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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 (IRIS)

October 2009

NOTICE

This document is an Interagency 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 disseminated 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.

1 U.S. Environmental Protection Agency

2 Washington, DC
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DISCLAIMER

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

2 distributed solely for the purpose of pre-dissemination peer review under applicable information 

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

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

5 names or commercial products does not constitute endorsement or recommendation for use.
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CONTENTS TOXICOLOGICAL REVIEW OF METHANOL

(CAS NO. 67-56-1)

1 1. INTRODUCTION ................................................................................................................. 1-26

2 2. CHEMICAL AND PHYSICAL INFORMATION ................................................................. 2-1

3 3. TOXICOKINETICS ................................................................................................................ 3-1

4 3.1. Overview.......................................................................................................................... 3-1

5 3.2. Key Studies...................................................................................................................... 3-5

6 3.3. Human Variability In Methanol Metabolism................................................................. 3-10

7 3.4. Physiologically Based Toxicokinetic Models................................................................ 3-15

8 3.4.1. Model Requirements for EPA Purposes................................................................. 3-15

9 3.4.1.1. MOA and Selection of a Dose Metric ............................................................ 3-15

10 3.4.1.2. Criteria for the Development of Methanol PBPK Models............................. 3-17

11 3.4.2. Methanol PBPK Models......................................................................................... 3-18

12 3.4.2.1. Ward et al. (1997)........................................................................................... 3-19

13 3.4.2.2. Bouchard et al. (2001).................................................................................... 3-19

14 3.4.2.3. Gentry et al. (2003, 2002) and Clewell et al. (2001)...................................... 3-20

15 3.4.3. Selected Modeling Approach ................................................................................. 3-21

16 3.4.3.1. Available PK Data.......................................................................................... 3-22

17 3.4.3.2. Model Structure .............................................................................................. 3-23

18 3.4.3.3. Model Parameters........................................................................................... 3-25

19 3.4.4. Mouse Model Calibration and Sensitivity Analysis............................................... 3-26

20 3.4.5. Rat Model Calibration............................................................................................ 3-31

21 3.4.6. Human Model Calibration...................................................................................... 3-35

22 3.4.6.1. Inhalation Route ............................................................................................. 3-35

23 3.4.6.2. Oral Route ...................................................................................................... 3-41

24 3.4.7. Monkey PK Data and Analysis.............................................................................. 3-42

25 3.4.7.1. PK Model Analysis for Monkeys................................................................... 3-44

26 3.4.8. Summary and Conclusions..................................................................................... 3-47

27 4. HAZARD IDENTIFICATION................................................................................................ 4-1

28 4.1. Studies in Humans – Case Reports, Occupational and Controlled Studies..................... 4-1

29 4.1.1. Case Reports............................................................................................................. 4-1

30 4.1.2. Occupational Studies................................................................................................ 4-9

31 4.1.3. Controlled Studies.................................................................................................. 4-11

32 4.2. Acute, Subchronic and Chronic Studies and Cancer Bioassays in Animals—Oral and 

33 Inhalation.......................................................................................................................... 4-13

34 4.2.1. Oral Studies............................................................................................................ 4-13

35 4.2.1.1. Acute Toxicity................................................................................................ 4-13

36 4.2.1.2. Subchronic Toxicity ....................................................................................... 4-13

37 4.2.1.3. Chronic Toxicity............................................................................................. 4-14

38 4.2.2. Inhalation Studies................................................................................................... 4-20

39 4.2.2.1. Acute Toxicity................................................................................................ 4-20

40 4.2.2.2. Subchronic Toxicity ....................................................................................... 4-21

41 4.2.2.3. Chronic Toxicity............................................................................................. 4-24

42 4.3. Reproductive AND Developmental Studies–Oral and Inhalation ................................. 4-32

43 4.3.1. Oral Studies............................................................................................................ 4-32

44 4.3.2. Inhalation Studies................................................................................................... 4-34
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1 4.3.3. Other Reproductive and Developmental Toxicity Studies.................................... 4-46

2 4.4. Neurotoxicity.................................................................................................................. 4-50

3 4.4.1. Oral Studies............................................................................................................ 4-51

4 4.4.2. Inhalation Studies................................................................................................... 4-53

5 4.4.3. Studies Employing In Vitro, S.C. and I.P. Exposures............................................ 4-60

6 4.5. Immunotoxicity.............................................................................................................. 4-64

7 4.6. Mechanistic Data and Other Studies in Support of the MOA........................................ 4-69

8 4.6.1. Role of Methanol and Metabolites in the Developmental Toxicity of Methanol .. 4-69

9 4.6.2. Role of Folate Deficiency in the Developmental Toxicity of Methanol................ 4-74

10 4.6.3. Methanol-Induced Formation of Free Radicals, Lipid Peroxidation, and Protein 

11 Modifications............................................................................................................... 4-75

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

13 Results from Methanol Exposure ................................................................................ 4-78

14 4.6.5. Mechanistic Data Related to the Potential Carcinogenicity of Methanol .............. 4-78

15 4.6.5.1. Genotoxicity ................................................................................................... 4-78

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

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

18 and Methyl Tertiary Butyl Ether), and a Metabolite (Formaldehyde)............... 4-81

19 4.6.5.2.1. Ethanol....................................................................................................... 4-83

20 4.6.5.2.2. Aspartame. ................................................................................................. 4-84

21 4.6.5.2.3. MTBE. .......................................................................................................4-89

22 4.6.5.2.4. Formaldehyde. ...........................................................................................4-90

23 4.7. Synthesis of Major Noncancer Effects........................................................................... 4-92

24 4.7.1. Summary of Key Studies in Methanol Toxicity..................................................... 4-92

25 4.7.1.1. Oral................................................................................................................. 4-95

26 4.7.1.2. Inhalation........................................................................................................ 4-96

27 4.8. NONCANCER MOA Information ................................................................................ 4-99

28 4.9. Evaluation of Carcinogenicity...................................................................................... 4-102

29 4.9.1. Summary of Overall Weight-of-Evidence ........................................................... 4-102

30 4.9.2. Synthesis of Human, Animal, and Other Supporting Evidence........................... 4-103

31 4.9.3. MOA Information ................................................................................................ 4-109

32 4.10. Susceptible Populations and Life Stages ................................................................... 4-110

33 4.10.1. Possible Childhood Susceptibility...................................................................... 4-110

34 4.10.2. Possible Gender Differences.............................................................................. 4-111

35 4.10.3. Genetic Susceptibility ........................................................................................ 4-112

36 5. DOSE-RESPONSE ASSESSMENT AND CHARACTERIZATION .................................... 5-1

37 5.1. Inhalation RfC for Effects Other than Cancer ................................................................ 5-1

38 5.1.1. Choice of Principal Study and Critical Effect(s)...................................................... 5-1

39 5.1.1.1. Key Inhalation Studies ..................................................................................... 5-1

40 5.1.1.2. Selection of Critical Effect(s)........................................................................... 5-2

41 5.1.2. Methods of Analysis for the POD—Application of PBPK and BMD Models........ 5-6

42 5.1.2.1. Application of the BMD/BMDL Approach ..................................................... 5-7

43 5.1.2.2. BMD Approach Applied to Brain Weight Data in Rats................................... 5-9

44 5.1.3. RfC Derivation – Including Application of Uncertainty Factors........................... 5-13

45 5.1.3.1. Comparison Between Endpoints and BMDL Modeling Approaches ............ 5-13

46 5.1.3.2. Application of UFs......................................................................................... 5-15

47 5.1.3.2.1. Interindividual Variation UFH....................................................................5-15

48 5.1.3.2.2. Animal-to-Human Extrapolation UFA. ......................................................5-17
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1 5.1.3.2.3. Database UFD.............................................................................................5-17

2 5.1.3.2.4. Extrapolation from Subchronic to Chronic and LOAEL-to NOAEL 

3 Extrapolation UFs......................................................................................................5-18

4 5.1.4. Previous RfC Assessment ...................................................................................... 5-18

5 5.2. Oral RfD......................................................................................................................... 5-18

6 5.2.1. Choice of Principal Study and Critical Effect–with Rationale and Justification ... 5-19

7 5.2.1.1. Expansion of the Oral Database by Route-to-Route Extrapolation................ 5-20

8 5.2.2. RfD Derivation–Including Application of UFs..................................................... 5-21

9 5.2.1.2. Consideration of Inhalation Data.................................................................... 5-21

10 5.2.1.3. Selection of Critical Effect(s) from Inhalation Data ...................................... 5-21

11 5.2.1.4. Selection of the POD...................................................................................... 5-22

12 5.2.2. RfD Derivation–Application of UFs...................................................................... 5-22

13 5.2.3. Previous RfD Assessment ...................................................................................... 5-22

14 5.3. Uncertanties in the Inhalation RfC and Oral RfD.......................................................... 5-23

15 5.3.1. Choice of Endpoint................................................................................................. 5-24

16 5.3.2. Choice of Dose Metric ........................................................................................... 5-25

17 5.3.3. Choice of Model for BMDL Derivations............................................................... 5-26

18 5.3.4. Choice of Animal-to-Human Extrapolation Method ............................................. 5-27

19 5.3.5. Route-to-Route Extrapolation ................................................................................ 5-27

20 5.3.6. Statistical Uncertainty at the POD ......................................................................... 5-28

21 5.3.7. Choice of Bioassay................................................................................................. 5-28

22 5.3.8. Choice of Species/Gender...................................................................................... 5-28

23 5.3.9. Human Population Variability ............................................................................... 5-28

24 5.4. 5.4. Cancer Assessment ................................................................................................ 5-29

25 5.4.1. Oral Exposure......................................................................................................... 5-29

26 5.4.1.1. Choice of Study/Data—with Rationale and Justification............................... 5-29

27 5.4.1.2. Dose-Response Data....................................................................................... 5-29

28 5.4.1.3. Dose Adjustments and Extrapolation Method................................................ 5-30

29 5.4.1.4. Oral Slope Factor............................................................................................ 5-33

30 5.4.2. Inhalation Exposure................................................................................................ 5-33

31 5.4.2.1. Choice of Study/Data–with Rationale and Justification................................. 5-33

32 5.4.2.2. Dose-Response Data....................................................................................... 5-34

33 5.4.2.3. Dose Adjustments and Extrapolation Method................................................ 5-35

34 5.4.2.4. IUR ................................................................................................................. 5-36

35 5.4.3. Uncertainties in Cancer Risk Assessment.............................................................. 5-36

36 5.4.3.1. Quality of Studies that are the Basis for the PODs ........................................ 5-37

37 5.4.3.2. Interpretation of Results of the Studies that are the Basis for the PODs........ 5-40

38 5.4.3.3. Consistency across Chronic Bioassays for Methanol..................................... 5-43

39 5.4.3.4. Choice of Endpoint for POD Derivation........................................................ 5-45

40 5.4.3.5. Choice of Species/Gender .............................................................................. 5-45

41 5.4.3.6. Choice of Model for POD Derivation ............................................................ 5-45

42 5.4.3.7. Choice of Dose Metric.................................................................................... 5-46

43 5.4.3.8. Choice of Animal-to-Human Extrapolation Method...................................... 5-47

44 5.4.3.9. Human Relevance of Cancer Responses Observed in Rats and Mice............ 5-48

45 6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF HAZARD AND DOSE 

46 RESPONSE.................................................................................................................................. 6-1

47 6.1. Human Hazard Potential .................................................................................................. 6-1

48 6.2. Dose Response ................................................................................................................. 6-3
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1 6.2.1. Noncancer/Inhalation ............................................................................................... 6-3

2 6.2.2. Noncancer/Oral ........................................................................................................ 6-5

3 6.2.3. Cancer/Oral and Inhalation ...................................................................................... 6-6

4 7. REFERENCES ........................................................................................................................ 7-1

5 APPENDIX A. SUMMARY OF EXTERNAL PEER REVIEW AND PUBLIC COMMENTS 

6 AND DISPOSITION .................................................................................................................. A-1

7 APPENDIX B. DEVELOPMENT, CALIBRATION AND APPLICATION OF A METHANOL 

8 PBPK MODEL ............................................................................................................................B-1

9 APPENDIX C. RFC DERIVATION OPTIONS.........................................................................C-1

10 APPENDIX D. RFC DERIVATION – COMPARISON OF DOSE METRICS........................ D-1

11 APPENDIX E. EVALUATION OF THE CANCER POTENCY OF METHANOL .................E-1
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LIST OF TABLES

1 Table 2-1. Relevant physical and chemical properties of methanol ........................................... 2-1

2 Table 3-1. Background blood methanol and formate levels in humans ................................... 3-11

3 Table 3-2. Human blood methanol and formate levels following methanol exposure............. 3-12

4 Table 3-3. Monkey blood methanol and formate levels following methanol exposure ........... 3-13

5 Table 3-4. Mouse blood methanol and formate levels following methanol exposure.............. 3-13

6 Table 3-5. Rat blood methanol and formate levels following methanol exposure ................... 3-14

7 Table 3-6. Plasma formate concentrations in monkeys............................................................ 3-15

8 Table 3-7. Serum folate concentrations in monkeys................................................................. 3-15

9 Table 3-8. Routes of exposure optimized in models – optimized against blood concentration data3-20

10 Table 3-9. Key methanol kinetic studies for model validation................................................. 3-22

11 Table 3-10. Parameters used in the mouse, rat and human PBPK models............................... 3-25

12 Table 3-11. Primate Kms reported in the literature ................................................................... 3-38

13 Table 3-12. Parameter estimate results obtained using acslXtreme to fit all human data using 

14 either saturable or first-order metabolism ................................................................. 3-38

15 Table 3-13. Comparison of LLFs for Michaelis-Menten and first-order metabolism.............. 3-40

16 Table 3-14. Monkey group exposure characteristics................................................................ 3-47

17 Table 4–1. Mortality rate for subjects exposed to methanol-tainted whiskey in relation to their 

level of acidosisaa 18 ........................................................................................................ 4-2

19 Table 4-2. Incidence of carcinogenic responses in Sprague-Dawley rats exposed to methanol in 

20 drinking water for up to 2 years ................................................................................ 4-18

21 Table 4-3. Incidence of malignant lymphoma responses in Swiss mice exposed to methanol in 

22 drinking water for life................................................................................................ 4-20

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

24 inhalation for 18 months............................................................................................ 4-28

25 Table 4-5. Histopathological changes in lung and adrenal tissues of F344 rats exposed to 

26 methanol via inhalation for 24 months...................................................................... 4-31

27 Table 4-6. Reproductive and developmental toxicity in pregnant Sprague-Dawley rats exposed to 

28 methanol via inhalation during gestation .................................................................. 4-36

29 Table 4-7. Reproductive parameters in Sprague-Dawley dams exposed to methanol during 

30 pregnancy then allowed to deliver their pups............................................................ 4-37

31 Table 4-8. Developmental effects in mice after methanol inhalation........................................ 4-39

32 Table 4-9. Benchmark doses at two added risk levels............................................................... 4-40

33 Table 4-10. Reproductive parameters in monkeys exposed via inhalation to methanol during 

34 prebreeding, breeding, and pregnancy ...................................................................... 4-42

35 Table 4-11. Mean serum levels of testosterone, luteinizing hormone, and corticosterone (± S.D.) 

36 in male Sprague-Dawley rats after inhalation of methanol, ethanol, n-propanol or n37 butanol at threshold limit values ............................................................................... 4-43

38 Table 4-12. Maternal and litter parameters when pregnant female C57BL/6J mice were injected 

39 i.p. with methanol...................................................................................................... 4-46

40 Table 4-13. Reported thresholds concentrations (and author-estimated ranges) for the onset of 

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

42 methanol, formaldehyde, and formate....................................................................... 4-49

43 Table 4-14. Brain weights of rats exposed to methanol vapors during gestation and lactation 4-58

44 Table 4-15. Effect of methanol on Wistar rat acetylcholinesterase activities............................ 4-61

45 Table 4-16. Effect of methanol on neutrophil functions in in vitro and in vivo studies in male 

46 Wistar rats.................................................................................................................. 4-65
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1 Table 4-17. Effect of intraperitoneally injected methanol on total and differential leukocyte 

2 counts and neutrophil function tests in male Wistar rats........................................... 4-66

3 Table 4-18. Effect of methanol exposure on animal weight/organ weight ratios and on cell counts 

4 in primary and secondary lymphoid organs of male Wistar rats............................... 4-68

5 Table 4-19. The effect of methanol on serum cytokine levels in male Wistar rats ................... 4-69

Table 4-20. Developmental outcome on GD10 following a 6-hour 10,000 ppm (13,104 mg/m3 6 ) 

7 methanol inhalation by CD-mice or formate gavage (750 mg/kg) on GD8.............. 4-70

8 Table 4-21. Summary of ontogeny of relevant enzymes in CD-1 mice and humans................ 4-71

9 Table 4-22. Dysmorphogenic effect of methanol and formate in neurulating CD-1 mouse 

10 embryos in culture (GD8).......................................................................................... 4-72

11 Table 4-23. Time-dependent effects of methanol administration on serum liver and kidney 

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

13 of male Wistar rats .................................................................................................... 4-77

14 Table 4-24. Effect of methanol administration on male Wistar rats on malondialdehyde 

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

16 effect of methanol on antioxidants in spleen............................................................. 4-77

17 Table 4-25. Summary of genotoxicity studies of methanol....................................................... 4-80

18 Table 4-26. Incidence of carcinogenic responses in Sprague-Dawley rats exposed to ethanol in 

19 drinking water for up to 2 years ................................................................................ 4-84

20 Table 4-27. Incidence of lymphomas and leukemias in Sprague-Dawley rats exposed to 

21 aspartame via the diet................................................................................................ 4-85

22 Table 4-28. Incidence of combined dysplastic hyperplasias, papillomas and carcinomas of the 

23 pelvis and ureter and of malignant schwannomas in peripheral nerve in Sprague24 Dawley rats exposed to aspartame via the diet.......................................................... 4-86

25 Table 4-29. Incidence of tumors in Sprague-Dawley rats exposed to aspartame from GD12 to 

26 natural death .............................................................................................................. 4-87

27 Table 4-30. Comparison of the incidence of combined lymphomas and leukemias in female 

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

29 postnatally or postnatally only .................................................................................. 4-88

30 Table 4-31. Incidence of Leydig cell testicular tumors and combined lymphomas and leukemias 

31 in Sprague-Dawley rats exposed to MTBE via gavage for 104 weeks..................... 4-90

32 Table 4-32. Incidence of hemolymphoreticular neoplasms on Sprague-Dawley rats exposed to 

33 formaldehyde in drinking water for 104 weeks......................................................... 4-91

34 Table 4-33. Incidence of leukemias in breeder and offspring Sprague-Dawley rats exposed to 

35 formaldehyde in drinking water for 104 weeks (Test BT 7005)............................... 4-92

36 Table 4-34. Summary of studies of methanol toxicity in experimental animals (oral) ............. 4-92

37 Table 4-35. Summary of studies of methanol toxicity in experimental animals (inhalation 

38 exposure)................................................................................................................... 4-93

39 Table 5-1. Summary of studies considered most appropriate for use in derivation of an RfC.... 5-5

40 Table 5-2. The EPA PBPK model estimates of methanol blood levels (AUC) in rats following 

41 inhalation exposures.................................................................................................. 5-10

42 Table 5-3. Comparison of benchmark dose modeling results for decreased brain weight in male 

43 rats at 6 weeks of age using modeled AUC of methanol as a dose metric................ 5-11

44 Table 5-4. Summary of PODs for critical endpoints, application of UFs and conversion to HEC 

45 values using BMD and PBPK modeling ................................................................... 5-14

46 Table 5-5. Summary of uncertainties in methanol noncancer risk assessment.......................... 5-23

47 Table 5-6. Incidence data for lymphoma, lympho-immunoblastic, and all lymphomas in male and 

48 female Sprague-Dawley rats ..................................................................................... 5-30

49 Table 5-7. BMD results and oral CSF using all lymphoma in male rats................................... 5-33
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1 Table 5-8. Incidence data for tumor responses in male and female F344 rats........................... 5-34

2 Table 5-9. BMD results and IUR using pheochromocytoma in female rats.............................. 5-36

3 Table 5-10. Summary of uncertainty in the methanol cancer risk assessment .......................... 5-37

4 Table B-1. Parameters used in the mouse and human PBPK models.........................................B-5

5 Table B-2. Primate kms reported in the literature......................................................................B-29

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

7 either saturable or first-order metabolism .................................................................B-31

8 Table B-4. Comparison of LLF for Michaelis-Menten and first-order metabolism.................B-32

9 Table B-5. PBPK model predicted Cmax and 24-hour AUC for mice and humans exposed to 

10 MeOH........................................................................................................................B-35

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

12 exposures...................................................................................................................B-90

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

14 exposures...................................................................................................................B-91

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

16 exposures...................................................................................................................B-91

17 Table B-9. Repeated daily oral dosing of humans with MeOH.................................................B-93

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

19 inhalation exposures....................................................................................................C-3

20 Table C-2. Comparison of BMD1SD results for decreased brain weight in male rats at 6 weeks of 

21 age using modeled AUC of methanol as a dose metric...............................................C-4

22 Table C-3. Comparison of BMD05 results for decreased brain weight in male rats at 6 weeks of 

23 age using modeled AUC of methanol as a dose metric...............................................C-9

24 Table C-4. EPA’s PBPK model estimates of methanol blood levels (Cmax) in rats following 

25 inhalation exposures..................................................................................................C-13

26 Table C-5. Comparison of BMD1SD results for decreased brain weight in male rats at 8 weeks of 

27 age using modeled Cmax of methanol as a dose metric..............................................C-14

28 Table C-6. Comparison of BMD05 modeling results for decreased brain weight in male rats at 8 

29 weeks of age using modeled Cmax of methanol as a common dose metric ...............C-20

30 Table C-7. EPA’s PBPK model estimates of methanol blood levels (Cmax) in mice following 

31 inhalation exposures..................................................................................................C-24

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

33 modeled Cmax of methanol as a common dose metric ...............................................C-25

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

35 modeled Cmax of methanol as a common dose metric ...............................................C-33

36 Table C-10. Comparison of BMD modeling results for VDR in female monkeys using AUC 

37 blood methanol as the dose metric ............................................................................C-42

38 Table E-1. Calculation of mg/kg-day doses..............................................................................E-12

39 Table E-2. Incidence for neoplasms considered for dose-response modeling..........................E-12

40 Table E-3. Results from multistage (1o) quantal modeling rat data using mg/kg-day exposures 

41 and default HED derivation method..........................................................................E-13

42 Table E-4. Results from time-to-tumor modeling data using mg/kg-day exposures and default 

43 HED derivation method.............................................................................................E-13

44 Table E-5. PBPK model estimated dose-metrics for doses ......................................................E-14

45 Table E-6a. Results from time-to-tumor modeling of data using PBPK dose metrics.............E-14

46 Table E-6b. HEDs from time-to-tumor modeling of data using PBPK dose metrics...............E-14

47 Table E-7. Results of Multistage (1o) quantal modeling of data using PBPK dose metrics.....E-15

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

49 quantal modeling of data using PBPK dose metrics.................................................E-15
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1 Table E-9. Incidence for neoplasms considered for dose-response modeling...........................E-16

2 Table E-10. PBPK dose metrics for doses................................................................................E-16

3 Table E-11. Benchmark results from multistage quantal dose-response modeling data using 

4 PBPK dose-metrics....................................................................................................E-17

5 Table E-12. Application of human PBPK model to derive HECs from BMDL10 estimates in 

6 Table E-11 using multistage quantal modeling.........................................................E-17

7 Table E-13. Incidence for malignant lymphoma (Apaja, 1980)................................................E-17

8 Table E-14. PBPK dose metrics for doses in Apaja (1980)......................................................E-18

9 Table E-15. Benchmark results from Multistage-cancer dose-response modeling data for 

10 malignant lymphoma in Swiss Webster mice (Apaja, 1980) using PBPK dose-metricsE-18

11 Table E-16. Application of human PBPK model to derive HEDs from BMDL10 estimates of 

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

13 1980) using PBPK dose metrics................................................................................E-19

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

15 dose” models and PBPK dose-metrics......................................................................E-19
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LIST OF FIGURES

1 Figure 3-1. Methanol metabolism and key metabolic enzymes in primates and rodents........... 3-3

2 Figure 3-2. Folate-dependent formate metabolism. Tetrahydrofolate (THF)-mediated one carbon 

3 metabolism is required for the synthesis of purines, thymidylate, and methionine. ... 3-4

4 Figure 3-3. Plot of fetal (amniotic) versus maternal methanol concentrations in GD20 rats. .... 3-7

5 Figure 3-4. Schematic of the PBPK model used to describe the inhalation, oral, and i.v. route 

6 pharmacokinetics of methanol................................................................................... 3-24

7 Figure 3-5. Model fits to data sets from GD6, GD7, and GD10 mice for 6- to 7-hour inhalation 

8 exposures to 1,000–15,000 ppm methanol................................................................ 3-27

9 Figure 3-6. Simulation of inhalation exposures to methanol in NP mice from Perkins et al. 

10 (1995a) (8-hour exposures) and GD8 mice from Dorman et al. (1995)(6-hour 

11 exposures).................................................................................................................. 3-28

12 Figure 3-7. Conceptus versus maternal blood AUC values for rats and mice plotted (A) on a log13 linear scale, as in Figure 8 of Ward et al. (1997), and (B) on a linear-linear scale... 3-30

14 Figure 3-8. NP rat i.v. route methanol blood kinetics............................................................... 3-33

15 Figure 3-9. Model fits to data sets from inhalation exposures to 200 (triangles), 1,200

16 (diamonds), or 2,000 (squares) ppm methanol in male F-344 rats............................ 3-33

17 Figure 3-10. Model fits to datasets from oral exposures to 100 and 2,500 mg/kg methanol in 

18 female Sprague-Dawley rats. .................................................................................... 3-35

19 Figure 3-11. Urinary methanol elimination concentration (upper panel) and cumulative amount 

20 (lower panel) following inhalation exposures to methanol in human volunteers. .... 3-37

21 Figure 3-12. Data showing the visual quality of the fit using optimized first-order or Michaelis22 Menten kinetics to describe the metabolism of methanol in humans........................ 3-39

23 Figure 3-13. Inhalation exposures to methanol in human volunteers....................................... 3-41

24 Figure 3-14. Blood methanol concentration data from NP and pregnant monkeys.................. 3-44

25 Figure 3-15. Chamber concentration profiles for monkey methanol exposures....................... 3-46

26 Figure 4-1. Hemolymphoreticular neoplasms in male and female Sprague-Dawley rats in 

27 formaldehyde........................................................................................................... 4-110

28 Figure 5-1. Hill model BMD plot of decreased brain weight in male rats at 6 weeks age using 

29 modeled AUCof methanol in blood as the dose metric, 1 control mean S.D........... 5-12

Figure 5-2. PODs (in mg/m3 30 ) for selected endpoints with corresponding applied UFs............ 5-15

31 Figure 5-3. All lymphomas versus methanol metabolized (mg/day) for female and male rats. 5-31

32 Figure 5-4. All lymphomas versus Cmax (mg/L) for female and male rats. ............................... 5-31

33 Figure 5-5. All lymphomas versus AUC (hr x mg/L) for male and female rats........................ 5-31

34 Figure 5-6. Lympho-immunoblastic Lymphoma minus “lung-only” response in rats of Soffritti et 

35 al. (2002a) methanol study versus methanol metabolized (mg/day)......................... 5-42

36 Figure 5-7. Total amount metabolized per day (after periodicity is reached) in a 420 g rat ..... 5-47

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

38 pharmacokinetics of MeOH. .......................................................................................B-3

39 Figure B-2. Model fits to data sets from GD6, GD7, and GD10 mice for 7-hour inhalation 

40 exposures to 1,000–15,000 ppm MeOH......................................................................B-7

41 Figure B-3. Simulation of inhalation exposures to MeOH in NP mice from Perkins et al. (1995a) 

42 (8-hour exposures) and Dorman et al. (1995), (6-hour exposures).............................B-8

43 Figure B-4. Oral exposures to MeOH in pregnant mice on GD8 (Dorman et al., 1995) or NP and 

44 GD18. ........................................................................................................................B-10

45 Figure B-5. Mouse intravenous route MeOH blood kinetics....................................................B-11

46 Figure B-6. Mouse model inhalation route sensitivity coefficients for metabolic parameters.B-15
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1 Figure B-7. Mouse model inhalation route sensitivity coefficients for flow rates (QCC: cardiac 

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

3 reported for blood MeOH AUC. ...............................................................................B-15

4 Figure B-8. Mouse model sensitivity coefficients for oral exposures to MeOH.......................B-16

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

6 concentration for a 6 d/wk exposure (B)...................................................................B-17

7 Figure B-10. NP rat i.v.-route methanol blood kinetics............................................................B-19

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

9 (diamonds), or 2,000 (squares) ppm MeOH in male F-344 rats. ..............................B-20

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

11 mg/kg MeOH in female Sprague-Dawley rat............................................................B-22

12 Figure B-13. Simulated Sprague-Dawley rat inhalation exposures to 500, 1,000, or 2,000 ppm 

13 MeOH........................................................................................................................B-24

14 Figure B-14. Simulated rat oral exposures of Sprague-Dawley rats to 65.9, 624.2, or 2,177 mg 

15 MeOH/kg/day............................................................................................................B-25

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

17 concentration for a 2-day exposure (B).....................................................................B-26

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

19 (lower panel), following inhalation exposures to MeOH in human volunteers. .......B-28

20 Figure B-17. Control (nonexposed) blood methanol concentrations.........................................B-30

21 Figure B-18. Data showing the visual quality of the fit using optimized first-order or Michaelis22 Menten kinetics to describe the metabolism of MeOH in humans. ..........................B-31

23 Figure B-19. Inhalation exposures to MeOH in human volunteers..........................................B-33

24 Figure B-20. Predicted 24-hour AUC (upper left), Cmax (upper right), and amount metabolized 

25 (lower) for MeOH inhalation exposures in the mouse (average over a 10-day exposure 

26 at 7 hours/day) and humans (steady-state values for a continuous exposure)...........B-36

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

28 optimizations. ............................................................................................................B-41

29 Figure B-22. Fit of the model to oral data using different clearance and uptake parameter 

30 optimizations. ............................................................................................................B-42

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

32 optimizations. ............................................................................................................B-42

33 Figure B-24. Simulated human oral exposures to 0.1 mg MeOH/kg/-day comparing the first few 

34 days for four exposure scenarios...............................................................................B-92

35 Figure C-1. Hill model, BMR of 1 Control Mean S.D. - Decreased Brain weight in male rats at 6 

36 weeks age versus AUC, F1 Generation inhalational study .........................................C-7

37 Figure C-2. Hill model, BMR of 0.05 relative risk - Decreased Brain weight in male rats at 6 

38 weeks age versus AUC, F1 Generation inhalational study. .......................................C-12

39 Figure C-3. Hill model, BMR of 1 Control Mean S.D. - Decreased Brain weight in male rats at 8 

40 weeks age versus Cmax, Gestation only inhalational study........................................C-18

41 Figure C-4. Exponential4 model, BMR of 0.05 relative risk - Decreased Brain weight in male 

42 rats at 8 weeks age versus Cmax, Gestation only inhalational study. .........................C-23

43 Figure C-5. Nested Logistic Model, 0.1 Extra Risk - Incidence of Cervical Rib in Mice versus 

44 Cmax Methanol, GD 6-15 inhalational study..............................................................C-32

45 Figure C-6. Nested Logistic Model, 0.05 Extra Risk - Incidence of Cervical Rib in Mice versus 

46 Cmax Methanol, GD 6-15 inhalational study..............................................................C-39

47 Figure C-7. 3rd Degree Polynomial Model, BMR of 1 Control Mean S.D. - VDR in female 

48 monkeys using AUC blood methanol as the dose metric..........................................C-44
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1 Figure D-1. Exposure-response data for methanol-induced CR plus SNR malformations in mice 

2 at various concentration-time combinations............................................................... D-2

3 Figure D-2. Internal dose-response relationships for methanol-induced CR plus SNR 

4 malformations in mice at various concentration-time combinations for three dose 

5 metrics. ....................................................................................................................... D-3

6 Figure E-1. Female rat survival. ...............................................................................................E-20

7 Figure E-2. Male rats survival. .................................................................................................E-20

8 Figure E-3. Female – Lymphomas lympho-immunoblastic – Multistage Weibull Model –

9 Approach 1. ...............................................................................................................E-21

10 Figure E-4. Female – All lymphomas – Multistage Weibull Model – Approach 1..................E-21

11 Figure E-5. Male – Hepatocellular carcinoma – Multistage Weibull Model – Approach 1.....E-22

12 Figure E-6. Male – Lymphomas lympho-immunoblastic – Multistage Weibull Model –

13 Approach 1. ...............................................................................................................E-22

14 Figure E-7. Male – All lymphomas – Multistage Weibull Model – Approach 1. ....................E-23

15 Figure E-8. Female rats –All lymphomas time-to-tumor model fit and Kaplan Meier curves.E-23

16 Figure E-9. Male rats –All lymphomas time-to-tumor model fit and Kaplan Meier curves. ...E-24

17 Figure E-10. Male rats- All lymphomas; dose = amount metabolized (mg/day); 1o multistage 

18 model.........................................................................................................................E-24

19 Figure E-11. Female rat survival. ............................................................................................E-25

20 Figure E-12. Male rat survival..................................................................................................E-25

21 Figure E-13. Female rats- pheochromocytomas; dose = amount metabolized (mg/d); 3o 

22 multistage model. ......................................................................................................E-26

23 Figure E-14. Plots for female (top; p= 0.55) and male (bottom; p= 0.21) mice – malignant 

24 lymphoma; dose=amount metabolized (mg/d); 2o multistage model.......................E-26
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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

ATP adenosine triphosphate

ATSDR 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)

BMD1SD BMD for response one standard deviation from control mean

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

BMDL1SD 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

C1 pool one carbon pool

Cmax peak concentration of a substance in the blood during the 

exposure period

C-section Cesarean section

CA chromosomal aberrations

CAR conditioned avoidance response

CASRN Chemical Abstracts Service Registry Number

CAT catalase

CERHR Center for the Evaluation of Risks to Human Reproduction

CH3OH methanol

CHL Chinese hamster lung (cells)
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CHR contact hypersensitivity response

CI confidence interval

ClS clearance rate

CNS central nervous system

CO2 carbon dioxide

con-A concanavalin-A

CR crown-rump length

CSF Cancer slope factor

CT computed tomography

CYP450 cytochrome P450

d, δ, Δ delta, difference, change

D2 dopamine receptor

DA dopamine

DIPE diisopropylether

DMDC dimethyl dicarbonate

DNA deoxyribonucleic acid

DNT developmental neurotoxicity test(ing)

DOPAC dihydroxyphenyl acetic acid

DPC days past conception

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

F0 parental generation

F1 first generation

F2 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 fraction inhaled

FS folate sufficient

FSH follicular stimulating hormone

γ-GT gamma glutamyl transferase
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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 gastrointestinal track

GLM generalized linear model

GLP good laboratory practice

GSH glutathione

HAP hazardous air pollutant

Hb hemoglobin

HCHO formaldehyde

HCOO formate

Hct hematocrit

HEC human equivalent concentration

HED human equivalent dose

HEI Health Effects Institute

HH hereditary hemochromatosis

5_HIAA 5-hydroxyindolacetic acid

HMGSH S-hydroxymethylglutathione

Hp haptoglobin

HPA hypothalamus-pituitary-adrenal (axis)

HPLC high-performance liquid chromatography

HSDB Hazardous Substances Databank

HSP70 biomarker of cellular 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

K1 first order rate loss

K1C first 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 uptake from intestine
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KLH keyhole limpet hemocyanin

KLL alternate first order rate constant

Km substrate concentration at half the enzyme maximum 

velocity (Vmax)

Km2 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

LD50 median lethal dose

LDH lactate dehydrogenase

LH luteinizing hormone

LLF (maximum) log likelihood function

LMI leukocyte migration inhibition (assay)

LOAEL lowest-observed-adverse-effect level

M, mM, μM molar, millimolar, micromolar

MAA 2-methoxyacetic acid

MCH mean corpuscular hemoglobin

MCHC mean corpuscular hemoglobin concentration

MCV mean cell volume

MeOH methanol

MLE maximum likelihood estimate

M-M Michaelis-Menten

MN micronuclei

MOA mode of action

4-MP 4-methylpyrazale messenger RNA

MRI magnetic resonance imaging

MTBE methyl tertiary butyl ether

MTX methotrexate

N2O/O2 nitrous oxide

NAD+ nicotinamide adenine dinucleotide

NADH reduced form of nicotinamide adenine dinucleotide

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 Institute of Occupational Safety and Health

NK natural killer

nmol nanomole

NOAEL no-observed-adverse-effect level

NOEL no-observed-effect level
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NP nonpregnant

NR not reported

NRC National Research Council

NS not specified

NTB nitroblue tetra zolium

NTP National 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

PFC 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 complex 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.

R2 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 sister chromatid exchange

S.D. standard deviation

S.E. standard error

SEM standard error of mean
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SGPT serum glutamate pyruvate transaminase

SHE Syrian hamster embryo

SOD superoxide dismutase

SOP standard operating procedure(s)

t time

T1⁄2, t1⁄2 half-life

T wave the 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)

UFA UF associated with interspecies (animal to human) extrapolation

UFD UF associated with deficiencies in the toxicity database

UFH UF associated with variation in sensitivity within the human

population

UFS UF associated with subchronic to chronic exposure

Vd volume of distribution

Vmax maximum enzyme velocity

VmaxC maximum velocity of the high-affinity/low-capacity pathway

v/v volume/volume

VDR visually directed reaching test

VitC vitamin C

VYS visceral yolk sac

WBC white blood cell

WOE weight of evidence

w/v weight/volume

χ

2 chi square
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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, 

reference 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 referred to EPA’s IRIS Hotline at (202) 566-1676 (phone), (202) 566-1749 (fax), or 

hotline.iris@epa.gov. 
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AUTHORS, CONTRIBUTORS, AND REVIEWERS

CHEMICAL MANAGER

Jeffrey Gift, Ph.D.

National Center for Environmental Assessment

U.S Environmental Protection Agency

Research Triangle Park, NC

AUTHORS

Stanley Barone, Ph.D.

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Allen Davis, MSPH

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Jeffrey 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 Boulevard

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, SC

Hugh Barton, Ph.D.

National Center for Computational Toxicology

U.S. Environmental Protection Agency

Research Triangle Park, NC
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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 Protection 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 Agency

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

Washington, DC

Sharon Oxendine, 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, WA
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John Rogers, 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 National 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 Street

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, TN
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Errol Zeiger, Ph.D.

Alpha-Gamma Technologies, Inc.

3301 Benson Drive

Raleigh, NC

REVIEWERS

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

2 federal agencies and White House offices.

INTERNAL EPA REVIEWERS

Jane Caldwell, Ph.D.

National 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

Robert Dewoskin Ph.D., DABT

National Center for 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 Laboratory

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 Protection and Environmental Education

U.S Environmental Protection Agency

Washington, DC

Jennifer Jinot, Ph.D.

National Center for Environmental Assessment
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U.S Environmental Protection Agency

Washington, DC

Eva McLanahan, Ph.D.

National Center for Environmental 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 Environmental Assessment

U.S Environmental Protection Agency

Research Triangle Park, NC
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1. INTRODUCTION

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

2 Information System (IRIS) Summary of the hazard and dose-response assessment of methanol.

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

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

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

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

7 mode of action (MOA). The RfD (expressed in units of milligrams per kilogram per day

8 [mg/kg-day]) is defined as an estimate (with uncertainty spanning perhaps an order of 

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

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

RfC (expressed in units of milligrams per cubic meter [mg/m3 11 ]) is analogous to the oral RfD but 

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

13 for both the respiratory system (portal-of-entry) and for effects peripheral to the respiratory 

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

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

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

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

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

19 duration.

20 The carcinogenicity assessment provides information on the carcinogenic hazard 

21 potential of the substance in question, and quantitative estimates of risk from oral and inhalation 

22 exposure may be derived. The information includes a weight-of-evidence (WOE) judgment of 

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

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

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

26 upper bound on the estimate of risk per mg/kg-day of oral exposure. Similarly, an inhalation unit 

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

(μg/m3 28 ) air breathed. 

29 Development of these hazard identification and dose-response assessments for methanol

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

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

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

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

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

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

36 Toxicity Risk Assessment (U.S. EPA, 1991), Interim Policy for Particle Size and Limit 
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1 Concentration Issues in Inhalation Toxicity Studies (U.S. EPA, 1994a), Methods for Derivation 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

18 2009.
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2. CHEMICAL AND PHYSICAL INFORMATION

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

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

3 naphtha; and wood spirit (Chemfinder, 2002). Some relevant physical and chemical properties 

4 are listed below (Hazardous Substances Data Bank [HSDB], 2002, International Programme on 

5 Chemical Safety [IPCS], 1997).

Table 2-1. Relevant physical and chemical properties of methanol

CASRN: 67-56-1

Empirical formula: CH3OH

Molecular weight: 32.04

Vapor pressure: 160 mmHg at 30 oC

Vapor Density: 1.11

Specific gravity: 0.7866 g/mL (25 oC)

Boiling point: 64.7 oC

Melting point: -98 oC

Water solubility: Miscible

Log octanol-water partition 

coefficient:

-0.82 to -0.68

Conversion factor (in air): 1 ppm = 1.31 mg/m3; 

1 mg/m3 = 0.763 ppm

6 Methanol is a clear, colorless liquid that has an alcoholic odor (IPCS, 1997). Endogenous 

levels of methanol are present in the human body as a result of both metabolism1 7 and dietary 

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

2 8 and can be measured in 

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

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

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

12 methanol as they are metabolized (Stegink et al., 1989). In general, aspartame exposure does not 

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

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

15 also occur. 

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

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

 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).
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1 construction, housing or automotive industries, with its use as a transportation fuel accounting 

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

3 include varnishes, shellacs, paints, windshield washer fluid, antifreeze, adhesives, de-icers, and 

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

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

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

7 methanol among the top 10 chemicals on the list entitled “On-site and Off-site Reported 

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

9 Chemicals” (U.S. EPA, 2006c). Natural gas is the primary feedstock for methanol and as such, 

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

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

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

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

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

15 increase in demand is its use in the production of biodiesel, a low-sulfur, high-lubricity fuel 

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

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

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

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

3.1. OVERVIEW

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

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

3 CERHR 2004; IPCS 1997). Table 3-1 summarizes background blood methanol levels in healthy 

4 humans which were found to range from 0.25–4.7 mg/L. One study reported a higher 

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

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

7 naturally in the human body (IPCS 1997). Table 3-1 outlines background levels of formate in 

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

following restriction of methanol-producing foods from the diet.3 9

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

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

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

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

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

15 inhalation, oral, and dermal routes of exposure. Table 3-2 outlines increases in human blood 

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

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

18 summarized in Tables 3-3, 3-4, and 3-5, respectively. Once absorbed, methanol pharmacokinetic

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

20 distribution to all organs and tissues according to water content, as an aqueous-soluble alcohol. 

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

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

23 bolus oral exposure versus longer-term inhalation). Because smaller species generally have 

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

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

26 concentration in air.

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

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

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

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

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

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

 3 In general, background levels among people who are on normal/non-restricted diets will be higher than those 

reported.
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1 were limited to the linear range, but the enzymes involved in primate metabolism are also 

2 saturable.

