg/m 3 methanol 2.5 h ou r s /day, 7 days/week during premating/mating 22

and throughout gestation ( approximately 168 days). Delayed sensorimotor development as 23

measured by a VDR test was the only effect in this study that exhibited a dose

- response and is a 24

measure of a functional deficit that is consistent with early developmental CNS effects ( e.g., 25

br ain weight changes) that have been observed in rats (NEDO, 1987). Though there is 26

uncertainty associated with this effect and its relation to methanol exposure, a BMD analysis was 27

performed for comparative purposes. BM D / BM D L values for the VDR endpoint w ere estimated 28

using AUCs derived from a monkey PBPK model of blood methanol data reported in the 29

Burbacher et al . (1999a) study. A human methanol PBPK modeling was then used to convert the 30

internal AUC BMDL to a n HEC, and a UF of 100 was applied to yield a reference value estimate 31

of 1.7 mg/m 3

. 32

Reduced brain weight was evaluated based on the results of a two

- generation study by 33

NEDO (1987) in which fetal rats and their dams were exposed from the first day of gestation 34

until 8 weeks of age

, and brain weight s were determined at 3, 6, and 8 weeks of age. To obtain 35

reference value estimates from these studies, a rat PBPK model was used to predict PODs in 36

terms of internal doses, which were divided by UFs and converted to HEC reference values via a 376

- 5 DRAFT

— DO NOT CITE OR QUOTE human PBPK m odel (see Table 5

- 4). BMD modeling was executed using two different BMRs, 1

one S.D. (as is usual with continuous data) and 5% relative (to control response) risk. The 2

resulting reference value estimate s were 2.4 and 1.8 mg/m 3 (5% relative risk and 1 S.D.

, 3

respectively) for reduced brain weight at 6 weeks of age following gestational and postnatal 4

exposure. 5

Despite the variety of approaches, different critical effects, and different data sources, all 6

reference value estimates fell within a narrow range. T he reference value associated with the 7

BMD estimate of the dose corresponding to a one S.D. decrease in brain weight in male rats at 6 8

weeks post

- birth observed in the NEDO (1987) developmental toxicity study is considered most 9

suitable for derivation of the methanol chronic RfC due to the relevance of the exposure 10

scenario/study design (see Section s 5.1.2.2 and 5.3 ) , and endpoint (see Section 5.3 ) to the 11

potential for developmental effects in neonatal humans, the relative robustness of the dose

12

response d ata and because it resulted in one of the lowest reference values of the BMD 13

derivations (see Table 5

- 4). Thus, the proposed chronic RfC for exposure to methanol is 2 14

mg/m 3 , an evaluation that includes a UF H of 10 for intraspecies variability, a UF A of 3 to address 15

the pharmacodynamic component of interspecies variability , and a UF D of 3 for database 16

uncertainty. 17

The confidence in this RfC is medium to high. Confidence in the NEDO (1987) 18

developmental studies is medium to high. While there are issues w ith the lack of reporting 19

detail, the critical effect (brain weight reduction) has been reproduced in an oral study of adult 20

rats ( U.S. EPA, 1986 c )

, and the exposure regimen involving pre

- and postnatal exposures 21

addresses a potentially sensitive human sub population. Confidence in the database is medium. 22

Despite the fact that skeletal and brain effects have been demonstrated and corroborated in 23

multiple animal studies in rats, mice, and monkeys, some study results we re not quantifiable, 24

the re is uncertain ty regarding which is the most relevant test species

, and there is limited data 25

regarding reproductive or developmental toxicity of methanol in humans. There is also 26

uncertainty regarding the potential active agent

— the parent compound, methanol, formaldeh yde, 27

or formic acid. There are deficiencies in our knowledge of the metabolic pathways of methanol 28

in the human fetus during early organogenesis, when the critical effects can be induced in 29

animals. Thus, the medium

- to

- high confidence in the critical stu dy and the medium confidence 30

in the database together warrant an overall confidence descriptor of medium to high. 31

6.2.2. Noncancer/Oral

T here is a paucity of scientific data regarding the outcomes of chronic oral exposure to 32

methanol. No data exist for long

- term methanol exposure of humans. A subchronic (90

- day) 33

oral study in Sprague

- Dawley rats reported brain and liver weight changes, with some evidence 34

for minor liver damage at 2,500 mg/kg

- day that was not supported by histopathologic findings 356

- 6 DRAFT

— DO NOT CITE OR QUOTE (U.S. EPA, 1986c ). Liver necrosis was reported in Eppley Swiss Webster mice that consumed 1

approximately 2000 mg/kg

- day (Apaja, 1980). In the only other study that administered 2

methanol chronically to animals by the oral route, Soffritti et al . (2002a) reported that, ove rall, 3

there was no pattern of compound

- related clinical signs of toxicity in Sprague

- Dawley rats 4

exposed to up to approximately 2,000 mg/kg

- day . The authors further reported that there were 5

no compound

- related signs of gross pathology nor histopathologic lesions indicative of 6

noncancer toxicological effects in response to methanol ; however, they did not provide any 7

detailed data to illustrate these findings. 8

As discussed above and in Section 5.1.1, reproductive and developmental effects are 9

considered the most sensitive and quantifiable effects reported in studies of methanol. Oral 10

reproductive and developmental studies employed single doses that were too high to be of use. 11

In the absence of sui table reproductive or developmental data from oral exposure s tudies

, it was 12

decided to conduct a route

- to

- route extrapolation and to use the critical effect from the inhalation 13

study ( brain weight ) to derive an RfD. Thus, the POD (in terms of AUC methanol in blood) used 14

for the derivation of the RfC was also used f or the derivation of the RfD. This POD was divided 15

by a UF of 100

, and a human PBPK model was used to obtain an RfD value of 0.4 mg/kg

- day. 16

As for the RfC, the 100

- fold UF includes a UF H of 10 for intraspecies variability, a UF A of 3 to 17

address pharmacod ynamic uncertainty, and a UF D of 3 for database uncertainty. 18

The confidence in the RfD is medium to high . Despite the relatively high confidence in 19

the critical studies, all limitations to confidence as presented for the RfC also apply to the RfD. 20

Confid ence in the RfD is slightly lower than for the RfC due to the lack of adequate oral studies 21

for the RfD derivation , necessitating a route

- to

- route extrapolation. 22

6.2.3. Cancer/Oral and Inhalation

Under the current Guidelines for Carcinogen Risk Assessment (U.S. EPA 2005a, 2005 b), 23

methanol fulfils the criteria to be described as likely to be a human carcinogen by all routes of 24

exposure

. This descriptor is based principally on findings of dose

- related, statistically significant 25

increases in the incidence of: lympho reticular tumors in lifetime stud ies of both sexes of Eppley 26

Swiss Webster mice (Apaja, 1980) and both sexes of Sprague

- Dawley rats (Soffritt et al., 2002a)

, 27

a slight but significant (compared to historical controls) increase in relatively rare hepatocellu lar 28

carcinomas in male Sprague

- Dawley rats following oral exposure (Soffritti et al ., 2002a)

, and 29

dose

- related occurrences of pulmonary adenomas/adenocarcinomas and pheochromocytomas in 30

F344 rats by inhalation exposure (NEDO

, 1985/2008b). This determinati on is supported by the 31

results of other studies that have shown tumorigenic responses similar to those observed by 32

Soffritti et al . (2002a) in rats exposed to formaldehyde, a metabolite of methanol, and to the 33

metabolic precursors of methanol and formaldeh yde, aspartame and MTBE. In addition, 34

epidemiological studies have associated exposure to formaldehyde with increases in the 356

- 7 DRAFT

— DO NOT CITE OR QUOTE incidence of both leukemias and lymphomas (IARC, 2004). However, the key studies

, Soffritti 1

et al

. ( 2002a )

, NEDO ( 1985/2008b) and Apaja (1980)

, have associated uncertainties (see below 2

and discussions in Sections 4. 9 .2 and 5.4.3) that reduce confidence in the chosen descriptor. 3

The statistically significant increase in the incidence of lymphoreticular tumors observed 4

in the Soffri tti et al . (2002a) drinking water study of Sprague

- Dawley rats was used in the 5

determination of the POD for estimating the methanol oral CSF . A PBPK model was developed

, 6

and several model predictions of internal dose metrics were considered for use in the dose

7

response analysis and derivation of the human equivalent dose. Methanol metabolized was 8

selected as the dose metric best suited for derivation of the oral POD because of its superior fit to 9

the response data and consistency with the hypothesis that formaldehyde may be the 10

carcinogenic agent associated with methanol exposure. The EPA multistage cancer model was 11

used to derive a BMDL 10 for the male rat in terms of mg methanol metabolized/day. Assuming 12

that metabolized methanol distributes in the body according to body weight to the 3⁄4 power, the 13

rat BMDL 10 of 6 3.9 mg

- day was converted to a human BMDL 10 of 3, 553 mg

- day. The human 14

PBPK model was then used to convert this human mg

- day value for total methanol metabolized 15

back to a human equivalent metha nol oral dose HED(BMDL 10 ) of 51.5 mg/kg

- day for 16

lymphomas in the male rat. The oral CSF of 2 E

- 0 3 (mg/kg

- day)

- 1 was then derived based on a 17

linear extrapolation from this POD to estimated background levels. 18

Pulmonary adenomas/adenocarcinomas in male F344 rats and pheochromocytomas in 19

female F344 rats observed in the chronic inhalation study of NEDO (1985/2008b) were 20

considered in the determination of the POD for estimating the inhalation CSF . In this case, all 21

dose metrics estimated by the PBPK model prov ided a similar accep table fit to the tumor 22

response data. Methanol metabolized was selected as the dose metric for derivation of the 23

inhalation POD for consistency with the approach used for the derivation of the oral POD and 24

with the hypothesis that form aldehyde may be the carcinogenic agent associated with methanol 25

exposure. As for the oral POD, the EPA multistage cancer model was used to derive a BMDL 10 26

for the rat in terms of mg methanol metabolized/day. Assuming that metabolized methanol 27

distributes in the body according to body weight to the 3⁄4 power, the rat BMDL 10 of 1 5 mg

- day 28

was converted to a human BMDL 10 of 971 mg

- day. The human PBPK model was then used to 29

convert this human mg

- day value for total methanol metabolized back to a human equivalen t 30

methanol inhalation concentration HEC( BM C L 10 ) of 81,900 μ g/m 3 for pheochromocytomas in 31

the female rat. The inhalation cancer unit risk of 1 E

- 06 (μg/m 3 )

- 1 was then derived based on a 32

linear extrapolation from this POD to estimated background levels. 33

Section 5.4.3 of this assessment documents several uncertainti es with the quanti fic ation 34

of cancer risk . The main uncertainties can be grouped into issues related to study quality, the 35

interpretation of study results

, and the consistency of the results with other laboratories. Other 36

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and limit concentration issues in inhalation toxicity studie s. Federal Register 59(206):53799. 20

Available from: http://www.epa.gov/iris/backgr

- d.htm

. 21

22

U.S. EPA (Environmental Protection Agency). (1994b) Methods for derivation of 23

inhalation reference concentratio ns and application of inhalation dosimetry. Office of Research 24

and Development, Washington, DC; EPA/600/8

- 90/066F. Available from: 25

http://www.epa.gov/iris/backgr

- d.htm

. 26

27

U.S. EPA (Environmental Protect ion Agency). (1995) Use of the benchmark dose 28

approach in health risk assessment. Risk Assessment Forum, Washington, DC; EPA/630/R

29

94/007. Available from: http://cfpub.epa.gov/ncea/r af/recordisplay.cfm?deid=42601

. 30

31

U.S. EPA (Environmental Protection Agency). (1996) Guidelines for reproductive 32

toxicity risk assessment. Federal Register 61(212):56274

– 56322. Available from: 33

http://www .epa.gov/iris/backgr

- d.htm

. 34

35

U.S. EPA (Environmental Protection Agency). (1997) Guidance on cumulative risk 36

assessment, Part 1 Planning and Scoping. Science Policy Council, Washington, D.C. 37

38DRAFT

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- 35 U.S. EPA (Environmental Protection Agency). (1998) Guidelines f or neurotoxicity risk 1

assessment. Federal Register 63(93):26926

– 26954. Available from: 2

http://www.epa.gov/iris/backgr

- d.htm

. 3

4

U.S. EPA (Environmental Protection Agency). (2000a) Science policy council handbook: 5

risk characterization. Office of Science Policy, Office of Research and Development, 6

Washington, DC; EPA 100

- B

- 00

- 002. Available from: http://www.epa.gov/iris/backgr

- d.htm

. 7

8

U.S. EPA (Environ mental Protection Agency). (2000b) Benchmark dose technical 9

guidance document [external review draft]. Risk Assessment Forum, Washington, DC; 10

EPA/630/R

- 00/001. Available from: http://www.epa.gov/iris/bac kgr

- d.htm

. 11

12

U.S. EPA (Environmental Protection Agency). (2000c) Supplementary guidance for 13

conducting for health risk assessment of chemical mixtures. Risk Assessment Forum, 14

Washington, DC; EPA/630/R

- 00/002. Available from: http://www.epa.gov/iris/backgr

- d.htm

. 15

16

U.S. EPA (Environmental Protection Agency) ( 2000 d ) Toxicological review of vinyl 17

chlo ri de. EPA.635R

- 00/004

. 18

19

U.S. EPA (Environmental Protection Agency). (2002a) A review of the reference dose 20

and reference concentration processes. Risk Assessment Forum, Washington, DC; EPA/630/P

21

02/0002F. Available from: http://www.epa.gov/iris/backgr

- d.htm

. 22

23

U.S. EPA (Environmental Protection Agency). (200 2b) Health assessment of 1,3

- 24

butadiene. EPA/600/P

- 98/001F. 25

26

U.S. EPA (Enviornmental Protection Agency) ( 2004) . Integrated risk information system: 27

vinyl chloride. Available from www.epa.gov/iris/subst/ 1001.htm

. 28

29

U.S. EPA (Environmental Protection Agency). (2005a) Guidelines for carcinogen risk 30

assessment. Risk Assessment Forum, Washington, DC; EPA/630/P

- 03/001B. Available from: 31

http://www.epa.gov/iris/backgr

- d.htm

. 32

33

U.S. EPA (Environmental Protection Agency). (2005b) Supplemental guidance for 34

assessing susceptibility from early

- life exposure to carcinogens. Risk Assessment Forum, 35

Washington, DC; EPA/630/R

- 03/003F. Available fro m: http://www.epa.gov/iris/backgr

- d.htm

. 36

37DRAFT

– DO NOT CITE OR QUOTE 7

- 36 U.S. EPA (Environmental Protection Agency). (2006a) Science policy council handbook: 1

peer review. Third edition. Office of Science Policy, Office of Research a nd Development, 2

Washington, DC; EPA/100/B

- 06/002. Available from: http://www.epa.gov/iris/backgr

- d.htm

. 3

4

U.S. EPA (Environmental Protection Agency). (2006b) A Framework for Assessing 5

Health Risk of Env ironmental Exposures to Children. National Center for Environmental 6

Assessment, Washington, DC, EPA/600/R

- 05/093F. Available from: 7

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=158363

. 8

9

U.S. EPA (Environmental Protection Agency). (2006c) Toxic Release Inventory (TRI) 10

On

- site and Off

- site Reported Disposed of or Otherwise Released (in pounds), for facilities in All 11

Industries, for All Chemicals, U.S., 2003. Available from: http://www.epa.gov/tri . Washington, 12

D.C. 13

14

Vara

- Castrodeza, A; Peréz

- Castrillón, JL; Dueñas

- Laita, A. (2007) Magnetic resonance 15

imaging in methanol poisoning. Clin Toxicol 45:429

– 430. 16

17

Ward, KW; Pollack, GM. (1996) Compa rative toxicokinetics of methanol in pregnant and 18

NP rodents. Drug Metab Dispos 24:1062

– 1070. 19

20

Ward, KW; Perkins, RA; Kawagoe, et al. (1995) Comparative toxicokinetics of methanol 21

in the female mouse and rat. Fundam Appl Toxicol 26:258

– 264. 22

23

Ward, KW; Blum enthal, GM; Welsch, F; et al. (1997) Development of a physiologically 24

based pharmacokinetic model to describe the disposition of methanol in pregnant rats and mice. 25

Toxicol Appl Pharmacol 145:311

– 322. 26

27

Weiss, B; Stern, S; Soderholm, S C; et al . (1996) Developmental neurotoxicity of 28

methanol exposure by inhalation in rats. HEI Research Report Number 73. 29

30

Wentzel, P; Rydberg, U; Eriksson, UJ. (2006) Antioxidative treatment diminishes 31

ethanol

- induced congenital malformations in the rat. Alcohol Clin Exp Res. 30(10):1752

– 60. 32

33

Wentzel, P; Eriksson, UJ. (2006) Ethanol

- induced fetal dysmorphogenesis in the mouse 34

is diminished by high antioxidative capacity of the mother. Toxicol Sci 2(2):416

– 22. 35

36

White, LR; Marthinsen, ABL; Richards, RJ; e t al . (1983) Biochemical and cytological 37

studies of rat lung after inhalation of methanol vapour. Toxicol Lett 17:1

– 5. 38DRAFT

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- 37 1

Wilson, SL; Cradock, MM. (2004) Review: Accounting for prematurity in developmental 2

assessment and the use of age

- adjusted scores. J Ped iatr Pschol 29:641

– 649. 3

4

Wu, AH; Kelly, T; McKay, C; et al . (1995) Definitive identification of an exceptionally 5

high methanol concentration in an intoxication of a surviving infant: methanol metabolism by 6

first

- order elimination kinetics. J Forensic Sci 4 0(2):315

– 320. 7

8

Yuan J. (1993) Modeling blood/plasma concentrations in dosed feed and dosed drinking 9

water toxicology studies. Toxicol Appl Pharmacol. Mar;119(1):131

- 41. PubMed PMID: 10

8470117. 11

12

Zorzano, A; Herrera, E. (1989) Disposition of ethanol and acetal dehyde in late pregnant 13

rats and their fetuses. Pediatr Res 25(1):102

– 6. 14DRAFT

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- 1 APPENDIX A. SUMMARY OF EXTERNAL PEER REVIEW AND PUBL IC

COMMENTS AND DISPOSI TION

[Page intentionally left blank] 1DRAFT

– DO NOT CITE OR QUOTE B

- 1 APPENDIX B. DEVELOPMENT, CALIBRA TION AND APPLICATION OF A

METHANOL PBPK MODEL

This appendix is adapted from a report prepared for the U.S. EPA under contract by the 1

Center for Biological Monitoring and Modeling, Battelle Northwest Laboratories (WA 09, 2

Battelle Project No. 48746, January 31, 2007). 3

B.1. SUMMARY

Th is appendix describes the develop ment , calibrat ion , and approach for application of 4

mouse, rat, and human PBPK models to extrapolate mouse and rat methanol inhalation

- route 5

internal dose metrics to human inhalation exposure concentrations that result in the same internal 6

dose ( HEC). The human oral methanol dose (s) yielding internal dose (s) equivalent to the mouse 7

or rat internal dose at the ( HED) is also presented. 8

A PBPK model was developed to describe the blood kinetics of methanol (MeOH) in 9

mice and humans. The model includ es compartments for lung/blood MeOH exchange, liver, fat, 10

and the rest of the body. To describe blood MeOH kinetics, the model employs two saturable 11

descriptions of MeOH metabolic clearance in mice and rats

, and one first

- order metabolic 12

clearance

, and a first

- order description of renal clearance (from blood) in humans. Renal 13

clearance is a minor pathway and does not appreciably affect MeOH blood kinetics. 14

This model is a revision of the model reported by Ward et al

. (1997), reflecting 15

significant simplif ications (removal of compartments for placentae, embryo/fetus, and 16

extraembrionic fluid) and two elaborations (addition of an intestine lumen compartment to the 17

existing stomach lumen compartment and addition of a bladder compartment which impacts 18

simulati ons for human urinary excretion.) , while maintaining the ability to describe MeOH blood 19

kinetics. The model reported here uses a single consistent set of parameters; the Ward et al

. 20

model employed a number of data

- set specific parameters. Other biokineti c MeOH models that 21

were considered as starting points for the current model also used varied parameters by dataset to 22

achieve model fits to the data. For example, the model of Bouchard et al . (2001) used different 23

respiratory rates and fractional inhalatio n absorbed for different human exposures. 24

The mouse model was calibrated against inhalation

- route blood MeOH kinetic data and 25

verified using intravenous

- route blood MeOH kinetic data. The rat model was calibrated against 26

low

- dose intravenous data and valid ated with inhalation

- route data. The human model was 27

calibrated against inhalation

- route MeOH kinetic data. The models accurately described the 28

inhalation route pharmacokinetics of MeOH. Mouse model simulations of oral

- and i

. v

. - route 29

kinetics compare well to some but not all the experimental data. 30

The MeOH HECs predicted by the model (based on 1,000 ppm inhalation exposure in 31

mice ) were > 1,000 ppm using either blood AUC or C

max as the dose metrics. The MeOH HED 32DRAFT

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- 2 derived by cross

- route extrapolation of th is inhalation

- route HEC was 110 mg/kg

- day

, based on 1

MeOH blood AUC following zero order uptake of MeOH (a constant rate of delivery). Because 2

of the lack of human data from high

- dose exposures, it was not possible to calibrate the model 3

for inhalation exposu res above 1,000 ppm or oral exposures above 110 mg/kg

- day

. However, 4

because the BMD approach was used to estimate an internal experimental animal dose and UFs 5

were applied to the internal dose, the human model can be used to back

- calculate that internal 6

d ose to an RfC and an RfD below 1,000 ppm or 110 mg/kg

- day , respectively. 7

B.2. MODEL DEVELOPMENT

B.2.1. Model Structure

This model is a revision of the model reported by Ward et al

. (1997), reflecting 8

significant simplifications and two elaborations, while maintainin g the ability to describe MeOH 9

blood kinetics in mice, rats, and humans (Figure B

- 1). The kidney, pregnancy and the fetal 10

compartment have been removed. The kidney was lumped with the body compartment because 11

the blood:tissue partition coefficients for t hese tissues were similar. The elaborate time

12

dependent descriptions of pregnancy were removed because analysis of the available 13

pharmacokinetic data indicates that blood MeOH kinetics in NP and pregnant mice are not 14

different enough to warrant separate d escriptions. Because the maternal blood:fetal blood 15

partition coefficients were near 1, there was no need to explicitly model fetal kinetics; they will 16

be equivalent to maternal blood kinetics. Further supporting data exist for ethanol, which is 17

quite si milar to MeOH in its partitioning and transport properties. In rats (Zorzano and Herrara, 18

1989; Guerri and Sanchis 1985), sheep (Brien et al., 1985; Cumming et al., 1984), and guinea 19

pigs (Clarke et al., 1986), fetal and maternal blood concentrations of e thanol are virtually 20

superimposable; maternal to fetal blood ratios are very close to 1, including during late gestation. 21

Also, fetal brain concentrations in guinea pigs (Clarke et al., 1986) were also very similar to the 22

mothers’. 23

In addition to the absol ute maternal

- fetal concentration similarity noted above, it is 24

common practice to use blood concentrations as an appropriate metric for risk extrapolation via 25

PBPK modeling for effects in various tissues, based on the reasonable expectation that any 26

tissue :blood differences will be similar in both the test species and humans. For example, even if 27

the brain:blood ratio was around 1.2 in the mouse or rat, the similar biochemical make

- up of 28

brain tissue and blood in rats and humans leads to the expectation th at the brain:blood levels in 29

humans (which depend on the biochemical make

- up) will also be close to 1.2, and so the relative 30

"error" that might occur by using blood instead of brain concentration in evaluating the dose

31

response in rats will be cancelled ou t by using blood instead of brain concentration in the human. 32

The fact that measured fetal blood levels are virtually identical to maternal levels for methanol 33

(and ethanol) tells us that the rate of metabolism in the fetus is not sufficient to significan tly 34DRAFT

– DO NOT CITE OR QUOTE B

- 3 reduce the fetal concentration versus maternal, and use of a PBPK model to predict maternal 1

levels will give a better estimate of fetal exposure than use of the applied dose or exposure, 2

because there are animal

- human differences in adult PK of MeOH fo r which the model accounts, 3

based on PK data from humans as well as rodents. 4

Figure B

- 1. Schematic of the PBPK model used to describe the inhalation,

oral, and i.v. route pharmacokinetics of MeOH. KAS, first

- order oral 5

absorption rate from stomach; KAI, first

- order uptake from the intestine; KSI, 6

first

- order transfer between stomach and intestine; Vmax and Km and Vmax2 and 7

Km2, Michaelis

- Menten rate constants for high affinity/low capacity and low 8

affinity/high capacity metabolic clearance of MeOH; KLL, alternate first

- order 9

rate constant; KBL, rate constant for urinary excretion from bladder. Both 10

metabolic pathways were used to describe MeOH clearance in the mouse and rat, 11

while a single pathway describes metabolic clearance in the human. 12

A lung compart ment was added to describe delivery of MeOH to blood as a function of

5

ventilation, partitioning, and blood flow rather than the less standard approach used by Ward 6

et al

. (1997). A term was added to the gas uptake equations to describe the fractional resp iratory 7

bioavailability of MeOH. A fat compartment was included because it is the only tissue with a 8

tissue:blood partitioning coefficient appreciably different than unity, and the liver is included 9

because it is the primary site of metabolism. A bladder compartment was added to better 10

describe the kinetics of human urinary data, where the drop in excretion rate is slower than the 11

predicted decline in blood methanol and hence rate of metabolite production. Also, to best 12

describe the observed rat dosimetr y after oral exposure while maintaining metabolic parameters 13DRAFT

– DO NOT CITE OR QUOTE B

- 4 fit to data from inhalation and IV exposure, a small rate of elimination from the intestine (lumen) 1

compartment to feces. (The mouse data could be adequately fit with this rate set to zero, 2

corr esponding to 100% absorption; humans were assumed to have zero fecal elimination, like the 3

mouse.) The final models thus include compartments for fat, liver, the rest of the body, bladder 4

(only used for humans), and lung. The mouse and rat models describe inhalation, oral, and 5

intravenous route dosing and the human model describes inhalation and oral route dosing and the 6

rat model includes a non

- zero rate of fecal elimination. Although there is an endogenous 7

background level of both MeOH and formate (See Section 3.3), the model does not explicitly 8

describe or account for background levels of MeOH or formate. In this analysis, when non

- zero 9

background levels have been measured (in blood), that background was simply subtracted from 10

the concentrations measure d during exposure. However, a zero

- order rate of infusion could be 11

added to the liver, blood, or stomach compartments to mimic background levels if that was 12

considered necessary. 13

MeOH is well absorbed by the inhalation and oral routes, and is readily me tabolized to 14

formaldehyde, which is rapidly converted to formate in both rodents and humans. Although the 15

enzymes responsible for metabolizing formaldehyde are different in rodents (CAT) and humans 16

(ALD) the metabolite, formate, is the same, and the metab olic rates are similar (Clary, 2003). 17

Most of the published rodent kinetic models for MeOH describe the metabolism of MeOH to 18

formaldehyde as a saturable process but differ in the handling of formate metabolism and 19

excretion (Bouchard et al., 2001; Fisher et al., 2000; Ward et al., 1997; Horton et al., 1992)

. 20

Ward et al. (1997) used one saturable and one first

- order pathway f or mice, and Horton et al. 21

(1992 ) applied two saturable pathways of metabolism to describe MeOH elimination in rats. 22

Bouchard et al. (2001) employed one metabolic pathway and a second pathway described as 23

urinary elimination in rats and humans. The need for two saturable metabolic pathways in the 24

mouse model was confirmed through simulation and optimization. High exposure (>2,000 ppm 25

MeOH) and low exposure (1,000 ppm MeOH) blood data could not be adequately fit either 26

visually or by more formal optimization without the second saturable metabolic pathway. The 27

optimization approach and results are found below and in the Additional Mater ials at the end of 28

this appendix. 29

While the PPK model explicitly describes the concentration of methanol, it only 30

describes the rate of metabolism or conversion of MeOH to its metabolites. Distribution and 31

metabolism of formaldehyde is not considered by the model, and this model tracks neither 32

formate nor formaldehyde. (The data that would be needed to parameterize or validate a specific 33

description of either of these metabolites is not available). Since the metabolic conversion of 34

formaldehyde to forma te is rapid (< 1 minute) in all species (Kavet and Nauss, 1990), the MeOH 35

clearance rate may approximate a formate production rate, though this has not been verified. 36

Thus the rate of MeOH metabolism predicted by the model can be used as a dose metric for 37

either or both of these metabolites, but scaling of that metabolic rate metric to humans would 38DRAFT

– DO NOT CITE OR QUOTE B

- 5 require use of an inter

- species scaling factor, (BW human /BW rodent ) 0.25

, to account for the general 1

expectation of slower clearance of the metabolites in humans

. 2

The model was initially coded in acslXtreme v1.4 and updated in acslXtreme v 2.3 3

(Aegis Technologies, Huntsville, AL). Most procedures used to generate this report, except those 4

for the optimization, may be run by executing the corresponding .m files. T he model code 5

(acslXtreme .csl file) and supporting .m files are available electronically and as text in the 6

Additional Materials at the end of this appendix. A key identifying .m files associated with 7

figures and tables in this report is also provided in the Additional Materials. 8

B.2.2. Model Parameters

Physiological parameters such as tissue volumes, blood flows, and ventilation rates were 9

obtained from the open literature (Table B

- 1). Parameters for blood flow, ventilation, and 10

metabolic capacity were scaled as a function of body weight raised to the 0.75 power, according 11

to the methods of Ramsey and Andersen (1984)

. 12

Table B

- 1. Parameters used in the mouse and human PBPK models

Mouse Rat

SD | F344 Human Source

Body weight (kg) 0.03 a 0.275 b 70 Measured/estimated

Tissue volume (% body weight)

Liver 5.5 3.7 2.6

Brown et a l

. 1997 Blood arterial 1.23 1.85 1.98

venous 3.68 4.43 5.93

Fat 7.0 7.0 21.4

Lung 0.73 0.50 0.8

Rest of body 72.9 73.9 58.3 Calculated c

Flows (L/hr/kg 0.75 )

Alveolar ventillation d 25.4 16.4 16.5 Perkins et al

. 1995a; Brown et al

. 1997;

U.S . EPA, 2004 Cardiac output 25.4 16.4 24.0

Percentage of cardiac output

Liver 25.0 25.0 22.7

Brown et al

. 1997

Fat 5.0 7.0 5.2

Rest of body 70.0 68 72.1 Calculated

Biochemical constants e 1 st

order saturable

V

max C (mg/hr/kg 0.75 ) 19 5.0 0 NA 3 3.1

Fitted Km (mg/L) 5.2 6.3 NA NA 23.7

V

max 2C (mg/hr/kg 0.75 ) 3.2 8.4 22.3 NA

Km2 (mg/L) 660 65 100 NA

K1C (BW 0.25 /hr) NA NA 0.0373 0.0342

KLLC (BW 0.25 /hr) f NA NA 95.7 NADRAFT

– DO NOT CITE OR QUOTE B

- 6 Oral absorption

VmASC (mg/hr/kg 0.75 ) 1830 5570 377 Mouse and rat f itted (mouse and human

KMASC assumed = rat); other human

values are those for ethanol from Sultatos et

al. (2004), with VmASC set so that for a

70

- kg person VmAS/KM = the first

- order

constant of Sultatos et al. KMASC (mg/kg) 620 620 620

KSI (hr

- 1 ) 2. 2 7.4 3.17

KAI (hr

- 1 ) 0.33 0.051 3.28

Kfec (hr

- 1 ) 0 0.029 0

Partition coefficients

Liver:Blood 1.06 1.06 0.583 h Ward et al., 1997; Fiserova

- Bergerova and

Diaz, 1986 Fat:Blood 0.083 0.083 0.142

Blood:Air 1350 i 1350 1626 Horton et al., 1992; F iserova

- Bergerova and

Diaz, 1986

Body:Blood 0.66 0.66 0.805 Rodent: estimated; human: Fiserova

Bergerova and Diaz, 1986 (human "body"

assumed = muscle) Lung:Blood 1 1 1.07

Bladder time

- constant

(KBL, hr

- 1 ) j NA 0.564 0.612 Fitted (human)

Inhalation fractional

availability (%) 0.665 0.20 0.866 k Rodent: fitted; human

Ernstgard et al., 2005

NA

- Not applicable for that species

a Both sources of mouse data report body weights of approximately 30 g

b The midpoints of rat weights reported for each study wa s used and ranged from 0.22 to 0.33 kg

c The volume of the other tissues was subtracted from 91% (whole body minus a bone volume of approximately 9%) to get

the volume of the remaining tissues

d Minute ventilation was measured and reported for much of the d ata from Perkins et al

. (1996) and the average alveolar

ventilation (estimated as 2/3 minute ventilation) for each exposure concentration was used in the model. When ventilation

rates were not available, a mouse QPC (Alveolar Ventilation/BW 0.75 ) of 25.4 wa s used (average from Perkins et al., 1995 a ).

The QPC used to fit the human data was obtained from U.S. EPA (2004). This QPC was somewhat higher than calculated

from Brown et al

. (1997) (~13 L/hr/kg 0.75 )

e V

max , Km, and V

max 2, Km2 represent the two saturable metabolic clearance processes assumed to occur solely in the liver.

The V

max used in the model = V

max C (mg/kg 0.75 ·hr)  BW 0.75 . K1C is the first

- order loss from the blood for human

simulations that represents urinary elimination. Allometric scaling for fir st

- order clearance processes was done as previously

described (Teeguarden et al ., 2005); The K1 used in the model= K1C / BW 0.25

f KLLC

– alternate human first

- order metabolism rate (used only when V

max C = V

max 2C = 0)

g Human oral simulations used a zero ord er dose rate equal to the mg/kg

- day dose

h Human liver:blood estimated from correlation to (measured) fat:blood, based on data from 28 other solvents

I Rat partition coefficient used for mice as done by Ward et al. (1997)

j KBL

– a first

- order rate constant f or clearance from the bladder compartment, used to account for the difference between

blood kinetics and urinary excretion data as observed in humans

k For human exposures, the fractional availability was from Šedivec et al. (1981), corrected for the fact t hat alveolar

ventilation is 2/3 of total respiration rate

Mouse model partition coefficients were used as reported (liver, fat, blood:air) or 1

estimated (lung, body). The mouse body compartment partition coefficient was set 2

approximately equal to the mea sured value for muscle (Ward et al., 1997). The mouse lung 3

partition coefficient was assumed to be 1.0, similar to the liver partition coefficient. This 4

parameter has no numerically significant impact on modeled blood dose metrics. 5

Human partition coeff icients were reported by Horton et al. (1992), but were in fact 6

measured in rat tissues. The reported rat fat partition coefficient was considerably closer to unity 7

than reported for MeOH or ethanol by other researchers (Ward et al

. , 1997; Pastino and Con olly, 8DRAFT

– DO NOT CITE OR QUOTE B

- 7 2000 ) and assumed to be in error. Human partition coefficients were obtained from Fiserova

1

Bergerova and Diaz (1986). 2

B.2.3. Mouse Model Calibration

B.2.3.1. Inhalation

- Route Calibration

For purposes of conducting interspecies ext rapolations of MeOH dosimetry, the 3

inhalation route was the most important route requiring calibration for the mouse model. The 4

critical endpoint and NOEL, which are the basis for the HEC estimation, are from inhalation

5

route studies. The ability to pre dict blood MeOH concentrations from inhalation exposures was 6

therefore a priority. Pharmacokinetic data from other routes, i.v. and oral, were used to verify 7

clearance terms derived by fitting to the inhalation data or to estimate a MeOH oral uptake rate 8

constants. Holding other parameters constant, the mouse PBPK model was calibrated against 9

inhalation

- route blood pharmacokinetic data (Figure B

- 2) by fitting five parameters: Michaelis

10

Menten constants for one high affinity/low capacity and one low

- affinit y high

- capacity enzyme 11

and the inhalation fractional availability term. 12

Figure B

- 2. Model fits to data sets from GD6, GD7, and GD10 mice for

7

- hour inhalation exposures to 1,000

– 15,000 ppm MeOH. Maximum

concentrations are from Table 2 in Rogers et al

. (1993). The complete data set for

GD7 mice exposed to 10,000 ppm is from Rogers et al

. (1997) and personal

communication (Additional Materials). Symbols are concentration means of a

minimum of n = 4 mice/concentration. Default ventilation rates (Table B

- 1) were

used to simulate these data.

For these mouse simulations, pulmonary ventilation was set to 25.4 (L/hr/kg 0.75 ), the 13

average value measured by Perkins et al

. (1995 a ), which is similar to the value of 29 (L/hr/kg 0.75 ) 14

reported in Brown et al

. (1997). Where ventilation rates were reported for individual exposure 15DRAFT

– DO NOT CITE OR QUOTE B

- 8 concentrations by Perkins et al

. (1995 a ), they were used directly in the model and a notation was 1

made in the figure legend. Reported ventilation rates varied from 592 to 857 L/kg x 8 hr, 2

dep ending on exposure concentration (Perkins et al

. , 1995 a ). Adjusting these values to 2/3 total 3

(for alveolar ventilation) and allometrically scaling by BW 0.75 , values used in the model for these 4

exposures ranged from 20.5 to 29.7 (L/hr/kg 0.75 ) (See Table B

- 1). A fractional availability of 5

73% of alveolar ventilation was visually optimized to best describe the inhalation

- route blood 6

MeOH pharmacokinetic data. This percentage of uptake for inhalation exposures is similar to 7

values reported for other alcohol s in rodents (Teeguarden et al

. , 2005), but considerably lower 8

than the value reported by Perkins et al

. (1995 a ) of 126% of alveolar ventilation (85% of total 9

ventilation). 10

The calibrated model predicted blood MeOH concentration time

- course agreed well wi th 11

measured values in adult mice in the inhalation studies of Rogers et al

. (1997, 1993) (Figure B

12

2), and Perkins et al

. (1995 a ), as well as in NP and early gestation (GD8) mice of Dorman et al

. 13

(1995) (Figure B

- 3). Parameter values used in the calibrate d model are given in Table B

- 1. 14

Figure B

- 3. Simulation of inhalation exposures to MeOH in NP mice from Perkins

et al

. (1995 a ) (8

- hour exposures) and Dorman et al

. (1995), (6

- hour exposures). Data 15

points represent measured blood MeOH concentrations and lines represent PBPK model 16

simulations. Note: data was obtained using DigitizIt (SharIt! Inc. Greensburg, PA) to 17

digitize data from Figure 2 of Perkins et al. (1995 a ) and Figure B

- 2 from Dorman et al., 18

(1995). Default ventilation rates (Table B

- 1) were used to simulate the Dorman data. The 19

alveolar ventilation rate for each data set from Perkins et al. (1995) was set equal to the 20

measured value reported in that manuscript. For the 2,500, 5,000, and 10,000 ppm exposure 21

groups, the alveolar ventilation ra tes were 29, 24, and 21 (L/hr/kg0.75), respectively. The 22

cardiac output for these simulations was set equal to the alveolar ventilation rate. 23DRAFT

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- 9 B.2.3.1. (should be B.2.3.2) Oral

- Route Calibration

The mouse model was calibrated for the oral route by fitting the rate constants for oral 1

uptake of MeOH. Calibration of the oral route was not required for interpretation of the critical 2

toxicology studies. This exercise was undertaken to estimate the rate constants for oral uptake so 3

it could be used to make dose

- route e xtrapolations for calculating human oral

- route exposures 4

equivalent to mouse exposures at the NOEL. 5

Ward et al

. (1997) described MeOH uptake as the sum of a fast and slow process (two 6

rate constants), with a fraction of the administered dose attributed t o each process. The rate 7

constants and the fraction of the dose attributed to each process were varied to describe oral

8

route blood MeOH kinetics for each GD. For instance, the fraction of the total oral dose 9

assigned to the fast absorption process varie d from 54 to 71%, depending on the data set. An 10

alternative approach with uptake attributed to stomach and intestine, which allows for greater 11

flexibility in fitting the data (Staats et al., 1991), was compared to a simpler one utilizing a single 12

rate of uptake. In both the current model and the model of Ward et al. (1997), orally ingested 13

MeOH was assumed to be 100 % absorbed. 14

Initially, a single oral absorption rate constant (KAS, hr

- 1 ) was fitted to oral

- route blood 15

MeOH kinetics reported by Ward et al

. (1997, 1995). Using these data, an average KAS 16

(0.62 hr

- 1 ) was estimated that provides adequate fits to MeOH blood kinetics following 2,500 17

mg/kg dose in NP and GD18 mice and 1,500 mg/kg in GD8 mice up to ~8 hours. At later time 18

points, however, a mode l using a single oral uptake rate constant consistently under predicts 19

blood concentrations of MeOH (results not shown). Fits were improved by using the two 20

compartment GI tract model (Figure B

- 4). However, when fitting the oral data in rats, it was 21

foun d that the fits were significantly improved if the uptake from the stomach was treated as a 22

saturable process. V

max (VMASC) was scaled as BW 0.75 , as is done for other V

max s, and the Km 23

(KMASC) was scaled as BW 1 to reflect that the variable used is the tot al amount in the stomach, 24

whose volume is expected to scale with BW 1 . For the mouse, model fits were not significantly 25

improved when KMASC was allowed to vary (change from the value fitted to rats, 1830 mg/kg), 26

so it was kept at the rat value. 27

Using the t wo

- compartment oral absorption model and adjusting only the absorption 28

parameters resulted in a good fit to the lower oral dose (1,500 mg/kg) (Dorman et al., 1995), but 29

consistently under

- prediction of the 2,500 mg/kg oral dosing blood levels (Ward et al., 1997). 30

When the metabolic constants (V

max C values) were decreased, the data from the higher dose 31

were fit, but the fit of the data for the 1,500 mg/kg dose was lost (see Additional Materials, 32

Figure B

- 19). Also, when using the lower clearance required to fit the data of Ward et al. (1997), 33

the inhalation data of Rogers et al. (1993) could no longer be fit by the model (see Additional 34

Materials, Figure B

- 20). The two

- compartment GI tract approach (with parameters that better fit 35

the low dose data) was ret ained in the model and used for all final mouse oral route simulations. 36

37DRAFT

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- 10 Figure B

- 4. Oral exposures to MeOH in pregnant mice on GD8 (Dorman et al

. , 1995)

or NP and GD18. Data points represent measured blood concentrations and lines represent 1

PBPK mode l estimations for NP mice. 2

Source: Ward et al

. , ( 1997)

.

B.2.3.1. (B2.3.3) Intravenous Route Simulation

The parameterization of MeOH clearance (high

- and

- low affinity metabolic pathways) 1

was verified by simulation of data sets describing the intravenous

- route pharma cokinetics of 2

MeOH. MeOH blood kinetics data in NP mice are only available for a single i.v. dose of 3

2,500 mg/kg (Ward et al

. , 1997). MeOH blood kinetics are also reported in GD18 mice 4

following administration of a broader range of doses: 100, 500, and 2, 500 mg/kg. Because 5

MeOH kinetics appear similar for NP and pregnant mice after administration of 2,500 mg/kg 6

prior to 20 hours, the model is expected to fit data for both pregnant and NP mice using the same 7

set of parameters, and hence, data for both life stages were used to verify metabolic clearance of 8

MeOH. 9

Initial blood concentrations of MeOH following i.v. administration were not proportional 10

to administered dose in the data from Ward et al

. (1997). Initial concentrations were 11

approximately 1.5

- fold lower in the 100 mg/kg dose group than expected if a dose

- independent 12

volume of distribution (V D ) is assumed (Figure B

- 5A). Initial blood concentrations were, 13

however, proportional to administered dose between 2,500 and 500 mg/kg. Two possible 14

explanatio ns were then considered: 15

1) the V D , which is not impacted by any other PBPK parameters and is only determined by 16

the biochemical partitioning properties of MeOH, is twofold lower at 100 mg/kg than at 17

the higher concentrations, while the V D at 500 and 2,50 0 mg/kg are exactly as predicted 18

by the PBPK model without adjustment; or 19DRAFT

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- 11 2) a dilution error occurred during preparation of the 100 mg/kg dosing solution used by 1

Ward et al. (1997). 2

Figure B

- 5. Mouse intravenous route MeOH blood kinetics. A) MeOH was 3

infused over 1.5 minutes into female CD

- 1 mice at target doses of 100 (circles), 4

500 (triangles) or 2,500 (squares) mg/kg. Mice were NP, GD9 or GD18 at the 5

time of dosing. Data points represent measured blood concentrations and lines 6

represent PBPK model simulations. B) Comparison of the 100 mg/kg dose data 7

(points) and PBPK model simulations assuming a 100 mg/kg dose as reported 8

(solid line) or to a presumed 200 mg/kg dose (dashed lines). Note: the 24

- hour 9

time point data from the 500 mg/kg NP an d GD 9 mice are below the reported 10

detection limit (2 μg/ml) and so are not shown. 11

Source: Adapted from Ward et al. ( 1997)

.

To account for this unexpected nonproportionality, Ward et al. (1997) used higher 3

partition coefficients for placenta and embryonic fluid and lower V

max for the metabolism of 4 

### A

BDRAFT

– DO NOT CITE OR QUOTE B

- 12 MeOH for the 100 and 500 mg/kg doses than for the 2,500 mg/kg dose. These adjustments to 1

partition coefficients effectively change the volume of distribution. However, the PBPK model 2

obtained with measured part ition coefficients and otherwise calibrated to inhalation data, as 3

described above, was capable of simulating both the 500 and 2,500 mg/kg data without adjust

4

ment or varying parameters between those 2 doses. Further, the data at the nominal dose of 100 5

m g/kg could also be adequately fit without other parameter adjustment simply by simulating a 6

dose of 200 mg/kg (dotted line, Figure B

- 5B). If the relative difference in the V D at 100 mg/kg 7

was not a round number and/or the apparent value at 500 mg/kg was intermediate between 100 8

and 2,500 mg/kg, the possibility of a dose

- related variation in this or other parameters would be 9

given more weight. Also it seems unlikely that the volumes of distribution would be different in 10

these animals solely based on diffe ring exposure concentrations. But the far simpler and more 11

likely explanation for the observed pattern of V D seems to be a dosing error (2). Therefore, a 12

single set of parameters was retained which describe the 2,500 and 500 mg/kg doses rather than 13

adjus t parameters to fit a data set (the 100 mg/kg dose group) that appeared inconsistent and may 14

be the result of a simple experimental error rather than attributable to a dose dependence. 15

Thus, h igh

- (2

, 500 mg/kg) and mid

- dose (500 mg/kg) intravenous

- route p harmacokinetic 16

data were used to validate the parameters calibrated from the inhalation studies for the metabolic 17

clearance of MeOH. Metabolic constants reasonably predict blood MeOH kinetics following a 18

2

, 500 mg/kg dose in NP animals until 12 hours poste xposure , but under predict blood MeOH in 19

GD 9 and GD 18 mice a t 8 hours of exposure and beyond, and under

- predict levels in both NP and 20

pregnant mice at 15 hours and beyond

. At th is high

- dose , where blood kinetics of MeOH were 21

reported in NP, GD9, and GD18 mice, the data for the GD18 mice was inconsistent with the 22

GD9 and NP animals . The GD9 data at 12 hours appears inconsistent with the NP data, but then 23

the 2 are nearly identical again at 15 hours, so it is not clear if that difference at 12 hours is real 24

or just due to experimental variability. Blood levels of MeOH were ~500 mg/L in GD18 mice at 25

24 hours , but were non detec table after 18 hours in the other groups (detection limit 2 mg/L). 26

Blood concentrations were accurately predicted following administrat ion of 500 mg/kg MeOH 27

( Figure B

- 5 A ). The model predictions did not match the 100 mg/kg data unless one assumed an 28

error in dose preparation, as described above ( Figure B

- 5 B ). The calibration of the MeOH 29

PBPK model is consistent with both the available in halation and oral

- route data. 30

B.2.3.1. (B2.3.4) Total Methanol Metabolic Clearance

Quantifying production of formaldehyde following MeOH exposure for use as an 31

alternative dose metric is of particular interest because fo rmaldehyde is also undergoing toxicity 32

assessment. However, it is important to understand that because the model was developed to 33

describe blood MeOH kinetics, metabolism of MeOH to neither formaldehyde nor formate is 34

specifically described; the model tra cks neither of these metabolites. While the metabolic 35

clearance of MeOH described by the model may be presumed to equate with formaldehyde 36DRAFT

– DO NOT CITE OR QUOTE B

- 13 production, this metabolic flux simply leaves the computational model system without specific 1

attribtution. Since t he metabolic conversion of formaldehyde to formate is rapid in all species 2

(< 1 minute) (Kavet and Nauss, 1990), the MeOH clearance rate may approximate a formate 3

production as well as a formaldehyde production rate, though this has not been verified. 4

Thus , production of formaldehyde or formate following exposure to MeOH can only be 5

estimated by summing the total amount of MeOH cleared by metabolic processes. If used, this 6

metric of formaldehyde or formate dose should be adjusted by an inter

- species scalin g factor 7

(SF = [BW human /BW rodent ] 0.25 ) to adjust for expected species differences in the clearance of these 8

two metabolites (this is a default factor based on the generally accepted assumption that total 9

metabolism scales as BW 0.75 and hence clearance per BW scales as 1/BW 0.25 ). The rate of 10

MeOH clearance may roughly be equated to the total amount of metabolites produced. Values of 11

total MeOH clearance as a function of exposure in mice and humans are presented in the 12

Additonal Materials (Tables 7

- 9). 13

B.2.3.1. (B.2 .3.5) Formal Optimization of Mouse Model Parameters

Formal optimization of five parameters (inhalation fractional availability and the V

max 14

and Kms for high and low affinity MeOH metabolism) was attempt ed using optimization 15

routines in acslXtreme v2.01.1.2. Under the best circumstances, formal optimizations offer the 16

benefit of repeatability and confirmation that global optima have not been significantly missed 17

by user

- guided visual optimization. Incor porating judgments regarding the value of specific data 18

sets, while possible when visually fitting, is more difficult when using optimization routines. 19

This is an important distinction between these approaches for this modeling exercise. 20

The mouse inhalati on route NOEL was less than 1,000 ppm MeOH. The model is 21

calibrated against inhalation

- route data because of the importance of this exposure route in the 22

assessment. Unfortunately, the vast majority of the MeOH data came from much higher 23

exposure concent rations. As expected, various attempts at formal optimization lead to improved 24

fits for some but never all data sets. This is to be expected when there is significant variability in 25

the underlying data. Various data

- weighting schemes were included to im prove overall 26

optimization while maintaining a good fit to the lowest concentration (1,000 ppm) data. In the 27

end, formal optimization provided no significant improvement over the fractional availability 28

and metabolic parameter values obtained by visual op timization, so these were retained in the 29

final version of the model. 30

Further details on the approach and results from the formal optimization are found in the 31

Additional Materials in outline format with supporting figures. More complete documentation 32

was not developed because the products of the optimizations were not used in the final model. 33

The documentation is intended only to demonstrate that appropriate optimizations were 34

conducted and what the results of those optimizations were. 35DRAFT

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- 14 B.2.4. Mouse Model Sensitivity Analysis

An evaluation of the importance of selected parameters on mouse model estimates of 1

blood MeOH AUC was performed by conducting a sensitivity analysis using the subroutines 2

within acslXtreme. Files for reproducing the sensitivity analysis are available in the model as 3

described in the additional materials. The analysis was conducted by measuring the change in 4

model output corresponding to a 1% change in a given model parameter when all other 5

parameters were held fixed. A normalized sensitivity coefficient of 1 indicates that there is a 6

one

- to

- one relationship between the fractional change in the parameter and model output; values 7

close to zero indicate a small effect on model output. A positive value for the normalized 8

sensitivity coefficient indicates that the output and the corresponding model parameter are 9

directly related while a negative value indicates they are inversely related. 10

Sensitivity analyses were conducted for the inhalation and oral rou tes. The 11

inhalation

- route analysis was conducted under the exposure conditions of Rogers and Mole 12

(1997) and Rogers et al

. (1993), 7

- hour inhalation exposures at the NOEL concentration of 13

1,000 ppm. The oral route sensitivity analysis was conducted for a n oral dose of 1,000 mg/kg. 14

Parameters with sensitivity coefficients less than 0.1 are not reported. The parameters 15

with the largest sensitivity coefficients for the inhalation route at 1,000 ppm were pulmonary 16

ventilation, V

max C, and partitioning to the body compartment (Figures B

- 6 [metabolism] and B

- 7 17

[flows and partition coefficients]). MeOH AUC was also sensitive to KM2 and V

max C. The 18

sensitivity coefficient for pulmonary ventilation increases from 1 to ~1.75 during the exposure 19

period as metabolism begins to saturate. The sensitivity coefficient is 1 for concentrations 100 20

ppm MeOH or less or when hepatic clearance is nonlimiting. 21

Oral

- route mouse blood MeOH AUC was sensitive to the rate constants for uptake. 22

Blood AUC was most sensitive to the fi rst

- order rate constant for uptake from the stomach, KAS, 23

during the first hour after exposure, becoming less important over time (Figure B

- 8). Blood 24

MeOH AUC was also modestly sensitive to KAI, and KSI, the rate constants for uptake from the 25

intestine an d transfer rates between compartments, respectively. 26

27DRAFT

– DO NOT CITE OR QUOTE B

- 15 -4 -3.5

-3 -2.5

-2 -1.5

-1 -0.5 0 0.5

1

0 2 4 6 8 10 12 14 16 Time (hr)Sensitivity coefficient Km

VmaxC Vmax2C Km2

Figure B

- 6. Mouse model inhalation route sensitivity coefficients for metabolic

parameters. Sensitivity coefficients calculated for an exposure of 1,000 ppm MeOH are 1

reported for blood MeOH AUC. Note: Km, Vmax refer to the high

- affinity, low

- capacity 2

pathway and Km2, Vmax2 refer to the low

- affinity, high

- capacity pathway. 3

-1 -0.5 0 0.5

1 1.5

2 2.5

3 3.5

4

0 2 4 6 8 10 12 14 16 Time (hr)Sensitivity coefficient

QPC

PR QLC QCC

Figure B

- 7. Mouse model inhalation route sensitivity coefficients for flow rates (QCC:

cardiac output; QPC: alveolar ventilat ion), and partitioning to the body (PR)

compart ment are reported for blood MeOH AUC. Sensitivity coefficients calculated for 1

an exposure to 1,000 ppm MeOH. 2DRAFT

– DO NOT CITE OR QUOTE B

- 16 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

1

0 2 4 6 8 10 12 14 16 Time (hr)Sensitivity coefficient

VmASC

KMASC KSI

KAI

Figure B

- 8. Mouse model sensitivity coefficients for oral exposures to MeOH. The 1

sensitivit y of blood MeOH AUC to oral absorption rate constants (KAS: stomach; KAI: 2

intestine; KSI: transfer between compartments) is reported. 3

B.2.5. Mouse Drinking Water Ingestion Pattern

To simulate exposures of mice via drink ing water under bioassay conditions, an ingestion 1

pattern first used by Keys et al. (2004) , based on data from Yuan (1993) was used. The pattern 2

specifies a fraction of percent of total daily ingestion consumed in each half

- hour interval. The 3

first inte rval was shifted to correspond to the beginning of the active (dark) period, for 4

consistency with patterns used for humans and rats. A Table function was used in acslXtreme to 5

interpolate an instantaneous rate between the measured (30

- min) values, with no rmalization so 6

that the 24

- hour integral equals 100%. The daily pattern is shown in Figure B

- 9A and the 7

resulting blood concentration for a mouse exposed for 6 days per week (2100 mg/kg) is shown in 8

Figure B

- 9B. 9

10

11DRAFT

– DO NOT CITE OR QUOTE B

- 17 1

2

Figure B

- 9. Mouse daily drinking wate r ingestion pattern (A) and resulting

predicted blood concentration for a 6 d/wk exposure (B). Mouse drinking

water exposures were simulated by multiplying the fractional rate (1/h) as a

function of clock time by the daily total dose ingested (mg) to obt ain a rate of

addition of methanol into the stomach lumen compartment (mg/h).