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

4 reactions that form formaldehyde, formate, and carbon dioxide (Figure 3-1). As noted in 

5 Figure 3-1, methanol is converted to formaldehyde by alcohol dehydrogenase-1 (ADH1) in 

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

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

8 report that the reaction proceeds at similar rates (Vmax = 30 and 48 mg/h/kg in rats and nonhuman 

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

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

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

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

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

14 2 times as fast in mice versus rats, with a Vmax for elimination of 117 and 60.7 mg/h/kg, 

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

16 consistently exhibited higher blood methanol levels than rats when inhaling equivalent methanol 

17 concentrations (See Tables 3-4 and 3-5). Possible explanations for the higher methanol 

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

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

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

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

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

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

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

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

26 take longer to reach that concentration.

27 In all species, formaldehyde is rapidly converted to formate, with the half-life for 

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

29 pathways (Teng et al., 2001). The first pathway (not shown in Figure 3-1) involves conversion 

30 of free formaldehyde to formate by the so-called low-affinity pathway (affinity = 1/Km = 

31 0.002/μM) mitochondrial aldehyde dehydrogenase-2 (ALDH2). The second pathway 

32 (Figure 3-1) involves a two-enzyme system that converts glutathione-conjugated formaldehyde 

33 (S-hydroxymethylglutathione [HMGSH]) to the intermediate S-formylglutathione, which is 

subsequently metabolized to formate and glutathione (GSH) by S-formylglutathione hydrolase.4 34

35 The first enzyme in this pathway, formaldehyde dehydrogenase-3 (ADH3), is rate limiting, and 

36 the affinity of HMGSH for ADH3 (affinity = 1/Km = 0.15/μM) is about a 100-fold higher than 

 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)
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1 that of free formaldehyde for ALDH2. In addition to the requirement of GSH for ADH3 activity, 

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

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

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

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

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

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

8 enzyme responsible for formaldehyde oxidation at physiologically relevant concentrations, 

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

10 and Cederbaum, 1986). 

11 

Primates CH3OH (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 hydrolase

Folate dependent pathway 

(see Figure 3-2) 

↓

↓

CO2 (Carbon dioxide) 

CAT-peroxide and 

Folate-dependent pathway 

(see Figure 3-2) 

Figure 3-1. Methanol metabolism and key metabolic enzymes in primates and rodents. 

Source: IPCS (1997). 

12 Rodents convert formate to carbon dioxide (CO2) through a folate-dependent enzyme 

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

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

two one-carbon groups. Formate can conjugate with THF to form N10 15 -formyl-THF and its 
3-4 DRAFT-DO NOT CITE OR QUOTE

isomer N5

-formyl-THF, both of which can be converted to N5, N10 1 -methenyl-THF and 

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

biosynthetic pathways (Figure 3-2). There is also evidence that formate generates CO2

- 3 radicals, 

and can be metabolized to CO2 via CAT and via the oxidation of N10 4 -formyl-THF (Dikalova 

5 et al., 2001). 

6

Figure 3-2. Folate-dependent formate metabolism. Tetrahydrofolate (THF)-mediated 

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

methionine. 

Source: Montserrat et al. (2006).

7 Unlike rodents, formate metabolism in primates occurs solely through a folate-dependent 

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

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

involving [14C]formate suggest that ~80% is exhaled as 14 10 CO2, 2-7% is excreted in the urine, and 

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

12 deficient in formyl-THF dehydrogenase exhibit no change in LD50 (via intraperitoneal [i.p.]) for 

13 methanol or in oxidation of high doses of formate. Thus it has been suggested that rodents 

14 efficiently clear formate via folate-dependent pathways, peroxidation by CAT, and by an 

15 unknown third pathway; conversely, primates do not appear to exhibit such capacity and are 

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

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

18 scenarios are reported in Table 3-2. As noted in Table 3-2, 75-minute to 6-hour exposures of 

19 healthy humans to 200 parts per million (ppm) methanol vapors, the American Council of 
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1 Governmental Industrial Hygienists (ACGIH) threshold limit value (TLV) for occupational 

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

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

4 magnitude lower than the TLV (IPCS, 1997). Table 3-3 indicates that exposure of monkeys to 

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

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

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

8 in Table 3-2 suggest that exposure to high concentrations of aspartame is unlikely to increase 

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

10 “abusive doses” (100–200 mg/kg) of aspartame (Stegink et al., 1981). Kerns et al. (2002) 

11 studied the kinetics of formate in 11 methanol-poisoned patients (mean initial methanol level of 

12 57.2 mmol/L or 1.83 g/L) and determined an elimination half-life of 3.4 hours for formate.

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

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

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

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

17 greater detail in Section 3.2, a limited study in folate-deficient monkeys demonstrated no 

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

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

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

3.2. KEY STUDIES

21 Some recent toxicokinetic and metabolism studies (Burbacher et al., 2004a, 1999b; 

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

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

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

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

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

toxicokinetic differences in pregnant and nonpregnant (NP) rats and mice. Methanol disposition5 27

28 was studied in Sprague-Dawley rats and CD-1 mice that were exposed to 100–2,500 mg/kg of 

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

30 were conducted in NP rats and mice, pregnant rats on gestation days (GD)7, GD14, and GD20, 

31 and pregnant mice on GD9 and GD18. Disposition was also studied in pregnant rats and mice 

32 exposed to 1,000–20,000 ppm methanol vapors for 8 hours. Three to five animals were 

33 examined at each dose and exposure condition.

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

detection (Pollack and Kawagoe, 1991)
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1 Based on the fit of various kinetic models to methanol measurements taken from all 

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

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

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

5 100–500 mg/kg of body weight, methanol was metabolized to formaldehyde and then formic 

6 acid through the saturable nonlinear pathway. A parallel, linear route characteristic of passive7 diffusion accounted for an increased fraction of total elimination at higher concentrations. 

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

9 2,500 mg/kg of body weight. 

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

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

12 GD7, GD14 and GD20 rats exposed to single oral gavage doses of 100 and 2,500 mg/kg, nor 

13 between NP and GD9 and GD18 mice at 2,500 mg/kg. The data as a whole suggested that the 

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

15 pregnant rodents versus NP rodents with the following exceptions:

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

17 distribution (Vd) to fractional bioavailability (F) by ~20% (while F decreased but not 

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

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

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

21 from the same data, making the model effectively over-parameterized. 

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

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

24 unchanged) and Vmax for clearance between NP and GD18 mice. No such differences 

25 were observed between NP and GD9 mice.

26 • The authors estimated a twofold higher Vmax for methanol elimination in mice versus rats 

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

28 would result in lower methanol concentrations in the mouse versus rat.

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

GD20 rats by microdialysis.7 30 A plot of the amniotic concentration versus maternal blood 

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

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

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

 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.
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1 fetal compartment. Nevertheless, this is a very minor departure from linearity, consistent with a 

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

3 total body water. 

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

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

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

Methanol").

Source: Pollack and Brouwer (1996).

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

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

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

7 concentrations measured following 8-hour inhalation exposures to concentrations ranging from 

8 1,000–20,000 ppm, the study authors (Pollack and Brown, 1996) concluded that methanol 

9 accumulation in the mouse occurred at a two- to threefold greater rate compared to the rat. They 

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

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

12 (see Section 4.3.2).

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

too was not statistically significant.
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1 The Pollack and Brouwer (1996) study was useful for comparing effects in pregnant and 

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

3 ambient levels of methanol is not clear (CERHR, 2004). 

4 Burbacher et al. (2004b, 1999a) examined toxicokinetics in Macaca fascicularis

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

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

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

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

9 were also examined and are discussed in Sections 4.3.2 and 4.4.2. In a 2-cohort design, 48 adult 

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

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

12 the entire breeding and gestation periods. Six-hour methanol clearance studies were conducted 

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

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

15 blood levels, which ranged from 2.2–2.4 mg/L throughout.

16  PK studies were performed initially (Study 1), after 90 days of pre-exposure and prior to 

17 mating (Study 2), between GD66 and GD72 (Study 3), and again between GD126 and 

18 GD132 (Study 4). These studies were analyzed using classical PK (one-compartment) 

19 models.

20  Disproportionate mean, dose-normalized, and net blood methanol dose-time profiles in 

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

22 pathway. Data from the 600 ppm group fit a linear model, while data from the 1,800 ppm 

23 group fit a Michaelis-Menten model. 

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

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

26 exposure. 

27  Blood methanol levels were measured every 2 weeks throughout pregnancy, and while 

28 there was measurement-to-measurement variation, there was no significant change or 

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

30 half-life and corresponding downward trend in blood methanol clearance between Studies 

31 2, 3, and 4. However, the changes are not statistically significant and the time-courses 

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

33  Significant differences between pre-breeding and gestational blood plasma formate levels 

34 were observed but were not dose dependent (Table 3-6).

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

36 were not dose dependent (Table 3-7). 
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1 An HEI review committee (Burbacher et al., 1999a) noted that this was a quality study 

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

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

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

metabolism and pharmacokinetics of [14C]methanol and [14 5 C]formate in normal and folate6 deficient cynomolgus, M. fascicularis monkeys that were exposed to environmentally relevant 

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

8 stage of the study, 4 normal 12-year-old cynomolgus monkeys were each exposed to 10, 45, 200, 

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

10 at least 2 months. After the first stage of the study was completed, monkeys were given a folate11 deficient diet supplemented with 1% succinylsulfathiozole (an antibacterial sulfonamide used to 

12 inhibit folic acid biosynthesis from intestinal bacteria) for 6–8 weeks in order to obtain folate 

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

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

hemoglobin level. The folate-deficient monkeys were exposed to 900 ppm [14 15 C]methanol for 

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

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

linear relationship between inhaled [14 18 C]methanol concentration and end-of-exposure 

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

[

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

21  Methanol concentration had no effect on elimination half-life (<1 hour) and percent 

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

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

[

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

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

 Peak blood [14 26 C]formate levels were significantly higher in folate-deficient monkeys, but 

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

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

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

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

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

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

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

34 folate deficiency.
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3.3. HUMAN VARIABILITY IN METHANOL METABOLISM

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

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

3 Li (1986), and Pietruszko (1980), discuss genetic polymorphisms for ADH. Class I ADH, the 

4 primary ADH in human liver, is a hetero- or homodimer composed of randomly associated 

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

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

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

8 The ADH1B*2 phenotype is estimated to occur in ~15% of Caucasians of European descent, 

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

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

11 there are two reports of polymorphisms in ADH3 (Cichoz-Lach et al., 2007; Hedberg et al., 

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

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

14 alleles in different populations have been reported, ADH enzyme kinetics (Vmax and Km) have not 

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

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

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

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

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

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

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

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

23 pollutant (CERHR, 2004). There is evidence that children under 5 years of age have reduced 

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

25 ethanol as a substrate and found that 2-month-old fetal livers have ~3–4% of adult ADH liver 

26 activity. ADH activity in 4–5 month old fetuses is ~10% of adult activity, and an infant’s activity 

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

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

29 activity (1,625–6,530/g liver wet weight or 2,030–5,430 mU/100 mg soluble protein). Smith 

30 et al. (1971) also compared liver ADH activity in 56 fetuses (9–22 weeks gestation), 37 infants 

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

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

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

34 levels, however, possibly via CAT (Tran et al., 2007). 

35 ADH3 exhibits little or no activity toward small alcohols, thus the previous studies 

36 provide no information about the ontogeny of formaldehyde clearance. While such data on 
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1 ADH3 activity does not exist, ADH3 mRNA is abundantly expressed in the mouse fetus (Ang 

2 et al., 1996) and is detectible in human fetal tissues (third trimester), neonates and children 

3 (Hines et al., 2002; Estonius et al., 1996). 

4 As noted earlier in this section, folate-dependent reactions are important in the 

5 metabolism of formate. Individuals who are commonly folate deficient include those who are 

6 pregnant or lactating, have gastrointestinal (GI) disorders, have nutritionally inadequate diets, are 

7 alcoholics, smoke, have psychiatric disorders, have pernicious anemia, or are taking folic acid 

8 antagonist medications such as some antiepileptic drugs (CERHR, 2004; IPCS, 1997). Groups 

9 which are known to have increased incidence of folate deficiencies include Hispanic and African 

10 American women, low-income elderly, and mentally ill elderly (CERHR, 2004). A 

11 polymorphism in methylene tetrahydrofolate reductase reduces folate activity and is found in 

12 21% of Hispanics in California and 12% of Caucasians in the United States. Genetic variations 

13 in folic acid metabolic enzymes and folate receptor activity are theoretical causes of folate 

14 deficiencies.

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 restricted diet (no 

methanol-containing or methanolproducing 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 methanolproducing 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 (second 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 no 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 fasted 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 

route

Exposure 

duration or 

method

Methanol 

exposure

concentration

Blood 

methanol 

mean or 

range

(mg/L)

Blood 

formate 

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 bwa <4 No data

10 mg/kg bwa 12.7 No data

15 mg/kg bwa 21.4 No data

20 mg/kg bwa 25.8

8.4–22.8

Infants administered 

aspartame; peak exposure 

level Oral

1 dose in 

beverage

0

No data

Stegink et al. 

(1983)

3.4 mg/kg bwa <3.5

5 mg/kg bwa 3.0

10 mg/kg bwa 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 bwa

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. 

191 ppm 1.881 (1991)

Males and females; 

postexposure serum levels Inhalation 4 hr

0 1.8 11.2 Osterloh 

200 ppm 6.5 14.3 et al. (1996)

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. 

Males with exercise; (1992)

postexposure blood methanol 

and plasma 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 assumed 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-sex Exposure 

route

Exposure 

duration

Methanol 

exposure

concentration

Blood 

methanol 

mean

in mg/L 

Blood 

formate 

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

2,000 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/strain/sex Exposure 

route

Exposure 

duration

Methanol exposure

concentration

Blood 

methanol 

mean

(mg/L)

Blood 

formate 

mean

(mg/L )

Reference

Mouse;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 34.5

Mouse;CD-1;female; 

postexposure blood 

methanol level

Inhalation 8 hr

2,500 ppm 1,883

No data

Pollack and 

Brouwer (1996); 

Perkins et al. 

(1995a)

5,000 ppm 3,580

10,000 ppm 6,028

15,000 ppm 11,165

Mouse;CD-1;female; 

mean postexposure plasma 

methanol level

Inhalation 7 hr/day on 

GD6–GD15

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

Mouse;CD-1;female; 

plasma level 1 hr 

postdosing

OralGavage GD6–GD15 4,000 mg/kg bw 3,856 No data

Mouse;CD-1;female; peak 

plasma level

OralGavage 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. Rat blood methanol and formate levels following methanol exposure

Species;strain/sex: 

type of sample 

collected

Exposure 

route

Exposure 

duration

Methanol exposure 

concentration

Blood 

methanol 

level in mg/L 

Blood 

formate 

level 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); 

Perkins et al. 

(1995a)

Rat;LongEvans;female; 

postexposure plasma 

level on GD7− GD12

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 

PND1− PND

21

4,500 ppm 1,260 No data

Rat/Fischer-344/male; 

postexposure blood 

level

Inhalation 6 hr

200 ppm

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 formate 

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

Oralgavage Single dose

3,500 mg/kg bw–FS

3,500 mg/kg bw–FP

3,500 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 sufficient; FR = Folate reduced; FP = Folate paire

Source: CERHR (2004).
3-15 DRAFT-DO NOT CITE OR QUOTE

Table 3-6. Plasma formate concentrations in monkeys

Exposure group 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 ppm 6.4 8.7 11 10

Source: Burbacher et al. (1999a).

Table 3-7. Serum folate concentrations in monkeys

Exposure group Mean serum folate level (μg/L) during each exposure period

Baseline Day 70

Pre-pregnancya

Day 98 

Pre-pregnancya

Day 55 

Pregnancya

Day 113

Pregnancya

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 BASED TOXICOKINETIC MODELS

1 In accordance with the needs of this human health risk assessment, particularly the 

2 derivation of human health effect benchmarks from studies of the developmental effects of 

3 methanol inhalation exposure in mice (Rogers et al., 1993a) and rats (NEDO, 1987) and 

4 carcinogenic effects of methanol in rats exposed via drinking water (Soffritti et al., 2002a) and 

5 inhalation (NEDO, 1987, 1985/2008b), mouse and rat models were developed to allow for the 

6 estimation of mouse and rat internal dose metrics. A human model was developed to extrapolate

7 those internal metrics to inhalation and oral exposure concentrations that would result in the 

8 same internal dose in humans (human equivalent concentrations [HECs] and human equivalent 

9 doses [HEDs]). The procedures used for the development, calibration and use of these models 

10 are summarized in this section, with further details provided in Appendix B, “Development, 

11 Calibration and Application of a Methanol PBPK Model.”

3.4.1. Model Requirements for EPA Purposes

3.4.1.1. MOA and Selection of a Dose Metric

12 Dose metrics closely associated with one or more key events that lead to the selected 

13 critical effect are preferred for dose-response analyses compared to metrics not clearly 

14 correlated. For instance, internal (e.g., blood, target tissue) measures of dose are preferred over 

15 external measures of dose (e.g., atmospheric or drinking water concentrations), especially when, 

16 as with methanol, blood methanol concentrations increase disproportionally with dose (Rogers 
3-16 DRAFT-DO NOT CITE OR QUOTE

1 et al., 1993a). This is likely due to the saturable metabolism of methanol in rodents. In addition, 

2 respiratory and GI absorption may vary between and within species. Mode of action (MOA)

3 considerations can also influence whether to model the parent compound with or without its 

4 metabolites for selection of the most adequate dose metric.

5 As discussed in Section 4, developmental effects following methanol exposures have 

6 been noted in both rats and mice, but are not as evident or clear in primate exposure studies

7 (Burbacher et al., 2004a; Clary, 2003; Rogers et al., 1993a, 1993b; Andrews et al., 1987; Nelson 

8 et al., 1985), and carcinogenic effects have been observed in a drinking water study of Sprague9 Dawley rats (Soffritti et al., 2002a) and an inhalation study of F344 rats (NEDO, 1985/2008b). 

10 The report of the New Energy Development Organization (NEDO, 1987) of Japan, which 

11 investigated developmental effects of methanol in rats, indicated that there is a potential that 

12 developing rat brain weight is reduced following maternal and neonatal exposures. These 

13 exposures included both in utero and postnatal exposures. The methanol PBPK models 

14 developed for this assessment do not explicitly describe these exposure routes. Mathematical 

15 modeling efforts have focused on the estimation of human equivalent external exposures that 

16 would lead to internal blood levels of methanol or its metabolites presumed to be associated with 

17 developmental effects as reported in rats (NEDO, 1987) and mice (Rogers et al., 1993a), and 

18 carcinogenic effects as reported in rats by Soffritti et al. (2002a).

19 In a recent review of the reproductive and developmental toxicity of methanol, a panel of 

20 experts concluded that methanol, not formate, is likely to be the proximate teratogen and 

21 determined that blood methanol level is a useful biomarker of exposure (CERHR, 2004; Dorman 

22 et al., 1995). The CERHR Expert Panel based their assessment of potential methanol toxicity on 

23 an assessment of circulating blood levels (CERHR, 2004). While recent in vitro evidence 

24 indicates that formaldehyde is more embryotoxic than methanol and formate (Harris et al., 2004, 

25 2003), the high reactivity of formaldehyde would limit its unbound and unaltered transport as 

26 free formaldehyde from maternal to fetal blood (Thrasher and Kilburn, 2001), and the capacity 

27 for the metabolism of methanol to formaldehyde is likely lower in the fetus and neonate versus 

28 adults (see discussion in Section 3.3). Thus, even if formaldehyde is ultimately identified as the 

29 proximate teratogen, methanol would likely play a prominent role, at least in terms of transport 

30 to the target tissue. Further discussions of methanol metabolism, dose metric selection, and 

31 MOA issues are covered in Sections 3.3, 4.6, 4.8 and 4.9.2.

32 It has been suggested that the lymphomas observed in Sprague-Dawley rats following 

33 methanol exposure are associated with formaldehyde because formaldehyde and other 

34 compounds that metabolize to formaldehyde have been reported to cause lymphomas in Sprague35 Dawley rats (Soffritti et al., 2005). Given the reactivity of formaldehyde, models that predict 

36 levels of formaldehyde in the blood are difficult to validate. However, production of 

37 formaldehyde or formate following exposure to methanol can be estimated by summing the total 
3-17 DRAFT-DO NOT CITE OR QUOTE

amount of methanol cleared by metabolic processes.9 1 This metric of formaldehyde or formate 

2 dose has limited value since it ignores important processes that may differ between species, such 

3 as clearance of these two metabolites, but it can be roughly be equated to the total amount of 

4 metabolites produced and may be the more relevant dose metric if formaldehyde is found to be 

5 the proximate toxic moiety. Thus, both blood methanol and total metabolism metrics are 

6 considered to be important components of the PBPK models. Dose metric selection and MOA 

7 issues are discussed further in Sections 3.3, 4.6, 4.8 and 4.9.2. 

3.4.1.2. Criteria for the Development of Methanol PBPK Models

8 The development of methanol PBPK models that would meet the needs of this 

9 assessment was organized around a set of criteria that reflect: 1) the MOA(s) being considered 

10 for methanol; 2) absorption, distribution, metabolism, and elimination characteristics; 3) dose 

11 routes necessary for interpreting toxicity studies or estimating HECs; and 4) general parameters 

12 needed for the development of predictive PK models. 

13 The criteria with a brief justification are provided below:

14 1) Must simulate blood methanol concentrations and total methanol metabolic clearance.

15 Blood methanol is the recommended dose metric for developmental effects, but total 

16 metabolic clearance may be a useful metric, particularly for cancer endpoints.

17 2) Must be capable of simulating experimental blood methanol and total metabolic 

18 clearance (mg/day) data for the inhalation route of exposure in mice and rats (a) and 

19 humans (b), and the oral route in rats (c) and humans (d). These routes are important for 

20 determining dose metrics in the most sensitive test species under the conditions of the 

21 toxicity study and in the relevant exposure routes in humans. 

22 3) The model code should easily allow designation of respiration rates during inhalation 

23 exposures. A standard variable in inhalation route risk assessments is ventilation rate.

24 Blood methanol concentrations will depend strongly on ventilation rate, which varies 

25 significantly between species. 

26 4) Must address the potential for saturable metabolism of methanol. Saturable metabolism 

27 has the potential to bring nonlinearities into the exposure:tissue dose relationship.

28 5) Model complexity should be consistent with modeling needs and limitations of the 

29 available data. Model should adequately describe the biological mechanisms that 

30 determine the internal dose metrics (blood methanol and metabolic clearance) to assure 

31 that it can be reliably used to predict those metrics in exposure conditions and scenarios 

32 where data are lacking. Compartments or processes should not be added that cannot be 

33 adequately characterized by the available data. 

 9 This assumption is more likely to be appropriate for formaldehyde than formate as formaldehyde is a direct 

metabolite of methanol. 
3-18 DRAFT-DO NOT CITE OR QUOTE

1 Although the rat and mouse models are useful for the evaluation of the dose metrics 

2 associated with methanol’s developmental effects and the relevant toxicity studies, including

3 gestational exposures, no pregnancy-specific PBPK model exists for methanol, and inadequate 

4 data exists for the development and validation of a fetal/gestational/conceptus compartment. 

5 However, EPA determined that nonpregnancy models for the appropriate species and routes of 

6 exposure could prove to be valuable because levels of methanol in NP, pregnant and fetal blood 

7 are expected to be similar following the same oral or inhalation exposure. Pollack and Brouwer 

8 (1996) determined that methanol distribution in rats and mice following repeated oral and i.v.

9 exposures up to day 20 of gestation is “virtually unaffected by pregnancy, with the possible 

10 exception of the immediate perinatal period.” The critical window for methanol induction of 

11 cervical rib malformations in CD-1 mice has been identified as occurring between GD6 and GD7

12 (Rogers and Mole, 1997; Rogers et al., 1993b), a developmental period roughly equivalent to 

13 week 3 of human development (Chernoff and Rogers, 2004). Methanol blood kinetics measured 

14 during and after inhalation exposure in NP and pregnant mice on GD6–GD10 and GD6–GD15

15 (Perkins et al., 1996, 1995a; Dorman et al., 1995; Rogers et al., 1993a) are also similar. Further, 

16 the available data indicate that the maternal blood:fetal partition coefficient is approximately 1 at 

17 dose levels most relevant to this assessment (Ward et al., 1997; Horton et al., 1992). The same 

18 has been found in rat (Zorzano et al., 1989; Guerri and Sanchis, 1985) and sheep (Brien et al.,

19 1985; Cumming et al., 1984) studies of ethanol, a structurally related chemical that also 

20 penetrates cellular membranes readily and distributes throughout total body water. 

21 Consequently, fetal methanol concentrations are expected to be roughly equivalent to that in the 

22 mother’s blood. Thus, pharmacokinetics and blood dose metrics for NP mice and humans are 

23 expected to provide reasonable approximations of pregnancy levels and fetal exposure, 

24 particularly during early gestation, that improve upon default estimations from external exposure 

25 concentrations. 

3.4.2. Methanol PBPK Models

26 As has been discussed, methanol is well absorbed by both inhalation and oral routes and 

27 is readily metabolized to formaldehyde, which is rapidly converted to formate in both rodents 

28 and humans. As was discussed in Section 3.1, the enzymes responsible for metabolizing 

29 methanol are different in rodents and humans. Several rat, mouse and human PBPK models 

30 which attempt to account for these species differences have been published (Fisher et al., 2000; 

31 Ward et al., 1997; Perkins et al., 1995a; Horton et al., 1992). In addition, a gestational model for 

32 a similar water soluble compound, isopropanol, with the potential to be adapted to methanol 

33 pharmacokinetics, was of interest (Gentry et al., 2003, 2002; Clewell et al., 2001). Three PK

34 models (Gentry et al., 2002; Bouchard et al., 2001; Ward et al., 1997) were identified as 

35 potentially appropriate for use in animal-to-human extrapolation of methanol metabolic rates and 

36 blood concentrations. An additional methanol PBPK model by Fisher et al. (2000) was 
3-19 DRAFT-DO NOT CITE OR QUOTE

1 considered principally because it had an important feature – pulmonary compartmentalization 

2 (see below for details) – worth adopting in the final model. 

3.4.2.1. Ward et al. (1997)

3 The PBPK model of Ward et al. (1997) describes inhalation, oral and i.v. routes of 

4 exposure and is parameterized for both NP and pregnant mice and rats (Table 3-8). The model 

5 has not been parameterized for humans. 

6 Respiratory uptake of methanol is described as a constant infusion into arterial blood at a 

7 rate equal to the minute ventilation times the inhaled concentration and includes a parameter for 

8 respiratory bioavailability, which for methanol is <100%. This simple approach is nonstandard 

9 for volatile compounds but is expected to be appropriate for a compound like methanol, for 

10 which there is little clearance from the blood via exhalation. Oral absorption is described as a 

11 biphasic process, dependent on a rapid and a slow first-order rate constant. This is conceptually 

12 similar to the isopropanol model discussed below (Gentry et al., 2002; Clewell et al., 2001), 

13 which also employs slow and fast absorption processes but functionally separates them into 

14 stomach and duodenal compartments.

15 Methanol elimination in the Ward et al. (1997) model is primarily via saturable hepatic 

16 metabolism. The parameters describing this metabolism come from the literature, primarily 

17 previous work by Ward and Pollack (1996) and Pollack et al. (1993). A first-order elimination of 

18 methanol from the kidney compartment includes a lumped metabolic term that accounts for both 

19 renal and pulmonary excretion. 

20 The model adequately fits the experimental blood kinetics of methanol in rat and mice 

21 and is therefore suitable for simulating blood dosimetry in the relevant test species and routes of 

22 exposure (oral and i.v.). The Ward et al. (1997) model meets criteria 1, 2a, 2c, 3, 4, and 5. The 

23 most significant limitation is the absence of parameters for the oral and inhalation routes in the 

24 human. A modified version of this model that includes human parameters and a standard PBPK 

25 lung compartment might be suitable for the purposes of this assessment. 

3.4.2.2. Bouchard et al. (2001)

26 The Bouchard et al. (2001) model is not actually a PBPK model but is an elaborate 

27 classical PK model, since the transfer rates are not determined from blood flows, ventilation, 

28 partition coefficients, and the like. The Bouchard et al. (2001) model uses a single compartment 

29 for methanol: a central compartment represented by a volume of distribution where the 

30 concentration is assumed to equal that in blood. The model was developed for inhalation and i.v. 

31 kinetics only. Methanol is primarily eliminated via saturable metabolism. The model adequately 

32 simulates blood kinetics in NP rats and humans following inhalation exposure and in NP rats 

33 following i.v. exposure; there is no description for oral absorption. Because methanol distributes 

34 with total body water (Ward et al., 1997; Horton et al., 1992), this simple model structure is 

35 sufficient for predicting blood concentrations of methanol following inhalation and i.v. dosing. 
3-20 DRAFT-DO NOT CITE OR QUOTE

1 The Bouchard et al. (2001) model has the advantage of simplicity, reflecting the 

2 minimum number of compartments necessary for representing blood methanol pharmacokinetics. 

3 Because volume of distribution can be easily and directly estimated for water-soluble compounds 

4 like methanol or fit directly to experimental kinetics data, concern over the scalability of this 

5 parameter is absent. The model has been parameterized for a required human exposure route, 

6 inhalation (Table 3-8). The model meets criteria 1, 2b, 3, 4, and 5 described in Section 3.4.1.2. 

7 However, the Bouchard model has specific and significant limitations. The model has neither 

8 been parameterized for the mouse, a test species of concern (Table 3-8), nor for the oral route in 

9 humans. As such, the model cannot be used to conduct the necessary interspecies extrapolation. 

Table 3-8. Routes of exposure 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) 

10 The model described in these three papers is for isopropanol, not methanol, and therefore 

11 lacks any immediately useful parameterization for the purposes of a methanol risk assessment. 

12 Although the overall model structure, the description of kinetics for both parent compound and 

13 primary metabolite, gestational compartments, lactational transfer, oral and i.v. routes, etc., are 

14 attractive for application to methanol, this model is not ideal. In particular, the model structure is 

15 more elaborate than necessary; because methanol partition coefficients are near 1 for all tissues 

16 except fat, there is no need to individually represent these tissues. Similarly, a fetal compartment 

17 may not be necessary because methanol kinetics in the fetus (conceptus) is expected to parallel 

18 maternal blood concentrations in the rodent. However, even if a fetal model was considered 

19 necessary, other than the partition coefficient, there are insufficient data to identify conceptus 

20 compartment parameters for methanol. This model would require the most modification and 

21 parameterization to be useful for methanol risk assessment since parameters would have to be 

22 estimated for all relevant species (at least mouse and humans) and for several routes of exposure. 

23 Therefore the isopropanol model was not considered further.
3-21 DRAFT-DO NOT CITE OR QUOTE

3.4.3. Selected Modeling Approach

1 As discussed earlier regarding model criteria, fetal methanol concentrations can 

2 reasonably be assumed to equal maternal blood concentration. Thus, as discussed in Section 

3 3.4.2.3, methanol pharmacokinetics and blood dose metrics for NP mice and humans are 

4 expected to improve upon default extrapolations from external exposures as estimates of fetal 

5 exposure during early gestation. The same level of confidence cannot be placed on the whole6 body rate of metabolism, in particular as a surrogate for formaldehyde dose. Because of 

7 formaldehyde’s reactivity and the limited fetal metabolic (ADH) activity (see Sections 3.3 and 

8 4.10.1), fetal formaldehyde concentration increases (from methanol) will probably not equal 

9 maternal increases in formaldehyde concentration. But since there is no model that explicitly 

10 describes formaldehyde concentration in the adult, let alone the fetus, the metabolism metric is 

11 the closest one can come to predicting fetal formaldehyde dose. This metric is expected to be a 

12 better predictor of formaldehyde dose than applied methanol dose or even methanol blood levels, 

13 which do not account for species differences in conversion of methanol to formaldehyde.

14 Most of the published rodent kinetic models for methanol describe the metabolism of 

15 methanol to formaldehyde as a saturable process but differ in the description of metabolism to 

16 and excretion of formate (Bouchard et al., 2001; Fisher et al., 2000; Ward et al., 1997). The 

17 model of Ward et al. (1997) used one saturable and one first-order pathway to describe methanol 

18 elimination in mice. The saturable pathway described in Ward et al. (1997) can specifically be 

19 ascribed to metabolic formation of formaldehyde in the liver, while the renal first-order 

20 elimination described in the model represents nonspecific clearance of methanol (e.g.,

21 metabolism, excretion, or exhalation). The model of Ward et al. (1997) does not describe 

22 kinetics of formaldehyde subsequent to its formation and does not include any description of 

23 formate.

24 Bouchard et al. (2001) employed a metabolic pathway for conversion of methanol to 

25 formaldehyde and a second pathway described as urinary elimination of methanol in rats and 

26 humans. They then explicitly describe two pathways of formaldehyde transformation, one to 

27 formate and the other to “other, unobserved formaldehyde byproducts.” Finally, formate 

28 removal is described by two pathways, one to urinary elimination and one via metabolism to CO2

29 (which is exhaled). All of these metabolic and elimination steps are described as first-order 

30 processes, but the explicit descriptions of formaldehyde and formate kinetics significantly 

31 distinguish the model of Bouchard et al. (2001) from that of Ward et al. (1997), which only 

32 describes methanol.

33 There are two other important distinctions between the Ward et al. (1997) and Bouchard 

34 et al. (2001) models. The former is currently capable of simulating blood data for all exposure 

35 routes in mice but not humans, while the latter is capable of simulating human inhalation route 

36 blood pharmacokinetics but not those in mice. The Ward et al. (1997) model has more 
3-22 DRAFT-DO NOT CITE OR QUOTE

1 compartments than is necessary to adequately represent methanol disposition but has been fit to 

2 PK data in pregnant and NP mice for all routes of exposure (i.v., oral, and inhalation). The Ward 

3 et al. (1997) model has also been fit to i.v. and oral route PK data in rats. Based primarily on the 

4 extensive amount of fitting that has already been demonstrated for this model, it was determined 

5 that a modified Ward et al. (1997) model, with the addition of a lung compartment as described 

6 by Fisher et al. (2000), should be used for the purposes of this assessment. See Appendix B for a 

7 more complete discussion of the selected modeling approach and modeling considerations.

3.4.3.1. Available PK Data

8 Although limited human data are available, several studies exist that contain PK and 

9 metabolic data in mice, rats, and nonhuman primates for model parameterization. Table 3-9 

10 contains references that were used to verify the model fits as reported in Ward et al. (1997). 

Table 3-9. Key methanol kinetic studies for model validation

Reference i.v. dose 

(mg/kg)

Inhalation 

(ppm)

Oral/dermal/

IP Species Samples Digitizedfiguresa

Batterman et al., 1998 Dermal Human Male/female Blood Figure 1

Batterman & Franzblau 1997 800 (8 hr)

Blood, 

urine, 

exhaled

Burbacher, 2004a, 2004b 0-1,800 (2.5 hr, 4 

mo)

Monkeys 

Cynomolgus 

Pregnant, NP

Blood

Osterloh et al., 1996;

Chuwers et al., 1995;

D’Alessandro et al., 1994

200 (4 hr) Human Male/female Blood, urine

Figure 1, 

Osterloh 

et al., 1996

Medinsky et al., 1997; 

Dorman et al., 1994 10-900 (2 hr)

Monkeys 

Cynomolgus Folate 

deficient 

Blood, 

urine, 

exhaled

Gonzalez-Quevedo et al., 

2002

IP: 2 mg/kgday, 2 wk Rat Blood

Horton et al., 1992 100 (rats 

only) 50-2,000 (6 hr) Rat & Monkey

Rhesus 

Blood,

urine, 

exhaled

Figure 7

Perkins et al., 1996, 1995a,

1995b 

1,000-20,000 (8 

hr) Mouse and Rat 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: SpragueDawley, & Mouse; 

CD-1 Pregnant, NP

Blood

Rogers and Mole, 1997; 

Rogers et al., 1993a; 

1,000-15,000 (7 

hr, 10 days)

Mouse

Pregnant Blood

Sedivec et al., 1981 78-231 (8 hr) Human Urine, 

blood

Figures 2, 3, 

6, 7, 8

Ward et al., 1997; Ward and 

Pollack, 1996

100, 500 

(Rat), 2,500 

(Mouse)

Oral: 

2,500 mg/kg

GD18 Mouse, 

GD14 & GD20 Rats

Blood, 

conceptus

a

data obtained from the reported figure
3-23 DRAFT-DO NOT CITE OR QUOTE

3.4.3.2. Model Structure

1 A model was developed which includes compartments for alveolar air/blood methanol 

2 exchange, liver, fat, bladder (human simulations) and the rest of the body (Figure 3-4). This 

3 model is a revision of the model reported by Ward et al. (1997), reflecting significant 

4 simplifications (removal of compartments for placenta, embryo/fetus, and extraembrionic fluid) 

5 and three elaborations (addition of an intestine lumen compartment to the existing stomach 

6 lumen compartment, use of a saturable rate of absorption from the stomach (but not intestine),

7 and addition of a bladder compartment which impacts simulations for human urinary excretion), 

8 while maintaining the ability to describe methanol blood kinetics in mice, rats, and humans. A 

9 fat compartment was included because it is the only tissue with a tissue:blood partitioning 

10 coefficient appreciably different than 1, and the liver is included because it is the primary site of 

11 metabolism. A bladder compartment was also added for use in simulating human urinary 

12 excretion to capture the difference in kinetics between changes in blood methanol concentration 

13 and urinary methanol concentration. The model code describes inhalation, oral, and i.v. dose 

14 routes, and data exist (Table 3-9) that were used to fit parameters and evaluate model predictions 

15 for all three of those routes in both mice and rats. In humans, inhalation exposure data were 

16 available for model calibration and validation but not oral or i.v. data. However, oral exposures 

17 were simulated in humans, assuming a continuous, zero-order ingestion rate, thereby obviating 

18 the need for oral uptake parameters. 

19 PK data from exposure routes other than inhalation and oral were used to test or further 

20 refine the parameters for methanol clearance. Monkey data were evaluated for insight into 

21 primate kinetics. Data from Osterloh et al. (1996) and Sedivec et al. (1981) were used to validate 

22 the modified Ward et al. (1997) model parameterization for humans. The fact that optimized 

23 human parameters were similar to those predicted in monkeys was important to the validation 

24 process (Bourchard et al., 2001)(see section 3.4.7 and Appendix B). Blood levels of methanol 

25 have been reported following i.v., oral, and inhalation exposure in rats and mice and inhalation 

26 exposure in nonhuman primates and humans.

27 The metabolic clearance of methanol was represented in mice, rats, and humans by 

28 specifying separate rate constants for the species-specific enzymes: two saturable processes for 

mice and rats10 29 and one for humans. The requirement for two saturable processes in the mouse 

30 and rat models may reflect saturation of CAT and ADH1. Simulated methanol clearance by these 

31 metabolic processes is not linked to production of formaldehyde or formate; it is simply cleared 

32 from the methanol mass balance leaving the system. Metabolism of formaldehyde is not 

33 explicitly simulated by the model, and this model tracks neither formate nor formaldehyde. 

34 Since the metabolic conversion of formaldehyde to formate is rapid (<1 minute) in all species 

 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 visually, or by more formal optimization, without the second saturable metabolic pathway. 
3-24 DRAFT-DO NOT CITE OR QUOTE

1 (Kavet and Nauss, 1990), the methanol clearance rate may approximate a formate production 

2 rate, though this has not been verified.

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; VmAS 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; Vmax and Km and Vmax2 and 

Km2, Michaelis-Menten rate constants for high affinity/low capacity and low 

affinity/high capacity metabolic clearance of methanol; KLL, alternate firstorder 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

5 The primary purpose of this assessment is for the determination of noncancer and cancer 

6 risk associated with increases in the levels of methanol or its metabolites (e.g., formate, 

7 formaldehyde). Thus, the focus of model development is on obtaining accurate predictions of 

8 increased body burdens over background. The PBPK models do not describe or account for 

9 background levels of methanol, formaldehyde or formate, and background levels were subtracted 

10 from the reported data before use in model fitting or validation (if not already subtracted by 

11 study authors), as described below. This approach is not expected to have a significant impact on 

12 PBPK model parameter estimates as background levels of methanol and its metabolites are low 
3-25 DRAFT-DO NOT CITE OR QUOTE

1 relative to exposure levels used in methanol bioassays. Further, while it is possible that 

2 background levels of methanol or its metabolites contribute to background responses for some 

3 adverse effects, the results of dose-response modeling of cancer endpoints using “background 

4 dose” models suggest that this contribution is relatively small (see discussion in Appendix E, 

5 Section E.4).

3.4.3.3. Model Parameters

6 The EPA methanol model uses a consistent set of physiological parameters obtained 

7 predominantly from the open literature (Table 3-10); the Ward et al. (1997) model employed a 

number of data-set specific parameters.11 8 Parameters for blood flow, ventilation, and metabolic 

9 capacity were scaled as a function of body weight raised to the 0.75 power, according to the 

10 methods of Ramsey and Andersen (1984). 

Table 3-10. Parameters used in the mouse, rat and human PBPK models

Mouse Rat

SD | F344 Human Source

Body weight (kg) 0.03a 0.275b 70 Measured/estimated

Tissue volume (% 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 Calculatedc

Flows (L/hr/kg0.75)

Alveolar ventillationd 25.4 16.4 16.5 Perkins et al. 1995a; Brown et al. 1997; 

Cardiac output 25.4 16.4 24.0 U.S. EPA, 2004

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 constantse 1st

order saturable

VmaxC (mg/hr/kg0.75) 19 5.0 0 NA 33.1

Fitted

Km (mg/L) 5.2 6.3 NA NA 23.7

Vmax2C (mg/hr/kg0.75) 3.2 8.4 22.3 NA

Km2 (mg/L) 660 65 100 NA

K1C (BW0.25/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 (BW0.25/hr)f NA NA 95.7 NA

Oral absorption

VmASC (mg/hr/kg0.75) 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 firstorder 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 0.583h Ward et al., 1997; Fiserova-Bergerova and 

Fat:Blood 0.083 0.083 0.142 Diaz, 1986

Blood:Air 1350i 1350 1626 Horton et al., 1992; Fiserova-Bergerova and 

Diaz, 1986

Body:Blood 0.66 0.66 0.805 Rodent: estimated; human: FiserovaBergerova and Diaz, 1986 (human "body" 

assumed = muscle) Lung:Blood 1 1 1.07

KBL (hr-1), bladder timeconstant j NA 0.564 0.612 Fitted (human)

FRACIN (%), nhalation 

fractional availability 0.665 0.20 0.866k 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

Minute 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 available, a mouse QPC (Alveolar Ventilation/BW0.75) 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/kg0.75

) e

Vmax, Km, and Vmax2, Km2 represent the two saturable metabolic clearance processes assumed to occur solely in the liver. 

The Vmax used in the model = VmaxC (mg/kg0.75·hr)BW0.75. K1C is the first-order loss from the blood for human 

simulations that represents 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 / BW0.25

f

KLLC – alternate human first-order metabolism rate (used only when VmaxC = Vmax2C = 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 

1 The process by which the mouse, rat, and human inhalation and oral models were 

2 calibrated is discussed in more detail in Appendix B, “Development, Calibration and Application 

3 of a Methanol PBPK Model.” The calibrated mouse inhalation model predicted blood methanol 

4 blood concentration time-course agreed well with measured values in adult mice in the critical 
3-27 DRAFT-DO NOT CITE OR QUOTE

1 inhalation studies of Rogers and Mole (1997) (Figure 3-5), Perkins et al. (1995a, 1995b), and 

2 Rogers et al. (1993a), as well as in NP and early gestation (GD8) mice of Dorman et al. (1995) 

3 (Figure 3-6). Parameter values used in the calibrated model are given in Table 3-10.

4 The mouse model was also calibrated for the oral route by fitting all but one of the rate 

5 constants for oral uptake of methanol to the oral-route blood methanol kinetics of Ward et al. 

6 (1997, 1995). The best model fit to the mouse oral route blood methanol PK data was obtained 

7 using a two-compartment GI tract model, as depicted in Figure 3-4. Because the oral data in rats 

8 led to the conclusion that a saturable rate of uptake from the stomach lumen was necessary (see 

9 section 3.4.5), the same equation was used for uptake in the mouse. But attempts to identify the 

10 uptake saturation constant, KMASC, from the mouse data were unsuccessful; therefore KMASC 

11 for the mouse was set equal to the value obtained for rats. Adjusting the other mouse oral uptake 

12 parameters gave an adequate fit to those data. This calibration allows inhalation to oral dose13 route extrapolations in the mouse, which can then be extrapolated to identify human oral route 

14 exposures equivalent to mouse inhalation exposures (if equivalent human exposures exist). 

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 used 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 inhalation exposures to methanol in NP mice from Perkins 

et al. (1995a) (8-hour exposures) and GD8 mice from Dorman et al. (1995)(6-hour 

exposures). 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 ventilation 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 exposure groups, the alveolar ventilation rates 

were 29, 24, and 21 (L/hours/kg0.75), respectively. The cardiac output for these 

simulations was set equal to the alveolar ventilation rate.

Source: Dorman et al. (1995); Perkins et al. (1995a).

1 The parameterization of methanol clearance (high-and low-affinity metabolic pathways) 

2 was also verified by simulation of datasets describing the pharmacokinetics of methanol 

3 following i.v. administration. The results of this calibration of the methanol PBPK model are 

4 described in Appendix B and were generally consistent with both the available inhalation and 

5 oral-route data. Up to 20 hours postexposure, blood methanol kinetics appears similar for NP 

6 and pregnant mice. However, some data suggests that clearance in GD18 mice is slower than in 

7 NP and earlier in gestation (GD10 and less), particularly beyond 20 hours postexposure (see the 

8 i.v. and oral data of Ward et al. [1997] in Appendix B). 

9 Intravenous-route blood methanol kinetic data in NP mice were only available for a

10 single i.v. dose of 2,500 mg/kg, but were available for GD18 mice following administration of a 

11 broader range of doses: 100, 500, and 2,500 mg/kg. The i.v. maternal PK data in GD18 mice 

12 appeared to show an unexpected dose-dependent nonlinearity in initial blood concentrations.

13 Before discussing the nonlinearity, it is first noted that data values used here were obtained from 

14 a computational “command file” provided by Ward et al. (1997). These values appear to be 
3-29 DRAFT-DO NOT CITE OR QUOTE

1 consistent with the plots in their publication but are inconsistent with some of the values in their 

2 Table 6 (Ward et al., 1997). In particular, the initial maternal blood concentration (i.e., the Cmax) 

3 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 

4 published table. The corresponding data point in their Figure 5A is distinctly centered above 

5 4,000 mg/L (digitizing yields 4,213 mg/L), and so must be 4,250 rather than 3,251 mg/L. 

6 Therefore the data values listed in the command file were used in the subsequent analysis, rather 

7 than those in the published table.

8 After i.v. dosing the ratio of the administered doses to the first concentrations measured 

9 by Ward et al. (1997) (5-minute time points) were 0.588 L/kg, 0.585 L/kg, and 0.397 L/kg at 

10 doses of 2,500, 500, and 100 mg/kg, respectively. The discrepancy between the first two values 

11 and the third value suggests either a dose dependence in the Vd or some source of experimental 

variability.12 12 It may be that Vd, which is not impacted by any other PBPK parameters and is only 

13 determined by the biochemical partitioning properties of methanol, is 1.5-fold lower at 100 

14 mg/kg than at the higher concentrations, while the Vd at 500 and 2,500 mg/kg are exactly as 

15 predicted by the PBPK model without adjustment. However, it was found that the PBPK model, 

16 obtained with measured partition coefficients and otherwise calibrated to inhalation data, could 

17 adequately fit the data at the nominal dose of 100 mg/kg without other parameter adjustment 

18 simply by simulating a dose of 200 mg/kg, as shown in Figure B-5. The fact that the alternate 

19 dose (200 mg/kg) differs by a factor of 2 from the nominal dose suggests that the data could also 

20 be the result of a simple dilution error in dose preparation. If the first two of the 

21 dose/concentration values were not virtually identical (0.588 and 0.585 L/kg), but instead the 

22 500 mg/kg value was more intermediate between those for 2,500 and 100 mg/kg, then a regular 

23 dose dependence in Vd would seem more likely. However, based on these values, the U.S. EPA 

24 has concluded that the apparent dose dependency is probably the result of a dosing error and

25 therefore, that dose-dependent parameter changes (e.g., in the partition coefficients) should not 

26 be introduced in an attempt to otherwise better fit these data.

27 Further, the nominal “nonlinearity” between the maternal blood and conceptus shown in 

28 Figure 8 of Ward et al. (1997) is the result of those data being plotted on a log-y/linear-x scale. 

29 Replotting the data from Tables 5 and 6 (using the value of 4,250 mg/L from the command file as 

30 the GD18 maternal Cmax for the 2,500 mg/kg) shows the results to be linear, especially in the 

31 low-dose region which is of the most concern (Figure 3-7). Therefore, the current model uses a 

32 consistent set of parameters that are not varied by dose and fit the 2,500 and 500 mg/kg i.v. data 

33 adequately, although they do not fit the 100 mg/kg i.v. data unless, as noted above, a presumed 

34 i.v. dose of 200 mg/kg is employed. With that exception, both the single set of parameters used 

35 herein and the assumption that maternal blood methanol is a good metric of fetal exposure are 

36 well supported by the data. 

 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 scale, 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 simple 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 to the conceptus is below 1:1.

Source: Ward et al. (1997).

1 To summarize the mouse model calibration: using the single set of parameters listed for 

2 the mouse in Table 3-10, the PBPK model has been shown to adequately fit or reproduce 

3 methanol PK data from a variety of laboratories and publications, including both NP mice and 

4 pregnant mice up to GD10. Two saturable metabolic pathways are thus described by the model 
3-31 DRAFT-DO NOT CITE OR QUOTE

1 and supported by the data. Also, it is thereby demonstrated that a model based on NP mouse 

2 physiology adequately describes (predicts) dosimetry in the pregnant mouse dam through GD10. 

3 Finally, as illustrated in Figure 3-7b, methanol PK in the conceptus and dam of both mice 

4 (including lower doses at GD18) and rats (GD14 and GD20) are virtually identical, except for 

5 the very highest doses in mice. Thus the existing model appears to be adequate for predicting 

6 internal methanol doses, including fetal exposures, at bioassay conditions.

7 An evaluation of the importance of selected parameters on mouse model estimates of 

8 blood methanol AUC was performed by conducting a sensitivity analysis using the subroutines 

9 within acslXtreme v2.3 (Aegis Technologies, Huntsville, Alabama). The analysis was conducted 

10 by measuring the change in model output corresponding to a 1% change in a given model 

11 parameter when all other parameters were held fixed. Sensitivity analyses were conducted for 

12 the inhalation and oral routes. The inhalation route analysis was conducted under the exposure 

13 conditions of Rogers and Mole (1997) and Rogers et al. (1993a): 7-hour inhalation exposures at 

14 the no-observed-effect level (NOEL) concentration of 1,000 ppm. The oral route sensitivity 

15 analysis was conducted for an oral dose of 1,000 mg/kg.

16 The parameters with the largest sensitivity coefficients for the inhalation route at 

17 1,000 ppm (absolute values >1) were pulmonary ventilation scaling coefficient (QPC) and 

18 maximum velocity of the high-affinity/low-capacity pathway (VmaxC). The sensitivity 

19 coefficient for QPC increases during the exposure period as metabolism begins to saturate. 

20 Following oral exposure, mouse blood methanol AUC was sensitive to the rate constants for oral 

21 uptake. Blood AUC was most sensitive to the maximum and saturation rate constants for uptake 

22 from the stomach (VmASC and KMASC). The sensitivity coefficient for VmASC decreased 

23 during the first hours after exposure from 1 to less than 0.1 at the end of exposure. Blood 

24 methanol AUC was also modestly sensitive to first-order uptake from the intestine (KAI), and 

25 first-order transfer between stomach and intestine (KSI), the rate constants for uptake from the 

26 intestine and transfer rates between compartments, respectively. For a more complete 

27 description of this sensitivity analysis for the mouse methanol PBPK model see Appendix B.

3.4.5. Rat Model Calibration

28 The rat model was calibrated to fit data from i.v., inhalation, and oral exposures in rats,

29 using data provided in the command file of Ward et al. (1997) and obtained from figures in 

30 Horton et al. (1992) using DigitizIt. Holding other parameters constant, the rat PBPK model was 

31 initially calibrated against the entire set of i.v.-route blood PK data (Figure 3-7) by fitting 

32 Michaelis-Menten constants for one high-affinity/low-capacity and one low-affinity/high33 capacity enzyme to both the Ward et al. (1997) data for Sprague-Dawley (SD) rats and the 

34 Horton et al. (1992) data for Fischer 344 (F344) rats, assuming that any difference between the 

35 two data sets (100 mg/kg data) were from experimental variability and that a single set of 

36 parameters could be fit to data for both strains of rat. However when the resulting parameters 
3-32 DRAFT-DO NOT CITE OR QUOTE

1 were then used to simulate the F344 inhalation uptake data of Horton et al. (with the fractional 

2 absorption for inhalation, FRACIN, adjusted to fit those data), it was found that the clearance 

3 rate predicted after the end of inhalation exposure was much more rapid than shown by the data. 

4 More careful examination of the i.v. data then revealed that there too the clearance for F344 rats 

5 was slower than for SD rats, and that the metabolic parameters obtained from fitting the 

6 combined i.v. data best represented the SD rat data. It was concluded that the combined data set 

7 indicated a true strain difference in metabolic parameters. The metabolic parameters for SD rats 

8 were then obtained by fitting only the Ward et al. (1997) i.v. data (both doses). 

9 The 100 mg/kg i.v. data of Horton et al. (1992) were combined with their inhalation data 

10 and a simultaneous optimization of the metabolic parameters and FRACIN for F344 rats was 

11 attempted over that data set. For this data set, however, the optimization either converged with 

12 the metabolic Vmax for the high affinity (low Km) pathway at zero, or with that Km value 

13 increasing to be statistically indistinguishable from the high Km value. Therefore the Vmax for 

14 the high affinity pathway was allowed to be zero, the Km for that pathway was not estimated, 

15 and only a single Vmax and low affinity (high Km) were fit to those data, with a simultaneous 

16 identification of FRACIN. Since there are no inhalation data for SD rats, this value of FRACIN 

17 was assumed to apply for both strains. The optimized parameters for both strains of rats are 

18 given in Table 3-10.

19 When the model was calibrated using the available inhalation and i.v. data for F344 rats

20 (Horton et al., 1992), a low fractional absorption of 20% was optimized to best fit the data, vs. 

21 66.5% for the mouse. This lower fractional absorption is consistent with values presented by 

22 Perkins et al. (1995), who also found that the fractional absorption of methanol from inhalation 

23 studies was lower in rats than in mice.

24

25

26

27
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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 represent PBPK 

model simulations.

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 from 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

1 Finally, oral absorption parameters were optimized to the oral absorption data reported by 

2 Ward et al. (1997), also using the optimization routines in acslXtreme v2.5.0.6 (Aegis 

3 Technologies, Huntsville, Alabama) (Table 3-10: Figure 3-9). While the two-compartment GI 

4 model (Figure 3-4) allows for both slow and fast absorption modes, it was not possible to fit both 

5 the 100 mg/kg data and the first several hours of the 2500 mg/kg data with that model structure 

6 using linear absorption and inter-compartment transfer rates. In particular the shorter-time data 

7 for 2500 mg/kg indicate a much slower rate of increase in blood levels than the linear-absorption 

8 model (top, thick line in upper panel of Figure 3-10), but the 100 mg/kg data (lower panel of 

9 Figure 3-10) are indeed consistent with a linear model, showing a rapid rise to a fairly narrow 

10 peak, then dropping rapidly. As long as linear rate equations were used, the shape of the 

11 absorption curve at 2500 mg/kg would mirror that at 100 mg/kg, but the data show a clear 

12 difference. It was concluded that the rate of absorption must at least partly saturate at the higher 

13 dose, and hence that Michaelis-Menten kinetics should be used. 

14 Even with the addition of saturable absorprtion from the stomach, it was also found that 

15 the 2500 mg/kg model simulations over-predicted all of those data (result not shown) and it was 

16 hypothesized that fecal elimination might become significant at such a high exposure level, so a 

17 term for fecal elimination from the intestine compartment was added. When that fecal rate 

18 constant and the saturable absorption from the stomach compartment were both used, the 

19 resulting fit to the data (thin, dashed line in upper panel) was considerably improved with an 

20 almost identical (excellent) fit to the 100 mg/kg data (saturable curve can be distinguished from 

21 the linear curve just after the peak is reached in the lower panel of Figure 3-4). For the purpose 

22 of scaling across individuals, strains, and species, the Km for absorption from the stomach 

(KMAS) was assumed to scale in proportion to the stomach (lumen) volume; i.e., with BW1 23 . 

The Vmax (VmAS) was assumed to scale as BW0.75 24 , with the result that for low doses the 

effective linear rate constant (VmAS/KMAS) scales as BW-0.25 25 , which is a standard assumption 

26 for linear rates. Since the quantity on which the rate depends is the total amount in the stomach 

27 (mg methanol), the resulting scaling constant for the Km, KMASC, conveniently has units of 

28 mg/kg BW; i.e., the standard units for oral dosing.
3-35 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 concentration 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

1 The mouse model was scaled to humans by setting either a standard human body weight 

2 (70 kg) or study-specific body weights and using human tissue compartment volumes and blood 

3 flows, and then calibrated to fit the human inhalation exposure data available from the open 

4 literature, which comprised data from four publications (Ernstgard et al., 2005; Batterman et al., 

5 1998; Osterloh et al., 1996; Sedivec et al., 1981). 

6 Since the human data included time-course data for urinary elimination, a first-order rate 

7 of loss of methanol from the blood (K1) was used to provide an estimate of methanol elimination 

8 to the bladder compartment in humans, and the rate of elimination from that compartment then 
3-36 DRAFT-DO NOT CITE OR QUOTE

1 characterized by a second constant (KBL). Note that the total amount eliminated by this route 

2 depends only on K1, while KBL affects the rate at which the material cleared from the blood 

3 then appears in the urine. Inhalation-route urinary methanol kinetic data described by Sedivec 

4 et al. (1981) (Figure 3-11) was used in the model calibration to inform this rate constant. 

5 Without use of the bladder compartment and rate constant, the fit of the model predictions to the 

6 data in Figure 3-11 is quite poor (results not shown), and a statistical test on the improvement of 

7 fit obtained by introducing the additional parameter (KBL) is significant (p < 0.0001). 

8 Conversion between the PBPK-model-predicted rate of urinary excretion (mg/hours) or 

9 cumulative urinary excretion (mg) and the urine methanol concentration data reported by the 

10 authors was achieved by assuming 0.5 mL/hours/kg body weight total urinary output (Horton 

11 et al., 1992). The resulting values of K1C and KBL, shown in Table 3-10, differ somewhat 

12 depending on whether first-order or saturable liver metabolism is used. These are only calibrated 

13 against a small dataset and should be considered an estimate. Urinary elimination is a minor 

14 route of methanol clearance with little impact on blood methanol kinetics.

15 Although the high doses used in the mouse studies clearly warrant the use of a second 

16 metabolic pathway with a high Km, the human exposure data all represent lower concentrations 

17 and may not require or allow for accurate calibration of a second metabolic pathway. Horton 

18 et al. (1992) employed two sets of metabolic rate constants to describe human methanol 

19 disposition, similar to the description used for rats and mice, but in vitro studies using monkey 

20 tissues with nonmethanol substrates were used as justification for this approach. Although 

21 Bouchard et al. (2001) described their metabolism using Michaelis-Menten metabolism, Starr 

22 and Festa (2003) reduced that to an effective first-order equation and showed adequate fits. 

23 Perkins et al. (1995a) estimated a Km of 320 ± 1273 mg/L (mean ± S.E.) by fitting a 

24 one-compartment model to data from a single estimated oral dose. In addition to the extremely 

25 high standard error, the large standard error for the associated Vmax (93 ± 87 mg/kg/hours) 

26 indicates that the set of Michaelis-Menten constants was not uniquely identifiable using this data. 

27 Other Michaelis-Menten constants have been used to describe methanol metabolism in various 

28 models for primates (Table 3-11).

29
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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 methanol elimination concentration (upper panel) and 

cumulative amount (lower panel) following inhalation exposures to methanol in

human volunteers. Data points in lower panel represent estimated 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 Kms reported in the literature

Km (mg/L) Reference Note

320 ±1273a Jacobsen et al., 1988 Human: oral poisoning, estimated dose

716 ± 489a Noker et al., 1980 Cynomolgus Monkey: 2 g/kg dose

278 Makar et al., 1968 Rhesus Monkey: 0.05-1 mg/kg dose

252 ± 116a Eells et al., 1983 Cynomolgus Monkey: 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 ± 8.7a,b (This analysis.) PBPK model, human: 100-800 ppm

a

The values reported are mean ± S.D. b

This Km was optimized while varying Vmax, 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. 

Source: 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 (optimized) -0.994 -24.1

Km 23.7 8.9

VmaxC 33.1 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 v2.3, which assumes all data points are discrete 

and not from sets of data obtained over time. Therefore a true S.D. would be a higher value.

2

3

4 To estimate both Michaelis-Menten and first-order rates, all human data under 

5 nonworking conditions (Batterman et al., 1998; Osterloh et al., 1996; Sedivec et al., 1981) were 

6 used (Table 3-12). The metabolic (first-order or saturable) and urinary elimination constants 

7 were numerically fit to the human datasets, while holding the value for FRACIN at 0.8655 

8 (estimated from the results of Sedivec et al. [1981]) and holding the ventilation rate constant at 

16.5 L/hours/kg0.75 and QPC at 24 L/hours/kg0.75 9 (values used by EPA [2000d] for modeling the 

10 inhalation-route kinetics of vinyl chloride). Other human-specific physiological parameters were 

11 used,as reported in Table 3-10. Final fitted parameters that have been used in the saturable 
3-39 DRAFT-DO NOT CITE OR QUOTE

1 model are given in Table 3-10. The resulting fits of two different possible parameterizations 

2 (first-order ["linear"] or optimized Km/Vmax) are shown in Figures 3-11 and 3-12.

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

5 Use of a first-order rate has the advantage of resulting in a simpler (one fewer variable) 

6 model, while providing an adequate fit to the data; however, the saturable model clearly fits 

7 some of the data better. To discriminate the goodness-of-fit resulting of the inclusion of an 

8 additional variable necessary to describe saturable metabolism versus using a single first-order 
3-40 DRAFT-DO NOT CITE OR QUOTE

rate, a likelihood ratio test was performed.13 1 The hypothesis that one metabolic description is 

2 better than another is calculated using the likelihood functions evaluated at the maximum 

3 likelihood estimates. Since the parameters are optimized in the model using the maximum log 

4 likelihood function (LLF), the resultant LLF is used for the statistical comparison of the models. 

The equation states that two times the log of the likelihood ratio follows a chi square (χ2 5 ) 

6 distribution with r degrees of freedom:

2[log( (model1) / (model 2))] 2[log (model1) log (model 2)] 2 λ λ λ λ χ r 7 − = − − ≅

8 The likelihood ratio test states that if the two times the difference between the maximum LLFs of 

the two different descriptions of metabolism is greater than the χ2 9 distribution then the model fit 

10 has been improved (Devore, 1995; Steiner et al., 1990) .

11 At greater than a 99.95% confidence level, using two metabolic rate constants (Km and 

12 VmaxC) is preferred over using a single rate constant (Table 3-13). Forcing the model to use the 

13 Km calculated by Perkins et al. (1995b) would result in model fits indistinguishable from the 

14 first-order case (results not shown). While the correlation coefficients (Table 3-12) indicate that 

15 VmaxC, and Km are highly correlated, that is not unexpected, and the S.D.s (Table B-3) indicate 

16 that each is reasonably bounded. If the data were indistinguishable from a linear system, Km in 

17 particular would not be so bounded from above since the Michaels-Menten model becomes 

18 indistinguishable from a linear model as VmaxC and Km tend to infinity. Further, the internal dose 

19 candidate points of departure (PODs), for example the BMDL10 for the inhalation-induced brain20 weight changes from NEDO (1987) with methanol blood AUC as the metric, is 90.9 mg-hr/L, 

21 which corresponds to an average blood concentration of 3.8 mg/L. Therefore, the Michaelis22 Menten metabolism rate equation appears to be sufficiently supported by the existing data with 

23 values in a concentration range in which the nonlinearity has an impact. 

Table 3-13. Comparison of LLFs for Michaelis-Menten and first-order metabolism

LLF (logλ) for 

M-M

LLF (logλ) for 

1st order

LLF

1st versus M-Ma

2 χ r (99% 

confidence)b

2 χ r (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 conditions. M-M: Michaelis-Menten a

obtained using this equation: − 2[log λ(model1) − log λ(model 2)] b

significance level at r=1 degree of freedom.

24

25 While the use of Michaelis-Menten kinetics might allow predictions across a wide 

26 exposure range (into the nonlinear region), extrapolation above 1,000 ppm is not suggested since 

27 the highest human exposure data are for 800 ppm. Extrapolation to higher concentrations is 

 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 described in "Reference Guide for Simusolv" (Steiner et al., 1990).
3-41 DRAFT-DO NOT CITE OR QUOTE

1 potentially misleading since the nonlinearity in the exposure-internal-dose relationship for 

2 humans is uncertain above this point. The use of a BMD or internally applied uncertainty factors 

3 (UFs) should place the exposure concentrations well within the linear range of the model.

4 The data from Ernstgard et al. (2005) were used to assess the use of the first-order

5 metabolic rate constant to a dataset collected under conditions of light work. Historical measures 

of QPC (52.6 L/hours/kg0.75) and QCC (26 L/hours/kg0.75 6 ) for individuals exposed under 

conditions of 50 watts of work from that laboratory (52.6 L/hours/kg0.75 7 ) (Ernstgard, 2005; 

8 Corley et al., 1994; Johanson et al., 1986) were used for the 2-hour exposure period (Figure. 3-

9 12). Otherwise, there were no changes in the model parameters (no fitting to these data). The 

10 results are remarkably good, given the lack of parameter adjustment to data collected in a 

11 different laboratory and using different human subjects than those to which the model was 

12 calibrated.

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/kg0.75 (Johansen et al., 1986), and a QCC of 26 L/hours/kg0.75

(Corley et al., 1994) was used by the model. 

Source: Ernstgard et al. (2005).

3.4.6.2. Oral Route

13 There were no methanol human data available for calibration or validation of the oral 

14 route for the human model. In the absence of methanol data to estimate rate constants for oral 

15 uptake, human oral absorption parameters reported values for ethanol (Sultatos et al., 2004) are 
3-42 DRAFT-DO NOT CITE OR QUOTE

1 set in the code, except that saturable absorption from the stomach was retained with the KMASC 

2 equal to the mouse value. The maximum rate of absorption form the stomach, VMASC, was 

3 then set such that for a 70-kg person, VMAS/KM (the effective first-order rate constant at low 

doses) matched the first-order absorption rate from Sultatos et al. (0.21 hr-1 4 ). Also, while 

5 Sultatos et al. included a rate of metabolism for ethanol in the stomach, the corresponding fecal 

6 elimination rate was set to zero here, effectively assuming 100% absorption of methanol for 

7 humans. However, human oral dosimetry was described as zero-order uptake, in which 

8 continuous infusion at a constant rate into the stomach equal to the daily dose/24 hours was 

9 assumed and human internal doses were computed at steady state. Since absorption is 100% for 

10 the human model, at steady state the net rate of absorption must equal the rate of infusion to the 

11 stomach, irrespective of the other parameter values. (Changes in the absorption constants simply 

12 cause the amount of methanol in each GI compartment at steady state to change until the net rate 

13 of absorption from the somtach and intestine equals the rate of infusion.) Thus the human 

14 absorption constants were set to what is considered a reasonable estimate, given the lack of 

15 human oral PK data, but the simulations are conducted in a way that makes the result insensitive 

16 to their values; having human values set does allow for simulations of non-constant infusion, 

17 should such be desired. Since the AUC was computed for a continuous oral exposure, its value 

18 is just 24 hours times the steady-state blood concentration at a given oral uptake rate.

3.4.7. Monkey PK Data and Analysis

19 In order to estimate internal doses (blood AUCs) for the monkey health-effects study of 

20 Burbacher et al. (1999b) and further elucidate the potential differences in methanol 

21 pharmacokinetics between NP and pregnant individuals (2nd and 3rd trimester), a focused 

22 reanalysis of the data of Burbacher et al. (1999a) was performed. Individual blood concentration 

23 measurements prior to and following exposure are shown in scatter plots in Appendix B of 

24 Burbacher et al. (1999a). More specifically, the monkeys in the study were exposed for 2.5 

25 hours/day, with the methanol concentration raised to approximately the target concentration for 

26 the first 2 hours of each exposure and the last 30 minutes providing a chamber "wash-out" 

27 period, when the exposure chamber concentration was allowed to drop to 0. Blood samples 

28 were taken and analyzed for methanol concentration at 30 minutes, 1, 2, 3, 4, and 6 hours after 

29 removal from the chamber (or 1, 1.5, 2.5, 3.5, 4.5, and 6.5 hours after the end of active 

30 exposure). These data were analyzed to compare the PK in NP versus pregnant animals, and 

31 fitted with a simple PK model to estimate 24-hour blood AUC values for each exposure level. 

32 Dr. Burbacher graciously provided the original data, which were used in this analysis.

33 Two cohorts of monkeys were examined, but the data (plots) did not indicate a systematic 

34 difference between the two, so the data from the two cohorts were combined. The data from the 

35 scatter plots of Burbacher et al. (1999a) for the NP (pre-pregnancy), first pregnancy (2nd 

36 trimester), and second pregnancy (3rd trimester) studies are compared in Figure 3-13, along with 
3-43 DRAFT-DO NOT CITE OR QUOTE

1 model simulations (explained below). Since the pregnancy time points were from animals that 

2 had been previously exposed for 87 days plus the duration of pregnancy to that time point, the 

3 pre-exposed NP animals were used for comparison, rather than naïve animals, with the 

4 expectation that effects due to changes in enzyme expression (i.e., induction) from the 

5 subchronic exposure would not be a distinguishing factor. Note that each exposure group 

6 included a pre-exposure baseline or background measurement, also shown. To aid in 

7 distinguishing the data visually, the NP data are plotted at times 5 minutes prior to the actual 

8 blood draws and the 3rd trimester at 5 minutes after each blood draw. 

9 Overall there appears to be no significant or systematic difference among the NP and 

10 pregnant groups. The solid lines are model simulations calibrated to only the 2nd trimester data 

11 (details below), but they just as adequately represent average concentrations for the NP and 3rd 

12 trimester data. Likewise, a PK model calibrated to the NP PK data adequately predicted the 

13 maternal methanol concentrations in the pregnant monkeys (results not shown). Since any 

14 maternal:fetal methanol differences are expected to be similar in experimental animals and 

15 humans (with the maternal:fetal ratio being close to one due to methanol's high aqueous 

16 solubility and relatively limited metabolism by the fetus), the predicted levels for the 2nd 

17 trimester maternal blood are used in place of measured or predicted fetal concentrations. 

18

19

20

21

22

23

24

25

26

27

28

29

30

31
3-44 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 minutes 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

1 To analyze and integrate the PK data of Burbacher et al. (1999a), the one-compartment 

2 model for Michaelis-Menten kinetics used by Burbacher et al. (1999a, 1999b) was extended by 

3 the addition of a chamber compartment to capture the kinetics of concentration change in the 

4 exposure chamber, as shown in Figure 3-14. The data in Figure 3-14 (digitized from Figure 5 of 

5 Burbacher et al., 1999a, 1999b) show an exponential rise to and fall from the approximate target 

6 concentration during the exposure period. The use of a single-compartment model for the 
3-45 DRAFT-DO NOT CITE OR QUOTE

1 chamber allows this dynamic to be captured, so that the full concentration-time course is used in 

2 simulating the monkey internal concentration rather than an approximate step function (i.e. rather 

3 than assuming an instantaneous rise and fall). The pair of equations representing the time-course 

4 in the chamber and monkey are as follows (bolded parameters are fit to data):

5 Chamber: dCch/dt = [(CCM·S – Cch)·Fch – Rinh]/Vch

6 Monkey: dCmk/dt = [Rinh – Vmax·Cmk/(Km + Cmk)]/(Vmk·BW)

with Rinh = Cch·RC·(1000·BW)0.74 7 ·F and Cnet = Cmk + Cbg.

8 d: delta, change

9 Cch: instantaneous chamber concentration (mg/L)

10 t: time (hour)

11 CCM: chamber in-flow methanol concentration (mg/L), which was set to the concentrations 

12 corresponding to those reported in Table 2 of Burbacher et al. (1999a), using the 

13 "Breeding" column for the NP (87 days pre-exposed; values in Table 3-14)

14 S: exposure switch, set to 1 when exposure is on (first 2 hours) and 0 when off

15 Fch:chamber air-flow, 25,200 L/hours, as specified by Burbacher et al. (1999a,1999b)

16 Rinh: net rate of methanol inhalation by the monkeys (mg/hr)

17 Vch (1,220 L): chamber volume, initially set to 1,380 L ("accessible volume" stated by 

18 Burbacher et al. [1999a, 1999b]), but allowed to vary below that value to account for 

19 volume taken by equipment, monkey, and to allow for imperfect mixing

20 Cmk: instantaneous inhalation-induced monkey blood methanol concentration (mg/L); this is 

21 added to the measured background/endogenous concentration before comparison to data

22 Vmax (39.3 mg/hr): fitted (nonscaled) Michaelis-Menten maximum elimination rate

23 Km (14.6 mg/L): fitted (nonscaled) Michaelis-Menten saturation constant

24 Vmk (0.75 L/kg): fitted volume of distribution for monkey

25 BW: monkey body weight (kg); for NP monkeys set to group average values in data of 

26 Burbacher et al. (1999a, 1999b; personal communication)

Rc: allometric scaling factor for total monkey respiration (0.12 L/hours/g0.74 27 = 

2 mL/minute/g0.74 28 ), as used by Burbacher et al. (1999a, 1999b)(note that scaling is to BW 

29 in g, not kg)

30 F: fractional absorption of inhaled methanol, set to 0.6 (60%), the (rounded) value measured 

31 in humans by Sedivec et al. (1981); F and Vmk cannot be uniquely identified, given the 

32 model structure, so F was set to the (approximate) human value to obtain a realistic 

33 estimate of Vmk

34 Cnet: net blood concentration, equal to sum of the inhalation-induced concentration (Cmk) and 

35 the background blood level (Cbg) (mg/L)

36 Cbg: background (endogenous) methanol concentration, set to the pre-exposure group37 specific mean from the data of Burbacher et al. (1999a, 1999b; personal communication)
3-46 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).

1 The model was specifically fit to the 2nd trimester monkey data, assuming that the 

2 parameters were the same for all the exposure groups and concentrations. While the discussion 

3 above and data show little difference between the NP and two pregnancy groups, the 2nd 

4 trimester group was presumed to be most representative of the average internal dosimetry over 

5 the entire pregnancy. Further, the results of Mooney and Miller (2001) show that developmental 

6 effects on the monkey brain stem following ethanol exposure are essentially identical for 

7 monkeys exposed only during early pregnancy versus full-term, indicating that early pregnancy 

8 is a primary window of vulnerability. 

9 Model simulation results are the lines shown in Figures 3-13 and 3-14. The model 

10 provides a good fit to the monkey blood and chamber air concentration data. While the chamber 

11 volume was treated as a fitted parameter, which was not done by Burbacher et al. (1999a), the 

12 chamber concentration data support this estimate. The model does an adequate job of fitting the 

13 data for all exposure groups without group-specific parameters. In particular, the data for all 

14 exposure levels can be adequately fit using a single value for the volume of distribution (Vmk) as 

15 well as each of the metabolic parameters. While one may be able to show statistically distinct 

16 parameters for different groups or exposure levels (by fitting the model separately to each), as 

17 was done by Burbacher et al. (1999a), it is unlikely that such differences are biologically 

18 significant, given the fairly large number of data points and the large variability evident in the 

19 blood concentration data. Thus, the single set of parameters listed with the parameter 

20 descriptions above will be used to estimate internal blood concentrations for the dose-response 
3-47 DRAFT-DO NOT CITE OR QUOTE

1 analysis. The chamber concentrations for “pregnancy” exposures recorded by Burbacher et al. 

2 (1999a; Table 2) and average body weights for each exposure group at the 2nd trimester time 

3 point were used along with the model to calculate 24-hour blood methanol AUCs (Table 3-14). 

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,1999b), 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

4 Mouse, rat, and human versions of a methanol PBPK model have been developed and 

5 calibrated to data available in the open literature. The model simplifies the structure used by 

6 Ward et al. (1997), while adding specific refinements such as a standard lung compartment 

7 employed by Fisher et al. (2000) and a two-compartment GI tract. 

8 Although the endpoints of concern are developmental effects which occur during in utero 

9 and (to a lesser extent) lactational exposure, no pregnancy-specific PBPK model exists for 

10 methanol and inadequate data exists for the development and validation of a 

11 fetal/gestational/conceptus compartment. The fact that the unique physiology of pregnancy and 

12 the fetus/conceptus are not represented in a methanol model would be important if methanol 

13 pharmacokinetics differed significantly during pregnancy or if the observed partitioning of 

14 methanol into the fetus/conceptus versus the mother showed a concentration ratio significantly 

15 greater than or less than 1. Methanol pharmacokinetics during GD6–GD10 in the mouse are not 

16 different from NP mice (Pollack and Brouwer, 1996), and the maternal blood:fetus/conceptus 

17 partition coefficient is reported to be near 1 (Ward et al., 1997; Horton et al., 1992). At GD18 in 

18 the mouse, maternal blood levels are only modestly different from those in NP animals (see 

19 Figures B-4 and B-5 [Appendix B] for examples), and in general the PBPK model simulations 

20 for the NP animal match the pregnancy data as well as the nonpregnancy data. Likewise, 

21 maternal blood kinetics in monkeys differs little from those in NP animals (see Section 3.4.7 for 

22 details). Further, in both mice and monkeys, to the extent that late-pregnancy blood levels differ 

23 from NP for a given exposure, they are higher; i.e., the difference between model predictions and 

24 actual concentrations is in the same direction. These data support the assumption that the ratio of 

25 actual target-tissue methanol concentration to (predicted) NP maternal blood concentrations will 

26 be about the same across species, and hence, that using NP maternal blood levels in place of fetal 

27 concentrations will not lead to a systematic error when extrapolating risks. 
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1 The findings in the mouse (similar blood methanol kinetics between NP and pregnant 

2 animals prior to GD18 and a maternal blood:fetal partition coefficient close to 1) are assumed to 

3 be applicable to the rat. However, the critical gestational window for the reduced brain weight 

4 effect observed in the NEDO (1987) rat study is broader than for the mouse cervical rib effect. 

5 In addition, NEDO (1987) rats were exposed not only to methanol gestationally but also 

6 lactationally and via inhalation after parturition. The additional routes of exposure presented to 

7 the pups in this study present uncertainties and suggest that average blood levels in pups might 

8 be greater than those of the dam. 

9 Methanol is transported directly from the maternal circulation to fetal circulation via the 

10 placenta, but transfer via lactation involves distribution to the breast tissue, then milk, then 

11 uptake from the pup’s GI tract. Therefore blood or target-tissue levels in the breast-feeding 

12 infant or pup are likely to differ more from maternal levels than do fetal levels. In addition, the 

13 health-effects data indicate that most of the effects of concern are due to fetal exposure, with a 

14 relatively small influence due to postbirth exposures. Further, it would be extremely difficult to 

15 distinguish the contribution of postbirth exposure from pre birth exposure to a given effect in a 

16 way that would allow the risk to be estimated from estimates of both exposure levels, even if one 

17 had a lactation/child PBPK model that allowed for prediction of blood (or target-tissue) levels in 

18 the offspring. Finally, one would still expect the target-tissue concentrations in the offspring to 

19 be closely related to maternal blood levels (which depend on ambient exposure and determine 

20 the amount delivered through breast milk), with the relationship between maternal levels and 

21 those in the offspring being similar across species. Further, as discussed to a greater extent in 

22 Sections 5.1.2 and 5.3, it is likely that the difference in blood levels between rat pups and dams 

23 would be similar to the difference between mothers and human offspring. Therefore, it is 

24 assumed that the potential differences between pup and dam blood methanol levels do not have a 

25 significant impact on this risk assessment and the estimation of HECs. The use of the full 

26 intrahuman UF of 10 is also expected to account for PK differences between children and adults.

27 Therefore, the development of a lactation/child PBPK model appears not to be necessary, 

28 given the minimal change that is likely to result in risk extrapolations, and use of (NP) maternal 

29 blood levels as a measure of risk in the offspring is considered preferable over use of default 

30 extrapolation methods. In particular, the existing human data allow for predictions of maternal 

31 blood levels, which depend strongly on the rate of maternal methanol clearance. Since bottle-fed 

32 infants do not receive methanol from their mothers, they are expected to have lower or, at most, 

33 similar overall exposures for a given ambient concentration than the breast-fed infant, so that use 

34 of maternal blood levels for risk estimation should also be adequately protective for that group.

35 The model fits to the mouse oral-route methanol kinetic data, using a consistent set of 

36 parameters (Figure B-4 in Appendix B), are fairly good for doses of 1,500 mg/kg but 

37 underpredict blood levels by 30% or more after a dose of 2,500 mg/kg. In particular, the oral 

38 mouse model consistently underpredicts the amount of blood methanol reported in two studies 
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1 (Ward et al., 1997, 1995). Ward et al. (1997) utilized a different Vmax for each oral absorption 

2 dataset; the GD18 and the GD8 data from Dorman et al. (1995) were both fit using a Vmax of 

3 ~80 mg/kg/hours (body weights were not listed; the model assumed that GD8 and GD18 mice 

4 were both 30 g; Ward et al. [1997] did not scale by body weight). Additionally, lower partition 

5 coefficients for placenta (1.63 versus 3.28) and embryonic fluid (0.0037 versus 0.77) were used 

6 for GD8 and GD18. The current refined model adequately fits the oral PK data using a single set 

7 of parameters that is not varied by dose or source of data.

8 The rat models were able to adequately predict the limited inhalation, oral and i.v. 

9 datasets available. Low-dose exposures were emphasized in model optimization due to their 

10 greater relevance to risk assessment. Based on a rat inhalation exposure to 500 ppm, the HEC 

11 would be 281 ppm (by applying an AUC of 201.3 [Figure B-12] to Equation 1 of Appendix B). 

12 The final mouse, rat, and human methanol PBPK models fit multiple datasets for 

13 inhalation, oral, and i.v., from multiple research groups using consistent parameters that are 

14 representative of each species but are not varied within species or by dose or source of data. 

15 Also, a simple PK model calibrated to NP monkey data, which were shown to be essentially 

16 indistinguishable from pregnant monkey PK data, was used to estimate blood methanol AUC 

17 values (internal doses) in that species. In Section 5, the models and these results are used to 

18 estimate chronic human exposure concentrations from internal dose metrics.
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4. HAZARD IDENTIFICATION

4.1. STUDIES IN HUMANS – CASE REPORTS, OCCUPATIONAL AND 

CONTROLLED STUDIES

4.1.1. Case Reports

1 An extensive library of case reports has documented the consequences of acute 

2 accidental/intentional methanol poisoning. Nearly all have involved ingestion, but a few have 

3 involved percutaneous and/or inhalation exposure. As many of the case reports demonstrate, the 

4 association of Parkinson-like symptoms with methanol poisoning is related to the observation 

5 that lesions in the putamen are a common feature both in Parkinson’s disease and methanol 

6 overexposure. These lesions are commonly identified using computed tomography (CT) or by 

7 Magnetic Resonance Imaging (MRI). Other areas of the brain (e.g., the cerebrum, cerebellum, 

8 and corpus callosum) also have been shown to be adversely affected by methanol overexposure. 

9 Various therapeutic procedures (e.g., ethanol infusion, sodium bicarbonate or folic acid 

10 administration, and hemodialysis) have been used in many of these methanol overexposures, and 

11 the reader is referred to the specific case reports for details in this regard. The reader also is 

12 referred to Kraut and Kurtz (2008) and Barceloux et al. (2002) for a more in-depth discussion of 

13 the treatments in relation to clinical features of methanol toxicity. A brief discussion of the 

14 terms cited in case report literature follows.

15 Basal ganglia, a group of interconnected subcortical nuclei in each cerebral hemisphere, 

16 refers to various structures in the grey matter of the brain that are intimately involved, for 

17 example, in coordinating motor function, maintaining ocular and respiratory function, and 

18 consciousness. The connectivity within the basal ganglia involves both excitatory and inhibitory 

19 neurotransmitters such as dopamine (associated with Parkinson’s disease when production is 

20 deficient).

21 The structures comprising the basal ganglia include but are not limited to: the putamen 

22 and the globus pallidus (together termed the lentiform nuclei), the pontine tegmentum, and the 

23 caudate nuclei. Dystonia or involuntary muscle contraction can result from lesions in the 

24 putamina; if there are concomitant lesions in the globus pallidus, Parkinsonism can result (Bhatia 

25 and Marsden, 1994). Bhatia and Marsden (1994) have discussed the various behavioral and 

26 motor consequences of focal lesions of the basal ganglia from 240 case-study reports. Lesions in 

27 the subcortical white matter adjacent to the basal ganglia often occur as well (Airas et al., 2008; 

28 Rubinstein et al., 1995; Bhatia and Marsden, 1994). In the case reports of Patankar et al. (1999), 

29 it was noted that the severity and extent of necrosis in the lenticular nuclei do not necessarily 

30 correlate with clinical outcome. 
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1 In one of the earliest reviews of methanol overexposure, Bennett et al. (1953) described a 

2 mass accidental poisoning when 323 persons, ranging in age from 10 to 78 years, in Atlanta, 

3 Georgia, consumed “whisky” adulterated with as much as 35–40% methanol. In all, 41 people 

4 died. Of the 323 individuals, 115 were determined to be acidotic with symptoms (visual 

5 impairment, headache [affecting ~62%], dizziness [affecting ~30%], nausea, abdominal pain and 

6 others) beginning around 24 hours postexposure. Visual impairment was mostly characterized 

7 by blurred or indistinct vision; some who were not acidotic experienced transient visual 

8 disturbances. The cardiovascular parameters were unremarkable. The importance of acidosis to 

9 outcome is shown in Table 4-1. Among the key pathological features were cerebral edema, lung 

10 congestion, gastritis, pancreatic necrosis, fatty liver, epicardial hemorrhages, and congestion of 

11 abdominal viscera. 

12 In another early investigation of methanol poisoning (involving 320 individuals), Benton 

13 and Calhoun (1952) reported on methanol’s visual disturbances.

Table 4–1. Mortality rate for subjects exposed to methanol-tainted whiskey in relation 

to their level of acidosisaa

Subjects Number Percent deaths

All patients 323 6.2

Acidotic (CO2 <20 mEq) 115 19

Acidotic (CO2 <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).

14 Riegel and Wolf (1966), in a case report involving a 60-year-old woman who ingested 

15 methanol, noted that nausea and dizziness occurred within 30 minutes of ingestion. She 

16 subsequently passed out and remained unconscious for 3 days. Upon awakening she had 

17 paralysis of the vocal cords and was clinically blind in one eye after 4 months. Some aspects of 

18 Parkinson-like symptoms were evident. There was a pronounced hypokinesia with a mask-like 

19 face resembling a severe state of Parkinson’s disease. The patient had difficulty walking and 

20 could only make right turns with difficulty. There was no memory loss.