Source: Yuan (1993); Keys et al. (2004)DRAFT

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- 18 B.2.6. Rat Model Calibration

The model was initially calibrated

- to

- fit data from intravenous, inhalation, an d oral 1

exposures in Sprague

- Dawley (SD) rats using the 100 and 2500 mg/kg intravenous (IV) data 2

provided in the command file of Ward et al. (1997). Holding other parameters constant, the rat 3

PBPK model was calibrated against the Ward et al. (1997) IV

- rout e blood pharmaco kinetic data 4

(Figure B

- 10) by fitting Michaelis

- Menten constants for one high affinity/low capacity and one 5

low

- affinity, high

- capacity enzyme, using the optimization routines in acslXtreme v2.3. Also 6

shown for comparison in Figure B

- 10A ar e the 100 mg/kg IV data of Horto n et al. (1992), 7

obtained using Fischer 344 (F344) rats (data extracted from figures using DigitizIt), with a model 8

simulation (heavy red line) which differs from that for the SD rat only due to the predicted effect 9

of kno w body weight differences. While the fit to the Ward et al. data for SD rats is excellent, 10

especially for the lower dose, the rate of clearance is over

- predicted for the F344 rat when 11

parameters fit to SD rat data are used. The 100 mg/kg IV data, with an alternate simulation for 12

the F344 rat obtained with distinct parameters (see below) is expanded in Figur B

- 10B, 13

emphasizing the difference in clearance between the two strains. 14

We then attempted to fit the model to the inhalation data of Horton et al. (19 92) by 15

adjusting only the inhalation fractional uptake (FRACIN). The results, shown in Figure B

- 11A, 16

are clearly poor. While the model does match the uptake portion of the inhalation data for the 17

1200 and 2000 ppm exposures, it under

- predicts the peak co ncentration reached at 200 ppm. 18

Further, the post

- exposure clearance predicted by the model is much more rapid than indicated 19

by the data, as occurred with the IV kinetics (Figure B

- 10). (Since the peak concentration for the 20

2000 ppm inhalation exposure actually occurred at 7 hr, we also simulated a 7

- hr exposure, 21

shown by the thin black line. The result indicates that the data are more consistent with and 22

better predicted by the longer exposure duration, but clearance is still over

- predicted post

23

exposu re.) Therefore we concluded that the data show a clear strain difference in metabolism, 24

and should support at least a partially independent set of parameters for SD and F344 rats. 25

We then combined the 100 mg/kg IV and inhalation data of Horton et al. (for F344 rats) 26

and attempted to simultaneously identify the four metabolic parameters (Vmax and Km for two 27

pathways) and FRACIN. However when this was done the resulting values for the two Km’s 28

were ~ 90 ± 50 mg/L and 70 ± 40 mg/L (Km and Km2, respectively), which are clearly 29

indistinguishable from a statistical standpoint. If instead the Km’s were fixed at the more distinct 30

values identified from the SD rat IV data (6.3 and 65 mg/L), the optimization routine tended to 31

set the Vmax associated with the lower Km to zero. Thus the F344 rat data of Horton et al. 32

(1992) appear to be most consistent with a single metabolic pathway, even though the observed 33

concentrations spanned almost 2 orders of magnitude. Therefore those data (including the 100 34

mg/kg IV data) were simultaneously fit by adjusting a single Vmax and Km, along with the 35DRAFT

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- 19 inhalation fraction, FRACIN, with the second metabolic pathway set to zero (Figures B

- 10B and 1

B

- 11B). 2

Figure B

- 10. NP rat i.v.

- route methanol blood kinetics

. MeOH was infused into: female 3

Sprague

- Dawley rats (275 g) at target doses of 100 (open squares and thin black 4

line) or 2,500 (filled squares and heavy blue line) mg/kg; or (filled diamonds and 5

heav y red lines) male F

- 344 rats (220 g) at target doses of 100 mg/kg. Data poi nts 6

represent measured blood concentrations and lines represent PBPK model 7

simulations with (A) metabolic parameters fit to the Sprague

- Dawley rat data or (B) 8

metabolic parameters fit to F

- 344 data (see text for further details). 9

Source: Ward et al

. (1997; squares); Hor to n et al. ( 1992; diamonds).DRAFT

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- 20 Figure B

- 11. Model fits to data sets from inhalation exposures to 200

(triangles), 1,200 (diamonds), or 2,000 (squares) ppm MeOH in male F

- 344

rats. (A) Model fits with metabolic parameters set to values obt aine from IV data

for Sprague

- Dawley rats, with only the inhaled fraction (FRACIN) adjusted. (B)

Model fits obtained by fitting metabolic parameters (Vmax and Km) for a single

pathway, along with FRACIN, to these data as well as the 100 mg/kg IV data

from F

- 344 rats (Figure B

- 9B).

Symbols are concentrations obtained using DigitizIt!. Lines represent PBPK

model fits. As the 7

- hour data point at 2,000 ppm is higher than the 6

- hour data

point (more evident on a linear scale) and appears more consistent with a 7

- hour

exposure, a model simulation for a 7

- hour exposure at 2,000 ppm is also shown

(lighter line).

Source: Horton et al. (1992)DRAFT

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- 21 Model simulations of the F344 rat data with the F344

- specific parameters are shown in 1

Figure B

- 10B (heavy red line) and Fig ure B

- 11B. Unfortunately we were unable to 2

simultaneously fit the inhalation data for all exposure levels, although a wide range of metabolic 3

saturation (Km) values were tested. We could obtain a better fit of the high

- concentration data 4

by constraining FRACIN to a higher value, for example, but then the fits to the lower 5

concentration data were compromised (not shown). Examining Figure 2 of Horton et al. (1992), 6

the experimental variability (indicated by the error bars) on the 2000 ppm data was much lar ger 7

than the 200 or 1200 ppm data, and as indicated by the simulations in Figure B

- 11 here, there is 8

at least the appearance that the exposure was actually for 7 hr instead of 6 hr. (To be clear, the 9

2000 ppm data were used in the optimization with the du ration of inhalation set to 6 hr, but the 10

routine selected parameters which only poorly fit those data.) Since our greatest concern is in 11

predicting dosimetry at lower exposure levels, near to the points of departure, we decided to 12

retain the fits shown h ere. The corresponding paramete r s are listed in Table B

- 1. The fractional 13

absorption (20%) was lower than that estimated for mice (66.5%), but Perkins et al. (1995) also 14

found lower fractional absorption of inhaled methanol in rats vs. mice. 15

Finally, fir st

- order oral absorption parameters were first fit to the lower dose (100 mg/kg) 16

oral absorption data reported by Ward et al. (1997), using the optimization routines in 17

acslXtreme v2.3 (Figure B

- 12, heavy/solid lines). (Since the animals used were SD rats, the SD

18

specific metabolic parameters were used.) While the fit to the low

- concentration data was quite 19

good (Figure B

- 12, lower panel), the fit to the 2500 mg/kg data (Figure B

- 12, upper panel) 20

exhibited a much faster and higher peak than shown by the da ta. Even when the model was fit to 21

both the high

- and low

- concentration data simultaneously, the fit to the high

- concentration data 22

could not be significantly improved without completely degrading the low

- concentration fit (not 23

shown). Also note that the 2500 mg/kg linear simulation completely over

- estimates all the data 24

points; i.e., the area

- under

- the

- curve for this dose is higher than indicated by the data, indicating 25

that the assumption of 100% absorption is not valid. Therefore, an alterna tive mode l using a 26

saturable (Michaelis

- Menten) equation for absorption from the stomach and fecal elimination 27

(linear term) from the intestine was considered (thin lines) and found to significantly improve the 28

high

- concentration simulation, with a nearly identical fit the low

- concentration data. While 29

methanol absorption is not known to be regulated by transporters or other processes that would 30

give rise to rate saturation, it is clear form the discrepancy between the linear model and the 2500 31

mg/kg data that upta ke is slower than predicted by such a model and its use would lead to an 32

over

- prediction of internal concentrations. Therefore parameters for the saturable uptake model 33

are reported in Table B

- 1 and the KMASC applied to mice and humans. Note that since t he 34

saturation constant corresponds to a fairly large dose (620 mg/kg), the model is still effectively 35

linear at low

- to moderate dose rates. 36DRAFT

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- 22 Figure B

- 12. Model fits to data sets from oral exposures to 100 (squares) or 2,500

(diamonds) mg/kg MeOH in f emale Sprague

- Dawley rat (Expanded scale in lower 1

panel). Symbols are concentrations obtained from the command file. The thick lines 2

represent PBPK model fits using a linear (first

- order) equation for absorption from 3

the stomach compartment with no fecal elimination, while the thin lines use a 4

Michaelis

- Menton equation with a small fraction eliminated in the feces. All other 5

GI rates, including absorption from the intestine, are first order. 6

Source: Ward et al.(1997).DRAFT

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- 23 B.2.7. Rat Model Simulations

A range of adverse developmental effects was noted in rat pups exposed to methanol 1

throughout embryogenesis (NEDO, 1987). SD rats were exposed in utero over different periods 2

of pregnancy and as neonates via inhalation or in drinking water. Inhalation exposures to 3

methanol were carried out for 18

– 22 hours, depending on the exposure group. Simulations of 4

predicted C

max , AUC, and total metabolized from 22

- hour exposures to 500, 1,000, and 5,000 5

ppm MeOH are shown in Figure B

- 13. Simulations of oral exposures of SD ra ts to 65.9, 624.1, 6

or 2,177 mg/kg

- day (500, 5,000, 20,000 ppm in drinking water), daily dose estimations from the 7

study of Soffritti et al. (2002), based on measured water consumption, kindly provided by 8

Cynthia Van Landingham, Environ International, Rusto n, Louisiana, are shown in Figure B

- 13. 9

Although the exposures in these studies are to rats over long periods and in some cases exposures 10

of the newborn pups, the model simulations are to NP adult rats only, using the dose

- group 11

specific average body weig hts of 0.33

- 0.34 kg BW from the study of Soffritti et al. (2002) and do 12

not take into account changes is body weight or composition. These simulated values are 13

presumed to be a better surrogate for and predictor of target

- tissue concentrations in developi ng 14

rats, and the corresponding estimated human concentrations a better predictor of developmental 15

risk in humans than would be obtained using the applied concentration or dose and default 16

extrapolations. The logic here is simply that the ratio of actual t arget tissue concentration (in the 17

developing rat pup or human) to the simulated concentration in the NP adult is expect ed to be the 18

same in both species and hence, that proportionality drops out in calculating a HEC. 19

Figure B

- 13 depicts simulations run t o determine internal doses for 22 hours/day 20

inhalation exposures at 500, 1,000, or 2,000 ppm. Simulation results for continuous inhalation 21

exposures are shown for contrast. The simulations show that for all but the highest dose (2

, 000 22

ppm) steady state i s reached within 22 hours

, and that “periodicity,” where the concentration 23

time course is the same for each subsequent day, is reached by the 2nd day of exposure. At 24

2,000 ppm, however, steady state is not reached until after 48 hours for the continous ex posure

. 25

Therefore, the C

max , 24

- hour AUC and amount metabolized per day (AMET) were by simulating 26

22 hours/day exposures for 5 day s and calculating values of AUC and AMET over the last day 27

(24 hours) of that period. 28

29DRAFT

– DO NOT CITE OR QUOTE B

- 24 0 10 20 30 40 50

0 12 24 36 48 60 72 84 96

Time (hr)Blood MeOH (mg/L)

500 ppm 2,000 ppm

1,000 ppm

Exposure

concentration

(ppm) C

max (m g/L) AUC

(hr·mg/L

) AMET

(mgEq)

500 3.6 79 17.6

1,000 10.6 227 34.8

2000 48.5 968 67.2

Figure B

- 13. Simulated Sprague

- Dawley rat inhalation exposures to 500, 1,000, or

2,000 ppm MeOH. Rat BW was set to 0.33 kg. Simulations are shown for both 1

con tinuous (thin, dashed/dotted lines in plot) and 22 hours/day exposures (thick, solid lines 2

in plot). Cmax, AUC, and amount metabolized (AMET) are determined from the 22 3

hour/day simulations, run for a total of 5 days (120 hours), with the AUC and AMET 4

cal culated for the last 24 hours of the simulation. 5

1

Figure B

- 14 depicts simulations run to mimic a single oral exposure, treated as a 2

continuous infusion for 12 hours (assuming 12

- hour period when rats are awake and active). 3

Total AUC and AMET and AUC24 and AMET24 for the first 24 hours after start of exposure 4

were calculated. 5DRAFT

– DO NOT CITE OR QUOTE B

- 25 1

2

Figure B

- 14. Simulated rat oral exposures of Sprague

- Dawley rats to 65.9, 624.2, or

2,177 mg MeOH/kg/day. Dosing was simulated as a 12

- hour, zero

- order infusion to the 3

liver compartment. The AU C and total amount metabolized are given for a period 4

sufficient for the MeOH to clear (84 hours), and the AUC24 and AMET24 values represent 5

just the first 24 hours of exposure. (Results shown for illustrative purposes. Dosimetry used 6

in assessment was sim ulated using a more realistic water ingestion pattern.) 7

To simulate ingestion of methanol in drinking water by rats under bioassay conditions, an 3

ingestion pattern based on the observations of Spiteri (1982) and Peng et al. (1990). While mice 4

ingest water in frequent, small bouts (Gannon et al., 1992) that are reasonably described as a 5

continuous delivery to the stomach, rats exhibit clear periods of ingestion alternating with 6

periods where no ingestion occurs (Spiteri, 1982; Peng et al., 1990). Based on those data a 7

reasonable representation of rat water ingestion can be described as serious of pulses. During the 8

dark/active period of each day (first 12 hr) each bout of drinking was assumed to last 45 min 9

followed by 45 min without ingestion (total of 8 bouts). During the light/inactive period (next 12 10

hr) drinking bouts were assumed to last only 30 min followed by 2.5 hr (150 min) without 11

drinking (4 bouts). An equal amount was assumed to be consumed in each bout within the dark 12

period, likewise within each light

- period bout, with the respective amounts adjusted such that 13

80% of the total ingestion occurs during the dark and 20% during the light (Burwell et al., 1992). 14

The resulting absorption pattern is shown in Figure B

- 15A and a simulated blood conce ntration 15

time

- curve (for 50 mg/kg/day dosing) is shown in Figure B

- 15B. 16 Exposure

dose

(mg/kg·day) Body

weight (kg) C

max (mg/L) AUC

(hr·mg/L) AUC24

(hr·mg/L) AMET

(mgEq) AMET24

(mgEq)

66.0 0.33 9.3 104.8 104.8 21.2 21.2

624.1 0.33 631.6 9,525 8,817 155. 6 122.6

2177 0.34 2,832.4 72,617 45,138 347.4 138.4DRAFT

– DO NOT CITE OR QUOTE B

- 26 1

2

Figure B

- 15. Rat daily drinking water ingestion pattern (A) and resulting predicted

blood concentration for a 2

- day exposure (B). Rat drinking water exposures were

simulated by mu ltiplying the fractional absorption rate (1/hr) as a function of clock

time by the daily total dose ingested (mg) to obtain a rate of addition of methanol

into the stomach lumen compartment (mg/h).

3

B.2.8. Human Model Calibration

B.2.8.1. Inhalation Route

The mouse model was scaled to human body weight (70 kg or study

- specific average), 4

using human tissue compartment volumes and blood flows, and calibrated to fit the human 5

inhalation

- exposure data available from the open literature, which comprised data from four 6

publicat ions (Ernstgard et al., 2005; Batterman et al., 1998; Osterloh et al., 1996; Sedivec et al., 7

1981)

. 8

A first

- order rate of loss of MeOH from the blood, K1C, and a first

- order bladder 9

compart ment time constant, KBL, were used to provide an estimate of urin ary MeOH 10DRAFT

– DO NOT CITE OR QUOTE B

- 27 elimination. The inhalation

- route urinary MeOH kinetic data described by Sedivec et al

. (1981) 1

(Figure B

- 16) were used to inform these parameters. The urine MeOH concentration data 2

reported by the authors were converted to amount in urine by ass uming 0.5 mL/hr/kg total 3

urinary output (Horton et al

. , 1992). Sedivec et al. (1981) measured a fractional uptake of 4

57.7%, based on total amount inhaled. Since the PBPK model uses alveolar rather than total 5

ventilation and this is typically assumed to b e 2/3 of total ventilation the fractional uptake of 6

Sedivec et al. (1981) was corrected by dividing by 2/3 to obtain a value for FRACIN of 0.8655. 7

The resulting values of K1C and KBL, shown in Table B

- 1, differ somewhat depending on 8

whether first

- order or saturable liver metabolism is used. These are only calibrated against a 9

small data set and should be considered an estimate. Urine is a minor route of MeOH clearance 10

with little impact on blood MeOH kinetics. 11

Although the high

- doses used in the mouse st udies warrant the use of a second metabolic 12

pathway with a high Km, the human exposure data all represent lower concentrations and may 13

not require or allow for accurate calibration of a second metabolic pathway. Horton et al

. (1992) 14

employed two sets of m etabolic rate constants to describe human MeOH disposition, similar to 15

the description used for rats and mice, but in vitro studies using monkey tissues with non

- MeOH 16

substrates were used as justification for this approach. Although Bouchard et al. (2001) 17

described their metabolism using Michaelis

- Menten metabolism, Starr and Festa (2003) reduced 18

that to an effective first

- order equation and showed adequate fits. Perkins et al. (1995) estimated 19

a Km of 320 **±** 1273 mg/L (mean **±** S.E.) by fitting a one

- compar tment model to data from a 20

single oral poisoning to an estimated dose. In addition to the extremely high standard error, the 21

large standard error for the associated V

max (93 **±** 87 mg/kg/hr) indicates that the set of 22

Michaelis

- Menten constants was not uni quely identifiable using this data. Other Michaelis

23

Menten constants that have been used to describe MeOH metabolism in various models for 24

primates are given in Table B

- 2. Because the Km calculated by Perkins et al. (1995) from the 25

high

- dose oral exposur e is 320 mg/L, while the highest observed concentration in the data sets 26

considered here is 14 mg/L (Batterman et al., 1998), forcing the model to use this higher Km 27

would simply result in fits that are effectively indistinguishable from the linear model. A simple, 28

linear model is preferred over the use of a Km value that high. 29DRAFT

– DO NOT CITE OR QUOTE B

- 28 Sedivec et al., urine

concentration

0 1 2 3 4 5 6 7 8 9 10

0 4 8 12 16 20 24

Time (h)Conccentration (mg/L) 231 ppm saturable sim

157 ppm saturable sim

78 ppm saturable sim

231 ppm linear sim

157 linear sim

78 ppm linear sim

231 ppm data

157 ppm data

78 ppm data

Sedivec et al.,

total urinary excretion

0 0.5

1 1.5

2 2.5

3

0 4 8 12 16 20 24

Time (h)Amount (mg)

Figure B

- 16. Urinary MeOH elimination concentration (upper panel) and cumulative

amount (lower panel), following inhalation exposures to MeOH in human volunteers

. 1

Data points in lower panel represent estimated total urinary MeOH elimination from 2

humans exposed to 78 (diamonds), 157 (triangles), and 231 (circles) ppm MeOH for 8 3

hours, and lines represent PBPK model simulations. Solid lines are model results with the 4

saturable e quation for hepatic metabolism while dashed lines show results for liner 5

metabolism. Data digitized from Sedivec et al. (1981) and provided for modeling by the 6

EPA. 7

Source: Sedivec et al

. ( 1 981).DRAFT

– DO NOT CITE OR QUOTE B

- 29 Table B

- 2. Primate kms reported in the literature

Km (mg/ L) Reference Note

320 **±** 1273 Perkins et al., 1995 Human: oral poisoning, estimated dose

716 **±** 489 Perkins et al., 1995 Cynomolgus monkey: 2 g/kg dose

278 Perkins et al., 1995 Rhesus monkey: 0.05

- 1 mg/kg dose

252 **±** 116 Perkins et al., 1995 Cynomolgus mon key: 1 g/kg dose

33.9 Horton et al., 1992 PBPK model: adapted from rat Km

0.66 Fisher et al., 2000 PBPK model, Cynomolgus monkey:10

- 900 ppm

23.7 **±** 8.7 a (This analysis.) PBPK model, human: 100

- 800 ppm

Note

- the values from Perkins et al. (1995b) , are **±** S.E.

a Mean **±** S.D. This Km was optimized while also varying V

max , K1C, and KBL, from all of the at

- rest human

inhalation data as a part of this project. The S.D. given for this analysis is based on the Optimize function of

acslXtreme, which assumes all d ata points are discrete and not from sets of data obtained over time and

therefore a true S.D. would be a higher value. The final value reported in Table B

- 1 (21 mg/L) was obtained by

sequentially rounding and fixing these parameters, then re

- optimizing th e remaining ones. For more detail, see

text and Table B

- 3.

To estimate both the Michaelis

- Menten and first

- order rates, all human data under 1

nonworking conditions (Batterman et al., 1998; Osterloh et al., 1996; Sedivec et al., 1981) were 2

used. Before d iscussing the parameter estimation, however, adjustments were made to one of 3

these data sets (Osterloh et al., 1996). Batterman et al., (1998) and Sedivec et al. (1981) both 4

subtracted background levels before reporting their results. However, Osterloh e t al. (1996) 5

measured and reported (plotted) blood methanol in nonexposed controls (data shown in 6

Figure B

- 17). The data for Osterloh et al. (1996) clearly show a time

- dependent trend which is 7

close to linear, and a linear regression is also included. Ho wever, the blood concentration 8

(average) in the exposed group of that study was ~1.2 mg/L, whereas the data and regression in 9

Figure B

- 17 indicate a value of ~ 0.9 mg/L. Therefore, the exposure data for Osterloh et al 10

(1996) were corrected by subtracting time

- zero value for the exposed group plus a time

11

dependent factor obtained by multiplying the slope of this regression (0.093 mg/L

- hr) by the 12

measurement time. 13

The metabolic (first

- order or saturable) and urinary elimination constants were 14

numerically fit to the nonworking human data sets while holding the value for FRACIN at 15

0.8655 (estimated from the results of Sedivec et al. as described above) and holding the 16

ventilation rate constant at 16.5 L/hr/kg 0.75 and QPC at 24 L/hr/kg 0.75 (values used by EPA 17

[2 000d] for modeling the inhalation

- route kinetics vinyl chloride). Other human

- specific 18

physiological parameters were set as reported in Table B

- 1. 19DRAFT

– DO NOT CITE OR QUOTE B

- 30 Control blood concentrations

(Osterloh et al., 1996)

y = 0.093x + 0.9094

R 2 = 0.9391

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

0 2 4 6 8

Time (hr)Concentration (mg/L)

Figure B

- 17. Control (nonexposed) blood methanol concentrations

Source: Ernstgard et al. (2005); Osterlo h et al. (1996)

.

Either (a) the set of V

max C, Km, K1C, and KBL were simultaneously varied while fitting 1

the entire data set or (b) KLLC, K1C, and KBL were so varied and fit. Thus the two model fits 2

are separated by a single degree of freedom (one additi onal parameter in case [a]). Statistical 3

results given in Tables B

- 2 and B

- 3 are from these global fitting exercises. Final fitted 4

parameters that have been used in the model for the risk assessment are given in Table B

- 1. The 5

resulting fits of the two parameterizations (1 st order or optimized Km/V

max ) are shown in 6

Figures B

- 16 and B

- 18. 7

Use of a first

- order rate has the advantage of resulting in one fewer variable in the model 8

and results in an adequate fit to the data, but the saturable model clearly f its some of the data 9

better (Figures B

- 16 and B

- 18). To discriminate the goodness

- of

- fit resulting from the inclusion 10

of an additional variable necessary to describe saturable metabolism versus using a single first

11

order rate, a likelihood ratio test was performed. Models are considered to be nested when the 12

basic model structures are identical except for the addition of complexity, such as the added 13

metabolic rate. Under these conditions, the likelihood ratio can be used to statistically compare 14

the rel ative ability of the two different metabolism scenarios to describe the same data, as 15

described by Collins et al. (1999). The hypothesis that one metabolic description is better than 16

another is calculated using the likelihood functions evaluated at the ma ximum likelihood 17

estimates. Since the parameters are optimized in the model using the maximum LLF, the 18

resultant LLF is used for the statistical comparison of the models. The equation states that two 19

times the log of the likelihood ratio follows a χ 2 distribution with r degrees of freedom: 20



### ( **) (**  **) (**  **)** 

### [ **] (**  **) (**  **)** 

### [ **]** 2 model log 1 model log 2 2 model / 1 model log 2 2 **χ λ**  **λ λ**  **λ**

r **≅**

**− −**  **=**

**−** 21DRAFT

– DO NOT CITE OR QUOTE B

- 31 The likelihood ratio test states that if twice the difference between the maximum LLF of the two 1

different descriptions of metabol ism is greater than the χ 2 distribution, then the model fit has 2

been improved (Devore, 1995; Collins et al., 1999; Steiner et al., 1990)

. 3

Batterman et al., blood concentration

(800 ppm exposures)

0 2 4 6 8 10 12 14 16 18

0 2 4 6 8

Time (h)Conccentration (mg/L) Saturable metabolsim

First-order metabolism

2 h data

1 h data

30 min data

Osterloh et al.

(200 ppm inhalation)

0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

5

0 2 4 6 8 Time (hr)Blood concentration (mg/L)

Data

Saturable metabolsim

First-order metabolism

Figure B

- 18. Data showing the visual quality of the fit using optimized first

- order or

Michaelis

- Menten kinetics to describe the metabolism of MeOH in humans. The rate 4

constants used for each simulati on are given in Table B

- 3. 5

Source: Batterman et al. (1998: top); Osterloh et al . (1996: bottom)

.

Table B

- 3. Parameter estimate results obtained using acslXtreme to fit all human data

using either saturable or first

- order metabolism

Parameters Optimized va lue S.D. Correlation matrix LLF

Michaelis

- Menten (optimized)

- 0.994

- 24.1

Km 23.8 8.8DRAFT

– DO NOT CITE OR QUOTE B

- 32 V

max C 33.2 10.1

First Order NA

- 31.0

KLLC 95.7 5.4

Note: The S.D.s are based on the Optimize function of acslXtreme, which assumes all data points are dis crete

and not from sets of data obtained over time and therefore a true S.D. would be a higher value.

At greater than a 99.95% confidence level, using 2 metabolic rate constants (Km and 1

V

max C) is preferred over utilizing a single rate constant (Table B

- 4 ). While the correlation 2

coefficients (Table B

- 3) indicate that V

max and Km are highly correlated, that is not unexpected, 3

and the S.D.s (Table B

- 3) indicate that each is reasonably bounded. If the data were 4

indistinguishable from a linear system, Km in particular would not be so bounded from above, 5

since the Michaels

- Menten model becomes indistinguishable from a linear model as V

max C and 6

Km tend to infinity. Moreover, the internal dose candidate POD, for example the BMDL 10 for 7

the inhalation

- induced bra in

- weight changes from NEDO (1987), with methanol blood AUC as 8

the metric, is 374.67 mg

- hr/L, which corresponds to an average blood concentration of 15.6 9

mg/L. Therefore the Michaelis

- Menten metabolism rate equation appears to be sufficiently 10

supported by the existing data, and its use is expected to improve the accuracy of the HEC 11

calculations, since those are being conducted in a concentration range in which the nonlinearity 12

has an impact. 13

Table B

- 4. Comparison of LLF for Michaelis

- Menten and first

- o rder metabolism

LLF (logλ) for M

- M LLF (logλ) for 1 st

order LLF

1st versus M

- M a 2

r **χ** (99% confidence) b 2

r **χ** (99.95%

confidence) b

- 24.1

- 31.0 34.1 13.8 12.22

Note: The models were optimized for all of the human data sets under non working conditions. M

- M:

Michaelis

- Menten

a obtained using this equation: 

### ( **) (**  **)** 

### [ **]** 2 model log 1 model log 2 **λ λ** 

**− −** 

b significance level at r = 1 degree of freedom.

While the use of Michaelis

- Menten kinetics might allow predictions across a wide 14

exposure ra nge (into the nonlinear region), extrapolation above 1,000 ppm is not suggested since 15

the highest human exposure data are for 800 ppm. Extrapolations to higher concentrations are 16

potentially misleading since the nonlinearity in the exposure

- internal

- dose relationship for 17

humans is uncertain above this point. The use of a BMD or internally applied UFs should place 18

the exposure concentrations within the range of the model. 19

The data from Ernstgard et al

. (2005) was used to assess the use of the first

- order 20

m etabolic rate constant to a dataset collected under conditions of light work. Historical measures 21

of QPC (52.6 L/hr/kg 0.75 ) and QCC (26 L/hr/kg 0.75 ) for individuals exposed under conditions of 22

50 w of work from that laboratory (52.6 L/hr/kg 0.7 ) (Ernstgard , personal communication; Corley 23DRAFT

– DO NOT CITE OR QUOTE B

- 33 et al., 1994; Johanson et al., 1986) were used for the 2

- hour exposure period (Figure B

- 19). 1

Otherwise, there were no changes in the model parameters (no fitting to these data). The results 2

are remarkably good, given the l ack of parameter adjustment to data collected in a different 3

laboratory, using different human subjects than those to which the model was calibrated. 4

Ernstgard et al. (2005)

0 1 2 3 4 5 6 7 8

0 2 4 6 8 10

Time (hr)Blood methanol (mg/L)

100 ppm 200

ppm

Figure B

- 19. Inhalation exposures to MeOH in human volunteers. Data points 5

represent measured blood M eOH concentrations from humans (4 males and 4 females) 6

exposed to 100 ppm (open symbols) or 200 ppm (filled symbols) for 2 hours during light 7

physical activity. Solid lines represent PBPK model simulations with no fitting of model 8

parameters. For the fir st 2 hours, a QPC of 52.6 L/hr/kg0.75 (Johansen et al. 1986), and a 9

QCC of 26 L/hr/kg0.75 (Corley et al., 1994) were used by the model. 10

Source: Ernstgard et al

. (2005 )

.

B.2.8.1. (Should be B.2.8.2) Oral Route

There were no human data available for calibration or va lidation of the oral route for the 5

human model. In the absence of data to estimate rate constants for oral uptake, the ‘humanset.m’ 6

file which sets parameters for human simulations applies the KMAS for the mouse with the other 7

absorption parameters set to match those identified for ethanol in humans by Sultatos et al. 8

(2004); VmASC was set such that for a 70

- kg person, VMAS/KMAS matches the first

- order 9

uptake constant of Sultatos et al. (2004) (0.21 hr

- 1 ). While Sultatos et al. include a term for 10

ethanol metabolism in the stomach, no such term is included here and the rate of fecal 11

elimination is set to zero, corresponding to 100% absorption. However zero

- order ingestion, a 12

continuous infusion at a constant rate into the stomach lumen equal to the daily d ose/24 hours, 13

was assumed for all human simulations. Since absorption was assumed to be 100% of 14

administered MeOH, at steady state the rate of uptake from the stomach and intestine 15

compartments (combined) must equal the rate of infusion to the stomach. Si nce C

max is driven by 16

the oral absorption rate, which was assumed rather than fitted and verified, C

max was not used as 17

a dose metric for human oral route simulations. AUC, which is less dependent on rate of uptake, 18DRAFT

– DO NOT CITE OR QUOTE B

- 34 was used as the dose metric and for esti mation of HEDs. Since the AUC was computed for a 1

continuous oral exposure, its value is just 24

- hours times the steady

- state blood concentration at 2

a given oral uptake rate. 3

B.2.8.1. (B.2.8.3) Inhalation Route HECs and Oral Route HEDs

The atmospheric MeOH concentr ation resulting in a human daily average blood MeOH 4

AUC (hr **×** mg/L) or C

max (mg/L) equal to that occurring in experimental animals following 5

exposure at the POD concentration is termed the HEC. Similarly, the oral dose (rate) resulting in 6

human daily average blood MeOH AUC (hr **×** mg/L) equivalent to that occurring in an 7

experimental animal at the POD concentration is termed the HED. 8

To determine the HEC for specific exposures in mice, the mouse PBPK model is first 9

used to determine the daily blood MeOH 24

- hours AUC and C

max associated with 7 hour/day 10

inhalation exposures. Mice were exposed each day for 10 days, so the full 10

- day exposure was 11

simulated and an average 24

- hours AUC calculated over that time, so no other duration 12

adjustment was needed

. The human AU C was determined for the last 24 hours of a continuous 13

1,000

- hour exposure, to assure steady state was achieved. The human C

max was determined at 14

steady state and so is equivalent to the steady state blood MeOH concentration. Results are given 15

in Table B

- 5 and for inhalation shown in Figure B

- 20. 16

For example, for a 1,000 ppm exposure this resulted in model

- predicted peak blood of 17

133 mg/L and an AUC of 770 ( hr **×** mg/L). The human model can then be used to determine the 18

human MeOH exposure concentration leadin g to the same daily average AUC or C

max under 19

continuous exposure conditions. Based on AUC, the HEC of the 1,000

- ppm exposure is 20

684 ppm, while based on peak (human steady

- state) concentration, the HEC is predicted to be 21

1110 ppm. The parameters used in the human model for these simulations are listed in Table B

- 1 22

for saturable kinetics. 23

The HED was calculated by using the human model to find the oral dose ( mg/kg

- day ) that 24

gave a blood MeOH AUC equivalent to the mouse AUC following an exposure at the POD. 25

Zero

- order absorption was assumed. For example, the human oral exposure equivalent to a 26

1,000

- ppm inhalation exposure in mice (i.e., with an AUC of 770 mg

- hr/L) is 163 mg/kg

- day. 27

Since a 200 mg/kg

- day oral exposure gave a human AUC of 1,320 mg

- hr/L, whi ch falls between 28

the values predicted for inhalation exposures at 800 ppm (1,090 mg

- hr/L) and 1,000 ppm (2,090 29

mg

- hr/L), this oral exposure rate was taken to be the upper end for the model to accurately 30

estimate an HED. 31DRAFT

– DO NOT CITE OR QUOTE B

- 35 Table B

- 5. PBPK model predicted C

ma x and 24

- hour AUC for mice and humans

exposed to MeOH

Exposure

concentration

(ppm) Inhalation Route Oral Route

Mouse a Human b Human

AUC

(mg

- hr/L) C

max (mg/L) AUC

(mg

- hr/L) Css (mg/L) Dose

(mg/kg

- day) AUC

(mg

- hr/L)

1 0.15 0.021 0.59 0.025 0.1 0.204

10 1.52 0.22 5.97 0.25 1 2.05

50 7.98 1.14 30.6 1.28 10 21.2

100 17.0 2.45 63.3 2.64 50 124

250 53.4 7.77 177 7.36 100 315

500 170 26.1 447 18.6 200 1320

1,000 770 133 2090 87.2 500 39400

2000 3310 524 31100 1300 1,000 125000

5000 17300 2000 147000 61 30 2000 297000

1,0000 51200 4710 341,000 14200 5000 814000

a The mouse 24

- hour average AUC were calculated under the conditions of the bioassay: 10 days of exposure

with 7 hours of exposure during each 24 hour period.

b Human simulation results are consid ered unreliable above 1,000 ppm (inhalation) or 200 mg/kgday (oral), but are

included for comparison

Again, since the available human exposure data is to, at most, 800 ppm, the model could 1

not be calibrated for higher exposures that approximate most of th e mouse and rat exposure 2

concentrations. The AUC in humans for similar exposure levels is ~3 times greater than in the 3

mouse, primarily because human exposure estimates are expected to result from 24

- hour 4

exposures and the mice were exposed for 7 hours. 5DRAFT

– DO NOT CITE OR QUOTE B

- 36 0.1

1 10 100 1000 10000 100000 1000000

1 10 100 1000 10000

Concentration (ppm)AUC (mg-hr/L) Mouse

Human

Human-linear

0.01 0.1

1 10 100 1000 10000

1 10 100 1000 10000

Concentration (ppm)Cmax (mg/L)

0.1

1 10 100 1000 10000

1 10 100 1000 10000

Concentration (ppm)Amount metabolized(mgkg0.75)

Mouse

Human

Human-linear

Figure B

- 20. Predicted 24

- hour AUC (upper left), Cmax (upper right), and amount

metabolized (lower) for MeOH inhalation exposures in the mouse (average over a 10

- day exposure at 7 hours/day) and humans (steady

- state values for a continuous

exposure). C

max for human exposures is equal to the steady

- st ate blood concentration. For

humans, the long

- dashed lines are model predictions using Michaelis

- Menten

metabolism (optimized Km of 23.8 mg/L) and the short

- dashed lines are model

predictions using first

- order kinetics. Amount metabolized normalized to BW 0.75 to

reflect cross

- species scaling (Human simulation results above 1,000 ppm are not

considered reliable but are shown for comparison).

While the PBPK computational code can be used in the future to de rive HECs or HEDs 1

for other exposures, an alternative approach was developed that allows non

- PBPK model users to 2

estimate MeOH HECs and HEDs from benchmark doses in the form of AUCs. This approach 3

uses algebraic equations describing the relationship betwe en predicted MeOH 24

- hour AUC or 4

total amount metabolized in the liver (MET, mg [per day]) (constant 24

- hour exposure) and the 5

inhalation exposure level (i.e., an HEC in ppm) (Equations 1, 1b, 3 or 3b) or oral exposure rate 6

(i.e., an HED in mg/kg

- day) (Equ ations 2, 2b, 4 or 4b). To use the equations to derive an HEC or 7

HED, the target human AUC is applied to the appropriate equation. Since these relationships are 8

for continuous exposures, blood concentration is constant, and hence extrapolation for a C

max is 9

obtained by simply using AUC = 24*C

max . 10DRAFT

– DO NOT CITE OR QUOTE B

- 37 AUC 794 AUC 1334 AUC 0224

. 0 ) (ppm HEC 1000 **+ ×** 

**+ ×**  **= <**  Equation 1a 1

UF ** **  ** **  ** ** 

**+ ×** 

**+ ×**  **=** AUC 794 AUC 1334 AUC 0224

. 0 (ppm) RfC Equation 1b 2

AUC 569 AUC 4

. 75 2 AUC 0058

. 0 ) (mg/kg/day HED 200 **+ ×** 

**+ ×**  **= <**  Equation 2a 3

UF ** **  ** **  ** ** 

**+ ×** 

**+ ×**  **=** AUC 569 AUC 4

. 75 2 AUC 0058

. 0 ) (mg/kg/day RfD Equation 2b 4



### ( **)** MET 9

. 19282 MET

14.69 MET 0.063554 ) (ppm HEC 1000

**− ×** 

**+ ×**  **= <**  Equation 3a 5



### ( **)** UF ** ** 

** ** 

** ** 

** ** 

**− ×** 

**+ ×**  **=** MET 9

. 19282 MET

14.69 MET 0.063554 (ppm) RfC Equation 3b 6



### ( **)** MET 9

. 19282 MET 3

. 3 MET 0.014287 ) (mg/kg/day HED 200

**− ×** 

**+ ×**  **= <**  Equation 4a 7



### ( **)** UF ** ** 

** ** 

** ** 

** ** 

**− ×** 

**+ ×**  **=** MET 9

. 19282 MET 3

. 3 MET 0.014287 ) (mg/kg/day RfD Equation 4b 8

B.2.9. Conclusions and Discussion

Mouse, rat, and human MeOH PBPK models have been developed and ca librated to data 9

in the open literature. The model simplifies the structure used by Ward et al

. (1997) while adding 10

specific refinements (e.g., a standard lung compartment and a two

- compartment GI tract). 11

Although the endpoints of concern are developmenta l effects which occur during in utero 12

and (to a lesser extent) lactational exposure, it is not necessary for a MeOH PBPK model to 13

specifically describe pregnancy (i.e., specify a fetal/gestational/conceptus compartment) and 14

lactation in order for it to pro vide better cross

- species extrapolation of risk than default methods. 15

Representation of the unique physiology of pregnancy and the fetus/conceptus would be 16

necessary if MeOH pharmacokinetics differed significantly during pregnancy or if the observed 17

partit ioning of MeOH into the fetus/conceptus versus the mother showed a concentration ratio 18

significantly greater than or less than 1. MeOH pharmacokinetics GD6

– GD10 in the mouse, are 19

not different from NP mice (Pollack and Brouwer, 1996), and the maternal bloo d:fetus/conceptus 20

partition coefficient is reported to be near 1 (Ward et al., 1997; Horton et al., 1992). At GD18 in 21

the mouse, maternal blood levels are only modestly different from those in NP animals (see 22

Figures B

- 4 and B

- 5 for examples), and in gene ral the PBPK model simulations for the NP 23

animal match the pregnancy data as well as the NP data. Likewise maternal blood kinetics in 24

monkeys differs little from those in NP animals (see Section 3.4.7 ). Further, in both mice and 25

monkeys, to the extent tha t late

- pregnancy blood levels differ from NP for a given exposure, they 26

are higher; i.e., the difference between model predictions and actual concentrations is in the same 27

direction. These data support the assumption that the ratio of actual target

- tissue methanol 28

concentration to (predicted) NP maternal blood concentrations will be about the same across 29DRAFT

– DO NOT CITE OR QUOTE B

- 38 species, and hence that using NP maternal blood levels in place of fetal concentrations will not 1

lead to a systematic error when extrapolating risks. Thu s, a full representation of pregnancy and 2

the fetal/conceptus compartment appears to be unnecessary. 3

While lactational exposure is less direct than fetal exposure and blood or target

- tissue 4

levels in the breast

- feeding infant or pup are likely to differ m ore from maternal levels, the 5

health

- effects data indicate that most of the effects of concern are due to fetal exposure, with 6

only a small influence due to postbirth exposures. Separating out the contribution of postbirth 7

exposure from pre

- birth exposure to a given endpoint in a way that would allow the risk to be 8

estimated from estimates of both exposure levels would be extremely difficult, even if one had a 9

lactation/child PBPK model that allowed for prediction of blood (or target

- tissue) levels in the 10

offspring. And one would still expect the target

- tissue concentrations in the offspring to be 11

closely related to maternal blood levels (which depend on ambient exposure and determine the 12

amount delivered through breast milk), with the relationship between maternal levels and those 13

in the offspring being similar across species. 14

Therefore, the development of a lactation/child PBPK model appears not to be supported, 15

given the minimal change that is likely to result in risk extrapolations and use of (NP) mater nal 16

blood levels as a measure of risk in the offspring is still considered preferable over use of default 17

extrapolation methods. In particular, the existing human data allow for accurate predictions of 18

maternal blood levels, which depend strongly on the r ate of maternal methanol clearance. 19

Failing to use the existing data (via PBPK modeling) on human methanol clearance (versus that 20

in other species) would be to ignore this very important determinant of exposure to breast

- fed 21

infants. And since bottle

- fed infants do not receive methanol from their mothers, they are 22

expected to have lower or, at most, similar overall exposures for a given ambient concentration 23

than the breast

- fed infant, so that use of maternal blood levels for risk estimation should also b e 24

adequately protective for that group. 25

During model development, several inconsistencies between experimental blood MeOH 26

kinetic data embedded in the Ward et al

. model (1995) and the published figures first reporting 27

these data were discovered. Therefore , data were digitized from the published literature when a 28

figure was available, and the digitized data was compared to the provided data. When the 29

digitized data and the data embedded in the computational files (i.e., provided to Battelle under 30

contract f rom the EPA) were within 3% of each other, the provided data was used; when the 31

difference was greater than 3%, the digitized data was used. Often, using the published figures 32

as a data source resulted in substantial improvements of the fit to the data in the cases where the 33

published figures were different from the embedded data. 34

The final MeOH PBPK model fits well inhalation

- route blood kinetic data from separate 35

laboratories in rodents and humans. Intravenous

- route blood MeOH kinetic data in NP mice 36

w ere only available for a single i.v. dose of 2,500 mg/kg, but were available for GD18 mice 37

following administration of a broader range of doses: 100, 500, and 2,500 mg/kg. Up to 38DRAFT

– DO NOT CITE OR QUOTE B

- 39 20 hours postexposure, blood MeOH kinetics appear similar for NP and pregnant mice after 1

administration of 2,500 mg/kg. The intravenous pharmacokinetic data in GD18 mice showed an 2

unexpected dose

- dependent nonlinearity in initial blood concentrations, suggesting either a dose 3

dependence on the volume distribution, which is unlikel y, or some source of experimental 4

variability. To account for this nonlinearity, Ward et al

. (1997) used dose

- specific partition 5

coefficients for placenta and embryonic fluid and V

max for the metabolism of MeOH. The 6

current model uses a consistent set of parameters that are not varied by dose and therefore does 7

not fit these 100 mg/kg dose intravenous data. The model does fit the 500 and 2,500 mg/kg 8

doses, and if a presumed i.v. dose of 200 mg/kg (twice the reported 100 mg/kg) is employed, is 9

able to pre dict initial blood concentrations for the lowest dose data, as expected. The i.v. data 10

from the Ward et al

. (1995) model does match the corresponding published figures. 11

The model fits to the mouse oral

- route MeOH kinetic data using a consistent set of 12

par ameters (Figure B

- 4) are reasonably good but not as good as fits to the inhalation data. The 13

model consistently underpredicts the amount of blood MeOH reported in two studies (Ward 14

et al

., 1997, 1995). Ward et al

. (1997) utilized a different V

max for eac h oral absorption data set. 15

In the report by Ward et al. (1997) the GD18 and the GD8 data from Dorman et al

. (1995) were 16

both fit using a V

max of ~80 mg/kg/hr (body weights were not listed, the model assumed that 17

GD8 and GD18 mice were both 30 g; Ward et a l. (1997) did not scaled by body weight), but 18

lower partition coefficients for placenta (1.63 versus 3.28) and embryonic fluid (0.0037 versus 19

0.77). The current model adequately fits the oral pharmacokinetic data using a single set of 20

parameters that is no t varied by dose or source of data. 21

The fits of the rat model to the limited dataset readily available were quite good. The low

22

dose exposures of all routes were emphasized in model optimization since they were the doses 23

most relevant to risk assessment. B ased on a rat inhalation exposure to 500 ppm, the human HEC 24

would be 301 ppm (by applying an AUC of 226 [Figure B

- 12] to Equation 1). 25

The mouse, rat, and human models fit multiple datasets from multiple research groups 26

using consistent parameters that are representative of each species, but are not varied within 27

species. Using the model, it will be possible to ascertain chronic human exposure concentrations 28

that are likely to be without an appreciable risk of deleterious effects. 29

B.3. ADDITIONAL MATERIAL

**•** Results from Optimizations 30

**•** acslXtreme Program (.csl) File (Electronic Attachment) 31

**•** acslXtreme procedure (.cmd) file 32

**•** Key to

. m files for reproducing the results in this report 33

**•** Code for .m files 34DRAFT

– DO NOT CITE OR QUOTE B

- 40 **•** Personal communication from Lena Ernstgard regarding human exposures re ported in the 1

Ernstgard and Johanson, 2005 SOT poster 2

**•** Personal communication from Dr. Rogers regarding mouse exposures to MeOH 3

**•** Data and s imulations for MeOH Metabolic Clearance/Total Metabolites Produced 4

**•** Multiple daily oral dosing for humans 5

B.3.1. Results From O ptimizations

B.3.1.1. Approach for and Results of the Optimization of Metabolic Parameters and

Inhalation Route Fractional Availability in Mice

The approach and results are presented below in outline format with supporting figures. More 6

complete documentation was not developed because the products of the optimizations were not 7

used in the final model. The documentation here is intended only to demonstrate that appropriate 8

optimizations were conducted and what the results of those optimizations were. 9

1. The V

max fo r the low affinity pathway was set to 0 and the remaining V

max C, Km, and 10

fractional availability were optimized using inhalation data only. 11

a. The optimizer was unable to find a value for Km that was greater than 0. 12

b. The resulting metabolic parameters essentia lly represented a zero order loss process. 13

2. The V

max for the low affinity pathway was set to 0 and the remaining V

max C and Km were 14

optimized using all (oral, intravenous and inhalation) data. 15

a. The optimized single Km, 135 mg/L, was equal to the average of th e 2 original Kms. 16

b. Fits to the MeOH blood levels following inhalation exposures > 2

, 000 ppm are slightly 17

improved, but the model fits to the 1,000 ppm exposure concentration overpredict 18

reported values by 20%. 19

3. Parameters for both metabolic pathways were opt imized using all (oral, intravenous and 20

inha la tion) data. 21

a. The fit to the high

- dose intravenous data from Ward et al . (1997) (2

, 500 mg/kg) was 22

improved ( Figure B

- 21 ). 23

b. The fit to the high

- dose oral data, also from Ward et al . (1997), (2

, 500 mg/kg) was 24

impr oved ( Figure B

- 22 ). 25

c. The fit to the mid

- dose i

. v

. data (500 mg/kg) dose was not as good as using the visually 26

fit parameters ( Figure B

- 21 ) 27

d. The fit to the low

- dose oral data (1

, 500 mg/kg) was not as good when the visually fit 28

parameters were used ( Figure B

- 22 ). The low

- dose data was from Dorman et al

. ( 1995 )

. 29DRAFT

– DO NOT CITE OR QUOTE B

- 41 e. Neither set of parameters resulted in an adequate fit to the low

- dose intravenous data 1

(100 mg/kg; Figure B

- 21 ). 2

f. Fits to the inhalation data following exposures to < 5

, 000 ppm MeOH were substantially 3

wo rse than when using the visually fit parameters ( Figure B

- 23 ) 4

10 100 1000 10000

0 5 10 15 20 25 30

Time (hr)Blood MeOH (mg/L)

Figure B

- 21. Fit of the model to i.v. data using different clearance and uptake

parameter optimizations. Solid blue lines

- visually optimized; dashed red lines

- 5

clearance parameters (Km, Km2, VmaxC, Vmax2C) optimized using all inhalation data 6

sets; dash/dot green lines

- clearance parameters optimized using all data sets (inhalation, 7

oral, and intravenous). 8

Source: Ward et al. (1997).DRAFT

– DO NOT CITE OR QUOTE B

- 42 0 500 1000 1500 2000 2500 3000 3500

0 5 10 15 20 25 30

Time (hr)Blood MeOH (mg/L)

Figure B

- 22. Fit of the model to oral data using di fferent clearance and uptake

parameter optimizations. Solid blue lines

- visually optimized; dashed red lines

- 1

clearance parameters (Km, Km2, V

max C, V

max 2C) optimized using all inhalation data sets; 2

dash/dot green lines

- clearance parameters optimized using all data sets (inhalation, oral, 3

and intravenous). 4

Source: Ward et al

. ( 1997 ) ; Dorman et al. (1995).