21 Treatment of a 13-year-old girl who ingested an unspecified amount of a windshield22 washer solution containing 60% methanol was described by Guggenheim et al. (1971). She 

23 displayed profound acidosis; her vital signs, once she was treated for acidosis, were normal by 

24 36 hours after hospital admission. During the ensuing 6 months after discharge from the 

25 hospital, visual acuity (20/400, both eyes) worsened, and she experienced muscle tremors, arm 

26 pain, and difficulty in walking. A regimen of levadopa treatment greatly improved her ability to 

27 function normally.
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1 Ley and Gali (1983) also noted symptoms that are Parkinson like following methanol 

2 intoxication. In this case report respiratory support was needed; the woman was in a coma. 

3 Once stabilized, she exhibited symptoms similar to those noted in other case study reports, such 

4 as blurred vision, movement difficulty, and tremors. Computerized Axial Tomography scan 

5 findings highlighted the central nervous system (CNS) as an important site for methanol 

6 poisoning.

7 Rubinstein et al. (1995) presented evidence that a methanol blood level of 36 mg/dL 

8 (360 mg/L) is associated with a suite of CNS and ocular deficits that led to a 36-year-old man 

9 (who subsequently died) becoming comatose. CT scans at 1–2 days following ingestion were 

10 normal. However, MRI scans at day 4 revealed lesions in the putamen and peripheral white 

11 matter of the cerebral and cerebellar hemispheres. Bilateral cerebellar cortical lesions had been 

12 reported in an earlier case of methanol poisoning by Chen et al. (1991).

13 Finkelstein and Vardi (2002) reported that long-term inhalation exposure of a woman 

14 scientist to methanol without acute intoxication resulted in a suite of delayed neurotoxic 

15 symptoms (e.g., hand tremor, dystonia, bradykinesia, and other decrements in body movement). 

16 Despite treatment with levadopa, an increase in the frequency and severity of effects occurred. 

17 Exposure to bromine fumes was concomitant with exposure to methanol.

18 Hantson et al. (1997a) found, in four cases, that MRI and brain CT scans were important 

19 tools in revealing specific brain lesions (e.g., in the putamina and white matter). The first subject 

20 was a 57-year-old woman who complained of blurred vision, diplopia, and weakness 24 hours

21 after ingesting 250 mL of a methanolic antifreeze solution. Upon hospital admission she was 

22 comatose and in severe metabolic acidosis. An MRI scan at 9 days indicated abnormal 

23 hyperintense foci in the putamina (decreased in size by day 23) and subtle lesions (no change by 

24 day 23) in the white matter. Upon her discharge, bilateral deficits in visual acuity and color 

25 discrimination persisted.

26 Similar deficits (metabolic acidosis, visual acuity, and color discrimination) were seen in 

27 a man who ingested 300 mL of 75% methanol solution. His blood methanol level was 

28 163 mg/dL (1,630 mg/L). An MRI administered 24 hours after hospital admission revealed 

29 abnormal hyperintense foci in the putamina, with less intense lesions in the white matter. Like 

30 the first subject, a subsequent MRI indicated the foci decreased in size over time, but visual 

31 impairments persisted.

32 The third individual, a male, ingested an unspecified amount of a methanolic solution. 

33 His blood methanol level was 1,290 mg/dL (12,900 mg/L), and he was in a coma upon hospital 

34 admission. An MRI revealed lesions in the putamina and occipital subcortical white matter. A 

35 follow-up CT scan was performed after 1 year and showed regression of the putaminal lesions 
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1 but no change in the occipital lesions. Upon his discharge, severe visual impairment remained 

2 but no extrapyramidal signs were observed.

3 The last case was a man who became comatose 12 hours after ingesting 100 mL 

4 methanol. His blood methanol level at that time was 60 mg/dL (600 mg/L). An MRI revealed 

5 lesions in the putamina; at 3 weeks these lesions were observed to have decreased in size. Upon 

6 his discharge, the neurological signs had improved but optic neuropathy (in visual evoked 

7 potential) was observed.

8 In a separate publication, Hantson et al. (1997b) reported a case of a 26-year-old woman 

9 who had ingested 250–500 mL methanol during the 38th week of pregnancy. Her initial blood 

10 methanol level was 230 mg/dL (2,300 mg/L) (formate was 33.6 mg/dL or 336 mg/L), yet only a 

11 mild metabolic acidosis was indicated. No distress to the fetus was observed upon gynecologic 

12 examination. Six days after therapy was initiated (methanol was not present in blood), she gave 

13 birth. No further complications with either the mother or newborn were noted. 

14 There have been several case reports involving infant or toddler exposures to methanol 

15 (De Brabander et al., 2005; Wu et al., 1995; Brent et al., 1991; Kahn and Blum, 1979). The 

16 report by Wu et al. (1995) involved a 5-week-old infant with moderate metabolic acidosis and a 

17 serum methanol level of 1,148 mg/dL (11,480 mg/L), a level that is ordinarily fatal. However, 

18 this infant exhibited no toxic signs and survived without any apparent permanent problems. De 

19 Brabander et al. (2005) reported the case of a 3-year-old boy who ingested an unknown amount 

20 of pure methanol; at 3 hours after ingestion, the blood methanol level was almost 30 mg/dL (300 

21 mg/L). Ethanol infusion as a therapeutic measure was not well tolerated; at 8 hours after 

22 ingestion, fomepizole was administered, and blood methanol levels stabilized below 20 mg/dL

23 (200 mg/L), a level above which is considered to be toxic by the American Academy of Clinical 

24 Toxicology (Barceloux et al., 2002). Neither metabolic acidosis nor visual impairment was 

25 observed in this individual. Hantson et al. (1997a), in their review, touted the efficacy of 

26 fomepizole over ethanol in the treatment of methanol poisoning

27 Bilateral putaminal lesions, suggestive of nonhemorrhagic necrosis in the brain of a man 

28 who accidentally ingested methanol, were reported by Arora et al. (2005). Approximately

29 10 hours after MRI examination, he developed blurred vision and motor dysfunction. After 

30 5 months, visual deficits persisted along with extrapyramidal symptoms. Persistent visual 

31 dysfunction was also reported in another methanol poisoning case (Arora et al., 2007); the vision 

32 problems developed ~46 hours subsequent to the incident. 

33 Vara-Castrodeza et al. (2007) applied diffusion-weighted MRI on a methanol-induced 

34 comatose woman. Diffusion-weighted MRI provides an image contrast distinct from standard 

35 imaging in that contrast is dependent on the molecular motion of water (Schaefer et al., 2000). 

36 The neuroradiological findings were suggestive of bilateral putaminal hemorrhagic necrosis, 
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1 cerebral and intraventricular hemorrhage, diffuse cerebral edema, and cerebellar necrosis. 

2 Diffusion-weighted MRI allows for differentiation of restricted diffusion which is indicative of 

3 nonviable tissue. In this case, treatment for acidosis (blood methanol levels had risen to 

4 1,000 mg/L) was unsuccessful and the patient died. 

5 Emergency treatment was unable to save the life of a 38-year-old man who presented 

6 with abdominal pain and convulsions after methanol intoxication (Henderson and Brubacher, 

7 2002). A review of a head CT scan performed before the individual went into respiratory arrest 

8 revealed bilateral globus pallidus ischemia.

9 Discrete lesions of the putamen, cerebral white matter, and corpus callosum were 

10 observed upon MRI (8 days post ingestion) in a man exposed to methanol (blood level 370 

11 mg/L) complaining of vision loss (Keles et al., 2007). Standard treatments corrected the acidosis 

12 (pH 6.8), and at 1-month follow-up, his cognitive function improved but blindness and bilateral 

13 optic atrophy were described as permanent. The follow-up MRI showed persistent putaminal 

14 lesions with cortical involvement. 

15 Fontenot and Pelak (2002) described a case of a woman who presented with persistent 

16 blurred vision and a worsening mental status 36 hours after ingestion of an unspecified amount 

17 of methanol. The initial CT scan revealed mild cerebral edema. The blood methanol level at this 

18 time was 86 mg/dL (860 mg/L). A repeat CT scan 48 hours after presentation showed 

19 hypodensities in the putamen and peripheral white matter. One month after discharge, cognitive 

20 function improved, and the patient experienced only a mild lower-extremity tremor.

21 Putaminal necrosis and edema of the deep white matter (the corpus callosum was not 

22 affected) was found upon MRI examination of a 50-year-old woman who apparently ingested an 

23 unknown amount of what was believed to be pure laboratory methanol (Kuteifan et al., 1998). 

24 Her blood methanol level was 39.7 mM (127 mg/dL; 1,272 mg/L) upon hospital admission and 

25 dropped to 102 mg/dL (1,020 mg/L) at 10 hours and to 71 mg/dL (710 mg/L) at 34 hours. The 

26 woman, a chronic alcoholic, was in a vegetative state when found and did not improved over the 

27 course of a year.

28 MRI and CT scans performed on a 51-year-old man with generalized seizures who had a 

29 blood methanol level of 95 mM (304 mg/dL; 3,044 mg/L) revealed bilateral hemorrhagic 

30 necrosis of the putamen and caudate nuclei (Gaul et al., 1995). In addition, there was extensive 

31 subcortical necrosis and bilateral necrosis of the pontine tegmentum and optic nerve. The patient 

32 died several hours after the scans were performed.

33 The relation of methanol overexposure to brain hemorrhage was a focus of the report by 

34 Phang et al. (1988), which followed the treatment of 7 individuals, 5 of whom died within 

35 72 hours after hospital admission. In two of the deceased individuals, CT scans and autopsy 

36 revealed putaminal hemorrhagic necrosis. The investigators postulated that the association of 
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1 methanol with hemorrhagic necrosis may be complicated by the use of heparin during 

2 hemodialysis treatment for acidosis

3 Treatment of two men who had drunk a solution containing 58% methanol and presented 

4 with impaired vision, coma, and seizures was discussed in a case report by Bessell-Browne and 

5 Bynevelt (2007). A CT scan on one individual revealed bilateral putaminal and cerebral lesions. 

6 Blood methanol levels were 21 mg/L. This individual, despite standard treatments, never 

7 regained consciousness. The second individual, upon MRI, showed scattered hemorrhage at the 

8 grey-white interface of the cerebral hemispheres. 

9 There have been two case reports (Adanir et al., 2005; Downie et al., 1992) that involved 

10 percutaneous and inhalation exposure. Use of a methanol-containing emollient by a woman with 

11 chronic pain led to vision loss, hyperventilation and finally, coma (Adanir et al., 2005). 

12 Subsequent to standard treatment followed by hospital discharge, some visual impairment and 

13 CNS decrements remained. The methanol blood threshold for ocular damage and acidosis 

14 appeared to be ~20 mg/L. Dutkiewicz et al. (1980) have determined the skin absorption rate to 

be 0.192 mg/cm2 15 /minute. In the case report of Aufderheide et al. (1993), two firefighters were 

16 transiently exposed to methanol by inhalation and the percutaneous route. Both only complained 

17 of a mild headache and had blood methanol levels of 23 and 16 mg/dL (230 and 160 mg/L), 

18 respectively.

19 Bebarta et al. (2006) conducted a prospective observational study of seven men who had 

20 purposefully inhaled a methanol-containing product. Four had a blood methanol level upon 

21 hospital presentation of >24 mg/dL (240 mg/L); the mean formic acid level was 71 μg/dL. One 

22 individual had a blood methanol level of 86 mg/dL (860 mg/L) and a blood formic acid level of 

23 250 μg/mL upon hospital admission. This latter individual was treated with fomepizole. No 

24 patient had an abnormal ophthalmologic examination. All seven stabilized quickly and acidosis 

25 was normalized in 4 hours.

26 Numerous other case reports documenting putaminal necrosis/hemorrhage and/or 

27 blindness have been reported (Blanco et al., 2006; Feany et al., 2001; Hsu et al., 1997; Pelletier 

28 et al., 1992; Chen et al., 1991).

29 Hovda et al. (2005) presented a combined prospective and retrospective case series study 

30 of 51 individuals in Norway (39 males and 12 females, many of whom were alcoholics) who 

31 were hospitalized after consuming tainted spirits containing 20% methanol and 80% ethanol. In 

32 general, serum methanol concentrations were highest among those most severely affected. The 

33 poor outcome was closely correlated with the degree of metabolic acidosis. It was noted by the 

34 investigators that the concomitant consumption of ethanol prevented more serious sequelae in 

35 2/5 individuals who presented with detectable ethanol levels and were not acidotic despite 2

36 having the highest blood methanol levels. However, others with detectable levels of ethanol 
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1 along with severe metabolic acidosis (two of whom died) presumably had subtherapeutic levels 

2 of ethanol in their system.

3 In a later report, Hovda et al. (2007) focused on formate kinetics in a 63-year-old male 

4 who died 6 days after being admitted to the hospital with headache, vomiting, reduced vision, 

and dizziness. The investigators speculated that the prolonged metabolic acidosis observed (T1/2 5

6 for formic acid was 77 hours before dialysis, compared to a typical normal range of 2.5–

7 12 hours) may have been related to retarded formate elimination.

8 Hovda and colleagues (Hunderi et al., 2006) found a strong correlation between blood 

methanol concentration and the osmolal gap (R2 9 = 0.92) among 17 patients undergoing dialysis 

10 after consuming methanol-contaminated spirits. They concluded that the osmolal gap could be 

11 taken as a priori indication of methanol poisoning and be used to guide initiation and duration of 

12 dialysis. As they indicated, many hours of dialysis could be safely dispensed with. The osmolal 

13 gap pertains to the effect that methanol (and other alcohols) has on the depression of the freezing 

14 point of blood in the presence of normal solutes. Braden et al. (1993) demonstrated in case 

15 studies that the disappearance of the osmolal gap correlates with the correction of acidosis; they 

16 cautioned that methanol and ethanol should not be assumed to be the main factors in causing 

17 osmolal gap as glycerol and acetone and its metabolites can as well. A more detailed discussion 

18 of the anion and osmolal gap has been provided by Henderson and Brubacher (2002).

19 Hassanian-Moghaddam et al. (2007) compiled data on the prognostic factor relating to 

20 outcome in methanol-poisoning cases in Iran. They examined 25 patients, 12 of whom died; 3

21 of the survivors were rendered blind. There was a significant difference in mean pH of the first 

22 arterial blood gas measurements of those who subsequently died compared with survivors. It 

23 was concluded that poor prognosis was associated with pH <7, coma upon admission, and 

24 >24-hours delay from intake to admission.

25 The use of blood methanol levels as predictors of outcome is generally not recommended

26 (Barceloux et al., 2002). These investigators cited differences in sampling time, ingestion of 

27 ethanol, and levels of toxic (e.g., formic acid) metabolites among the complicating factors. As 

28 an illustration, the case report by Prabhakaran et al. (1993) cites two women who ingested a 

29 methanol solution (photocopying diluent) at about the same time, were admitted to the hospital 

30 about the same time (25–26 hours after ingestion) and had identical plasma methanol 

31 concentrations (83 mg/dL; 830 mg/L) upon admission, but different outcomes. Patient #1 was in 

32 metabolic acidosis and had an unstable conscious state even after treatment. Upon discharge at 

33 day 6, there were no apparent sequelae. Patient #2 had severe metabolic acidosis, fixed and 

34 dilated pupils, and no brain stem reflexes. This patient died at day 3 even though therapeutic 

35 measures had been administered.
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1 In a discussion of 3 fatal methanol-overexposure cases, Andresen et al. (2008) found 

2 antemortem blood methanol levels of 540 and 740 mg/dL (5,400 and 7,400 mg/L) in 2

3 individuals. At autopsy brain stem blood levels were 738 and 1,008 mg/dL (7,380 and 

4 10,080 mg/L), respectively. These brain levels were much higher than blood levels postmortem. 

5 Autopsy revealed brain and pulmonary edema in all three individuals; in the two who had the 

6 longer survival times, there was hemorrhagic necrosis of the putamen and hemorrhages of the 

7 tissue surrounding the optic nerve. In their study of 26 chronic users of methylated spirits, 

8 Meyer et al. (2000) found that the best predictor of death or a poor outcome in chronic abusers 

9 was a pH <7.0; there was no correlation between blood methanol levels and outcome. Mahieu 

10 et al. (1989) considered a latency period before treatment exceeding 10 hours and a blood 

11 formate level >50 mg/dL (500 mg/L) as predictive of possible permanent sequelae. Liu et al. 

12 (1998) in their examination of medical records of 50 patients treated for methanol poisoning over 

13 a 10-year period found that 1) deceased patients had a higher mean blood methanol level than 

14 survivors; and 2) initial arterial pH levels <7.0 (i.e., severe metabolic acidosis). Coma or seizure 

15 was also associated with higher mortality upon hospital admission. 

16 Numerous cases of methanol poisoning have been documented in a variety of countries. 

17 In Tunisia, 16 cases of methanol poisoning were discussed by Brahmi et al. (2007). Irreversible 

18 blindness occurred in two individuals, with others reporting CNS symptoms, GI effects, visual 

19 disturbances, and acidosis. Putaminal necrosis was also described in case reports from Iran 

20 (Sefidbakht et al., 2007). Of 634 forensic autopsies carried out in Turkey during 1992–2003, 18 

21 appeared to be related to methanol poisoning (Azmak, 2006). Brain edema and focal necrosis of 

22 the optic nerve were among various sequelae noted. Dethlefs and colleagues (Naraqi et al., 

23 1979; Dethlefs and Naraqi, 1978) described permanent ocular damage in 8/24 males who 

24 ingested methanol in Papua New Guinea.

25 In summary, most cases of accidental/intentional methanol poisoning reveal a common 

26 set of symptoms, many of which are likely to be presented upon hospital admission. These 

27 include:

28 • blurred vision and bilateral or unilateral blindness

29 • convulsions, tremors, and coma

30 • nausea, headache, and dizziness

31 • abdominal pain

32 • diminished motor skills

33 • acidosis

34 • dyspnea

35 • behavioral and/or emotional deficits

36 • speech impediments
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1 Acute symptoms generally are nausea, dizziness, and headache. In the case reports cited 

2 above, the onset of symptom sets as well as their severity varies depending upon how much 

3 methanol was ingested, whether or not and when appropriate treatment was administered, and 

4 individual variability. A longer time between exposure and treatment, with few exceptions, 

5 results in more severe outcomes (e.g., convulsions, coma, blindness, and death). The diminution 

6 of some acute and/or delayed symptoms may reflect concomitant ingestion of ethanol or how 

7 quickly therapeutic measures (one of which includes ethanol infusion) were administered in the 

8 hospital setting. 

9 Those individuals who are in a metabolic acidotic state (e.g., pH <7.0) are typically the 

10 individuals who manifest the more severe symptoms. Many case reports stress that, unlike blood 

11 pH levels <7.0, blood levels of methanol are not particularly good predictors of health outcome. 

12 According to a publication of the American Academy of Clinical Toxicology (Barceloux et al., 

13 2002), “the degree of acidosis at presentation most consistently correlates with severity and 

14 outcome.”

15 As the case reports demonstrate, those individuals who present with more severe 

16 symptoms (e.g., coma, seizures, severe acidosis) generally exhibit higher mortality (even after 

17 treatment) than those without such symptoms. In survivors of poisoning, persistence or 

18 permanence of vision decrements and particularly blindness often have been observed 

19 Correlation of symptomatology with blood levels of methanol has been shown to vary 

20 appreciably between individuals. Blood methanol levels in the case reports involving ingestion 

21 ranged from values of 30 to over 1,000 mg/dL (300 to over 10,000 mg/L). The lowest value 

22 (20 mg/dL; 200 mg/L) reported (Adanir et al., 2005) involved a case of percutaneous absorption 

23 (with perhaps associated inhalation exposure) that led to vision and CNS deficits after hospital 

24 discharge. In one case report (Rubinstein et al., 1995) involving ingestion, coma and subsequent 

25 death were associated with an initial blood methanol level of 36 mg/dL (360 mg/L).

26 Upon MRI and CT scans, the more seriously affected individuals typically have focal 

27 necrosis in both brain white matter and more commonly, in the putamen. Bilateral hemorrhagic 

28 and nonhemorrhagic necrosis of the putamen is considered by many radiologists as the most 

29 well-known sequelae of methanol overexposure. 

4.1.2. Occupational Studies

30 Occupational health studies have been carried out to investigate the potential effects of 

31 chronic exposure to lower levels of methanol than those seen in acute poisoning cases such as 

32 those described above. For example, Frederick et al. (1984) conducted a health hazard 

33 evaluation on behalf of the National Institute for Occupational Safety and Health (NIOSH) to 
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1 determine if vapor from duplicating fluid (which contains 99% methanol) used in mimeograph 

2 duplicating machines caused adverse health effects in exposed persons. A group of 84 teacher’s 

3 aides were selected for study, 66 of whom responded with a completed medical questionnaire. A 

4 group of 297 teachers (who were not exposed to methanol vapors to the same extent as the 

5 teacher’s aides) completed questionnaires as a control group. A 15-minute breathing zone 

6 sample was taken from 21 duplicators, 15 of which were greater than the NIOSH-recommended 

short term ceiling concentration of 800 ppm (1048 mg/m3 7 ). The highest breathing zone 

8 concentrations were in the vicinity of duplicators for which no exhaust ventilation had been 

provided (3,080 ppm [4,036 mg/m3 9 ] was the highest value recorded). Upon comparison of the 

10 self-described symptoms of the 66 teacher’s aides with those of 66 age-matched teachers chosen 

11 from the 297 who responded, the number of symptoms potentially related to methanol were 

12 significantly higher in the teacher’s aides. These included blurred vision (22.7 versus 1.5%), 

13 headache (34.8 versus 18.1%), dizziness (30.3 versus 1.5%), and nausea (18 versus 6%). By 

14 contrast, symptoms that are not usually associated with methanol exposure (painful urination, 

15 diarrhea, poor appetite, and jaundice) were similar in incidence among the groups. 

16 To further investigate these disparities, NIOSH physicians (not involved in the study) 

17 defined a hypothetical case of methanol toxicity by any of the following four symptom 

18 aggregations: 1) visual changes; 2) one acute symptom (headache, dizziness, numbness, 

19 giddiness, nausea or vomiting) combined with one chronic symptom (unusual fatigue, muscle 

20 weakness, trouble sleeping, irritability, or poor memory); 3) two acute symptoms; or 4) three 

21 chronic symptoms. By these criteria, 45% of the teacher’s aides were classified as being 

22 adversely affected by methanol exposure compared to 24% of teachers (p < 0.025). Those 

23 teacher’s aides and teachers who spent a greater amount of time using the duplicators were 

24 affected at a higher rate than those who used the machines for a lower percentage of their work 

25 day.

26 Tanner (1992) reviewed the occupational and environmental causes of Parkinsonism, 

27 spotlighting the potential etiological significance of manganese, carbon monoxide, repeated head 

28 trauma (such as suffered by boxers), and exposure to solvents. Among the latter, Tanner (1992) 

29 discussed the effects of methanol and n-hexane on the nervous system. Acute methanol 

30 intoxication resulted in inebriation, followed within hours by GI pain, delirium, and coma. 

31 Tanner (1992) pinpointed the formation of formic acid, with consequent inhibition of 

32 cytochrome oxidase, impaired mitochondrial function, and decreased ATP formation as relevant 

33 biochemical and physiological changes for methanol exposure. Nervous system injury usually 

34 includes blindness, Parkinson-like symptoms, dystonia, and cognitive impairment, with injury to 

35 putaminal neurons most likely underlying the neurological responses.
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1 Kawai et al. (1991) carried out a biomarker study in which 33 occupationally exposed

2 workers in a factory making methanol fuel were exposed to concentrations of methanol of up to 

3,577 ppm (4,687 mg/m3 3 ), as measured by personal samplers of breathing zone air. Breathing 

4 zone exposure samples were correlated with the concentrations of methanol in urine at the end of 

5 the shift in 38 exposed individuals and 30 controls (r = 0.82). Eleven of 22 individuals who 

experienced high exposure to methanol (geometric mean of 459 ppm [601 mg/m3 6 ]) complained 

7 of dimmed vision during work while 32% of this group of workers experienced nasal irritation. 

8 These incidences were statistically significant compared to those of persons who worked in lowexposure conditions (geometric mean of 31 ppm [41 mg/m3 9 ]). One 38-year-old female worker 

10 who had worked at the factory for only 4 months reported that her visual acuity had undergone a 

11 gradual impairment. She also displayed a delayed light reflex.

12 Lorente et al. (2000) carried out a case control study of 100 mothers whose babies had 

13 been born with cleft palates. Since all of the mothers had worked during the first trimester, 

14 Lorente et al. (2000) examined the occupational information for each subject in comparison to 

15 751 mothers whose babies were healthy. Industrial hygienists analyzed the work histories of all 

16 subjects to determine what, if any, chemicals the affected mothers may have been exposed to 

17 during pregnancy. Multivariate analysis was used to calculate odds ratios, with adjustments 

18 made for center of recruitment, maternal age, urbanization, socioeconomic status, and country of 

19 origin. Occupations with positive outcomes for cleft palate in the progeny were hairdressing 

20 (OR = 5.1, with a 95% confidence interval [CI] of 1.0–26) and housekeeping (OR = 2.8, with a 

21 95% CI of 1.1–7.2). Odds ratios for cleft palate only and cleft lip with or without cleft palate 

22 were calculated for 96 chemicals. There seemed to be no consistent pattern of association for 

23 any chemical or group of chemicals with these impairments, and possible exposure to methanol 

24 was negative for both outcomes.

4.1.3. Controlled Studies

25 Two controlled studies have evaluated humans for neurobehavioral function following 

exposure to ~200 ppm (262 mg/m3 26 ) methanol vapors in a controlled setting. The occupational 

27 TLV established by the American Conference of Governmental Industrial Hygienists (ACGIH, 

2000) is 200 ppm (262 mg/m3 28 ). In a pilot study by Cook et al. (1991), 12 healthy young men 

29 (22–32 years of age) served as their own controls and were tested for neurobehavioral function 

following a random acute exposure to air or 191 ppm (250 mg/m3 30 ) methanol vapors for 

31 75 minutes. The majority of results in a battery of neurobehavioral endpoints were negative. 

32 However, statistical significance was obtained for results in the P-200 and N1-P2 component of 

33 event-related potentials (brain wave patterns following light flashes and sounds), the Sternberg 

34 memory task, and subjective evaluations of concentration and fatigue. As noted by the Cook et 
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1 al. (1991), effects were mild and within normal ranges. Cook et al. (1991) acknowledged 

2 limitations in their study design, such as small sample size, exposure to only one concentration 

3 for a single duration time, and difficulties in masking the methanol odor from experimental 

4 personnel and study subjects.

5 In a randomized double-blind study, neurobehavioral testing was conducted on 15 men 

and 11 women (healthy, aged 26–51 years) following exposure to 200 ppm (262 mg/m3 6 ) 

7 methanol or water vapors for 4 hours (Chuwers et al., 1995); subjects served as their own 

8 controls in this study. Exposure resulted in elevated blood and urine methanol levels (up to peak 

9 levels of 6.5 mg/L and 0.9 mg/L, respectively) but not formate concentrations. The majority of 

10 study results were negative. No significant findings were noted for visual, neurophysiological, 

11 or neurobehavioral tests except for slight effects (p < 0.05) on P-300 amplitude (brain waves 

12 following exposure to sensory stimuli) and Symbol Digit testing (ability to process information 

13 and psychomotor skills). Neurobehavioral performance was minimally affected by methanol 

14 exposure at this level. Limitations noted by Chuwers et al. (1995) are that studies of alcohol’s

15 affect on P-300 amplitude suggest that this endpoint may be biased by unknown factors and 

16 some experimenters and subjects correctly guessed if methanol was used.

17 Although the slight changes in P-200 and P-300 amplitude noted in both the Chuwers 

18 et al. (1995) and Cook et al. (1991) studies may be an indication of moderate alterations in 

19 cognitive function, the results of these studies are generally consistent and suggest that the 

20 exposure concentrations employed were below the threshold for substantial neurological effects. 

21 This is consistent with the data from acute poisoning events which have pointed to a serum 

22 methanol threshold of 200 mg/L for the instigation of acidosis, visual impairment, and CNS

23 deficits.

24 Mann et al. (2002) studied the effects of methanol exposure on human respiratory 

25 epithelium as manifested by local irritation, ciliary function, and immunological factors. Twelve 

healthy men (average age 26.8 years) were exposed to 20 and 200 ppm (26.2 and 262 mg/m3 26 , 

27 respectively) methanol for 4 hours at each concentration; exposures were separated by 1-week 

intervals. The 20 ppm (26.2 mg/m3 28 ) concentration was considered to be the control exposure 

29 since previous studies had demonstrated that subjects can detect methanol concentrations of 

20 ppm (26.2 mg/m3 30 ) and greater. Following each single exposure, subclinical inflammation was 

31 assessed by measuring concentrations of interleukins (IL-8, IL-1β, and IL-6) and prostaglandin 

32 E2 in nasal secretions. Mucociliary clearance was evaluated by conducting a saccharin transport 

33 time test and measuring ciliary beat frequency. Interleukin and prostaglandin data were 

34 evaluated by a 1-tailed Wilcoxon test, and ciliary function data were assessed by a 2-tailed 

Wilcoxon test. Exposure to 200 (262 mg/m3) versus 20 ppm (26.2 mg/m3 35 ) methanol resulted in a 

36 statistically-significant increase in IL-1β (median of 21.4 versus 8.3 pg/mL) and IL-8 (median of 
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1 424 versus 356 pg/mL). There were no significant effects on IL-6 and prostaglandin E2 

2 concentration, ciliary function, or on the self-reported incidence of subjective symptoms of 

irritation. The authors concluded that exposure to 200 ppm (262 mg/m3 3 ) methanol resulted in a 

4 subclinical inflammatory response. 

In summary, adult human subjects acutely exposed to 200 ppm (262 mg/m3 5 ) methanol 

6 have experienced slight neurological (Chuwers et al., 1995) and immunological effects 

7 (increased subclinical biomarkers for inflammation) with no self-reported symptoms of irritation 

8 (Mann et al., 2002). These exposure levels were associated with peak methanol blood levels of 

9 6.5 mg/L (Chuwers et al., 1995), which is approximately threefold higher than background 

10 methanol blood levels reported for adult human subjects on methanol-restrictive diets 

11 (Table 3-1). Nasal irritation effects have been reported by adult workers exposed to 459 ppm 

(601 mg/m3 12 ) methanol (Kawai et al., 1991). Frank effects such as blurred vision, bilateral or 

13 unilateral blindness, coma, convulsions/tremors, nausea, headache, abdominal pain, diminished 

14 motor skills, acidosis, and dyspnea begin to occur as blood levels approach 200 mg methanol/L, 

15 while 800 mg/L appears to be the threshold for lethality. Data for subchronic, chronic or in utero 

16 human exposures are very limited and inconclusive.

4.2. ACUTE, SUBCHRONIC AND CHRONIC STUDIES AND CANCER BIOASSAYS IN 

ANIMALS—ORAL AND INHALATION

17 A number of studies in animals have investigated the acute, subchronic, and chronic 

18 toxicity of methanol. Most are via the inhalation route. Presented below are summaries of these 

19 investigations.

4.2.1. Oral Studies

4.2.1.1. Acute Toxicity

20 Although there are few studies that have examined the short-term toxic effects of 

21 methanol via the oral route, a number of median lethal dose (LD50) values have been published 

22 for the compound. As listed in Lewis (1992), these include 5,628 mg/kg in rats, 7,300 mg/kg in 

23 mice, and 7,000 mg/kg in monkeys.

4.2.1.2. Subchronic Toxicity

24 An oral repeat dose study was conducted by the EPA (1986c) in rats. Sprague-Dawley 

25 rats (30/sex/dose) were gavaged with 0, 100, 500, or 2,500 mg/kg-day of methanol. Six weeks 

26 after dosing, 10 rats/sex/dose group were subjected to interim sacrifice, while the remaining rats 

27 continued on the dosing regimen until the final sacrifice (90 days). This study generated data on 

28 weekly body weights and food consumption, clinical signs of toxicity, ophthalmologic

29 evaluations, mortality, blood and urine chemistry (from a comprehensive set of hematology, 
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1 serum chemistry, and urinalysis tests), and gross and microscopic evaluations for all test animals. 

2 Complete histopathologic examinations of over 30 organ tissues were done on the control and 

3 high-dose rats. Histopathologic examinations of livers, hearts, and kidneys and all gross lesions 

4 seen at necropsy were done on low-dose and mid-dose rats. There were no differences between 

5 dosed animals and controls in body weight gain, food consumption, or upon gross or microscopic 

evaluations. Elevated levels (p ≤ 0.05 in males) of serum alanine transaminase (ALT)14 6 and 

7 serum alkaline phosphatase (SAP), and increased (but not statistically significant) liver weights 

8 in both male and female rats suggest possible treatment-related effects in rats bolus dosed with 

9 2,500 mg methanol/kg-day despite the absence of supportive histopathologic lesions in the liver. 

10 Brain weights of high-dose group (2,500 mg/kg-day) males and females were significantly less 

11 than those of the control group at terminal sacrifice. Based on these findings, 500 mg/kg-day of 

12 methanol is considered an NOEL from this rat study.

4.2.1.3. Chronic Toxicity

13 A report by Soffritti et al. (2002a) summarized a European Ramazzini Foundation (ERF) 

chronic duration experimental study of methanol15 14 in which the compound was provided to 

15 100 Sprague-Dawley rats/sex/group ad libitum in drinking water at concentrations of 0, 500, 

16 5,000, and 20,000 ppm (v/v). The animals were 8 weeks old at the beginning of the study. In 

17 general, ERF does not randomly assign animals to treatment groups, but assigns all animals from 

18 a given litter to the same treatment group (Bucher, 2002). All rats were exposed for up to 

19 104 weeks, then maintained until they died naturally. Rats were housed in groups of 5 in 

20 Makrolon cages (41 × 25 × 15 cm) in a room that was maintained at 23 ± 2°C and 50–60% 

21 relative humidity. The in-life portion of the experiment ended at 153 weeks with the death of the 

22 last animal. Mean daily drinking water, food consumption, and body weights were monitored 

23 weekly for the first 13 weeks, every 2 weeks thereafter for 104 weeks, then every 8 weeks until 

24 the end of the experiment. Clinical signs were monitored 3 times/day, and the occurrence of 

25 gross changes was evaluated every 2 weeks. All rats were necropsied at death then underwent 

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 Histopathology 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 glandular), intestine (four levels), urinary bladder, prostate, 

gonads, interscapular fat pad, subcutaneous and mesenteric lymph nodes, and any other organs or tissues with

pathologic lesions.
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1 Soffritti et al. (2002a) reported no substantial dose-related differences in survival, but no 

data were provided. Using individual animal data available from the ERF website,17 2 Cruzan 

3 (2009) reports that male rats treated with methanol generally survived better than controls, with 

4 50% survival occurring at day 629, 686, 639 and 701 in the 0, 500, 5,000, and 20, 000 mg/L 

5 groups, respectively. There were no significant differences in survival between female control 

6 and treatment groups, with 50% survival occurring at day 717, 691, 678 and 708 in the 0, 500, 

7 5,000, and 20, 000 mg/L groups, respectively. Body weight and water and food consumption 

8 were monitored in the study, but the data were not documented in the published report. 

9 However, based on data available from the ERF website, average doses of 0, 53.2, 524, and 

10 1,780 mg/kg-day in males and 0, 66.0, 624.1, and 2,177 mg/kg-day in females could be 

11 calculated (see Appendix E) from drinking water concentrations of 0, 500, 5,000, and 

12 20,000 ppm.

13 Soffritti et al. (2002a) reported that water consumption in high-dose females was reduced 

14 compared to controls between 8 and 56 weeks and that the mean body weight in high-dose males 

15 tended to be higher than that of control males. Overall, there was no pattern of compound16 related clinical signs of toxicity, and the available data did not provide any indication that the 

17 control group was not concurrent with the treated group (Cruzan, 2009). Soffritti et al. (2002a) 

18 further reported that there were no compound-related signs of gross pathology or histopathologic 

19 lesions indicative of noncancer toxicological effects in response to methanol.

20 Soffritti et al. (2002a) reported a number of oncogenic responses to methanol (Table 4-2), 

21 principally hemolymphoreticular neoplasms, the majority of which were reported to be lympho22 immunoblastic lymphomas. In ERF bioassays, including this methanol study, 

23 hemolymphoreticular neoplasms are generally divided into specific histological types 

24 (lymphoblastic lymphoma, lymphoblastic leukemia, lymphocytic lymphoma, lympho25 immunoblastic lymphoma, myeloid leukemia, histocytic sarcoma, and monocytic leukemia) for 

26 identification purposes. According to Soffritti et al. (2007), the overall incidence of 

27 hemolymphoreticular tumors (lymphomas/leukemias) in ERF studies is 13.3% (range, 4.0–

28 25.0%) in female historical controls (2,274 rats) and 20.6% (range, 8.0–30.9%) in male historical 

29 controls (2,265 rats). The high-dose responses, shown in Table 4-2, of 28% and 40% for females 

and males, respectively, are above their corresponding historical ranges.18 30 

31 The National Toxicology Program (NTP) does not routinely subdivide lymphomas into 

32 specific histological types as was done by the ERF. In 2004, a Pathology Working Group 

33 (PWG) of National Institute of Environmental Health Sciences (NIEHS) performed a limited 

34 review of about 75 slides provided by ERF as representive of lesions in Sprague-Dawley rats 

 17 http://www.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. 
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1 associated with aspartame exposure (EFSA, 2006; Hailey, 2004). The primary objective of this 

2 review was to “provide a second opinion for this set of lesions by a group of pathologists 

experienced in Toxicologic Pathology.”19 3 Eleven of the slides reviewed by the PWG were 

4 related to lymphomas, and three of these had been classified by ERF as lympho-immunoblastic. 

5 The PWG concluded that “The diagnoses of lymphatic and histocytic neoplasms in the cases 

6 reviewed were generally confirmed” (Hailey, 2004). In particular, the PWG accepted the more 

7 specific diagnoses of ERF when the lesions were considered to be consistent with a neoplasm of 

8 lymphocytic, histocytic, monocytic, and/or myeloid origin. The PWG noted, however, that while 

9 lymphoblastic lymphomas, lymphocytic lymphomas, lympho-immunoblastic lymphomas, and

10 lymphoblastic leukemias as malignant lymphomas can be combined, myeloid leukemias, 

11 histocytic sarcomas, and monocytic leukemia should be treated as separate malignancies and not 

12 combined with the other lymphomas since they are of different cellular origin (Hailey, 2004). 

13 McConnell et al. (1986) and Cruzan (2009) have also noted that myeloid leukemia, histocytic 

14 sarcoma, and monocytic leukemia are of a different cell line and are not typically combined with 

15 other lymphomas for statistical significance or dose-response modeling. Consistent with these 

16 judgments, EPA has not included the myeloid leukemia, histocytic sarcoma, and monocytic 

17 leukemia in combination with lymphoblastic lymphoma, lymphoblastic leukemia, lymphocytic 

18 lymphoma, and lympho-immunoblastic lymphoma in its consideration of tumorgenic responses 

19 reported by ERF (see Section 5.4.1; Table 5-6). Thus, EPA’s analysis of this tumorogenic 

20 response differs from the lymphoreticular tumor response shown in Table 4-2 and reported by 

21 Soffriti et al. (2002a). As described in Section 5.4.1.1, EPA’s reassessment indicates a 

22 significant increase in tumor response at the two highest doses for males and across all doses for 

23 females (Fisher’s exact, p < 0.05), as well as a significant dose-response trend (Cochran 

24 Armitage trend test; p < 0.05).

25 Schoeb et al. (2009) have suggested that the interpretation of lesions in ERF studies, 

26 including the Soffritti et al. (2002a) methanol study, may have been confounded by a respiratory 

27 infection referred to as Mycoplasma pulmonis (M. pulmonis) disease and that lesions of this

28 disease were interpreted as lymphoma. They noted that lympho-immunoblastic lymphoma is not

29 listed as a lymphoma type in rats in available reference sources and that the cellular morphology

30 of the lung lympho-immunoblastic lymphomas reported by ERF for aspartame (Soffritti et al., 

31 2005) and MTBE (Belpoggi et al., 1999) studies are more consistent with M. pulmonis disease. 

32 As noted above, an NIEHS PWG (Hailey, 2004) has confirmed the ERF diagnosis of the several 

33 lymphomas, including three lymphomas from the lung, thymus and medullary lymph node and 

34 mesenteric lymph node that were characterized by ERF as “lympho-immunoblastic.” Hailey 

 19 This review 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.”
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1 (2004) reports that the PWG “accepted their [ERF’s] more specific diagnosis if the lesion was 

2 considered to be consistent with a neoplasm of lymphocytic, histocytic, monocytic, and/or 

3 myeloid origin.” The concerns of Schoeb et al. (2009) regarding the possibility of infection 

4 confounding the interpretation of lung lesions in the ERF study are not unfounded. Chronic 

5 inflammatory changes are apparently a common finding in ERF studies (Caldwell et al., 2008), 

6 probably caused by the ERF bioassay design that does not employ specific pathogen-free (SPF) 

7 rats (EFSA, 2006) and allows the rats to live out their “natural life span” in the absence of 

8 disease barriers (e.g., fully enclosed cages). However, an infection of the ERF colony with 

9 M. pulmonis has not been confirmed (Caldwell et al., 2008) and, without confirmation, cannot be 

10 used to discount an existing dose-related trend (U.S. EPA, 2005). Further, even if the rats of the 

11 ERF methanol study were suffering from a respiratory infection that confounded the 

12 interpretation of lung lesions, 60% of reported lymphoma incidences involved other organ 

13 systems, and the dose-response for lymphomas in other organ systems is not remarkably 

14 different than for all lymphomas (see analysis in Section 5.4.3.2).

15 Another cancer response, reported by Soffritti et al. (2002a), that is considered to be 

16 potentially related to methanol exposure was an increase in rare hepatocellular carcinomas in 

17 male rats. Although the increase was not statistically increased compared to concurrent controls, 

18 EPA has analyzed historical data for this tumor type in this species and determined that the 

19 incidence in all dose groups was significantly elevated relative to historical controls (Fisher’s 

20 exact p < 0.05 for all doses and p < 0.01 for the high-dose group). The historical control group 

21 (n = 407) used was the combined control groups from ERF studies for which individual animal 

pathology data have been made available via the ERF website20 22 and include data for methanol, 

23 formaldehyde, aspartame, MTBE, and TAME.

24 As noted in Table 4-2, increased incidences of carcinomas of the ear ducts and 

25 osteosarcomas of the head were reported for both female and male rats, with a statistically 

26 significant increase in only the high-dose male ear duct carcinomas. Ear duct carcinomas are a 

27 rare finding in Charles River rats and NTP historical databases of Sprague-Dawley rats (Cruzan, 

28 2009). In their limited review of pathology slides from the ERF aspartame bioassay (Soffritti 

29 et al., 2006, 2005), NTP pathologists interpreted a majority of such head pathologies, including

30 in the ear duct, as being hyperplastic in nature, not carcinogenic (EFSA, 2006; Hailey, 2004). 

31 Soffritti et al. (2002a) also noted an increased incidence of testicular hyperplasia in high-dose 

32 males and uterine sarcomas in high-dose females compared to controls. However, these 

33 increases were not statistically significant and were within historical control ranges for this 

34 species and strain (NTP, 2007, 1999; Haseman et al., 1998). The group-specific total number of 

35 malignant tumors was also shown to increase with dose in both sexes of rats. 