10 100 1000 10000

0 5 10 15 20 25 30

Time (hr)Blood MeOH (mg/L)

Figure B

- 23. Fit of the model to inhalation data using different clearance and uptake

parameter optimizations. Dotted black lines

- model optim ized fractional inhalation, solid 1

blue lines

- visually optimized; dashed red lines

- clearance parameters (Km, Km2, VmaxC, 2

Vmax2C) optimized using all inhalation data sets; dash/dot green lines

- clearance 3

parameters optimized using all data sets (inhalat ion, oral, and intravenous). 4

Source: Rogers et al. (1997).DRAFT

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- 43 B.3.1.1. [B3.1.2.] Conclusion

Under the best circumstances, formal optimizations offer the benefit of repeatability and 1

confirmation that global optima have not been missed by user

- guided visual optimizat ion. 2

Incorporating judgments regarding the value of specific data sets while easy when visually 3

fitting, is difficult at best when using optimization routines. This is an important distinction 4

between these approaches for this modeling exercise. 5

The mouse NOEL was 1,000 ppm MeOH. Fitting the blood MeOH concentration data at 6

this exposure drove our modeling exercises because of the importance of this exposure group in 7

the risk assessment. Unfortunately, the vast majority of the blood MeOH data came from mu ch 8

higher exposures. As expected, our various attempts at optimization led to fits that were better 9

for some, but never all, data sets. This is to be expected when there is clearly significant 10

variability in the underlying data. Various data weighting sch emes were included to improve 11

overall optimization while maintaining a good fit to the 1,000 ppm data. In the end, optimization 12

offered no significant improvement over the fractional uptake and metabolic parameter values 13

obtained by visual optimization, so these were retained in the final version of the model. 14

B.3.2. acslXtreme Program (.csl) File

PROGRAM MeOH

-- PBPK Model for Methanol 15

PROGRAM MeOH

-- PBPK Model for Methanol 16

! Based on MeOH Model by Ward et al 1997 with these revisions: 17

! TS Poet, P Hinderliter and J Teeguarden, 18

! Center for Biological Monitoring and Modeling 4/16/05 19

! Pacific Northwest National Laboratory 20

! Model contains inhalation, iv, and oral (multiple patterns). 21

! 1) Removed fetal compartment and other tissues that could be lumped 22

! base d on similarity of partition coefficients or did not need to be 23

! specified directly (Bone, mammary tissue) for the modeling purposes here. 24

! 2) Changed day to hr. 25

! 3) Flows (scaled to BW or BW**0.75), Metabolism (BW**0.75) and 26

! tissue volumes (BW) ar e scaled in the model. 27

! Final has somach and intestine compartments which provide fast and 28

! slow absorption rates, respectively. 29

! 4) Bladder compartment (for human simulations) added by Paul 30

! Schlosser, U.S. EPA, Oct. 2008 31

! 5) "Sipping" drinking water exposure code for rats, to match data 32

! from Peng et al. (1990) 33

! 6) Time

- variable drinking pattern for mice from Keys et al. (2004) 34

! added by Paul Schlosser, U.S. EPA, Aug. 2009 35

! Version is final version used for simulations 36

! ************ MODEL UNIT S ***************** 37

! Concentration, mg/L 38

! Mass of Chemical, mg 39

! Volume, L 40

! Flow, L/hr 41

! Body Weight Kg 42

!==========================72 Character Line==========================! 43

INITIAL 44

! Initialize some Variables before start 45DRAFT

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- 44 Integer IDS, MULTE 1

REAL DRT( 6), DRP(6) !store drink water times, percents in array! 2

3

CONSTANT BW = 0.030 ! Body weight (kg) 4

CONSTANT QPC = 15. ! Alveolar ventilation (L/hr/kg**0.75) 5

6

! Blood Flows (fraction of cardiac output) 7

CONSTANT QCC = 15.0 ! Cardiac output (L/hr/kg**0.75) 8

CON STANT QFC = 0.05 ! Fat 9

CONSTANT QLC = 0.25 ! Liver 10

! Blood flow to rest of body Calculated by Flow Balance! 11

QRC = 1.0

- (QFC + QLC) 12

QC = QCC*BW**0.75 13

QP = QPC*BW**0.75 14

15

! Tissue Volumes for mice (fraction of body weight) 16

CONSTANT VAC = 0.0123 ! Arteri al blood 17

CONSTANT VFC = 0.07 ! Fat 18

CONSTANT VLC = 0.055 ! Liver 19

CONSTANT VLuC = 0.0073 ! Lung tissue 20

CONSTANT VVBC = 0.0368 ! Venous blood 21

VRC = 0.91

- (VAC+VFC+VLC+VLuC+VVBC) 22

23

! Partition Coefficients (Mouse values from Ward et al used as default) 24

C ONSTANT PB = 1350 ! MeOH Blood:Air; Use Horton value! 25

CONSTANT PF = 0.08 ! MeOH Fat:Blood 26

CONSTANT PL = 1.1 ! MeOH Liver:Blood 27

CONSTANT PLU = 1.0 ! MeOH Lung:Blood, compartment for dosing only 28

CONSTANT PR = 0.8 ! MeOH Rest of body:Blood 29

30

! Hepatic Meta bolism of MeOH 31

CONSTANT KM = 45.0 ! mg/L 32

CONSTANT VMAXC = 15.0 ! mg/hr/BW**0.75 33

VMAX = VMAXC*BW**0.75 ! mg/hr 34

CONSTANT VMAX2C = 15.0 ! 2nd saturable pathway 35

VMAX2 = VMAX2C*BW**0.75 36

CONSTANT KM2= 45.0 37

CONSTANT KLLC = 0.0 ! First

- order metabolism 38

! Se t VMAXC = VMAX2C = 0, when KLLC > 0 39

KLL = KLLC/BW**0.25 40

41

! MeOH Clearance from Blood! 42

CONSTANT K1C = 0.01 ! First

- order clearance, BW**0.25/hr 43

K1 = K1C/BW**0.25 ! Scaled blood elimination, hr

- 1 44

! This lumped term was used in the WARD model an accounte d for 45

! renal elimination and "additional" non

- hepatic metabolism of 46

! MeOH associated only with high dose i.v. data. 47

! A 1st

- order term should not be used to represent two processes 48

! with different dose

- dependencies. 49

! This has not been used for mouse data (set=0), but was used to 50

! approximate human urinary data! 51

52

! Bladder compartment added (Paul Schlosser, U.S. EPA, 10/2008) 53

CONSTANT KBL=0.0 ! Bladder constant, 1/hr 54

55

! Fractional Absorption of MeOH 56

CONSTANT FRACin = 0.85 ! Inhalation, value from Pe rkins et al 57DRAFT

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- 45 CONSTANT KFEC = 0.0 ! Fecal elimination constant, 1/hr 1

! KFEC determines oral bioavailability 2

3

! Molecular Weight of MeOH 4

CONSTANT MWMe = 32.0 ! mol wt, g/mol! 5

6

! Closed Chamber Parameters 7

CONSTANT VChC = 100.0 ! Volume of closed chamber (L) 8

CONSTANT Rats = 0.0 ! Number of rats in chamber 9

CONSTANT kLoss = 0.0 ! Chamber loss rate /hr 10

! Set RATS = 0.0 and KLOSS = 0.0 for open chamber 11

12

! Blood Flows (L/hr) 13

QF = QFC*QC ! Fat 14

QL = QLC*QC ! Liver 15

QR = QRC*QC ! Rest of Body 16

17

! Tissue Volum es (mL) 18

VAB = VAC*BW ! Arterial blood volume 19

VF = VFC*BW ! Fat 20

VL = VLC*BW ! Liver 21

VLu = VLuC*BW ! Lung 22

VR = VRC*BW ! Rest of the body 23

VVB = VVBC*BW ! Venous blood 24

VBL = VAB + VVB ! Total blood 25

26

!

---------- Timing commands

------------ ! 27

CONSTANT T CHNG = 6.0 ! End of exposure! 28

CONSTANT TSTOP = 24.0 ! End of experiment/simulation! 29

CONSTANT POINTS = 1000.0 ! No. points for simulation output! 30

CONSTANT REST = 100000.0 ! End of work period for human exercise 31

CONSTANT WORK = 100000.0 ! Start of work period for human exercise 32

SCHEDULE DS1.AT.REST ! Change from work to rest conditions 33

SCHEDULE DS2.AT.WORK ! Change from rest to work conditions 34

! Human Rest/Work (changes in blood

- flow fractions to fat/liver not currently used0 35

CONSTANT QPCHR=15.0, QC CHR=15.0, QLCHR=0.25, QFCHR=0.05 ! Rest 36

CONSTANT QPCHW=52.0, QCCHW=26.0, QLCHW=0.16, QFCHW=0.06 ! Work 37

38

!

--------- Simulation Control

------------ ! 39

! Exposure Conditions Based on User Defined Initial Amounts of 40

! Chemical (mg) 41

CONSTANT CONCppm = 0.0 ! Air Concentration in ppm 42

VCh = VChC

- (Rats*BW) ! Volume of Occupied Chamber 43

CONCmg = CONCppm*MWMe/24451 ! Convert ppm to mg/Liter! 44

ACHO = CONCmg*VCH ! Init Amt in Chamber, mg! 45

46

! Oral dosing 47

CONSTANT KAS = 0.1 ! 1st order oral abs, hr

- 1 48

CONSTANT KMASC = 550 ! Saturable oral abs Kmasc [=] mg/kg 49

KMAS = KMASC*BW 50

CONSTANT VASC = 1740 ! Saturable oral ab VmaxC, mg/hr/kg^0.75 51

VAS = VASC*BW**0.75 ! Saturable oral ab Vmax, mg/hr 52

CONSTANT KAI = 0.1 ! 1st order oral abs from intestine, hr

- 1 53

CONSTANT KSI = 0 .5 ! 1st order transfer stom to intes hr

- 1 54

CONSTANT DOSE = 0.0 ! Oral dose in mg/kg BW 55

CONSTANT ODS = 0.0 ! Switch for zero order oral uptake 56

! (Set to 1 for zero order, set to 0 for first order) 57DRAFT

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- 46 ODOSE = DOSE*BW*(1.0

- ODS) ! Convert mg/kg to mg total (o ral) 1

RAOZ = DOSE*BW*ODS/24.0 ! mg/hr for zero order dosing 2

3

! Daily dose for steady drinking water by "sipping" (by rats) 4

CONSTANT DWDOSE = 0 ! mg/kg/d by periodic sipping 5

CONSTANT PER1 = 1.5 ! Period between sipping episodes (hr) during dark 6

! "Bet ween" means from the start of 1 to the start of the next episode 7

CONSTANT DUR1 = 0.75 ! Duration of sipping episodes during dark (hr) 8

CONSTANT PER2 = 3.0 ! Period during light (hr) between sipping episodes 9

CONSTANT DUR2 = 0.5 ! Duration of sipping ep isodes during light (hr) 10

CONSTANT FNIGHT = 0.8 ! Fraction of drinking during night 11

constant days = 7.0 ! days/week of oral exposure 12

constant metd = 7.0 ! number of days at end over which AUCBF and AMETF 13

! are averaged 14

tmetf = metd*24.0 15

dayo n=24.0*days 16

! Night sipping rate (mg/h) during episodes 17

DWRNIGHT = DWDOSE*BW*FNIGHT*PER1/(12.0*DUR1) 18

! Day sipping rate (mg/h) during episodes 19

DWRDAY = DWDOSE*BW*(1

- FNIGHT)*PER2/(12.0*DUR2) 20

IDOSE=0 21

! Above assumes 12

- hr each for day/night 22

23

! Drinki ng Table from Deborah Keys for mice, as used in 24

! A quantitative description of suicide inhibition of dichloroacetic acid in rats and mice. 25

! Keys DA, Schultz IR, Mahle DA, Fisher JW. 26

! Toxicol Sci. 2004 Dec;82(2):381

- 93. 27

! Based on data of Yuan, J. (1993) . Modeling blood/plasma concentrations in dosed feed and dosed 28

! drinking water toxicology studies. Toxicol. Appl. Pharmacol. 119, 131

- 141. 29

constant rdrink = 1.0 ! Default for use of sipping w/ DWDOSE abopve 30

! set rdrink = 0.0 to use pattern below 31

table m drinkp,1,49 / 0., .5, 1., 1.5, 2., 2.5, 3., 3.5, & 32

4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, & 33

8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, & 34

12., 12.5, 13., 13.5, 14., 14.5, 15., 15.5, & 35

16., 16.5, 17., 17.5, 18., 18.5, 19., 19.5, & 36

20., 20.5, 21., 21.5, 22., 22.5, 23., 23.5, 24.0, & 37

0.12 , 0.9, 1.6, 1.8, 1.9, 2.9, 4.0, 4.5, 4.9, 4.9, & 38

4.8, 4.4, 4.0, 5.0, 5.9, 5.3, 4.5, 3.9, & 39

3.2, 3.0, 2.7, 2.5, 2.3, 2.3, 2.3, 1.9, & 40

1.4, 1.4, 1.3, 1. 3, 1.3, 1.1, 0.8, 0.8, & 41

0.8, 0.6, 0.5, 0.7, 0.8, 0.6, 0.4, 0.2, & 42

0.05, 0.08, 0.14, 0.07, 0.06, 0.08, 0.12 / 43

44

! Larger bolus dosing 45

CONSTANT DRDOSE=0.0 ! Total dose by drinking water in boluses, mg/kg day 46

! Times for multiple oral dr inks/day *after* 0 47

! Must be ascending, 0 <= times < 24 hr 48

! CONSTANT DRT=0, 2, 4, 6, 8, 10 ! Rat values 49

Constant DRT = 0.0, 3.0, 5.0, 8.0, 11.0, 15.0 ! Human values 50

! DRTIME(1) assumed = 0 and not used 51

! Fraction consumed by drinking at those tim es 52

CONSTANT DRP = 0.25, 0.1, 0.25, 0.1, 0.25, 0.05 53

54

!Total oral bolus dose; initial value given at t=0 via initial condition 55

TODOSE = DRP(1)*DRDOSE*BW*(1.0

- ODS) + ODOSE 56

57DRAFT

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- 47 ! IV dosing 1

CONSTANT IVDOSE = 0.0 ! IV dose, mg/kg 2

CONSTANT TINF = 0.025 ! Leng th of exposure (hrs), default = 1.5 min (bolus) 3

! 1.5 min reported by Ward and Pollack, DMD 1996 4

TIV = IVDOSE*BW ! Expected amt infused, mg 5

IV1 = TIV/TINF ! Rate of infusion, mg/hg 6

7

! For I.V. Runs, control step size if necessary by changing MaxT, not POINTs or CINT 8

MAXT = 1.0 ! Maximum Step Size, Hours 9

!IF (IVDOSE.GE.1.0E

- 4) MAXT = 1.0E

- 4 10

11

! Liver infusion 12

CONSTANT LIVR0 = 0.0 ! Zero

- order liver total, mg/kg/day 13

RLIV0 = LIVR0*BW/TCHNG ! Rate in mg/hr 14

15

!

-------- Dose Scheduling

----------------- 16

CON STANT MULTE=0 ! Default is *no* repeated dosing/inhalation 17

CIZONE = 1.0 ! Start with inhalation on 18

IVZONE = 1.0 ! Start with IV on 19

SCHEDULE OFF.AT.TCHNG ! Turn off exposure at TCHNG 20

DAY = 0; 21

NEWDAY = 0; IDS = 2 ! First dose given as initial conditio n 22

IF (MULTE) SCHEDULE ORALDOSE.AT.DRT(2) 23

ALGORITHM IALG = 2 ! Gear algorithm 24

END ! END OF INITIAL 25

26

DYNAMIC 27

28

DERIVATIVE 29

!************************* MeOH ************************* 30

IVR = IVZONE*IV1 ! IV dosing; IVR = ate of infusion, mg/hg 31

! Oral Dosing 32

D WING = ( (DWRNIGHT*PULSE(0.0,PER1,DUR1)*PULSE(0.0,24.0,12.0) + & 33

DWRDAY*PULSE(0.0,PER2,DUR2)*(1

- PULSE(0.0,24.0,12.0)) )*rdrink + & 34

(1

- rdrink)*mdrinkp(mod(T,24.0))*0.02*DWDOSE*BW )*PULSE(0.0,168,dayon) 35

RAS = KAS*STOM + VAS*STOM/(KMAS+STOM) 36

RSTOM = DWI NG + RAOZ

- RAS

- KSI*STOM ! Change in stomach (mg/hr) 37

RINT = KSI*STOM

- RFEC

- KAI*AINTEST ! Change in intestines (mg/hr) 38

RLZ = RLIV0*CIZONE ! Zero

- order to liver 39

RAO = RAS + KAI*AINTEST + RLZ ! Oral absorption (mg/hr) 40

RFEC = KFEC*AINTEST 41

FEC = INTEG (RFEC, 0.0) 42

STOM = INTEG(RSTOM, TODOSE) ! Amt in stomach (mg) 43

AINTEST = INTEG(RINT, 0.0) ! Amt in intestines (mg) 44

OralDoseCheck = INTEG(RAO, 0.0) 45

46

! Arterial Blood 47

RAAB = QC*(CVLU

- CAB) 48

AAB = INTEG(RAAB, 0.0) ! Amount, mg 49

CAB = AAB/VAB ! Concentr ation, mg/L 50

AAUCB = INTEG(CAB, 0.0) ! AUC, hr*mg/L 51

52

! Fat 53

RF = QF*(CAB

- CVF) 54

AF = INTEG(RF, 0.0) ! Amount, mg 55

CF = AF/VF ! Concentration, mg/L 56

CVF = CF/PF ! AUC, hr*mg/L 57DRAFT

– DO NOT CITE OR QUOTE B

- 48 1

! Liver 2

RAL = QL*(CAB

- CVL) + RAO

- RMETL

- RMETL2

- RMETL3 3

AL = INTEG(RAL , 0.0) ! Amount, mg 4

CL = AL/VL ! Concentration, mg/L 5

CVL = CL/PL ! Concentration, mg/L 6

AUCL = INTEG(CL, 0.0) ! AUC, hr*mg/L 7

8

! Liver Metabolism 9

RMETL = VMAX*CVL/(KM + CVL) 10

METL = INTEG(RMETL, 0.0) 11

RMETL2 = VMAX2*CVL/(KM2 + CVL) 12

METL2 = INTEG(RMET L2, 0.0) 13

RMETL3 = KLL*CVL 14

METL3 = INTEG(RMETL3, 0.0) 15

16

! Total Amount Metabolized (Formate and Formaldehyde) 17

! Does not include K1C for human MeOH excretion estimate 18

AMET = METL + METL2 + METL3 19

AMET24 = AMET*24.0/TSTOP 20

! Total amount metabolized in last tmetf hr of exposure, averaged per day 21

AMETF = INTEG((RMETL+RMETL2+RMETL3)*PULSE(TSTOP

- tmetf,TSTOP,tmetf),0.0)* & 22

24.0/tmetf ! (tmetf = 24.0*metd) 23

! Chamber concentration (mg/L) 24

RACh = (Rats*QP*CLEx)

- (FRACin*Rats*QP*CCh)

- (kLoss*ACh) 25

ACh = INTEG( RACh, AChO) 26

27

! The following calculation yields an air concentration equal to the 28

! closed chamber value if a closed chamber run is in place and a 29

! specified constant air concentration if an open chamber run is in place 30

CCh = ACh*Cizone/VCh 31

CCPPM = CC h*24451/MWMe 32

CLoss = INTEG(kLoss*ACh, 0.0) 33

34

! Lungs 35

RALu = QP*(FRACin*CCh

- CLEx) + QC*(CVB

- CVLu) 36

ALu = INTEG(RALu, 0.0) 37

CLu = ALu/VLu ! Concentration, mg/L 38

CVLu = CLu/PLu ! Exiting Concentration, mg/L 39

40

! Amount Inhaled 41

RInh = FRACin*QP*CCh 42

AInh = INTEG(RInh, 0.0) ! mg per rat 43

AInhC = AInh*Rats ! mg for a group of rats 44

45

! Amount Exhaled 46

CLEx = CVB/PB ! Concentration, mg/L 47

RAEx = QP*CLEx 48

AEx = INTEG(RAEx, 0.0)*PULSE(0,TCHNG,TSTOP) ! Amount, mg per rat 49

AExC = AEx*Rats ! Amount, mg, for a grou p of rats 50

AxF = INTEG(RAEx*PULSE(TCHNG,24,24), 0.0) ! Amount exhaled post

- exposure 51

52

! Rest of Body 53

RAR = QR*(CAB

- CVR) 54

AR = INTEG(RAR, 0.0) ! Amount, mg 55

CR = AR/VR ! Concentration, mg/L 56

CVR = CR/PR ! Exiting Venous Concentration, mg/L 57DRAFT

– DO NOT CITE OR QUOTE B

- 49 AUCR = INTEG( CR, 0.0) ! AUC, hr*mg/L 1

2

! Venous Blood (mg) 3

RURB = K1*CVB*VVB ! Lumped Clearance from Blood 4

RAVB = QF*CVF + QL*CVL + QR*CVR + IVR

- QC*CVB

- RURB 5

AVB = INTEG(RAVB, 0.0) ! Amount, mg 6

CVB = AVB / VVB ! Concentration, mg/L 7

AUCB = INTEG(CVB, 0.0) ! AUC , hr*mg/L (total over entire exposure) 8

AUCBB = AUCB*24.0/TSTOP ! Average over exposure, hr*mg/(L*day) 9

AUCBF = INTEG(CVB*PULSE(TSTOP

- tmetf,TSTOP,tmetf),0)*24.0/tmetf 10

! AUCBF = Last tmetf AUC averaged/day (tmetf = 24.0*metd) 11

! For "steady state" AUC i n blood over a day, set exposures to 12

! several weeks to reach "periodicity", then use AUCBF w/ metd = 7 13

14

! Bladder compartment, added by PS, U.S. EPA, 10/2008 15

RBL = KBL*ABL ! Rate of clearance from bladder (mg/hr) 16

ABL = INTEG((RURB

- RBL),0.0) ! Amount in bladder (mg) 17

RUR= RBL/(BW*0.5e

- 3) ! Urine concentration = rate/[BW*(0.5e

- 3 L/h/kg BW)] 18

URB = INTEG(RBL, 0.0) ! Amount cleared to urine, mg 19

URBF = INTEG(RURB*PULSE(TSTOP

- tmetf,TSTOP,tmetf),0)*24.0/tmetf 20

! Amount cleared to urine in last tmetf avera ged/day (tmetf = 24.0*metd) 21

22

!************************* Mass Balance ************************** 23

Tbody = AAB + AF + AL + ALU + AR + AVB + ABL + STOM + AINTEST 24

MetabORClrd = METB + METL + METL2 + METL3 + AEX + FEC 25

TMass = Tbody + MetabORClrd 26

TDose = AinH + INTEG(IVR+DWING+RAOZ+RLZ,0.0) + TODOSE 27

MassBal=100*(TDose

- TMass)/(TMass+1e

- 12) 28

!compare to TIV, ODOSE, or AINHC 29

! Check Blood Flows 30

QTOT = QF + QL + QR 31

QRECOV = 100.0*QTOT/QC 32

END ! End of Derivative 33

TERMT(T.GE.TStop) 34

35

!

------- Exposure Control

------- ----- 36

DISCRETE ORALDOSE ! Stom is amount in stomach 37

IDOSE = DRP(IDS)*DRDOSE*BW 38

STOM = STOM + IDOSE ! Drinking percent 39

TODOSE = TODOSE + IDOSE 40

IF (IDS.EQ.1) THEN 41

STOM = STOM + ODOSE 42

TODOSE = TODOSE + ODOSE 43

ENDIF 44

IDS = IDS+1 45

IF (IDS.EQ.7) THEN ! F or 6 doses 46

IDS = 1 47

NEWDAY = NEWDAY + 24 48

SCHEDULE ORALDOSE.AT.NEWDAY ! Go to start of the next day 49

ELSE 50

SCHEDULE ORALDOSE.AT.(NEWDAY+DRT(IDS)) ! Go to next drink time 51

ENDIF 52

END ! OF DISCRETE ORALDOSE 53

54

DISCRETE OFF ! Turn INHAL exposure off 55

CIZONE = 0.0 56

IVZONE = 0.0 57DRAFT

– DO NOT CITE OR QUOTE B

- 50 DAY=DAY+1 1

IF (MULTE) SCHEDULE ON.AT.(DAY*24.0) 2

END ! OF DISCRETE OFF 3

4

DISCRETE ON 5

CIZONE=1.0 6

SCHEDULE OFF.AT.(T+TCHNG) 7

END ! OF DISCRETE ON 8

9

DISCRETE DS1 ! Human at rest 10

! Equations scheduled for change during simulation repeated here 11

QC = QCCHR*BW**0.75 12

QP = QPCHR*BW**0.75 13

QF = QFC*QC ! QFCHR*QC ! Equations for alternate flow fractions 14

QL = QLC*QC ! QLCHR*QC ! But QFC and QLC taken to be 'at rest' values 15

QRC = 1.0

- (QFC + QLC) 16

QR = QRC*QC 17

END ! OF DISCRETE DS1 18

19

DISCRETE DS2 ! Human at work (50W) 20

! Equations scheduled for change during simulation repeated here 21

QC = QCCHW*BW**0.75 22

QP = QPCHW*BW**0.75 23

QF = QFC*QC ! QFCHW*QC ! Equations for alternate flow fractions 24

QL = QLC*QC ! QLCHW*QC ! But don't seem to work (fit data) well 25

QRC = 1.0

- (QFC + QLC) 26

QR = QRC*QC 27

END ! OF DISCRETE DS2 28

29

END ! End of Dynamic 30

END ! End of Program 31

B.3.3. acslXtreme procedure (.cmd) file

! File MEOHCBMMfinal.CMD

- FOR PBPK MODEL FOR METHANOL 32

! taken from .cmd file from Ward et al ., Edited by KWW

- 06/ 02/96 33

! Developed for this (CBMM) model

- 4/15/15 34

! Final with Digitized Data

- 5/25/05 35

! Final Version has fast and slow rates of oral absorption 36

! Version 4 is final version used for simulations 37

! Final Version 1.10.06 38

! Beyond this comment, this file is left "as is" for archival purposes. But most if not all of 39

! the functions and data sets defined here are replicated and/or replaced in the .m files below. 40

! Only use these when there is no corresponding .m file. 41

!

– Paul Schlosser, U.S. EPA, Oct. 2008 42

!

------------------------------------------------------------ 43

PREPARE T,CVB,MetB 44

45

! Procedural blocks for general mouse/rat data 46

PROCED CDMICE ! Anatomic/physiologic data for mice 47

SET BW=0.03, TSTOP=1.5 48

SET IVDose=0, DOSE=0, CONCppm=0 49

SET PL=1.06, PF=0.083 , PR=0.66, PB=1350 50DRAFT

– DO NOT CITE OR QUOTE B

- 51 SET QPC=25.4,QCC=25.4,fracin=0.73 1

SET QLC = 0.25,QFC=0.05 2

SET KM=12, V

max C=14.3,KLC=0.0,KAS=2 3

SET V

max 2c=19,km2=210,KAI=0.22,KSI=1.1 4

SET VAC = 0.0123,VFC = 0.07,VLC = 0.055 5

SET VLuC = 0.0073, VVBC = 0.0368 6

!Volumes from Brown et al 7

!Mous e QPC avg from Brown 29, 24 used in Corley et al and others 8

!AVG of measured vent rates by Perkins et al 25.4 L/hr/kg^0.75 9

!Blood volume 4.9% total. As per Brown 25:75 split art:ven 10

!Metab originally from Ward et al

- KldC for mice =0 11

END 12

13

PROCED HUMAN 14

SET BW=70 15

SET IVDose=0, DOSE=0, CONCppm=0 16

SET PL=1.06, PR=0.66, fracin=0.75 17

SET VFC=0.214, VLC=0.026,VLUC=0.008 18

SET VAC= 0.0198,VVBC=0.0593 19

SET QPC=18.5, QCC=18.5, QLC=0.227, QFC=0.052 20

SET KM=12, V

max C=11,KLC=0.044,KAS=2.0 21

SET KAI=0.22,KSI=1.1 22

SET PB = 1626, P F=0.14 23

SET V

max 2c=0 24

!Volumes from Brown et al 25

!QPC from Brown, upper end 13.4 L/hr/kg^0.75 26

!Need higher for data, 15 L/hr/kg^0.75 used in several published human models 27

!Blood volume 7.9% total. As per Brown 25:75 split art:ven 28

!Frac absorbed from Ernstgar d SOT poster + personal communication 29

!Human Partition Coef. equal to mice. Horton et al . used rat 30

!Except Human Partition Coef blood and fat

- from Fiserova

- Bergerova and Diaz, 1986 31

!

- but rat values are inconsistent with expected fat partitioning for an alcohol like this 32

!

- for example Pastino and Conolly EtOH model, fat PC =0.1 33

END 34

35

PROCED SDRAT !Anatomic/physiologic data for rats 36

SET BW=0.3, TSTOP=1.5 37

SET IVDose=0, DOSE=0, CONCppm=0 38

SET PL=1.6, PF=0.1, PR=1.3 39

SET KM=45, V

max C=15,KLC=0.1,KAS=5 40

SET VAC = 0.0185, VFC=0.07, VLC= 0.034, VLuC=0.005, VVBC=0.0555 41

!Volumes from Brown et al 42

!PC from horton et al ., PF reduced to 0.1 from Horton's 1.1 43

!Blood volume 7.4% total. As per Brown 25:75 split art:ven 44

!Metab originally from Ward et al

- KldC for mice =0 45

!Ra t model not calibrated 46

END 47

48

PROCED PREG 49DRAFT

– DO NOT CITE OR QUOTE B

- 52 !For GD 18 mice, BW increased as estimated from Roger's et al 1

!Increased VFC as per Corley CRT development review 2

!This just to give a WAG as to how data might change from BW and different volume of 3

distribution 4

!No t invoked for any PROCs below as the default 5

!Liver to 140% of NP 6

SET BW = 0.055, VFC=0.08,VLC=0.11,VVBC=0.05 7

END 8

9

PROCED CLEARIT 10

SET IVDose=0, DOSE=0, CONCppm=0 11

END 12

13

PROCED SHOWIT 14

display V

max c,km,klc,pb,pf,pr,pl,kas,fracin 15

END 16

17

!Procedural blocks for al l non

- pregnant mouse data 18

! IV 19

PROCED MWARDIV25 20

!Ward et al ., TAP 1997 21

! Figure 2, data from Ward model cmd 22

!Data was checked via digitizit

- within +/

- 5% of cmd file 23

CLEARIT 24

CDMICE 25

SET TSTOP=24.0 26

SET IVDOSE=2500., tchng=0.025 27

END 28

29

PROCED PMWARDIV25 30

PLOT /D =MWARDIV25, CVB 31

END 32

33

DATA MWARDIV25(T,CVB) 34

0.08 4481.8 35

0.25 4132.2 36

0.5 3888 37

1.00 3164.8 38

2.0 2303.5 39

4.00 1921.5 40

6 1883.8 41

8 1620 42

12 838 43

18 454.7 44

24 NaN 45

END 46

47

PROCED MWARD95IV25 48

!Ward et al ., FAT 1995 49DRAFT

– DO NOT CITE OR QUOTE B

- 53 ! Figure 2 1

!Data via digitizit 2

CLEARIT 3

CDMICE 4

SET TSTOP=24.0 5

SET IVDOSE=2500., tchng=0.025 6

END 7

8

PROCED PMWARD95IV25 9

PLOT /D=MWARD95IV25, CVB 10

END 11

12

DATA MWARD95IV25(T,CVB) 13

0.53 3299.60 14

1.06 3244.54 15

1.54 3190.71 16

3.07 2803.13 17

4.07 2544.36 18

5.02 2237.77 19

6.02 2063.59 20

7.02 1873.10 21

8.02 1521.92 22

9.03 1670.30 23

10.03 1423.12 24

EN D 25

26

! Procs for pegnant IV below: MWARDGD9IV25, MWARDGD18IV25, MWARDGD18IV5, 27

MWARDGD18IV1 28

! Oral 29

PROCED MWARDPO25 30

!Ward et al ., FAT 1995 31

! Figure 2, data from Ward model cmd 32

!Data was checked via digitizit

- within +/

- 5% of cmd file 33

CLEARIT 34

CDMICE 35

SET TSTOP= 24, DOSE=2500 36

END 37

38

PROCED PMWARDPO25 39

PLOT /D=MWARDPO25, CVB 40

END 41

42

DATA MWARDPO25(T,CVB) 43

0.504 2370 44

0.96 2645 45

1.44 2705 46

1.992 2719 47

2.208 2781 48

3 2704 49DRAFT

– DO NOT CITE OR QUOTE B

- 54 4.008 2370 1

4.992 2617 2

6 2516 3

7.008 2635 4

7.992 2213 5

9 2370 6

10.008 2028 7

10.992 1916 8

12 1347 9

13.008 1467 10

13.992 1354 11

15 1175 12

16.008 864.3 13

16.992 745.2 14

18 422.4 15

19.01 428 16

21 243 17

24 136 18

END 19

20

!Procs for pregnant Oral below: MDORGD8PO15, MWARDGD18PO25 21

!Inhalation 22

! QPC set to measured as in Perkins et al ., FAT, 1995 for each concentration 23

24

PROCED MPERKIN25 25

!Perkins et a l ., FAT, 1995 26

!Fig. 2 data in Ward cmd file 27

CLEARIT 28

CDMICE 29

SET TSTOP=24, CONCppm=2500, vchc=5000 30

SET QPC = 29., QCC=29. 31

SET TCHNG=8 32

END 33

34

PROCED PMPERKIN25 35

PLOT /D=MPERKIN25, CVB 36

END 37

38

!This data from DigitizIt 39

DATA MPERKIN25(T,CVB) 40

2.0 414.0 41

4.0 453.0 42

6.0 5 86.0 43

8.25 694.0 44

12 282.0 45

16 0.6 46

END 47

48

!This data from cmd file 49DRAFT

– DO NOT CITE OR QUOTE B

- 55 !DATA MPERKIN25(T,CVB) 1

!1.99 386.49 2

!4.01 617.57 3

!6.00 816.22 4

!8.26 970.27 5

!12.00 393.24 6

!16.0 13.51 7

END 8

9

PROCED MPERKIN50 10

!Perkins et al ., FAT, 1995 11

!Fig. 2, data in Ward cmd file 12

!Data i n command file higher than appears in figure 13

CLEARIT 14

CDMICE 15

SET TSTOP=24, CONCppm=5000, vchc=5000 16

SET TCHNG=8, qpc=24.,qcc=24. 17

END 18

19

PROCED PMPERKIN50 20

PLOT /D=MPERKIN50, CVB 21

END 22

23

!this from Digitizit, Fig 2 Perkins et al 24

DATA MPERKIN50(T,CVB) 25

1 644.00 26

2 877 .00 27

3 1340.00 28

4 1450.00 29

6 2040.00 30

8.25 2290.0 31

12 1410.0 32

16 583.0 33

20 271.0 34

24 9.7 35

END 36

37

!This data from cmd file 38

!DATA MPERKIN50(T,CVB) 39

!1.0 906.76 40

!2.0 1202.7 41

!3.0 1828.38 42

!4.0 1986.49 43

!6.0 2800 44

!8.3 3125.68 45

!12.0 1914.86 46

!16.0 806.76 47

!20.0 367.57 48

!24.0 10. 81 49DRAFT

– DO NOT CITE OR QUOTE B

- 56 !END 1

2

PROCED MPERKIN100 3

!Perkins et al ., FAT, 1995 4

!Fig. 2 data in Ward cmd file 5

!Note, Table 6 in Ward paper

- max value of 3260 +/

- 151 6

CLEARIT 7

CDMICE 8

SET TCHNG=8, CONCppm=1,0000, tstop=36,vchc=5000 9

SET QPC=21,qcc=21 10

END 11

12

PROCED PMPERKIN100 13

PLOT /D=MP ERKIN100, CVB 14

END 15

16

!this from Digitizit, Fig 2 Perkins et al 17

DATA MPERKIN100(T,CVB) 18

2.0 2080.0 19

4.0 2530.0 20

6.0 3350.0 21

8.25 3350.0 22

12 2370.0 23

16 1830.0 24

20 1080.0 25

24 591.0 26

28 44.6 27

END 28

29

!DATA MPERKIN100(T,CVB) 30

!This from original cmd file 31

!2.0 2809.46 32

!4.0 3405 .4 33

!6.0 4528.38 34

!8.3 4524.32 35

!12.0 3212.16 36

!16.0 2456.76 37

!20.0 1439.19 38

!24.0 798.65 39

!28.0 55.4 40

!END 41

42

! Procs for Preg mouse Inhalaiton date below: MDOR8IN10,MDOR8IN15 43

!and:MROGGD7IN10, MROGGD6IN1, MROGGD6IN2, MROGGD6IN5, 44

MROGGD6IN10 45

46

!pregnant mice 47

! IV 48

P ROCED MWARDGD9IV25 49DRAFT

– DO NOT CITE OR QUOTE B

- 57 !Ward et al ., DMD, 1996 1

!Not used in the manuscript, only in cmd file 2

CLEARIT 3

CDMICE 4

SET TSTOP=24 5

SET IVDOSE=2500., TINF=0.025 6

END 7

8

PROCED PMWARDGD9IV25 9

PLOT /D=MWARDGD9IV25, CVB 10

END 11

12

DATA MWARDGD9IV25(T,CVB) 13

0.0833 4606.2 14

0.25 4079.5 15

0. 5 3489.3 16

1 2939.6 17

2 3447.6 18

4 2605.0 19

6 2690.5 20

8 2574.9 21

12 1506.1 22

18 498.6 23

24. NaN 24

END 25

26

PROCED PROCED MWARDGD18IV25 27

!Ward et al ., DMD, 1996 28

!Note, Table 6 in Ward paper

- max value of 3521+/

- 492 29

CLEARIT 30

CDMICE 31

SET TSTOP=24 32

SET IVDOSE=2500., TINF=0.025 33

END 34

35

PROCED PMWARDGD18IV25 36

PLOT /D=MWARDGD18IV25, CVB 37

END 38

39

DATA MWARDGD18IV25(T,CVB) 40

0.0833 4250.0 41

0.25 3445.1 42

0.5 2936.8 43

1.0 2470.5 44

2.0 2528.1 45

4.0 2292.3 46

6.0 2269.4 47

8.0 2057.0 48

12 1805.9 49DRAFT

– DO NOT CITE OR QUOTE B

- 58 18 1482.2 1

24.0 496.1 2

END 3

4

PROCED MWARDGD18IV5 5

!Ward et al ., DMD, 1996 6

!N ote, Table 6 in Ward paper

- max value of 868.8 +/

- 53.9 7

CLEARIT 8

CDMICE 9

SET TSTOP=6 10

SET IVDOSE=500., TINF=0.025 11

END 12

13

PROCED PMWARDGD18IV5 14

PLOT /D=MWARDGD18IV5, CVB 15

END 16

17

DATA MWARDGD18IV5(T,CVB) 18

0.25 854.7 19

0.5 720.2 20

1.0 624.1 21

2.0 453.2 22

3.0 307.6 23

4.0 217.7 24

4.5 202.6 25

END 26

27

PROCED MWARDGD18IV1 28

!Ward et al ., DMD, 1996 29

!Ward Proc GD8, but must be 18 as per PBPK manuscript 30

!Note, Table 6 in Ward paper

- max value of 252 +/

- 12.9 31

! table matches file 32

CLEARIT 33

CDMICE 34

SET TSTOP=4 35

SET IVDOSE=100. 36

37

PROCED PMWARDGD18IV1 38

P LOT /D=MWARDGD18IV1, CVB 39

END 40

41

DATA MWARDGD18IV1(T,CVB) 42

0.25 252 43

0.52 242.2 44

1.0 222.7 45

2 176.4 46

3 134.2 47

3.5 94.41 48

END 49DRAFT

– DO NOT CITE OR QUOTE B

- 59 1

! Oral 2

3

PROCED MDORGD8PO15 4

!Ward et al ., cmd file 5

!Note, Table 6 in Ward paper

- max value of 1610 +/

- 704 6

! Table and file match w/in round off 7

!Data must be from Dorman 8

!Dorman Teratology, 1995, Fig. 1 9

!within error for Digitiz data the same 10

CLEARIT 11

CDMICE 12

SET TSTOP=24, DOSE=1500 13

END 14

15

PROCED PMDORGD8PO15 16

PLOT /D=MDORGD8PO15, CVB 17

END 18

19

DATA MDORGD8PO15(T,CVB) 20

1 1609.6 21

2 1331.2 22

4 1241.6 23

8 707 .2 24

16 160.0 25

24 38.4 26

END 27

28

PROCED MWARDGD18PO25 29

!Ward et al ., DMD, 1996 30

!Note, Table 6 in Ward paper

- max value of 3205 +/

- 291 31

CLEARIT 32

CDMICE 33

SET TSTOP=24, DOSE=2500 34

END 35

36

PROCED PMWARDGD18PO25 37

PLOT /D=MWARDGD18PO25, CVB 38

END 39

40

!from cmd file, replaced with digitized 41

!DATA MWARDGD18PO25(T,CVB) 42

!0.25 2770. 43

!0.5 3299. 44

!1 3336. 45

!2 3502. 46

!4 3217. 47

!6 2999. 48

!10 2036. 49DRAFT

– DO NOT CITE OR QUOTE B

- 60 !12 1832. 1

!15 949.1 2

!18 403.5 3

!21 40.47 4

!24. 16.03 5

!END 6

7

!Digitizit data 8

DATA MWARDGD18PO25(T,CVB) 9

0.5 2024 10

1 2554 11

2 3193 12

4 3002 13

6 2933 14

10 1976 15

12 192 2 16

15 1339 17

18 1033 18

21 832 19

24 580 20

END 21

22

!Inhalation 23

24

PROCED MDOR8IN10 25

! Ward et al ., TAP 1997 26

!Note, Table 6 in Ward paper

- max value of 2080 +/

- 800 27

!Fig 7? Table 6 attributes to Dorman 28

!Digitizit of Dorman Fig 2 matches cmd file 29

!actual exposure ppm 9900 30

C LEARIT 31

CDMICE 32

SET TCHNG=6, CONCppm=9900, tstop=36 33

END 34

35

PROCED PMDOR8IN10 36

PLOT /D=MDOR8IN10, CVB 37

END 38

39

DATA MDOR8IN10(T,CVB) 40

1 771.2 41

2 1017.6 42

4 1788.8 43

6 2076.8 44

8 2281.6 45

16 1152.0 46

24 268.8 47

END 48

49DRAFT

– DO NOT CITE OR QUOTE B

- 61 PROCED MDOR8IN15 1

! Ward et al ., TAP 1997 2

!Note, Table 6 in Ward paper

- max value of 7136 +/

- 736 3

!Fig 7? Table 6 attributes to Dorman 4

!Digitizit of Dorman Fig 2 matches cmd file 5

CLEARIT 6

CDMICE 7

SET TCHNG=6, CONCppm=15000, tstop=36 8

SET vchc=5000000000 9

END 10

11

PROCED PMDOR8IN15 12

PLOT /D=MDOR8IN15, CVB 13

END 14

15

DATA MDOR8IN15(T,C VB) 16

1 1475.2 17

2 2486.4 18

4 4588.8 19

6 7123.2 20

8 5888.0 21

16 3456.0 22

24 1446.4 23

END 24

25

!Files above provided in cmd file from Ward PBPK model, TAP 1997 26

!Files below added for this evaluation, 27

!sources described in proc files and in notebook 28

29

PROCED MROGGD 7IN10 30

! Rogers et al ., Teratology, 1997 31

! Actual Values kindly Provided by Rogers 32

CLEARIT 33

CDMICE 34

SET TCHNG=7, CONCppm=1,0000, tstop=36 35

SET vchc=500000000,bw=0.032 36

END 37

38

PROCED PMROGGD7IN10 39

PLOT /D=MROGGD7IN10, CVB 40

END 41

42

DATA MROGGD7IN10(T,CVB) 43

1 930 44

4 2800 45

6 3360 46

7 3990 47

7.5 3980 48

8 4120 49DRAFT

– DO NOT CITE OR QUOTE B

- 62 9 3270 1

12 2630 2

16 1690 3

26 60 4

END 5

6

PROCED MROGGD6IN1 7

CLEARIT 8

!Rogers et al ., Teratology, 1993 9

!Rogers data from GD 6 and 10 10

!In Table 2 11

CDMICE 12

SET TCHNG=7, CONCppm=1,000, tstop=36,vchc=500000000,bw=0.032 13

END 14

15

PROCED PMROGGD6IN1 16

PLOT /D=MROGGD6IN1, CVB 17

END 18

19

DATA MROGGD6IN1(T,CVB) 20

7 63 21

7 131 22

END 23

24

25

PROCED MROGGD6IN2 26

! Rogers et al ., Teratology, 1993 27

!Rogers data from GD 6 and 10 28

!In Table 2 29

CLEARIT 30

CDMICE 31

SET TCHNG=7, CONCppm=2000, tstop=36, vchc=500000000,bw=0.032 32

END 33

34

PROCED PMROG GD6IN2 35

PLOT /D=MROGGD6IN2, CVB 36

END 37

38

DATA MROGGD6IN2(T,CVB) 39

7 487 40

7 641 41

END 42

43

PROCED MROGGD6IN5 44

! Rogers et al ., Teratology, 1993 45

!Rogers data from GD 6 and 10 46

!In Table 2 47

CLEARIT 48

CDMICE 49DRAFT

– DO NOT CITE OR QUOTE B

- 63 SET TCHNG=7, CONCppm=5000, tstop=36,vchc=500000000,bw=0.032 1

END 2

3

PROC ED PMROGGD6IN5 4

PLOT /D=MROGGD6IN5, CVB 5

END 6

7

DATA MROGGD6IN5(T,CVB) 8

7 2126 9

7 1593 10

END 11

12

PROCED MROGGD6IN10 13

! Rogers et al ., Teratology, 1993 14

!Rogers data from GD 6, 10, 15 15

!In Table 2 16

CLEARIT 17

CDMICE 18

SET TCHNG=7, CONCppm=1,0000, tstop=36,vchc=500000000,bw=0 .032 19

END 20

21

PROCED PMROGGD6IN10 22

PLOT /D=MROGGD6IN10, CVB 23

END 24

25

DATA MROGGD6IN10(T,CVB) 26

7 4653 27

7 4304 28

7 3655 29

END 30

31

!Human inhalation dta 32

33

PROCED HJOHIN1 34

!Ernstgard et al. SOT poster 200 ppm human 35

!Digitized from Fig 2 36

!Also personal communication

- Ernstgar d 37

!QPC from Johanson et al . Scand J. Work Env. 86 =52.6 38

!If Assume value = alveolar. similar to Astrand '83 value of 56 L/hr/kr^0.75 39

!Fracin

- 50% of total (from poster) ~76% 40

!QCC from Corley et al TAP 129, 1994 41

CLEARIT 42

HUMAN 43

SET TCHNG=2, CONCppm=100, tsto p=16 44

SET QPC=52.6,qcc=26,vchc=500000000 45

END 46

47

PROCED PHJOHIN1 48

PLOT /D=HJOHIN1, CVB 49DRAFT

– DO NOT CITE OR QUOTE B

- 64 END 1

2

DATA HJOHIN1(T,CVB) 3

0.20 0.87 4

0.46 1.50 5

0.97 2.31 6

1.46 3.24 7

1.91 3.65 8

2.17 3.52 9

2.50 2.55 10

2.91 2.23 11

3.51 1.59 12

4.01 1.72 13

5.02 0.41 14

6.00 0.50 15

9.24 0.12 16

END 17

18

PROCED HJOHIN2 19

!Ernstgard et al . SOT poster 200 ppm human 20

!Digitized from Fig 2 21

!Also personal communication

- Ernstgard 22

!QPC from Johanson et al . Scand J. Work Env. 86 =52.6 23

!If Assume value = alveolar. similar to Astrand '83 value of 56 L/hr/kr^0.75 24

!Fracin

- 50% of t otal (from poster) ~75% 25

!QCC from Corley et al TAP 129, 1994 26

CLEARIT 27

HUMAN 28

SET TCHNG=2, CONCppm=200, tstop=16 29

SET QPC=52.6,qcc=26,vchc=500000000 30

END 31

32

PROCED PHJOHIN2 33

PLOT /D=HJOHIN2, CVB 34

END 35

36

DATA HJOHIN2(T,CVB) 37

0.22 1.63 38

0.49 2.92 39

0.92 4.76 40

1.47 6.30 41

1.90 7.65 42

2.16 6.20 43

2.47 5.49 44

2.91 4.96 45

3.50 3.64 46

4.00 3.43 47

4.99 1.94 48

5.97 1.03 49DRAFT

– DO NOT CITE OR QUOTE B

- 65 8.90 0.21 1

END 2

3

PROCED HOSTERIN2 4

! Osterloh et al ., JOEM 1996 5

! Digitized data provided by EPA 6

! Subtracted background from exposure blood levels 7

CLEARIT 8

HUMAN 9

SET TCHNG=4, CONCppm= 200, tstop=16 10

SET vchc=500000000, BW=78.2 11

END 12

13

PROCED PHOSTERIN2 14

PLOT /D=HOSTERIN2, CVB 15

END 16

17

DATA HOSTERIN2(T,CVB) 18

0.05 0.54 19

0.25 1.39 20

0.50 1.82 21

0.75 2.28 22

1.00 2.42 23

1.50 2.94 24

2.00 3.37 25

2.50 3.90 26

3.00 4.21 27

3.50 4.61 28

4.00 4.82 29

5.00 2.99 30

6.00 2.30 31

7.00 1.40 32

7 .95 1.07 33

END 34

35

PROCED HBATIN82 36

!Batterman et al ., Int Arch Occ Health 1998 37

!Digitized Data 38

CLEARIT 39

HUMAN 40

SET TCHNG=2, CONCppm=800, tstop=16 41

SET vchc=500000000 42

END 43

44

PROCED PHBATIN82 45

PLOT /D=HBATIN82, CVB 46

END 47

48

DATA HBATIN82(T,CVB) 49DRAFT

– DO NOT CITE OR QUOTE B

- 66 2.223 13.658 1

2.495 13.282 2

2. 742 11.928 3

3.230 9.456 4

4.231 6.197 5

5.247 3.953 6

6.262 2.325 7

7.251 1.551 8

8.216 1.176 9

END 10

11

PROCED HBATIN81 12

!Batterman et al ., Int Arch Occ Health 1998 13

!Digitized Data 14

CLEARIT 15

HUMAN 16

SET TCHNG=1, CONCppm=800, tstop=16 17

SET vchc=500000000 18

END 19

20

PROCED PHBATIN81 21

PL OT /D=HBATIN81, CVB 22

END 23

24

DATA HBATIN81(T,CVB) 25

1.096 6.477 26

1.398 6.136 27

1.644 5.345 28

2.143 4.270 29

3.178 2.661 30

4.188 1.307 31

5.199 0.732 32

6.266 0.552 33

7.292 0.356 34

8.209 0.093 35

END 36

37

PROCED HBATIN830 38

!Batterman et al ., Int Arch Occ Health 1998 39

!Digitized Data 40

!body we ight not provided 41

CLEARIT 42

HUMAN 43

SET TCHNG=0.5, CONCppm=800, tstop=16 44

SET vchc=500000000 45

END 46

47

PROCED PHBATIN830 48

PLOT /D=HBATIN830, CVB 49DRAFT

– DO NOT CITE OR QUOTE B

- 67 END 1

2

DATA HBATIN830(T,CVB) 3

0.579 4.608 4

0.857 4.685 5

1.137 4.870 6

1.650 3.452 7

2.650 2.082 8

3.662 0.910 9

4.693 0.316 10

5.713 0.320 11

6.643 0.292 12

7.696 0.547 13

END 14

15

PROCED HSEDIN231 16

!Sedivec et al ., Int Arch Occ Health 1981 17

!Digitized Data 18

!Note, urine volumes not given, these are estimates 19

!urine production of 0.75 mg/hr, this for info purposes only!!! 20

CLEARIT 21

HUMAN 22

SET TCHNG=8, CONCppm= 231, tstop=24 23

SET vchc=500000000 24

END 25

26

PROCED PHSEDIN231 27

PLOT /D=HSEDIN231, Metb 28

END 29

30

DATA HSEDIN231(T,Metb) 31

0.043 0.0042 32

2.174 0.33 33

4.478 0.87 34

6.478 1.46 35

8.522 2.15 36

10.348 2.63 37

12.130 2.91 38

14.044 3.07 39

18.870 3.32 40

23.696 3.52 41

END 42

43

PROCED HSEDIN157 44

!Sedivec et al ., Int Arch Occ Health 1981 45

!Digitized Data 46

!Note, urine volumes not given, these are estimates 47

!urine production of 0.75 mg/hr, this for info purposes only!!!CLEARIT 48

HUMAN 49DRAFT

– DO NOT CITE OR QUOTE B

- 68 SET TCHNG=8, CONCppm=157, tstop=24 1

SET vchc=500000000 2

END 3

4

PROCED PHSEDIN157 5

P LOT /D=HSEDIN157, Metb 6

END 7

8

DATA HSEDIN157(T,Metb) 9

0.126 0.0038 10

2.204 0.228 11

4.242 0.576 12

6.196 0.975 13

8.326 1.47 14

10.163 1.81 15

12.094 2.00 16

14.016 2.12 17

18.8966 2.34 18

23.776 2.53 19

END 20

21

PROCED HSEDIN78 22

!Sedivec et al ., Int Arch Occ Health 1981 23

!Digitized Data 24

!Note , urine volumes not given, these are estimates 25

!urine production of 0.75 mg/hr, this for info purposes only!!! 26

CLEARIT 27

HUMAN 28

SET TCHNG=8, CONCppm=78, tstop=24 29

SET vchc=500000000 30

END 31

32

PROCED PHSEDIN78 33

PLOT /D=HSEDIN78, Metb 34

END 35

36

DATA HSEDIN78(T,Metb) 37

0.03 0 .013 38

2.06 0.189 39

3.96 0.397 40

6.09 0.652 41

8.09 0.820 42

10.11 0.933 43

11.93 1.02 44

13.92 1.09 45

18.89 1.27 46

END 47

48

!AUC, C

max estimation procedures 49DRAFT

– DO NOT CITE OR QUOTE B

- 69 Proced mousin 1

!To determine AUC for 7 hr exposure in mice 2

CLEARIT 3

CDMICE 4

SET TCHNG=7, tstop=24 5

SET vchc=50000000000 6

SET CONC ppm=1 7

start /nc 8

d concppm,AUCB,amet,cvb 9

SET CONCppm=5 10

start /nc 11

d concppm,AUCB,amet,cvb 12

SET CONCppm=10 13

start /nc 14

d concppm,AUCB,amet,cvb 15

SET CONCppm=25 16

start /nc 17

d concppm,AUCB,amet,cvb 18

SET CONCppm=50 19

start /nc 20

d concppm,AUCB,amet,cvb 21

SET CONCppm=75 22

start /nc 23

d concppm,AUCB,amet,cvb 24

SET CONCppm=100 25

start /nc 26

d concppm,AUCB,amet,cvb 27

SET CONCppm=175 28

start /nc 29

d concppm,AUCB,amet,cvb 30

SET CONCppm=208.3 31

start /nc 32

d concppm,AUCB,amet,cvb 33

SET CONCppm=250 34

start /nc 35

d concppm,AUCB,amet,cvb 36

SET CONCppm=325 37

start /nc 38

d concppm,AUCB,amet,cvb 39

SET CONCppm=500 40

start /nc 41

d concppm,AUCB,amet,cvb 42

SET CONCppm=750 43

start /nc 44

d concppm,AUCB,amet,cvb 45

SET CONCppm=1,000 46

start /nc 47

d concppm,AUCB,amet,cvb 48

SET CONCppm=2000 49DRAFT

– DO NOT CITE OR QUOTE B

- 70 start /nc 1

d concppm,AUCB,amet,cvb 2

SET CONCppm=2500 3

start /nc 4

d concppm,AUCB,amet,cvb 5

SET CONCppm=5000 6

start /nc 7

d concppm,AUCB,amet,cvb 8

SET CONCppm=1,0000 9

start /nc 10

d concppm,AUCB,amet,cvb 11

SET CONCppm=50000 12

start /nc 13

d concppm,AUCB,amet,cvb 14

END 15

16

Proced mousinC 17

!To determine 7 hr C

max , note

- not at SS 18

CLEARIT 19

CDMICE 20

S ET TCHNG=7, tstop=7,VCHC=50000000000 21

SET CONCppm=1 22

start /nc 23

d conc ppm,cvb 24

SET CONCppm=10 25

start /nc 26

d concppm,CVB 27

SET CONCppm=50 28

start /nc 29

d concppm,CVB 30

SET CONCppm=100 31

start /nc 32

d concppm,CVB 33

SET CONCppm=250 34

start /nc 35

d concppm,CVB 36

SET CONCppm=500 37

sta rt /nc 38

d concppm,CVB 39

SET CONCppm=1,000 40

start /nc 41

d concppm,CVB 42

SET CONCppm=2000 43

start /nc 44

d concppm,CVB 45

SET CONCppm=2500 46

start /nc 47

d concppm,CVB 48

SET CONCppm=5000 49DRAFT

– DO NOT CITE OR QUOTE B

- 71 start /nc 1

d concppm,CVB 2

SET CONCppm=1,0000 3

start /nc 4

d concppm,CVB 5

SET CONCppm=50000 6

star t /nc 7

d concppm,CVB 8

END 9

10

Proced humin 11

! To determine 24 hr AUC, C

max at SS for human 12

CLEARIT 13

human 14

SET TCHNG=360, tstop=1,000 15

SET vchc=5000000000, points=48 16

SET Concppm=1 17

Start /nc 18

d concppm,aucBb,cvb 19

SET CONCppm=10 20

start /nc 21

d concppm,aucBb,cvb 22

SET CONCpp m=50 23

start /nc 24

d concppm,aucBb,cvb 25

SET CONCppm=100 26

start /nc 27

d concppm,aucBb,cvb 28

SET CONCppm=250 29

start /nc 30

d concppm,aucBb,cvb 31

SET CONCppm=500 32

start /nc 33

d concppm,aucBb,cvb 34

SET CONCppm=625 35

start /nc 36

d concppm,aucBb,cvb 37

SET CONCppm=750 38

start /nc 39

d concppm,a ucBb,cvb 40

SET CONCppm=875 41

start /nc 42

d concppm,aucBb,cvb 43

SET CONCppm=1,000 44

start /nc 45

d concppm,aucBb,cvb 46

SET CONCppm=2000 47

start /nc 48

d concppm,aucBb,cvb 49DRAFT

– DO NOT CITE OR QUOTE B

- 72 SET CONCppm=2500 1

start /nc 2

d concppm,aucBb,cvb 3

SET CONCppm=5000 4

start /nc 5

d concppm,aucBb,cvb 6

SET CONCppm= 1,0000 7

start /nc 8

d concppm,aucBb,cvb 9

SET CONCppm=50000 10

start /nc 11

d concppm,aucBb,cvb 12

END 13

14

Proced humor 15

! To determine 24 hr AUC 16

! Oral exposure 17

CLEARIT 18

human 19

SET TCHNG=1,000, tstop=1,000 20

SET vchc=5000000000, points=48 21

SET dose=0.1 22

SET ODS=1 23

start /nc 24

d dos e,aucBb 25

SET dose=1 26

Start /nc 27

d dose,aucBb 28

SET dose=5 29

start /nc 30

d dose,aucBb 31

SET dose=10 32

start /nc 33

d dose,aucBb 34

SET dose=50 35

start /nc 36

d dose,aucBb 37

SET dose=100 38

start /nc 39

d dose,aucBb 40

SET dose=250 41

start /nc 42

d dose,aucBb 43

SET dose=350 44

start /nc 45

d dose,aucBb 46

SE T dose=500 47

start /nc 48

d dose,aucBb 49DRAFT

– DO NOT CITE OR QUOTE B

- 73 SET dose=750 1

start /nc 2

d dose,aucBb 3

SET dose=1,000 4

start /nc 5

d dose,aucBb 6

SET dose=2500 7

start /nc 8

d dose,aucBb 9

SET dose=5000 10

start /nc 11

d dose,aucBb 12

S ODS=0 13

END 14

15

!Procedural blocks for all non

- pregnant rat data 16

!Not calibra ted!!!!!! 17

!Procs from Ward CMD file 18

19

PROCED WARDIV25 20

CLEARIT 21

SDRAT 22

SET TSTOP=48. 23

SET IVDOSE=2500. 24

END 25

26

PROCED PWARDIV25 27

PLOT /D=WARDIV25, CVB 28

END 29

30

DATA WARDIV25(T,CVB) 31

0.072 4849 32

0.168 3926 33

0.24 2965 34

0.504 2836 35

1.008 3248 36

1.992 2589 37

3 2619 38

4.008 25 14 39

7.008 2315 40

19.992 1495 41

22.992 1272 42

24 1214 43

25.992 982 44

28.008 957 45

30 860 46

37.992 238 47

39 200 48

40.008 150 49DRAFT

– DO NOT CITE OR QUOTE B

- 74 40.992 167 1

43.008 77 2

END 3

4

PROCED WARDIV1 5

CLEARIT 6

SDRAT 7

SET TSTOP=8 8

SET IVDOSE=100., tchng=0.016 9

END 10

11

PROCED PWARDIV1 12

PLOT /D=RG0IV1, CVB 13

END 14

15

DATA WARDIV1 (T,CVB) 16

0.072 141.7 17

0.168 121.8 18

0.24 111.6 19

0.504 99.7 20

0.744 97.4 21

1.008 86.3 22

1.488 80.3 23

1.992 58 24

3 44.4 25

4.008 22.8 26

4.992 10.9 27

6 3.8 28

7.008 1.4 29

END 30

31

PROCED WARDPO25 32

CLEARIT 33

cdmice 34

SET BW=0.3 35

SET TSTOP=48 36

SET DOSE=2500 37

END 38

39

PROCED P WARDPO25 40

PLOT /D=WARDPO25, CVB 41

END 42

43

DATA WARDPO25(T,CVB) 44

0.072 862.7 45

0.168 1243 46

0.24 1356 47

0.504 1621 48

1.008 1641 49DRAFT

– DO NOT CITE OR QUOTE B

- 75 1.992 1611 1

3 1869 2

4.008 1896 3

7.008 2181 4

24 1365 5

25.992 1081 6

28.008 921 7

30 958.4 8

31.008 969.8 9

45 42.9 10

46.008 27.1 11

46.992 16.4 12

48 23.9 13

49.008 41.9 14

49.992 13.1 15

52.008 2.3 16

52.992 1 17

END 18

19

PROCED WARDPO1 20

CLEARIT 21

cdmice 22

SET BW=0.3 23

SET DOSE=100, tstop=8 24

END 25

26

PROCED PWARDPO1 27

PLOT /D=WARDPO1, CVB 28

END 29

30

DATA WARDPO1(T,CVB) 31

0.072 85.5 32

0.168 95.6 33

0.24 95.5 34

0.504 91.1 35

0.744 86.6 36

1.008 80.6 37

1.488 71.3 38

1.992 61.1 39

3 45.1 40

4.008 27.4 41

4.992 16.4 42

6 8.9 43

7.008 4.2 44

END 45DRAFT

– DO NOT CITE OR QUOTE B

- 76 B.3.4. Procedural .m files for reproducing the results in Appendix B and Chapter 3

B.3.4.1. Key to ACSL Extreme v2.5.0.6 .m files

Found in the Runtime Files Folder 1

CDmice.m Sets parameters for (CD) mouse simulat ions 2

Rogers

- mouse

- inhal.m Figure B

- 2

- Simulations of mouse inhalation exposures from GD 6, 3

7, 8 and 10 mice from Rogers et al

. , 1993. 4

Perkins Dorm

- mouse

- inh.m Figure B

- 3

- Simulations of inhalation exposures to MeOH in NP 5

mice from Perkins et al

. 1995 (8 hr exposures) a nd GD 8 mice from 6

Dorman et al

. 1995 (6 hr exposures) 7

Ward_mouse_GD18.m Figure B

- 4

- Oral exposures to MeOH in pregnant and non

- pregnant 8

mice Data from Dorman et al ., 1995 and Ward et al ., 1997 9

Ward

- mouse

- iv.m Figure B

- 5

- Simulations of m ou se IV exposures to MeOH from 10

Ward et al

., 1997 11

Apaja

- mouse

- drink.m Calculates internal doses for mice in Apaja (1980) 12

13

SDrat.m Sets parameters for Sprague

- Dawley (SD) rat simulations 14

F344rat.m Sets parameters for F344 rat simulations 15

Ward

- rat

- iv.m Figure B

- 10

– Simulations rat IV exposures from Ward et al

. , 1997 and 16

Horton et al

. , 1992 17

Horton

- rat

- inhal.m Figure B

- 11

– Simulations rat inhalation exposures from Horton et al

. , 1992 18

Ward

- rat

- oral.m Figure B

- 12

– Simulations rat oral exposures from Ward et al

. , 1997 19

Nedo

- rat

- inhal

- devpmt

- rat.m Figure B

- 13

– Simulations rat inhalation (bioassay) exposures 20

(200, 500, 1,000, 2000, & 5000 ppm) 21

Nedo

- rat

- inhal

- cancer.m Simulations for NEDO F344 rat cancer inhalation study 22

rat

- infu

- sims.m Figure B

- 14

– Simulations rat " oral" exposures (bioassay doses, but using 23

liver infusion; for illustration only) 24

25

humanset.m Sets human MeOH PBPK parameters 26

Sedivec_human_inh.m Figure B

- 16

- Simulation of human u rinary MeOH elimination 27

following Inhalation exposures from Sedivec et al

. 1981 28

Batterman_human_inh.m Figure B

- 17 (upper panel)

- Simulations of human inhalation exposure 29

data of Batterman et al

. 1998 30

Osterloh_human_inh.m Figure B

- 17 (lower panel)

- Simulations of human inhalation exposure 31

data of Osterloh et al

. 1996 32

Ernstgard_ human_inh.m Figure B

- 18

- Simulations of human inhalation exposures to MeOH 33

from Ernstgard et al

. 2005 34

35

mouse_inh_sim.m Produces data for Table B

- 5, mouse inhalation exposures 36

human_inh_sim.m Produces data for Table B

- 5, human inhalation expsoures 37

human_or al_sim.m Produces data for Table B

- 5, human oral exposures 38

human_drink_compare.m Figure B

- 24 and Table B

- 9 (altenate drinking pattern comparison) 39

40

Found in the Sensitivity Analysis Files Folder 41

Fig_B

- 6 Sensitivity of the mouse model to metabolic parameters (e.g., Km and V

max ) 42

for the inhalation route 43

Fig_B

- 7 Sensitivity of the mouse model to flow parameters (e.g., blood flow to liver) 44

and to the rest

- of

- body partition coefficient for the inhalation route 45DRAFT

– DO NOT CITE OR QUOTE B

- 77 Fig_B

- 8 Sensitivity analysis of the rat model to oral absorption parameters for a bolus 1

oral exposure (1,000 mg/kg) 2

B.3.4.1. [B.3.4.2.] Code for .m files

% File CDmice.m 3

% Sets parameters for mouse simulations, MeOH PBPK model 4

CONCPPM=10; WESITG=0; WEDITG=0; CINT

- 0.1; 5

start @nocallback 6

BW=0.03; TSTOP=24; TCHNG=7; RES T=20000; WORK=20000; 7

IVDOSE=0; DOSE=0; DRDOSE=0; RATS=0; KLOSS=0; LIVR0=0; 8

PL=1.06; PF=0.083; PR=0.66; PLU=1; PB=1350; 9

QPC=25.4; QCC=25.4; FRACIN=0.73; KFEC=0; 10

QLC=0.25; QFC=0.05; 11

KM=12; VMAXC=14.3; K1C=0.0; KAS=0.0; KLLC=0; 12

VMAX2C=19; KM2=210; KAI=0.5; KS I=5.0; 13

VAC=0.0123; VFC=0.07; VLC=0.055; VLUC=0.0073; VVBC=0.0368; 14

CONCPPM=0.0; IVDOSE=0.0; DOSE=0.0; DWDOSE=0; MULTE=0; RDRINK=1; 15

% Volumes from Brown et al 16

% Mouse QPC avg from Brown 29; 24 used in Corley et al and others 17

% AVG of measured vent rates by Perkins et al 25.4 L/hr/kg^0.75 18

% Blood volume 4.9% total. As per Brown 25:75 split art:ven 19

% Metab originally from Ward et al

- KldC for mice = 0 20

21

% use mouseINH_fit

- params.m % File contents copied below 22

% Updated parameters as obtained by Paul Schlosser, U.S. EPA 23

% August 11, 2009 [this file updated] 24

25

% Values generated through parameter estimation script 'mouseINH_fit.m' 26

VMAX2C = 3.222500e+00; KM2 = 660; VMAXC = 19; KM = 5.2; FRACIN = 6.650939e

- 01; 27

28

% Values generated through parameter estimation script 'mouseor_fit.m' 29

VASC = 1.833246e+03; KSI = 2.2; KAI = 0.33; MASC = 620; 30

_____________________________________________________________________________________________ 31

% File Rogers_mouse_inhal.m (Figure B

- 2) 32

% Produces MeOH PBPK figures for Rogers' mouse in halation exposures 33

% Variables in the plot command are case sensitive 34

use CDmice 35

% set mouse parameters 36

%

------- DATA BLOCKS 37

% These data blocks taken directly from MeOH CBMMv3.cmd 38

% Data for are T (hours), CV (mg/L) 39

% semicolons (";") creates a new line in a data file 40

41

% Rogers et al., Teratology, 1997 42

D7IN10 = [1, 930; 4, 2800; 6, 3360; 7, 3990; 7.5, 3980; 43

8, 4120; 9, 3270; 12, 2630; 16, 1690; 26, 60]; 44

45

%Rogers et al., Teratology, 1993 46

D6IN1 = [7, 63; 7, 131]; D6IN2 = [7, 487; 7, 641]; 47

D6IN5 = [7, 2126; 7, 1593]; D6IN7p5 = [7, 2801; 7, 3455]; 48

D6IN10 = [7, 4653; 7, 4304]; D6IN15 = [7, 7720; 7, 7394]; 49

50

%

----- RUN MODEL 51

RATS=0.0; KLOSS=0.0; %

- > open chamber 52

TCHNG=7; CONCPPM=10000; TSTOP=27.0; MULTE=0; BW=0.032; 53

CINT=TSTOP/1000; cs=[]; prepare @clear T CVB 54DRAFT

– DO NOT CITE OR QUOTE B

- 78 for CONCPPM=[1, 2, 5, 7.5, 10, 15]*1000 1

start @nocallback 2

cs=[cs,_cvb]; 3

% Since TSTOP & CINT not changing, assume _t also the same. 4

end 5

6

%

----- PLOT COMMANDS 7

% The rogers.aps file will retain changes made using the plot 8

% editor as long as the editor is called by clicking the 9

% words EDIT PLOT PROPERTIES not the little icon in the 10

% properties dialogue box 11

plot(_t,cs(:,1), _t,cs(:,2), _t,cs(:,3), _t,cs(:,4), _t,cs(:,5), _t,cs(:,6), ... 12

D6IN1(:,1),D6IN1(:,2),D6IN2(:,1),D6IN2(:,2),D6IN5(:,1), D6IN5(:,2), ... 13

D6IN7p5(:,1),D6IN7p5(:,2),D6IN10(:,1),D6IN10(:,2), ... 14

D7IN10(:,1),D7IN10(:,2),D6IN15(:,1),D6IN15(:,2), 'rogers.aps') 15

16

%

----- WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL 17

% Cannot save data with different # of rows to the same table. 18

cs=[_t,cs]; 19

save cs @file='Rogersplotdata.csv' @format=ASCII @separator=ascii 20

_____________________________________________________________________________________________ 21

% File: PerkinsDorm

- mouse

- inh.m (Figure B

- 3) 22

% Produces MeOH PBPK simula tions Perkins 1995 inhalation exposures, 23

% and Ward 1997 (pregnant) and Dorman 1995 for comparison) 24

% Includes all nonpregnant and "early" GD (<GD 10) sets 25

% GD18 not included 26

27

%

------- DATA BLOCKS 28

% These data blocks taken directly from MeOH CBMMv3.cm d 29

% Data for are T (hours), CV (mg/L) 30

% Perkins et al, FAT, 1995 31

Perk25 =[2, 414; 4, 453; 6, 586; 32

8.25, 694; 12, 282; 16 0.6]; 33

%Perkins et al, FAT, 1995 34

Perk50= [1, 666; 2, 905; 3, 1370; 35

4, 1480; 6, 2090; 8.25, 2310; 36

12, 1420; 16, 597; 20, 276; 24 , 36.2]; 37

%Perkins et al, FAT, 1995 38

Perk100=[2, 2080.0; 4, 2530; 6, 3350; 39

8.25, 3350; 12, 2370; 16, 1830; 40

20, 1080; 24, 591; 28, 44.6]; 41

%Ward et al., TAP 1997 42

Dor815=[1, 1475.2; 2, 2486.4; 4, 4588.8; 43

6, 7123.2; 8, 5888; 16, 3456; 24, 1446.4]; 44

45

%table 6, TAP 1997 46

%estimate all Cmax at end of exposure 47

%this is to compare model fits to published values that may be different from cmd file 48

%the last value (2300) is not in table, it is estimated from figure in Perkins et al from 5000 ppm exposure 49

% 8 3250

- non pregnant mouse 10,000 ppm 50

% 6 7136

- GD 8 mouse 15,000 ppm 51

% 8 2300

- non preg mouse 15,000 ppm 52

53

%

----- RUN MODEL 54

use CDmice 55

RATS=0; KLOSS=0; %

- > open chamber 56

MULTE=0; TSTOP=24; CONCPPM=2500; QPC = 29; QCC=29; TCHNG=8; 57DRAFT

– DO NOT CITE OR QUOTE B

- 79 start @nocallback 1

Cs25 = _cvb ; Ts25 = _t; 2

CONCPPM=5000; QPC=24; QCC=24; start @nocallback 3

Cs50 = _cvb; Ts50 = _t; 4

CONCPPM=10000; TSTOP=36; QPC=21; QCC=21; start @nocallback 5

Cs100 = _cvb; Ts100 = _t; 6

use CDmice 7

RATS=0; KLOSS=0; %

- > open chamber 8

TCHNG=6; CONCPPM=15000; TSTOP=36; start @nocallback 9

10

%

----- PLOT COMMANDS 11

% The .aps file will retain changes made using the plot 12

% editor as long as the editor is called by clicking the 13

% words EDIT PLOT PROPERTIES not the little icon in the 14

% properties dialogue box 15

plot(Ts25, Cs25, Ts50, Cs50, Ts100, Cs100,_t, _cvb, ... 16

Perk25(:,1), Perk25(:,2), Perk50(:,1), Perk50(:,2), ... 17

Perk100(:,1), Perk100(:,2),Dor815(:,1), Dor815(:,2), 'inhalation.aps') 18

19

%

----- WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL 20

% Can't save data with dif ferent # of rows to the same table. 21

mytable1 = [Ts25, Cs25, Ts50, Cs50, Ts100, Cs100,_t, _cvb]; 22

save mytable1 @file='PerkinDormanplotdata.csv' @format=ASCII @separtor=comma 23

___________________________________________________________________________________ __________ 24

% File WardGD18.m 25

% Creates Figure B

- 4, including Ward et al 1997 NP and GD 18 mouse data 26

% and Dorman et al 1995 GD 8 mouse data. 27

CDmice 28

TSTOP=25; DOSE=1500; CONCPPM=0; MULTE=0; 29

prepare @clear T CVB 30

start @nocallback 31

T1=_t;P1=_cvb; 32

DOSE=2500; s tart @nocallback 33

D15=[1, 1609.6; 2, 1331.2; 4, 1241.6; 34

8, 707.2; 16, 160; 24, 38.4]; 35

D25a=[0.5, 2370; 0.96, 2645; 1.44, 2705; 2, 2719; 36

2.2, 2781; 3, 2704; 4, 2370; 5, 2617; 6, 2516; 37

7, 2635; 8, 2213; 9, 2370; 10, 2028; 11, 1916; 38

12, 1347; 13, 1467; 14, 1354; 15, 1175; 16, 864.3; 39

17, 745.2; 18, 422.4; 19, 428; 21, 243; 24, 136]; 40

D25b=[0.5, 2024; 1, 2554; 2, 3193; 4, 3002; 6, 2933; 41

10, 1976; 12, 1922; 15, 1339; 18, 1033; 21, 832; 24, 580]; 42

43

plot(D15(:,1),D15(:,2),D25a(:,1),D25a(:,2),D25b(:,1),D25b(:,2),.

.. 44

T1,P1,_t,_cvb,"wardgd18plot.aps") 45

% File Ward

- mouse

- iv.m 46

% M File for reproducing MeOH PBPK Figure B

- 5 For WARD iv mouse exposures 47

% (also Ward Pregnant Includes all nonpregnant and Pregnant) 48

____________________________________________________________ _________________________________ 49

%

------- DATA BLOCKS 50

%Taken directly from MeOH CBMMv3.cmd, values are [T (hours), CV (mg/L)] 51

%Ward et al, FAT 1995 52

NPIV25=[0.08, 4481.8; 0.25, 4132.2; 0.5, 3888; 1, 3164.8; 2, 2303.5; 53

4, 1921.5; 6, 1883.8; 8, 1620 ; 12, 838; 18, 454.7; 24, 1.41]; 54

%PROCED MWARDGD8IV25 55

GD8IV25=[0.0833, 4606.2; 0.25, 4079.5; 0.5, 3489.3; 1, 2939.6; 2, 3447.6; 56

4, 2605.0; 6, 2690.5; 8, 2574.9; 12, 1506.1; 18, 498.6; 24, 0.554]; 57DRAFT

– DO NOT CITE OR QUOTE B

- 80 %!Ward et al., DMD, 1996 1

GD18IV25=[0.0833, 4250.0; 0.25, 3 445.1; 0.5, 2936.8; 1, 2470.5; 2, 2528.1; 2

4, 2292.3; 6, 2269.4; 8, 2057.0; 12, 1805.9; 18, 1482.2; 24.0, 496.1]; 3

%Ward et al., DMD, 1996 4

GD18IV5=[0.25, 854.7; 0.5, 720.2; 1, 624.1; 5

2, 453.2; 3, 307.6; 4, 217.7; 4.5, 202.6]; 6

%Ward et al., DMD, 1996 7

GD18 IV1=[0.25, 252; 0.52, 242.2; 1, 222.7; 2, 176.4; 3, 134.2; 3.5, 94.41]; 8

9

%

----- RUN MODEL 10

use CDMICE 11

TSTOP=24.0; IVDOSE=2500; TCHNG=0.025; start @nocallback 12

CVs25 = _cvb; Ts25 = _t; TSTOP=6; IVDOSE=500; start @nocallback 13

CVs5 = _cvb; Ts5 = _t; TSTOP=4; IVD OSE=100; start @nocallback 14

CVs1 = _cvb; Ts1 = _t; IVDOSE=200; start @nocallback 15

16

%

----- PLOT COMMANDS 17

% The .aps file will retain changes made using the plot 18

% editor as long as the editor is called by clicking the 19

% words EDIT PLOT PROPERTIES not the litt le icon in the 20

% properties dialogue box 21

plot(Ts25, CVs25, Ts5, CVs5, Ts1, CVs1, NPIV25(:,1), NPIV25(:,2),... 22

GD8IV25(:,1), GD8IV25(:,2), GD18IV25(:,1), GD18IV25(:,2), ... 23

GD18IV5(:,1), GD18IV5(:,2), GD18IV1(:,1), GD18IV1(:,2), 'iv.aps') 24

plot(_t, _cvb, T s1, CVs1, GD18IV1(:,1), GD18IV1(:,2), 'ivb.aps') 25

26

%

----- WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL 27

% Cant save data with different # of rows to the same table. 28

mytable1 = [Ts25, CVs25, Ts5, CVs5, _t, _cvb, Ts1, CVs1]; 29

save mytable1 @file='Ward IV.csv' @format=ASCII @separator=comma 30

_____________________________________________________________________________________________ 31

% File Apaja

- mouse

- drink.m 32

% Calculates internal doses for mice in Apaja (1980) 33

use CDmice 34

DWDOSE=1; start @nocallback 35

DWDS =[0.045, 550; 0.045, 970; 0.045, 1800; 36

0.040, 560; 0.040, 1000; 0.040. 2100]; 37

% Above are BWs and doses for males, then females, from Apaja (1980) 38

ODS=1; TSTOP=24*3*7; MULTE=1; DAYS=6.0; simres=[]; LIVR0=0; 39

PER1=1.5; DUR1=0.75; PER2=3.0; DUR2=0.5; FNIGHT =0.8; CINT=0.01; 40

prepare @clear T CVB STOM 41

for RDRINK =0 %[1, 0] % 0

- > mouse drinking pattern 42

for i=1:length(DWDS) 43

BW=DWDS(i,1); DWDOSE=DWDS(i,2); start @nocallback 44

simres=[simres;[TDOSE*(24/TSTOP)/BW,BW,AUCBF,max(_cvb),AMF]]; 45

end 46

plot(_t,_cvb) 47

end 48

simr es=[simres(:,1)*7/6, simres]; 49

simres/100 % Print values to screen (/100) 50

TDOSE*(24/TSTOP)/BW % Check that final total dose/day is correct 51

save simres @file='Apaja_mouse_drink_sims.csv' @format=ascii @separator=comma 52

________________________________________ _____________________________________________________ 53

% File SDrat.m 54

% Sets parameters for rat simulations, MeOH PBPK model 55

CONCPPM=10; WESITG=0; WEDITG=0; TSTOP=24; TCHNG=6; MULTE=0; 56

REST=20000; WORK=20000; 57DRAFT

– DO NOT CITE OR QUOTE B

- 81 start @nocallback 1

BW=0.275; TSTOP=24; FRACIN=0.2 ; 2

IVDOSE=0; DOSE=0; CONCPPM=0; DRDOSE=0; DWDOSE=0; ODS=0; LIVR0=0; 3

QCC=16.4; QPC=16.4; QFC=0.07; QLC=0.25; 4

PL=1.06; PF=0.083; PR=0.66; PB=1350; 5

KM=6.3; VMAXC=5.0; VMAX2C=8.4; KM2=65; 6

KLLC=0.0; K1C=0.0; 7

VAC=0.0185; VFC=0.07; VLC=0.037; VLUC=0.005; VVBC=0.0 443; 8

9

KAS=10.9; KSI=6.8; KAI=0.039; KFEC=0.0; VASC=0; 10

% Just above are linear absorption params fit to 100 mg/kg 11

% oral data, w/ no fecal elimination 12

13

KAS=0.0; % Below are for saturable uptake model 14

% Values generated through parameter estimation script ' ratoral_fit.m' 15

KSI = 7.4; KAI = 0.051; KFEC = 0.029; VASC = 5.573125e+03; KMASC = 620; 16

_____________________________________________________________________________________________ 17

% File F344rat.m: parameters specific to F344 rat 18

% Created by Paul Schloss er, U.S. EPA, Aug. 2009 19

use SDrat 20

VMAXC=0; VMAX2C=22.3; KM2=100; 21

_____________________________________________________________________________________________ 22

% File: Ward

- rat

- iv.m 23

% Creates Figure B

- 9; rat MeOH PBPK model, to simulate 24

% Ward '97 rat iv 25 00 & 100 mg/kg (BW=275, SD) 25

% and Horton '92 iv 100 mg/kg (BW=100, F344) 26

rwi25 =[0.072, 4849; 0.168, 3926; 0.24, 2965; 0.5, 2836; 1, 3248; 27

2, 2589; 3, 2619; 4, 2514; 7, 2315; 20, 1495; 23, 1272; 24, 1214; 28

26, 982; 28, 957; 30, 860; 38, 238; 39, 200; 40, 150; 41, 167; 43, 77]; 29

30

% ward '97 rat iv 100 mg/kg (BW 275, SD) 31

rwi1=[0.072, 141.7; 0.168, 121.8; 0.24, 111.6; 0.5, 99.7; 0.744, 97.4; 32

1, 86.3; 1.488, 80.3; 2, 58; 3, 44.4; 4, 22.8; 5, 10.9; 6, 3.8]; 33

34

% horton '92 rat iv 100 mg/kg (BW 220, F344) 35

rhi1=[0. 24, 91.13; 0.52, 80.14; 0.90, 70.29; 1.38, 61.22; 36

1.86, 60.63; 2.79, 39.40; 4.30, 26.05; 5.81, 12.51]; 37

38

use SDrat 39

prepare @clear T CVB 40

TSTOP=48; IVDOSE=2500; TCHNG=0.016; BW=0.275; CINT=0.1; start @nocallback 41

Twi25 = _t; Cwi25 = _cvb; 42

IVDOSE=100; start @n ocallback 43

Twi1 = _t; Cwi1 = _cvb; 44

BW=0.22; start @nocallback 45

46

plot(Twi25, Cwi25, Twi1, Cwi1, _t, _cvb, rwi25(:,1), rwi25(:,2), ... 47

rwi1(:,1), rwi1(:,2), rhi1(:,1), rhi1(:,2), 'rwi2500.aps') 48

49

use F344rat 50

IVDOSE=100; BW=0.22; TCHNG=0.016; CINT=0.1; start @n ocallback 51

plot(Twi25, Cwi25, Twi1, Cwi1, _t, _cvb, rwi25(:,1), rwi25(:,2), ... 52

rwi1(:,1), rwi1(:,2), rhi1(:,1), rhi1(:,2), 'rwi100.aps') 53

_____________________________________________________________________________________________ 54

% File: Horton

- rat

- inhal .m 55

% MeOH PBPK model rat simulations for Horton '92 rat inhalation data 56

% Creates Figure B

- 10 57DRAFT

– DO NOT CITE OR QUOTE B

- 82 hi20=[0.46, 18.70; 1, 23.76; 3, 59.73; 6, 80.12 1

7, 83.25; 9, 53.49; 12, 16.54; 15, 0.91]; 2

hi12=[0.46, 4.89; 1, 8.02; 3, 20.57; 6, 26.63; 3

7, 16.12; 8, 9.28; 9, 5. 23; 10.5, 2.93; 12, 0.98]; 4

hi2=[0.48, 1.2; 3, 3.1; 6, 3.7; 6.47, 2.7; 5

7, 2.0; 8, 1.6; 9, 1.2]; 6

7

use F344rat 8

prepare @clear T CVB 9

TSTOP=16; CONCPPM=2000; TCHNG=6; BW=0.22; CINT=0.1; start @nocallback 10

t20 = _t; c20 = _cvb; 11

CONCPPM=1200; TSTOP=13; start @noc allback 12

t12 = _t; c12 = _cvb; 13

CONCPPM=200; TSTOP=10; start @nocallback 14

t2 = _t; c2 = _cvb; 15

TSTOP=16; CONCPPM=2000; TCHNG=7; start @nocallback 16

17

plot(t20, c20, t12, c12, t2, c2, hi20(:,1), hi20(:,2), ... 18

hi12(:,1), hi12(:,2), hi2(:,1), hi2(:,2), _t, _cvb, ' hi2000.aps') 19

_____________________________________________________________________________________________ 20

% File: Ward

- rat

- oral.m 21

% MeOH PBPK model rat simulations for Ward '97 rat oral data 22

% Creates Figre B

- 11 23

use SDRat 24

KAS=10.9; KSI=6.8; KAI=0.039; KFE C=0.0; VASC=0; 25

BW=0.3; ODS=0; prepare @clear T CVB 26

DOSE=100; TSTOP=10; start @nocallback 27

t1=_t;c1=_cvb; 28

DOSE=2500; TSTOP=36; start @nocallback 29

t2=_t;c2=_cvb; 30

31

KAS=0; use ratoral_fit

- params 32

DOSE=100; TSTOP=10; start @nocallback 33

t1A=_t;c1A=_cvb; 34

DOSE=2500; TSTOP=36; start @nocallback 35

t2A=_t;c2A=_cvb; 36

37

d1=[0.072, 85.5; 0.168, 95.6; 0.24, 95.5; 0.504, 91.1; 38

0.744, 86.6; 1.008, 80.6; 1.488, 71.3; 1.992, 61.1; 39

3, 45.1; 4.008, 27.4; 4.992, 16.4; 6, 8.9; 7.008, 4.2]; 40

41

d25=[0.072, 862.7; 0.168, 1243; 0.24, 1356; 0.504, 1621; 42

1.008, 1641; 1.992, 1611; 3, 1869; 4.008, 1896; 7.008, 2181; 43

24, 1365; 25.992, 1081; 28.008, 921; 30, 958.4; 31.008, 969.8]; 44

45

plot(t2,c2, t2A,c2A, d25(:,1),d25(:,2), ... 46

t1,c1, t1A,c1A, d1(:,1),d1(:,2),"wardratoralplot.aps") 47

plot(t2,c2, t2A,c 2A, d25(:,1),d25(:,2), ... 48

t1,c1, t1A,c1A, d1(:,1),d1(:,2),"wardratoralplotb.aps") 49

_____________________________________________________________________________________________ 50

% File: Nedo

- rat

- inhal

- devpmt.m 51

% MeOH PBPK model rat simulations for rat inha lation exposures 52

% 200, 500, 1000, 2000, and 5000 ppm 53

% Internal doses for NEDO developmental inhalation exposures, Sprague

- Dawley rats 54

% Creates Figure B

- 13 ('simres' is tabulated results) 55

use SDRat 56

prepare @clear T CVB 57DRAFT

– DO NOT CITE OR QUOTE B

- 83 1

simres=[]; ts=[]; cs=[]; ts2=[]; c s2=[]; TSTOP=24*2*7; CINT=1; 2

for CONCPPM=[200, 500, 1000, 2000, 5000] 3

TCHNG=22; MULTE=1; start @nocallback 4

res=[CONCPPM,max(_cvb),AUCBF,AMF]; 5

ts=[ts,_t]; cs=[cs,_cvb]; 6

TCHNG=TSTOP; MULTE=0; %CONCPPM=22*cp/24; 7

start @nocallback 8

simres=[simres;[res,CON CPPM,max(_cvb),AUCBF,AMF]]; 9

ts2=[ts2,_t]; cs2=[cs2,_cvb]; 10

end 11

12

simres 13

plot(ts(:,2),cs(:,2),ts(:,3),cs(:,3),ts(:,4),cs(:,4), ... 14

ts2(:,2),cs2(:,2),ts2(:,3),cs2(:,3),ts2(:,4),cs2(:,4), ... 15

[24 24],[0,95], 'fig13.aps') 16

save simres @file='Nedo_devpomt_rat_i nhal_sims.csv' @format=ascii @separator=comma 17

cs=[ts(:,1),cs,cs2]; save cs @file='Fig13_sims.csv' @format=ascii @separator=comma 18

_____________________________________________________________________________________________ 19

% File Nedo

- rat

- inhal

- cancer.m 20

% Simulations for NEDO F344 rat cancer inhalation study 21

use F344rat 22

TCHNG=22.7; TSTOP=5*7*24; MULTE=1; 23

res=[]; CONCPPM=200; prepare @clear T CVB 24

start @nocallback 25

TCHNG=19.5; ODS=1; cppm=[0,10,100,1000]; 26

bwm=[422.1, 418.3, 417.7, 410.0]; 27

bwf=[268.7, 270.6, 2 67.0, 264.9]; 28

for sdose=[0,25.4863] 29

for i=1:length(cppm) 30

CONCPPM=cppm(i); 31

BW=bwm(i)/1000; 32

DOSE=sdose/(BW^0.25); 33

start @nocallback 34

res=[res;[CONCPPM,TCHNG,BW,AUCBF,max(_cvb),AMF]] 35

BW=bwf(i)/1000; 36

DOSE=sdose/(BW^0.25); 37

start @nocallback 38

re s=[res;[CONCPPM,TCHNG,BW,AUCBF,max(_cvb),AMF]] 39

end 40

end 41

42

save res @file='Nedo_rat_cancer_sims.csv' @format=ascii @separator=comma 43

_____________________________________________________________________________________________ 44

% File: rat

- infu

- sims.m 45

% MeOH P BPK model rat simulations for zero

- order liver infusions 46

% Creates Figre B

- 14 47

use SDRat 48

lv0=[0.33, 65.9; 0.33, 624.1; 0.34, 2177; 49

0.49, 53.2; 0.50, 524; 50

0.54 1780]; 51

% Above are BWs and doses from Soffretti et al. 2002a 52

prepare @clear T CVB 53

TCHNG=12; MULTE =0; simres=[]; ts=[]; cs=[]; 54

for i=1:3 55

BW=lv0(i,1); LIVR0=lv0(i,2); TSTOP=24; start @nocallback 56

res=[LIVR0,BW,max(_cvb),0,AUCB,0,AMET]; 57DRAFT

– DO NOT CITE OR QUOTE B

- 84 TSTOP=84; start @nocallback 1

res(4)=AUCB; res(6)=AMET; simres=[simres;res]; 2

ts=[ts,_t]; cs=[cs,_cvb]; 3

end 4

simres/100 5

plot(ts(:,1),cs(:,1),ts(:,2),cs(:,2),ts(:,3),cs(:,3),'fig14b.aps') 6

save simres @file='rat_liver

- infusion_sims.csv' @format=ascii @separator=comma 7

_____________________________________________________________________________________________ 8

% File: humanse t.m 9

% Sets parameters for human simulations. Expects the user to define 10

% metabf = "linear" to use 1st

- order metabolism parameters; otherwise 11

% metabf set to "non

- linear" and Michaelis

- Menten parameters used. 12

BW = 70; FRACIN = 0.8655; IVDOSE=0; DOSE=0; CO NCPPM=0; LIVR0=0; 13

PB = 1626; PL = 0.583; %1.06; 14

PF = 0.142; PR = 0.805; %0.66; 15

PLU=1.07; %1.0; 16

VFC = 0.214; VLC = 0.026; VLUC = 0.008; 17

VAC = 0.0198; VVBC = 0.0593; 18

QPC = 18.5; QCC = 18.5; QLC = 0.227; QFC = 0.052; 19

KM = 12.76; VMAXC = 0; %VMAXC=11.72; %low KM optimum 20

KM2 = 460; VMAX2C = 0; %VMAX2C=304.5; %high KM optimum 21

KLLC = 60.7; %linear liver metabolism optimum 22

K1C = 0.0397; KAI = 0.22; KSI = 1.1; KAS = 2.0; 23

RATS=0; KLOSS=0; % constant exposure/no chamber losses 24

QPC=24.0; QCC=16.5; REST=3000; WORK =3000; 25

26

% Below are optimal values for Michaelis

- Menten liver metabolism 27

K1C = 0.0342; KLLC = 0.0; KBL=0.612; 28

KM = 23.7; VMAXC = 33.1; 29

30

% Mouse oral uptake KMASC; others set to match ethanol values 31

% for humans from Sultatos et al. (2004), with VASC set so that 32

% VASC/KMAS = 0.21/h, the Sultatos et al. 1st

- order constant, 33

% and KFEC = 0 corresponding to assumed 100% absorption. 34

VASC = 377; KSI = 3.17; KAI = 3.28; KMASC = 620; KFEC=0; 35

36

exist metabf; % check if metabf defined 37

if ~ans % If not... 38

metabf = "non

- linear" 39

end 40

if metabf=="linear" 41

% Below are optimal values for 1st

- order liver metabolism 42

K1C = 0.0373; KLLC = 95.7; KBL=0.564; VMAXC=0.0; 43

% Below are 'no bladder values; uncomment next line to use 44

%K1C = 0.0278; KLLC = 70.3; KBL=1000.0; VMAXC=0. 0; 45

else metabf="non

- linear"; 46

end 47

disp(['Simulation for ',ctot(metabf),' human kinetics']); 48

_____________________________________________________________________________________________ 49

% File: Sedivec_human_inh.m 50

% Creates MeOH PBPK Figure B

- 16 51

% For human inhalation exposures, w/ data of Sedivec et al 52

53

%

------- DATA BLOCKS 54

% These data blocks taken directly from MeOH CBMMv3.cmd 55

% Data are T (hours), CV (mg/L), cumulative urinary clearance (mg) 56

% Rounded to 3

- 4 sig figs 57DRAFT

– DO NOT CITE OR QUOTE B

- 85 % Sedivec et al., Int Arch Occ Health 1981, urine 1

HS231 = [2, 3.338, 0.1168; 4, 5.776, 0.4358; 6, 7.371, 0.8960; 2

8, 8.581, 1.454; 10, 6.576, 1.985; 12, 3.243, 2.328; 3

14, 1.32 , 2.488; 19, 0.333, 2.632; 24, 0, 2.661]; 4

5

HS157 = [2, 2.185, 0.0765; 4, 3.941, 0.291; 6, 4.896, 0.600; 6

8, 5.708, 0.971; 10, 4.360, 1.324; 12, 1.738, 1.537; 7

14, 0.776, 1.625; 19, 0.231, 1.713; 24, 0, 1.733]; 8

9

HS78 = [2, 0.881, 0.0308; 4, 1.648, 0.1193; 6, 2.285, 0.2570; 10

8, 2.551, 0.4263; 10, 1.515, 0.5686; 12, 0.708, 0.6464; 11

14, 0.430, 0.6863; 19, 0.094, 0.7321; 24, 0, 0.7403]; 12

13

%

----- RUN MODEL 14

use humanset 15

TCHNG=8; TSTOP=24; CONCPPM=231; 16

prepare @clear T RUR METB 17

start @nocallback 18

ur1 = _metb; t1 = _t; cu1=_rur;, % Save time series for urine MeOHc 19

CONCPPM=157; start @nocallback 20

ur2 = _metb; t2 = _t; cu2= _rur; 21

CONCPPM=78; start @nocallback 22

23

%

----- PLOT COMMANDS 24

plot(t1,ur1,t2,ur2,_t,_metb,HS231(:,1),HS231(:,3),... 25

HS157(:,1),HS157(:,3),HS78(:,1),HS78(:,3), 'sedivic.aps') 26

plot(t1,cu1,t2,cu2,_t,_rur,HS231(:,1),HS231(:,2),... 27

HS157(:, 1),HS157(:,2),HS78(:,1),HS78(:,2), 'sedivic2.aps') 28

29

%

----- WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL 30

% Cant save data with different # of rows to the same table. 31

mytable1 = [t1,ur1,cu1,t2,ur2,cu2,_t,_metb,_rur]; 32

eval(['save mytable1 @file =Sedv_fit_KLLC.',num2str(round(KLLC)),'.csv @format=ascii 33