 20 http://www.ramazzini.it/fondazione/foundation.asp.
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Table 4-2. Incidence of carcinogenic responses in Sprague-Dawley rats exposed to 

methanol in drinking water for up to 2 years

Tissues/affected sites

Dose (mg/kg-day)

Males Females

0 53.2 524 1780 0 66.0 624.1 2177

Ear duct (carcinomas) 9/100 13/100 17/100 24/100b 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

Hemolymphoreticular tumors 28/100 35/100 36/100 40/100 13/100 24/100 24/100 28/100a

Liver (hepatocarcinomas) 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 70/100b 43/100 48/100 48/100 63/100b

a

p < 0.05 using the χ2 test. b

p < 0.01 using the χ2 test.

Source: Soffritti et al. (2002a).

1 Apaja (1980) performed dermal and drinking water chronic bioassays in which male and 

2 female Eppley Swiss Webster mice (25/sex/dose group; 8 weeks old at study initiation) were 

3 exposed 6 days per week until natural death to various concentrations of malonaldehyde and 

4 methanol. The stated purpose of the study was to determine the carcinogenicity of 

5 malonaldehyde, a product of oxidative lipid deterioration in rancid beef and other food products 

6 in advanced stages of degradation. However, due to its instability, malonaldehyde was obtained 

7 from the more stable malonaldehyde bis(dimethyacetal), which was hydrolyzed to 

8 malonaldehyde and methanol in dilute aqueous solutions in the presence of a strong mineral acid. 

9 In the drinking water portion of this study, mice were exposed to 3 different concentrations of 

10 the malonaldehyde/methanol solution and three different control solutions of methanol alone, 

11 0.222%, 0.444% and 0.889% methanol in drinking water (222, 444 and 889 ppm, assuming a 

12 density of 1 g/ml), corresponding to the stoichiometric amount of methanol liberated by 

13 hydrolysis of the acetal in the three test solutions. The methanol was described as Mallinckrodt 

14 analytical grade. No unexposed control groups were included in these studies. However, the 

15 author provided pathology data from historical records of untreated Swiss mice of the Eppley 

16 colony used in two separate chronic studies, one involving 100 untreated males and 100 

17 untreated females (Toth et al., 1977) and the other involving 100 untreated females 

18 histopathological analyzed by Apaja (Apaja, 1980). 

19 Mice in the Apaja (1980) study were housed five/plastic cage and fed Wayne Lab-Blox 

20 pelleted diet. Water was available ad libitum throughout life. Liquid consumption per animal 

21 was measured 3 times/week. The methanol dose in the dermal study (females only) was 21.3 mg 

22 (532 mg/kg-day using an average weight of 0.04 kg as approximated from Figure 4 of the study), 

23 three times/week. The methanol doses in the drinking water study were reported as 22.6, 40.8 
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1 and 84.5 mg/day (560, 1000 and 2100 mg/kg-day using an average weight of 0.04 kg as 

2 approximated from Figures 14-16 of the study) for females, and 24.6, 43.5 and 82.7 mg/day 

3 (550, 970, and 1800 mg/kg-day using an average weight of 0.045 kg as approximated from 

4 Figures 14-16 of the study) for males, 6 days/week. The animals were checked daily and body 

5 weights were monitored weekly. The in-life portion of the experiment ended at 120 weeks with 

6 the death of the last animal. Like the Soffritti et al. (2002a) study, test animals were sacrificed 

and necropsied when moribund.21 7

8 The authors reported that survival of the methanol exposed females of the drinking water 

9 study was lower than untreated historical controls (p < 0.05), but no significant differences in 

10 survival was noted for males. An increase in liver parenchymal cell necrosis was reported in the 

11 male and female high-dose groups, with the incidence in females (8%) being significant 

12 (p < 0.01) relative to untreated historical controls. Incidence of acute pancreatitis was higher in 

13 high-dose males (p <0.001), but did not appear to be dose-related in females, increasing at the

14 mid- (p <0.0001) and low-doses (p <0.01) when compared to historical controls but not 

15 appearing at all in the high-dose females. Significant increases relative to untreated historical 

16 controls were noted in amyloidosis of the spleen, nephropathy and pneumonia, but the increases 

17 did not appear to be dose related. 

18 The author reported incidences of malignant lymphoma in females of 15%, 16%, 36%, 

19 and 40% for 532 mg/kg-day (dermal), 560, 1,000, and 2,100 mg/kg-day (drinking water), 

20 respectively. Males from the drinking water study had incidences of malignant lymphoma of 4, 

21 24, and 16% for 550, 970, and 1,800 mg/kg-day. The lymphomas were classified according to 

22 Rappaport’s classification (Rappaport, 1966), but location of the lymphoma (organ system) was 

23 not reported. The distributions of lymphomas according to subclasses reported by the author are 

24 shown in Table 4-3 for historical untreated and methanol exposed mice in the drinking water 

25 studies. The author indicates that the incidences in both males and females were “within the 

26 normal range of occurrence of malignant lymphomas in Eppley Swiss mice,” but provides no 

27 references or supporting data for this statement and reports elsewhere that the response in high28 dose females and mid-dose males were significantly different from unexposed mice from 

29 “historical data of untreated controls (Table 9)” of Toth et al. (1977) (p < 0.05). Though not 

30 statistically significant (Fishers exact p = 0.06), the malignant lymphoma response in the mid31 dose females was also more than double that of untreated controls from another study (18/100) 

32 for which the histopathology was also performed by Apaja (Apaja, 1980). 

 21 The following tisues were fixed in 10% formalin (pH 7.5), embedded in paraffin, sectioned, stained routinely with 

hematoxylineosin (special 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 other gross pathological lesions.
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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 (%)

0a

n=100

550

n=25

970

n=25

1800

n=24

0a

n=100

0b

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 24c 17 20 18 16 36d 40c

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 untreated 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

1 Lewis (1992) reported a 4-hour median lethal concentration (LC50) for methanol in rats of 

64,000 ppm (83,867 mg/m3 2 ).

3 Japan’s NEDO sponsored a series of toxicological tests on monkeys (M. fascicularis), 

rats, and mice, using inhalation exposure.22 4 A short-term exposure study evaluated monkeys (sex 

unspecified) exposed to 3,000 ppm (3,931 mg/m3 5 ), 21 hours/day for 20 days (1 animal), 

5,000 ppm (6,552 mg/m3) for 5 days (1 animal), 5,000 ppm (6,552 mg/m3 6 ) for 14 days (2 

animals), and 7,000 and 10,000 ppm (9,173 and 13,104 mg/m3 7 , respectively) for up to 6 days (1 

8 animal at each exposure level) (NEDO, 1987, unpublished report). Most of the experimental 

9 findings were discussed descriptively in the report, without specifying the extent of change for 

10 any of the effects in comparison to seven concurrent controls. However, the available data 

11 indicate that clinical signs of toxicity were apparent in animals exposed to 5,000 ppm (all 

12 exposure durations) or higher concentrations of methanol. These included reduced movement, 

13 crouching, weak knees, involuntary movements of hands, dyspnea, and vomiting. In the 

 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 

wild-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 infectious disease was observed in monkeys” and that “subjects were healthy throughout 

the experiment.”
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1 discussion section of the summary report, the authors stated that there was a sharp increase in the 

blood levels of methanol and formic acid in monkey exposed to >3,000 ppm (3,931 mg/m3 2 ) 

3 methanol. They reported that methanol and formic acid concentrations in the blood of monkeys

exposed to 3,000 ppm or less were 80 mg/L and 30 mg/L, respectively.23 4 In contrast, monkeys 

5 exposed to 5,000 ppm or higher concentrations of methanol had blood methanol and formic acid 

6 concentrations of 5,250 mg/L and 1,210 mg/L, respectively. Monkeys exposed to 7,000 ppm and 

7 10,000 ppm became critically ill and had to be sacrificed prematurely. Food intake was said to 

8 be little affected at 3,000 ppm, but those exposed to 5,000 ppm or more showed a marked 

9 reduction. Clinically, the monkeys exposed to 5,000 ppm or more exhibited reduced movement, 

10 weak knees, and involuntary movement of upper extremities, eventually losing consciousness

11 and dying. 

12 There were no significant changes in growth, with the exception of animals exposed to 

13 the highest concentration, where body weight was reduced by 13%. There were few compound14 related changes in hematological or clinical chemistry effects, although animals exposed to 7,000 

15 and 10,000 ppm showed an increase in white blood cells. A marked change in blood pH values 

16 at the 7,000 ppm and 10,000 ppm levels (values not reported) was attributed to acidosis due to 

17 accumulation of formic acid. A range of histopathologic changes to the CNS was apparently 

18 related to treatment. Severity of the effects was increased with exposure concentration. Lesions 

19 included characteristic degeneration of the bilateral putamen, caudate nucleus, and claustrum, 

20 with associated edema in the cerebral white matter. Necrosis of the basal ganglia was noted 

21 following exposure to 5,000 ppm for 5 days (1 animal) and 14 days(1 animal). Exposure to 

22 3,000 ppm was considered to be close to the threshold for these necrotic effects, as the monkeys 

23 exposed at this level experienced little more than minimal fibrosis of responsive stellate cells of 

24 the thalamus, hypothalamus and basal ganglion. The authors reported that no clinical or 

25 histopathological effects of the visual system were apparent, but that exposure to 3,000 ppm 

(3,931 mg/m3 26 ) or more caused dose-dependent fatty degeneration of the liver, and exposure to 

5,000 ppm (6,552 mg/m3 27 ) or more caused vacuolar degeneration of the kidneys, centered on the 

28 proximal uniferous tubules.

4.2.2.2. Subchronic Toxicity

29 A number of experimental studies have examined the effects of subchronic exposure to 

30 methanol via inhalation. For example, Sayers et al. (1944) employed a protocol in which 2 male 

31 dogs were repeatedly exposed (8 times daily for 3 minutes/exposure) to 10,000 ppm 

(13,104 mg/m3 32 ) methanol for 100 days. One of the dogs was observed for a further 5 days 

33 before sacrifice; the other dog was observed for 41 days postexposure. There were no clinical 

 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).
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1 signs of toxicity, and both gained weight during the study period. Blood samples were drawn on 

2 a regular basis to monitor hematological parameters, but few if any compound-related changes

3 were observed. Ophthalmoscopic examination showed no incipient anomalies at any point 

4 during the study period. Median blood concentrations of methanol were 65 mg/L (range 0–280 

5 mg/L) for one dog, and 140 mg/L (70–320 mg/L) for the other. 

6 White et al. (1983) exposed 4 male Sprague-Dawley rats/group, 6 hours/day, 5 days/week 

to 0, 200, 2,000, or 10,000 ppm (0, 262, 2,621, and 13,104 mg/m3 7 ) methanol for periods of 1, 2, 

8 4, and 6 weeks. Additional groups of 6-week-exposure animals were granted a 6-week 

9 postexposure recovery period prior to sacrifice. The lungs were excised intact and lavaged 

10 6 times with known volumes of physiological saline. The lavage supernatant was then assayed 

11 for lactate dehydrogenase (LDH) and N-acetyl-β-D-glucosamidase (β-NAG) activities. Other 

12 parameters monitored in relation to methanol exposure included absolute and relative lung 

13 weights, lung DNA content, protein, acid RNase and acid protease, pulmonary surfactant, 

14 number of free cells in lavage/unit lung weight, surface protein, LDH, and β-NAG. As discussed 

15 by the authors, none of the monitored parameters showed significant changes in response to 

16 methanol exposure. 

17 Andrews et al. (1987) carried out a study of methanol inhalation in 5 Sprague-Dawley 

18 rats/sex/group and 3 M. fascicularis monkeys/sex/group, 6 hours/day, 5 days/week, to 0, 500, 

2,000, or, 5,000 ppm (0, 660, 2,620, and 6,552 mg/m3 19 ) methanol for 4 weeks. Clinical signs 

20 were monitored twice daily, and all animals were given a physical examination once a week. 

21 Body weights were monitored weekly, and animals received an ophthalmoscopic examination 

22 before the start of the experiment and at term. Animals were sacrificed at term by 

23 exsanguination following i.v. barbiturate administration. A gross necropsy was performed, 

24 weights of the major organs were recorded, and tissues and organs taken for histopathologic 

25 examination. As described by the authors, all animals survived to term with no clinical signs of 

26 toxicity among the monkeys and only a few signs of irritation to the eyes and nose among the 

27 rats. In the latter case, instances of mucoid nasal discharges appeared to be dose related. There

28 were no differences in body weight gain among the groups of either rats or monkeys, and overall, 

29 absolute and relative organ weights were similar to controls. The only exception to this was a 

30 decrease in the absolute adrenal weight of female high-concentration monkeys and an increase in 

31 the relative spleen weight of mid-concentration female rats. These changes were not considered 

32 by the authors to have biological significance. For both rats and monkeys, there were no 

33 compound-related changes in gross pathology, histopathology, or ophthalmoscopy. These data 

suggest a NOAEL of 5,000 ppm (6,600 mg/m3 34 ) for Sprague-Dawley rats and monkeys under the 

35 conditions of the experiment.
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1 Two studies by Poon et al. (1995, 1994) examined the effects of methanol on Sprague2 Dawley rats when inhaled for 4 weeks. The effects of methanol were evaluated in comparison to 

3 those of toluene and toluene/methanol mixtures (Poon et al., 1994), and to gasoline and 

4 gasoline/methanol mixtures (Poon et al., 1995). In the first case (Poon et al., 1994), 10 Sprague5 Dawley rats/sex/group were exposed via inhalation, 6 hours/day, 5 days/week to 0, 300, or 

3,000 ppm (0, 393, 3,930 mg/m3 6 ) methanol for 4 weeks. Clinical signs were monitored daily, 

7 and food consumption and body weight gain were monitored weekly. Blood was taken at term 

8 for hematological and clinical chemistry determinations. Weights of the major organs were 

9 recorded at necropsy, and histopathologic examinations were carried out. A 10,000 × g liver 

10 supernatant was prepared from each animal to measure aniline hydroxylase, aminoantipyrine N11 demethylase, and ethoxyresorufin-O-deethylase activities. For the most part, the responses to 

12 methanol alone in this experiment were unremarkable. All animals survived to term, and there 

13 were no clinical signs of toxicity among the groups. Body weight gain and food consumption 

14 did not differ from controls, and there were no compound-related effects in hematological or 

15 clinical chemistry parameters or in hepatic mixed function oxidase activities. However, the 

16 authors described a reduction in the size of thyroid follicles that was more obvious in female than 

17 male rats. The authors considered this effect to possibly have been compound related, although 

18 the incidence of this feature for the 0, 300, and 3,000 ppm-receiving females was 0/6, 2/6, and 

19 2/6, respectively.

20 The second experimental report by Poon et al. (1995) involved the exposure of 

21 15 Sprague-Dawley rats/sex/group, 6 hours/day, 5 days/week for 4 weeks to 0 or 2,500 ppm (0 

and 3,276 mg/m3 22 ) to methanol as part of a study on the toxicological interactions of methanol 

23 and gasoline. Many of the toxicological parameters examined were the same as those described 

24 in Poon et al. (1994) study. However, in this study urinalysis featured the determination of 

25 ascorbic and hippuric acids. Additionally, at term, the lungs and tracheae were excised and 

26 aspirated with buffer to yield bronchoalveolar lavage fluid that was analyzed for ascorbic acid, 

27 protein, and the activities of gamma-glutamyl transferase (γ-GT), AP and LDH. Few if any of 

28 the monitored parameters showed any differences between controls and those animals exposed to 

29 methanol alone. However, two male rats had collapsed right eyes, and there was a reduction in 

30 relative spleen weight in females exposed to methanol. Histopathologic changes in methanol31 receiving animals included mild panlobular vacuolation of the liver in females and some mild 

32 changes to the upper respiratory tract, including mucous cell metaplasia. The incidence of the 

33 latter effect, though higher, was not significantly different than controls in rats exposed to 

2,500 ppm (3,267 mg/m3 34 ) methanol. However, there were also signs of an increased severity of 

35 the effect in the presence of the solvent. No histopathologic changes were seen in the lungs or 

36 lower respiratory tract of rats exposed to methanol alone.
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4.2.2.3. Chronic Toxicity

1 Information on the chronic toxicity of methanol has come from NEDO (1987, 

2 unpublished report) which includes the results of experiments on 1) monkeys exposed for up to 3 

3 years, 2) rats and mice exposed for 12 months, 3) mice exposed for 18 months, and 4) rats 

4 exposed for 2 years.

5 In the monkeys, 8 animals (sex unspecified) were exposed to 10, 100, or 1,000 ppm (13, 

131, and 1,310 mg/m3 6 ) methanol, 21 hours/day, for 7 months (2 animals), 19 months, 

7 (3 animals), or 29 months (3 animals). There was no indication in the NEDO (1987) report that 

8 this study employed a concurrent control group. One of the 3 animals receiving 100 ppm 

9 methanol and scheduled for sacrifice at 29 months was terminated at 26 months. Clinical signs 

10 were monitored twice daily, body weight changes and food consumption were monitored weekly, 

11 and all animals were given a general examination under anesthetic once a month. Blood was 

12 collected for hematological and clinical chemistry tests at term, and all animals were subject to a 

13 histopathologic examination of the major organs and tissues. 

14 While there were no clinical signs of toxicity in the low-concentration animals, there was 

15 some evidence of nasal exudate in monkeys in the mid-concentration group. High-concentration 

16 (1,000 ppm) animals also displayed this response and were observed to scratch themselves over 

17 their whole body and crouch for long periods. Food and water intake, body temperature, and 

18 body weight changes were the same among the groups. NEDO (1987) reported that there was no 

19 abnormality in the retina of any monkey. When animals were examined with an 

20 electrocardiogram, there were no abnormalities in the control or 10 ppm groups. However, in the 

21 100 ppm group, one monkey showed a negative change in the T wave. All 3 monkeys exposed 

to 1,000 ppm (1,310 mg/m3 22 ) displayed this feature, as well as a positive change in the Q wave. 

This effect was described as a slight myocardial disorder and suggests that 10 ppm (13.1 mg/m3 23 ) 

24 is a NOAEL for chronic myocardial effects of methanol and mild respiratory irritation. There 

25 were no compound-related effects on hematological parameters. However, 1 monkey in the 

100 ppm (131 mg/m3 26 ) group had greater than normal amounts of total protein, neutral lipids, 

27 total and free cholesterol, and glucose, and displayed greater activities of ALT and aspartate 

28 transaminase (AST). The authors expressed doubts that these effects were related to methanol

exposure and speculated that the animal suffered from liver disease.24 29 

30 Histopathologically, no degeneration of the optical nerve, cerebral cortex, muscles, lungs, 

31 trachea, tongue, alimentary canal, stomach, small intestine, large intestine, thyroid gland, 

32 pancreas, spleen, heart, aorta, urinary bladder, ovary or uterus were reported (neuropathological 

33 findings are discussed below Section 4.4.2. Most of the internal organs showed no compound-

 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.
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1 related histopathologic lesions. However, there were signs of incipient fibrosis and round cell 

infiltration of the liver in monkeys exposed to 1,000 ppm (1,310 mg/m3 2 ) for 29 months. NEDO 

3 (1987) indicated that this fibrosis occurred in 2/3 monkeys of the 1,000 ppm group to a “strictly 

4 limited extent.” They also qualitatively reported a dose-dependent increase in “fat granules” in 

5 liver cells “centered mainly around the central veins” at all doses, but did not provide any 

response data. The authors state that 1,000 ppm (1,310 mg/m3 6 ) represents a chronic lowest7 observed-adverse-effect level (LOAEL) for hepatic effects of inhaled methanol, suggesting that 

the no effect level would be 100 ppm (131 mg/m3 8 ). However, this is a tenuous determination 

9 given the lack of information on the pathological progression and significance of the appearance 

10 of liver cell fat granules at exposures below 1,000 ppm and the lack detail (e.g., time of sacrifice) 

11 for the control group. 

12 Dose-dependent changes were observed in the kidney; NEDO (1987) described the 

appearance of Sudan-positive granules in the renal tubular epithelium at 100 ppm (131 mg/m3 13 ) 

and 1,000 (1,310 mg/m3 14 ) and hyalinization of the glomerulus and penetration of round cells into 

the renal tubule stroma of monkeys exposed to methanol at 1,000 (1,310 mg/m3 15 ). The former 

16 effect was more marked at the higher concentration and was thought by the authors to be 

compound-related. This would indicate a no effect level at 10 ppm (13.1 mg/m3 17 ) for the chronic 

18 renal effects of methanol. The authors observed atrophy of the tracheal epithelium in four

19 monkeys. However, the incidence of these effects was unrelated to dose and therefore, could not 

20 be unequivocally ascribed to an effect of the solvent. No other histopathologic abnormalities 

21 were related to the effects of methanol. Confidence in these determinations is considerably 

weakened by concern over whether a concurrent control group was used in the chronic study.25 22

23 NEDO (1987) describes a 12-month inhalation study in which 20 F344 rats/sex/group 

were exposed to 0, 10, 100, or 1,000 ppm (0, 13.1, 131, and 1,310 mg/m3 24 ) methanol, 

25 approximately 20 hours/day, for a year. Clinical signs of toxicity were monitored daily; body 

26 weights and food consumption were recorded weekly for the first 13 weeks, then monthly. 

27 Blood samples were drawn at term to measure hematological and clinical chemistry parameters. 

28 Weights of the major organs were monitored at term, and a histopathologic examination was 

29 carried out on all major organs and tissues. Survival was high among the groups; one high30 concentration female died on day 337 and one low-concentration male died on day 340. As 

31 described by the authors, a number of procedural anomalies arose during this study. For 

32 example, male controls in two cages lost weight because of an interruption to the water supply. 

33 Another problem was that the brand of feed was changed during the study. Fluctuations in some 

34 clinical chemistry and hematological parameters were recorded. The authors considered the 

35 fluctuations to be minor and within the normal range. Likewise, a number of histopathologic 

 25 All control group responses were reported in a single table in the section of the NEDO (1987) report that describes 

the acute monkey study, with no indication as to when the control group was sacrificed.
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1 changes were observed, which, in every case, were considered to be unrelated to exposure level 

2 or due to aging.

3 A companion experiment featured the exposure of 30 B6C3F1 mice/sex/group for 1 year 

4 to the same concentrations as the F344 rats (NEDO, 1987). Broadly speaking, the same suite of 

5 toxicological parameters was monitored as described above, with the addition of urinalysis. 

6 10 mice/sex/group were sacrificed at 6 months to provide interim data on the parameters under 

7 investigation. A slight atrophy in the external lacrimal gland was observed in both sexes and was 

8 significant in the 1,000 ppm male group compared with controls. An apparently dose-related 

9 increase in moderate fatty degeneration of hepatocytes was observed in males (1/20, 4/20, 6/20 

10 and 8/20 in the 0, 10, 100, and 1,000 ppm dose groups, respectively) which was significantly 

11 increased over controls at the 1,000 ppm dose. However the incidence of moderate to severe 

12 fatty degeneration was observed in untreated animals maintained outside of the chamber. In 

13 addition, there was a clear correlation between fatty degeneration and body weight (a change 

14 which was not associated with treatment at 12 months); heavier animals tended to have more 

15 severe cases of fatty degeneration. The possibility of renal deficits due to methanol exposure 

16 was suggested by the appearance of protein in the urine. However, this effect was also seen in 

17 controls and did not display a dose-response effect. Therefore, it is unlikely to be a consequence 

18 of exposure to methanol. NEDO (1987) reported other histopathologic and biochemical (e.g.,

19 urinalysis and hematology) findings that do not appear to be related to treatment, including a 

20 number of what were considered to be spontaneous tumors in both control and exposure groups.

NEDO (1987, 1985/2008a, unpublished reports)

26 21 exposed 52 male and 53 female 

22 B6C3F1 mice/group for 18 months at the same concentrations of methanol (0, 10, 100 and 

1,000 ppm) and with a similar experimental protocol to that described in the 12-month studies.27 23 

24 The fact that the duration of this study was only 18 months and not the more typical 2 years

25 limits its ability to detect carcinogenic responses with relatively long latency periods. Animals 

26 were sacrificed at the end of the 18-month exposure period. NEDO (1985/2008a) reported that 

27 “there was no microbiological contamination that may have influenced the result of the study” 

28 and that the study included an assessment of general conditions, body weight change, food 

29 consumption rate, laboratory tests (urinalysis, hematological, and plasma biochemistry) and 

pathological tests (pathological autopsy,28 organ weight check and histopathology29 30 ). As stated 

 26 This study is described in a summary report (NEDO, 1987) and a more detailed, eight volume translation of the 

original chronic 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 duration 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.
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1 in the summary report (NEDO, 1987), a few animals showed clinical signs of toxicity, but the 

2 incidence of these responses was not related to dose. Likewise, there were no compound-related 

changes in body weight increase, food consumption, 30 3 urinalysis, hematology, or clinical 

4 chemistry parameters. High-concentration males had lower testis weights compared to control 

5 males. Significant differences were detected for both absolute and relative testis weights. One

6 animal in the high-dose group had severely atrophied testis weights, approximately 25% of that 

7 of the others in the dose group. Exclusion of this animal in the analysis still resulted in a 

8 significant difference in absolute testis weight compared to controls but resulted in no difference 

9 in relative testis weight. High-concentration females had higher absolute kidney and spleen 

10 weights compared to controls, but there was no significant difference in these organ weights 

11 relative to body weight. At necropsy, there were signs of swelling in spleen, preputial glands, 

12 and uterus in some animals. Some animals developed nodes in the liver and lung although, 

13 according to the authors, none of these changes were treatment-related. NEDO (1985/2008a) 

14 reported that all nonneoplastic changes were “nonspecific and naturally occurring changes that 

15 are often experienced by 18-month old B6C3F1 mice” and that fatty degeneration of liver that 

16 was suspected to occur dose-dependently in the 12-month NEDO (1987) study was not observed 

17 in this study. Similarly, though the study found various neoplastic changes across dose groups, 

18 there was no compound-related formation of tumors in any organ or tissue.

19 EPA reviewed the cancer findings documented in a recent translation of the original 

20 NEDO report on this chronic mouse study (NEDO, 1985/2008a) to identify possible compound21 related effects. Hyperplastic and neoplastic histopathological findings have been tabulated and 

22 are as shown in Table 4-4.

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

via inhalation for 18 months

Tissues/tumor type

Exposure concentration (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).

1 There is no clear evidence for treatment-related carcinogenic effects in the mice in this 

2 study. However, the fact that the study duration was limited to 18 months rather than the 

3 traditional 2-year bioassay makes it difficult to draw a definitive conclusion, particularly 

4 regarding pulmonary adenomas, which were marginally increased in high-dose male mice of this 

5 study and were also increased in male rats of the NEDO chronic rat study (NEDO, 1987,

6 1985/2008b, unpublished reports). In this study, the lack of adenomatosis in control or treated 

7 male mice supports the conclusion of the authors that the observed tumors were probably 

8 unrelated to methanol exposure. There was no apparent relationship to treatment in any 

9 neoplastic findings in the liver. Of relevance to the findings of treatment-related lymphomas and 

10 leukemias in Sprague-Dawley rats receiving methanol in drinking water in the Soffritti et al. 

11 (2002a) study, few lymphomas and leukemias were identified in the NEDO (1987, 1985/2008b) 

12 study reports, with no sign of a dose-related trend. 

Another study reported in (NEDO (1987, 1985/2008b)31 13 was a 24-month carcinogenicity 

14 bioassay in which 52 F344 rats/sex/group were kept in whole body inhalation chambers 

containing 0, 10, 100, or 1,000 ppm (0, 13.1, 131, and 1,310 mg/m3 15 ) methanol vapor. Animals 

16 were maintained in the exposure chambers for approximately 19.5 hours/day for a total of 733–

17 736 days (males) and 740–743 days (females). Animals were monitored once a day for clinical 

18 signs of toxicity, body weights were recorded once a week, and food consumption was measured 

19 weekly in a 24-animal subset from each group. Urinalysis was carried out on the day prior to 

20 sacrifice for each animal, the samples being monitored for pH, protein, glucose, ketones, 

21 bilirubin, occult blood, and urobilinogen. Routine clinical chemistry and hematological 

 31 This study is described in a summary report (NEDO, 1987) and a more detailed, eight-volume translation of the 

original chronic rat study report (NEDO, 1985/2008b).
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1 measurements were carried out and all animals were subject to necropsy at term, with a 

comprehensive histopathological examination of tissues and organs.32 2

3 There was some fluctuation in survival rates among the groups in the rat study, though 

apparently unrelated to exposure concentration. 33 4 In all groups, at least 60% of the animals 

5 survived to term. A number of toxicological responses were described by the authors, including 

6 atrophy of the testis, cataract formation, exophthalmia, small eye ball, alopecia, and paralysis of 

7 the hind leg. However, according to the authors, the incidence of these effects were unrelated to 

8 dose and more likely represented effects of aging. NEDO (1985/2000b) reported a mild, 

9 nonsignificant (4%) body weight suppression among 1,000 ppm females between 51 and 

10 72 weeks, but that body weight gain was largely similar among the groups for the duration of the 

11 experiment. Food consumption was significantly lower than controls in high-concentration male 

12 rats during the day 210–365 time interval, but no corresponding weight loss was observed. 

13 Among hematological parameters, mid- and high-concentration females had a significantly 

14 (p > 0.05) higher differential leukocyte count than controls, but dose dependency was not 

15 observed. Serum total cholesterol, triglyceride, free fatty acid, and phospholipid concentrations 

16 were significantly (p > 0.05) lower in high-concentration females compared to controls. 

17 Likewise, serum sodium concentrations were significantly (p > 0.05) lower in mid- and high18 concentration males compared to controls. High-concentration females had significantly lower

19 (p > 0.05) serum concentrations of inorganic phosphorus but significantly (p > 0.05) higher 

20 concentrations of potassium compared to controls. Glucose levels were elevated in the urine of 

21 high-concentration male rats relative to controls, and female rats had lower pH values and higher 

22 bilirubin levels in mid- and high-concentration groups relative to controls. In general, NEDO 

23 (1987, 1985/2008b) reported that these variations in urinary, hematology, and clinical chemistry 

24 parameters were not related to chemical exposure.

25 NEDO (1987) reported that there was little change in absolute or relative weights of the 

26 major organs or tissues. When the animals were examined grossly at necropsy, there were some 

27 signs of swelling in the pituitary and thyroid, but these effects were judged to be unrelated to 

28 treatment. The most predominant effect was the dose-dependent formation of nodes in the lung 

29 of males (2/52, 4/52, 5/52, and 10/52 [p < 0.01] for control, low-, mid-, and high-concentration 

30 groups, respectively). Histopathologic examination pointed to a possible association of these 

31 nodes with the appearance of pulmonary adenoma (1/52, 5/52, 2/52, and 6/52 for control, low-, 

32 mid- and high-concentration groups, respectively) and a single pulmonary adenocarcinoma in the 

 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 found 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-exposure groups. 33Survival 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. 
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1 high-dose group (1/52). Other examples of tumor formation that were increased in high2 concentration animals versus controls included an increased incidence of pituitary adenomas in 

3 high-concentration males (17/52 compared to 12/52 controls), hyperplastic change in the testis in 

4 high-concentration males (10/52 compared to 4/52 controls), and chromaffinoma 

(pheochromocytomas)34 5 in the adrenals of high-concentration females (7/52 compared to 2/52 

6 controls). Individually, these changes did not achieve statistical significance, and in general, the 

7 authors concluded that few if any of the observed changes were effects of methanol.

8 EPA reviewed the cancer findings of this study that are documented in a recent translation 

9 of the original NEDO (1985/2008b) report to identify possible compound-related effects. High10 dose incidences of pituitary adenomas (17/52; 33%) and hyperplastic change in testes (10/52; 

19%) mentioned above were within historical incidences for this rat strain.35 11 However, the 

12 observed incidence rate for pulmonary adenoma/adenocarcinoma in high-dose males of 13.5% 

13 (7/52) was significantly elevated (Fisher’s exact test p < 0.05) over the concurrent control rate of 

14 2% (1/52) and historical control rates of 2.5% ± 2.6% (n = 1054) and 3.84% ± 2.94% (n = 1199) 

15 reported by NTP for the pre-1995 control F344 male rats fed NIH-07 diet (NTP, 1999) and post16 1994 control F344 male rats fed NTP-2000 diet (NTP, 2007), respectively. Also, the incidence of 

17 pulmonary adenoma/adenocarcinoma in male rats exhibited a dose-response trend (Cochrane18 Armitage p < 0.05). While the observed incidence rate for pheochromocytomas in high-dose 

19 females of 13.7% (7/51) was not significantly elevated over the concurrent control rate of 4% 

20 (2/50), it was significantly elevated (Fisher’s exact test p < 0.05) over NTP historical control 

21 rates for total (benign, complex and malignant) pheochromocytomas of 2.5% ± 2.6% (n = 1054) 

22 and 3.84% ± 2.94% (n = 1199) reported by NTP for pre-1995 control F344 female rats fed NIH23 07 diet (NTP, 1999) and post-1994 control F344 female rats fed NTP-2000 diet (NTP, 2007), 

respectively.36 24 Also, the incidence of pheochromocytomas in female rats exhibited a dose25 response trend (Cochrane-Armitage p < 0.05). The histopathological incidences for pulmonary 

26 and adrenal effects reported by NEDO (1987, 1985/2008b) are shown in Table 4-5. 

 34 There were some differences in nomenclature 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 

reported, 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). 
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Table 4-5. Histopathological changes in lung and adrenal tissues of F344 rats exposed 

to methanol via inhalation for 24 months

Tissues/

tumor 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).

1 In contrast to the conclusions of the NEDO (1987) summary report that there were no 

2 compound-related changes in F344 rats exposed to methanol via inhalation, EPA identifies 

3 potential treatment-related changes in the lungs of male rats and the adrenal medulla of female 

4 rats in the more detailed translation of the original report (NEDO, 1985/2008b). The NEDO 

5 (1987) summary report did not report the statistically significant combined pulmonary adenoma 

6 and adenocarcinoma finding in the high-dose group of male rats. Table 6 (page 146) of the 

7 NEDO (1987) summary reports only “Tumural changes occurring at a rate of over 5%.” The 

8 lung response of the male rats as shown in Table 4-5 suggests a proliferative change in cells of 

9 the alveolar epithelium involving a progression towards adenoma and adenocarcinoma that 

10 appears to be more pronounced with increasing methanol exposure and considerably elevated 

11 over historical controls. Similarly, for female rats, the observed increase in medullary 

12 hyperplasia in the 100 ppm dose group, in conjunction with a higher incidence of 

13 pheochromocytoma in the adrenal gland is suggestive of a methanol-induced progressive change 

14 leading to a carcinogenic response.
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4.3. REPRODUCTIVE AND DEVELOPMENTAL STUDIES–ORAL AND INHALATION

1 Many studies have been conducted to investigate the reproductive and developmental 

2 toxicity of methanol. The purpose of these studies was principally to determine if methanol has 

3 a similar toxicology profile to another widely studied teratogen, ethanol.

4.3.1. Oral Studies

4 Three studies were identified that investigated the reproductive and developmental effects 

5 of methanol in rodents via the oral route (Fu et al., 1996; Sakanashi et al., 1996; Rogers et al., 

6 1993a). Two of these studies also investigated the influence of folic acid-deficient (FAD) diets on 

7 the effects of methanol exposures (Fu et al., 1996; Sakanashi et al., 1996). 

8 Rogers et al. (1993a) conducted a developmental toxicity study in which methanol in 

9 water was administered to pregnant female CD-1 mice via gavage on GD6–GD15. Eight test 

10 animals received 4 g/kg-day methanol given in 2 daily doses of 2g/kg; 4 controls received 

11 distilled water. By analogy to the protocol of an inhalation study of methanol that was described 

12 in the same report, it is assumed that dams were sacrificed on GD17, at which point implantation 

13 sites, live and dead fetuses, resorptions/litter, and the incidences of external and skeletal 

14 anomalies and malformations were determined. In the brief summary of the findings provided 

15 by the authors, it appears that cleft palate (43.5% per litter versus 0% in controls) and 

16 exencephaly (29% per litter versus 0% in controls) were the prominent external defects 

17 following maternal methanol exposure by gavage. Likewise, an increase in totally resorbed 

18 litters and a decrease in the number of live fetuses per litter were evident. However, it is possible 

19 that these effects may have been caused or exacerbated by the high bolus dosing regimen 

20 employed. It is also possible that effects were not observed due to the limited study size. The 

21 small number of animals in the control group relative to the test group limits the power of this 

22 study to detect treatment-related responses.

23 Sakanashi et al. (1996) tested the influence of dietary folic acid intake on various 

24 reproductive and developmental effects observed in CD-1 mice exposed to methanol. Starting 

25 5 weeks prior to breeding and continuing for the remainder of the study, female CD-1 mice were 

26 fed folic acid free diets supplemented with 400 (low), 600 (marginal) or 1,200 (sufficient) nmol 

27 folic acid/kg. After 5 weeks on their respective diets, females were bred with CD-1 male mice. 

28 On GD6–GD15, pregnant mice in each of the diet groups were given twice-daily gavage doses of 

29 2.0 or 2.5 g/kg-day methanol (total dosage of 4.0 or 5.0 g/kg-day). On GD18, mice were 

30 weighed and killed, and the liver, kidneys and gravid uteri removed and weighed. Maternal liver 

31 and plasma folate levels were measured, and implantation sites, live and dead fetuses, and 

32 resorptions were counted. Fetuses were weighed individually and examined for cleft palate and 

33 exencephaly. One third of the fetuses in each litter were examined for skeletal morphology. 
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1 They observed an approximate 50% reduction in liver and plasma folate levels in the mice fed 

2 low versus sufficient folic acid diets in both the methanol exposed and unexposed groups. 

3 Similar to Rogers et al. (1993a), Sakanashi et al. (1996) observed that an oral dose of 4–5 g/kg4 day methanol during GD6–GD15 resulted in an increase in cleft palate in mice fed sufficient 

5 folic acid diets, as well as an increase in resorptions and a decrease in live fetuses per litter. They 

6 did not observe an increase in exencephaly in the FAS group at these doses, and the authors 

7 suggest that this may be due to diet and the source of CD-1 mice differing between the two 

8 studies. 

9 In the case of the animals fed the folate deficient diet, there was a 50% reduction in 

10 maternal liver folate concentration and a threefold increase in the percentage of litters affected by 

11 cleft palate (86.2% versus 34.5% in mice fed sufficient folic acid) and a 10-fold increase in the 

12 percentage of litters affected by exencephaly (34.5% versus 3.4% in mice fed sufficient folic 

13 acid) at the 5 g/kg methanol dose. Sakanashi et al. (1996) speculate that the increased methanol

14 effect from the FAD diet could have been due to an increase in tissue formate levels (not 

15 measured) or to a critical reduction in conceptus folate concentration following the methanol 

16 exposure. Plasma and liver folate levels at GD18 within each dietary group were not 

17 significantly different between exposed versus unexposed mice. However, these measurements 

18 were taken 3 days after methanol exposure. Dorman et al. (1995) observed a transient decrease 

19 in maternal red blood cells (RBCs) and conceptus folate levels within 2 hours following 

20 inhalation exposure to 15,000 ppm methanol on GD8. Thus, it is possible that short-term 

21 reductions in available folate during GD6–GD15 may have affected fetal development.

22 Fu et al. (1996) also tested the influence of dietary folic acid intake on reproductive and 

23 developmental effects observed in CD-1 mice exposed to methanol. This study was performed 

24 by the same laboratory and used a similar study design and dosing regimen as Sakanashi et al. 

25 (1996), but exposed the pregnant mice to only the higher 2.5 g/kg-day methanol (total dosage of 

26 5.0 g/kg-day) on GD6–GD10. Like Sakanashi et al. (1996), Fu et al. (1996) measured maternal 

27 liver and plasma folate levels on GD18 and observed similar, significant reductions in these 

28 levels for the FAD versus FAS mice. However, Fu et al. (1996) also measured fetal liver folate 

29 levels at GD18. This measurement does not address the question of whether methanol exposure 

30 caused short-term reductions in fetal liver folate because it was taken 8 days after the GD6–

31 GD10 exposure period. However, it did provide evidence regarding the extent to which a 

32 maternal FAD diet can impact fetal liver folate levels in this species and strain. Significantly, the 

33 maternal FAD diet had a greater impact on fetal liver folate than maternal liver folate levels. 

34 Relative to the FAS groups, fetal liver folate levels in the FAD groups were reduced 2.7-fold for 

35 mice not exposed to methanol (1.86 ± 0.15 nmol/g in the FAD group versus 5.04 ± 0.22 nmol/g

36 in the FAS group) and 3.5-fold for mice exposed to methanol (1.69 ± 0.12 nmol/g in the FAD 
4-34 DRAFT-DO NOT CITE OR QUOTE

1 group versus 5.89 ± 0.39 nmol/g in the FAS group). Maternal folate levels in the FAD groups 

2 were only reduced twofold both for mice not exposed (4.65 ± 0.37 versus 9.54 ± 0.50 nmol/g) 

3 and exposed (4.55 ± 0.19 versus 9.26 ± 0.42 nmol/g). Another key finding of the Fu et al. (1996) 

4 study is that methanol exposure during GD6–GD10 appeared to have similar fetotoxic effects, 

5 including cleft palate, exencephaly, resorptions, and decrease in live fetuses, as the same level of 

6 methanol exposure administered during GD6–GD15 (Sakanashi et al., 1996; Rogers et al., 

7 1993a). This is consistent with the hypothesis made by Rogers et al. (1993b) that the critical 

8 period for methanol-induced cleft palate and exencephaly in CD-1 mice is within GD6–GD10. 

9 As in the studies of Sakanashi et al. (1996) and Rogers et al. (1993a), Fu et al. (1996) reported a 

10 higher incidence of cleft palate than exencephaly. 

4.3.2. Inhalation Studies

11 Nelson et al. (1985) exposed 15 pregnant Sprague-Dawley rats/group to 0, 5,000, 10,000, 

or 20,000 ppm (0, 6,552, 13,104, and 26,209 mg/m3 12 ) methanol (99.1% purity) for 7 hours/day. 

13 Exposures were conducted on GD1–GD19 in the two lower concentration groups and GD7–

14 GD15 in the highest concentration group, apparently on separate days. Two groups of 15 control 

15 rats were exposed to air only. Day 1 blood methanol levels measured 5 minutes after the 

16 termination of exposure in NP rats that had received the same concentrations of methanol as 

17 those animals in the main part of the experiment were 1.00 ± 0.21, 2.24 ± 0.20, and 8.65 ± 0.40 

18 mg/mL for those exposed to 5,000, 10,000 and 20,000 ppm methanol, respectively. Evidence of 

19 maternal toxicity included a slightly unsteady gait in the 20,000 ppm group during the first few 

20 days of exposure. Maternal bodyweight gain and food intake were unaffected by methanol. 

21 Dams were sacrificed on GD20, and 13–30 litters/group were evaluated. No effect was observed 

22 on the number of corpora lutea or implantations or the percentage of dead or resorbed fetuses. 

23 Statistical evaluations included analysis of variance (ANOVA) for body weight effect, Kruskal24 Wallis test for endpoints such as litter size and viability and Fisher’s exact test for 

25 malformations. Fetal body weight was significantly reduced at concentrations of 10,000 and 

26 20,000 ppm by 7% and 12–16%, respectively, compared to controls. An increased number of 

27 litters with skeletal and visceral malformations were observed at ≥10,000 ppm, with statistical 

28 significance obtained at 20,000 ppm. Numbers of litters with visceral malformations were 0/15, 

29 5/15, and 10/15 and with skeletal malformations were 0/15, 2/15, and 14/15 at 0, 10,000, and 

30 20,000 ppm, respectively. Visceral malformations included exencephaly and encephaloceles. 

31 The most frequently observed skeletal malformations were rudimentary and extra cervical ribs. 

32 The developmental and maternal NOAELs for this study were identified as 5,000 ppm (6,552 

mg/m3

) and 10,000 ppm (13,104 mg/m3 33 ), respectively.
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1 NEDO (1987) sponsored a teratology study in Sprague-Dawley rats that included an 

2 evaluation of postnatal effects in addition to standard prenatal endpoints. Thirty-six pregnant 

females/group were exposed to 0, 200, 1,000, or 5,000 ppm (0, 262, 1,310, and 6,552 mg/m3 3 ) 

4 methanol vapors (reagent grade) on GD7–GD17 for 22.7 hours/day. Statistical significance of 

results was evaluated by t-test, Mann-Whitney U test, Fisher’s exact test, and/or Armitage’s χ

2 5

6 test.

7 Contrary to the Nelson et al. (1985) report of a 10,000 ppm NOAEL for this rat strain, in 

8 the prenatal portion of the NEDO (1987) study, reduced body weight gain and food and water 

9 intake during the first 7 days of exposure were reported for dams in the 5,000 ppm group. 

10 However, it was not specified if these results were statistically significant. One dam in the 

11 5,000 ppm group died on GD19, and one dam was sacrificed on GD18 in moribund condition. 

12 On GD20, 19–24 dams/group were sacrificed to evaluate the incidence of reproductive deficits 

13 and such developmental parameters as fetal viability, weight, sex, and the occurrence of 

14 malformations. As summarized in Table 4-6, adverse reproductive and fetal effects were limited 

15 to the 5,000 ppm group and included an increase in late-term resorptions, decreased live fetuses, 

16 reduced fetal weight, and increased frequency of litters with fetal malformations, variations, and 

17 delayed ossifications. Malformations or variations included defects in ventricular septum, 

18 thymus, vertebrae, and ribs.

19 Postnatal effects of methanol inhalation were evaluated in the remaining 12 dams/group 

20 that were permitted to deliver and nurse their litters. Effects were only observed in the 

21 5,000 ppm group, and included a 1-day prolongation of the gestation period and reduced post22 implantation survival, number of live pups/litter, and survival on PND4 (Table 4-7). When the 

23 delay in parturition was considered, methanol treatment had no effect on attainment of 

24 developmental milestones such as eyelid opening, auricle development, incisor eruption, testes 

25 descent, or vaginal opening. There were no adverse body weight effects in offspring from 

26 methanol treated groups. The weights of some organs (brain, thyroid, thymus, and testes) were 

27 reduced in 8-week-old offspring exposed to 5000 ppm methanol during prenatal development. 

28

29

30

31
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Table 4-6. Reproductive and developmental toxicity in pregnant Sprague-Dawley rats 

exposed to methanol via inhalation during gestation

Effect Exposure concentration (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 resorptions 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 ± 4.04a

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 ± 0.27c

Fetal weight (female) 3.51 ± 0.19 3.60 ± 0.25 3.60 ± 0.30 2.83 ± 0.26c

Total resorption rate (%) 11.2 ± 9.0 15.6 ± 21.3 10.6 ± 8.4 23.3 ± 22.7a

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) 35.2 ± 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 costotransversium 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

Excessive 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, bp < 0.01, cp < 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

4
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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 ± 0.5c

Number of implantations 15.8 ± 1.6 14.8 ± 1.2 15.3 ± 1.3 14.6 ± 1.1a

Number of pups 15.2 ± 1.6 14.4 ± 1.3 14.5 ± 1.4 13.1 ± 2.2a

Number of live pups 15.2 ± 1.6 14.1 ± 1.4 14.3 ± 1.4 12.6 ± 2.5b

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 87/85 103/70a 75/81

Postimplantation embryo 

survival rate 96.3 ± 4.2 94.9 ± 5.1 93.6 ± 6.1 86.2 ±16.2a

a

p < 0.05, bp < 0.01, cp < 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 NEDO (1987) contains an account of a two-generation reproductive study that evaluated 

3 the effects of pre- and postnatal methanol (reagent grade) exposure (20 hours/day) on 

4 reproductive and other organ systems of Sprague-Dawley rats. The F0 generation (30 males and 

30 females per exposure group)37 5 was exposed to 0, 10, 100, and 1,000 ppm (0, 13.1, 131, and 

1,310 mg/m3 6 ) from 8 weeks old to the end of mating (males) or to the end of lactation period 

7 (females). The F1 generation was exposed to the same concentrations from birth to the end of 

8 mating (males) or to weaning of F2 pups 21 days after delivery (females). Males and females of 

9 the F2 generation were exposed from birth to 21 days old (one animal/sex/litter was exposed to 

10 8 weeks of age). NEDO (1987) noted reduced brain, pituitary, and thymus weights, and early 

11 testicular descent in the offspring of F0 and F1 rats exposed to 1,000 ppm methanol. The early 

12 testicular descent is believed to be an indication of earlier fetal development as indicated by the 

13 fact that it was correlated with increased pup body weight. However, no histopathologic effects 

14 of methanol were observed. As discussed in the report, NEDO (1987) sought to confirm the 

15 possible compound-related effect of methanol on the brain by carrying out an additional study in 

16 which Sprague-Dawley rats were exposed to 0, 500, 1,000, and 2,000 ppm (0, 655, 1,310, and 

2,620 mg/m3 17 ) methanol from the first day of gestation through the F1 generation (see 

18 Section 4.4.2). 

 37 A second control group of 30 animals/sex was maintained in a separate room to “confirm that environmental conditions inside 

the chambers were not unacceptable to the animals.”
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1 Rogers et al. (1993a) evaluated development toxicity in pregnant female CD-1 mice 

2 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, 

9,894, 13,104, and 19,656 mg/m3 3 ) methanol vapors (≥ 99.9% purity) in a chamber for 

4 7 hours/day on GD6–GD15 in a 3-block design experiment. The numbers of mice exposed at 

5 each dose were 114, 40, 80, 79, 30, 30, and 44, respectively. During chamber exposures to air or 

6 methanol, the mice had access to water but not food. In order to determine the effects of the 

7 chamber exposure conditions, an additional 88 control mice were not handled and remained in 

8 their cages; 30 control mice were not handled but were food deprived for 7 hours/day on GD6–

9 GD15. Effects in dams and litters were statistically analyzed using the General Linear Models 

10 procedure and multiple t-test of least squares means for continuous variables and the Fisher’s 

11 exact test for dichotomous variables. An analysis of plasma methanol levels in 3 pregnant 

12 mice/block/treatment group on GD6, GD10, and GD15 revealed a dose-related increase in 

13 plasma methanol concentration that did not seem to reach saturation levels, and methanol plasma 

14 levels were not affected by gestation stage or number of previous exposure days. Across all 

15 3 days, the mean plasma methanol concentrations in pregnant mice were approximately 97, 537, 

16 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 

17 exposure groups, respectively.

18 The dams exposed to air or methanol in chambers gained significantly less weight than 

19 control dams that remained in cages and were not handled. There were no methanol-related 

20 reductions in maternal body weight gain or overt signs of toxicity. Dams were sacrificed on 

21 GD17 for a comparison of developmental toxicity in methanol-treated groups versus the chamber 

22 air-exposed control group. Fetuses in all exposure groups were weighed, assessed for viability, 

23 and examined for external malformations. Fetuses in the control, 1,000, 2,000, 5,000, and 

24 15,000 ppm groups were also examined for skeletal and visceral defects. Incidence of 

25 developmental effects is listed in Table 4-8. A statistically significant increase in cervical 

26 ribs/litter was observed at concentrations of 2,000, 5,000, and 15,000 ppm. At doses of 

27 ≥5,000 ppm the incidences of cleft palates/litter and exencephaly/litter were increased with 

28 statistical significance achieved at all concentrations with the exception of exencephaly which 

increased but not significantly at 7,500 ppm.38 29 A significant reduction in live pups/litter was 

30 noted at ≥7,500 ppm, with a significant increase in fully resorbed litters occurring at 

31 ≥10,000 ppm. Fetal weight was significantly reduced at ≥10,000 ppm. Rogers et al. (1993a)

32 identified a developmental NOAEL and LOAEL of 1,000 ppm and 2,000 ppm, respectively. 

33 They also provide BMD maximum likelihood estimates (benchmark concentration [BMC]; 

34 referred to by the authors as MLE) and estimates of the lower 95% confidence limit on the BMC

 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 higher are considered biologically significant.
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1 (benchmark concentration, 95% lower bound [BMCL]; referred to as benchmark dose [BMD] by 

2 Rogers et al. (1993a) for 5% and 1% added risk, by applying a log-logistic dose-response model 

3 to the mean percent/litter data for cleft palate, exencephaly and resorption. The BMC05 and 

4 BMCL05 values for added risk estimated by Rogers et al. (1993a) are listed in Table 4-9. From 

5 this analysis, the most sensitive indicator of developmental toxicity was an increase in the 

6 proportion of fetuses per litter with cervical rib anomalies. The most sensitive BMCL and BMC

from this effect for 5% added risk were 305 ppm (400 mg/m3

) and 824 ppm (1,080 mg/m3 7 ), 

respectively.39 8

Table 4-8. Developmental effects in mice after methanol inhalation

Endpoint Exposure concentration (ppm)

0 1,000 2,000 5,000 7,500 10,000 15,000

No. live pups/litter 9.9 9.5 12.0 9.2 8.6b 7.3c 2.2c

No. fully resorbed litters 0 0 0 0 3 5a 14c

Fetus weight (g) 1.20 1.19 1.15 1.15 1.17 1.04c 0.70c

Cleft palate/ litter (%) 0.21 0.65 0.17 8.8b 46.6c 52.7c 48.3c

Exencephaly/litter (%) 0 0 0.88 6.9a 6.8 27.4c 43.3c

Anomalies

Cervical ribs/litter (%) 28 33.6 49.6b 74.4c ND ND 60.0a

Sternebral defects/litter (%) 6.4 7.9 3.5 20.2c ND ND 100c

Xiphoid defects/litter (%) 6.4 3.8 4.1 10.9 ND ND 73.3c

Vertebral arch defects/litter (%) 0.3 ND ND 1.5 ND ND 33.3c

Extra lumbar ribs/litter (%) 8.7 2.5 9.6 15.6 ND ND 40.0c

Ossifications (values are means of litter means)

Sternal 5.96 5.99 5.94 5.81 ND ND 5.07c

Caudal 5.93 6.26 5.71a 5.42 ND ND 3.20a

Metacarpal 7.96 7.92 7.96 7.93 ND ND 7.60b

Proximal phalanges 7.02 7.04 7.04 6.12 ND ND 3.33c

Metatarsals 9.87 9.90 9.87 9.82 ND ND 8.13c

Proximal phalanges 7.18 7.69 6.91 5.47 ND ND 0c

Distal phalanges 9.64 9.59 9.57 8.46b ND ND 4.27c

Supraoccipital score+ 1.40 1.65 1.57 1.48 ND ND 3.20c

ND = Not determined. + = on a scale of 1–4, where 1 is fully ossified and 4 is unossified.

Statistical significance: ap < 0.05, bp < 0.01, cp < 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 external 

exposure concentrations, whereas the analyses in Section 5 used extra risk and internal dose metrics that were then 

converted to human equivalent exposure concentrations.
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Table 4-9. Benchmark doses at two added risk levels

Endpoint BMC05 (ppm) BMCL05 (ppm) BMC01 (ppm) BMCL01 (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

Resorptions (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).

1 Rogers and Mole (1997) investigated the critical period of sensitivity to the 

2 developmental toxicity of inhaled methanol in the CD-1 mouse by exposing 12–17 pregnant 

females to 0 or 10,000 ppm (0 and 13,104 mg/m3 3 ), 7 hours/day on 2 consecutive days during 

4 GD6–GD13, or to a single exposure to the same methanol concentration during GD5–GD9. 

5 Another group of mice received a single 7-hour exposure to methanol at 10,000 ppm. The latter 

6 animals were sacrificed at various time intervals up to 28 hours after exposure. Blood samples 

7 were taken from these animals to measure the concentration of methanol in the serum. Serum 

8 methanol concentrations peaked at ~4 mg/mL 8 hours after the onset of exposure. Methanol 

9 concentrations in serum had declined to pre-exposure levels after 24 hours. All mice in the main 

10 body of the experiment were sacrificed on GD17, and their uteri removed. The live, dead, and 

11 resorbed fetuses were counted, and all live fetuses were weighed, examined externally for cleft 

12 palate, and then preserved. Skeletal abnormalities were determined after the carcasses had been 

13 cleaned and eviscerated. Cleft palate, exencephaly, and skeletal defects were observed in the 

14 fetuses of exposed dams. For example, cleft palate was observed following 2-day exposures to 

15 methanol on GD6–GD7 through GD11–GD12. These effects also were apparent in mice 

16 receiving a single exposure to methanol on GD5–GD9. This effect peaked when the dams were 

17 exposed on GD7. Exencephaly showed a similar pattern of development in response to methanol 

18 exposure. However, the data indicated that cleft palate and exencephaly might be competing 

19 malformations, since only one fetus displayed both features. Skeletal malformations included 

20 exoccipital anomalies, atlas and axis defects, the appearance of an extra rudimentary rib on 

21 cervical vertebra No.7, and supernumerary lumbar ribs. In each case, the maximum time point 

22 for the induction of these defects appeared to be when the dams were exposed to methanol on or 

23 near GD7. When dams were exposed to methanol on GD5, there was also an increased 

24 incidence of fetuses with 25 presacral vertebrae (26 is normal). However, an increased incidence 

25 of fetuses with 27 presacral vertebrae was evident when dams were exposed on GD7. These 

26 results indicate that gastrulation and early organogenesis is a period of increased embryonic 

27 sensitivity to methanol.
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1 Burbacher et al. (1999a, 1999b) carried out toxicokinetic and reproductive/developmental 

2 studies of methanol in M. fascicularis monkeys that were published by the Health Effects 

3 Institute (HEI) in a two-part monograph. Some of the data were subsequently published in the 

4 open scientific literature (Burbacher et al., 2004a, 2004b). The experimental protocol featured 

5 exposure to 2 cohorts of 12 monkeys/group to low exposure levels (relative to the previously 

discussed rodent studies) of 0, 200, 600, or 1,800 ppm (0, 262, 786, and 2,359 mg/m3 6 ) methanol 

7 vapors (99.9% purity), 2.5 hours/day, 7 days/week, during a premating period and mating period 

8 (−180 days combined) and throughout the entire gestation period (−168 days). The monkeys 

9 were 5.5–13 years old and were a mixture of feral-born and colony-bred animals. The study 

10 included an evaluation of maternal reproductive performance and tests to assess infant postnatal 

11 growth and newborn health, reflexes, behavior, and development of visual, sensorimotor, 

12 cognitive, and social behavioral function (see Section 4.4.2 for a review of the developmental 

13 neurotoxicity findings from this study). Blood methanol levels, clearance, and the appearance of 

14 formate were also examined and are discussed in Section 3.2.

15 With regard to reproductive parameters, there was a statistically significant decrease

16 (p = 0.03) in length of pregnancy in all treatment groups, as shown in Table 4-10. Maternal 

17 menstrual cycles, conception rate, and live birth index were all unaffected by exposure. There 

18 were also no signs of an effect on maternal weight gain or clinical toxicity among the dams. The 

19 decrease in pregnancy length was largely due to complications of pregnancy requiring Cesarean 

20 section (C-section) deliveries in the methanol exposure groups. The C-section deliveries were 

21 performed in response to signs of difficulty in the pregnancy and thus may serve as supporting 

22 evidence of reproductive dysfunction in the methanol-exposed females. 

23 While pregnancy duration was virtually the same in all exposure groups, there were some 

24 indications of increased pregnancy duress only in methanol-exposed monkeys. C-sections were 

25 done in 2 monkeys from the 200 ppm group and 2 from the 600 ppm group due to vaginal 

bleeding, presumed, but not verified, to be from placental detachment.40 26 A monkey in the 

27 1,800 ppm group also received a C-section after experiencing nonproductive labor for 3 nights. 

28 In addition, signs of prematurity were observed in 1 infant from the 1,800 ppm group that was 

29 born after a 150-day gestation period. The authors speculated that the shortened gestation length 

30 could be due to a direct effect of methanol on the fetal hypothalamus-pituitary-adrenal (HPA) 

31 axis or an indirect effect of methanol on the maternal uterine environment. Other fetal 

32 parameters such as crown-rump length and head circumference were unchanged among the 

33 groups. Infant growth and tooth eruption were unaffected by prenatal methanol exposure.

 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.
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Table 4-10. Reproductive parameters in monkeys exposed via inhalation to methanol 

during prebreeding, breeding, and pregnancy

Exposure (ppm) Conception rate Weight gain (kg) Pregnancy duration (days)a Live born delivery rate

0 9/11 1.67 ± 0.07 168 ± 2 8/9

200 9/12 1.27 ± 0.14 160 ± 2b 9/9

600 9/11 1.78 ± 0.25 162 ± 2b 8/9

1,800 10/12 1.54 ± 0.20 162 ± 2b 9/10

Values are means ± SE.; 

a

Live-born offspring only; bp < 0.05, as calculated by the authors.

Source: Burbacher et al. (2004a).

1 In later life, 2 females out of the total of 9 offspring in the 1,800 ppm group experienced 

2 a wasting syndrome at 12 and 17 months of age. Food intake was normal and no cause of the 

3 syndrome could be determined in tests for viruses, hematology, blood chemistry, and liver, 

4 kidney, thyroid, and pancreas function. Necropsies revealed gastroenteritis and severe 

5 malnourishment. No infectious agent or other pathogenic factor could be identified. Thus, it 

6 appears that a highly significant toxicological effect on postnatal growth can be attributed to 

prenatal methanol exposure at 1,800 ppm (2,300 mg/m3 7 ).

8 In summary, the Burbacher et al. (2004a, 2004b, 1999b) studies suggest that methanol 

9 exposure can cause reproductive effects, manifested as a shortened mean gestational period due 

10 to pregnancy complications that precipitated delivery via a C-section, and developmental 

11 neurobehavioral effects which may be related to the shortened gestational period (see 

12 Section 4.4.2). The low exposure of 200 ppm may signify a LOAEL for reproductive effects. 

13 However, the decrease in gestational length was marginally significant and largely the result of 

14 human intervention (C-section) for reasons (presumably pregnancy complications) that were not 

15 objectively confirmed with clinical procedures (e.g., placental ultrasound). Also, this effect did 

16 not appear to be dose related, the greatest gestational period decrease having occurred at the 

17 lowest (200 ppm) exposure level. Thus, a clear NOAEL or LOAEL cannot be determined from 

18 this study. 

19 In a study of the testicular effects of methanol, Cameron et al. (1984) exposed 5 male 

20 Sprague-Dawley rats/group to methanol vapor, 8 hours/day, 5 days/week for 1, 2, 4, and 6 weeks 

at 0, 200, 2,000, or 10,000 ppm (0, 262, 2,620, and 13,104 mg/m3 21 ). The authors examined the 

22 possible effects of methanol on testicular function by measuring blood levels of testosterone, 

23 luteinizing hormone (LH), and follicular stimulating hormone (FSH) using radioimmunoassay. 

24 When the authors tabulated their results as a percentage of the control value for each duration 

25 series, the most significant changes were in blood testosterone levels of animals exposed to 

26 200 ppm methanol, the lowest concentration evaluated. At this exposure level, animals exposed 

27 for 6 weeks had testosterone levels that were 32% of those seen in controls. However, higher 
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1 concentrations of methanol were associated with testosterone levels that were closer to those of 

2 controls. However, the lack of a clear dose-response is not necessarily an indication that the 

3 effect is not related to methanol. The higher concentrations of methanol could be causing other 

4 effects (e.g., liver toxicity) which can influence the results. Male rats exposed to 10,000 ppm 

5 methanol for 6 weeks displayed blood levels of LH that were about 3 times higher (mean ± S.D.) 

6 than those exposed to air (311 ± 107% versus 100 ± 23%). In discussing their results, the authors 

7 placed the greater emphasis on the fact that an exposure level equal to the ACGIH TLV 

8 (200 ppm) had caused a significant depression in testosterone formation in male rats.

9 A follow-up study report by the same research group (Cameron et al., 1985) described the 

10 exposure of 5 male Sprague-Dawley rats/group, 6 hours/day for either 1 day or 1 week, to 

11 methanol, ethanol, n-propanol, or n-butanol at their respective TLVs. Groups of animals were 

12 sacrificed immediately after exposure or after an 18-hour recovery period, and the levels of 

13 testosterone, LH, and corticosterone measured in serum. As shown in Table 4-11, the data were 

14 consistent with the ability of these aliphatic alcohols to cause a transient reduction in the 

15 formation of testosterone. Except in the case of n-butanol, rapid recovery from these deficits can 

16 be inferred from the 18-hour postexposure data.

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 ± 16a 98 ± 18 81 ± 22 82 ± 27

Ethanol 1,000 64 ± 12a 86 ± 16 88 ± 14 101 ± 13

n-Propanol 200 58 ± 15a 81 ± 13 106 ± 28 89 ± 17

n-Butanol 50 37 ± 8a 52 ± 22a 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

Ethanol 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 ± 23
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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 ND 60 ± 25 ND

n-Propanol 200 112 ± 21 ND 79 ± 14 ND

n-Butanol 50 143 ± 11a ND 85 ± 26 ND

ND = No data.; 

a

p < 0.05, as calculated by the authors.

Source: Cameron et al. (1985).

1

2 In a series of studies that are relevant to the reproductive toxicity of methanol in males, 

3 Lee et al. (1991) exposed 8-week-old male Sprague-Dawley rats (9–10/group) to 0 or 200 ppm 

(0 and 262 mg/m3 4 ) methanol, 8 hours/day, 5 days/week, for 1, 2, 4, or 6 weeks to measure the 

5 possible treatment effects on testosterone production. Study results were evaluated by one factor 

6 ANOVA followed by Student’s t-test. In the treated rats, there was no effect on serum 

7 testosterone levels, gross structure of reproductive organs, or weight of testes and seminal 

8 vesicles. Lee et al. (1991) also studied the in vitro effect of methanol on testosterone production 

9 from isolated testes, but saw no effect on testosterone formation either with or without the 

10 addition of human chorionic gonadotropin hormone.

11 In a third experiment from the same report, Lee et al. (1991) examined testicular 

12 histopathology to determine if methanol exposure produced lesions indicative of changing 

13 testosterone levels; the effects of age and folate status were also assessed. This is relevant to the 

14 potential toxicity of methanol because folate is the coenzyme of tetrahydrofolate synthetase, an 

15 enzyme that is rate limiting in the removal of formate. Folate deficiency would be expected to 

16 cause potentially toxic levels of methanol, formaldehyde, and formate to be retained. The same 

17 authors examined the relevance of folate levels, and by implication, the overall status of formate 

18 formation and clearance in mediating the testicular functions of Long-Evans rats. Groups of 

19 4-week-old male Long-Evans rats were given diets containing either adequate or reduced folate 

levels plus 1% succinylsulfathiazole, an antibiotic that, among other activities, 41 20 would tend to 

21 reduce the folate body burden. At least 9 rats/dietary group/dose were exposed to 0, 50, 200, or 

800 ppm (0, 66, 262, and 1,048 mg/m3 22 ) methanol vapors starting at 7 months of age while 8–

23 12 rats/dietary group/dose were exposed to 0 or 800 ppm methanol vapors at 15 months of age. 

 41 Succinylsulfathiazone antibiotic may have a direct impact on the effects being measured, the extent of which was 

not addressed by the authors of this study.
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1 The methanol exposures were conducted continuously for 20 hours/day for 13 weeks. Without 

2 providing details, the study authors reported that visual toxicity and acidosis developed in rats 

3 fed the low folate diet and exposed to methanol. No methanol-related testicular lesions or 

4 changes in testes or body weight occurred in rats that were fed either the folate sufficient or 

5 deficient diets and were 10 months old at the end of treatment. Likewise, no methanol-lesions 

6 were observed in 18-month-old rats that were fed diets with adequate folate. However, the 

7 incidence but not severity of age-related testicular lesions was increased in the 18-month-old rats 

8 fed folate-deficient diets. Subcapsular vacuoles in germinal epithelium were noted in 3/12 

9 control rats and 8/13 rats in the 800 ppm group. One rat in the 800 ppm group had atrophied 

10 seminiferous tubules and another had Leydig cell hyperplasia. These effects, as well as the 

11 transient decrease in testosterone levels observed by Cameron et al. (1985, 1984), could be the 

12 result of chemically-related strain on the rat system as it attempts to maintain hormone 

13 homeostasis.

14 Dorman et al. (1995) conducted a series of in vitro and in vivo studies of developmental 

15 toxicity in ICR BR (CD-1) mice associated with methanol and formate exposure. The studies 

16 used HPLC grade methanol and appropriate controls. PK and developmental toxicity parameters 

17 were measured in mice exposed to a 6-hour methanol inhalation (10,000 or 15,000 ppm), 

18 methanol gavage (1.5 g/kg) or sodium formate (750 mg/kg by gavage) on GD8. In the in vivo

19 inhalation study, 12–14 dams/group were exposed to 10,000 ppm methanol for 6 hours on 

GD8,42 20 with and without the administration of fomepizole (4-methylpyrazole) to inhibit the 

21 metabolism of methanol by ADH1. Dams were sacrificed on GD10, and folate levels in 

22 maternal RBC and conceptus (decidual swelling) were measured, as well as fetal neural tube 

23 patency (an early indicator of methanol-induced dysmorphogenic response). The effects 

24 observed included a transient decrease in maternal RBC and conceptus folate levels within 

25 2 hours following exposure and a significant (p < 0.05) increase in the incidence of fetuses with 

26 open neural tubes (9.65% in treated versus 0 in control). These responses were not observed 

27 following sodium formate administration, despite peak formate levels in plasma and decidual

28 swellings being similar to those observed following the 6-hour methanol inhalation of 

29 15,000 ppm. This suggests that these methanol-induced effects are not related to the 

30 accumulation of formate. As this study provides information relevant to the identification of the 

31 proximate teratogen associated with developmental toxicity in rodents, it is discussed more 

32 extensively in Section 4.6.1.

 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.
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4.3.3. Other Reproductive and Developmental Toxicity Studies

1 Additional information relevant to the possible effects of methanol on reproductive and 

2 developmental parameters has been provided by experimental studies that have exposed 

3 experimental animals to methanol during pregnancy via i.p. injections (Rogers et al., 2004). 

4 Relevant to the developmental impacts of the chemical, a number of studies also have examined 

5 the effects of methanol when included in whole-embryo culture (Hansen et al., 2005; Harris 

6 et al., 2003; Andrews et al., 1998, 1995, 1993).

7 Pregnant female C57BL/6J mice received 2 i.p. injections of methanol on GD7 (Rogers 

8 et al., 2004). The injections were given 4 hours apart to provide a total dosage of 0, 3.4, and 4.9 

9 g/kg. Animals were sacrificed on GD17 and the litters were examined for live, dead, and 

10 resorbed fetuses. Rogers et al. (2004) monitored fetal weight and examined the fetuses for 

11 external abnormalities and skeletal malformations. Methanol-related deficits in maternal and 

12 litter parameters observed by Rogers et al. (2004) are summarized in Table 4-12.

Table 4-12. Maternal and litter parameters 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–GD8 (g) 0.33 ± 0.10 0.37 ± 0.15 -0.24 ± 0.14a

Wt gain GD7–GD10 (g) 1.63 ± 0.18 2.20 ± 0.20 1.50 ± 0.20

Live fetuses/litter 7.5 ± 0.30 6.3 ± 0.5a 3.7 ± 0.4a

Resorbed fetuses/litter 0.4 ± 0.1 1.3 ± 0.4a 4.4 ± 0.4a

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 ± 0.02a

Values are means ± SEM.

a

p < 0.05, as calculated by the authors.

Source: Rogers et al. (2004).

13 Rogers et al. (2004) used a number of sophisticated imaging techniques, such as confocal 

14 laser scanning and fluorescence microscopy, to examine the morphology of fetuses excised at 

15 GD7, GD8, and GD9. They identified a number of external craniofacial abnormalities, the 

16 incidence of which was, in all cases, significantly increased in the high-dose group compared to 

17 controls. For some responses, such as microanophthalmia and malformed maxilla, the incidence 

18 was also significantly increased in animals receiving the lower dose. Fifteen compound-related 

19 skeletal malformations were tabulated in the report. In most cases, a dose-response effect was 

20 evident, resulting in statistically significant incidences in affected fetuses and litters, when 

21 compared to controls. Apparent effects of methanol on the embryonic forebrain included a 
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1 narrowing of the anterior neural plate, missing optical vesicles, and holoprosencephaly (failure of 

2 the embryonic forebrain to divide). The authors noted that there was no sign of incipient cleft 

3 palate or exencephaly, as had been observed in CD-1 mice exposed to methanol via the oral and 

4 inhalation routes (Rogers et al., 1993a).

5 In order to collect additional information on cell proliferation and histological changes in 

6 methanol-treated fetuses, Degitz et al. (2004a) used an identical experimental protocol to that of 

7 Rogers et al. (2004) by administering 0, 3.4, or 4.9 g methanol/kg in distilled water i.p. (split 

8 doses, 4 hours apart) to C57BL/6J mice on GD7. Embryos were collected at various times on 

9 GD8 and GD10. Embryos from dams exposed to 4.9 g/kg and examined on GD8 exhibited 

10 reductions in the anterior mesenchyme, the mesenchyme subjacent to the mesencephalon and the 

11 base of the prosencephalon (embryonic forebrain), and in the forebrain epithelium. The optic 

12 pits were often lacking; where present their epithelium was thin and there were fewer neural 

13 crest cells in the mid- and hindbrain regions.

14 At GD9, there was extensive cell death in areas populated by the neural crest, including 

15 the forming cranial ganglia. Dose-related abnormalities in the development of the cranial nerves 

16 and ganglia were seen on GD7. In accordance with an arbitrary dichotomous scale devised by 

17 the authors, scores for ganglia V, VIII, and IX were significantly (not otherwise specified) 

18 reduced at all dose levels, and ganglia VII and X were reduced only at the highest dose. At the 

19 highest dose (4.9 g/kg), the brain and face were poorly developed and the brachial arches were 

20 reduced in size or virtually absent. Flow cytometry of the head regions of the embryos from the 

21 highest dose at GD8 did not show an effect on the proportion of cells in S-phase.

22 Cell growth and development were compared in C57BL/6J and CD-1 mouse embryos 

23 cultured in methanol (Degitz, et al., 2004b). GD8 embryos, with 5–7 somites, were cultured in 

24 0, 1, 2, 3, 4, or 6 mg methanol/mL for 24 hours and evaluated for morphological development. 

25 Cell death was increased in both strains in a developmental stage- and region-specific manner at 

26 4 and 6 mg/mL after 8 hours of exposure. The proportions of cranial region cells in S-phase 

27 were significantly (p < 0.05) decreased at 6 mg/mL following 8- and 18-hour exposures to 

28 methanol. After 24 hours of exposure, C57BL/6J embryos had significantly (p < 0.05) decreased 

29 total protein at 4 and 6 mg/kg. Significant (p < 0.05) developmental effects were seen at 3, 4, 

30 and 6 mg/kg, with eye dysmorphology being the most sensitive endpoint. CD-1 embryos had 

31 significantly decreased total protein at 3, 4, and 6 mg/kg, but developmental effects were seen 

32 only at 6 mg/kg. It was concluded that the C57BL/6J embryos were more severely affected by 

33 methanol in culture than the CD-1 embryos.

34 Andrews et al. (1993) carried out a comparative study of the developmental toxicity of 

35 methanol in whole Sprague-Dawley rat or CD-1 mouse embryos. Nine-day rat embryos were 

36 explanted and cultured in rat serum containing 0, 2, 4, 8, 12, or 16 mg/mL methanol for 24 hours 
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1 then transferred to rat serum alone for a further 24 hours. Eight-day mouse embryos were 

2 cultured in 0, 2, 4, 6, or 8 mg/mL methanol in culture medium for 24 hours. At the end of the 

3 culture period, embryos were examined for growth, development and dysmorphogenesis. For 

4 the rats, doses of 8 mg/mL and above resulted in a concentration-related decrease in somite 

5 number, head length, and developmental score. Some lethality was seen in embryos incubated at 

6 12 mg/mL methanol. For the mouse embryos, incubation concentrations of 4 mg/mL methanol 

7 and above resulted in a significant decrease in developmental score and crown-rump length. The 

8 high concentration (8 mg/mL) was associated with embryo lethality. These data suggest that 

9 mouse embryos are more sensitive than rat embryos to the developmental effects of methanol. 

10 Using a similar experimental system to examine the developmental toxicity of formate and 

11 formic acid in comparison to methanol, Andrews et al. (1995) showed that the formates are 

12 embryotoxic at doses that are four times lower than equimolar doses of methanol. Andrews et al. 

13 (1998) showed that exposure to combinations of methanol and formate was less embryotoxic 

14 than would be expected based on simple toxicity additivity, suggesting that the embryotoxicity 

15 observed following low-level exposure to methanol is mechanistically different from that 

16 observed following exposure to formate.

17 A study by Hansen et al. (2005) determined the comparative toxicity of methanol and its 

18 metabolites, formaldehyde and sodium formate, in GD8 mouse (CD-1) and GD10 rat (Sprague19 Dawley) conceptuses. Incubation of whole embryos was for 24 hours in chemical-containing 

20 media (mouse: 4–12 mg/mL methanol, 1–6 μg/mL formaldehyde, 0.5–4 mg/mL sodium formate; 

21 rat: 8–20 mg/mL, 1–8 μg/mL, 0.5–8 mg/mL). Subsequently, the visceral yolk sac (VYS) was 

22 removed and frozen for future protein and DNA determination. The embryos were examined 

23 morphologically to determine growth and developmental parameters such as viability, flexure 

24 and rotation, crown-rump length, and neuropore closure. In other experiments, the chemicals 

25 were injected directly into the amniotic space. For each response, Table 4-13 provides a 

26 comparison of the concentrations or amounts of methanol, formaldehyde, and formate that 

27 resulted in statistically significant changes in developmental abnormalities compared to controls.

28 For a first approximation, these concentrations or amounts may be taken as threshold29 dose ranges for the specific responses under the operative experimental conditions. The data 

30 show consistently lower threshold values for the effects of formaldehyde compared to those of 

31 formate and methanol. The mouse embryos were more sensitive towards methanol toxicity than 

32 rat embryos, consistent with in vivo findings, whereas the difference in sensitivity disappeared 

33 when formaldehyde was administered. Hansen et al. (2005) hypothesized that, while the MOA 

34 for the initiation of the organogenic defects is unknown, the relatively low threshold levels of 

35 formaldehyde for most measured effects suggest formaldehyde involvement in the embryotoxic 

36 effects of methanol. By contrast, formate, the putative toxicant for the acute effects of methanol 
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1 poisoning (acidosis, neurological deficits), did not appear to reproduce the methanol-induced 

2 teratogenicity in these whole embryo culture experiments.

Table 4-13. Reported thresholds concentrations (and author-estimated ranges) for the 

onset of embryotoxic effects when rat and mouse conceptuses were incubated in vitro 

with methanol, formaldehyde, 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

CRa length No change No change No change No change No change No change

Neural tube closure (%) 8.0 0.001 2.0 12.0 No change No change

Reduced embryo protein 8.0 0.003 4.0 8.0 0.004 2.0

Reduced VYSb protein 10.0 0.004 4.0 12.0 0.004 NR

Reduced embryo 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

CRa 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 embryo protein 1–45 0.501–1.0 No change No change 1.51–2.0 0.51–1.0

Reduced VYSb protein 135–178 1.01–1.5 No change No change No change 1.01–1.5

Reduced embryo DNA 46–89 0.501–1.0 No change No change No change 0.51–1.0

Reduced VYSb 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

Source: Hansen et al. (2005); Harris et al. (2004) (adapted).

3 Harris et al. (2003) provided biochemical evidence consistent with the concept that 

4 formaldehyde might be the ultimate embryotoxicant of methanol by measuring the activities of 

5 enzymes that are involved in methanol metabolism in mouse (CD-1) and rat (Sprague-Dawley)

6 whole embryos at different stages of development. Specific activities of the enzymes ADH1, 

7 ADH3, and CAT, were determined in rat and mouse conceptuses during the organogenesis period 

8 of 8–25 somites. Activities were measured in heads, hearts, trunks, and VYS from early- and 

9 late-stage mouse and rat embryos. While CAT activities were similar between rat and mouse 

10 embryos, mouse ADH1 activities in the VYS were significantly lower throughout organogenesis 

11 when compared to the rat VYS or embryos of either species. ADH1 activities of heads, hearts, 

12 and trunks from mouse embryos were significantly lower than those from rats at the 7–12 somite 
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1 stage. However, these interspecies differences were not evident in embryos of 20–22 somites. 

2 ADH3 activities were lower in mouse versus rat VYS, irrespective of the stage of development. 

3 However, while ADH3 activities in mouse embryos were markedly lower than those of rats in 

4 the early stages of development, the levels of activity were similar to at the 14–16 somite stage 

5 and beyond. A lower capacity to transform formaldehyde to formate might explain the increased 

6 susceptibility of mouse versus rat embryos to the toxic effects of methanol. The hypothesis that 

7 formaldehyde is the ultimate embryotoxicant of methanol is supported by the demonstration of 

8 diminished ADH3 activity in mouse versus rat embryos and by the demonstration by Hansen 

9 et al. (2005) that formaldehyde has a far greater embryotoxicity than either formate or methanol 

10 itself.

11 That formate can induce similar developmental lesions in whole rat and mouse 

12 conceptuses was demonstrated by Andrews et al. (1995), who evaluated the developmental 

13 effects of sodium formate and formic acid in rodent whole embryo cultures in vitro. Day 9 rat 

14 (Sprague-Dawley) embryos were cultured for 24 or 48 hours and day 8 mouse (CD-1) cultures 

15 were incubated for 24 hours. As tabulated by the authors, embryos of either species showed 

16 trends towards increasing lethality and incidence of abnormalities with exposure concentration. 

17 Among the anomalies observed were open anterior and posterior neuropores, plus rotational 

18 defects, tail anomalies, enlarged pericardium, and delayed heart development.

4.4. NEUROTOXICITY

19 A substantial body of information exists on the toxicological consequences to humans 

20 who consume or are exposed to large amounts of methanol. As discussed in Section 4.1, 

21 neurological consequences of acute methanol intoxication in humans include Parkinson-like

22 responses, visual impairment, confusion, headache, and numerous subjective symptoms. The 

23 occurrence of these symptoms has been shown to be associated with necrosis of the putamen 

24 when neuroimaging techniques have been applied (Salzman, 2006). Such profound changes 

25 have been linked to tissue acidosis that arises when methanol is metabolized to formaldehyde 

26 and formic acid through the actions of ADH1 and ADH3. However, the well-documented impact 

27 of the substantial amounts of formate that are formed when humans and animals are exposed to 

28 large amounts of methanol may obscure the potentially harmful effects that may arise when 

29 humans and animals exposed to smaller amounts. Human acute exposure studies (Chuwers et al., 

30 1995; Cook et al., 1991) (See Section 4.1.3) at TLV levels of 200 ppm would indicate that some 

31 measures of neurological function (e.g., sensory evoked potentials, memory testing and 

32 psychomotor testing) were impaired in the absence of measureable formate production. 
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4.4.1. Oral Studies

1 Two rodent studies investigated the neurological effects of developmental methanol 

2 exposure via the oral route (Aziz et al., 2002; Infurna and Weiss, 1986). One of these studies also 

3 investigated the influence of FAD diets on the effects of methanol exposures (Aziz et al., 2002). 

4 In the first, Infurna and Weiss (1986) exposed 10 pregnant female Long-Evans rats/dose to 2% 

5 methanol (purity not specified) in drinking water on either GD15–GD17 or GD17–GD19. Daily 

6 methanol intake was calculated at 2,500 mg/kg-day by the study authors. Dams were allowed to 

7 litter and nurse their pups. Data were analyzed by ANOVA with the litter as the statistical unit. 

8 Results of the study were equivalent for both exposure periods. Treatment had no effect on 

9 gestational length or maternal bodyweight. Methanol had no effect on maternal behavior as 

10 assessed by the time it took dams to retrieve pups after they were returned to the cage following 

11 weighing. Litter size, pup birth weight, pup postnatal weight gain, postnatal mortality, and day

12 of eye opening did not differ from controls in the methanol treated groups. Two neurobehavioral 

13 tests were conducted in offspring. Suckling ability was tested in 3–5 pups/treatment group on 

14 PND1. An increase in the mean latency for nipple attachment was observed in pups from the 

15 methanol treatment group, but the percentage of pups that successfully attached to nipples did 

16 not differ significantly between treatment groups. Homing behavior, the ability to detect home 

17 nesting material within a cage containing one square of shavings from the pup’s home cage and 

18 four squares of clean shavings, was evaluated in 8 pups/group on PND10. Pups from both of the 

19 methanol exposure groups took about twice as long to locate the home material and took less 

20 direct paths than the control pups. Group-specific values differed significantly from controls. 

21 This study suggests that developmental toxicity can occur at this drinking water dose without 

22 readily apparent signs of maternal toxicity.

23 Aziz et al. (2002) investigated the role of developmental deficiency in folic acid and

24 methanol-induced developmental neurotoxicity in PND45 rat pups. Wistar albino female rats 

(80/group) were fed FAD43 25 and FAS diets separately. Following 14–16 weeks on the diets, liver 

26 folate levels were estimated and females exhibiting a significantly low folic acid level were 

27 mated. Throughout their lactation period, dams of both the FAD and the FAS group were given 

28 0, 1, 2, or 4% v/v methanol via drinking water, equivalent to approximately 480, 960 and 

1,920 mg/kg-day.44 29 Pups were exposed to methanol via lactation from PND1–PND21. Litter size 

30 was culled to 8 with equal male/female ratios maintained as much as possible. Liver folate 

31 levels were determined at PND21 and neurobehavioral parameters (motor performance using the 

 43 Along with the FAD diet, 1% succinylsulphathiazole 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 60 mL/kg-day = 0.48 g/kg-day = 480 mg/kg-day.
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1 spontaneous locomotor activity test and cognitive performance using the conditioned avoidance 

2 response [CAR] test), and neurochemical parameters (dopaminergic and cholinergic receptor 

3 binding and dopamine levels) were measured at PND45. The expression of growth-associated 

4 protein (GAP 43), a neuro-specific protein in the hippocampus that is primarily localized in 

5 growth cone membranes and is expressed during developmental regenerative neurite outgrowth, 

6 was examined using immunohistochemistry and western blot analysis.

7 A loss in body weight gain was observed at PND7, PND14, and PND21 in animals 

8 exposed to 2% (11, 15 and 19% weight gain reduction) and 4% (17, 24 and 29% weight gain 

9 reduction) methanol in the FAD group and only at 4% (9, 14 and 17% weight gain reduction) 

10 methanol in the FAS group. No significant differences in food and water intake were observed 

11 among the different treatment groups. Liver folate levels in the FAD group were decreased by 

12 63% in rats prior to mating and 67% in pups on PND21. 

13 Based on reports of Parkinson-like symptoms in survivors of severe methanol poisoning 

14 (see Section 4.1), Aziz et al. (2002) hypothesized that methanol may cause a depletion in 

dopamine levels and degeneration of the dopaminergic nigrostriatal pathway.45 15 Consistent with 

16 this hypothesis, they found dopamine levels were significantly decreased (32% and 51%) in the 

17 striatum of rats in the FAD group treated with 2% and 4% methanol, respectively. In the FAS 

18 group, a significant decrease (32%) was observed in the 4% methanol-exposed group. 