@separator=comma']); 34

_____________________________________________________________________________________________ 35

% File: Batterman_human_inh.m 36

% Creates MeOH PBPK Figure B

- 17 (upper panel) 37

% For h uman inhalation exposures of Batterman et al 1998 38

39

%These data blocks taken directly from MeOH CBMMv3.cmd 40

%Data are T (hours), CV (mg/L) 41

% Batterman et al., Int Arch Occ Health 1998 42

HB82=[2, 13.6; 2.25, 13.4; 2.5, 12; 3, 9.6; 43

4, 6.4; 5, 4.1; 6, 2. 6; 7, 1.8; 8, 1.4]; 44

HB81=[1, 6.5; 1.25, 6.2; 1.5, 5.4; 2, 4.3; 3, 2.8; 45

4, 1.5; 5, 0.94; 6, 0.72; 7, 0.52; 8, 0.23]; 46

HB830=[0.5, 4.6; 0.75, 4.7; 1, 4.9; 1.5, 3.5; 2.5, 2.2; 47

3.5, 1; 4.5, 0.52; 5.5, 0.51; 6.5, 0.47; 7.5, 0.68]; 48

49

use humanset 50

prepare @clear T CVB 51

TCHNG=2; CONCPPM=800; TSTOP=16; start @nocallback 52

t2=_t; c2=_cvb; TCHNG=1; start @nocallback 53

t1=_t; c1=_cvb; TCHNG=0.5; start @nocallback 54

t30=_t; c30=_cvb; 55

56

%

----- PLOT COMMANDS 57DRAFT

– DO NOT CITE OR QUOTE B

- 86 plot(t2,c2, t1,c1, t30,c30, HB82(:,1),HB82(:,2), ... 1

HB81(:,1),HB81(:,2) ,HB830(:,1),HB830(:,2), 'batterman.aps') 2

3

%

----- WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL 4

% Cant save data with different # of rows to the same table. 5

le=1:min([length(t1),length(t2),length(t30)]); 6

mytable1 = [t2(le),c2(le),t1(le),c1(le) ,t30(le),c30(le)]; 7

eval(['save mytable1 @file=Batter_fit_KLLC.',num2str(round(KLLC)),'.csv ' ... 8

'@format=ascii @separator=comma']); 9

_____________________________________________________________________________________________ 10

% File: Osterloh_human_inh.m 11

% Creates Fig B

- 17 (lower panel) 12

% Data from Osterloh et al., JOEM 1996 13

% Digitized data provided by EPA (dat1) 14

% Subtracted background from exposure blood levels 15

% by Paul Schlosser, U.S. EPA 16

use humanset 17

BW=78.2; 18

dat1=[0.05, 0.54; 0.25, 1.39; 0.50, 1.82 ; 0.75, 2.28; 1, 2.42; 19

1.5, 2.94; 2, 3.37; 2.5, 3.90; 3, 4.21; 3.5, 4.61; 20

4, 4.82; 5, 2.99; 6, 2.30; 7, 1.40; 8, 1.07]; 21

dat=[0.25, 1.183; 0.5, 1.526; 0.75, 1.948; 1, 2.073; 1.5, 2.741; 22

2, 3.118; 2.5, 3.495; 3, 3.998; 3.5, 4.181; 4, 4.48; 23

5, 2.7 90; 6, 1.943; 7, 1.106; 8, 0.687]; 24

25

prepare @clear T CVB 26

TCHNG=4; CONCPPM=200; TSTOP=16; start @nocallback 27

plot(_t,_cvb,dat(:,1),dat(:,2), 'osterloh.aps') 28

mytable1=[_t,_cvb]; 29

eval(['save mytable1 @file=Oster_fit_KLLC.',num2str(round(KLLC)),'.txt @format=as cii']); 30

_____________________________________________________________________________________________ 31

% File: Ernstgard_human_inh.m 32

% Creates MeOH PBPK Figure B

- 18, w/ data of Ernstgard et al 2005a,b 33

% For human inhalation exposures w/ exercise 34

%

------- DA TA BLOCKS 35

%These data blocks taken directly from MeOH CBMMv3.cmd 36

%Data are T (hours), CV (mg/L) 37

% Ernstgard et al. SOT poster, 100 ppm & 200 ppm human 38

ern1 =[0.20, 0.87; 0.46, 1.50; 0.97, 2.31; 1.46, 3.24; 39

1.91, 3.65; 2.17, 3.52; 2.50, 2.55; 2 .91, 2.23; 3.51, 1.59; 40

4.01, 1.72; 5.02, 0.41; 6.00, 0.50; 9.24, 0.12]; 41

ern2 =[0.22, 1.63; 0.49, 2.92; 0.92, 4.76; 1.47, 6.30; 42

1.90, 7.65; 2.16, 6.20; 2.47, 5.49; 2.91, 4.96; 3.50, 3.64; 43

4.00, 3.43; 4.99, 1.94; 5.97, 1.03; 8.90, 0.21]; 44

45

%

----- RUN MODE L 46

use humanset 47

QPCHR=QPC; QCCHR=QCC; REST=2.0; WORK=0.0; 48

TCHNG=2.0; CONCPPM=100; TSTOP=10.0; QPCHW=52.6; QCCHW=26.0; 49

%FRACIN=0.9509; %FRACIN=0.9324; 50

prepare T CVB QP QC 51

start @nocallback, 52

cv1 = _cvb; t1 = _t; CONCPPM=200; start @nocallback 53

54

%

----- PLOT C OMMANDS 55

plot(t1, cv1, _t, _cvb,... 56

ern1(:,1),ern1(:,2),ern2(:,1),ern2(:,2), 'ernstgard.aps') 57DRAFT

– DO NOT CITE OR QUOTE B

- 87 %

----- WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL 1

% Cant save data with different # of rows to the same table. 2

mytable1 = [t1, cv1, _t, _cvb]; 3

eval(['save mytable1 @file=Ernst_nofit_KLLC.',num2str(round(KLLC)),'.csv ' ... 4

'@format=ascii @separator=comma']); 5

_____________________________________________________________________________________________ 6

% File: mouse_inh_sim.m 7

% Runs simulations for Table B

- 5, mouse internal

- dose calculations 8

% from inhalation exposure, over the concentration range specified 9

% in the 'for' statement below. 10

% Results saved to file 'MouseInhalSims.csv'. 11

use CDMice 12

BW=0.03; TCHNG=7; % 7 hr/day exposures 13

TSTOP=240; MULT E=1; % Run for 10 days; multi

- day exposure 'on' 14

RATS=0.0; KLOSS=0.0; %

- > open chamber 15

prepare @clear T CVB 16

CONCPPM=10000; CINT=0.02; start @nocallback 17

% plot(_t,_cvb) % uncomment to see/check that periodicity reached by TSTOP 18

inhres=[]; CINT=0.2; 19

for CONC PPM=[1, 10, 50, 100, 250, 500, 1000, 2000, 5000, 10000] 20

start @nocallback 21

inhres=[inhres;[CONCPPM, AUCBB, max(_cvb), AMET24/(BW^0.75)]]; 22

end 23

save inhres @file=MouseInhalSims.csv @format=ASCII @separator=comma 24

____________________________________________ _________________________________________________ 25

% File: human_inh_sim.m 26

% Runs simulations for Table B

- 5, human internal

- dose calculations 27

% from inhalation exposure, over the concentration range specified 28

% in the 'for' statement below. 29

% Results saved to file 'HumanInhSims_KLLC.#.csv', where # is 30

% value of KLLC used (0 if non

- linear/Michaelis

- Menten kinetics). 31

% If metab="linear", 1st order kinetics used; otherwise non

- linear. 32

use humanset 33

WESITG=0; WEDITG=0; simres=[]; MULTE=0; CINT=1.0; RATS=0.0; KL OSS=0.0; 34

CONCPPM=0; TSTOP=1000; TCHNG=1000; DOSE=0; DWDOSE=0; ODS=1; 35

prepare @clear T CVB STOM 36

start @nocallback 37

for CONCPPM=[1, 10, 50, 100, 250, 500, 1000, 2000, 5000, 10000] 38

start @nocallback 39

simres=[simres;[CONCPPM,AUCBF,max(_cvb),AMF/(BW^0.75)]] 40

end 41

disp(['Simulation for ',ctot(metabf),' human kinetics']); 42

eval(['save simres @file=HumanInhSims_KLLC.',num2str(round(KLLC)), ... 43

'.csv @format=ascii @separator=comma']); 44

___________________________________________________________________________________ __________ 45

% File: human_oral_sim.m 46

% Runs simulations for Table B

- 5, human internal

- dose calculations from 47

% oral exposure, over the exposure range specified in the 'for' statement below. 48

% Results saved to file 'Hum_DW_Sims_KLLC.#.csv', where # is value of KLLC 49

% used (0 if non

- linear/Michaelis

- Menten kinetics). 50

% If metab="linear", 1st order kinetics used; otherwise non

- linear. 51

use humanset 52

WESITG=0; WEDITG=0; MULTE=0; CINT=0.1; RATS=0.0; KLOSS=0.0; 53

CONCPPM=0; TSTOP=1000; DWDOSE=0; DOSE=1; ODS=1; DRDOSE =0; 54

prepare @clear T CVB STOM 55

start @nocallback 56

simres=[]; 57DRAFT

– DO NOT CITE OR QUOTE B

- 88 for DOSE=[0.1, 1, 10, 50, 100, 250, 500, 1000, 2000, 5000] 1

start @nocallback 2

simres=[simres;[DOSE,AUCBF,max(_cvb)],AMF]; 3

end 4

disp(['Simulation for ',ctot(metabf),' human kinetics']); 5

eval(['save simres @file=Hum_DW_Sims_KLLC.',num2str(round(KLLC)),'.csv ' ... 6

'@format=ascii @separator=comma']); 7

_____________________________________________________________________________________________ 8

% file human_drink_compare.m 9

% creates Figure B

- 24 and Table B

- 9 10

% Created by Paul Schlosser, U.S. EPA, 8/26/09 11

use humanset 12

WESITG=0; WEDITG=0; MULTE=0; CINT=0.1; TSTOP=48; DOSE=0.1; ODS=1; DRDOSE=0; 13

prepare @clear T CVB 14

start @nocallback 15

T1=_t; C1=_cvb; DOSE=0; LIVR0=0.1; TCHNG=12; MULTE=1; start @nocallback 16

T2=_ t; C2=_cvb; LIVR0=0; ODS=0; DOSE=0.1; start @nocallback 17

T3=_t; C3=_cvb; DOSE=0; DRDOSE=0.1; start @nocallback 18

plot(T1,C1,T2,C2,T3,C3,_t,_cvb,'humoralsim.aps') 19

20

tbl=[]; metd = 1.0; 21

for dse=[0.1, 1.0, 10, 100, 250, 500] 22

row=[]; LIVR0=0; DOSE=0; DRDOSE=dse; start @nocallback 23

row=[dse,max(_cvb),AUCBF,AMETF]; 24

LIVR0=dse; DOSE=0; DRDOSE=0; start @nocallback 25

row=[row,max(_cvb),AUCBF,AMETF]; 26

LIVR0=0; DOSE=dse; DRDOSE=0; start @nocallback 27

tbl=[tbl;[row,max(_cvb),AUCBF,AMETF]]; 28

end 29

tbl 30

31

B.3.5. Personal Communication f rom Lena Ernstgard Regarding Human Exposures

Reported in the Ernstgard and Johanson, 2005 SOT Poster

From: Lena Ernstgård [Lena.Ernstgard@imm.ki.se] 32

Sent: Wednesday, March 23, 2005 12:39 AM 33

To: Poet, Torka S 34

Subject: RE: Human MeOH poster 35

Hi, 36

We measured t he ventilation rate and they ought to be similar to those reported by Dr. Johanson at the same 37

workload. 38

Sincerly, 39

Lena Ernstgård 40

41

At 18:41 2005

- 03

- 22, you wrote: 42

43

Thank you very much. Your net uptake is what we thought. Did you measure ventilation ra tes? 44

45

Thanks again, 46

Torka 47

48

Torka Poet, PhD 49

Center for Biological Monitoring and Modeling 50

Pacific Northwest National Laboratories 51

902 Battelle Blvd. 52

P.O. Box 999, MSIN P7

- 59 53DRAFT

– DO NOT CITE OR QUOTE B

- 89 Richland, WA 99352 1

ph: (509)376

- 7740 2

f ax: (509)376

- 9449 3

e

- mail: Torka.poet@pnl.gov 4

(Express Mail Delivery: 790 Sixth Street, Zip Code 99354) 5

6

From: Lena Ernstgård [ mailto:Lena.Ernstgard@imm.ki.se ] 7

Sent: Sunday, March 20, 2005 11:21 PM 8

To: Poe t, Torka S 9

Subject: Re: Human MeOH poster 10

Hi, 11

The manuscript has not been submitted yet, but it will be soon I hope. I will save your mail and send you a 12

copy as soon as possible. 13

When I say % of net uptake, i mean the relative uptake. It is calculated as : conc in exposure chamber

- 14

(minus) exhaled conc / (divided by) conc in exposure chamber. I hope you understand how we have done. 15

Sincerly, 16

Lena Ernstgård 17

B.3.6. Personal Communication from Dr. Rogers Regarding Mouse Exposures

Jeff Gift, Ph.D. 18

National Center f or Environmental Assessment EPA (B243

- 01) RTP, NC 27711 19

919

- 541

- 4828 20

919

- 541

- 0245 (fax) 21

gift.jeff@epa.gov 22

23

----- Forwarded by Jeff Gift/RTP/USEPA/US on 04/04/2005 04:31 PM

----- 24

John Rogers 25

To: Jeff Gift/RTP/USEPA/US@EPA 26

04/04/2005 03:50 PM 27

Subject: Re: report(Document link: Jeff Gift) 28

29

Hi Jeff: 30

It's easie r just to give you the numbers from that figure in tabular form: 31

32

Time (hr) 33

(exposure Blood [MeOH] SEM 34

ends @ 7 hr) (mg/ml) 35

1 0.93 0.05 36

4 2.80 0.20 37

6 3.36 0.08 38

7 3.99 0.13 39

7.5 3.98 0.21 40

8 4.12 0.07 41

9 3.27 0.16 42

12 2.63 0.21 43

16 1.69 0.08 44

26 0.06 0.02 45

46

Wh ew! Surprised I could find these numbers that fast, I get a little worried when someone asks 47

for 12 year old data, I'm not as organized as I'd like to be,to say the least. 48

49DRAFT

– DO NOT CITE OR QUOTE B

- 90 John 1

2

John M. Rogers, Ph.D. 3

Chief, Developmental Biology Branch (MD

- 67) Reproducti ve Toxicology Division National 4

Health and Environmental Effects Research Laboratory Research Triangle Park, NC 27711 5

T: (919)541

- 5177 6

F: (919)541

- 4017 7

e

- mail: rogers.john@epa.gov 8

B.3.7. Total MeOH Metabolic Clearance/Metabolites Produced

Table B

- 6. Mouse total M eOH metabolic clearance/metabolites produced

following inhalation exposures a

Exposure concentration

(ppm) AUC (mg/L

- hr) C

max (mg/L) Total MeOH metabolically cleared

(mg)

1 1.51E

- 01 2.16E

- 02 1.20E

- 02

10 1.53E+00 2.18E

- 01 1.20E

- 01

50 8.03E+00 1.15E+00 6.0 1E

- 01

100 1.72E+01 2.46E+00 1.20E+00

250 5.38E+01 7.83E+00 2.99E+00

500 1.72E+02 2.64E+01 5.89E+00

525 1.89E+02 2.94E+01 6.17E+00

550 2.09E+02 3.26E+01 6.45E+00

575 2.29E+02 3.62E+01 6.73E+00

600 2.51E+02 3.99E+01 7.01E+00

625 2.74E+02 4.40E+01 7.2 8E+00

675 3.24E+02 5.30E+01 7.83E+00

750 4.09E+02 6.84E+01 8.63E+00

875 5.77E+02 9.88E+01 9.93E+00

1,000 7.76E+02 1.34E+02 1.12E+01

2000 5.12E+03 7.57E+02 2.37E+01

5000 1.73E+04 2.00E+03 3.77E+01

1,0000 4.98E+04 4.60E+03 5.50E+01

a Total over a 36

- h our period during which mice were exposed for 7 hours to MeOH according to the conditions of

the dose

- response study.DRAFT

– DO NOT CITE OR QUOTE B

- 91 Table B

- 7. Human total MeOH metabolic clearance/metabolites produced

from inhalation exposures a

Exposure concentration (ppm) AUC

(mg/L

- hr) C

max (mg/L) Total MeOH metabolically cleared

(mg)

1 0.7142 0.0300 10.23

10 7.142 0.300 102.3

50 35.71 1.498 511.7

100 71.42 2.997 1023

250 178.6 7.491 2559

500 357.1 14.98 5117

625 446.4 18.73 6396

750 535.7 22.47 7676

875 625.0 26.22 8955

1 ,000 714.2 29.97 10234

a Total over a 24

- hour period during which humans were exposed continuously to MeOH.

Table B

- 8. Human total MeOH metabolic clearance/metabolites produced

following oral exposures a

Exposure concentration ( mg/kg

- day ) AUC (mg/L

- hr) To tal MeOH metabolically cleared (mg)

0.1 0.3795 6.2152

1 3.7954 62.152

5 18.977 310.8

10 37.954 621.5

50 189.8 3108

100 379.5 6215

250 948.8 15538

a Total over a 24

- hour period during which humans were exposed continuously to MeOH.

Note: MeOH in the model is eliminated via exhalation, metabolism, and urinary excretion (human only). Total

MeOH metabolically cleared approximates total production of down stream metabolites, but as a dose metric is not

equivalent to formaldehyde or formate concentration.DRAFT

– DO NOT CITE OR QUOTE B

- 92 B.3.8. Multiple Daily Oral Dosing for Humans

Current mode simulations of oral exposures to humans use a constant rate of infusion to 1

the stomach lumen. This approach results in a steady rate of absorption from the stomach equal 2

to the exposure rate irregardele ss of the oral uptake rate constants (assumed equal to the mouse), 3

hence avoids the difficulty that independent values of these constants are not available for 4

humans due to a lack of human oral PK data.. A more likely drinking scenario was tested by 5

usin g additional code within the model to simulate a 6

- times/day drinking schedule, over the 6

course of 15 h (see code below). The schedule is still an approximation, as it assumes 6 episodes 7

of drinking, each considered to be a bolus. Specifically, it was as sumed that humans drank at 0, 8

3, 5, 8, 11, and 15 hours from the first ingestion of each day, with the respective fractions of daily 9

consumption being 25, 10, 25, 10, 25, and 5% at those times. The predicted blood 10

concentrations resulting from simulations of six daily boluses, once/day boluses, 12 h/d infusion 11

(zero order), or constant (zero order) are shown in Figure B

- 24 for a total dose of 0.1 mg/kg. 12

Table B

- 9 shows PBPK model predicted C

max , AUC, and Amet (for the last 24 hours of repeated 13

exposures) for humans exposed to MeOH via six daily boluses, 12 h/d infusion, or a single daily 14

gavage. 15

Figure B

- 24. Simulated human oral exposures to 0.1 mg MeOH/kg/

- day

comparing the first few days for four exposure scenarios : continuous (zero

order) infusion; 12 h/d infusion, a single daily bolus, and a pattern of six boluses

per day (see text).DRAFT

– DO NOT CITE OR QUOTE B

- 93 Table B

- 9. Repeated daily oral dosing of humans with MeOH *

Dose

(mg/kg) Six daily boluses 12 h/d infusion Single

- daily bolus

C

max (mg/L) AUC

(mg

- h/L) Amet

(mg) C

max (mg/L) AUC

(mg

- h/L) Amet

(mg) C

max (mg/L) AUC

(mg

- h/L) Amet

(mg)

0.1 0.0204 0.0581 1.97 0.0170 0.0583 1.97 0.0569 0.0584 1.98

1 0.205 0.584 19.7 0.171 0.586 19.8 0.579 0.594 19.8

10 2.16 6.17 197 1.83 6.21 197 6.64 7.01 197

100 33.4 10 9 1,950 41.4 125 1,950 95.2 204 1,920

250 182 720 4,400 237 857 4,420 296 1,150 4,440

500 746 3,290 5,180 866 3,540 5,190 903 4,250 5,270

*AUC in blood and Amet (amount metabolized) computed from 24

- 48 hDRAFT

– DO NOT CITE OR QUOTE C

- 1 APPENDIX C. R f C DERIVATION OPTIONS

C.1. RFC DERIVATIONS USIN G THE NEDO METHANOL REPORT (NEDO, 1987)

The BMD approach was utilized in the derivation of potential chronic inhalation 1

reference values. In the application of the BMD approach, continuous models in the EPA’s 2

BMDS, version 2. 1

, were fit to datasets for decreas ed brain weight in male rats exposed 3

throughout gestation and the postnatal period to 6 weeks and male rats exposed during gestation 4

on days 7

– 17 only (NEDO, 1987). Although there remains uncertainty surrounding the 5

identification of the proximate teratog en of importance (methanol, formaldehyde, or formate), 6

the dose metrics chosen for the derivation of RfCs were based on blood methanol levels. This 7

decision was primarily based on evidence that the toxic moiety is not likely to be the formate 8

metabolite o f methanol (CERHR, 2004), and evidence that levels of the formaldehyde metabolite 9

following methanol maternal and/or neonate exposure would be much lower in the fetus and 10

neonate than in adults. While recent in vitro evidence indicates that formaldehyde i s more 11

embryotoxic than methanol and formate, the high reactivity of formaldehyde would significantly 12

limit its transport from maternal to fetal blood

, and the capacity for the metabolism of methanol 13

to formaldehyde is lower in the fetus and neonate versus adults. Further discussions of methanol 14

metabolism, dose metric selection and MOA issues are covered in Sections 3.3, 4.5 and 4.6. 15

C.1.1. Decreased Brain Weight in Male Rats Exposed t hroughout Gestation and into the

Postnatal Period (NEDO, 1987)

The results o f NEDO (1987), shown in Table 4

- 14 , indicate that there is not a cumulative 16

effect of ongoing exposure on brain

- weight decrements in rats exposed postnatally; i.e., the dose 17

response in terms of percent of control is about the same at 3 weeks postnatal as at 8 weeks 18

postnatal in rats exposed throughout gestation and the F 1 generation. However, there does 19

appear to be a greater brain

- weight effect in rats exposed postnatally versus rats exposed only 20

during organogenesis (GD7

– GD 17). In male rats exposed dur ing organogenesis only, there is no 21

statistically significant decrease in brain weight at 8 week after birth at the 1,000 ppm exposure 22

level. Conversely, in male rats exposed to the same level of methanol throughout gestation and 23

the F 1 generation, there was an approximately a 5% decrease in brain weights (statistically 24

significant at the p < 0.01 level). The extent to which this observation is due to recovery in rats 25

for which exposure was discontinued at birth versus a cumulative effect in rats exposed 26

postnatally is not clear. The fact that male rats exposed to 5

, 000 ppm methanol only during 27

organogenesis experienced a decrease of brain weight of 10% at 8 weeks postnatal indicates that 28

postnatal exposure is not necessary for the observation of persiste nt postnatal effects. However, 29

the fact that this decrease was less than the 13% decrease observed in male rats exposed to 30DRAFT

– DO NOT CITE OR QUOTE C

- 2 2

, 000 ppm methanol throughout gestation

, and the 8 week postnatal period indicates that the 1

absence of postnatal exposure allows for some measure of recovery. 2

It appears that once methanol exposure is discontinued, continuous biological processes 3

that are disrupted by exposure, manifesting as decreased brain weight, undergo some recovery 4

and brain weights begin to return to normal va lues. This indicates that brain weight is 5

susceptible to both the level and duration of exposure. Therefore, a dose metric that incorporates 6

a time component would be the most appropriate metric to use. For these reasons and because it 7

is more typicall y used in internal

- dose

- based assessments and better reflects total exposure 8

within a given day, daily AUC (measured for 22

- hour exposure/day) was chosen as the most 9

appropriate dose metric for modeling the effects of methanol exposure on brain weights in rats 10

exposed throughout gestation and continuing into the F 1 generation. 11

Application of the EPA methanol PBPK model ( described in Section 3.4 ) to the NEDO 12

(1987) study in which developing rats were exposed during gestation and the postnatal period 13

presents complications that need to be discussed. The neonatal rats in this study were exposed to 14

methanol gestationally before parturition, as well as lactationally and inhalationally after 15

parturition. The PBPK model developed by the EPA only estimates interna l dose metrics for 16

methanol exposure in NP adult mice and rats. Experimental data indicate that inhalation

- route 17

blood methanol kinetics in NP mice and pregnant mice on GD6

– GD 10 are similar (Dorman 18

et al ., 1995; Perkins et al ., 1995a,b; Rogers et al ., 199 3 a,b ). In addition, experimental data 19

indicate that the maternal blood:fetal partition coefficient for mice is approximately 1 (see 20

Section 3.4.1.2)

. Assuming that these findings apply for rats , they indicate that pharmacokinetic 21

and blood dose metrics f or NP rats are appropriate surrogates for fetal exposure during early 22

gestation. However, as is discussed to a greater extent in Section 5.3, the additional routes of 23

exposure presented to the pups in this study (lactation and inhalation) present uncertai nties that 24

make it reasonable to assume that average blood levels in pups in the NEDO report are also 25

greater than those of the dam. However, it is also reasonable to assume that any differences seen 26

between the pups and dams would also be seen in mothers and human offspring. Therefore, the 27

presumed differences between pup and dam blood methanol levels are deemed relatively 28

inconsequential

, and the PBPK model

- estimated adult blood methanol levels are assumed to be 29

appropriate dose metrics for the purpose of this analysis

. 30

The first step in the current analysis is to convert the inhalation doses, given as ppm 31

values from the studies, to an internal dose surrogate or dose metric using the EPA PBPK model 32

(see Section 3.4 )

. Predicted AUC values for methanol i n the blood of rats and humans are 33

summarized in Table C

- 1

. 34

35DRAFT

– DO NOT CITE OR QUOTE C

- 3 Table C

- 1 . EPA ’s PBPK model estimates of methanol blood levels (AUC) in rats

following inhalation exposures

Exposure l evel (ppm) Methanol in b lood AUC (hr × mg/L) a in r ats

500 79.2

1,000 226.7

2000 967.8

a AUC values were obtained by simulating 22 h ou r/day exposures for 5 days and calculated for the last 24 hours of

that period.

The c urrent BMD technical guidance (U.S. EPA, 2000 b ) suggests that in the absence of 1

knowledge as to what level of response to consider adverse, a change in the mean equal to 2

1 control S.D. from the control mean can be used as a BMR for continuous endpoint s

. However, 3

it has been suggested that other BMRs, such as 5% change relative to estimated control mean, 4

are also appropriate when performing BMD analyses on fetal weight change as a developmental 5

endpoint (Kavlock et al ., 1995). Therefore, in this assessment, both a 1 control mean S.D. 6

change and a 5% change relative to estimated control mean were considered. All m odels were fit 7

using restrictions and option settings suggested in the EPA BMD technical guidance document 8

(U.S. EPA, 2000 b ). 9

C.1.2. BMD Approach with a BMR of 1 Control Mean S.D.

– Gestation and into the

Postnatal Period (NEDO, 1987)

A summary of the results mos t relevant to the development of a POD using the BMD 10

approach (BMD, BMDL, and model fit statistics) for decreased brain weight at 6 weeks in male 11

rats exposed to methanol throughout gestation and continuing into the F 1 generation, with a 12

BMR of 1 control m ean S.D

, is provided in Table C

- 2

. The 6 week male brain weight responses 13

were chosen because they resulted in lower BMD and BMDL estimates than male responses at 3 14

and 8 week s and female responses at any time point (data not shown)

. Model fit and was 15

det ermined by statistics (AIC and χ 2 residuals of individual dose groups) and visual inspection

, 16

as recommended by EPA (2000 b ). There is a 2.5

- fold range of BMDL estimates from adequately 17

fitting models, indicating considerable model dependence. In addition , the fit of the Hill and 18

more complex Exponential models is better than the other models in the dose region of interest 19

as indicated by a lower scaled residual at the dose group closest to the BMD (0.09 versus

- 0.67 20

or

- 0.77) and visual inspection. I n ac cordance with EPA BMD Technical Guidance (EPA, 21

2000 b ), the BMDL from the Hill model (bolded), is selected as the most approriate basis for an 22

RfC derivation because it results in the lowest BMDL from among a broad range of BMDLs and 23

provides a superior fit in the low dose region nearest the BMD

. The BMDL 1SD was determined 24

to be 90.9 hr × mg /L using the 95% lower confidence limit of the dose

- response curve expressed 25

in terms of the AUC for methanol in blood. 26DRAFT

– DO NOT CITE OR QUOTE C

- 4 Table C

- 2 . Comparison of BMD 1SD results for decr eased brain weight in male rats at 6

weeks of age using modeled AUC of methanol as a dose metric

Model BMD 1SD (AUC,

hr × mg/L ) a BMDL 1SD (AUC,

hr × mg/L ) a p

- value AIC c Scaled r esidual d

Linear 278.30 225.30 0.5376

- 203.84

- 0.77

2 nd degree p olynomial 278.3 0 225.30 0.5376

- 203.84

- 0.77

3rd degree p olynomial 278.30 225.30 0.5376

- 203.84

- 0.77

Power 278.30 225.30 0.5376

- 203.84

- 0.77

Hill b 170.57 90.93 0.8366

- 203.04 0.09

Exponential 2 260.94 209.10 0.612

- 204.10

- 0.67

Exponential 3 260.94 209.10 0.612

- 2 04. 10

- 0.67

Exponential 4 172.08 96.93 0.8205

- 203. 10 0.09

Exponential 5 172.08 96.93 0.8205

- 203. 10 0.09

a T he BMDL is the 95% lower confidence limit on the AUC estimated to decrease brain weight by 1 control mean

S.D. using BMDS model options and restr ictions suggested by EPA BMD technical guidance (U.S. EPA, 2000b)

. b There is a 2.5

- fold range of BMDL estimates from adequately fitting models, indicating considerable model

dependence. In addition, the fit of the Hill and more complex Exponential models is better in the dose region of

interest as indicated by a lower scaled residual at the dose group closest to the BMD (0.09 versus

- 0.67 or

- 0.77)

and visual inspection. Thus, i n accordance with EPA BMD Technical Guidance (EPA, 2000), the BMDL from the

Hi ll model (bolded) is considered the most appropriate POD for us in an RfC derivation

.

c AIC = Akaike Information Criterion =

- 2L + 2P, where L is the log

- likelihood at the maximum likelihood

estimates for the parameters, and P is the number of modeled degr ees of freedom (usually the number of parameters

estimated)

. d χ

2 d residual (measure of how model

- predicted responses deviate from the actual data) for the dose group closest to

the BMD scaled by an estimate of its S.D

. Provides a comparative measure of model fit near the BMD. Residuals

that exceed 2.0 in absolute v alue should cause one to question model fit in this region.

Source: NEDO (1987).

==================================================================== 1

Hill Model. (Version: 2.14; Date: 06/26/2008) 2

Input Data File: C: \ Usepa \ BMDS21 \ Data \ Methan ol \ NEDO \ Gest

- F1 \ hilm

- 6wk

- brwHil

3

Restrict.(d) 4

Gnuplot Plotting File: C: \ Usepa \ BMDS21 \ Data \ Methanol \ NEDO \ Gest

- F1 \ hilm

- 6wk

5

brwHil

- Restrict.plt 6

Mon Aug 24 13:08:09 2009 7

==================================================================== 8

9

BM DS Model Run 10

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 11

12

The form of the response function is: 13

14

Y[dose] = intercept + v*dose^n/(k^n + dose^n) 15

16

17

Dependent variable = Mean 18

Independent variable = Dose 19

rho is set to 0 20

v is set to

- 0.5 21

Power parameter restricted to be greater than 1 22

A constant variance model is fit 23

24

Total number of dose groups = 4 25

Total number of records with missing values = 0 26

Maximum number of iterations = 250 27

Relative Function Convergence has been set to: 1e

- 008 28DRAFT

– DO NOT CITE OR QUOTE C

- 5 Parameter Convergence has been set to: 1e

- 008 1

2

3

4

Default Initial Parameter Values 5

alpha = 0.00539333 6

rho = 0 Specified 7

intercept = 1.78 8

v =

- 0.5 Specified 9

n = 1.2699 10

k = 924.206 11

12

13

Asymptotic Correlation Matrix of Parameter Estimates 14

15

( *** The model parameter(s)

- rho

- v 16

have been estimated at a boundary point, or have been specified by 17

the user, 18

and do not appear in the correlation matrix ) 19

20

alpha intercept n k 21

22

alpha 1 1.7e

- 008

- 1.7e

- 008

- 2.9e

- 009 23

24

intercept 1.7e

- 008 1

- 0.56

- 0.73 25

26

n

- 1.7e

- 008

- 0.56 1 0.15 27

28

k

- 2.9e

- 009

- 0.73 0.15 1 29

30

31

32

Parameter Estimates 33

34

95.0% Wald Confidence Interval 35

Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 36

alpha 0.004 96114 0.0010023 0.00299667 0.00692562 37

intercept 1.77806 0.0193152 1.7402 1.81591 38

n 1.07212 0.217805 0.645228 1.49901 39

k 915.874 1 79.818 563.437 1268.31 40

41

42

43

Table of Data and Estimated Values of Interest 44

45

Dose N Obs Mean Est Mean Obs Std Dev Est Std Dev Scaled Res. 46

------ --- -------- -------- ----------- ----------- ----- ----- 47

48

0 12 1.78 1.78 0.07 0.0704 0.0956 49

79.2 12 1.74 1.74 0.09 0.0704

- 0.21 50

226.7 11 1.69 1.69 0.06 0.0704 0.159 51

967.8 14 1.52 1.52 0.07 0.0704

- 0.0355 52

53

54

55

Model Descriptions for likelihoods calculated 56

57

58

Model A1: Yij = Mu(i) + e(ij) 59

Var{e(ij)} = Sigma^2 60

61

Model A2: Yij = Mu(i) + e(ij) 62

Var{e(ij)} = Sigma(i)^2 63

64

Model A3: Yij = Mu(i) + e(ij) 65

Var{e(ij)} = Sigma^2 66

Model A3 uses any fixed variance parameters that 67DRAFT

– DO NOT CITE OR QUOTE C

- 6 were specified by the user 1

2

Model R: Yi = Mu + e(i) 3

Var{e(i)} = Sigma^2 4

5

6

Likeliho ods of Interest 7

8

Model Log(likelihood) # Param's AIC 9

A1 105.539862 5

- 201.079724 10

A2 106.570724 8

- 197.141449 11

A3 105.539862 5

- 201.079724 12

fitted 105.499930 4

- 202.999859 13

R 77.428662 2

- 150.857324 14

15

16

Explanation of Tests 17

18

Test 1: Do responses and/or variances differ among Dose levels? 19

(A2 vs. R) 20

Test 2: Are Variances Homogeneous? (A1 vs A2) 21

Test 3: Are variances adequately modeled? (A2 vs. A3) 22

Test 4: Does the Model for the Mean Fit? (A3 vs. fitted) 23

(Note: When rho=0 the results of Test 3 and Test 2 will be the same.) 24

25

Tests of Interest 26

27

Test

- 2*log(Likelihood Ratio) Test df p

- value 28

29

Test 1 58.2841 6 <.0001 30

Test 2 2.06173 3 0.5597 31

Test 3 2.06173 3 0.5597 32

Test 4 0.0798644 1 0.7775 33

34

The p

- value for Test 1 is less than .05. There appears to be a 35

difference between response and/or variances among the dose levels 36

It seems appropriate to model the data 37

38

T he p

- value for Test 2 is greater than .1. A homogeneous variance 39

model appears to be appropriate here 40

41

42

The p

- value for Test 3 is greater than .1. The modeled variance appears 43

to be appropriate here 44

45

The p

- value for Test 4 is greater than .1. The mod el chosen seems 46

to adequately describe the data 47

48

49

Benchmark Dose Computation 50

51

Specified effect = 1 52

53

Risk Type = Estimated standard deviations from the control mean 54

55

Confidence level = 0.95 56

57

BMD = 169.597 58

59DRAFT

– DO NOT CITE OR QUOTE C

- 7 BMDL = 103.912

1.45

1.5 1.55

1.6 1.65

1.7 1.75

1.8 1.85

0 200 400 600 800 1000Mean Response

dose Hill Model with 0.95 Confidence Level

09:05 08/24 2009 BMD BMDL Hill

Figure C

- 1. Hill mod el, BMR of 1 Control Mean S.D.

- Decreased Brain weight in

male rats at 6 weeks age versus AUC, F1 Generation inhalational study

Source: NEDO (1987).

Once the BMDL 1SD was obtained in uni ts of hr × mg/L, it was used to derive a chronic 1

inhalation reference value. The first step is to calculate the HEC using the PBPK model 2

described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B ) that 3

describes the relationship between predicted methanol AUC and the human equivalent inhalation 4

exposure concentration (HEC) in ppm. 5

BMD L HEC (ppm) = 0.0224* BMDL 1SD +(1334* BMDL 1SD )/ (794+ BMDL 1SD ) 6

BMD L HEC (ppm) = 0.0224* 374.7 +(1334* 374.7)/ (794+ 90.9 ) = 139 ppm 7

Next, because RfCs a re typically expressed in units of mg/m 3 , the HEC value in ppm 8

was converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m 3 : 9DRAFT

– DO NOT CITE OR QUOTE C

- 8 HEC (mg/m 3 ) = 1.31 × 139 ppm = 182 mg/m 3 1

Finally, this HEC value was divided by a composite 100

- fold UF ( 3 for uncertainty 2

associated with animal to human differences, 10 for consideration of human variability, and 3 for 3

database deficiencies ) to obtain the chronic inhalation reference value: 4

RfC (mg/m 3 ) = 182 mg/m 3 ÷ 100 = 1.8 mg/m 3 5

C.1.3. BMD Approach with a BMR of 0.05 Change Relative to Estimated Control Mean

–

Gestation and into the Postnatal Period (NEDO, 1987)

A summary of the results most relevant to the development of a POD using the BMD 6

approach (BMD, BMDL, and model fit statistics) for decreased brai n weight at 6 weeks in male 7

rats exposed to methanol throughout gestation and continuing into the F 1 generation, with a 8

BMR of 0.05 change relative to estimated control mean

, is provided in Table C

- 3

. The 6 week 9

male brain weight responses were chosen bec ause they resulted in lower BMD and BMDL 10

estimates than male responses at 3 and 8 week s and female responses at any time point (data not 11

shown)

. Model fit was determined by statistics (AIC and χ 2 residuals of individual dose groups) 12

and visual inspection

, as recommended by U.S. EPA (2000 b ). There is a 2.4

- fold range of 13

BMDL estimates from adequately fitting models, indicating considerable model dependence. In 14

addition, the fit of the Hill and more complex Exponential models is better than the other models 15

in the dose region of interest as indicated by a lower scaled residual at the dose group closest to 16

the BMD (0.09 versus

- 0.67 or

- 0.77) and visual inspection. In accordance with EPA BMD 17

Technical Guidance (EPA, 2000b), the BMDL from the Hill model (bold ed), is selected as the 18

most approriate basis for an RfC derivation because it results in the lowest BMDL from among a 19

broad range of BMDLs and provides a superior fit in the low dose region nearest the BMD

. The 20

BMDL 05 was determined to be 123.9 hr × mg /L

, using the 95% lower confidence limit of the 21

dose

- response curve expressed in terms of the AUC for methanol in blood. 22DRAFT

– DO NOT CITE OR QUOTE C

- 9 Table C

- 3 . Comparison of BMD 05 results for decreased brain weight in male rats at 6

weeks of age using modeled AUC of methanol as a dose metric

Model BMD 05 (AUC,

hr × mg/L ) a BMDL 05 (AUC,

hr × mg/L ) a p

- value AIC c Scaled Residual d

Linear b 344.49 297.92 0.5376

- 203.84

- 0.77

2 nd degree p olynomial 344.49 297.92 0.5376

- 203.84

- 0.77

3rd degree p olynomial 344.49 297.92 0.5376

- 203.84

- 0.77

Po wer 344.49 297.92 0.5376

- 203.84

- 0.77

Hill 223.18 123.87 0.8366

- 203.0 4

- 0.0 9

Exponential 2 325.82 278.26 0.612

- 204.10

- 0.67

Exponential 3 325.82 278.26 0.612

- 204.10

- 0.67

Exponential 4 223.94 129.97 0.8205

- 203.10 0.09

Exponential 5 223.94 129.97 0.8205

- 203.10 0.09

a T he BMDL is the 95% lower confidence limit on the AUC estimated to decrease brain weight by 5% using BMDS

model options and restrictions suggested by EPA BMD technical guidance (U.S. EPA, 2000b)

. b There is a 2. 4

- fold range of BMDL e stimates from adequately fitting models, indicating considerable model

dependence. In addition, the fit of the Hill and more complex Exponential models is better in the dose region of

interest as indicated by a lower scaled residual at the dose group clos est to the BMD (0.09 versus

- 0.67 or

- 0.77)

and visual inspection. Thus, in accordance with EPA BMD Technical Guidance (EPA, 2000), the BMDL from the

Hill model (bolded) is considered the most appropriate POD for us in an RfC derivation.

c AIC = Akaike In formation Criterion =

- 2L + 2P, where L is the log

- likelihood at the maximum likelihood

estimates for the parameters, and P is the number of modeled degrees of freedom (usually the number of parameters

estimated)

. d χ

2 d residual (measure of how model

- predicted responses deviate from the actual data) for the dose group closest to

the BMD scaled by an estimate of its S.D

. Provides a comparative measure of model fit near the BMD. Residuals

that exceed 2.0 in absolute v alue should cause one to que stion model fit in this region.

Source: NEDO (1987).

==================================================================== 1

Hill Model. (Version: 2.14; Date: 06/26/2008) 2

Input Data File: C: \ Usepa \ BMDS21 \ Data \ Methan ol \ NEDO \ Gest

- F1 \ hilm

- 6wk

- brwHil

3

Restrict.(d) 4

Gnuplot Plotting File: C: \ Usepa \ BMDS21 \ Data \ Methanol \ NEDO \ Gest

- F1 \ hilm

- 6wk

5

brwHil

- Restrict.plt 6

Mon Aug 24 09:40:25 2009 7

==================================================================== 8

9

BM DS Model Run 10

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 11

12

The form of the response function is: 13

14

Y[dose] = intercept + v*dose^n/(k^n + dose^n) 15

16

17

Dependent variable = Mean 18

Independent variable = Dose 19

rho is set to 0 20

Power parameter restricted to be greater than 1 21

A constant variance model is fit 22

23

Total number of dose groups = 4 24

Total number of records with missing values = 0 25

Maximum number of iterations = 250 26

Relative Function Convergence has been set to: 1e

- 008 27

Parameter Convergence has been set to: 1e

- 008 28

29DRAFT

– DO NOT CITE OR QUOTE C

- 10 1

2

Default Initial Parameter Values 3

alpha = 0.00539333 4

rho = 0 Specified 5

interce pt = 1.78 6

v =

- 0.26 7

n = 1.08282 8

k = 401.076 9

10

11

Asymptotic Correlation Matrix of Parameter Estimates 12

13

( *** The model par ameter(s)

- rho

- n 14

have been estimated at a boundary point, or have been specified by 15

the user, 16

and do not appear in the correlation matrix ) 17

18

alpha intercept v k 19

20

alph a 1 4e

- 009

- 1.2e

- 008 6.2e

- 009 21

22

intercept 4e

- 009 1 0.54

- 0.64 23

24

v

- 1.2e

- 008 0.54 1

- 0.99 25

26

k 6.2e

- 009

- 0.64

- 0.99 1 27

28

29

30

Parameter Estimates 31

32

95.0% Wald Confidence Interval 33

Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 34

alpha 0.00495736 0.00100154 0.00 299438 0.00692034 35

intercept 1.77823 0.0184949 1.74198 1.81448 36

v

- 0.600699 0.340055

- 1.2672 0.0657974 37

n 1 NA 38

k 1284.65 1317. 9

- 1298.39 3867.68 39

40

NA

- Indicates that this parameter has hit a bound 41

implied by some inequality constraint and thus 42

has no standard error. 43

44

45

46

Table of Data and Estimated Values of Interest 47

48

Dose N Obs Mean Est Mean Obs Std Dev Est Std Dev Scaled Res. 49

------ --- -------- -------- ----------- ----------- ---------- 50

51

0 12 1.78 1.78 0.07 0.0704 0.0872 52

79.2 12 1.74 1.74 0 .09 0.0704

- 0.165 53

226.7 11 1.69 1.69 0.06 0.0704 0.0884 54

967.8 14 1.52 1.52 0.07 0.0704

- 0.00677 55

56

57

58

Model Descriptions for likelihoods calculated 59

60

61

Model A1: Yij = Mu(i) + e(ij) 62

Var{e(ij)} = Sigma^2 63

64

Model A2: Yij = Mu(i) + e(ij) 65

Var{e(ij)} = Sigma(i)^2 66

67DRAFT

– DO NOT CITE OR QUOTE C

- 11 Model A3: Yij = Mu(i) + e(ij) 1

Var{e(ij)} = Sigma^2 2

Model A3 uses any fixed variance parameters that 3

were specified by the user 4

5

Model R: Yi = Mu + e(i) 6

Var{e(i)} = Sigma^2 7

8

9

Likelihoods of Interest 10

11

Model Log(likelihood) # Param's AIC 12

A1 105.539862 5

- 201.079724 13

A2 106.570724 8

- 197.141449 14

A3 105.539862 5

- 201.079724 15

fitted 105.518584 4

- 203.037168 16

R 77.428662 2

- 150.857324 17

18

19

Explanation of Tests 20

21

Test 1: Do responses and/or variances differ among Dose levels? 22

(A2 vs. R) 23

Test 2: Are Variances Homogeneous? (A1 vs A2) 24

Test 3: Are variances adequately modeled? (A2 vs. A3) 25

Test 4 : Does the Model for the Mean Fit? (A3 vs. fitted) 26

(Note: When rho=0 the results of Test 3 and Test 2 will be the same.) 27

28

Tests of Interest 29

30

Test

- 2*log(Likelihood Ratio) Test df p

- value 31

32

Test 1 58.2841 6 <.0001 33

Test 2 2.06173 3 0.5597 34

Test 3 2.06173 3 0.5597 35

Test 4 0.0425562 1 0.8366 36

37

The p

- value for Test 1 is less than .05. The re appears to be a 38

difference between response and/or variances among the dose levels 39

It seems appropriate to model the data 40

41

The p

- value for Test 2 is greater than .1. A homogeneous variance 42

model appears to be appropriate here 43

44

45

The p

- value for Test 3 is greater than .1. The modeled variance appears 46

to be appropriate here 47

48

The p

- value for Test 4 is greater than .1. The model chosen seems 49

to adequately describe the data 50

51

52

Benchmark Dose Computation 53

54

Specified effect = 0.05 55

56

Risk Ty pe = Relative risk 57

58

Confidence level = 0.95 59

60

BMD = 223.178 61

62DRAFT

– DO NOT CITE OR QUOTE C

- 12 BMDL = 123.872

1.45

1.5 1.55

1.6 1.65

1.7 1.75

1.8 1.85

0 200 400 600 800 1000Mean Response

dose Hill Model with 0.95 Confidence Level

09:40 08/24 2009 BMD BMDL Hill

Figure C

- 2. Hill model, BMR of 0.05 relative risk

- Decreased Brain weight in male

rats at 6 weeks age versus AUC, F 1 Generation inhalational study

.

Source: NEDO (1987)

.