19 Methanol treatment at 2% and 4% was associated with significant increases in activity, in 

20 the form of distance traveled in a spontaneous locomotor activity test, in the FAS group (13%

21 and 39%, respectively) and more notably, in the FAD group (33% and 66%, respectively) when 

22 compared to their respective controls. Aziz et al. (2002) suggest that these alterations in 

23 locomotor activity may be caused by a significant alteration in dopamine receptors and 

24 disruption in neurotransmitter availability. Dopamine receptor (D2) binding in the hippocampus 

25 of the FAD group was significantly increased (34%) at 1% methanol, but was significantly 

26 decreased at 2% and 4% methanol exposure by 20% and 42%, respectively. In the FAS group, 

27 D2 binding was significantly increased by 22% and 54% in the 2% and 4% methanol-exposed 

28 groups. 

29 At PND45, the CAR in FAD rats exposed to 2% and 4% methanol was significantly 

30 decreased by 48% and 52%, respectively, relative to nonexposed controls. In the FAS group, the 

31 CAR was only significantly decreased in the 4% methanol-exposed animals and only by 22% as 

32 compared to their respective controls. Aziz et al. (2002) suggest that the impairment in CAR of 

33 the methanol-exposed FAD pups may be due to alterations in the number of cholinergic 

34 (muscarinic) receptor proteins in the hippocampal region of the brain. Muscarinic receptor 

 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 pathological features of 

Parkinson's disease (Kim et al., 2003),
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1 binding was significantly increased in the 2% (20%) and 4% (42%) methanol-exposed group in 

2 FAD animals, while FAS group animals had a significant increase in cholinergic binding only in 

3 the 4% methanol exposed group (21%). High concentrations of methanol may saturate the 

4 body’s ability to remove toxic metabolites, including formaldehyde and formate, and this may be 

5 exacerbated in FAD pups having a low store of folate. 

6 Immunohistochemistry showed an increase in the expression of GAP-43 protein in the 

7 dentate granular and pyramidal cells of the hippocampus in 2% and 4% methanol-exposed 

8 animals in the FAD group. The FAS group showed increased expression only in the 4% 

9 methanol-exposed group. The Western blot analysis also confirmed a higher expression of 

10 GAP-43 in the 2% and 4% methanol-exposed FAD group rats. Aziz et al. (2002) suggested that 

11 up-regulation of GAP-43 in the hippocampal region may be associated with axonal growth or 

12 protection of the nervous system from methanol toxicity.

13 The Aziz et al. (2002) study provides evidence that hepatic tetrahydrofolate is an 

14 important contributing factor in methanol-induced developmental neurotoxicity in rodents. The 

15 immature blood-brain barrier and inefficient drug-metabolizing enzyme system make the 

16 developing brain a particularly sensitive target organ to the effects of methanol exposure.

4.4.2. Inhalation Studies

17 A review by Carson et al. (1981) has summarized a number of older reports of studies on 

18 the toxicological consequences of methanol exposure. In one example relevant to neurotoxicity, 

19 the review cites a research report of Chen-Tsi (1959) who exposed 10 albino rats/group (sex and 

strain unstated) to 1.77 and 50 mg/m3 20 (1.44 and 40.7 ppm) methanol vapor, 12 hours/day, for 

21 3 months. Deformation of dendrites, especially the dendrites of pyramidal cells, in the cerebral 

22 cortex was included in the description of histopathological changes observed in adult animals 

following exposure to 50 mg/m3 23 (40.7 ppm) methanol vapor. One out of ten animals exposed to 

24 the lower methanol concentration also displayed this feature.

25 Information on the neurotoxicity of methanol inhalation exposure in adult monkeys 

26 (M. fascicularis) has come from NEDO (1987) which describes the results of a number of 

27 experiments. The study included an acute study, a chronic study monkeys, and a repeated 

28 exposure experiment (of variable duration depending upon exposure level), followed by recovery 

29 period (1–6 months), and an experiment looking at chronic formaldehyde exposure (1 or 5 ppm), 

30 a combustion product of methanol. This last experiment was apparently only a pilot and included 

31 only one monkey per exposure condition.

32 In the chronic experiment 8 monkeys were included per exposure level (control, 10, 100, 

1,000 ppm or 13, 131, and 1,310 mg/m3 33 , respectively, for 21 hours/day); however, animals were 

34 serially sacrificed at 3 time points: 7 months, 19 months, or >26 months. This design reduced 
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1 the number of monkeys at each exposure level to 2 subjects at 7 months and 3 subjects at the 

2 subsequent time points (see Section 4.2.2). One of the 3 animals receiving 100 ppm methanol 

3 and scheduled for sacrifice at 29 months was terminated at 26 months. 

4 Histopathologically, no overt degeneration of the retina, optical nerve, cerebral cortex, or 

5 other potential target organs (liver and kidney) was reported in the chronic experiment. 

Regarding the peripheral nervous system, 1/3 monkeys exposed to 100 ppm (131 mg/m3 6 ) and 2/3 

exposed to 1,000 ppm (1,310 mg/m3 7 ) for 29 months showed slight but clear changes in the 

8 peroneal nerves. There was limited evidence of CNS degeneration inside the nucleus of the 

thalamus of the brain at exposure to 100 ppm (131 mg/m3) or 1,000 ppm (1,310 mg/m3 9 ) for 

10 7 months or longer. Abnormal appearance of stellate cells (presumed astroglia) within the 

11 cerebral white matter was also observed in a high proportion (7/8 in both mid- and high-exposure 

12 groups) of monkeys exposed to 100 ppm and 1,000 ppm for 7 months or more. All monkeys that 

13 had degeneration of the inside nucleus of the thalamus also had degeneration of the cerebral 

14 white matter. According to NEDO (1987), the stellate cell response was transient and “not 

15 characteristic of degeneration.” The authors also noted that the stellate cell response was “nearly 

16 absent in normal monkeys in the control group” and “in the groups exposed to a large quantity of 

17 methanol or for a long time their presence tended to become permanent, so a relation to the long 

18 term over which the methanol was inhaled is suspected.” However, all control group responses 

19 are reported in a single table in the section of the NEDO (1987) report that describes the acute 

20 monkey study, with no indication as to when the control group was sacrificed.

21 In the recovery experiment, monkeys were exposed to 1,000, 2,000, 3,000, or 5,000 ppm 

22 methanol, followed by recovery periods of various duration. Monkeys exposed to 3,000 ppm for 

23 20 days followed by a 6-month recovery period experienced relatively severe fibrosis of 

24 responsive stellate cells and lucidation of the medullary sheath. However, resolution of some of 

25 the glial responses was noted in the longer duration at lower exposure levels, with no effects 

26 observed on the cerebral white matter in monkeys exposed for 7 months to 1,000 ppm methanol 

27 followed by a 6-month recovery period. In general, the results from the recovery experiment 

28 corroborated results observed in the chronic experiment. NEDO (1987) interpreted the lack of 

29 glial effects after a 6-month recovery as an indication of a transient effect. The authors failed to 

30 recognize that glial responses to neural damage do not necessarily persist following resolution of 

31 neurodegeneration (Aschner and Kimmelberg, 1996).

32 The limited information available from the NEDO (1987) summary report suggests that 

100 ppm (131 mg/m3 33 ) may be an effect level following continuous, chronic exposure to 

34 methanol. However, the current report does not indicate a robust dose response for the 

35 neurodegenerative changes in the thalamus and glial changes in the white matter. The number of 

36 animals at each exposure level for each serial sacrifice also limits statistical power 

37 (2–3 monkeys/time point/exposure level). Confidence in this study is also weakened by the lack 

38 of documentation for a concurrent control group.
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1 Weiss et al. (1996) exposed 4 cohorts of pregnant Long-Evans rats (10–12 dams/

treatment group/cohort) to 0 or 4,500 ppm (0 and 5,897 mg/m3 2 ) methanol vapor (high3 performance liquid chromatography [HPLC] grade), 6 hours/day, from GD6 to PND21. Pups 

4 were exposed together with the dams during the postnatal period. Average blood methanol levels 

5 in pups on PND7 and PND14 were about twice the level observed in dams. However, methanol 

6 exposure had no effect on maternal gestational weight gain, litter size, or postnatal pup weight 

gain up to PND1846 7 . Neurobehavioral tests were conducted in neonatal and adult offspring; the 

8 data generated from those tests were evaluated by repeated measures ANOVA. Three 

9 neurobehavioral tests conducted in 13–26 neonates/group included a suckling test, conditioned 

10 olfactory aversion test, and motor activity test. In contrast to earlier test results reported by 

11 Infurna and Weiss (1986), methanol exposure had no effect on suckling and olfactory aversion 

12 tests conducted on PND5 and PND10, respectively. Results of motor activity tests in the 

13 methanol group were inconsistent, with decreased activity on PND18 and increased activity on 

14 PND25. Tests that measured motor function, operant behavior, and cognitive function were 

15 conducted in 8–13 adult offspring/group. Some small performance differences were observed 

16 between control and treated adult rats in the fixed wheel running test only when findings were 

17 evaluated separately by sex and cohort. The test requires the adult rats to run in a wheel and 

18 rotate it a certain amount of times in order to receive a food reward. A stochastic spatial 

19 discrimination test examined the rats’ ability to learn patterns of sequential responses. Methanol 

20 exposure had no effect on their ability to learn the first pattern of sequential responses, but 

21 methanol-treated rats did not perform as well on the reversal test. The result indicated possible 

22 subtle cognitive deficits as a result of methanol exposure. A morphological examination of 

23 offspring brains conducted on PND1 and PND21 indicated that methanol exposure had no effect 

24 on neuronal migration, numbers of apoptotic cells in the cortex or germinal zones, or 

25 myelination. However, neural cell adhesion molecule (NCAM) 140 and NCAM 180 gene 

26 expression in treated rats was reduced on PND4 but not 15 months after the last exposure. 

27 NCAMs are glycoproteins required for neuron migration, axonal outgrowth, and establishing 

28 mature neuronal function patterns.

29 Stanton et al. (1995) exposed 6–7 pregnant female Long-Evans rats/group to 0 or 

15,000 ppm (0 and 19,656 mg/m3 30 ) methanol vapors (≥ 99.9% purity) for 7 hours/day on GD7–

31 GD19. Mean serum methanol levels at the end of the 1st, 4th, 8th, and 12th days of exposure 

32 were, 3,836, 3,764, 3,563, and 3,169 μg/mL, respectively. As calculated by authors, dams 

33 received an estimated methanol dose of 6,100 mg/kg-day. A lower body weight on the first 

 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.
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1 2 days of exposure was the only maternal effect; there was no increase in postimplantation loss. 

2 Dams were allowed to deliver and nurse litters. Parameters evaluated in pups included mortality, 

3 growth, pubertal development, and neurobehavioral function. Examinations of pups revealed 

4 that two pups from the same methanol-exposed litter were missing one eye; aberrant visually 

5 evoked potentials were observed in those pups. A modest but significant reduction in body 

6 weight gain on PND1, PND21, and PND35 was noted in pups from the methanol group. For 

7 example, by PND35, male pups of dams exposed to methanol had a mean body weight of 

8 129 grams versus 139 grams in controls (p < 0.01). However, postnatal mortality was unaffected 

9 by exposure to methanol. The study authors did not consider a 1.7-day delay in vaginal opening 

10 in the methanol group to be an adverse effect. Preputial separation was not affected by prenatal 

11 methanol exposure. Neurobehavioral status was evaluated using 8 different tests on specific 

12 days up to PND160. Tests included motor activity on PND13–PND21, PND30, and PND60, 

13 olfactory learning and retention on PND18 and PND25, behavioral thermoregulation on 

14 PND20-21, T-maze delayed alternation learning on PND23–PND24, acoustic startle reflex on 

15 PND24, reflex modification audiometry on PND61–PND63, passive avoidance on PND73, and 

16 visual evoked potentials on PND160. A single pup/sex/litter was examined in most tests, and 

17 some animals were subjected to multiple tests. The statistical significance of neurobehavioral 

18 testing was assessed by one-way ANOVA, using the litter as the statistical unit. Results of the 

19 neurobehavioral testing indicated that methanol exposure had no effect on the sensory, motor, or 

20 cognitive function of offspring under the conditions of the experiment. However, given the 

21 comparatively small number of animals tested for each response, it is uncertain whether the 

22 statistical design had sufficient power to detect small compound-related changes.

23 NEDO (1987, unpublished report) sponsored a teratology study that included an 

24 evaluation of postnatal effects in addition to standard prenatal endpoints in Sprague-Dawley rats. 

25 Thirty-six pregnant females/group were exposed to 0, 200, 1,000, or 5,000 ppm (0, 262, 1,310, 

and 6,552 mg/m3 26 ) methanol vapors (reagent grade) on GD7–GD17 for 22.7 hours/day. Statistical 

27 significance of results was evaluated by t-test, Mann-Whitney U test, Fisher’s exact test, and/or 

Armitage’s χ

2 28 test.

29 Postnatal effects of methanol inhalation were evaluated in the remaining 12 dams/group 

30 that were permitted to deliver and nurse their litters. Effects were only observed in the 

31 5,000 ppm. There were no adverse effects on offspring body weight from methanol exposure. 

32 However, the weights of some organs (brain, thyroid, thymus, and testes) were reduced in 

33 8-week-old offspring following prenatal-only exposure to 5,000 ppm methanol. An unspecified 

34 number of offspring were subjected to neurobehavioral testing or necropsy, but results were 

35 incompletely reported.
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1 NEDO (1987, unpublished report) also contains an account of a two-generation 

2 reproductive study that evaluated the effects of pre- and postnatal methanol (reagent grade) 

3 exposure (20 hours/day) on reproductive and other organ systems of Sprague-Dawley rats and in 

particular the brain. The F0 generation (30 males and 30 females per exposure group)47 4 was 

exposed to 0, 10, 100, and 1,000 ppm (0, 13.1, 131, and 1,310 mg/m3 5 ) from 8 weeks old to the 

6 end of mating (males) or to the end of lactation period (females). The F1 generation was exposed 

7 to the same concentrations from birth to the end of mating (males) or to weaning of F2 pups 

8 21 days after delivery (females). Males and females of the F2 generation were exposed from 

9 birth to 21 days old (1 animal/sex/litter was exposed to 8 weeks of age). NEDO (1987) noted 

10 reduced brain, pituitary, and thymus weights, in the offspring of F0 and F1 rats exposed to 

11 1,000 ppm methanol. As discussed in the report, NEDO (1987) sought to confirm the possible 

12 compound-related effect of methanol on the brain by carrying out an additional study in which 

13 Sprague-Dawley rats were exposed to 0, 500, 1,000, and 2,000 ppm (0, 655, 1,310, and 

2,620 mg/m3 14 ) methanol from the first day of gestation through the F1 generation. Brain weights 

15 were measured in 10–14 offspring/sex/group at 3, 6, and 8 weeks of age. As illustrated in 

16 Table 4-14, brain weights were significantly reduced in 3-week-old males and females exposed 

17 to ≥ 1,000 ppm. At 6 and 8 weeks of age, brain weights were significantly reduced in males 

18 exposed to ≥ 1,000 ppm and females exposed to 2,000 ppm. Due to the toxicological 

19 significance of this postnatal effect and the fact that it has not been measured or reported 

20 elsewhere in the peer-reviewed methanol literature, the brain weight changes observed by NEDO 

21 (1987) following gestational and postnatal exposures and following gestation-only exposure (in 

22 the teratology study discussed above) are evaluated quantitatively and discussed in more detail in 

23 Section 5 of this review.

 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 unacceptable to the animals.”
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Table 4-14. 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 1,000 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 ± 0.05c

(96%)

1.33 ± 0.07d

(94%)

1.27 ± 0.06e

(88%)

1.26 ± 0.09e

(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 ± 0.06d

(95%)

1.62 ± 0.07

(96%)

1.52 ± 0.07e

(85%)

1.55 ± 0.05e

(92%)

--

--

8 wka

Male

Female

1.99 ± 0.06

1.85 ± 0.05

--

--

1.98 ± 0.09

(99%)

1.83 ± 0.07

(99%)

1.88 ± 0.08d

(94%)

1.80 ± 0.08

(97%)

1.74 ± 0.05e

(87%)

1.67 ± 0.06e

(90%)

--

--

8 wkb

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 ± 0.16d

(91%)

1.76 ± 1.09

(95%)

a

Exposed throughout gestation and F1 generation. b

Exposed on gestational days 7-17 only. c

p < 0.05, dp < 0.01, ep < 0.001, as calculated by the authors.

Values are means ± S.D.

Source: NEDO (1987).

1 Burbacher et al. (1999a,1999b) carried out toxicokinetic, reproductive, developmental

2 and postnatal neurological and neurobehavioral studies of methanol in M. fascicularis monkeys 

3 that were published by HEI in a two-part monograph. Some of the data were subsequently 

4 published in the open scientific literature (Burbacher et al., 2004a, 2004b). The experimental 

5 protocol featured exposure to 2 cohorts of 12 monkeys/group to low-exposure levels (relative to 

6 the previously discussed rodent studies) of 0, 200, 600, or 1,800 ppm (0, 262, 786, and 2,359 

mg/m3 7 ) methanol vapors (99.9% purity), 2.5 hours/day, 7 days/week, during a premating period 

8 and mating period (−180 days combined) and throughout the entire gestation period (−168 days). 

9 The monkeys were 5.5–13 years old and were a mixture of feral-born and colony-bred animals. 

10 The outcome study included an evaluation of maternal reproductive performance (discussed in 

11 Section 4.3.2) and tests to assess infant postnatal growth and newborn health, neurological 

12 outcomes included reflexes, behavior, and development of visual, sensorimotor, cognitive, and 

13 social behavioral function. Blood methanol levels, clearance, and the appearance of formate 

14 were also examined and are discussed in Section 3.2. The effects observed were in the absence of 

15 appreciable increases in maternal blood formate levels.
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1 Neurobehavioral function was assessed in 8–9 infants/group during the first 9 months of 

2 life (Burbacher et al., 2004a, 1999b). Although results in 7/9 tests were negative, 2 effects were 

3 possibly related to methanol exposure. The Visually Directed Reaching (VDR) test is a measure 

4 of sensorimotor development and assessed the infants’ ability to grasp for a brightly colored 

5 object containing an applesauce-covered nipple. Beginning at 2 weeks after birth, infants were 

6 tested 5 times/day, 4 days/week. Performance on this test, measured as age from birth at 

7 achievement of test criterion (successful object retrieval on 8/10 consecutive trials over 2 testing 

8 sessions), was reduced in all treated male infants. The times (days after birth) to achieve the 

9 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 ± 

10 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 

11 ± 4.0 (n = 7) days for females in the control to 1,800 ppm groups, respectively. Statistical 

12 significance was obtained in the 1,800 ppm group when males and females were evaluated 

13 together (p = 0.04) and in the 600 ppm (p = 0.007) for males only. However, there was no 

14 significant difference between responses and/or variances among the dose levels for males and 

15 females combined (p = 0.244), for males only (p = 0.321) and for males only, excluding the high16 dose group (p = 0.182). Yet there was a significant dose-response trend for females only 

17 (p = 0.0265). The extent to which VDR delays were due to a direct effect of methanol on 

18 neurological development or a secondary effect due to the methanol-induced decrease in length 

19 of pregnancy and subsequent prematurity is not clear. Studies of reaching behavior have shown 

20 that early motor development in pre-term human infants without major developmental disorders 

21 differs from that of full-term infants (Fallang et al., 2003). Clinical studies have indicated that 

22 the quality of reaching and grasping behavior in pre-term infants is generally less than that in 

23 full-term infants (Fallang et al., 2003; Plantinga et al., 1997). For this reason, measures of 

24 human infant development generally involve adjustment of a child’s “test age” if he or she had a 

25 gestational age of fewer than 38 weeks, often by subtracting weeks premature from the age 

26 measured from birth (Wilson and Cradock, 2004). When this type of adjustment is made to the 

27 Burbacher et al. (2004a, 1999b) VDR data, the dose-response trend for males only becomes 

28 worse (p = 0.448) and the dose-response trend for the females only is improved (p = 0.009), 

29 though the variance in the data could not be modeled adequately. Thus, only the unadjusted 

30 VDR response for females only exhibited a dose response that could be adequately modeled for 

31 the purposes of this assessment (see Appendix C). 

32 At 190–210 days of age, the Fagan Test of infant intelligence was conducted. The 

33 paradigm makes use of the infant’s proclivity to direct more visual attention to novel stimuli 

34 rather than familiar stimuli. The test measures the time infants spend looking at familiar versus 

35 novel items. Deficits in the Fagan task can qualitatively predict deficits in intelligence quotient 

36 (IQ) measurements assessed in children at later ages (Fagan and Singer, 1983). Control monkey 
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1 infants in the Burbacher et al. (2004a, 1999b) study spent more than 62% ± 4% (mean for both 

2 cohorts) of their time looking at novel versus familiar monkey faces, while none of the treated 

3 monkeys displayed a preference for the novel faces (59% ± 2%, 54% ± 2% and 59% ± 2% in 

4 200, 600 and 1,800 ppm groups, respectively). Unlike the VDR results discussed previously, 

5 results of this test did not appear to be gender specific and were neither statistically significant 

6 (ANOVA p = 0.38) nor related to exposure concentration. The findings indicated a cohort effect 

7 which appeared to reduce the statistical power of this analysis. The authors’ exploratory analysis 

8 of differences in outcomes between the 2 cohorts indicated an effect of exposure in the second 

9 cohort and not the first cohort due to higher mean performance in controls of cohort 2 (70% +

10 5% versus 55% ± 4% for cohort 1). In addition, this latter finding could reflect the inherent 

11 constraints of this endpoint. If the control group performs at the 60% level and the most 

12 impaired subjects perform at approximately the 50% chance level (worse than chance 

13 performance would not be expected), the range over which a concentration-response relationship 

14 can be expressed is limited. Because of the longer latency between assessment and birth, these 

15 results would not be confounded with the postulated methanol-induced decrease in gestation 

16 length of the exposed groups of this study. Negative results were obtained for the remaining 

17 seven tests that evaluated early reflexes, gross motor development, spatial and concept learning 

18 and memory, and social behavior. Infant growth and tooth eruption were unaffected by methanol 

19 exposure.

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

20 There is some experimental evidence that the presence of methanol can affect the activity 

21 of acetylcholinesterase (Tsakiris, et al., 2006). Although these experiments were carried out on 

22 erythrocyte membranes in vitro, the apparent compound-related changes may have implications 

23 for possible impacts of methanol and/or its metabolites on acetylcholinesterase at other centers, 

24 such as the brain. Tsakiris et al. (2006) prepared erythrocyte ghosts from blood samples of 

25 healthy human volunteers by repeated freezing-thawing. The ghosts were incubated for 1 hour at 

37o 26 C in 0, 0.07, 0.14, 0.6 or 0.8 mmol/L methanol and the specific activities of 

27 acetylcholinesterase monitored. Respective values (in change of optical density units/minute-mg 

28 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 ± 

29 0.09 (p < 0.001). More recently, Simintzi et al. (2007) carried out an in vitro experiment to 

30 investigate the effects of aspartame metabolites, including methanol, on 1) a pure preparation of 

31 acetylcholinesterase, and 2) the same activity in homogenates of frontal cortex prepared from the 

32 brains of (both sexes of) Wistar rats. The activities were measured after incubations with 0, 0.14, 

33 0.60, or 0.8 mmoles/L (0, 4.5, 19.2, and 25.6 mg/L) methanol, and with methanol mixed with the 

34 other components of aspartame metabolism, phenylalanine and aspartic acid. After incubation at 
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37o 1 C for 1 hour, the activity of acetylcholinesterase was measured spectrophotometrically. As 

2 shown in Table 4-15, the activities of the acetylcholinesterase preparations were reduced dose

3 dependently after incubation in methanol. Similar results were also obtained with the other 

4 aspartame metabolites, aspartic acid, and phenylalanine, both individually or as a mixture with 

5 methanol. While the implications of this result to the acute neurotoxicity of methanol are 

6 uncertain, the authors speculated that methanol may bring about these changes through either 

7 interactions with the lipids of rat frontal cortex or perturbation of proteinaceous components.

Table 4-15. Effect 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 ± 0.007a 1.18 ± 0.06

0.60 0.223 ± 0.009b 1.05 ± 0.04b

0.80 0.204 ± 0.008b 0.98 ± 0.05b

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).

8 In another experiment of relevance to neurotoxicity, the impact of repeat methanol 

9 exposure on amino acid and neurotransmitter expression in the retina, optic nerve, and brain was 

10 examined by Gonzalez-Quevedo et al. (2002). The goal of the study was to determine whether a 

11 sustained increase in formate levels, at concentrations below those known to produce toxic 

12 effects from acute exposures, can induce biochemical changes in the retina, optical nerve, or 

13 certain regions of the brain. Male Sprague-Dawley rats (5–7/group; 100–150 g) were divided 

14 into 6 groups and treated for 4 weeks according to the following plan. Four groups of animals 

15 received tap water ad libitum as drinking water for 1 week. During the second week, groups 1 

16 and 2 (control and methanol respectively) received saline subcutaneously, (s.c.) and groups 3 and 

4 (methotrexate48 17 [MTX] and methotrexate-methanol [MTX-methanol], respectively) received 

18 MTX s.c. (0.2 mg/kg-day). During the 3rd week, MTX was reduced to 0.1 mg/kg and 20% 

19 methanol (2g/kg-day) was given i.p. to groups 2 (methanol) and 4 (MTX-methanol). Groups 1 

20 (control) and 3 (MTX) received equivalent volumes of saline administered i.p. The treatment 

was continued until the end of the fourth week. Groups 5, (taurine49 21 [Tau]) and 6, (Tau-MTX-

 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, and was administered here 

to explore its possible protective effect (Gonzalez-Quevedo et al., 2002)
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1 methanol) received 2% Tau in their drinking water ad libitum during the first 4 weeks, after 

2 which they were treated in the same manner as groups 1 and 4, respectively. Weights were 

3 documented weekly on all animals. Blood for formate and amino acid determinations and 

4 biopsy samples of retina, optic nerve, hippocampus, and posterior cortex of each animal were 

5 collected at the end of the experiment. Formate levels were not affected by Tau alone or MTX 

6 alone. While methanol alone increased blood formate levels, MTX-methanol, and Tau-MTX7 methanol produced a threefold increase in blood formate levels as compared to controls and a 

8 twofold increase as compared to methanol alone. The amino acids aspartate, glutamate, 

9 asparagine, serine, histidine, glutamine, threonine, glycine, arginine, alanine, hypotaurine, 

10 gamma-aminobutyric acid (which is also a neurotransmitter), and tyrosine were measured in 

11 blood, brain, and retinal regions.

12 None of the amino acids measured were altered in the blood of methanol-, MTX-, or

13 MTX-methanol-treated animals. Tau was increased in the blood of animals treated with taurine 

14 in the drinking water (Tau and Tau-MTX-methanol) and histidine was increased in the Tau group 

15 but not in the Tau-MTX-methanol group.

16 The levels of aspartate, Tau, glutamine, and glutamate were found to be altered by 

17 treatment in various areas of the brain. Aspartate was increased in the optic nerve of animals 

18 treated with MTX-methanol and Tau-MTX-methanol, indicating a possible relation to formate 

19 accumulation. The authors note that L-aspartate is a major excitatory amino acid in the brain and 

20 that increased levels of excitatory amino acids can trigger neuronal cell damage and death (Albin 

21 and Greenamyre, 1992). Aspartate, glutamine and Tau were found to be increased with respect 

22 to controls in the hippocampus of the three groups receiving methanol. Glutamate was 

23 significantly increased in the hippocampus in the methanol and the Tau-MTX-methanol groups 

24 with respect to controls, but no statistically significant difference was found in the MTX25 methanol group when compared to controls, methanol alone, or the Tau-MTX-methanol groups. 

26 The authors suggest that increased levels of aspartate and glutamine in the hippocampus could 

27 provide an explanation for some of the CNS symptoms observed in methanol poisonings on the 

28 basis of their observed impact on cerebral arteries (Huang et al., 1994). The fact that these 

29 increases resulted primarily from methanol without MTX is significant in that it indicates 

30 methanol can cause excitotoxic effects without formate mediation. The treatments used did not 

31 produce any significant changes in amino acid levels in the posterior cortex.

32 The neurotransmitters serotonin (5-HT) and dopamine (DA) and their respective 

33 metabolites, 5-hydroxyindolacetic acid (5-HIAA) and dihydroxyphenylacetic acid (DOPAC),

34 were measured in the brain regions described. The levels of these monoamines were not affected 

35 by formate accumulation, as the only increases were observed for 5-HT and 5-HIAA following 

36 methanol-only exposure. 5-HT was increased in the retina and hippocampus of methanol-only 
4-63 DRAFT-DO NOT CITE OR QUOTE

1 treated animals, and the metabolite 5-HIAA was increased in the hippocampus of methanol-only 

2 treated animals; DA and DOPAC levels were not altered by the treatments in any of the areas 

3 measured. The posterior cortex did not show any changes in monoamine levels for any treatment 

4 group.

5 Rajamani et al. (2006) examined several oxidative stress parameters in male Wistar rats 

6 following methotrexate-induced folate deficiency. Animals (6/group) were divided into 3e 

7 groups: saline controls, methotrexate (MTX) controls, and MTX-methanol treated animals. 

8 Animals in the MTX-only group were treated with 0.2 mg/kg-day MTX s.c. injection for 7 days 

9 and following confirmation of folate deficiency, received either saline for MTX control and 

10 saline controls or a single dose of 3 g/kg methanol (20% w/v in saline) i.p. on day 8. On the 9th 

11 day, all animals were sacrificed and blood and tissue samples were collected. The optic nerve, 

12 retina, and brain were collected and the brain was dissected into the following regions: cerebral 

13 cortex, cerebellum, mid-brain, pons medulla, hippocampus and hypothalamus. Each region was 

14 homogenized, then centrifuged at 300 × g for 15 minutes and the supernatant was examined for 

15 indicators of oxidative stress including the free radical scavengers superoxide dismutase (SOD), 

16 CAT, glutathione peroxidase, and reduced GSH levels. The levels of protein thiols, protein 

17 carbonyls, and amount of lipid peroxidation were also measured. Compared to controls the 

18 levels of SOD, CAT, GSH peroxidase, oxidized GSH, protein carbonyls and lipid peroxidation 

19 were elevated in all of the brain regions where it was measured, with greater increases observed 

20 in the MTX-methanol treated animals than in the MTX alone group. The level of GSH and 

21 protein thiols was decreased in all regions of the brain, with a greater decrease observed in the 

22 MTX-methanol-treated animals than MTX-treated animals. In addition, expression of HSP70, a 

23 biomarker of cellular stress, was increased in the hippocampus. Overall, these results suggest that 

24 methanol treatment of folate-deficient rats results in increased oxidative stress in the brain, retina 

25 and optic nerve.

26 To determine the effects of methanol intoxication on the HPA axis, a combination of 

27 oxidative stress, immune and neurobehavioral parameters were observed (Parthasarathy et al., 

28 2006a). Adult male Wistar albino rats (6 animals/group) were treated with either 0 or 2.37g/kg29 day methanol i.p. for 1, 15 or 30 days. Oxidative stress parameters examined included SOD, 

30 CAT, GSH peroxidase, GSH, and ascorbic acid (Vitamin C). Plasma corticosterone levels were 

31 measured, and lipid peroxidation was measured in the hypothalamus and the adrenal gland. An 

32 assay for DNA fragmentation was conducted in tissue from the hypothalamus, the adrenal gland 

33 and the spleen. Immune function tests conducted included the footpad thickness test for delayed 

34 type hypersensitivity (DTH), a leukocyte migration inhibition assay, the hemagglutination assay 

35 (measuring antibody titer), the neutrophil adherence test, phagocytosis index, and a nitroblue 

36 tetrazolium (NBT) reduction and adherence assay used to measure the killing ability of 
4-64 DRAFT-DO NOT CITE OR QUOTE

1 polymorphonuclear leukocytes (PMNs). The open field behavior test was used to measure 

2 general locomotor and explorative activity during methanol treatment in the 30-day treatment 

3 group, with tests conducted on days 1, 4, 8, 12, 16, 20, 24, and 28. All enzymatic (SOD, CAT, 

4 and GSH peroxidase) and nonenzymatic antioxidants (GSH and Vitamin C) were significantly 

5 increased in the 1-day methanol-exposed group as compared to controls. However, with 

6 increasing time of treatment, all of the measured parameters were significantly decreased when 

7 compared with control animals. Lipid peroxidation was significantly increased in both the 

8 hypothalamus and the adrenal gland at 1, 15, and 30 days, with the 30-day treated animals also 

9 significantly increased when compared to the 15-day methanol-treated animals.

10 Leukocyte migration and antibody titer were both significantly increased over controls 

11 for all time points, while footpad thickness was significantly decreased in 15- and 30-day treated 

12 animals. Neutrophil adherence was significantly decreased after 1 and 30 days of exposure. A 

13 significant decrease in the NBT reduction and adherence was found when comparing PMNs from 

14 the 30-day treated animals with cells from the 15-day methanol-treated group.

15 The open field behavior tests showed a significant decrease in ambulation from the 4th 

16 day on and significant decreases in rearing and grooming from the 20th day on. A significant 

17 increase was observed in immobilization from the 8th day on and in fecal bolus from the 24th 

18 day on in methanol-exposed animals.

19 While corticosterone levels were significantly increased following 1 or 15 days of 

20 methanol treatment, they were significantly decreased after 30 days of treatment, as compared to 

21 controls. Following 30 days of methanol treatment, DNA from the hypothalamus, the adrenal 

22 gland, and the spleen showed significant fragmentation. The authors conclude that exposure to 

23 methanol-induced oxidative stress, disturbs HPA-axis function, altering corticosterone levels and 

24 producing effects in several nonspecific and specific immune responses.

4.5. IMMUNOTOXICITY

25 Parthasarathy et al. (2005a) provided data on the impact of methanol on neutrophil 

26 function in an experiment in which 6 male Wistar rats/group were given a single i.p. exposure of 

27 2,370 mg/kg methanol mixed 1:1 in saline. Another group of 6 animals provided blood samples 

28 that were incubated with methanol in vitro at a methanol concentration equal to that observed in 

29 the in vivo-treated animals 30 and 60 minutes postexposure. Total and differential leukocyte 

30 counts were measured from these groups in comparison to in vivo and in vitro controls. 

31 Neutrophil adhesion was determined by comparing the neutrophil index in the untreated blood 

32 samples to those that had been passed down a nylon fiber column. The cells’ phagocytic ability 

33 was evaluated by their ability to take up heat-killed Candida albicans. In another experiment, 

34 neutrophils were assessed for their killing potential by measuring their ability to take up then 
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convert NBT to formazan crystals.50 1 One hundred neutrophils/slides were counted for their total 

2 and relative percent formazan-positive cells.

3 The blood methanol concentrations 30 and 60 minutes after dosing were 2,356 ± 162 and 

4 2,233 ± 146 mg/L, respectively. The mean of these values was taken as the target concentration 

5 for the in vitro methanol incubation. In the in vitro studies, there were no differences in total and 

6 differential leukocyte counts, suggesting that no lysis of the cells had occurred at this methanol 

7 concentration. This finding contrasts with the marked difference in total leukocytes observed as 

8 a result of methanol incubation in vivo, in which, at 60 minutes after exposure, 16,000 ± 1,516 

cells/mm3 9 were observed versus 23,350 ± 941 in controls (p < 0.001). Some differences in 

10 neutrophil function were observed in blood samples treated with methanol in vitro and in vivo. 

11 These differences are illustrated for the 60-minute postexposure samples in Table 4-16.

Table 4-16. 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 ± 2.2a 66.0 ± 4.8 84.0 ± 7.0b

Avidity index 4.53 ± 0.6 4.47 ± 0.7 2.4 ± 0.1 3.4 ± 0.3a

NBT reduction (%) 31.6 ± 4.6 48.6 ± 4.3b 4.6 ± 1.2 27.0 ± 4.6b

Adherence (%) 50.2 ± 5.1 39.8 ± 2.4a 49.0 ± 4.8 34.6 ± 4.0b

Values are means ± S.D. for six animals.

a

p < 0.01. b

p < 0.001.

Source: Parthasarathy et al. (2005a).

12 Parthasarathy et al. (2005a) observed differences in the neutrophil functions of cells 

13 exposed to methanol in vitro versus in vivo, most notably in the phagocytic index that was 

14 reduced in vitro but significantly increased in vivo. However, functions such as adherence and 

15 NBT reduction showed consistency in the in vitro and in vivo responses. The authors noted that, 

16 by and large, the in vivo effects of methanol on neutrophil function were more marked than those 

17 in cells exposed in vitro.

18 Another study by Parthasarathy et al. (2005b) also exposed 6 male Wistar rats/group i.p.

19 to methanol at approximately 1/4 the LD50 (2.4 g/kg). The goal was to further monitor possible 

20 methanol-induced alterations in the activity of isolated neutrophils and other immunological 

21 parameters. The exposure protocol featured daily injections of methanol for up to 30 days in the 

22 presence or absence of sheep RBCs. Blood samples were assessed for total and differential 

23 leukocytes, and isolated neutrophils were monitored for changes in phagocytic and avidity 

 50 Absence of NBT reduction indicates a defect in some of the metabolic pathways involved in intracellular 

microbial killing.
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1 indices, NBT reduction, and adherence. In the latter test, blood samples were incubated on a 

2 nylon fiber column, then eluted from the column and rechecked for total and differential 

3 leukocytes. Phagocytosis was monitored by incubating isolated buffy coats from the blood 

4 samples with heat-killed C. albicans. NBT reduction capacity examined the conversion of the 

5 dye to formazan crystals within the cytoplasm. The relative percentage of formazan-positive 

6 cells in each blood specimen gave a measure of methanol’s capacity to bring about cell death. 

7 As tabulated by the authors, there was a dose-dependent reduction in lymphoid organ weights 

8 (spleen, thymus, and lymph node) in rats exposed to methanol for 15 and 30 days via i.p. 

9 injection, irrespective of the presence of sheep RBCs. Methanol also appeared to result in a 

10 reduction in the total or differential neutrophil count. These and potentially related changes to 

11 neutrophil function are shown in Table 4-17.

Table 4-17. 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 

methanol

Organ weights (mg)

Spleen 1223 ± 54 910 ± 63a 696 ± 83a,b 1381 ± 27 1032 ± 39a 839 ± 35a,b

Thymus 232 ± 12 171 ± 7a 121 ± 10a,b 260 ± 9 172 ± 10a 130 ± 24a,b

Lymph node 32 ± 2 24 ± 3a 16 ± 2a,b 39 ± 2 28 ± 1a 23 ± 1a,b

Leukocyte counts

Total leukocytes 23,367 ± 946 16,592 ± 1219a 13,283 ± 

2553a,b

18,633 ± 2057 16,675 ± 1908 14,067 ± 930a,b

% neutrophils 24 ± 8 21 ± 3 16 ± 3a 8 ± 3 23 ± 4a 15 ± 5a,b

% Lymphocytes 71 ± 7 76 ± 3 79 ± 5 89 ± 4 78.5 ± 4a 82 ± 6

Neutrophil function tests

Phagocytic index 

(%)

91.0 ± 2.0 80.0 ± 4.0a 79.0 ± 2.0a 87.0 ± 4.0 68.0 ± 3.0a 63.0 ± 4.0a

Avidity index 2.6 ± 0.3 3.2± 0.5a 3.2 ± 0.1a 4.1 ± 0.1 2.6 ± 0.3a 2.1 ± 0.3a

NBT reduction (%) 6.3 ± 2.0 18.2 ± 2.0a 15.0 ± 1.0a,b 32.0 ± 3.3 22.0 ± 3.0a 19.0 ± 2.4a

Adherence (%) 49.0 ± 5.0 44.0 ± 5.0 29.5 ± 5.0a,b 78.0 ± 9.2 52.0 ± 9.0a 30.0 ± 4.3a,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).

12 The study provided data that showed altered neutrophil functions following repeated 

13 daily exposures of rats to methanol for periods up to 30 days. This finding is indicative of a 

14 possible effect of methanol on the immunocompetence of an exposed host.
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1 Parthasarathy et al. (2006a) reported on additional immune system indicators as part of a 

2 study to determine the effects of methanol intoxication on the HPA axis. As described in 

3 Section 4.4.3. immune function tests conducted included the footpad thickness test for DTH, a 

4 leukocyte migration inhibition assay, the hemagglutination assay (measuring antibody titer), the 

5 neutrophil adherence test, phagocytosis index, and a NBT reduction and adherence assay used to 

6 measure the killing ability of PMNs.

7 Leukocyte migration and antibody titer were both significantly increased over controls 

8 for all time points, while footpad thickness was significantly deceased in 15- and 30-day treated 

9 animals. Neutrophil adherence was significantly decreased after 1 and 30 days of exposure. A 

10 significant decrease in the NBT reduction and adherence was found when comparing PMNs from 

11 the 30-day treated animals with cells from the 15-day methanol-treated group.

12 Parthasarathy et al. (2007) reported the effects of methanol on a number of specific 

13 immune functions. As before, 6 male Wistar rats/group were treated with 2,370 mg/kg methanol 

14 in a 1:1 mixture in saline administered intraperitoneally for 15 or 30 days. Animals 

scheduled/designated for termination on day 15 were immunized intraperitoneally with 5 × 109 15

16 sheep RBCs on the 10th day. Animals scheduled for day 30 termination were immunized on the 

17 25th day. Controls were animals that were not exposed to methanol but immunized with sheep 

18 RBCs as described above. Blood samples were obtained from all animals at sacrifice and 

19 lymphoid organs including the adrenals, spleen, thymus, lymph nodes, and bone marrow were 

removed. Cell suspensions were counted and adjusted to 1 × 108 20 cells/mL. Cell-mediated 

21 immune responses were assessed using a footpad thickness assay and a leucocyte migration 

22 inhibition (LMI) test, while humoral immune responses were determined by a hemagglutination 

23 assay, and by monitoring cell counts in spleen, thymus, lymph nodes, femoral bone marrow, and 

24 in splenic lymphocyte subsets. Plasma levels of corticosterone were measured along with levels 

25 of such cytokines as TNF-α, IFN-γ, IL-2, and IL-4. DNA damage in splenocytes and thymocytes 

26 was also monitored using the Comet assay.

27 Table 4-18 shows decreases in the animal weight/organ weight ratios for spleen, thymus, 

28 lymph nodes and adrenal gland as a result of methanol exposure. However, the splenocyte, 

29 thymocyte, lymph node, and bone marrow cell counts were time-dependently lower in methanol30 treated animals.
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Table 4-18. Effect of methanol exposure on animal weight/organ weight ratios and on 

cell counts in primary and secondary lymphoid organs of male Wistar rats.

Organ

Immunized

Control 15 days 30 days

Animal weight/organ weight ratio

Spleen 3.88 ± 0.55 2.85 ± 0.36a 2.58 ± 0.45a

Thymus 1.35 ± 0.29 0.61 ± 0.06a 0.63 ±0.04a

Lymph node 0.10 ± 0.01 0.08 ± 0.01a 0.06 ± 0.02a

Adrenal 0.14 ± 0.01 0.15 ± 0.01 0.12 ± 0.01a, b

Cell counts

Splenocytes (× 108) 5.08 ± 0.06 3.65 ±0.07a 3.71 ± 0.06a

Thymocytes (× 108) 2.66 ± 0.09 1.95 ± 0.03a 1.86 ± 0.09a

Lymph node (× 107) 3.03 ± 0.04 2.77 ± 0.07a 2.20 ± 0.06a, b

Bone marrow (× 107) 4.67 ± 0.03 3.04 ± 0.09a 2.11 ± 0.05a,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).