Once the BMDL 05 was obtained in units of hr × mg/L, it was used to derive a chronic 1

inhalation reference value. The first step is to calculate the HEC using the PBPK model 2

described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B ) that 3

describes the relationship between predicted methanol AUC and the human equivalent inhalation 4

exposure concentration (HEC) in ppm. 5

BMD L HEC (ppm) = 0.0224* BMDL 05 +(1334* BMDL 05 )/ (794+ BMDL 05 ) 6

BMD L HEC (ppm) = 0.0224* 503.0 + ( (1334* 123.9 )/ (794+ 123.9 ) ) = 183 ppm 7

Next, because RfCs are typically expressed in units of mg/m 3 , the HEC value in 8

ppm was converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m 3 : 9DRAFT

– DO NOT CITE OR QUOTE C

- 13 HEC ( mg/m 3 ) = 1.31 × 183 ppm = 240 mg/m 3 1

Finally, this HEC value was divided by a composite 100

- fold UF (3 for uncertainty 2

associated with animal to human differences, 10 for consideration of human variability, and 3 for 3

database deficiencies ) to obtain the ch ronic inhalation reference value: 4

RfC (mg/m 3 ) = 240 mg/m 3 ÷ 100 = 2.4 mg/m 3 5

C.2. DECREASED BR AIN WEIGHT IN MALE R ATS EXPOSED DURING

GESTATION ONLY ( GD 7

– GD 17) (NEDO, 1987)

C

max , as calculated by the EPA ’s PBPK model, was selected as the dose metric for this 6

exp osure scenario , in concordance with the choice of this dose metric for the increased incidence 7

of cervical rib in mice in the Rogers et al . study (1993). Exposures occurred only during the 8

major period of organogenesis in both studies. As there is eviden ce that C

max is a better predictor 9

of response than AUC for incidence of cervical rib (see Appendix D), it was assumed appropriate 10

to consider C

max the better predictor for decreased brain weight as well. 11

The first step in the current analysis is to con vert the inhalation doses, given as ppm 12

values from the studies, to an internal dose surrogate or dose metric using the EPA PBPK model 13

(see Section 3.4 )

. Predicted C

max values for methanol in the blood of rats are summarized in 14

Table C

- 4

. 15

Table C

- 4. EPA ’s PBPK model estimates of methanol blood levels ( C

max ) in rats

following inhalation exposures

Exposure l evel (ppm) Methanol in lood C

max (mg /L) a in r ats

200 1.2

1,000 10.6

5000 630.5

a C

max values were obtained by simulating 22 hr/day exposures

The c u rrent BMD technical guidance (U.S. EPA, 2000 b ) suggests that in the absence of 16

knowledge as to what level of response to consider adverse, a change in the mean equal to 17

1 control S.D. from the control mean can be used as a BMR for continuous endpoint s

. Ho wever, 18

it has been suggested that other BMRs, such as 5% change relative to estimated control mean, 19

are also appropriate when performing BMD analyses on fetal weight change as a developmental 20

endpoint (Kavlock et al ., 1995). Therefore, in this assessment, both a 1 control mean S.D. 21

change and a 5% change relative to estimated control mean were considered. All models were fit 22

using restrictions and option settings suggested in the EPA ’s BMD technical guidance document 23

(U.S. EPA, 2000 b ). 24DRAFT

– DO NOT CITE OR QUOTE C

- 14 C.2.1. BMD Approach with a BMR of 1 Control Mean S.D. (GD7

– GD 17) (NEDO, 1987)

A summary of the results most relevant to the development of a POD using the BMD 1

approach (BMD, BMDL, and model fit statistics) for decreased brain weight at 8 weeks in male 2

rats exposed to methanol durin g gestation from days 7

– 17, with a BMR of 1 control mean S.D

, is 3

provided in Table C

- 5

. M ale brain weight responses were chosen because they resulted in lower 4

BMD and BMDL estimate s than female responses (data not shown)

. Model fit was determined 5

by stati stics (AIC and χ 2 residuals of individual dose groups) and visual inspection , as 6

recommended by EPA (2000 b ). T he polynomial and power models reduced to linear form and 7

returned identical modeling results. In contrast, the more complex Hill and Exponentia l4 8

models, which estimate a response “plateau” or asymptote, returned similar, markedly nonlinear 9

results . This is because these models approximated the response “plateau” to be near the 10

maximum drop in brain weight observed in the study (approximately 10 % at the high dose)

, 11

resulting in a distinctly “L” shaped dose

- response curve

. 89 In this case, t he only PBPK model 12

estimated C

max dose that is associated with a significant response over controls, the high

- dose, is 13

60

- fold higher than the mid

- dose C

max esti mate

. Thus, there are many plausible curve shapes 14

and,consequently, a wide range of BMDL estimates. Per EPA (2000b) guidance and to err on the 15

side of public health protection , the lowest BMDL 1SD of 10.2 6 mg methanol/L in blood estimated 16

from adequate and plausible models was chosen for use in the RfC derivation

. However, it 17

should be noted that there is a great deal of uncertainty and model dependence associated with 18

these dose

- response data. 19

Table C

- 5. Comparison of BMD 1SD results for decreased brain wei ght in male rats at 8

weeks of age u sing m odeled C

max of m ethanol as a dose metric

Model BMD 1SD ( C

max , mg/L) a BMDL 1SD ( C

max , mg/L ) a p

- value AIC c Scaled r esidual d

Linear 207.18 135.22 0.7881

- 173.12

- 0.43

2 nd degree polynomial 207.18 135.22 0.7881

- 173.12

- 0.43

3rd degree polynomial 207.18 135.22 0.7881

- 173.12

- 0.43

Power 207.18 135.22 0.7881

- 173.12

- 0.43

Hill b 43.08 10.26 0. 9602

- 171.59

- 0. 1 0

Exponential 2 199.98 127.55 0.9494

- 173.13

- 0.42

Exponential 3 199.98 127.55 0.9494

- 173.13

- 0.42

Exponent ial 4 b 39.53 10.2 6 Not reported

- 171.59 0.10

89 The extent of the “L” shape is dependent on the asymptote term, or “plateau” level, estimated for the data. If the

asymptote term (v) in the Hill model is set to

- .4 ( representing a 20% drop from the control brain weight of 2

grams), the model result is more linear and the BMD and BMDL estimates are approximately 4

- fold higher.DRAFT

– DO NOT CITE OR QUOTE C

- 15 a The BMDL is the 95% lower confidence limit on the C

max estimated to decrease brain weight by 1 control mean

S.D. using BMDS model options and restrictions suggested by EPA BMD technical guidance (U.S. EPA, 200 0b).

b Per EPA (2000b) guidance and to err on the side of public health protection, the lowest BMDL 1SD of 10.2 6 mg

methanol/L in blood estimated from adequate and plausible models was chosen for use in the RfC derivation

c AIC = Akaike Information Criterion =

- 2L + 2P, where L is the log

- likelihood at the maximum likelihood

estimates for the parameters, and P is the number of modeled degrees of freedom (usually the number of parameters

estimated).

d χ

2 d residual (measure of how model

- predicted responses devia te from the actual data) for the dose group closest

to the BMD scaled by an estimate of its S.D . Provides a comparative measure of model fit near the BMD.

Residuals that exceed 2.0 in absolute value should cause one to question model fit in this region

.

Source: NEDO (1987)

==================================================================== 1

Hill Model. (Version: 2.14; Date: 06/26/2008) 2

Input Data File: C: \ Usepa \ BMDS21 \ Data \ Methanol \ NEDO \ Gest

- only \ hilm

- 8wk

3

brwHil

- Restrict.(d) 4

Gn uplot Plotting File: C: \ Usepa \ BMDS21 \ Data \ Methanol \ NEDO \ Gest

- only \ hilm

5

8wk

- brwHil

- Restrict.plt 6

Tue Aug 25 12:40:30 2009 7

==================================================================== 8

9

BMDS Model Run 10

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 11

12

The form of the response function is: 13

14

Y[dose] = intercept + v*dose^n/(k^n + dose^n) 15

16

17

Dependent variable = Mean 18

Independent variable = Dose 19

Power parameter restricted to be greater than 1 20

The variance i s to be modeled as Var(i) = exp(lalpha + rho * ln(mean(i))) 21

22

Total number of dose groups = 4 23

Total number of records with missing values = 0 24

Maximum number of iterations = 250 25

Relative Function Convergence has been set to: 1e

- 008 26

Parameter Convergence has been set to: 1e

- 008 27

28

29

30

Default Initial Parameter Values 31

lalpha =

- 4.68678 32

rho = 0 33

intercept = 2 34

v =

- 0.19 35

n = 0.861776 36

k = 303.331 37

38

39

Asymptotic Correlation Matrix of Parameter Estimates 40

41

( *** The model parameter(s)

- n 42

have been estimated at a boundary point, or have been specified by the user, 43

and do not appear in the correlation matrix ) 44

45

lalpha rho intercept v k 46

47

lalpha 1

- 1

- 0.083 0.6

- 0.18 48

49

rho

- 1 1 0.096

- 0.6 0.18 50

51DRAFT

– DO NOT CITE OR QUOTE C

- 16 intercept

- 0.083 0.096 1 0.19

- 0.55 1

2

v 0.6

- 0.6 0.19 1

- 0.73 3

4

k

- 0.18 0.18

- 0.55

- 0.73 1 5

6

7

8

Parameter Estimates 9

10

95.0% Wald Confidence Interval 11

Variable Estimate Std. Err. Lower Conf. L imit Upper Conf. Limit 12

lalpha 7.03732 4.98399

- 2.73112 16.8058 13

rho

- 18.1432 7.32604

- 32.502

- 3.78448 14

intercept 2.0068 0.0134454 1.98045 2.03316 15

v

- 0.232906 0.0881494

- 0.405676

- 0.0601362 16

n 1 NA 17

k 121.949 194.687

- 259.631 503.529 18

19

NA

- Indicates that this parameter has hit a b ound 20

implied by some inequality constraint and thus 21

has no standard error. 22

23

24

25

Table of Data and Estimated Values of Interest 26

27

Dose N Obs Mean Est Mean Obs Std Dev Est Std Dev Scaled Res. 28

------ --- -------- ----- --- ----------- ----------- ---------- 29

30

0 11 2 2.01 0.047 0.0608

- 0.371 31

1.2 11 2.01 2 0.075 0.0614 0.295 32

10.6 12 1.99 1.99 0.072 0. 0662 0.0954 33

630.5 10 1.81 1.81 0.161 0.154

- 0.0338 34

35

36

37

Model Descriptions for likelihoods calculated 38

39

40

Model A1: Yij = Mu(i) + e(ij) 41

Var{e(ij)} = Sigma^2 42

43

Model A2: Yij = Mu(i) + e(ij ) 44

Var{e(ij)} = Sigma(i)^2 45

46

Model A3: Yij = Mu(i) + e(ij) 47

Var{e(ij)} = exp(lalpha + rho*ln(Mu(i))) 48

Model A3 uses any fixed variance parameters that 49

were specified by the user 50

51

Model R: Yi = Mu + e(i) 52

Var{e(i)} = Sigma^2 53

54

55

Likelihoods of Interest 56

57

Model Log(likelihood) # Param's AIC 58

A1 83.205960 5

- 156.411920 59

A2 92.060485 8

- 168. 120970 60

A3 90.797178 6

- 169.594356 61

fitted 90.795933 5

- 171.591867 62

R 70.761857 2

- 137.523714 63

64

65

Explanation of Tests 66

67DRAFT

– DO NOT CITE OR QUOTE C

- 17 Test 1: Do responses and/or variances differ among Dose levels? 1

(A2 vs. R) 2

Test 2: Are Variances Homogeneous? (A1 vs A2) 3

Test 3: Are variances adequately modeled? (A2 vs. A3) 4

Test 4: Does the Model for the Mean Fit? (A3 vs. fitted) 5

(Note: When rho =0 the results of Test 3 and Test 2 will be the same.) 6

7

Tests of Interest 8

9

Test

- 2*log(Likelihood Ratio) Test df p

- value 10

11

Test 1 42.5973 6 <.0001 12

Test 2 17.7091 3 0.000505 13

Test 3 2.52661 2 0.2827 14

Test 4 0.00248896 1 0.9602 15

16

The p

- value for Test 1 is less than .05. There appears to be a 17

difference between response and/or variances among the dose levels 18

It seems appropriate to model the data 19

20

The p

- value for Test 2 is less than .1. A non

- homogeneous variance 21

model appears to be appropriate 22

23

The p

- value for Test 3 is greater than .1. The modeled variance appears 24

to be appropriate here 25

26

The p

- value for Test 4 is greater than .1. The model chosen seems 27

to adequately describe the data 28

29

30

Benchmark Dose Computation 31

32

Specified effect = 1 33

34

Risk Type = Estimated standard deviations from the control mean 35

36

Conf idence level = 0.95 37

38

BMD = 43.0842 39

40

BMDL = 10.2551 41DRAFT

– DO NOT CITE OR QUOTE C

- 18 1.7 1.75

1.8 1.85

1.9 1.95 2 2.05

0 100 200 300 400 500 600Mean Response

dose Hill Model with 0.95 Confidence Level

12:40 08/25 2009 BMD BMDL Hill

Figure C

- 3. Hill model, BMR of 1 Control Mean S.D.

- Decreased Brain weight in

male rats at 8 weeks age versus C

max , Gestation only inhalational study

.

Source: NEDO (1987).

Once the BMDL 1SD was obtained in units of mg/L, it was used to derive a chronic 1

inhalation reference value. The first step is to calculate the HEC using the PBPK model 2

described in Appendix B. An algebraic equation is provided (Equ ation 1 of Appendix B ) that 3

describes the relationship between predicted methanol AUC and the human equivalent inhalation 4

exposure concentration (HEC) in ppm. This equation can also be used to estimate model 5

predictions for HECs from C

max values because C

max values and AUC values

, were estimated at 6

steady

- state for constant 24

- hour exposures ( i.e., AUC = 24 x C

max ). 7

BMD L HEC (ppm) = 0.0224 * BMDL 1SD * 24 +(1334* BMDL 1 SD * 24)/ (794+ BMDL 1 SD * 24 ) 8

BMD L HEC (ppm) = 0.0224* 10.3 * 24 + ( (1334* 10.3 * 24)/ (794+ 10.3 * 24 ) ) = 32 2 ppm 9DRAFT

– DO NOT CITE OR QUOTE C

- 19 Next, because RfCs are typically expressed in units of mg/m 3 , the HEC value in ppm was 1

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m 3 : 2

HEC (mg/m 3 ) = 1.31 × 322 ppm = 422 mg/m 3 3

Finally, this HEC value was divided b y a composite 100

- fold UF (3 for uncertainty 4

associated with animal to human differences, 10 for consideration of human variability, and 3 for 5

database deficiencies ) to obtain the chronic inhalation reference value: 6

RfC (mg/m 3 ) = 422 mg/m 3 ÷ 100 = 4.2 mg/ m 3 7

C.2.2. BMD Approach with a BMR of 0.05 Change Relative to Control Mean (GD7

– GD 17)

(NEDO, 1987)

A summary of the results most relevant to the development of a POD using the BMD 8

approach (BMD, BMDL, and model fit statistics) for decreased brain weight at 8 weeks in male 9

rats exposed to methanol during gestation from days 7 to 17, with a BMR of 0.05 change relative 10

to estimated control mean

, is provided in Table C

- 6

. Model fit was determined by statistics (AIC 11

and χ 2 residuals of individual dose groups) and visua l inspection , as recommended by EPA 12

(2000 b ). Modeling considerations and uncertainties for this dataset were discussed in C.2.1 and, 13

as was done for the BMR of 1 S.D., the lowest BMDL 05 of 21

. 07 mg methanol/L in blood 14

estimated from adequate and plausible models was chosen for use in the RfC derivation

. 15DRAFT

– DO NOT CITE OR QUOTE C

- 20 Table C

- 6. Comparison of BMD 05 modeling results for decreased brain weight in male

rats at 8 weeks of age u sing m odeled C

max of m ethanol as a common dose metric

Model BMD 05 (C

max , mg/L) a BMDL 05 (C

max , mg/ L) a p

- value AIC c Scaled r esidual d

Linear b 328.84 226.08 0.7881

- 173.12 0.02

2 nd degree p olynomial 328.84 226.08 0.7881

- 173.12 0.02

3rd degree p olynomial 328.84 226.08 0.7881

- 173.12 0.02

Power 328.84 226.08 0.9446

- 173.12 0.02

Hill b 92.30 Not reporte d 0. 9602

- 171.59 0. 1 0

Exponential 2 320.62 215.13 0.9494

- 173.13 0.02

Exponential 3 320.62 215.13 0.9494

- 173.13 0.02

Exponential 4 b 76.36 21.07 Not reported

- 171.59 0.10

a The BMDL is the 95% lower confidence limit on the C

max estimated to decrease bra in weight by 5% using BMDS

model options and restrictions suggested by EPA BMD technical guidance (U.S. EPA, 2000b).

b Per EPA (2000b) guidance and to err on the side of public health protection, the lowest BMDL 05 of 21

. 07 mg

methanol/L in blood estimated f rom adequate and plausible models was chosen for use in the RfC derivation

.

c AIC = Akaike Information Criterion =

- 2L + 2P, where L is the log

- likelihood at the maximum likelihood

estimates for the parameters, and P is the number of modeled degrees of free dom (usually the number of parameters

estimated).

d χ

2 d residual (measure of how model

- predicted responses deviate from the actual data) for the dose group closest to

the BMD scaled by an estimate of its S.D . Provides a comparative measure of model fit n ear the BMD. Residuals

that exceed 2.0 in absolute value should cause one to question model fit in this region.

Source: NEDO (1987)

.

==================================================================== 1

Exponential Model. (Version: 1.61; Date: 7/24/2009) 2

Input Data File: C: \ Usepa \ BMDS21 \ Data \ Methanol \ NEDO \ Gest

- only \ expm

- 8wk

3

brwSetting.(d) 4

Gnuplot Plotting File: 5

Tue Aug 25 14:15:15 2009 6

==================================================================== 7

8

BMDS Model Run 9

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 10

11

The form of the response function by Model: 12

Model 2: Y[dose] = a * exp{sign * b * dose} 13

Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 14

Model 4: Y[do se] = a * [c

- (c

- 1) * exp{

- b * dose}] 15

Model 5: Y[dose] = a * [c

- (c

- 1) * exp{

- (b * dose)^d}] 16

17

Note: Y[dose] is the median response for exposure = dose; 18

sign = +1 for increasing trend in data; 19

sign =

- 1 for decreasing trend. 20

21

Model 2 is nested within Models 3 and 4. 22

Model 3 is nested within Model 5. 23

Model 4 is nested within Model 5. 24

25

26

Dependent variable = Mean 27

Independent variable = Dose 28

Data are assumed to be distributed: normally 29

Variance Model: exp(lnalpha +rho *ln(Y[dose])) 30

The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 31DRAFT

– DO NOT CITE OR QUOTE C

- 21 1

Total number of dose groups = 4 2

Total number of records with missing values = 0 3

Maximum number of iterations = 250 4

Relative Function Convergence has been set to: 1e

- 008 5

Parameter Convergence has been set to: 1e

- 008 6

7

MLE solution provided: Exact 8

9

10

Initial Parameter Values 11

12

Variable Model 2 Model 3 Model 4 Model 5 13

-------- ------- ------- ------- ------- 14

lnalpha NC NC 7.32457 NC 15

rho NC NC

- 18.5236 NC 16

a NC NC 2.1105 NC 17

b NC NC 0.00239093 NC 18

c NC NC 0.816778 NC 19

d NC NC

-- NC 20

21

22

23

Parameter Estimates by Model 24

25

Variable Model 2 Model 3 Model 4 Model 5 26

-------- ------- ------- ------- ------- 27

lnalpha NC NC 7.03418 NC 28

rho NC NC

- 18.1386 NC 29

a NC NC 2.00677 NC 30

b NC NC 0.00941775 NC 31

c NC NC 0.902498 NC 32

d NC NC

-- NC 33

34

NC = No Convergence 35

36

37

Table of Stats From Input Data 38

39

Dose N Obs Mean Obs Std Dev 40

----- --- ---------- ------------- 41

0 11 2 0.047 42

1 .2 11 2.01 0.075 43

10.6 12 1.99 0.072 44

630.5 10 1.81 0.161 45

46

47

Estimated Values of Interest 48

49

Model Dose Est Mean Est Std Scaled Residual 50

---- --- ------ ---------- --------- ---------------- 51

4 0 2.007 0.06082

- 0.3692 52

1.2 2.005 0.06142 0.2932 53

10.6 1.988 0.06617 0.095 27 54

630.5 1.812 0.1538

- 0.03335 55

56

57

58

Other models for which likelihoods are calculated: 59

60

Model A1: Yij = Mu(i) + e(ij) 61

Var{e(ij)} = Sigma^2 62

63

Model A2: Yij = Mu(i) + e(ij) 64

Var{e(ij)} = Sigma(i)^2 65

66

Model A3: Yij = Mu(i) + e(ij) 67DRAFT

– DO NOT CITE OR QUOTE C

- 22 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 1

2

Model R: Yij = Mu + e(i) 3

Var{e(ij)} = Sigma^2 4

5

6

Likelihoods of Interest 7

8

Model Log(likelihood) DF AIC 9

------- ----------------- ---- ------------ 10

A1 83.20596 5

- 156.4119 11

A2 92.06049 8

- 168.121 12

A3 90.61606 6

- 169.2321 13

R 70.76186 2

- 137.5237 14

4 90.79579 5

- 171.5916 15

16

17

Addi tive constant for all log

- likelihoods =

- 40.43. This constant added to the 18

above values gives the log

- likelihood including the term that does not 19

depend on the model parameters. 20

21

22

Explanation of Tests 23

24

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 25

Test 2: Are Variances Homogeneous? (A2 vs. A1) 26

Test 3: Are variances adequately modeled? (A2 vs. A3) 27

28

Test 6a: Does Model 4 fit the data? (A3 vs 4) 29

30

31

Tests of Interest 32

33

Test

- 2*log(Likelihood Ratio) D. F. p

- value 34

-------- ------------------------ ------ -------------- 35

Test 1 42.6 6 < 0.0001 36

Test 2 17.71 3 0.000505 37

Test 3 2.889 2 0.2359 38

Test 6a

- 0.3595 1 N/A 39

40

41

The p

- value for Test 1 is less than .05. There appears to be a 42

difference between response and/or variances among the dose 43

levels, it seems appropriate to model the data. 44

45

The p

- value for Test 2 is less than .1. A non

- homogeneous 46

variance model appears to be appropriate. 47

48

The p

- value for Test 3 is greater than .1. The modeled 49

variance appears to be appropriate here. 50

51

The p

- value for Test 6a is less than .1. Model 4 may not adequately 52

describe the data; you may want to consider another model. 53

54

55

Benchm ark Dose Computations: 56

57

Specified Effect = 0.050000 58

59

Risk Type = Relative deviation 60

61

Confidence Level = 0.950000 62

63

64

BMD and BMDL by Model 65

66

Model BMD BMDL 67DRAFT

– DO NOT CITE OR QUOTE C

- 23 ------- ------------ ---------- 1

2 0 0 Not computed 2

3 0 0 Not computed 3

4 76.3561 21.0664 4

5 0 0 Not computed 5

1.7 1.75

1.8 1.85

1.9 1.95 2 2.05

0 100 200 300 400 500 600Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

14:15 08/25 2009 BMD BMDL Exponential

Figure C

- 4. Exponential4 model, BMR of 0.05 relative risk

- Decreased Brain weight

in male rats at 8 weeks age versus C

max , Gestation only inhalational study

.

Source: NEDO (1987).

Once the BMDL 05 was obtained in units of mg/L, it was u sed to derive a chronic 6

inhalation reference value. The first step is to calculate the HEC using the PBPK model 7

described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B ) that 8

describes the relationship between predicted methan ol AUC and the human equivalent inhalation 9

exposure concentration (HEC) in ppm. This equation can also be used to estimate model 10

predictions for HECs from C

max values because C

max values

, and AUC values were estimated at 11

steady

- state for constant 24

- hour exposures ( i.e., AUC = 24 x C

max ). 12

BMD L HEC (ppm) = 0.0224 * BMDL 05 * 24 +(1334* BMDL 05 * 24)/ (794+ BMDL 05 * 24 ) 13

BMD L HEC (ppm) = 0.0224* 21.07 * 24 + ( (1334* 21.07 * 24)/ (794+ 21.07 * 24 ) ) = 530 ppm 14DRAFT

– DO NOT CITE OR QUOTE C

- 24 Next, because RfCs are typically expressed in units of mg/m 3 , the HEC va lue in ppm was 1

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m 3 : 2

HEC (mg/m 3 ) = 1.31 × 530 ppm = 695 mg/m 3 3

Finally, this HEC value was divided by a composite 100

- fold UF (3 for uncertainty 4

associated with animal to human di fferences, 10 for consideration of human variability, and 3 for 5

database deficiencies ) to obtain the chronic inhalation reference value: 6

RfC (mg/m 3 ) = 695 mg/m 3 ÷ 100 = 7.0 mg/m 3 7

C.3. R f C DERIVATIONS USING ROGERS ET AL . (1993)

For the purposes of deriving an RfC for methanol from developmental endpoint s using 8

the BMD method and mouse data, cervical rib incidence data were evaluated from Rogers et al

. 9

(1993). In this paper, Rogers et al. (1993) also utilized a BMD methodology, examining the 10

dosimetric threshol d for cervical ribs and other developmental impacts by applying a log

- logistic 11

maximum likelihood model to the dose

- response data. Using air exposure concentrations (ppm) 12

as their dose metric, a value for the lower 95% confidence limit on the benchmark do se for 5% 13

additional risk in mice was 305 ppm (400 mg/m 3 ), using the log

- logistic model. Although the 14

teratology portion of the NEDO study (1987) also reported increases in cervical rib incidence in 15

Sprague

- Dawley rats, the Roger s et al

. (1993) study was chosen for dose

- response modeling 16

because effects were seen at lower doses, it was peer

- reviewed and published in the open 17

literature, and data on individual animals were available for a more statistically robust analysis 18

utilizing nested models available in BMDS. 19

The first step in the current BMD analysis is to convert the inhalation doses, given as 20

ppm values from the studies, to an internal dose surrogate or dose metric using the EPA ’s PBPK 21

model (see Section 3.4). For cervical rib malformations, C

max o f methanol in blood (mg/L) is 22

chosen as the appropriate internal dose metric metric (see Appendix D for further explanation)

. 23

Predicted C

max values for methanol in the blood of mice are summarized in Table C

- 7

. 24

Table C

- 7 . EPA ’s PBPK model estimates of me thanol blood levels ( C

max ) in mice

following inhalation exposures

Exposure c oncentration (ppm) Methanol in b lood C

max (mg/L) a in m ice

1 0.0216

10 0.218

50 1.14

100 2.46

250 7.83DRAFT

– DO NOT CITE OR QUOTE C

- 25 Exposure c oncentration (ppm) Methanol in b lood C

max (mg/L) a in m ice

500 26.4

1,000 134

a Rounded to three significant figures.

These C

max values are then used as the dose metric for the BMD analysis of cervical rib 1

incidence. 2

A 10% BMR level is the value typically calculated for comparisons across chemicals and 3

endpoint s for dichotomous responses because this level is near the low end of th e observable 4

range for many types of toxicity studies. However, reproductive and developmental studies 5

having a nested design often have a greater sensitivity, and a 5% BMR is typically appropriate 6

for determination of a POD (U.S. EPA, 2000 b ; Allen et al

. , 1994). Rogers et al . (1993) utilized a 7

5% added risk for the BMR in the original study

. T his assessment utilizes both a 10% and 5% 8

extra risk level as a BMR for the determination of a POD

. 90 The nested suite of models available 9

in BMDS was used to mode l the cervical rib data. In general, data from developmental toxicity 10

studies are best modeled using nested models, as these models account for any intralitter 11

correlation ( i.e., the tendency of littermates to respond similarly to one another relative to other 12

litters in a dose group). All models were fit using restrictions and option settings suggested in 13

the EPA ’ s BMD technical guidance document (U.S. EPA, 2000 b ). 14

C.3.1. BMD Approach with a BMR of 0.10 Extra Risk (Rogers et al ., 1993)

A summary of the results most relevant to the development of a POD using the BMD 15

approach (BMD, BMDL, and model fit statistics) for increased incidence of cervical rib in mice 16

exposed to methanol during gestation from days 6 to 15, with a BMR of 0.10 extra risk

, is 17

provided in Ta ble C

- 8

. Model fit was determined by statistics (AIC and χ 2 residuals of individual 18

dose groups) and visual inspection , as recommended by U.S. EPA (2000 b ). The best model fit to 19

these data (from visual inspection and comparison of AIC values) was obtaine d using the Nested 20

Logistic (NLogistic) model. The BMDL 10 was determined to be 94.3 mg/L using the 95% lower 21

confidence limit of the dose

- response curve expressed in terms of the C

max for methanol in blood

. 22

Table C

- 8 . Comparison of BMD modeling results f or cervical rib incidence in mice

using modeled C

max of methanol as a common dose metric

Model BMD 10 ( C

max , mg/L) a BMDL 10 ( C

max , mg/L) a p

- value AIC c Scaled r esidual d

NLogistic b 141.492 94.264 0.293 1046.84 0.649

NCTR 207.945 103.972 0.241 1048.92 0.662

90 Starr and Festa (2003) have argued that the Rogers et al . (1993) study’s experimental desi gn lacked the statistical

power to detect a 5% risk and that a 5% level lay below the observable response data. However, EPA’s BMD

guidance (U.S. EPA, 2000b) does not preclude the use of a BMR that is below observable response data and EPA

has deemed that the Rogers et al. (1993) is adequate for the consideration of a 5% BMR.DRAFT

– DO NOT CITE OR QUOTE C

- 26 Model BMD 10 ( C

max , mg/L) a BMDL 10 ( C

max , mg/L) a p

- value AIC c Scaled r esidual d

Rai and Van Ryzin 221.509 110.754 0.163 1051.65 0.661

a Daily C

max was estimated using a mouse PBPK model as described in section 3.4 of the methanol toxicological

review; the BMDL is the 95% lower confidence limit on the C

max for a 10% extra risk (dichot omous endpoint s )

estimated by the model using the likelihood profile method (U.S. EPA, 2000b).

b Model choice based on adequate p value (> 0.1), visual inspection, low AIC, and low (absolute) scaled residual

.

c AIC = Akaike Information Criterion =

- 2L + 2P, where L is the log

- likelihood at the maximum likelihood

estimates for the parameters, and P is the number of modeled degrees of freedom (usually the number of parameters

estimated)

. d χ

2 d residual (measure of how model

- predicted responses deviate from the a ctual data) for the dose group closest to

the BMD scaled by an estimate of its S.D. Provides a comparative measure of model fit near the BMD. Residuals

exceeding 2.0 in absolute value should cause one to question model fit in this region

.

Source: Rogers et al . (1993).

==================================================================== 1

NLogistic Model. 2

(Version: 2.13; Date: 02/20/2007) 3

Input Data File: U: \ Methanol \ BMDS \ CervicalRib \ C

max \ NLog_ C

max _10_default.(d) 4

Wed Nov 07 15:45:40 2007 5

============ ======================================================== 6

7

**BMD Method for RfC: Incidence of Cervical Rib in Mice versus** C

max **Methanol, GD 6**

**- 15**  8

**inhalational study (Rogers, et**  **al ., 1993)**  9

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 10

The probability function is: 11

12

Prob. = alpha + theta1*Rij + [1

- alpha

- theta1*Rij]/ 13

14

[1+exp(

- beta

- theta2*Rij

- rho*log(Dose))], 15

16

where Rij is the litter specific covariate. 17

18

Restrict Power rho >= 1. 19

20

Total number of ob servations = 166 21

Total number of records with missing values = 0 22

Total number of parameters in model = 9 23

Total number of specified parameters = 0 24

25

Maximum number of iterations = 250 26

Relative Function Convergence has been set to: 1e

- 008 27

Parameter Conv ergence has been set to: 1e

- 008 28

29

Default Initial Parameter Values 30

alpha = 0.297863 31

beta =

- 7.94313 32

theta1 = 0 33

thet a2 = 0 34

rho = 1.09876 35

phi1 = 0.213134 36

phi2 = 0.309556 37

phi3 = 0.220142 38

phi4 = 0.370587 39

40

Parameter Estimates 41

42

Variable Estimate Std. Err. 43

alpha 0.102434 * 44

beta

- 4.80338 * 45

theta1 0.0325457 * 46

theta2

- 0.436115 * 47DRAFT

– DO NOT CITE OR QUOTE C

- 27 rho 1 * 1

phi1 0.200733 * 2

phi2 0.307656 * 3

phi3 0.212754 * 4

phi4 0.368426 * 5

6

*

- Indicates that this value is not calculated. 7

8

Log

- likelihood:

- 515.422 AIC: 1046.84 9

10

Litter Data 11

12

Lit.

- Spec. Litter Scaled 13

Dose Cov. Est._Prob. Size Expected Observed Residual 14

-------------------------------------------------------------------------- 15

0.0000 1.0000 0.135 1 0.135 0

- 0.3950 16

0.0000 1.0000 0.135 1 0.135 0

- 0.3950 17

0.0000 2.0000 0.168 2 0.335 0

- 0.5790 18

0.0000 2.0000 0.168 2 0.335 1 1.1490 19

0.0000 2.0000 0.168 2 0.335 0

- 0.5790 20

0.0000 2.0000 0.168 2 0.335 2 2.8770 21

0.0000 2.0000 0.168 2 0.335 0

- 0.5790 22

0.0000 3.0000 0.200 3 0.600 0

- 0.7317 23

0.0000 3.00 00 0.200 3 0.600 0

- 0.7317 24

0.0000 3.0000 0.200 3 0.600 1 0.4874 25

0.0000 3.0000 0.200 3 0.600 1 0.4874 26

0.0000 4.0000 0.233 4 0 .930 0

- 0.8699 27

0.0000 4.0000 0.233 4 0.930 1 0.0650 28

0.0000 4.0000 0.233 4 0.930 0

- 0.8699 29

0.0000 4.0000 0.233 4 0.930 1 0.0650 30

0 .0000 5.0000 0.265 5 1.326 0

- 1.0004 31

0.0000 5.0000 0.265 5 1.326 1

- 0.2458 32

0.0000 5.0000 0.265 5 1.326 3 1.2632 33

0.0000 5.0000 0.265 5 1.326 1

- 0.2458 34

0.0000 5.0000 0.265 5 1.326 0

- 1.0004 35

0.0000 5.0000 0.265 5 1.326 1

- 0.2458 36

0.0000 5.0000 0.265 5 1.326 1

- 0.2458 37

0.0000 5.0000 0.265 5 1.326 0

- 1.0004 38

0.0000 5.0000 0.265 5 1.326 0

- 1.0004 39

0.0000 5.0000 0.265 5 1.326 1

- 0.2458 40

0.0000 6.0000 0.298 6 1.786 3 0.7656 41

0.0000 6.0000 0.298 6 1.786 6 2.6578 42

0.0000 6.0000 0.298 6 1.786 1

- 0.4959 43

0.0000 6.0000 0.298 6 1.786 0

- 1.1267 44

0.0000 6.0000 0.298 6 1.786 0

- 1.1267 45

0.0000 6.0000 0.298 6 1.786 1

- 0.4959 46

0.0000 6.0000 0.298 6 1.786 2 0.1348 47

0.0000 6.0000 0.298 6 1.786 0

- 1.1267 48

0.0000 6.0000 0.298 6 1.786 2 0.1348 49

0.0000 6.0000 0.298 6 1.786 3 0.7656 50

0.0000 6.0000 0.298 6 1.786 5 2.0271 51

0.0000 6.0000 0.298 6 1.786 0

- 1.1267 52

0.0000 6.0000 0.298 6 1.786 3 0.7656 53

0.0000 6.0000 0.298 6 1.786 3 0.7 656 54

0.0000 6.0000 0.298 6 1.786 3 0.7656 55

0.0000 6.0000 0.298 6 1.786 5 2.0271 56

0.0000 7.0000 0.330 7 2.312 0

- 1.2513 57

0.0000 7.0000 0. 330 7 2.312 1

- 0.7100 58

0.0000 7.0000 0.330 7 2.312 2

- 0.1688 59

0.0000 7.0000 0.330 7 2.312 3 0.3725 60

0.0000 7.0000 0.330 7 2.312 2

- 0.1688 61

0.0000 7.0000 0.330 7 2.312 3 0.3725 62

0.0000 7.0000 0.330 7 2.312 5 1.4551 63

0.0000 7.0000 0.330 7 2.312 0

- 1.2513 64

0.0000 7 .0000 0.330 7 2.312 2

- 0.1688 65

0.0000 7.0000 0.330 7 2.312 5 1.4551 66

0.0000 7.0000 0.330 7 2.312 1

- 0.7100 67DRAFT

– DO NOT CITE OR QUOTE C

- 2 8 0.0000 7.0000 0.330 7 2.312 2

- 0.1688 1

0.0000 7.0000 0.330 7 2.312 1

- 0.7100 2

0.0000 8.0000 0.363 8 2.902 1

- 0.9020 3

0.0000 8.0000 0.363 8 2.902 4 0.5204 4

0.0000 8.0000 0.363 8 2.902 3 0.0463 5

0.0000 8.0000 0.363 8 2.902 8 2.4170 6

0.0000 8.0000 0.363 8 2.902 2

- 0.4279 7

8

134.0000 1.0000 0.494 1 0.494 0

- 0.9887 9

134.0000 1.0000 0.494 1 0.494 0

- 0.9887 10

134.0000 2.0000 0.430 2 0.859 0

- 1.0732 11

134.0000 2.0000 0.430 2 0.859 2 1.4251 12

134.0000 3.0000 0.383 3 1.150 3 1.7287 13

134.0000 3.0000 0.383 3 1.150 1

- 0.1400 14

134.0000 3.0000 0.383 3 1.150 2 0.7944 15

134.0000 3.000 0 0.383 3 1.150 1

- 0.1400 16

134.0000 4.0000 0.356 4 1.425 3 1.1858 17

134.0000 4.0000 0.356 4 1.425 0

- 1.0729 18

134.0000 5.0000 0.346 5 1. 732 0

- 1.0898 19

134.0000 5.0000 0.346 5 1.732 4 1.4275 20

134.0000 5.0000 0.346 5 1.732 0

- 1.0898 21

134.0000 5.0000 0.346 5 1.732 1

- 0.4604 22

134. 0000 5.0000 0.346 5 1.732 0

- 1.0898 23

134.0000 6.0000 0.350 6 2.099 3 0.4839 24

134.0000 6.0000 0.350 6 2.099 2

- 0.0534 25

134.0000 7.0000 0.363 7 2.543 3 0.2128 26

134.0000 7.0000 0.363 7 2.543 2

- 0.2530 27

134.0000 7.0000 0.363 7 2.543 2

- 0.2530 28

134.0000 7.0000 0.363 7 2.543 2

- 0.2530 29

134.0000 7.0000 0.363 7 2.543 2

- 0.2530 30

134.0000 7.0000 0.363 7 2.543 0

- 1.1847 31

134.0000 8.0000 0.383 8 3.068 2

- 0.4373 32

134.0000 8.0000 0.383 8 3.068 0

- 1.2562 33

134.0000 8.0000 0.383 8 3.068 8 2.0195 34

35

526.0000 2.0000 0.703 2 1.406 2 0.8346 36

526.0000 3.0000 0.631 3 1.892 3 1.1101 37

526.0000 4.0000 0.562 4 2.250 2

- 0.1967 38

526.0000 4.0000 0.562 4 2.250 1

- 0.9842 39

526.0000 5.0000 0.506 5 2.530 3 0.3091 40

526.0000 5.0000 0.506 5 2.530 5 1.6241 41

526.0000 5.0000 0.506 5 2.530 3 0.3091 42

526.0000 5.0000 0.506 5 2.530 1

- 1.0058 43

526.0000 6.0000 0.466 6 2.796 3 0.1162 44

526.0000 6.0000 0.466 6 2.796 3 0.1162 45

526.0000 6.0000 0.466 6 2.796 3 0.1162 46

526.0000 6.0000 0.466 6 2.796 5 1.2 556 47

526.0000 6.0000 0.466 6 2.796 6 1.8253 48

526.0000 6.0000 0.466 6 2.796 5 1.2556 49

526.0000 6.0000 0.466 6 2.796 2

- 0.4534 50

526.0000 6.0000 0. 466 6 2.796 0

- 1.5928 51

526.0000 6.0000 0.466 6 2.796 2

- 0.4534 52

526.0000 6.0000 0.466 6 2.796 0

- 1.5928 53

526.0000 6.0000 0.466 6 2.796 5 1.2556 54

526.0000 6.0000 0.466 6 2.796 4 0.6859 55

526.0000 6.0000 0.466 6 2.796 3 0.1162 56

526.0000 6.0000 0.466 6 2.796 2

- 0.4534 57

526.0000 6 .0000 0.466 6 2.796 4 0.6859 58

526.0000 6.0000 0.466 6 2.796 2

- 0.4534 59

526.0000 7.0000 0.444 7 3.105 0

- 1.5658 60

526.0000 7.0000 0.444 7 3.105 4 0.4511 61

526.0000 7.0000 0.444 7 3.105 5 0.9554 62

526.0000 7.0000 0.444 7 3.105 1

- 1.0615 63

526.0000 7.0000 0.444 7 3.105 4 0.4511 64

526.0000 7.0000 0.444 7 3.105 1

- 1.0615 65

526.0000 7.0000 0.444 7 3.105 5 0.9554 66

526.0000 7.0000 0.444 7 3.105 3

- 0.0531 67DRAFT

– DO NOT CITE OR QUOTE C

- 29 526.0000 7.0000 0.444 7 3.105 4 0.4511 1

526.0000 7.0000 0.444 7 3.105 1

- 1.0615 2

526.0000 7.0000 0.444 7 3.105 3

- 0.0531 3

526.0000 7.0000 0.444 7 3.105 3

- 0.0531 4

526.0000 8.0000 0.437 8 3.496 0

- 1.5793 5

526.0000 8.0000 0.437 8 3.496 7 1.5832 6

526.0000 8.0000 0.437 8 3.496 5 0.6796 7

526.0000 9.0000 0.443 9 3.985 0

- 1.6270 8

526.0000 9.0000 0.443 9 3.985 6 0.8225 9

10

2005.0000 1.0000 0.926 1 0.926 1 0.2834 11

2005.0000 1.0000 0.926 1 0. 926 1 0.2834 12

2005.0000 1.0000 0.926 1 0.926 1 0.2834 13

2005.0000 2.0000 0.894 2 1.789 1

- 1.5502 14

2005.0000 2.0000 0.894 2 1.789 2 0.4157 15

2005. 0000 3.0000 0.853 3 2.559 3 0.5454 16

2005.0000 3.0000 0.853 3 2.559 1

- 1.9294 17

2005.0000 3.0000 0.853 3 2.559 1

- 1.9294 18

2005.0000 3.0000 0.853 3 2.559 3 0.5454 19

2005.0000 4.0000 0.802 4 3.208 4 0.6851 20

2005.0000 4.0000 0.802 4 3.208 4 0.6851 21

2005.0000 4.0000 0.802 4 3.208 4 0.6851 22

2005.0000 4.0000 0.802 4 3.208 2

- 1.0440 23

2005.0000 4.0000 0.802 4 3.208 3

- 0.1795 24

2005.0000 4.0000 0.802 4 3.208 4 0.6851 25

2005.0000 4.0000 0.802 4 3.208 4 0.6851 26

2005.0000 5.0000 0.743 5 3.714 1

- 1.7660 27

2005.0000 5.0000 0.743 5 3.714 3

- 0.4648 28

2005.0000 5.0000 0.743 5 3.714 5 0.8364 29

2005.0000 5.0000 0.743 5 3.714 5 0.8364 30

2005.0000 5.0000 0.743 5 3.714 4 0.1858 31

2005.0000 5.0000 0.743 5 3.714 4 0.1858 32

2005.0000 6.0000 0.681 6 4.086 6 0.9945 33

2005.0000 6.0000 0.681 6 4.086 2

- 1.0836 34

2005.0000 6.0000 0.681 6 4.086 4

- 0.0445 35

2005.0000 6.0000 0.681 6 4.086 5 0.4750 36

2005.0000 6.0000 0.681 6 4.086 6 0.9945 37

2005.0000 6.0000 0.681 6 4.086 5 0.4750 38

2005.0000 6.0000 0.681 6 4.086 4

- 0.04 45 39

2005.0000 6.0000 0.681 6 4.086 5 0.4750 40

2005.0000 6.0000 0.681 6 4.086 3

- 0.5641 41

2005.0000 6.0000 0.681 6 4.086 6 0.9945 42

2005.0000 6.0000 0.6 81 6 4.086 0

- 2.1227 43

2005.0000 6.0000 0.681 6 4.086 0

- 2.1227 44

2005.0000 7.0000 0.623 7 4.361 7 1.1486 45

2005.0000 7.0000 0.623 7 4.361 5 0.2781 46

2005.0000 7.0000 0.623 7 4.361 5 0.2781 47

2005.0000 7.0000 0.623 7 4.361 7 1.1486 48

2005.0000 7.0000 0.623 7 4.361 6 0.7133 49

2005.0000 8. 0000 0.576 8 4.606 0

- 1.7419 50

51

Combine litters with adjacent levels of the litter

- specific covariate 52

within dose groups until the expected count exceeds 3.0, to help improve 53

the fit of the X^2 statistic to chi

- square. 54

55

Grouped Data 56

Mean Scaled 57

Dose Lit.

- Spec. Cov. Expected Observed Residual 58

------------------------------------------------------------- 59

0.0000 1.0000 0.270 0

- 0.5586 60

0.0000 2.0000 1.675 3 1.0237 61

0.0000 3.0000 2.401 2

- 0.2443 62

0.0000 4.0000 3.722 2

- 0.8049 63

0.0000 5.0000 3.977 4 0.0098 64

0.0000 5.0000 3.977 2

- 0.8614 65

0.0000 5.0000 3.977 1

- 1.2970 66

0.0000 5.0000 1.326 1

- 0.2458 67DRAFT

– DO NOT CITE OR QUOTE C

- 30 0.0000 6.0000 3.57 3 9 2.4207 1

0.0000 6.0000 3.573 1

- 1.1474 2

0.0000 6.0000 3.573 1

- 1.1474 3

0.0000 6.0000 3.573 2

- 0.7013 4

0.0000 6.0000 3.573 5 0.6367 5

0.0000 6.0000 3.573 5 0.6367 6

0.0000 6.0000 3.573 6 1.0827 7

0.0000 6.0000 3.573 8 1.9747 8

0.0000 7.0000 4.624 1

- 1.3869 9

0.0000 7.0000 4.624 5 0.1441 10

0.0000 7.0000 4.624 5 0.1441 11

0.0000 7.0000 4.624 5 0.1441 12

0.0000 7.0000 4.624 7 0.9096 13

0.0000 7.0000 4.624 3

- 0.6214 14

0.0000 7.0000 2.312 1

- 0.7100 15

0.0000 8.0000 5.805 5

- 0.2698 16

0.0000 8.0000 5.805 11 1.7418 17

0.0000 8.0000 2.902 2

- 0.4279 18

19

134.0000 1.0000 0.989 0

- 1.3982 20

134.0000 2.0000 1.718 2 0.2488 21

134.0000 3.0000 3.449 6 1. 3759 22

134.0000 3.0000 1.150 1

- 0.1400 23

134.0000 4.0000 2.850 3 0.0799 24

134.0000 5.0000 3.463 4 0.2388 25

134.0000 5.0000 3.463 1

- 1.0962 26

134.0000 5.0000 1.732 0

- 1.0898 27

134.0000 6.0000 4.199 5 0.3044 28

134.0000 7.0000 5.086 5

- 0.0284 29

134.0000 7.0000 5.086 4

- 0.3578 30

13 4.0000 7.0000 5.086 2

- 1.0166 31

134.0000 8.0000 3.068 2

- 0.4373 32

134.0000 8.0000 3.068 0

- 1.2562 33

134.0000 8.0000 3.068 8 2.0195 34

35

526.0 000 2.0000 1.406 2 0.8346 36

526.0000 3.0000 1.892 3 1.1101 37

526.0000 4.0000 4.500 3

- 0.8351 38

526.0000 5.0000 5.060 8 1.3670 39

526.0000 5.0000 5.060 4

- 0.4926 40

526.0000 6.0000 5.592 6 0.1644 41

526.0000 6.0000 5.592 8 0.9700 42

526.0000 6.0000 5.592 11 2.1785 43

526.0000 6.0000 5.592 2

- 1.4469 44

526.0000 6.0000 5.592 2

- 1.4469 45

526.0000 6.0000 5.592 9 1.3729 46

526.0000 6.0000 5.592 5

- 0.2384 47

526.0000 6 .0000 5.592 6 0.1644 48

526.0000 7.0000 3.105 0

- 1.5658 49

526.0000 7.0000 3.105 4 0.4511 50

526.0000 7.0000 3.105 5 0.9554 51

526.0000 7.000 0 3.105 1

- 1.0615 52

526.0000 7.0000 3.105 4 0.4511 53

526.0000 7.0000 3.105 1

- 1.0615 54

526.0000 7.0000 3.105 5 0.9554 55

526.0000 7.0000 3.105 3

- 0.0531 56

526.0000 7.0000 3.105 4 0.4511 57

526.0000 7.0000 3.105 1

- 1.0615 58

526.0000 7.0000 3.105 3

- 0.0531 59

526.0000 7.0000 3.105 3

- 0.0531 60

526.0000 8.0000 3.496 0

- 1.5793 61

526.0000 8.0000 3.496 7 1.5832 62

526.0000 8.0000 3.496 5 0.6796 63

526.0000 9.0000 3.985 0

- 1.6270 64

526.0000 9.0000 3.985 6 0.8225 65

66

2005.0000 1.0000 2.777 3 0.4909 67DRAFT

– DO NOT CITE OR QUOTE C

- 31 2005.0000 2.0000 3.577 3

- 0.8022 1

2005.0000 3.0000 5. 118 4

- 0.9786 2

2005.0000 3.0000 5.118 4

- 0.9786 3

2005.0000 4.0000 3.208 4 0.6851 4

2005.0000 4.0000 3.208 4 0.6851 5

2005.0000 4.0000 3.208 4 0.6851 6

2005.0000 4.0000 3.208 2

- 1.0440 7

2005.0000 4.0000 3.208 3

- 0.1795 8

2005.0000 4.0000 3.208 4 0.6851 9

2005.0000 4.0000 3.208 4 0.6851 10

2005.0000 5.0000 3.714 1

- 1.7660 11

2005.0000 5.0000 3.714 3

- 0.4648 12

2005.0000 5.0000 3.714 5 0.8364 13

2005.0000 5.0000 3.714 5 0.8364 14

2005.0000 5.0000 3.714 4 0.1858 15

2005.0000 5.0000 3.714 4 0.1858 16

2005.0000 6.0000 4.086 6 0.9945 17

2005.0000 6.0000 4.086 2

- 1.0836 18

2005.0000 6.0000 4.086 4

- 0.0445 19

2005.0000 6.0000 4.086 5 0.4750 20

2005.0000 6.0000 4.086 6 0.9945 21

2005.0000 6.0000 4.086 5 0 .4750 22

2005.0000 6.0000 4.086 4

- 0.0445 23

2005.0000 6.0000 4.086 5 0.4750 24

2005.0000 6.0000 4.086 3

- 0.5641 25

2005.0000 6.0000 4.086 6 0.994 5 26

2005.0000 6.0000 4.086 0

- 2.1227 27

2005.0000 6.0000 4.086 0

- 2.1227 28

2005.0000 7.0000 4.361 7 1.1486 29

2005.0000 7.0000 4.361 5 0.2781 30

20 05.0000 7.0000 4.361 5 0.2781 31

2005.0000 7.0000 4.361 7 1.1486 32

2005.0000 7.0000 4.361 6 0.7133 33

2005.0000 8.0000 4.606 0

- 1.7419 34

35

Chi

- square = 105.13 DF = 98 P

- value = 0.2930 36

37

To calculate the BMD and BMDL, the litter specific covariate is fixed 38

at the mean litter specific covariate of all the data: 5.379518 39

==================================================================== 40DRAFT

– DO NOT CITE OR QUOTE C

- 32 Specified effect = 0.1

Risk Type = Extra risk

Confidence level = 0.95

BMD = 141.492

BMDL = 94.264

0.2 0.3 0.4 0.5 0.6 0.7 0.8

0 500 1000 1500 2000Fraction Affected

dose Nested Logistic Model with 0.95 Confidence Level

13:20 11/13 2007 BMDL BMD Nested Logistic

Figure C

- 5. Nested Logistic Model, 0.1 Extra Risk

- Incidence of Cervical Rib in Mice

versus C

max Methanol, GD 6

- 15 inhalational study

.

Source: Rogers et al. (1993).

Once the BMDL 10 was obtained in units of mg/L, it was used to derive a chronic 1

inhalation reference value. The first step is to calculate the HEC using the PBPK model 2

de scribed in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B ) that 3

describes the relationship between predicted methanol AUC and the human equivalent inhalation 4

exposure concentration (HEC) in ppm. This equation can also be used to estimate model 5

predictions for HECs from C

max values because C

max values and AUC values were estimated at 6

steady

- state for constant 24

- hour exposures ( i.e., AUC = 24 x C

max ). 7

BMD L HEC (ppm) = 0.0224 * BMDL 10 * 24 +(1334* BMDL 10 * 24)/ (794+ BMDL 10 * 24 ) 8

BMD L HEC (pp m) = 0.0224* 94.3 * 24 + ( (1334* 94.3 * 24)/ (794+ 94.3 * 24 ) ) = 1038 ppm 9

Next, because RfCs are typically expressed in units of mg/m 3 , the HEC value in ppm was 10

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m 3 : 11

HEC (mg/m 3 ) = 1.31 × 1038 ppm = 1360 mg/m 3 12DRAFT

– DO NOT CITE OR QUOTE C

- 33 Finally, this HEC value was divided by a composite 100

- fold UF (3 for uncertainty 1

associated with animal to human differences, 10 for consideration of human variability, and 3 for 2

database deficiencies ) to obtain the chronic inhalat ion reference value: 3

4

RfC (mg/m 3 ) = 1360 mg/m 3 ÷ 100 = 13.6 mg/m 3 5

C.3.2. BMD Approach with a BMR of 0.05 Extra Risk (Rogers et al ., 1993)

A summary of the results most relevant to the development of a POD using the BMD 6

approach (BMD, BMDL, and model fit statistic s) for increased incidence of cervical rib in mice 7

exposed to methanol during gestation from days 6 to 15, with a BMR of 0.05 extra risk

, is 8

provided in Table C

- 9

. Model fit was determined by statistics (AIC and χ 2 residuals of individual 9

dose groups) and visual inspection , as recommended by U.S. EPA (2000 b ). The best model fit to 10

these data (from visual inspection and comparison of AIC values) was obtained using the 11

NLogistic model. The BMDL 05 was determined to be 44.7 mg/L using the 95% lower 12

confidence limit of the dose

- response curve expressed in terms of the C

max for methanol in blood

. 13

Table C

- 9 . Comparison of BMD modeling results for cervical rib incidence in mice

using modeled C

max of methanol as a common dose metric

Model BMD 05 ( C

max , mg/L) a BMDL 05 ( C

max , mg/L) a p

- value AIC c Scaled re sidual d

NLogistic b 67.022 44.651 0.293 1046.84 0.649

NCTR 101.235 50.618 0.241 1048.92 0.662

Rai and Van

Ryzin 107.838 53.919 0.163 1051.65 0.661

a Daily C

max was estimat ed using a mouse PBPK model as described in section 3.4 of the methanol

toxicological review; the BMDL is the 95% lower confidence limit on the C

max for a 5% extra risk

(dichotomous endpoint s ) estimated by the model using the likelihood profile method (U.S . EPA,

2000b).

b Model choice based on adequate p value (> 0.1), visual inspection, low AIC, and low (absolute)

scaled residual

.

c AIC = Akaike Information Criterion =

- 2L + 2P, where L is the log

- likelihood at the maximum

likelihood estimates for the param eters, and P is the number of modeled degrees of freedom (usually

the number of parameters estimated)

. d χ

2 d residual (measure of how model

- predicted responses deviate from the actual data) for the dose

group closest to the BMD scaled by an estimate of its S.D

. Provides a comparative measure of model

fit near the BMD. Residuals exceeding 2.0 in absolute value should cause one to question model fit

in this region

.

Source: Rogers et al. (1993).

============================================================== ====== 14

NLogistic Model. 15

(Version: 2.13; Date: 02/20/2007) 16

Input Data File: U: \ Methanol \ BMDS \ CervicalRib \ C

max \ NLog_ C

max _10_default.(d) 17

Wed Nov 07 15:45:40 2007 18

==================================================================== 19DRAFT

– DO NOT CITE OR QUOTE C

- 34 **BMD Method for RfC: I ncidence of Cervical Rib in Mice versus**  C

max **Methanol, GD 6**

**- 15**  1

**inhalational study (Rogers, et**  **al ., 1993)**  2

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 3

The probability function is: 4

5

Prob. = alpha + theta1*Rij + [1

- alpha

- theta 1*Rij]/ 6

7

[1+exp(

- beta

- theta2*Rij

- rho*log(Dose))], 8

9

where Rij is the litter specific covariate. 10

11

Restrict Power rho >= 1. 12

13

Total number of observations = 166 14

Total number of records with missing values = 0 15

Total number of parameters in model = 9 16

Total number of specified parameters = 0 17

18

Maximum number of iterations = 250 19

Relative Function Convergence has been set to: 1e

- 008 20

Parameter Convergence has been set to: 1e

- 008 21

22

Default Initial Parameter Va lues 23

alpha = 0.297863 24

beta =

- 7.94313 25

theta1 = 0 26

theta2 = 0 27

rho = 1.09876 28

phi1 = 0.213134 29

phi2 = 0.309556 30

phi3 = 0.220142 31

phi4 = 0.370587 32

33

Parameter Estimates 34

35

Variable Es timate Std. Err. 36

alpha 0.102434 * 37

beta

- 4.80338 * 38

theta1 0.0325457 * 39

theta2

- 0.436115 * 40

rho 1 * 41

phi1 0.200733 * 42

phi2 0.307656 * 43

phi3 0.212754 * 44

phi4 0.368426 * 45

46

*

- Indicates that this value i s not calculated. 47

48

Log

- likelihood:

- 515.422 AIC: 1046.84 49

Litter Data 50

51

Lit.

- Spec. Litter Scaled 52

Dose Cov. Est._Prob. Size Expected Observed Residual 53

--- ----------------------------------------------------------------------- 54

0.0000 1.0000 0.135 1 0.135 0

- 0.3950 55

0.0000 1.0000 0.135 1 0.135 0

- 0.3950 56

0.0000 2.0000 0.168 2 0.335 0

- 0.5790 57

0.0000 2.0000 0.168 2 0.335 1 1.1490 58

0.0000 2.0000 0.168 2 0.335 0

- 0.5790 59

0.0000 2.0000 0.168 2 0.335 2 2.8770 60

0.0000 2.0000 0.168 2 0.335 0

- 0.5790 61

0.0000 3.0000 0.200 3 0.600 0

- 0.7317 62

0.0000 3.0000 0.200 3 0.600 0

- 0.7317 63

0.0000 3.0000 0.200 3 0.600 1 0.4874 64

0.0000 3.0000 0.200 3 0.600 1 0.4874 65

0.0000 4.0000 0.233 4 0.930 0

- 0.8699 66DRAFT

– DO NOT CITE OR QUOTE C

- 35 0.0000 4.0000 0.233 4 0.930 1 0.0650 1

0.0000 4.0000 0.233 4 0.930 0

- 0.8699 2

0.0000 4.0000 0.233 4 0.930 1 0.0650 3

0.0000 5.0000 0.265 5 1.326 0

- 1.0004 4

0.000 0 5.0000 0.265 5 1.326 1

- 0.2458 5

0.0000 5.0000 0.265 5 1.326 3 1.2632 6

0.0000 5.0000 0.265 5 1.326 1

- 0.2458 7

0.0000 5.0000 0.265 5 1.326 0

- 1.0004 8

0.0000 5.0000 0.265 5 1.326 1

- 0.2458 9

0.0000 5.0000 0.265 5 1.326 1

- 0.2458 10

0.0000 5.0000 0.265 5 1.326 0

- 1. 0004 11

0.0000 5.0000 0.265 5 1.326 0

- 1.0004 12

0.0000 5.0000 0.265 5 1.326 1

- 0.2458 13

0.0000 6.0000 0.298 6 1.786 3 0.7656 14

0.0000 6.0000 0 .298 6 1.786 6 2.6578 15

0.0000 6.0000 0.298 6 1.786 1

- 0.4959 16

0.0000 6.0000 0.298 6 1.786 0

- 1.1267 17

0.0000 6.0000 0.298 6 1.786 0

- 1.1267 18

0.0000 6.0000 0.298 6 1.786 1

- 0.4959 19

0.0000 6.0000 0.298 6 1.786 2 0.1348 20

0.0000 6.0000 0.298 6 1.786 0

- 1.1267 21

0.0000 6.0000 0.298 6 1.786 2 0.1348 22

0.0000 6.0000 0.298 6 1.786 3 0.7656 23

0.0000 6.0000 0.298 6 1.786 5 2.0271 24

0.0000 6.0000 0.298 6 1.786 0

- 1.1267 25

0.0000 6.0000 0.298 6 1.786 3 0.7656 26

0.0000 6.0000 0.298 6 1.786 3 0.7656 27

0.0000 6.0000 0.298 6 1.786 3 0.7656 28

0.0000 6.0000 0.298 6 1.786 5 2.0271 29

0.0000 7.0000 0.330 7 2.312 0

- 1.2513 30

0.0000 7.0000 0.330 7 2.312 1

- 0.7100 31

0.0000 7.0000 0.330 7 2.312 2

- 0.1688 32

0.0000 7.0000 0.330 7 2.312 3 0.3725 33

0.0000 7.0000 0.330 7 2.312 2

- 0.1688 34

0.0000 7.0000 0.330 7 2.312 3 0.3725 35

0.0000 7.0000 0.330 7 2.312 5 1.4551 36

0.0000 7.0000 0.330 7 2.312 0

- 1.2513 37

0.0000 7.0000 0.330 7 2.312 2

- 0.1688 38

0.0000 7.000 0 0.330 7 2.312 5 1.4551 39

0.0000 7.0000 0.330 7 2.312 1

- 0.7100 40

0.0000 7.0000 0.330 7 2.312 2

- 0.1688 41

0.0000 7.0000 0.330 7 2. 312 1

- 0.7100 42

0.0000 8.0000 0.363 8 2.902 1

- 0.9020 43

0.0000 8.0000 0.363 8 2.902 4 0.5204 44

0.0000 8.0000 0.363 8 2.902 3 0.0463 45

0. 0000 8.0000 0.363 8 2.902 8 2.4170 46

0.0000 8.0000 0.363 8 2.902 2

- 0.4279 47

48

134.0000 1.0000 0.494 1 0.494 0

- 0.9887 49

134.0000 1.0000 0.494 1 0.494 0

- 0.9887 50

134.0000 2.0000 0.430 2 0.859 0

- 1.0732 51

134.0000 2.0000 0.430 2 0.859 2 1.4251 52

134.0000 3.0000 0.383 3 1.150 3 1.7287 53

134.0000 3.0000 0.383 3 1.150 1

- 0.1400 54

134.0000 3.0000 0.383 3 1.150 2 0.7944 55

134.0000 3.0000 0.383 3 1.150 1

- 0.1400 56

134.0000 4.0000 0.356 4 1.425 3 1.1858 57

134.0000 4.0000 0.356 4 1.425 0

- 1.0729 58

134.0000 5.0000 0.346 5 1.732 0

- 1.0898 59

134.0000 5.0000 0.346 5 1.732 4 1.4275 60

134.0000 5.0000 0.346 5 1.732 0

- 1.0898 61

134.0000 5.0000 0.346 5 1.732 1

- 0.4604 62

134.0000 5.0000 0.346 5 1.732 0

- 1.0898 63

134.0000 6.0000 0.350 6 2.099 3 0.4839 64

134.0000 6.0000 0.350 6 2.099 2

- 0.0534 65

134.0000 7.0000 0.363 7 2.543 3 0.2128 66

134.0000 7.0000 0.363 7 2.543 2

- 0.2530 67DRAFT

– DO NOT CITE OR QUOTE C

- 36 134.0000 7.0000 0.363 7 2.543 2

- 0.2530 1

134.0000 7.0000 0.363 7 2.543 2

- 0.2530 2

134.0000 7.0000 0.363 7 2.543 2

- 0.2 530 3

134.0000 7.0000 0.363 7 2.543 0

- 1.1847 4

134.0000 8.0000 0.383 8 3.068 2

- 0.4373 5

134.0000 8.0000 0.383 8 3.068 0

- 1.2562 6

134.0000 8.0000 0. 383 8 3.068 8 2.0195 7

8

526.0000 2.0000 0.703 2 1.406 2 0.8346 9

526.0000 3.0000 0.631 3 1.892 3 1.1101 10

526.0000 4.0000 0.562 4 2.250 2

- 0.1967 11

526.0000 4.0000 0.562 4 2.250 1

- 0.9842 12

526.0000 5.0000 0.506 5 2.530 3 0.3091 13

526.0000 5.0000 0.506 5 2.530 5 1.6241 14

526.0000 5.0000 0.506 5 2.530 3 0.3091 15

526.0000 5.0000 0.506 5 2.530 1

- 1.0058 16

526.0000 6.0000 0.466 6 2.796 3 0.1162 17

526.0000 6.0000 0.466 6 2.796 3 0.1162 18

526.0000 6.0000 0.466 6 2.796 3 0.1162 19

526.0000 6.0000 0.466 6 2.796 5 1.2556 20

526.0000 6.0000 0.466 6 2.796 6 1.8253 21

526.0000 6.0000 0.466 6 2.796 5 1.2556 22

526.0000 6.0000 0.466 6 2.796 2

- 0.4534 23

526.0000 6.0000 0.466 6 2.796 0

- 1.5928 24

526.0000 6.0000 0.466 6 2.796 2

- 0.4534 25

526.0000 6.0000 0.466 6 2.796 0

- 1.5928 26

526.0000 6.0000 0.466 6 2.796 5 1.2556 27

526.0000 6.0000 0.466 6 2.796 4 0.6859 28

526.0000 6.0000 0.466 6 2.796 3 0.1162 29

526.0000 6.0000 0.466 6 2.796 2

- 0.4534 30

526.0000 6.0000 0.466 6 2.796 4 0.6859 31

526.0000 6.000 0 0.466 6 2.796 2

- 0.4534 32

526.0000 7.0000 0.444 7 3.105 0

- 1.5658 33

526.0000 7.0000 0.444 7 3.105 4 0.4511 34

526.0000 7.0000 0.444 7 3. 105 5 0.9554 35

526.0000 7.0000 0.444 7 3.105 1

- 1.0615 36

526.0000 7.0000 0.444 7 3.105 4 0.4511 37

526.0000 7.0000 0.444 7 3.105 1

- 1.0615 38

526. 0000 7.0000 0.444 7 3.105 5 0.9554 39

526.0000 7.0000 0.444 7 3.105 3

- 0.0531 40

526.0000 7.0000 0.444 7 3.105 4 0.4511 41

526.0000 7.0000 0.444 7 3.105 1

- 1.0615 42

526.0000 7.0000 0.444 7 3.105 3

- 0.0531 43

526.0000 7.0000 0.444 7 3.105 3

- 0.0531 44

526.0000 8.0000 0.437 8 3.496 0

- 1.5793 45

526.0000 8.0000 0.437 8 3.496 7 1.5832 46

526.0000 8.0000 0.437 8 3.496 5 0.6796 47

526.0000 9.0000 0.443 9 3.985 0

- 1.6270 48

526.0000 9.0000 0.443 9 3.985 6 0.8225 49

50

2005.0000 1.0000 0.926 1 0.926 1 0.2834 51

2005.0000 1.0000 0.926 1 0.926 1 0.2834 52

2005.0000 1.0000 0.926 1 0.926 1 0.2834 53

2005.0000 2.0000 0.894 2 1.789 1

- 1.5502 54

2005.0000 2.0000 0.894 2 1.789 2 0.4157 55

2005.0000 3.0000 0.853 3 2.559 3 0.5454 56

2005.0000 3.0000 0.853 3 2.559 1

- 1.9294 57

2005.0000 3.0000 0.853 3 2.559 1

- 1.9294 58

2005.0000 3.0000 0.853 3 2.559 3 0.5454 59

2005.0000 4.0000 0.802 4 3.208 4 0.6851 60

2005.0000 4.0000 0.802 4 3.208 4 0.6851 61

2005.0000 4.0000 0.802 4 3.208 4 0.6851 62

2005.0000 4.0000 0.802 4 3.208 2

- 1.0 440 63

2005.0000 4.0000 0.802 4 3.208 3

- 0.1795 64

2005.0000 4.0000 0.802 4 3.208 4 0.6851 65

2005.0000 4.0000 0.802 4 3.208 4 0.6851 66

2005.0000 5.0000 0. 743 5 3.714 1

- 1.7660 67DRAFT

– DO NOT CITE OR QUOTE C

- 37 2005.0000 5.0000 0.743 5 3.714 3

- 0.4648 1

2005.0000 5.0000 0.743 5 3.714 5 0.8364 2

2005.0000 5.0000 0.743 5 3.714 5 0.8364 3

2005.0000 5.0000 0.743 5 3.714 4 0.1858 4

2005.0000 5.0000 0.743 5 3.714 4 0.1858 5

2005.0000 6.0000 0.681 6 4.086 6 0.9945 6

2005.0000 6 .0000 0.681 6 4.086 2

- 1.0836 7

2005.0000 6.0000 0.681 6 4.086 4

- 0.0445 8

2005.0000 6.0000 0.681 6 4.086 5 0.4750 9

2005.0000 6.0000 0.681 6 4.086 6 0.9945 10

2005.0000 6.0000 0.681 6 4.086 5 0.4750 11

2005.0000 6.0000 0.681 6 4.086 4

- 0.0445 12

2005.0000 6.0000 0.681 6 4.086 5 0.4750 13

2 005.0000 6.0000 0.681 6 4.086 3

- 0.5641 14

2005.0000 6.0000 0.681 6 4.086 6 0.9945 15

2005.0000 6.0000 0.681 6 4.086 0

- 2.1227 16

2005.0000 6.0000 0.681 6 4.086 0

- 2.1227 17

2005.0000 7.0000 0.623 7 4.361 7 1.1486 18

2005.0000 7.0000 0.623 7 4.361 5 0.2781 19

2005.0000 7.0000 0.623 7 4.361 5 0.2781 20

2005.0000 7.0000 0.623 7 4.361 7 1.1486 21

2005.0000 7.0000 0.623 7 4.361 6 0.7133 22

2005.0000 8.0000 0.576 8 4.606 0

- 1.7419 23

24

Combine litters wi th adjacent levels of the litter

- specific covariate 25

within dose groups until the expected count exceeds 3.0, to help improve 26

the fit of the X^2 statistic to chi

- square. 27

28

Grouped Data 29

Mean Scaled 30

Dose Lit.

- Spec. Cov. Expected Observed Residual 31

------------------------------------------------------------- 32

0.0000 1.0000 0.270 0

- 0.5586 33

0.0000 2.0000 1.675 3 1.0237 34

0.0000 3.0000 2.401 2

- 0.2443 35

0.0000 4.0000 3.722 2

- 0.8049 36

0.0000 5.0000 3.977 4 0.0098 37

0.0000 5.0000 3.977 2

- 0.8614 38

0.0000 5.0000 3.977 1

- 1.2970 39

0.0000 5.0000 1.326 1

- 0.2458 40

0.0000 6.0000 3.573 9 2.4207 41

0.0000 6.0000 3.573 1

- 1.1474 42

0.0000 6.0000 3.573 1

- 1.1474 43

0.0000 6.0000 3.573 2

- 0.7013 44

0.0000 6.0000 3.573 5 0.6367 45

0.0000 6.0000 3.573 5 0.63 67 46

0.0000 6.0000 3.573 6 1.0827 47

0.0000 6.0000 3.573 8 1.9747 48

0.0000 7.0000 4.624 1

- 1.3869 49

0.0000 7.0000 4.624 5 0.1441 50

0.0000 7.0000 4.624 5 0.1441 51

0.0000 7.0000 4.624 5 0.1441 52

0.0000 7.0000 4.624 7 0.9096 53

0.0000 7.0000 4.624 3

- 0.6214 54

0. 0000 7.0000 2.312 1

- 0.7100 55

0.0000 8.0000 5.805 5

- 0.2698 56

0.0000 8.0000 5.805 11 1.7418 57

0.0000 8.0000 2.902 2

- 0.4279 58

59

134.000 0 1.0000 0.989 0

- 1.3982 60

134.0000 2.0000 1.718 2 0.2488 61

134.0000 3.0000 3.449 6 1.3759 62

134.0000 3.0000 1.150 1

- 0.1400 63

134.0000 4.0000 2.850 3 0.0799 64

134.0000 5.0000 3.463 4 0.2388 65

134.0000 5.0000 3.463 1

- 1.0962 66

134.0000 5.0000 1.732 0

- 1.0898 67DRAFT

– DO NOT CITE OR QUOTE C

- 38 134.0000 6.0000 4.199 5 0.3044 1

134.0000 7.0000 5.086 5

- 0.0284 2

134.0000 7.0000 5.086 4

- 0.3578 3

134.0000 7.0000 5.086 2

- 1.0166 4

134.0000 8.0 000 3.068 2

- 0.4373 5

134.0000 8.0000 3.068 0

- 1.2562 6

134.0000 8.0000 3.068 8 2.0195 7

8

526.0000 2.0000 1.406 2 0.8346 9

526.0000 3.0000 1.892 3 1.1101 10

526.0000 4.0000 4.500 3

- 0.8351 11

526.0000 5.0000 5.060 8 1.3670 12

526.0000 5.0000 5.060 4

- 0.4926 13

526.0000 6.0000 5.592 6 0.1644 14

526.0000 6.0000 5.592 8 0.9700 15

526.0000 6.0000 5.592 11 2.1785 16

526.0000 6.0000 5.592 2

- 1.4469 17

526.0000 6.0000 5.592 2

- 1.4469 18

526.0000 6.0000 5.592 9 1.3729 19

526.0000 6.0000 5.592 5

- 0.2384 20

526.0000 6.0000 5.592 6 0.1644 21

526.0000 7.0000 3.105 0

- 1.5658 22

526.0000 7.0000 3.105 4 0.4511 23

526.0000 7.0000 3.105 5 0.9554 24

526.0000 7.0000 3.105 1

- 1.0615 25

526.0000 7.0000 3.10 5 4 0.4511 26

526.0000 7.0000 3.105 1

- 1.0615 27

526.0000 7.0000 3.105 5 0.9554 28

526.0000 7.0000 3.105 3

- 0.0531 29

526.0000 7.0000 3.105 4 0.4511 30

526.0000 7.0000 3.105 1

- 1.0615 31

526.0000 7.0000 3.105 3

- 0.0531 32

526.0000 7.0000 3.105 3

- 0.0531 33

526.0000 8.0000 3.496 0

- 1.5793 34

526.0000 8.0000 3.496 7 1.5832 35

526.0000 8.0000 3.496 5 0.6796 36

526.0000 9.0000 3.985 0

- 1.6270 37

526.0000 9.0000 3.985 6 0.8225 38

39

2005.0000 1.0000 2.777 3 0.4909 40

2005.0000 2.0000 3.577 3

- 0.8022 41

2005.0000 3.0000 5.118 4

- 0.9786 42

2005.0000 3.0000 5.118 4

- 0.9786 43

2005.0000 4.0000 3.208 4 0.6851 44

2005.0000 4.0000 3.208 4 0.6851 45

2005.0000 4.0000 3.208 4 0.6851 46

2005.0000 4.0000 3.208 2

- 1. 0440 47

2005.0000 4.0000 3.208 3

- 0.1795 48

2005.0000 4.0000 3.208 4 0.6851 49

2005.0000 4.0000 3.208 4 0.6851 50

2005.0000 5.0000 3.714 1

- 1.7660 51

2005.0000 5.0000 3.714 3

- 0.4648 52

2005.0000 5.0000 3.714 5 0.8364 53

2005.0000 5.0000 3.714 5 0.8364 54

2005.0000 5.0000 3.714 4 0.1858 55

200 5.0000 5.0000 3.714 4 0.1858 56

2005.0000 6.0000 4.086 6 0.9945 57

2005.0000 6.0000 4.086 2

- 1.0836 58

2005.0000 6.0000 4.086 4

- 0.0445 59

2005.00 00 6.0000 4.086 5 0.4750 60

2005.0000 6.0000 4.086 6 0.9945 61

2005.0000 6.0000 4.086 5 0.4750 62

2005.0000 6.0000 4.086 4

- 0.0445 63

2005.0000 6.0000 4.086 5 0.4750 64

2005.0000 6.0000 4.086 3

- 0.5641 65

2005.0000 6.0000 4.086 6 0.9945 66

2005.0000 6.0000 4.086 0

- 2.1227 67DRAFT

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- 39 2005.0000 6.0000 4.086 0

- 2.1227 1

2005.0000 7.0000 4.361 7 1.1486 2

2005.0000 7.0000 4.361 5 0.2781 3

2005.0000 7.0000 4.361 5 0.2781 4

2005.0000 7. 0000 4.361 7 1.1486 5

2005.0000 7.0000 4.361 6 0.7133 6

2005.0000 8.0000 4.606 0

- 1.7419 7

8

Chi

- square = 105.13 DF = 98 P

- value = 0.2930 9

10

To calculate the BMD and BMDL , the litter specific covariate is fixed 11

at the mean litter specific covariate of all the data: 5.379518 12

==================================================================== 13

Specified effect = 0.05

Risk Type = Extra risk

Confidence level = 0.95

BMD = 67.0227

BMDL = 44.6514

0.2 0.3 0.4 0.5 0.6 0.7 0.8

0 500 1000 1500 2000Fraction Affected

dose Nested Logistic Model with 0.95 Confidence Level

11:42 11/16 2007 BMDL BMD Nested Logistic

Figure C

- 6. Nested Logistic Model, 0.05 Extra Risk

- Incidence of Cervical Rib in

Mice versus C

max Methanol, GD 6

- 15 inhalational study

.

Source: Rogers et al. (1 993).

Once the BMDL 05 was obtained in units of mg/L, it was used to derive a chronic 14

inhalation reference value. The first step is to calculate the HEC using the PBPK model 15

described in Appendix B. An algebraic equation is provided (Equation 1 of Appendi x B ) that 16

describes the relationship between predicted methanol AUC and the human equivalent inhalation 17

exposure concentration (HEC) in ppm. This equation can also be used to estimate model 18

predictions for HECs from C

max values because C

max values and AU C values were estimated at 19

steady

- state for constant 24

- hour exposures ( i.e., AUC = 24 x C

max ). 20

BMD L HEC (ppm) = 0.0224 * BMDL 05 * 24 +(1334* BMDL 05 * 24)/ (794+ BMDL 05 * 24 ) 21DRAFT

– DO NOT CITE OR QUOTE C

- 40 BMD L HEC (ppm) = 0.0224* 44.7 * 24 + ( (1334* 44.7 * 24)/ (794+ 44.7 * 24 ) ) = 791 ppm 1

Next, because R fCs are typically expressed in units of mg/m 3 , the HEC value in ppm was 2

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m 3 : 3

HEC (mg/m 3 ) = 1.31 × 791 ppm = 1036 mg/m 3 4

Finally, this HEC value was divided by a composite 100

- fol d UF (3 for uncertainty 5

associated with animal to human differences, 10 for consideration of human variability, and 3 for 6

database deficiencies ) to obtain the chronic inhalation reference value: 7

RfC (mg/m 3 ) = 1036 mg/m 3 ÷ 100 = 10.4 mg/m 3 8

C.4. RFC DERIVATIONS USING BURBACHER ET AL . (1999A,B)

The BMD approach was utilized in the derivation of potential chronic inhalation 9

reference values from effects seen in monkeys due to prenatal methanol exposure. Deficits in 10

VDR were evaluated from Burbacher et al . (1999a, 1999 b). In the application of the BMD 11

approach, continuous models in the EPA’s BMDS, version 1.4.1c

, were fit to the dataset for 12

increased latency in VDR in neonatal monkeys. As the EPA ’s PBPK model was not 13

parameterized for monkeys, external concentrati on (ppm) was used as the dose metric. 14

The VDR test, which assesse s time (from birth) it takes for an infant to grasp for a 15

brightly colored object containing an applesauce

- covered nipple, is a measure of sensorimotor 16

development. Beginning at 2 weeks aft er birth, infants were tested 5 times/day, 4 days/week. 17

Performance on that test

, measured as age from birth at achievement of test criterion (successful 18

object retrieval on 8 / 10 consecutive trials over 2 testing sessions)

, was reduced in all treated male 19

infants. The times (days after birth) to achieve the criteria for the V DR t est were 2 3.7 ± 4.8 20

(n

= 3) , 32 .4 ± 4.1 (n = 5) , 4 2.7 ± 8.0 (n = 3) , and 4 0.5 ± 12.5 (n = 2) days for males and 21

34 .2 ± 1.8 (n = 5) , 33 .0 ± 2.9 (n = 4) , 2 7.6 ± 2.7 (n = 5) , and 40

. 0 ± 4.0 (n = 7) days for females 22

in the control to 1800 ppm groups, respectively. As discussed in S ection 4.3.2, this type of 23

response data is sometimes adjusted to account for premature births by subtracting time (days) 24

premature from the time (days from birth) needed to meet the test criteria ( Wilson and Cradock, 25

2004 ). When this type of adjustment is applied, the times (days after birth or, if shorter, days 26

after control mean gestation length) to achieve the criteria for VDR t est were 22.0 ± 9.54 (n = 3)

, 27

26.2 ± 8.61 (n = 5)

, 33.3 ± 10.0 (n = 3) , and 39.5 ± 16.3 (n = 2) days for males and 32.0 ± 4.3 28

(n = 5)

, 21.8 ± 5.6 (n = 4)

, 24.0 ± 5.7 (n = 5) , and 32.0 ± 14.8 (n = 7) days for females in the 29

control to 1800 ppm groups, respectively. When these data were modeled within BMDS 30

(version 2.1), there was no significant difference between unadjusted responses and/or variances 31

among the dose levels for males and females combined ( p = 0.244), for males only ( p = 0.321) 32DRAFT

– DO NOT CITE OR QUOTE C

- 41 and for males only with the high

- dose gro up excluded ( p = 0.182), or for adjusted responses of 1

males and females combined ( p = 0.12), males only ( p = 0.448) and males only with the high

2

dose group excluded ( p = 0.586). 91 The only data that offered a significant dose

- response trend 3

was that for un adjusted ( p = 0.0265) and adjusted ( p = 0.009) female responses, but the model 4

fits for the adjusted female response data were unacceptable. Only the unadjusted female VDR 5

response data offered both a dose

- response trend and accep table model fits. The mo deling 6

results for this data set are presented in Table C

- 10

. 7

The c urrent BMD technical guidance (U.S. EPA, 2000 b ) suggests that in the absence of 8

knowledge as to what level of response to consider adverse, a change in the mean equal to 9

1 control S.D. fro m the control mean can be used as a BMR for continuous endpoint s

. A 10

summary of the results most relevant to the development of a POD using the BMD approach 11

(BMD, BMDL, and model fit statistics) for increased latency of VDR in female neonatal 12

monkeys expos ed to methanol with a BMR of 1 control mean S.D. is provided in Table C

- 1 0

. 13

Model fit was determined by statistics (AIC and χ 2 residuals of individual dose groups) and 14

visual inspection , as recommended by EPA (2000 b ). The 3 rd degree p olynomial model returned 15

a lower AIC than the other models

. 92 The BMDL 1 SD was determined to be 81.7 hr×mg/L

, using 16

the 95% lower confidence limi t of the dose

- response curve expressed in terms of the ppm of 17

external methanol concentration

. 18

91 BMDS continuous models contain a test for dose

- response trend, test 1, which compares a model that fits a

distinct mean and variance for each dose group to a model that contains a single mean and variance. The dose

response is considered to be significant if this comparison returns a p value < 0.05.

92 A detailed analysis of this dose response revealed that modeling results, particularly the BMDL estimation, are

very sensitive to the high

- dose response. There is no data to inform the shape of the curve between the mid

- and

high

- exposure levels, making the derivation of a BMDL very uncertain. The data were analyzed without the high

dose to determine if the downward trend in the l ow

- and mid

- exposure groups is significant. It was not, so

nonnegative restriction on the β coefficients of the poly models was retained.DRAFT

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- 42 Table C

- 10 . Comparison of BMD modeling results for VDR in female monkeys using

AUC blood methanol as the dose metric

Model BMD 1 SD ( AUC,

hr × mg/L ) a BMDL 1 SD (AUC,

hr × mg/L ) a p

- value AIC c Scaled r esidual d

Linear 119.058 5 1

. 9876 0.1 44 0 110. 4492 0.538 0

2 nd degree p olynomial 114.094 59.6412 0.2388 109.43782 0.0994

3rd degree p olynomial 120.176 81.6513 0.2718 109.17894 0.0199

Power b 133.517 63.0615 0.1112 111.1101 0 0.000 0

Hill 132.283

-- NA 113.1101 0 0.000 0

a AUC was estimated using a rat PBPK model as described in section 3.4 of the methanol toxicological review; the

BMDL is the 95% lower confidence limit on the AUC of a decrease of 1 control mean S.D. estimated b y the model

using the likelihood profile method (U.S. EPA, 2000b).

b Model choice based on adequate p value (> 0.1), visual inspection, low AIC, and low (absolute) scaled residual

.

c AIC = Akaike Information Criterion =

- 2L + 2P, where L is the log

- likeliho od at the maximum likelihood

estimates for the parameters, and P is the number of modeled degrees of freedom (usually the number of parameters

estimated)

. d χ

2 d residual (measure of how model

- predicted responses deviate from the actual data) for the dose g roup closest to

the BMD scaled by an estimate of its S.D

. Provides a comparative measure of model fit near the BMD. Residuals

that exceed 2.0 in absolute value should cause one to question model fit in this region.

Source: Burbacher et al . (1999a).

== ================================================================== 1

Polynomial Model. 2

(Version: 2.13; Date: 04/08/2008) 3

Input Data File: C: \ USEPA \ BMDS2 \ Data \ Burbacher \ PolfemSet.(d) 4

Gnuplot Plotting File: C: \ USEPA \ BMDS2 \ Data \ Burbacher \ PolfemSet.pl t 5

Fri Dec 12 15:30:29 2008 6

==================================================================== 7

**VDR in female monkeys using AUC blood methanol as the dose metric** 8

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 9

The form of the response function is: 10

11

Y[dose] = beta_0 + beta_1*dose + beta_2*dose^2 + ... 12

13

Dependent variable = F_VDR 14

Independent variable = F_Dose 15

The polynomial coefficients are restricted to be positive 16

The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 17

18

Total number of dose groups = 4 19

Total number of records with missing values = 0 20

Maximum number of iterations = 250 21

Relative Function Convergence has been set to: 1e

- 008 22

Parameter Convergence has been set to : 1e

- 008 23

24

Default Initial Parameter Values 25

lalpha = 4.07254 26

rho = 0 27

beta_0 = 34.2 28

beta_1 = 0 29

beta_2 = 0 30

beta_3 = 0 31

32

Asymptotic Correlation Matrix of Parameter Estimates 33

34

( *** The model parameter(s)

- beta_1

- beta_2 35DRAFT

– DO NOT CITE OR QUOTE C

- 43 have been estimated a t a boundary point, or have been specified by 1

the user, 2

and do not appear in the correlation matrix ) 3

4

lalpha rho beta_0 beta_3 5

6

lalpha 1

- 1

- 0.0076 0.018 7

rh o

- 1 1 0.0076

- 0.018 8

beta_0

- 0.0076 0.0076 1

- 0.37 9

beta_3 0.018

- 0.018

- 0.37 1 10

11

Parameter Estimates 12

95.0% Wald

CI 13

Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. 14

Limit 15

lalpha

- 13.5062 9.81148

- 32.7363 16

5.72395 17

rho 4.90831 2.77841

- 0.537284 18

10.3539 19

beta_0 31.5013 1.49057 28.5798 20

34.4228 21

beta_1 8.36431e

- 025 NA 22

beta_2 0 NA 23

beta_3 3.19775e

- 006 1.53534e

- 006 1.88544e

- 007 6.20695e

24

006 25

26

NA

- Indicates that this parameter has hit a bound 27

implied by some inequality constraint and thus 28

has no standard error. 29

30

Table of Data and Estimated V alues of Interest 31

32

Dose N Obs Mean Est Mean Obs Std Dev Est Std Dev Scaled Res. 33

------ --- -------- -------- ----------- ----------- ---------- 34

35

0 5 34.2 31.5 4.09 5.55 1.09 36

6.73 4 33 31.5 5.83 5.55 0.54 37

28.28 5 27.6 31.6 5.94 5.58

- 1.59 38

138.1 7 40 39.9 10.7 9.93 0.0199 39

40

Model Descriptions for likelihoods calculated 41

42

Model A1: Yij = Mu(i) + e(ij) 43

Var{e(ij)} = Sigma^2 44

Model A2: Yij = Mu(i) + e(ij) 45

Var{e(ij)} = Sigma(i)^2 46

Model A3: Yij = Mu(i) + e(ij) 47

Var{e(ij)} = exp(lalpha + rho*ln(Mu(i))) 48

Model A3 uses any fixed variance parameters that 49

were specified by the user 50

51

Model R: Yi = Mu + e(i) 52

Var{e(i)} = Sigma^2 53

54

Likelihoods of Interest 55

56

Model Log(likelihood) # Param's AIC 57

A1

- 51.042924 5 112.085848 58

A2

- 47.867444 8 111.734888 59

A3

- 49.286738 6 110.573475 60

fitted

- 50.589469 4 109. 178938 61

R

- 55.013527 2 114.027055 62

63

Explanation of Tests 64

65

Test 1: Do responses and/or variances differ among Dose levels? 66

(A2 versus R) 67DRAFT

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- 44 Test 2: Are Variances Homogeneous? (A1 vs A2) 1

T est 3: Are variances adequately modeled? (A2 versus A3) 2

Test 4: Does the Model for the Mean Fit? (A3 versus fitted) 3

(Note: When rho=0 the results of Test 3 and Test 2 will be the same.) 4

5

Tests of Interest 6

7

Test

- 2*log(Li kelihood Ratio) Test df p

- value 8

9

Test 1 14.2922 6 0.02654 10

Test 2 6.35096 3 0.09573 11

Test 3 2.83859 2 0.2419 12

Test 4 2.60546 2 0.2718 13

14

The p

- value for Test 1 is less than .05. There appears to be a 15

difference between response and/or variances among the dose levels 16

It seems appropriate to model the data 17

18

The p

- value for Test 2 is less than .1. A non

- homogeneous vari ance 19

model appears to be appropriate 20

21

The p

- value for Test 3 is greater than .1. The modeled variance appears 22

to be appropriate here 23

24

The p

- value for Test 4 is greater than .1. The model chosen seems 25

to adequately describe the data 26

27

28

29

Speci fied effect = 1

Risk Type = Estimated S.D. s from the control mean

Confidence level = 0.95

BMD = 120.176

BMDL = 81.6513

20 25 30 35 40 45 50

0 20 40 60 80 100 120 140Mean Response

dose Polynomial Model with 0.95 Confidence Level

15:30 12/12 2008 BMD BMDL Polynomial

Figure C

- 7. 3rd Degree Polynomial Model, BMR of 1 Control Mean S.D.

- VDR in

female monkeys using AUC blood methanol as the dose metric

.DRAFT

– DO NOT CITE OR QUOTE C

- 45 Source: Burbacher et al. (1999a, 1999b)

Once the BMDL 1 SD was obtained in units of ppm, it was used to derive a chronic 1

inhalation reference value. The first step is to calculate t he HEC using the PBPK model 2

described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B ) that 3

describes the relationship between predicted methanol AUC and the human equivalent inhalation 4

exposure concentration (HEC) in ppm. 5

BMD L HEC (ppm) = 0.0224* BMDL 1 SD +(1334* BMDL 1 SD )/ (794+ BMDL 1 SD ) 6

BMD L HEC (ppm) = 0.0224* 81.7 +(1334* 81.7)/ (794+ 81.7 ) = 126.3 ppm 7

Next, because RfCs are typically expressed in units of mg/m 3 , the HEC value in ppm was 8

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m 3 : 9

HEC (mg/m 3 ) = 1.31 × 126.3 ppm = 165 mg/m 3 10

Finally, this HEC value was divided by a composite 100

- fold UF (3 for uncertainty 11

associated with animal to human differences, 10 for consideration of human variability

, and 3 for 12

database deficiencies ) to obtain the chronic inhalation reference value: 13

RfC (mg/m 3 ) = 165 mg/m 3 ÷ 100 = 1.7 mg/m 3 14DRAFT

– DO NOT CITE OR QUOTE D

- 1 APPENDIX D. R f C DERIVATION

– COMPARISON OF DOSE M ETRICS

D.1. METHODS

D.1.1. Dose Metric Comparisons

Three potential dose metrics were evaluated for possible use in risk extrapolation of 1

methanol

- induced developmental effects: AUC of methanol in the blood ; C

max of methanol in the 2

blood ; and total metabolism of methanol. The la t ter metric was considered because 3

developmental effects may be caused by metabolites of methanol, particularly formaldehyde

, and 4

formate. These three metrics were evaluated by determining how well they were able to explain 5

the variation in response for incidence of cervical ribs (CR) and supernumerary ribs (SNR) in a 6

concentration

- time b ioassay by Rogers et al . ( 1995, raw data obtained from personal 7

communication ). In particular, pregnant CD

- 1 mice were exposed to 2,000, 5,000, 10,000, or 8

15,000 ppm methanol for 1, 2, 3, 5, or 7 h ou r s on GD 7 and developmental effects evaluated at 9

GD17

. This endpoint was selected because it was the most sensitive of those examined and gave 10

a reasonable dose

- response relationship overall. 11

Initially, the fraction of pups within each litter carrying either or both CR and SNR was 12

calculated, and then the average across all lit t ers in each concentration

- time combination was 13

computed. However, as shown in Figure D

- 1 , the resulting data appear to be nonmonotonic, 14

with the responses from 5

- h ou r exposures exceeding those from 7

- h ou r exposures

, and the 15

response s from 2

- h ou r exposures exceeding those from 3

- h ou r exposures. It was noted that the 16

study was done with a block

- design, where the dams/litters for some concentration

- time 17

combination were divided between multiple blocks and the average CR + SNR incidence in 18

controls varied from 30

– 52% among the 8 blocks. Therefore block

- control response ( percent ) 19

was subtracted from each exposed litter's response ( percent ) before calculating an average 20

response among litters in a given concentration

- time combination. Th e resulting data are 21

presented in Figure D

- 1

. 22DRAFT

– DO NOT CITE OR QUOTE D

- 2 A: Raw data starting with

grand-mean of controls

15 30 45 60 75 90 105

0 5000 10000 15000

Concentration (ppm)CR + SNR %

7-hr data

5-hr data

3-hr data

2-hr data

1-hr data

B: Data minus bkgd in

each block, then summarized

-15

0 15 30 45 60 75

0 5000 10000 15000

Concentration (ppm)CR + SNR % 7-hr data

5-hr data

3-hr data

2-hr data

1-hr data

Figure D

- 1

. Exposure

- response data for methanol

- induced CR plus SNR

malformations in mice at various concentration

- time combinations. The percent

response in each litter was first calculated, with dire ct averages shown in the

first panel relative to the grand

- mean for the controls. In the second panel,

the percent response in controls for each block of exposures in the study was

first subtracted from each litter's response in that block before taking

a verages across litters.

Source: Rogers et al

. ( 1995)

.

While the correction for background differences does not completely correct the apparent 1

nonmonotonic dose, the 2

- h ou r response is now less than or below the 3

- h ou r response at 5,000 2

and 10,000 ppm, an d the strong disparity that appeared between the 5

- and 7

- h ou r data at 2,000 3

ppm is eliminated. Overall

, the data show a more consistent dependence on duration of 4

exposure, except for the response to 3 h ou r s of 15,000 ppm methanol. Therefore these 5DRAFT

– DO NOT CITE OR QUOTE D

- 3 backgr ound

- corrected response measures will be used to evaluate the 3 dose metrics, with the 1

exception that the 3

- h ou r 15,000 ppm data point will be dropped as an outlier. In particular, the 2

dose

- response relationship based on these data will be plotted against each of the dose metrics to 3

determine which provides the most consistent overall dose

- response relationship. 4

D.2. RESULTS

D.2.1. Dose Metric Comparisons

The average incidence of CR plus SNR from the concentration

- time developmental 5

bioassay of Rogers et al . ( 1995 ), w ith block

- specific control values subtracted from each litter 6

average before calculating overall average responses, is plotted in Figure D

- 2 against three dose 7

metrics: AUC, C

max , and total amount metabolized of methanol (The volume units for C

max and 8

AUC were adjusted to put all three data sets on approximately the same scale for comparison)

. 9

10

11

Figure D

- 2

. Internal dose

- response relationships for methanol

- induced CR plus SNR

malformations in mice at various concentration

- time combinations for three dose

metrics. The percent response in controls for each block of exposures in the study was

first subtracted from each litter's response in that block before taking

averages across litters. The set of response values plotted for e ach metric is

the same, only the metric associated with those responses changes.

Source: Rogers et al

. (1995).

While none of the metrics result s are in complete alignment of the dose

- response data, 12

the scatter for the C

max dose

- response ( i.e., the range o f response values associated with a given 13

small range of the dose metric

– scatter in the y

- direction) is quite a bit less than either of the 14 - 15 0 15 30 45 60 75

0 10 20 30 40 50 60

Dose metric Cmax (mg/cL)

AUC (hr

- mg/mL)

Amount metabolized (mg)CR + SNR (%)DRAFT

– DO NOT CITE OR QUOTE D

- 4 other two metrics. Thus, C

max appears to be a better predictor of response than AUC or amount 1

metabolized. Looki ng at the exposure

- response data in Figure D

- 1 , one can see that 2

- and 2

3

- h ou r exposures at 5,000 ppm elicit no increase over control, while 5

- and 7

- h ou r exposures at 3

this level do. 4

If AUC or amount metabolized were true measures of risk, then one would expect a 5

graded response, where the 2

- and 3

- h ou r exposures were intermediate between controls and 5

– 6

7

- h ou r exposures. But the lack of response at those shorter times indicates that the concentration 7

( C

max ) has not risen high enough in such a short expos ure to cause a response, while it has at the 8

longer durations. From Figure D

- 2

, it appears that a C

max of 11 mg/cL (1,100 mg/L) is a 9

NOAEL , with a linear increase in CR + SNR from that level to 38 mg/cL, after which the 10

response begins to plateau. Note t hat while the plot is of response above background, the plateau 11

is effectively at 100% total incidence: the highest points in Figure D

- 1 are from the 7

- h ou r 12

exposures at 15,000 ppm, where actual incidence was 98% (30% in controls); and the next 13

highest poi nts are from the 5

- h ou r 15,000 and 10,000 ppm exposures, where the incidences were 14

94% and 93%, respectively (32% in controls; both from the same block)

. 15E

- 1 DRAFT

- DO NOT CITE OR QUOTE APPENDIX E. EVALUATION OF THE CANCER POTENCY OF METHANOL

E.1. INTRODUCTION

Two studies were selected for the evaluation of the cancer potency of methanol (Soffritti 1

et al

. , 2002a

, 2000 b

, 2002c ; NEDO

, 1987

, 1987/2008b)

. The Soffritti et al

. (2002a) study is the 2

only oral study available with effects that show a statistically significant increase in incidence of 3

any ca ncer endpoint s in the treated groups versus the concurrent contr ol group (pair

- wise 4

comparison) and is used to derive the POD for deriving an oral cancer slope factor. The NEDO 5

(1987, 1987/2008b) 24

- month rat study is the only inhalation study available with e ffects that 6

show a statistically significant increase in incidence of any ca ncer endpoint s and was used to 7

derive the POD for the inhalation cancer unit risk. A third study, Apaja (1980), reported 8

statistically significant increases in malignant lymphomas in Eppley Swiss Webster mice over 9

historical controls (pair

- wise comparison) following drinking water exposure to methanol. 10

Because this study did not involve a concurrent control group it is not used for the derivation of a 11

cancer oral slope factor, but its dose

- response is evaluated here for comparative purposes. 12

E.1.1. Oral CSF POD

The Soffritti et al . (2002a) study

, conducted by the Ramazzini Foundation

, presents a 13

number of challenges if these data are to be used in dose

- response modeling to assess the 14

ca rcinogenic potency of methanol. One challenge, determining the appropriate HED , is best 15

addressed using a PBPK model to derive a n HED dose that considers the kinetic differences in 16

humans and the animal model, i.e., species extrapolation. Such a model wa s developed by the 17

EPA and is not addressed in this appendix; however, the dose metrics derived from that PBPK 18

model are used in the modeling of the data. 19

The other major challenge, which is addressed in this appendix, is how to model the 20

nonstandard proto col by which methanol was tested, as reported in Soffritti et al . (2002a). In 21

most oncogenicity studies, typified by those conducted by the NTP, animals are dosed for 22

104 weeks, with a scheduled sacrifice of all surviving animals at the end of treatment. In the 23

study for methanol reported by Soffritti et al

. (2002a), while the animals were treated with 24

methanol for 104 weeks, animals were not euthanized and examined on a specified schedule but 25

were followed until their natural death. It is well known tha t the incidence of background tumors 26

in a number of organs is different between that seen at a scheduled sacrifice at 104 or 105 weeks 27

and in the same sex/strain that is followed for a lifetime. A higher background incidence can 28

increase the difficulty of detecting chemical ly related responses (Melnick et al ., 2007). Further, 29

performing pathological examinations on tissues collected after natural death can create 30DRAFT

– DO NOT CITE OR QUOTE E

- 2 difficulties associated with cell autolysis. 93 At the same time, the shorter duration of the 2

- year 1

bioassays used at the NTP misses about two thirds of the life span of the rodent, potentially 2

missing late stage or late appearing chemical ly related tumor responses (Melnick et al ., 2007). 3

ERF believes that “cutting short an experiment after two y ears may mask a possible carcinogenic 4

response,” but ERF further suggests that all chronic cancer studies “should continue until 5

spontaneous animal death” (Soffritti et al ., 2002a). Soffritti et al . (2002a) cite ERF studies of 6

benzene, xylenes, mancozeb, and vinyl acetate monomer as examples for which carcinogenic 7

responses were observed after the 2

- year treatment period. 8

These data were evaluated using three different approaches and two different dose

9

response models (EPA’s multistage cancer and a multist age Weibull time

- to

- tumor model). 10

These approaches involved using the administered dose and models that would rely upon the 11

published data (Option 1), while another would rely on unpublished information that would be 12

provided by the Ramazzini Foundation ( Options 1 and 2), while a third option (Option 3) would 13

also rely upon this unpublished information but would also incorporate results from PBPK 14

modeling for methanol developed by the EPA. 15

E.1.2. Selection of the Data Modeled and HED

The individual animal data from the Ramazzini Foundation study was provided to EPA in 16

the standard NTP format in which the number of days on study, the tissues examined and the 17

tumor types found were given for each animal. The tumors with incidences that were 18

statistically signific antly increased or were considered to be rare tumors and considered for dose

19

response modeling were the incidence of hepatocellular carcinoma in male rats and the incidence 20

of hemolymphoreticular neoplasms in both male and female rats. The incidence of ly mpho

21

immunoblastic lymphomas was modeled separately

, and the combined incidence of all the 22

lymphomas was considered for dose

- response modeling. Table E

- 2 provides the incidence of 23

these neoplasms reported in each dose group. The incidence of histiocytic sarcomas and myeloid 24

leukemias were not significantly increased in either sex. The incidence of these tumors was not 25

combined with the lymphoblastic lymphomas because they are of a different cell line and the 26

combination is not typically evaluated either for statistical significance or dose

- response 27

modeling (McConnell et al ., 1986). 28

The drinking water concentrations provided in the Soffritti et al . (2002a) study were 29

converted to doses in mg/kg

- day. Initially, an attempt was made to estimate the dose of methanol 30

to individual animals for development of an average dose; however, water consumption 31

information was available only on a cage

- by

- cage basis. Based on the available information, the 32

average water consumption for each treatment group was calculated using the available data 33

93 Autolysis may develop in carcasses of animals if they are not processed immediately after death or if the animals

b ecome severely moribund prior to death. These types of changes can compromise pathological diagnosis and

subclassification of neoplasms.DRAFT

– DO NOT CITE OR QUOTE E

- 3 reported for weeks 1

– 104. Although individual body weights were available, the corresponding 1

intake was not available. The average body weight over the period of dosing for the experiment 2

(using measurements taken on day 1

– day 7 36) was calculated for each dosed group. A weighted 3

average was calculated for the body weights using the number of animals for which body 4

weights were recorded at each time point. The average body weight and the average water 5

consumption in (mL/day) we re used to calculate the mg/kg

- day doses. The equation used for 6

this calculation is: 7

) kg ( BodyWeight 1000 ) day / mL ( mption WaterConsu ) ppm ( Dose ) day kg / mg ( Dose

**× ×** 

**=**

**−**

.

Table E

- 1 provides the values used to obtain the mg/kg

- day doses, as well as the resulting 8

mg/kg

- day doses. In addition, the average and media n times of death were calculated for each 9

group (both dosed and control), for the only the dosed groups combined (excluding the controls)

, 10

and for all the groups in the study combined (including control). These values were obtained 11

using the reported week s on study for each animal. One male rat (ID # 129) in the 20

, 000 ppm 12

group was not examined microscopically and was excluded from the time of death calculations 13

and all modeling. If this animal was included in the calculations for the average and median 14

times of death, the median time of death for all male rat dosed groups would increase from 97

– 15

98 weeks; all of the other average and median times of death that include the 20

, 000 ppm group 16

do not change. 17

In the absence of a kinetic model, extrapolation f rom animal to human was based on the 18

default assumption of body weight 3/4

. This extrapolation was applied to the animal POD 19

estimates to obtain the HEDs reported in Table E

- 1. This extrapolation was calculated using the 20

average body weight of the dosed a nimals excluding controls (0.33 kg for the female rats and 21

0.51 kg for the male rats) over the dosing period of the study (through day 736) and 70 kg for the 22

human body weight. The equation used for the body weight 3⁄4 extrapolation is 23

4 / 1

** ** 

** ** 

** ** 

** ** 

(kg) Weight Body Human (kg) Weight Body Animal

and results in a value of 0.26 for the female rats and 0.29 for the male rats. 24

E.1.2.1. Dose

- Response Modeling

Option 1

– Quantal Dose

- Response Modeling. Under this option, the standard default 25

modeling approach outlined in the Cancer Guidelines (U

. S

. EPA, 20 05 a ) was applied. The 26

cancer bioassay data were fit using a multistage model, which is the current model preferred by 27

EPA’s IRIS program for cancer dose

- response modeling. The PODs for use in the derivation of 28DRAFT

– DO NOT CITE OR QUOTE E

- 4 CSFs were calculated using the multistage

- ca ncer model available in the BMDS program 1

( http://www.epa.gov/NCEA/bmds ). 2

BMDS was used to estimate BMDs and 95% lower bounds on the BMDs or BMDLs 3

associated with a 10% extra risk (BMDL 10 ). For this assessm ent, the multistage model was 4

determinded to be an appropriate model for characterization of the dose

- response curve in the 5

observable range. At this time, the MOA for the tumors observed following exposure to 6

methanol is not known ; therefore, linear extr apolation was conducted to estimate a CSF

. 7

Option 2

– Time

- to

- Tumor Dose

- Response Modeling. This option is similar to 8

Option 1; however, rather than the use of quantal models, a time

- to

- tumor model was applied to 9

the selected datasets. Data for this anal ysis was provided by the Ramazzini Foundation and can 10

be obtained from their web site ( http://www.ramazzini.it/fondazione/study.asp ). The same 11

assumptions regarding the HED and low

- dose extrapo lation were applied. Because BMDS does 12

not include time

- to

- tumor modeling, the QRISK portion of Statox was relied upon for dose

13

response modeling. Statox is an internal EPA program that is used for gathering and analyzing 14

animal bioassay data and contain s the QRISK component for dose

- response modeling. The 15

QRISK component of Statox Version 5.5 fits a m ultistage Weibull model to the data. The 16

m ultistage Weibull model is m ultistage in dose and Weibull in time and essentially assesses the 17

probability that a tumor would have been identified at time t. The m ultistage Weibull model has 18

the form: 19

c k

k 

### t t d q d q d q q 

# e t d p ) ( ) ( 0 2

2 1 0 

# 1 )

, ( **− ×**  **× +**  **× +**  **× +** 

**−**



# − **=** 

with dose ( d ) and time ( t ) as the variables. The parameters estimated by fitting the model to the 20

data are the dose parameters q 0 through q k , the induction time (t 0 ) and the power term for 21

time (c)

. 22

If t 0 is interpreted as the time (assumed to be the same for all animals) from when a tumor 23

is observable ( i.e., capable of being detected if the animal were to be sacrificed and a necropsy 24

perfor med) to the time the tumor causes the death of the animal, then these models can be 25

applied to data on incidental and fatal tumors simultaneously. Note that t and t 0 only appear in 26

the model in the form of t

- t 0 . To make this explicit, we write P(d,t) = F (d,t

- t 0 ) . The probability 27

of an incidental tumor by time t is taken to be F(d,t) (t

0 = 0) and the probability of a fatal tumor 28

by time t is taken to be F(d,t

- t 0 ) . There are three possible types of incidence contexts for each 29

animal which contribute separ ately to the likelihood function for this model. These are: 30

**•** Censored response

– animal died without having the tumor(s) being modeled 31

**•** Incidental response

– the animal died with the tumor(s) but the death was not caused by 32

the tumor(s) ( i.e., the time to d eath from those tumors would have been later than the 33

actual death time); and 34DRAFT

– DO NOT CITE OR QUOTE E

- 5 **•** Fatal incidence

– t he tumor(s) being modeled w as the cause of death. 1

The contribution of each animal to the likelihood is then defined for its time of death (t)

. 2

The complete likelihood is defined as : 3



## ∏ **∏ ∏** 

**= =**  **= =**  ** **  ** **  ****

** **  ** **  ****

** **  ** **  ** ** 

**∂ −**  **∂**

**× **  ** **  ** **  ****

**− −**  **× **  ** **  ** **  ****

**−** g

1 j Fatal ) j

, i ( Incidence 0 j

Incidental ) j

, i ( Incidence 0 j j

Censored ) j

, i ( Incidence j )

t ) t t

, d ( F ( ) t t

, d ( F ) t

, d ( F ( ) t

, d ( F 1 ( 

## 

where g is the number of dose groups in the study

, including the control group

, and i varies from 4

1 to the total number of animals in the study examined for the tumor type(s) being modeled. 5

As with the qua ntal modeling, the lower bound on a dose at an extra risk of 10% was 6

estimated. Goodness

- of

- fit was determined by visually inspecting graphical output of the 7

modeling. AIC values were also calculated for the time

- to

- tumor model fit. 8

This option was propo sed because time

- to

- tumor modeling is typically applied to account 9

for differences in survival among treated and control groups. However, in this case there were 10

no differences detected in the survival times. Figures E

- 1 and E

- 2 are graphs of the proport ion 11

surviving versus the weeks on study for the female rats and male rats

, respectively. In addition, 12

the Life Table program (Thomas et al ., 1997) was run on the data. None of the statistical tests in 13

this program indicated a difference in survival betwe en the control and the dosed groups. 14

The protocol used by the Ramazzini Foundation was different from that typically 15

employed in chronic rat bioassays. In typical rodent bioassays, a compound is administered to 16

the animals for approximately 104 weeks

, a nd the animals sacrificed within a short period (days) 17

following the end of treatment. In the Ramazzini Foundation bioassays ( i.e., for methanol, 18

formaldehyde, MTBE, and aspartame), the animals were administered compounds for 104 weeks 19

but were allowed to live until a natural death, which, in some animals, occurred months after the 20

completion of chemical administration. This can have an impact on the tumor incidence and 21

therefore, the potential risk of tumor development associated with administration of a given 22

compound. Time

- to

- tumor modeling was used in this case to attempt to adjust for the extended 23

life span of the some of the animals in this study. 24

For time

- to

- tumor modeling, the POD must consider a specific time as well as a specific 25

risk level. Si nce this study was not a standard study with a fixed study length, several 26

assumptions can be made with regard to the time to be used. For this modeling exercise, T wo 27

different possible approaches were considered

. 28

For the first approach, the model was f it to animal data using all the times reported up to 29

the last death time or 153 weeks for the female rats and 148 weeks for the male rats. Every 30

tumor observed was assumed to be a fatal tumor or the cause of death in the animal. While the 31

animals lived l onger than 104 or 105 weeks, the POD was calculated at 105 weeks, since it was 32

assumed that an animal life span of 148

– 153 weeks would not correspond to the average 70

- year 33

human life span

. 34DRAFT

– DO NOT CITE OR QUOTE E

- 6 For the second approach, an attempt was made to simulate what mi ght have occurred if 1

the study had been a standard 2

- year protocol that was terminated at 105 weeks

, with all 2

surviving animals sacrificed at 105 weeks. It was assumed that the tumors discovered in the 3

animals that survived longer than 105 weeks would hav e been present and found at necropsy. 4

Therefore, all tumors in animals that died in weeks 105 and earlier were assumed to be fatal or 5

the cause of death in the animals, and all tumors that would have been discovered at the necropsy 6

of animals were assumed to be incidental or not the cause of death. The life span assumed for 7

this analysis was 105 weeks in the rat and the POD was calculated for a 10% risk to a human at 8

105 weeks. This approach was conducted mainly for comparative purposes to evaluate the 9

p otential impact on the POD if the study duration was shortened and for a more direct 10

comparison to the quantal PODs and serves only as bounding exercise for the risk. 11

Option 3

– Dose

- Response Modeling using PK Dose Metrics. For the third approach, 12

PK dose metrics obtained from the PBPK model (Section 3.4) were used as the doses. Both 13

time

- to

- tumor modeling and quantal modeling was done. The Statox program was used to 14

estimate MLEs and lower bounds on dose associated with a 10% extra risk (LED10s)

, and 15

BM DS multistage model was used to estimate BMDs and 95% lower bounds on the BMDs or 16

BMDLs associated with a 10% extra risk (BMDL10). Each of the dose metrics

, provided in 17

Table E

- 5

, were used in this option of the dose

- response modeling. 18

E.1.2.1. Results for Oral Slope Factor POD

Quantal Dose

- Response Modeling (Option 1). Quantal dose

- response modeling was 19

conducted using estimated mg/kg

- day doses and the incidence of lympho

- immunoblastic 20

neoplasms and the combined lymphomas for the female rat and the hepatocellu lar carcinomas, 21

the lympho

- immunoblastic neoplasms, and the combined lymphomas for the male rat. The 22

results of the quantal modeling for this option are given in Table E

- 3. The multistage model gave 23

an adequate fit ( p value > 0.05) and was able to derive BMDL 10 values for all of the lymphoma 24

data. However, for the male rat hepatocellular carcinomas, the multistage model failed to 25

estimate a BMDL 10

. Human equivalent BMDL 10 values 94 and CSF are also provided in 26

Table E

- 3. 27

The POD values calculated from the female rat data ranged from 259

– 405 mg/kg

- day for 28

the lympho

- immunoblastic neoplasms and 251

– 400 mg/kg

- day for the combined lymphomas. 29

For the HED POD values calculated from the male rat data, the range of values for the liver 30

hepatocellular carcinomas w as 537

– 567 mg/kg

- day; however, for 3 of the models (Multistage, 31

Quantal Linear and Log

- Probit), the BMDS program failed to estimate a BMDL 10 . The lympho

32

immunoblastic endpoint in the male rats produced POD values that ranged from 110

– 223 mg/kg

33

day

, and th e all lymphomas incidence data gave POD values of 113

– 227 mg/kg

- day. Figures E

- 3 34

94 Computed from the animal values by multiplying by the body weight 3⁄4 animal

- to

- human extrapolation value (0.26

for f emales and 0.29 for males)DRAFT

– DO NOT CITE OR QUOTE E

- 7 through E

- 7 show graphs of the quantal models that give the lowest POD for each of the 1

endpoint s modeled. 2

Time

- to

- Tumor Dose

- Response Modeling (Option 2). Results of the ti me

- to

- tumor 3

modeling using estimated mg/kg

- day doses with POD and CS F values for each approach 4

described above are given in Table E

- 4. Approach 2 gives smaller POD estimates than 5

A pproach 1. With A pproach 2, an artificial end of the study is assumed of 1 05 weeks and the 6

designation of approximately half of the total tumors changed from fatal to incidental. This 7

approach evaluates the potential impact on the POD of terminating the study at 105 weeks, rather 8

than allowing the animals to live until their na tural death, assuming that the same animals 9

bearing tumors would be “observed” at 105 weeks, rather than at later time points. For 10

A pproach 1, the model was fit to the actual observed weeks

- on

- study

, and a time of 105 weeks 11

was used in calculating the POD . For the female rat, the PODs based on the lympho

12

immunoblastic neoplasms were 349 mg/kg

- day for approach 1 and 179 mg/kg

- day 13

for A pproach 2 . The PODs for the combined lymphomas ranged were 321 and 198 mg/kg

- day 14

for the 2 appraoches. For the PODs calcul ated from the male rat data, the values were 783 and 15

612 mg/kg

- day for the hepatocellular carcinomas, 174 and 91 mg/kg

- day for the lympho

16

immunoblastic neoplasms

, and 192 and 92 mg/kg

- day for the combined lymphomas. Figures E

17

3 through E

- 7 show the modeli ng results for the time

- to

- tumor modeling using A pproache 1 18

where the time is fixed at 105 weeks and the doses are allowed to vary. Figures E

- 8 and E

- 9 19

show Kaplan

- Meier curves versus the model fit to the combined lymphoma data for the females 20

and males, respectively. In these graphs each line corresponds to a specific dose

, and time is 21

allowed to vary up to the study end of 153 or 148 weeks. For the male rat combined lymphomas 22

( Figure E

- 9), the multistage Weibull predicted values more closely match the Kaplan

- Meier at 23

105 weeks than at the average life span (94 weeks) or the end of study (148 weeks). For the 24

female rat combined lymphomas, the closest match of the Kaplan

- Meier curves to the model 25

predicted values appears to be around the average life spa n of 96 weeks. 26

The AIC values for the time

- to

- tumor modeling are all higher than those for the quantal 27

modeling. However, since the LLFs on which the AIC are based are different for time

- to

- tumor 28

versus quantal models, the AIC values from time

- to

- tumor mo dels cannot be compared to those 29

from quantal models. 30

Dose

- Response Modeling using PBPK Dose Metrics (Option 3). Both time

- to

- tumor 31

and quantal dose

- response modeling were conducted using the incidence of lympho

32

immunoblastic neoplasms and the combined l ymphomas for the female rat and the hepatocellular 33

carcinomas, the lympho

- immunoblastic neoplasms, and the combined lymphomas for the male 34

rat and the three PBPK dose metrics (blood methanol AUC, peak blood concentration

, and the 35

total metabolized per day) obtained from the EPA ’s PBPK model ( Table E

- 5). 36

Only A pproach 1 was used for the time

- to

- tumor dose

- response modeling using PBPK 37

dose metrics

, and results are given in Table E

- 6a ; H EDs are provided in Table E

- 6b. 38DRAFT

– DO NOT CITE OR QUOTE E

- 8 The results of the multistage quantal mode ling are given in Table E

- 7. Most model runs 1

gave an adequate fit to the data by the χ 2 Goodness of Fit p

- value ( e.g., p

- values > 0.05)

, 2

although for male rat hepatocellular carcinomas, the calculations using the AUC or amount 3

metabolized dose metric were unable to converge for the model fit or the derivation of a BMDL. 4

A plot of the model fit for male rat combined lymphomas using total methanol metabolized per 5

day as the dose metric (the endpoint and dose metric used in the derivation of the oral CSF ) is 6

shown in Figure E

- 10 ; HEDs are provided in Table E

- 8 where the BMDL was calculated. 7

E.2. INHALATION ORAL CANCER UNIT RISK POD

The NEDO (1987

, 1987/2008b) study was conducted using a standard protocol with 8

exposure for 104 weeks, followed by sacrifice of all an imals surviving to 104 weeks. As with 9

the Soffretti (2002a) study, pharmacokinetic dose metrics for use in the dose

- response 10

assessment were determined for the inhalation exposures using the EPA’s PBPK model. 11

E.2.1. Selection of the Data Modeled and H ED

The indi vidual animal data from the NEDO (1987/2008b) study were provided in a 2008 12

translation of the study from Japanese to English

. Although the translation provided the number 13

of days on study and the neoplastic responses seen in each animal, the translation d id not provide 14

results if a tissue was examined histopathologically with no neoplastic responses. This makes it 15

difficult to determine which of the individual animals were not examined

, although the tables did 16

indicate that

, for some of the animals

, sele cted organs (specifically a few lungs in males and a 17

few adrenal glands in females) were not examined. Therefore, time

- to

- tumor analysis could not 18

be conducted with results from the inhalation data as was done with the oral data. However, 19

survival analys is of all the data from the NEDO (1987/2008b) study did not indicate that there 20

were any survival problems (Figures E

- 11 and E

- 12). This suggests that a time

- to

- tumor analysis 21

is not necessary. 22

The tumors with significantly increased incidence that were c onsidered for dose

- response 23

modeling were the female rat adrenal gland pheochromocytomas and the male rat lung tumors 24

(papillary adenomas and adenocarcinomas combined or papillary adenomas, adenocarcinomas 25

and adenomatosis combined) ; Table E

- 9 gives the in cidence of these tumors. 26

E.2.1.1. Dose

- Response Modeling

Quantal Dose

- Response Modeling using Pharmacokinetic Dose Metrics. For the 27

selected endpoint s from the NEDO (1987) study

, only quantal dose

- response modeling using 28

pharmacokinetic internal dose metrics esti mated by the PBPK model (described in Section 3.4 29

and Appendix B) was conducted. Each of the dose metrics provided in Table E

- 10 w as used in 30

the BMDS software with the incidence data in Table E

- 9 to estimate the BMDs and 95% lower 31

confidence limits (BMDLs ) associated with a 10% extra risk (BMDL 10 ). 32DRAFT

– DO NOT CITE OR QUOTE E

- 9 E.2.2. Results for the I UR POD

Quantal dose

- response modeling was conducted using the incidence of adrenal gland 1

phoechromocytomas in female rats and the combined incidence of lung adenomas and 2

adenocarcinomas or lung adenomas, adenocarcinomas and adenomatosis in male rats using the 3

pharmacokinetics dose metrics derived from the EPA’s PBPK model. The results of this 4

modeling are given in Table E

- 11. The multistage model gave an adequate fit to the data in all 5

instance s as determined by the χ 2 goodness

- of

- fit p

- value ( e.g., p

- values > 0.05). A plot of the 6

model fit for female rat pheochromocytomas using total methanol metabolized per day as the 7

dose metric (the endpoint and dose metric used in the derivation of the IUR ) is shown in 8

Figure E

- 13. HECs are provided in Table E

- 12

. 9

E.3. ANALYSIS OF APAJA (1 980) DRINKING WATER STUDY

The Apaja (1980 ) study was similar to the Soffritti et al. (2002a) study in that it was a life 10

span drinking water study. The primary differences a re that Apaja (1980) used Eppley Swiss 11

Webster mice, did not stop exposure at 104 weeks and did not employ an untreated concurrent 12

control group. Methanol exposure groups of this study served as controls for malonaldehyde 13

exposed mice. As with the Soffre tti (2002a) study, pharmacokinetic dose metrics for use in the 14

dose

- response assessment were determined for the oral exposures using the EPA’s PBPK mouse 15

model. 16

E.3.1. Selection of the Data Modeled and H ED

I ndividual animal data from the Apaja (1980 ) study were n ot available. Therefore, time

17

to

- tumor analysis could not be conducted. The tumors with significantly increased incidence that 18

were considered for dose

- response modeling were malignant lymphomas in male and female 19

mice ; Table E

- 13 gives the incidence of these tumors. 20

E.3.1.1. Dose

- Response Modeling

Quantal Dose

- Response Modeling using Pharmacokinetic Dose Metrics. A 1 st degree 21

m ultisage model was used to evaluate the malignant lymphoma response from the Apaja (1980 ) 22

study versus pharmacokinetic dose metrics witho ut background 95 was conducted. Each of the 23

dos e metrics provided in Table E

- 14 was used in the BMDS software with the incidence data in 24

Table E

- 13 to estimate the BMDs and 95% lower confidence limits (BMDLs) associated with a 25

10% extra risk (BMDL 10 ). 26

95 The assumption of zero background dose is consistent with what was done for the derivation of the oral CSF and

EPA practice.DRAFT

– DO NOT CITE OR QUOTE E

- 10 E.3.2. Resul ts for the IUR POD

Quantal dose

- response modeling was conducted using the incidence of malignant 1

lymphoma in male and female Eppley Swiss Webster mice using the pharmacokinetics dose 2

metrics derived from the EPA’s PBPK model. The results of t his modeling are given in 3

Table E

- 1 5 . The 1 st degree multistage model gave an adequate fit to the data in all instances as 4

determined by the χ 2 goodness

- of

- fit p

- value (e.g., p

- values > 0.05). A plot of the model fit s for 5

male and female mice using total methanol metabolized per day as the dose metric is shown in 6

Figure E

- 14

. BMDL 10 HECs associated with each dose metric are provided in Table E

- 1 6

. 7

E.4. BACKGROUND DOSE ANAL YSES

The primary purpose of this cancer analys is is for the determination of cancer risk 8

associated with increases in the levels of methanol or its metabolites (e.g., formate, 9

formaldehyde) over background. Thus, t he PBPK mode l estimates of internal dose used in the 10

dose

- response analyses described above do not describe or account for background levels of 11

methanol or its metabolites

. However, b ackground levels of methanol may have contribute d to 12

the response levels reported for some of the tumors associated with methanol. If this 13

contribution is large, it could significantly impact cancer risk estimates. 14

Of the Soffritti et al. (2002a), NEDO (1985/2008b) and Apaja (1989) studies, only the 15

NEDO study reported methanol blood leve ls in test animals. Based on background levels of 16

methanol in the blood of F344 rats reported by NEDO (1987; 1985/2008b) PBPK model 17

estimates of AUC methanol (mg

- h/L), Cmax methanol (mg/L) and total metabolites (mg/day) in 18

control animals were obtained. AUC methanol, Cmax methanol and methanol metabolite 19

background levels were estimated to be 97.16 mg

- h/L, 4.05 mg/L and 7.79 mg/day for female 20

F344 rats and 96.02 , 4.00 and 10.81 mg/day for male F344 rats, respectively. 21

Available bioassays do not allow for the quantification of the relationship between 22

background levels and background responses , but the “ background dose ” Multistage

- cancer 23

model contained in version 2.1 of EPA’s BMDS software can be used to estimate what 24

background dose would be necessary to explain the dose

- response data evaluated in this 25

assessment if all of the background response were due to background levels of methanol or its 26

metabolites . T he results of dose

- response modeling of cancer endpoints evaluated in this 27

assessment using the M ultistage

- cancer “background dose” model are shown in Table E

- 17. 28

As can be seen from Table E

- 17, the Multistage

- cancer

- bgdose model predicts that AUC 29

methanol, Cmax methanol and methanol metabolite background doses necessary to explain the 30

dose

- response data if all of the background response were due to background levels of methanol 31

or its metabolites are well above the actual background levels for these metrics predicted by the 32

PBPK model. In the case of lymphoma responses in Sprague

- Dawley rats, the b ackground doses 33

for all metrics predicted by the Multistage

- cancer

- bgdose model are 20

- 2 ,000 fold higher than 34DRAFT

– DO NOT CITE OR QUOTE E

- 11 the actual background doses predicted in F344 rats by t he PBPK model . In the case of female 1

pheochromocytoma and male lung responses in F344 rats , the background doses predicted by the 2

Multistage

- cancer

- bgdose model are 3

- 5 fold higher than the actual background doses predicted 3

by the PBPK model. T he fact that background dose estimates from the Multistage

- cancer

- bgdose 4

model are several

- fold and so metimes more than 1,000

- fold higher than the PBPK model 5

predictions suggests that the contribution of background levels of methanol or its metabolites to 6

the background responses of these tumors is relatively small

. 7DRAFT

– DO NOT CITE OR QUOTE E

- 12 Table E

- 1

. Calculation of mg/kg

- day d oses

Female Sprague

- Dawley Rats Male Sprague

- Dawley Rats

Dose (ppm) Body

w eight

(kg) Water

c onsump

. (g/day or

mL/day) Dose

(mg/kg

- day) Average

t ime of

d eath

(wk) Median

t ime of

d eath

(wk) Body

w eight

(kg) Water

c onsump

. (g/day or

mL/day) Dose

(mg/kg

-

d ay) Average

t ime of

d eath

(wk) Median

t ime of

d eath

(wk)

0 0.33 42.55 0 98 102 0.50 52.57 0 91 91

500 0.33 43.05 66.0 96 99 0.49 52.06 53.2 97 98

5000 0.33 41.11 624.1 94 97 0.50 52.58 524 93 93

20000 0.34 37.26 2177 98 101 0.54 48.32 1780 93 100

Av eraged

over all

d osed g roups

(excluding

control) 0.33 96 99 0.51 94 97

Averaged

over all

g roups

(including

c ontrol) 0.33 97 100 0.51 93 96

Source: Soffretti et al . (2002a).

Table E

- 2

. Incidence for n eoplasms considered for dose

- response modeli ng

Dose

(ppm) Dose

(mg/kg

day) Number of

a nimals

e xamined Heptocellular

c arcinomas Histiocytic

s arcoma Leukemia

m onocytic Leukemia

m yloid Lymphoma

l ymphoblastic Lymphoma

l ympocytic Lymphoma

l ympho

i mmunoblastic All

l ymphomas

c ombined

Female Sprague

- Dawl ey r ats

0 0 100 1 3 0 0 9 9

500 66.0 100 2 3 1 1 17 19 a

5000 624.1 100 2 3 1 0 19 a 20 a

20000 2177 100 3 3 1 0 21 a 22 b

Cochran Armitage Trend Test 0.19 0.5 0.3 0.8 0.04 0.04

Male Sprague

- Dawley r ats

0 0 100 0 2 1 8 1 16 17

500 53.2 100 2 4 0 4 3 24 27

5000 524 100 2 1 0 6 1 2 8 a 29 a

20000 1780 99 3 1 0 1 1 37 b 38 b

Cochran Armitage Trend Test 0.10 0.9 0.8 0.98 0.7 0.0007 0.001

a Fisher’s Exact p

- value < 0.05 ; b Fisher’s Exact p

- value < 0.01

Source: Soffretti et al . (2002a).DRAFT

– DO NOT CITE OR QUOTE E

- 13 Table E

- 3. Results from m ultistage (1o) quantal modeling rat data using mg/kg

- day

exposures and default HED derivation method

AIC p

- v alue Scaled

residual at

observed

dose closest

to BMD Animal v alues Human v alues

BMD 10 BMDL 10 Human

e quivalent

BMDL 10 a CSF b

Female

Sprague

Dawley r at All organs

lympho

immunoblastic 359.16 0.19

- 0.07 2179.51 1058.99 277.5 3.6E

- 4

All organs

- all

lymphomas 371.95 0.11

- 0.07 2141.88 1033.69 270.9 3.7E

- 4

Male

Sprague

Dawley r at Hepatocellular

carcinoma 72.84 0.38 Failed a

A ll organs

lympho

immunoblastic 455.67 0.34 0.14 714.26 448.43 131.0 7.6E

- 4

All organs

– all

lymp homas 468.79 0.24 0.11 744.35 456.11 133.3 7.5E

- 4

a Model failed to optimize.

b Calculated as CSF = 0.1/Human BMDL 10

.

Source: Soffretti et al. (2002a).

Table E

- 4. Results from time

- to

- tumor modeling data using mg/kg

- day exposures and

default HED derivation method

Human v alues b

AIC Prediction

t ime (weeks) MLE

(mg/kg

- day) LED 10 (mg/kg

- day) CSF c

Approach 1

- Model fit to actual death times, dose estimates com puted at 105 weeeks a

Female

Sprague

Dawley r at All organs lympho

- immunoblastic 831.42 105 729.0 348.6 2.9E

- 4

All organs all lymphomas 900.46 105 679.1 320.9 3.1E

- 4

Male

Sprague

Dawley r at Hepatocellular carcinoma 105.45 105 4250.7 783.3 1.3E

- 04

All o rgans lympho

- immunoblastic 1254.58 105 302.0 173.8 5.8E

- 4

All organs all lymphomas 1309.86 105 356.7 191.8 5.2E

- 4

Approach 2

- Truncating s tudy at 105 Weeks a

Female

Sprague

Dawley r at All organs lympho

- immunoblastic 631.81 105 370.7 178.5 5.6E

- 4

All organs all lymphomas 664.43 105 431.9 198.0 5.1E

- 4

Male

Sprague

Dawley r at Hepatocellular carcinoma 1.16E+05 d 105 1013.7 612.2 1.6E

- 4

All organs lympho

- immunoblastic 914.42 105 150.8 91.2 1.1E

- 3

All organs

– all lymphomas 935.29 105 157.1 92.2 1.1E

- 3

a Individual animal pathology data needed for the modeling reported in this table can be obtained from the Ramazzini Foundation web site

( http://www.ramazzini.it/fondazione/study.asp ).

b Human va lues are computed by converting the animal doses to HED before modeling by multiplying by the body weight 3⁄4 animal

- to

human extrapolation value (0.26 for females and 0.29 for males).

c Calculated as CSF = 0.1/Human LED 10

. d Model failed to optimize.

Source: Soffretti et al. (2002a).DRAFT

– DO NOT CITE OR QUOTE E

- 14 Table E

- 5

. PBPK model estimated dose

- metrics for doses

Sex Dose (mg/kg

- day) Body w eight

(kg) AUC

(mg

- h/L) Peak (mg/L) Amount m etabolized

(mg /day )

Female

Sprague

Dawley 66 0.33 66.84 5.96 18.39

624.1 0.33 9543.63 500.59 126.6 8

2177 0.34 91262.27 4157.98 141.60

Male

Sprague

Dawley 53.2 0.49 55.76 4.81 21.82

524 0.5 0 7500.14 395.56 168.85

1780 0.54 80420.25 3629.08 200.03

Source: Soffretti et al . (2002a)

.

Table E

- 6 a. Results from time

- to

- tumor modeling of d ata using P BPK dose metri cs

Approach 1

- Model fit

to actual death times,

dose estimates

computed at 105

weeeks Prediction

Time (wk) AUC (mg

- h/L) Peak (mg/L) Amount Metabolized

(mg /d )

AIC MLE LED 10 AIC MLE LED 10 AIC MLE LED 10

Female

Sprague

Dawley

r at All organs

lympho

immunoblastic 105

832.43 152281 64486 832.35 6763.4 2895.3 829.24 160.45 90.7

All organs all

lymphomas 105 901.43 143138 59574 901.36 6356.1 2675.3 898.24 145.53 82.2

Male

Sprague

Dawley

r at Hepatocellular

carcinoma 105 110.47 undefined 119186 110.45 undefined 5385.9 109.60 undefined 300.4

All organs

lympho

immunoblastic 105

1242.72 55409 30059 1244.61 2460.2 1341.3 1242.66 141.63 77.3

All organs

– all

lymphomas 105 1297.74 66211 33315 1297.66 2939.5 1487.2 1295.95 143.14 82.7

Source: Sof fretti et al . (2002a)

Table E

- 6 b. HEDs from time

- to

- tumor modeling of d ata using PBPK dose metri cs

Approach 1

- Model fit to actual death times,

dose estimates computed at 105 weeeks AUC (mg

- h/L) Peak (mg/L) Amount

Metabolized (mg)

LED 10 ( mg/kg

- day ) LED 10 ( mg/kg

- day ) LED 10 ( mg/kg

- day )

Female

Sprague

Dawley r at All organs lympho

- immunoblastic 647.0 676.2 87.7

All organs all lymphomas 618.3 645.4 79.4

Male

Sprague

Dawley r at Hepatocellular carcinoma 965.4 1023.9 260.0

All organs lympho

- immunoblast ic 594.0 457.3 62.4

All organs

– all lymphomas 657.1 478.1 66.7

Source: Soffretti et al . (2002a)DRAFT

– DO NOT CITE OR QUOTE E

- 15 Table E

- 7. Results of Multistage (1o) quantal modeling of data u sing PBPK dose

metrics

AUC (mg

- h/L) Peak (mg/L) Amount Metabolized (mg/d)

AIC p

value Sc aled

residual

at dose

nearest

to BMD BMD 10 BMDL 10 AIC p

value Scaled

residual

at dose

nearest to

BMD BMD 10 BMDL 10 AIC p

value Scaled

residual

at dose

nearest to

BMD BMD 10 BMDL 10

Female Sprague

- Dawley r at s

All organs lympho

- immunoblastic

35 7 .9 0.1 3

- 0. 13 2 112790 49771 359. 9 0.1 3

- 1.56 5034.3 2240.3 35 7. 9 0. 34

- 0.913 139.8 77.2

All organs

– all lymphomas

372.78 0.075

- 0.128 111621 48789.6 372. 1 7 0. 07 7

- 0.1 45 4982 .2 2196.3 370.5 7 0. 1 8

- 0. 002 134.4 74.5

Male Sprague

- Dawley r at s

Hepatocellular carcinoma

73. 21 0.37 Failed a 73. 19 0.37 Failed a 72. 5 5 0.3 6 Failed a

All organs lympho

- immunoblastic

457. 45 0.1 4 1. 214 36865 21989 457. 3 0.1 5 1.1 73 1639.0 981.7 455. 4 6 0. 36

- 0. 750 94.4 61.6

All organs

– all lymphomas

46 8.09 0.15 0.8 78 37822 22195 467. 98 0.1 5 0.8 3 8 1686.5 993.1 467.3 5 0. 20

- 0. 9 01 10 1.7 63.9

a BMD computation failed. BMD is larger than three times maximum input doses.

Source: Soffretti et al . (2002a)

.

Table E

- 8

. Application of human PBPK model to derive HEDs from results of

m ultistage (1o) quan tal modeling of d ata using PBPK d ose m etrics

AUC (mg

- h/L) Peak (mg/L) Amount m etabolized

(mg /d ) a

HED BMDL 10 ( mg/kg

day ) HED BMDL 10 ( mg/kg

- day ) HED BMDL 10 ( mg/kg

- day )

Female

Sprague

Dawley rat All organs lympho

immunoblastic 560.96 584.37 74.54

All organs

– all

lymphomas 555.20 578.18 71.92

Male

Sprague

Dawley r at Hepatocellular

c arcinoma N/A N/A N/A

All organs lympho

immunoblastic 395.99 405.56 49.65

All organs

– all

lymphomas 397.25 407.22 51.50 b

a T otal metabolized methanol was selected as th e preferred dose metric (see discussion in section 5.4.1.3). B efore

applying human PBPK model to obtain these HED BMDL 10 estimates, Table E

- 7 mg/d values were converted to

human mg/d by multiplying by either: (BW human ) 3⁄4 / (BW

rat ) 3⁄4 =(70 kg) 3⁄4 / (0.33 kg ) 3⁄4 =55. 6 for male rats or,

(BW human ) 3⁄4 /(BW

rat ) 3⁄4 = (70 kg) 3⁄4 /(0.26 kg) 3⁄4 = 66.5 for female rats.

b This value was used in the derivation of the methanol oral cancer slope factor.

Source: Soffretti et al . (2002a)

.DRAFT

– DO NOT CITE OR QUOTE E

- 16 Table E

- 9. Incidence for n eoplasms considered for dose

- response modeling

Dose (ppm) Examined Adrenal gland

phoechromocytoma Lung adenoma and

adenocarcinoma Lung adenoma,

adenocarcinoma,

adenomatosis

Female F344 r ats

0 50 2

10 51 3

100 49 2

1,000 51 7

Male F344 r ats

0 52 1 5

10 50 5 6

10 0 52 2 7

1,000 52 7 a 11

Cochran Armitage Trend Test p_values 0.015 0.0259 0.0415

a Fisher's Exact p

- values < 0.05

Source: NEDO (1987, 1987/2008b).

Table E

- 10. PBPK dose metrics for doses

Dose (ppm) AUC

(mg

- h/L) Peak (mg/L) Amount

metabolized (mg)

Female F344 rats

0 0.00 0.00 0.00

10 3.70 0.19 0.30

100 37.53 1.93 2.94

1,000 434.29 22.69 28.96

Male F344 rats

0 0 0 0

10 3.70 0.19 0.41

100 37.53 1.93 4.11

1,000 433.61 22.68 40.19

Source: NEDO (1987, 1987/2008b).DRAFT

– DO NOT CITE OR QUOTE E

- 17 Table E

- 11. Benchmark resu lts from m ultistage quantal dose

- response modeling data

using PBPK dose

- metrics

Model AUC (mg

- h/L) Peak (mg/L) Amount m etabolized (mg/d)

AIC p

value Scaled

residual

at dose

nearest

to BMD BMD 10 BMDL 10 AIC p

- value Scaled

residual at

dose

nearest to

BMD BM D 10 BMDL 10 AIC p

- value Scaled

residual

at dose

nearest

to BMD BMD 10 BMDL 10

Female F344 r at s

Adrenal glands

– pheochromocytoma

Multi stage ( 3 o) 101.37 0.88 0.000 44 2.1 21 7.0 101.37 0.88 0.000 2 3.1 11. 3 101.37 0.88 0.000 29.5 1 4.6

Male F344 r at s

Lung

– adenomas and adenocarcinomas

Multistage ( 3 o) 107.99 0.16 0.000 454.9 23 0.7 107.99 0.16 0.000 2 3.8 12. 1 107.99 0.16 0.001 42.2 21.6

Lung

– adenomas, adenocarcinomas a n d adenomatosis

Multistage (1o) 168. 61 0. 89

- 0.03 5 378.9 168.8 168.6 1 0.8 9

- 0.03 5 19.8 8 .83 168.59 0.90

- 0.03 8 34.8 15.6

Source: NEDO (1987, 1987/2008b).

Table E

- 12 . Application of human PBPK model to derive HECs from BMDL 10

estimates in Table E

- 11 using multistage quantal modeling

AUC (mg

- h/L) Peak (mg/L) Amount m etabolized (mg /d ) a

HE C BM C L 10 (mg/m 3 ) HEC BM C L 10 (mg/m 3 ) HEC BM C L 10 (mg/m 3 )

Female F344

r at Adrenal glands

- phoechromocytoma 384.2262 464.79844 81.85 b

Male F344 r at Lung

- adenomas and

adenocarcinomas 401.96305 485.15995 101.27

Lung

- adenomas,

adenocarcinomas a n d

adenoma tosis 316.20336 386.70859 73.12

a T otal metabolized methanol was selected as the preferred dose metric (see discussion in section 5.4.2.3). B efore

applying human PBPK model to obtain these HEC BMCL 10 estimates, Table E

- 7 mg/d values were converted to

huma n mg/d by multiplying by either: (BW human ) 3⁄4 / (BW

rat ) 3⁄4 =(70 kg) 3⁄4 / (0.33 kg ) 3⁄4 =55.6 for male rats or,

(BW human ) 3⁄4 /(BW

rat ) 3⁄4 = (70 kg) 3⁄4 /(0.26 kg) 3⁄4 = 66.5 for female rats.

b This value was used in the derivation of the methanol inhalation unit risk.

Source: NED O (1985/2008b)

Table E

- 1 3

. Incidence for malignant lymphoma (Apaja, 1980)

Dose (ppm) Examined Malignant Lymphoma

Female Swiss Webster mice

Historical untreated controls a,b 200 38

10 25 4

100 25 9 c

1,000 25 10 dDRAFT

– DO NOT CITE OR QUOTE E

- 18 Dose (ppm) Examined Malignant Lymphoma

Male Swiss Webster mice

Historical un treated controls a 100 8

10 25 1

100 25 6 d

1,000 25 4

a Toth et al. (1977); b Hinderer et al. (1979); c p

- v alue

= 0.0 6; d p

- value s < 0.0 5

Source: Apaja (1980)

Table E

- 1 4 . PBPK dose metrics for doses in Apaja (1980)

Daily

Dose

(mg/kg

- d) Weekly

Avg. Dos e

(mg/kg

- d) B ody

W eight

(kg) AUC

(mg

- h/L) Peak (mg/L) Amount

metabolized (mg)

Female Swiss Webster mice

0 0 0.040 0 0 0

560.01 479.99 0.040 484.86 88.40 18.38

1000.00 857.13 0.040 3466.74 383.10 28.45

2099.98 1800.00 0.040 19503.87 146 2.07 39.18

Male Swiss Webster mice

0 0 0.045 0 0 0

550.00 471.42 0.045 500.63 89.77 20.29

970.00 831.39 0.045 3405.26 373.72 31.15

1 80 0.00 1542.86 0.045 14998.60 1162.69 41.86

Source: Apaja (1980).

Table E

- 1 5 . Benchmark results from Multistage

- can cer dose

- response modeling data

for malignant lymphoma in Swiss Webster mice (Apaja, 1980) using PBPK dose

metrics

Gender AUC (mg

- h/L) Peak (mg/L) Amount metabolized (mg/d)

AIC p

value Scaled

residual

at dose

nearest

to BMD BMD 10 BMDL 10 AIC p

- value Scale d

residual at

dose

nearest to

BMD BMD 10 BMDL 10 AIC p

- value Scaled

residual

at dose

nearest

to BMD BMD 10 BMDL 10

Female mice a 28 8.91 0 .32 1.354 5808.3 2957.6 288.42 0.43 1.090 428 .5 225.1 288.0 7 0.55

- 0.937 22.7 10.1

Male mice a 122.09 0. 083

- 0.883 11164.2 4343.04 121.58 0.12

- 0. 641 798.3 338.9 120.97 0.21 1.309 35.8 18.3

a Multistage

- cancer ( 1o ) used for AUC and Peak metrics; Multistage

- cancer (2 o ) used for Amount metabolized metric

Source: Apaja (1980)

.

1DRAFT

– DO NOT CITE OR QUOTE E

- 19 Table E

- 1 6

. Application of human PBPK model to derive HEDs from BMDL 10

estimates of Table E

- 15

, Multistage (1o) modeling of malignant lymphoma in Swiss

mice (Apaja, 1980) using PBPK d ose m etrics

AUC (mg

- h/L) Peak (mg/L) Amount m etabolized (mg /d ) a

HED BMDL 10 ( mg/kg

- day ) HED BMDL 10 ( mg/kg

day ) HED B MDL 10 ( mg/kg

- day )

Female mice 248 2 81 9. 7

Male mice 269 305 1 4.7

a Before applying human PBPK model Table 11 mg/day values were converted to human mg/day by multiplying by

either: (BW human ) 3⁄4 / (BW

rat ) 3⁄4 =(70 kg) 3⁄4 / (0.33 kg ) 3⁄4 =55.6 for male rats or, (BW human ) 3⁄4 /(BW

rat ) 3⁄4 = (70 kg) 3⁄4 /(0.26 kg ) 3⁄4

= 66.5 for female rats.

Table E

- 17. Benchmark results for all tumor types using BMDS 2.1 multistage

“background dose” models and PBPK dose

- metrics

Model AUC (mg

- h/L) Peak (mg/L) Amount metabolized (mg/d)

AIC p

value Ba ckground

Dose BMD 10 BMDL 10 AIC p

value Background

Dose BMD 10 BMDL 10 AIC p

value Background

Dose BMD 10 BMDL 10

Female Sprague

- Dawley r at s (Soffritti et al., 2002a)

All organs lympho

- immunoblastic

Multi stage ( 1 o) 361.99 0.045 168476 112790 49771 359.93 0. 14 7480.0 5034.3 2240.3 357.87 0.34 169.0 139.8 77.2

All organs

– all lymphomas

Multi stage ( 1 o) 372.78 0.075 179247 111621 48790 372.72 0.077 7961.1 4982.2 2196.3 370.57 0.18 175.1 134.4 74.1

M ale Sprague

- Dawley r at s (Soffritti et al., 2002a)

Hepatocel lular carcinoma

Multistage ( 1 o) 97.52 0.00 Failed a 73.19 0.37 Failed a 72.55 0.36 Failed a

All organs lympho

- immunoblastic

Multistage (1o) 455.11 0.18 85497.6 36501 21901 454.97 0.20 3781.3 1625.7 979.1 453.98 0.30 190.2 97.8 63.2

All organs

– all lympho mas

Multistage (1o) 468.09 0.15 96684.3 37822 22195 467.98 0.15 4285.6 1686.5 993.1 467.35 0.20 221.2 101.7 63.9

Female F344 r at s ( NEDO

, 1985/2008b)

Adrenal glands

– pheochromocytoma

Multi stage ( 2 o) 101.46 0.84 553.1 450.9 214.8 103.45 0.56 28.9 23.6 11.2 101.47 0.83 36.7 30.1 14.4

Male F344 r at s ( NEDO

, 1985/2008b)

Lung

– adenomas and adenocarcinomas

Multistage ( 1 o) 108.15 0.14 248.16 507.9 226.0 108.15 0.14 13.0 26.6 11.8 108.19 0.14 23.0 47.3 21.0

Lung

– adenomas, adenocarcinomas amd adenomatosis

Multistage (1o) 168.61 0.89 430.38 378.9 168.8 168.61 0.89 22.5 19.8 8.8 168.59 0.90 39.3 34.8 15.6

Female Swiss Webster mice (Apaja, 1989 )

Malignant lymphomas

Multistage ( 1 o) 288.91 0.32 11933.3 5808 2958 288.42 0.43 863.1 428.5 225.1 289.00 0.37 32. 8 16.8 9.1

Male Swiss Webster mice (Apaja, 1989 )

Multistage ( 1 o) 122.09 0.083 9573.9 11164 4343.0 121.58 0.12 646.9 798.3 338.9 121.30 0.20 28.6 38.1 17.6

a BMD computation failed. BMD is larger than t hree times maximum input doses.

Source: NEDO (1987 , 1987/2008b).DRAFT

– DO NOT CITE OR QUOTE E

- 20 Female Rats Survival

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0 20 40 60 80 100 120 140 160

Time on Study (weeks)Proportion Surviving

Control 500 ppm 5000 ppm 20000 ppm

Figure E

- 1. Female rat survival

.

Source: Soffretti et al . (2002a)

.

Male Rats Survival

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0 15 30 45 60 75 90 105 120 135 150

Time on Study (weeks)Proportion Surviving

Control 500 ppm 5000 ppm 20000 ppm

Figure E

- 2. Male rats survival

.

Source: Soffretti et al . (2002a)

.DRAFT

– DO NOT CITE OR QUOTE E

- 21 0 0.2 0.4 0.6 0.8

1

0 500 1000 1500 2000Fraction Affected

Animal Dose (mg/kg-day) Female Rats - Multistage Weibull Model (t=105 weeks) 14:27 01/11/2007

Time-to-Tumor Weibull

Observed (90%% Binomial limits)

Figure E

- 3. Female

– Lymphomas l ympho

- immunoblastic

– Multistage Weibull Model

– Approach 1.

S ource: Soffretti et al

. (2002a)

.

0 0.2 0.4 0.6 0.8

1

0 500 1000 1500 2000Fraction Affected

Animal Dose (mg/kg-day) Female Rats - Multistage Weibull Model (t=105 weeks) 14:02 01/11/2007

Time-to-Tumor Weibull

Observed (90%% Binomial limits)

Figure E

- 4. Female

– All l ymphomas

– Multistage Weibull Model

– Approach 1

.

Source: Soffretti et al

. (2002a)

.

1DRAFT

– DO NOT CITE OR QUOTE E

- 22 0 0.2 0.4 0.6 0.8

1

0 200 400 600 800 1000 1200 1400 1600 1800Risk

Dose (mg/kg/day) Risk Assessment : 2 ; Chemical : Methanol; Sex : Male 15:19 05/21/2008

Time-to-Tumor Weibull

Observed (90%% Binomial limits)

Figure E

- 5. Mal e

– Hepatocellular carcinoma

– Multistage Weibull Model

– Approach

1

.

Source: Soffretti et al

. (2002a)

.

0 0.2 0.4 0.6 0.8

1

0 200 400 600 800 1000 1200 1400 1600 1800Fraction Affected

Animal Dose (mg/kg-day) Male Rats - Multistage Weibull Model (t=105 weeks) 16:03 01/11/2007

Time-to-Tumor Weibull

Observed (90%% Binomial limits)

Figure E

- 6. Male

– Lymphomas l ympho

- immunoblastic

– Multistage Weibull Model

– Approach 1

.

Source: Soffretti et al

. (2002a)

.DRAFT

– DO NOT CITE OR QUOTE E

- 23 0 0.2 0.4 0.6 0.8

1

0 200 400 600 800 1000 1200 1400 1600 1800Fraction Affected

Animal Dose (mg/kg-day) Male Rats - Multistage Weibull Model (t=105 weeks) 16:04 01/11/2007

Time-to-Tumor Weibull

Observed (90%% Binomial limits)

Figure E

- 7. Male

– All l ymphomas

– Multistage Weibull Model

– Approach 1

.

Source: Soffretti et al

. (2002a )

.

Female Rats All Lymphomas Combined

Kaplan Meier Plots and Multistage Weibull Model

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0 20 40 60 80 100 120 140 160

Time in WeeksProbability of Response

2177 mg/kg-day 624.1 mg/kg-day 66 mg/kg-day Control 105 weeks 96

weeks 153

weeks

Figure E

- 8. Female r ats

– All lymphomas time

- to

- tumor model fit and Kaplan Meier

c urves

.

Source: Soffretti et al

. (2002a)

.DRAFT

– DO NOT CITE OR QUOTE E

- 24 Male Rats All Lymphomas Combined

Kaplan Meier Plots and Multistage Weibull Model

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Time in WeeksProbability of Response

1783 mg/kg-day 524 mg/kg-day 53.2 mg/kg-day Control 105

weeks 94

weeks 148 weeks

Figure E

- 9. Male r ats

– All lymphomas time

- to

- tumor model fit and Kaplan Meier

c urves

.

Source: Soffretti et al

. (2002a)

.

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.45

0.5

0 50 100 150 200Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

08:55 09/03 2009 BMD BMDL Multistage Cancer

Linear extrapolation

Figure E

- 10. Male r ats

- All lymphomas; dose = amount metabolized (mg/day) ; 1 o

multistage model

.

Source: Soffretti et al

. (2002a)

.DRAFT

– DO NOT CITE OR QUOTE E

- 25 Female Rats Survival

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

50 60 70 80 90 100

Time on Study (weeks)Proportion Surviving

Control 10 ppm 100 ppm 1000 ppm

Figure E

- 11. Female rat survival

.

Source: NEDO (1987, 1987/2008b)

.

Male Rats Survival

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

50 65 80 95

Time on Study (weeks)Proportion Surviving

Control 10 ppm 100 ppm 1000 ppm

Figure E

- 12. Male rat survival

.

Source: NEDO (1987, 1987/2008b)

.DRAFT

– DO NOT CITE OR QUOTE E

- 26 0 0.05 0.1 0.15 0.2 0.25

0 5 10 15 20 25 30Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

08:58 09/03 2009 BMD BMDL Multistage Cancer

Linear extrapolation

Figure E

- 13. Female rats

- pheochromocytomas; dose = amount metabolized (mg/d); 3 o

m ultistage m odel

.

Source: NEDO ( 1987, 1987/2008b )

0 0.1 0.2 0.3 0.4 0.5 0.6

0 5 10 15 20 25 30 35 40Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

09:58 09/03 2009 BMD BMDL Multistage Cancer

Linear extrapolation

0 0.1 0.2 0.3 0.4 0.5

0 5 10 15 20 25 30 35 40Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

09:56 09/03 2009 BMD BMDL Multistage Cancer

Linear extrapolation

Figure E

- 14. Plots for female (top; p= 0.55 ) and male (bottom ; p= 0.21 ) mice

– malignant lymph oma; do se=amount metabolized (mg/d); 2 o multistage model.

Source: Apaja (1980)