1 Parthasarathy et al. (2007) also documented their results on the cell-mediated and 

2 humoral immunity induced by methanol. Leucocyte migration was significantly increased 

3 compared to control animals, an LMI of 0.82 ± 0.06 being reported in rats exposed to methanol 

4 for 30 days. This compares to an LMI of 0.73 ± 0.02 in rats exposed for 15 days and 0.41 ± 0.10 

5 in controls. By contrast, footpad thickness and antibody titer were decreased significantly in 

6 methanol-exposed animals compared to controls (18.32 ± 1.08, 19.73 ± 1.24, and 26.24 ± 1.68% 

7 for footpad thickness; and 6.66 ± 1.21, 6.83 ± 0.40, and 10.83 ± 0.40 for antibody titer in 30-day, 

8 15-day exposed rats, and controls, respectively).

9 Parthasarathy et al. (2007) also provided data in a histogram that showed a significant 

10 decrease in the absolute numbers of Pan T cells, CD4, macrophage, major histocompatibility 

11 complex (MHC) class II molecule expressing cells, and B cells of the methanol-treated group 

12 compared to controls. The numbers of CD8 cells were unaffected. Additionally, as illustrated in 

13 the report, DNA single strand breakage was increased in immunized splenocytes and thymocytes 

14 exposed to methanol versus controls. Although some fluctuations were seen in corticosterone 

15 levels, the apparently statistically significant change versus controls in 15-day exposed rats was 

16 offset by a decrease in 30-day exposed animals. Parthasarathy et al. (2007) also tabulated the 

17 impacts of methanol exposure on cytokine levels; these values are shown in Table 4-19.
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Table 4-19. The effect of methanol on serum cytokine levels in male Wistar rats

Cytokines (pg/mL)

Immunized

Control 15 days 30 days

IL-2 1810 ± 63.2 1303.3 ± 57.1a 1088.3 ± 68.8a,b

IL-4 44.8 ± 2.0 74.0 ± 5.1a 78.8 ± 4.4a

TNF-α 975 ± 32.7 578.3 ± 42.6a 585 ± 45a

IFN-γ 1380 ± 55.1 961 6 ± 72.7a 950 ± 59.6a

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).

1 Drawing on the results of DNA single strand breakage in this experiment, the authors 

2 speculated that methanol-induced apoptosis could suppress specific immune functions such as 

3 those examined in this research report. Methanol appeared to suppress both humoral and cell4 mediated immune responses in exposed Wistar rats.

4.6. MECHANISTIC DATA AND OTHER STUDIES IN SUPPORT OF THE MOA

5 While the role of the methanol metabolite, formate, in inducing the toxic consequences of 

6 acute exposure to methanol, including ocular toxicity and metabolic acidosis, is well established

7 in humans (see Section 4.1), there is controversy over the possible roles of the parent compound, 

8 metabolites, and folate deficiency (potentially associated with methanol metabolism) in the 

9 developmental neurotoxicity of methanol. Experiments that have attempted to address these 

10 issues are reviewed in the following paragraphs.

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

11 Dorman et al. (1995) conducted a series of in vitro and in vivo studies that provide 

12 information for identifying the proximate teratogen associated with developmental toxicity in 

13 CD-1 mice. The studies used CD-1 ICR BR (CD-1) mice, HPLC grade methanol, and 

14 appropriate controls. PK and developmental toxicity parameters were measured in mice exposed 

15 to sodium formate (750 mg/kg by gavage), a 6-hour methanol inhalation (10,000 or 15,000 ppm), 

16 or methanol gavage (1.5 g/kg). In the in vivo inhalation study, 12–14 dams/ group were exposed 

to 10,000 ppm methanol for 6 hours on GD8,51 17 with and without the administration of 

18 fomepizole (4-methylpyrazole) to inhibit the metabolism of methanol by ADH1. Dams were 

19 sacrificed on GD10, and fetuses were examined for neural tube patency. As shown in 

20 Table 4-20, the incidence of fetuses with open neural tubes was significantly increased in the 

 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.
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1 methanol group (9.65% in treated versus 0 in control) and numerically but not significantly 

2 increased in the group treated with methanol and fomepizole (7.21% in treated versus 0 in 

3 controls). These data should not be interpreted to suggest that a decrease in methanol 

4 metabolism is protective. As discussed in Section 3.1, rodents metabolize methanol via both 

5 ADH1 and CAT. This fact and the Dorman et al. (1995) observation that maternal formate levels 

6 in blood and decidual swellings (swelling of the uterine lining) did not differ in dams exposed to 

7 methanol alone or methanol and fomepizole suggest that the role of ADH1 relative to CAT and 

8 nonenzymatic methanol clearance is not of great significance in adult rodents. 

Table 4-20. Developmental outcome on GD10 following a 6-hour 10,000 ppm 

(13,104 mg/m3

) 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 ± 3.13a 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 authors.

Source: Dorman et al. (1995) (adapted).

9 The data in Table 4-20 suggest that the formate metabolite is not responsible for the 

10 observed increase in open neural tubes in CD-1 mice following methanol exposure. Formate 

11 administered by gavage (750 mg/kg) did not increase this effect despite the fact that this formate 

12 dose produced the same toxicokinetic profile as a 6-hour exposure to 10,000 ppm methanol 

13 vapors (1.05 mM formate in maternal blood and 2.0 mmol formate/kg in decidual swellings). 

14 However, the data are consistent with the hypotheses that the formaldehyde metabolite of 

15 methanol may play a role. Both CAT and ADH1 activity are immature at days past conception 

16 (DPC)8 (Table 4-21). If fetal ADH1 is more mature than fetal CAT, it is conceivable that the 

17 decrease in the open neural tube response observed for methanol combined with fomepizole 

18 (Table 4-20) may be due to fomepizole having a greater effect on the metabolism of fetal 

19 methanol to formaldehyde than is observed in adult rats. Unfortunately, the toxicity studies were 

20 carried out during a period of development where ADH1 expression and activity are just starting 

21 to develop (Table 4-21); therefore, it is uncertain whether any ADH1 was present in the fetus to 

22 be inhibited by fomepizole. 
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Table 4-21. 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

 activitya

 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).

1 Dorman et al. (1995) provide additional support for their hypothesis that methanol’s 

2 developmental effects in CD-1 mice are not caused by formate in an in vitro study involving the 

3 incubation of GD8 whole CD-1 mouse embryos with increasing concentrations of methanol or 

4 formate. Developmental anomalies were observed on GD9, including cephalic dysraphism, 

5 asymmetry and hypoplasia of the prosencephalon, reductions of brachial arches I and II, 

6 scoliosis, vesicles on the walls of the mesencephalon, and hydropericardium (Table 4-22). The 

7 concentrations of methanol used for embryo incubation (0–375 mM) were chosen to be broadly 

8 equivalent to the peak methanol levels in plasma that have been observed (approximately 

100 mM) after a single 6-hour inhalation exposure to 10,000 ppm (13,104 mg/m3 9 ). As discussed 

10 above, these exposure conditions induced an increased incidence of open neural tubes on GD10 

11 embryos when pregnant female CD-1 mice were exposed on GD8. (Table 4-20). Embryonic 

12 lesions such as cephalic dysraphism, prosencephalic lesions, and brachial arch hypoplasia were 

13 observed with 250 mM (8,000 mg/L) methanol and 40 mM (1,840 mg/L) formate. The study 

14 authors noted that a formate concentration of 40 mM (1,840 mg/L) greatly exceeds blood 

15 formate levels in mice inhaling 15,000 ppm methanol (0.75 mM = 35 mg/L), a teratogenic dose.
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Table 4-22. Dysmorphogenic effect of methanol and formate in neurulating CD-1 

mouse embryos in culture (GD8)

Treatment

Concentration

(mM)

Live embryos 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 7a 1 8 6a

375 12 7 6a 5 11a 9a 1 10a 8a

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 14a 10a 4 14a 3 5a 8 13a

a

p < 0.05, as calculated by the authors.

Source: Dorman et al. (1995) (adapted).

1 As discussed in Section 4.3.3, a series of studies by Harris et al. (2004, 2003) also 

2 provide evidence as to the moieties that may be responsible for methanol-induced developmental 

3 toxicity. Harris et al. (2004) have shown that among methanol and its metabolites, viability of 

4 cultured rodent embryos is most affected by formate. In contrast, teratogenic endpoints (of 

5 interest to this risk assessment) in cultured rodent embryos are more sensitive to methanol and 

6 formaldehyde than formate. Data from these studies indicate that developmental toxicity may be 

7 more related to formaldehyde than methanol, as formaldehyde-induced teratogenicity occurs at 

8 several orders of magnitude lower than methanol (Table 4-14) (Hansen et al., 2005; Harris et al., 

9 2004). It should also be noted that CAT, ADH1, and ADH3 activities are present in both the rat 

10 embryo and VYS at stages as early as 6–12 somites (Harris et al., 2003); thus, it is presumable 

11 that in these ex vivo studies methanol is metabolized to formaldehyde and formaldehyde is 

12 subsequently metabolized to S-formylglutathione.

13 Studies involving GSH also lend support that formaldehyde may be a key proximal 

14 teratogen. Inhibition of GSH synthesis with butathione sulfoximine (BSO) has little effect on 

15 developmental toxicity endpoints, yet treatment with BSO and methanol or formaldehyde 

16 increases developmental toxicity (Harris et al., 2004). Among the enzymes involved in methanol 

17 clearance, only ADH3-mediated metabolism of formaldehyde is GSH dependent. This 

18 hypothesis that ADH3-mediated metabolism of formaldehyde is important for the amelioration 

19 of methanol’s developmental toxicity is also supported by the diminished ADH3 activity in the 

20 mouse versus rat embryos, which is consistent with the greater sensitivity of the mouse to 
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1 methanol developmental toxicity (Harris et al., 2003) (Section 4.3.3). Similarly reasonable 

2 explanations for this greater mouse sensitivity are not readily apparent for the two MOAs 

3 described below that attribute methanol toxicity to methanol metabolism per se, either through 

4 the depletion of folate (Section 4.6.2) or the generation of reactive oxidant species (Section 

5 4.6.3). Mouse livers actually have considerably higher hepatic tetrahydrofolate and total folate 

6 than rat or monkey liver. Harris et al. (2003) and Johlin et al. (1987) have shown that CAT 

7 activity in the embryo and VYS of rats and mice appear similar..

8 Without positive identification of the actual moiety responsible for methanol-induced 

9 teratogenicity, MOA remains unclear. If the moiety is methanol, then it is possible that 

10 generation of NADH during methanol oxidation creates an imbalance in other enzymatic 

11 reactions. Studies have shown that ethanol intake leads to a >100-fold increase in cellular 

NADH, presumably due to ADH1-mediated reduction of the cofactor NAD+ 12 to NADH 

13 (Cronholm, 1987; Smith and Newman, 1959). This is of potential importance because, for 

14 example, ethanol intake has been shown to increase the in vivo and in vitro enzymatic reduction 

15 of other endogenous compounds (e.g., serotonin) in humans (Svensson et al., 1999; Davis et al., 

16 1967). In rodents, CAT-mediated methanol metabolism may obviate this effect; in humans,

17 however, methanol is primarily metabolized by ADH1.

18 If the teratogenic moiety of methanol is formaldehyde, then reactivity with protein 

19 sulfhydryls and nonprotein sulfhydryls (e.g., GSH) or DNA protein cross-links may be involved. 

20 Metabolic roles ascribed to ADH3, particularly regulation of S-nitrosothiol biology (Foster and 

21 Stamler, 2004), could also be involved in the MOA. Recently, Staab et al. (2008) have shown 

22 that formaldehyde alters other ADH3-mediated reactions through cofactor recycling and that 

23 formaldehyde alters levels of cellular S-nitrosothiol, which plays a key role in cellular signaling 

24 and many cellular functions and pathways (Hess et al., 2005). 

25 Studies such as those by Harris et al. (2004, 2003) and Dorman et al. (1995, 1994) 

26 suggest that formate is not the metabolite responsible for methanol’s teratogenic effects. The 

27 former researchers suggest that formaldehyde is the proximate teratogen, and provide evidence 

28 in support of that hypothesis. However, questions remain. Researchers in this area have not yet 

29 reported using a sufficient array of enzyme inhibitors to conclusively identify formaldehyde as 

30 the proximate teratogen. Studies involving other inhibitors or toxicity studies carried out in 

31 genetically engineered mice, while not devoid of confounders, might further inform regarding 

32 the methanol MOA for developmental toxicity. Even if formaldehyde is ultimately identified as 

33 the proximate teratogen, methanol would likely play a prominent role, at least in terms of 

34 transport to the target tissue. The high reactivity of formaldehyde would limit its unbound and 

35 unaltered transport as free formaldehyde from maternal to fetal blood (Thrasher and Kilburn, 
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1 2001), and, as has been discussed, the capacity for the metabolism of methanol to formaldehyde 

2 is likely lower in the fetus and neonate versus adults (Section 3.3). 

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

3 As discussed in Sections 3.1 and 4.1, humans and other primates are susceptible to the 

4 effects of methanol exposure associated with formate accumulation because they have lower 

5 levels of hepatic tetrahydrofolate-dependent enzymes that help in formate oxidation. 

6 Tetrahydofolate-dependent enzymes and critical pathways that depend on folate, such as purine 

7 and pyrimidine synthesis, may also play a role in the developmental toxicity of methanol. 

8 Studies of rats and mice fed folate-deficient diets have identified adverse effects on reproductive 

9 performance, implantation, fetal growth and developmental defects, and the inhibition of folate 

10 cellular transport has been associated with several developmental abnormalities, ranging from 

11 neural tube defects to neurocristopathies such as cleft-lip and cleft-palate, cardiacseptal defects, 

12 and eye defects (Antony, 2007). Folate deficiency has been shown to exacerbate some aspects of 

13 the developmental toxicity of methanol in mice (see discussion of Fu et al., 1996, and Sakanashi 

14 et al., 1996, in Section 4.3.1) and rats (see discussion of Aziz et al., 2002, in Section 4.4.1). 

15 The studies in mice focused on the influence of FAD on the reproductive and skeletal 

16 malformation effects of methanol. Sakanashi et al. (1996) showed that dams exposed to 

17 5 g/kg-day methanol on GD6–GD15 experienced a threefold increase in the percentage of litters 

18 affected by cleft palate and a 10-fold increase in the percentage of litters affected by exencephaly 

19 when fed a FAD (resulting in a 50% decrease in liver folate) versus a FAS diet. They speculated

20 that the increased methanol effect from FAD diet could have been due to an increase in tissue 

21 formate or a critical reduction in conceptus folate concentration immediately following the 

22 methanol exposure. The latter appears more likely, given the high levels of formate needed to 

23 cause embryotoxicity (Section 4.3.3) and the decrease in conceptus folate that is observed within 

24 2 hours of GD8 methanol exposure (Dorman et al., 1995). Fu et al. (1996) confirmed the 

25 findings of Sakanashi et al. (1996) and also determined that the maternal FAD diet had a much 

26 greater impact on fetal liver folate than maternal liver folate levels. 

27 The rat study of Aziz et al. (2002) focused on the influence of FAD on the developmental 

28 neurotoxicity of methanol. Experiments by Aziz et al. (2002) involving Wistar rat dams and 

29 pups exposed to methanol during lactation provide evidence that methanol exposure during this 

30 postnatal period affects the developing brain. These effects (increased spontaneous locomotor 

31 activity, decreased conditioned avoidance response, disturbances in dopaminergic and 

32 cholinergic receptors and increased expression of GAP-43 in the hippocampal region) were more 

33 pronounced in FAD as compared to FAS rats. This suggests that folic acid may play a role in 

34 methanol-induced neurotoxicity. These results do not implicate any particular proximate 
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1 teratogen, as folate deficiency can increase levels of both methanol, formaldehyde and formate 

2 (Medinsky et al., 1997). Further, folic acid is used in a number of critical pathways such as 

3 purine and pyrimidine synthesis. Thus, alterations in available folic acid, particularly to the 

4 conceptus, could have significant impacts on the developing fetus apart from the influence it is 

5 presumed to have on formate removal.

4.6.3. Methanol-Induced Formation of Free Radicals, Lipid Peroxidation, and Protein 

Modifications

6 Oxidative stress in mother and offspring has been suggested to be part of the teratogenic 

7 mechanism of a related alcohol, ethanol. Certain reproductive and developmental effects (e.g.,

8 resorptions and malformation rates) observed in Sprague-Dawley rats following ethanol 

9 exposure were reported to be ameliorated by antioxidant (Vitamin E) treatment (Wentzel et al., 

10 2006; Wentzel and Eriksson, 2006). A number of studies have examined markers of oxidative 

11 stress associated with methanol exposure.

12 Skrzydlewska et al. (2005) provided inferential evidence for the effects of methanol on 

13 free radical formation, lipid peroxidation, and protein modifications, by studying the protective 

14 effects of N-acetyl cysteine and the Vitamin E derivative, U83836E, in the liver of male Wistar 

15 rats exposed to the compound via gavage. Forty-two rats/group received a single oral gavage 

16 dose of either saline or 50% methanol. This provided a dose of approximately 6,000 mg/kg, as 

17 calculated by the authors. Other groups of rats received the same concentration of methanol, but 

18 were also injected intraperitoneally with either N-acetylcysteine or U-83836E. N-acetylcysteine 

19 and U-83836E controls were also included in the study design. Animals in each group were 

20 sacrificed after 6, 14, and 24 hours or after 2, 5, or 7 days. Livers were rapidly excised for 

21 electron spin resonance (ESR) analysis, and 10,000 × g supernatants were used to measure GSH, 

22 malondialdehyde, a range of protein parameters, including free amino and sulfhydryl groups, 

23 protein carbonyls, tryptophan, tyrosine, and bityrosine, and the activity of cathepsin B.

24 Skrzydlewska et al. (2005) provided data that showed an increase in an ESR signal at 

25 g = 2.003 in livers harvested 6 and 12 hours after methanol exposure. The signal, thought to be 

26 indicative of free radical formation, was opposed by N-acetylcysteine and U83836E. Other 

27 compound-related changes included: 1) a significant decrease in GSH levels that was most 

28 evident in rats sacrificed 12 and 24 hours after exposure; 2) increased concentrations in the lipid 

29 peroxidation product, malondialdehyde (by a maximum of 44% in the livers of animals 

30 sacrificed 2 days after exposure); 3) increased specific concentrations of protein carbonyl groups 

31 and bityrosine; but 4) reductions in the specific level of tryptophan. Given the ability of N32 acetylcysteine and U83836E to oppose these changes, at least in part, the authors speculated that 

33 a number of potentially harmful changes may have occurred as a result of methanol exposure. 
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1 These include free radical formation, lipid peroxidation, and disturbances in protein structure. 

2 However, it is unclear whether or not the metabolites of methanol, formaldehyde, and/or formate, 

3 were involved in any of these changes.

4 Rajamani et al. (2006) examined several oxidative stress parameters in male Wistar rats 

5 following methotrexate-induced folate deficiency. Compared to controls, the levels of free 

6 radical scavengers SOD, CAT, GSH peroxidase, oxidized GSH, protein carbonyls, and lipid 

7 peroxidation were elevated in several regions of the brain, with greater increases observed in the 

8 MTX-methanol-treated animals than in the MTX-alone group. The level of GSH and protein 

9 thiols was decreased in all regions of the brain, with a greater decrease observed in the MTX10 methanol-treated animals than MTX-treated animals.

11 Dudka (2006) measured the total antioxidant status (TAS) in the brain of male Wistar rats 

12 exposed to a single oral gavage dose of methanol at 3 g/kg. The animals were kept in a nitrous 

13 oxide atmosphere (N2O/O2) throughout the experiment to reduce intrinsic folate levels, and 

14 various levels of ethanol and/or fomepizole (as ADH antidotes) were administered i.p. after 

15 4 hours. Animals were sacrificed after 16 hours, the brains homogenized, and the TAS 

16 determined spectrophotometrically. As illustrated graphically by the author, methanol 

17 administration reduced TAS in brain irrespective of the presence of ADH antidotes. The author 

18 speculated that, while most methanol is metabolized in the liver, some may also reach the brain. 

Metabolism to formate might then alter the NADH/NAD+ 19 ratio resulting in an increase in 

20 xanthine oxidase activity and the formation of the superoxide anion.

21 Parthasarathy et al. (2006b) investigated the extent of methanol-induced oxidative stress 

22 in rat lymphoid organs. Six male Wistar rats/group received 2,370 mg/kg methanol (mixed 1:1 

23 with saline) injected i.p. for 1, 15 or 30 days. A control group received a daily i.p. injection of 

24 saline for 30 days. At term, lymphoid organs such as the spleen, thymus, lymph nodes, and bone 

25 marrow were excised, perfused with saline, then homogenized to obtain supernatants in which 

26 such indices of lipid peroxidation as malondialdehyde, and the activities of CAT, SOD, and GSH 

27 peroxidase were measured. Parthasarathy et al. (2006b) also measured the concentrations of 

28 GSH and ascorbic acid (nonenzymatic antioxidants) and the serum concentrations of a number of 

29 indicators of liver and kidney function, such as ALT, AST, blood urea nitrogen (BUN), and 

30 creatinine.

31 Table 4-23 shows the time-dependent changes in serum liver and kidney function 

32 indicators that resulted from methanol administration. Treatment with methanol for increasing 

33 durations resulted in increased serum ALT and AST activities and the concentrations of BUN and 

34 creatinine.
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Table 4-23. Time-dependent effects of methanol administration on serum liver and 

kidney function, serum ALT, AST, BUN, and creatinine in control and experimental 

groups of male 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 ± 2.3a 60.4 ± 2.8a

AST (μmoles/min-mg) 5.8 ± 0.4 6.4 ± 0.3 9.0 ± 1.2a 13.7 ± 1.2a

BUN (mg/L) 301 ± 36 332 ± 29 436 ± 35a 513 ± 32a

Creatinine (mg/L) 4.6 ± 0.3 4.8 ± 0.3 5.6 ± 0.2a 7.0 ± 0.4a

Values are means ± S.D. of 6 animals.

a

p < 0.05 versus controls.

Source: Parthasarathy et al. (2006b) (adapted).

Table 4-24. Effect of methanol administration on male 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

Malondialdehyde in lymphoid organs

Spleen 2.62 ± 0.19 4.14 ± 0.25a 7.22 ± 0.31a 9.72 ± 0.52a

Thymus 3.58 ± 0.35 5.76 ± 0.36a 9.23 ± 0.57a 11.6 ± 0.33a

Lymph nodes 3.15 ± 0.25 5.08 ± 0.24a 8.77 ± 0.57a 9.17 ± 0.67a

Bone marrow 3.14 ± 0.33 4.47 ± 0.18a 7.20 ± 0.42a 9.75 ± 0.56a

Antioxidant levels in spleen

SOD (units/mg protein) 2.40 ± 0.16 4.06 ± 0.19a 1.76 ± 0.09a 1.00 ± 0.07a

CAT (μmoles H2O2 

consumed/min-mg protein

35.8 ± 2.77 52.5 ± 3.86a 19.1 ± 1.55a 10.8 ± 1.10a

GPx (μg GSH 

consumed/min-mg protein)

11.2 ± 0.60 20.0 ± 1.0a 7.07 ± 0.83a 5.18 ± 0.45a

GSH (μg/mg protein) 2.11 ± 0.11 3.75 ± 0.15a 1.66 ± 0.09a 0.89 ± 0.04a

Vit C (μg/mg protein) 0.45 ± 0.04 0.73 ± 0.05a 0.34 ± 0.18a 0.11 ± 0.03a

Values are means ± S.D. of six animals.

a p < 0.05, versus controls.

Source: Parthasarathy et al. (2006b) (adapted).

1 Table 4-24 gives the concentration of malondialdehyde in the lymphoid organs of control 

2 and experimental groups, and, as an example of all tissue sites examined, the levels of enzymatic 

3 and nonenzymatic antioxidants in spleen. The results show that malondialdehyde concentrations 

4 were time-dependently increased at each tissue site and that, in spleen as an example of all the 

5 lymphoid tissues examined, increasing methanol administration resulted in lower levels of all 

6 antioxidants examined compared to controls. Parthasarathy et al. (2006b) concluded that 
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1 exposure to methanol may cause oxidative stress by altering the oxidant/antioxidant balance in 

2 lymphoid organs in the rat.

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

Results from Methanol Exposure

3 In companion reports, Muthuvel et al. (2006a, 2006b) used 6 male Wistar rats/group to 

4 test the ability of exogenously-administered formate dehydrogenase (FD) to reduce the serum 

5 levels of formate that were formed when 3 g/kg methanol was administered i.p. to rats in saline.

6 In the first experiment, purified FD (from Candida boitinii) was administered by i.v. conjugated 

7 to the N-hydroxysuccinimidyl ester of monomethoxy polyethylene glycol propionic acid 

8 (PEG-FD) (Muthuvel et al., 2006a). In the second, rats were administered FD-loaded 

9 erythrocytes (Muthuvel et al., 2006b). In the former case, some groups of rats were made folate

10 deficient by means of a folate-depleted diet; in the latter, folate deficiency was brought about by 

11 i.p. administration of methotrexate. In some groups, the rats received an infusion of an 

12 equimolar mixture of carbonate and bicarbonate (each at 0.33 mol/L) to correct the formate13 induced acidosis. As illustrated by the authors, methanol-exposed rats receiving a folate14 deficient diet showed significantly higher levels of serum formate than those receiving a folate15 sufficient diet. However, administration of native or PEG-FD reduced serum formate in 

16 methanol-receiving folate-deficient rats to levels seen in animals receiving methanol and the 

17 folate-sufficient diet.

18 In the second report, Muthuvel et al. (2006b) carried out some preliminary experiments to 

19 show that hematological parameters of normal, reconstituted but unloaded, and reconstituted and 

20 FD-loaded erythrocytes, were similar. In addition, they showed that formate levels of serum 

21 were reduced in vitro in the presence of FD-loaded erythrocytes. Expressing blood formate 

22 concentration in mmol/L at the 1-hour time point after carbonate/bicarbonate and enzyme-loaded 

23 erythrocyte infusion via the tail vein, the concentration was reduced from 10.63 ± 1.3 (mean ± 

24 S.D.) in methanol and methotrexate-receiving controls to 5.83 ± 0.97 (n = 6). This difference 

25 was statistically significant at the p < 0.05 level. However, FD-loaded erythrocytes were less 

26 efficient at removing formate in the absence of carbonate/bicarbonate. Effective elimination of 

27 formate appears to require an optimum pH for the FD activity in the enzyme-loaded 

28 erythrocytes.

4.6.5. Mechanistic Data Related to the Potential Carcinogenicity of Methanol

4.6.5.1. Genotoxicity

29 The genotoxicity/mutagenicity of methanol has not been extensively studied, but the 

30 results of those studies that have thus far have been mostly negative. For example, in a survey of 
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1 the capacity of 71 drinking water contaminants to induce gene reversion in the Ames test, 

2 Simmon et al. (1977) listed methanol as one of 45 chemicals that gave negative results with 

3 Salmonella typhimurium strains TA 98, 100, 1535, 1537, and 1538, irrespective of the presence 

4 or absence of metabolic activation (an S9 microsomal fraction). This result was confirmed by 

5 DeFlora et al. (1984) and in NEDO (1987) for the same strains of Salmonella. DeFlora et al. 

6 (1984) also found methanol to be negative for induction of DNA repair in E. coli strains WP2, 

WP2 (uvrA-

, polA-), and CM871 (uvrA-, recA-, lexA- 7 ), again irrespectively of the presence or 

8 absence of S9.

Abbondandolo et al. (1980) used a ade6-60/rad10-198,h- 9 strain of Schizosaccharomyces 

10 pombe (P1 strain) to determine the capacity of methanol and other solvents to induce forward 

11 mutations. Negative results were obtained for methanol, irrespective of metabolic activation 

12 status. In other genotoxicity/mutagenicity studies of methanol using fungi, Griffiths (1981) 

13 reported methanol to be negative for the induction of aneuploidy in Neurospora crassa. By 

14 contrast, weakly positive results for the compound were obtained by Crebelli et al. (1989) for the 

15 induction of chromosomal malsegregation in the diploid strain P1 of Aspergillus nidulans.

16 In an extensive review of the capacity of a wide range of compounds to induce 

17 transformation in mammalian cell lines, Heidelberger et al. (1983) reported methanol to be 

18 negative in Syrian hamster embryo (SHE) cells. It also did not enhance the transformation of 

19 SHE cells by Simian adenovirus. However, McGregor et al. (1985) reported in an abstract that a 

statistically significant increase in forward mutations in the mouse lymphoma L5178Y tk+

/tk- 20 cell 

21 line occurred at a concentration of 7.9 mg/mL methanol in the presence of S9.

22 The capacity of methanol to bring about genetic changes in human cell lines was 

23 examined by Ohno et al. (2005), who developed a system in which the chemical activation of the 

24 p53R2 gene was assessed by the incorporation of a p53R2-dependent luciferase reporter gene 

25 into two human cell lines, MCF-7 and HepG2. Methanol, among 80 chemicals tested in this 

26 system, gave negative results. NEDO (1987) used Chinese hamster lung (CHL) cells to monitor 

27 methanol’s capacity to induce 1) forward mutations to azaguanine, 6-thioguanine, and ouabain 

28 resistance, and 2) chromosomal aberrations (CA) though with negative results throughout. 

29 However, methanol did display some capacity to induce sister chromatid exchanges (SCE) in 

30 CHL cells, since the incidence of these lesions at the highest concentration (28.5 mg/mL) was 

31 significantly greater than in controls (9.41 ± 0.416 versus 6.42 ± 0.227 [mean ± SE per 100 

32 cells]).

33 In an in vivo experiment examining the genotoxicity/mutagenicity of methanol, Campbell 

34 et al. (1991) exposed 10 male C57BL/6J mice/group to 0, 800, or 4,000 ppm (0, 1,048, and 

5,242 mg/m3 35 ) methanol, 6 hours/day, for 5 days. At sacrifice, blood cells were examined for the 

36 formation of micronuclei (MN). Excised lung cells for SCE, CA and MN, and excised testicular 
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1 germ cells were examined for evidence of synaptonemal damage, in each case with negative 

2 results.

3 There was no evidence of methanol-induced formation of MN in the blood of fetuses or 

4 pregnant CD-mice when the latter were gavaged twice daily with 2,500 mg/kg methanol on 

5 GD6–GD10 (Fu et al., 1996). The presence of marginal or adequate amounts of folic acid in the 

6 diet of the dams did not affect MN formation. NEDO (1987) carried out an in vivo MN test in 

7 6 male SPF mice/group who received a single gavage dose of 1,050, 2,110, 4,210, and 8,410 

8 mg/kg methanol. Twenty-four hours later, 1,000 cells were counted for MN in bone marrow 

9 smears. No compound-related effects on MN incidence were observed. Table 4-25 provides a 

10 summary of the genotoxicity/mutagenicity studies of methanol.

Table 4-25. Summary of genotoxicity studies of methanol

Test system Cell/strain Result Reference Comments

In vitro tests

Gene reversion/

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 repair/E. coli WP2, WP2 (uvrA-, 

polA-),CM871(uvrA-, recA-, 

lexA-)

- (+S9), - (-S9)

DeFlora et al. (1984)

Forward mutations/

S. pombe

P1

(ade6-60/rad10-198,h-) - (+S10), - (-S10)

Abbondandolo et al. 

(1980)

Molecular activation 

used a 10,000 × g (S10) 

supernatant from liver 

of induced Swiss mice

Aneuploidy/

N. crassa

(arg-1, ad-3A, ad-3B, nic2, 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)
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Test system Cell/strain Result Reference Comments

Genetic activation/

human cell lines

MCF-7 and HepG2 

containing a p53R2-

dependent 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 vivo 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)

Molecular activation 

not applicable

Mice/CA C57BL/6J

(Lung cells) – Campbell et al. (1991)

Molecular activation 

not applicable

Mouse/synaptonem

al damage

C57BL/6J

(Testicular germ cells) – Campbell et al. (1991)

Molecular activation 

not applicable

Mouse/MN 

formation

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)

1 The ERF or the European Foundation of Oncology and Environmental Sciences have 

2 conducted nearly 400 experimental bioassays on over 200 compounds/agents, using some 

148,000 animals over nearly 4 decades. Of the over 200 compounds tested by ERF,52 3 8 have 

4 been associated with an increased incidence of hemolymphoreticular tumors in Sprague-Dawley 

5 rats, suggesting that it may be a rare and potentially species/strain-specific finding. These eight

6 chemicals are: methanol, formaldehyde, aspartame, MTBE, DIPE, TAME, mancozeb, and 

7 toluene. Methanol, formaldehyde, aspartame, and MTBE share a common metabolite, 

8 formaldehyde, and DIPE, TAME, methanol and MTBE are all gasoline-oxygenate additives 

9 (Caldwell et al., 2008).

10 With the exception of a positive study for malignant lymphomas in Swiss Webster mice 

11 exposed to methanol (Apaja, 1989), lymphoma responses have not been reported by other

12 institutions performing long-term testing of these chemicals in various strains of rats, including 

 52 While ERF has tested over 200 chemicals in 398 long-term ERF bioassays, only 112 of their bioassays have been 

published to date (Caldwell, et al., 2008). The extent to which the unpublished studies are documented varies.
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formaldehyde inhalation studies in F344 (Kerns et al., 1983; Kamata et al., 1987)

53 1 and Sprague2 Dawley (Albert et al., 1982; Sellakumar et al., 1985) rats, formaldehyde oral studies in Wistar 

3 rats (Til et al., 1989; Tobe et al., 1989), toluene oral studies in F344 rats (NTP, 1990), MTBE 

4 inhalation studies in F344 rats (Chun et al., 1992), aspartame oral studies in Wistar (Ishii et al., 

5 1981) and Sprague-Dawley (Molinary, 1984) rats, and methanol inhalation studies in F344 rats 

6 (NEDO, 1987, 1985/2008b). Several differences in study design may contribute to the 

7 differences in responses observed across institutions, particularly study duration and test animal 

8 strain. Fischer-344 rats have a high background of mononuclear cell leukemia (20% in control 

females)

54 9 and a very low background rate of ‘‘lymphoma’’ (0% in control females) at 104 weeks 

10 (NTP, 2006). In contrast, Sprague-Dawley rats from NTP studies exhibit a low background rate 

11 of “leukemias” (0.8% in control females) and a higher background rate of “lymphomas” (1.08% 

12 in control females) at 104 weeks (NTP, 2006). Similarly, Chandra et al. (1992) report a 

13 background level of 1.6% for malignant lymphocytic lymphomas in female control Sprague14 Dawley rats for 17 2-year carcinogenicity studies. 

15 In lifetime studies of Sprague-Dawley rats at ERF, the overall incidence of 

16 lymphomas/leukemias has been reported to be 13.3% (range, 4.0–25.0%) in female historical 

17 controls (2,274 rats) and 20.6% (range, 8.0–30.9%) in male historical controls (2,265 rats)

18 (Soffritti et al., 2007). The difference in background rates reported by ERF versus other labs for 

19 this tumor type could be due to differences in study duration, differences in tumor classification

20 systems, and/or misdiagnoses due to confounding effects (see discussion in Section 4.2.1.3). A 

21 high background incidence can increase the difficulty of detecting chemically related responses 

22 (Melnick et al., 2007), and the background rate reported by ERF for this tumor type is considered 

23 to be high relative to other tumor types and relative to the background rate for this tumor type in 

Sprague-Dawley rats from other laboratories (EFSA, 2006; Cruzan, 2009).

55 24 However, it is in a

25 range that can be considered reasonable for studies that employ a large number of animals 

26 (Leakey et al., 2003; Caldwell et al., 2009).

27 Thus, with respect to the identification of hemolymphoreticular carcinogenic responses, 

28 life span studies of Sprague-Dawley rats performed by ERF may be more sensitive than the 

29 2-year studies of Fischer 344 (F344) strain of rats used by NTP (1990) and NEDO (1987,

 53 Though Kerns et al. (1983) did not report a positive response for lymphoma, a survival-adjusted analysis of the 

data from this study indicates 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 rats 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 

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).
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1 1985/2008b). The results of ERF studies of the carcinogenic potential of methanol, MTBE, and 

2 formaldehyde and related chemicals, ethanol and aspartame, are summarized in this section. 

3 4.6.5.2.1. Ethanol. In a study that was reported in the same article that described the 

4 carcinogenic responses of Sprague-Dawley rats to methanol, Soffritti et al. (2002a) exposed 

5 110 Sprague-Dawley rats/sex/group to ethanol in drinking water at concentrations of 0 or 10% 

6 (v/v) beginning at 39 weeks of age and ending at natural death, and including a single breeding 

7 cycle. Various numbers of the offspring (30 male controls, 39 female controls, 49 exposed 

8 males, and 55 exposed females) were exposed to ethanol in drinking water at the same 

9 concentrations as their parents. The experiment concluded with the death of the last offspring at 

10 179 weeks of age. Animals were examined for the same toxicological parameters as those 

11 described for methanol, and organs and tissues were grossly and histopathologically examined at 

12 necropsy. Soffritti et al. (2002a) reported that food and drinking water intake were lower in 

13 exposed animals compared to controls but that body weight changes were similar among the 

groups. 56 14 There were no compound-related clinical signs of toxicity and no differences in 

15 survival rates among the groups. While there were apparently no nononcogenic pathological 

16 changes evident on gross inspection or histopathologic examination, a number of benign and 

17 malignant tumors were considered by the authors to be compound-related. Compared to 

18 controls, these included increased incidences of: 1) total malignant tumors in male and female 

19 breeders (145/220 versus 99/220) and offspring (49/69 versus 54/104); 2) total malignant tumors 

20 per 100 animals in female breeders (130 versus 60.9) and offspring (164.1 versus 96.4); 3) 

21 carcinomas of the head and neck, especially to the oral cavity, lips and tongue in male and 

22 female breeders (27/220 versus 5/220) and offspring (26/69 versus 5/104); 4) squamous cell 

23 carcinomas of the forestomach in male and female breeders (5/220 versus 0/220) and offspring 

24 (2/69 versus 0/104); 5) interstitial cell adenomas of the testis in male breeders (23/110 versus 

25 9/110) and offspring (4/30 versus 4/49); 6) Sertoli-Leydig cell tumors in ovaries of female 

26 offspring (3/39 versus 1/55); 7) adenocarcinomas of the uterus in female breeders (9/110 versus 

27 2/110) and offspring (8/39 versus 6/55); 8) pheochromoblastomas in male and female breeders 

28 (13/220 versus 4/220) and offspring (4/69 versus 2/104); and 9) osteosarcomas in male and 

29 female breeders ([for the head] 14/220 versus 4/220) and offspring ([for the head] 10/69 versus

30 7/104). Notably, Soffritti et al. (2002a) did not observe increases in any of the lymphoma 

31 responses reported in their methanol bioassay. Incidence data for these responses and their 

statistical significance compared to controls are shown in Table 4-26.

 56 Test animals were likely receiving calories from ethanol exposure.
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Table 4-26. Incidence of carcinogenic responses in Sprague-Dawley rats exposed to 

ethanol in drinking water for up to 2 years

Tissues/affected sites

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 79/110b 23/49 23/30a 31/55 26/39

Oral cavity (carcinomas) 3/110 15/110b 2/110 12/110 2/49 10/30b 3/55 16/39b

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 23/110d 4/49 4/30

Sertoli-Leydig cell tumors 

(ovary)

1/110 2/110 0/55 3/39

Uterus (adenocarcinomas) 2/110 9/110c 6/55 8/39

Head (osteosarcomas) 0/110 8/110 4/110 6/110 4/49 6/30 3/55 4/39

Adrenal 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 89.1b 60.9 130b 61.2 136.7b 96.4 164.1b

a

p < 0.05 using the χ2 test, as calculated by the authors. b

p < 0.01 using the χ2 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.

Source: Soffritti et al. (2002a).

1 4.6.5.2.2. Aspartame. Soffritti et al. (2006, 2005) reported the results of a cancer bioassay on 

2 the artificial sweetener aspartame. The study has potential relevance to the carcinogenicity of 

3 methanol because aspartame has been shown to be metabolized to aspartic acid, phenylalanine, 

4 and methanol in the GI tract prior to absorption into systemic circulation. In the study, aspartame

5 (>98% purity) was given to 100 or 150 Sprague-Dawley rats/sex/group in feed at dietary 

6 concentrations of 0, 80, 400, 2,000, 10,000, 50,000, and 100,000 ppm. The authors reported 

7 these concentrations to be equivalent to approximate daily doses of 0, 4, 20, 100, 500, 2,500, and 

8 5,000 mg/kg-day, respectively, under the conditions of the study. Animals were maintained until 

9 their “natural death,” with the in-life phase of the experiment concluding with the death of the 

10 last animal at 151 weeks. All animals were monitored for body weight, food and water 

11 consumption. At death, animals were examined grossly and given a complete histopathological 

12 examination.

13 Soffritti et al. (2006, 2005) reported that there were no differences among the groups in 

14 mean body weight, survival, or daily water consumption. However, there appeared to be a dose15 related reduction in food consumption in both male and female rats.
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1 The principal histopathological finding was an increased incidence of lymphomas and 

2 leukemias in female rats, a response reported by the authors to be statistically significant 

3 compared to concurrently exposed controls (Table 4-27) and greater than the range of overall 

4 incidence of lymphomas and leukemias in historical controls at the ERF (13.4% [range, 7.0–

5 18.4] in females and 21.8% [range, 8.0–30.9] in males). Among the hemolymphoreticular 

6 neoplasms observed, the most frequent type observed was lympho-immunoblastic lymphoma. 

7 The authors concluded that aspartame causes a “dose-related statistically significant increase in 

8 lymphomas and leukemias in females at dose levels very near those to which humans can be

9 exposed.” They postulated that an increase in the incidence of lymphomas and leukemias could 

10 be associated with the formation of either methanol or formaldehyde. Other potentially 

11 compound-related effects of aspartame were (1) an increase in combined dysplastic hyperplasias, 

12 papillomas, and carcinomas of the renal pelvis and ureter, and (2) an increasing trend in the 

13 formation of malignant schwannomas in peripheral nerves (Table 4-28).

14 The European Commission asked the European Food Safety Authority (EFSA) to assess 

15 the study and review all ERF findings related to aspartame. An EFSA review panel assessed the 

16 study and considered additional unpublished data provided to it by the ERF. In their report, 

17 EFSA (2006) concluded that the Soffritti et al. (2005, 2006) study had flaws that brought into 

18 question the reported findings. The review panel noted the high background of chronic 

19 inflammatory changes in the lung and other vital organs. These background inflammatory 

20 changes were thought to contribute significant uncertainty to the interpretations of the study. In 

21 fact, the review panel concluded that most of the documented changes, in particular, the apparent 

22 compound-related increase in lymphomas and leukemias, may have been incidental findings and, 

23 therefore, unrelated to aspartame.

Table 4-27. 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) 30/150b (20)

IV 2,000 100 33/150 (22) 28/150a (19)

V 10,000 500 15/100 (15) 19/100a (19)

VI 50,000 2,500 20/100 (20) 25/100b (25)

VII 100,000 5,000 29/100 (29) 25/100b (25)

a

p < 0.05 using the poly-k test. b

p < 0.01 using the poly-k test.

Source: Soffritti et al. (2006, 2005).
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Table 4-28. Incidence of combined dysplastic hyperplasias, papillomas and carcinomas 

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

Sprague-Dawley 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) 2/150c (1.3) 1/150c (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) 9/150a (6) 3/150 (2) 0/150 (0)

IV 2,000 100 5/150 (3) 10/150a (7) 2/150 (1.3) 3/150 (2)

V 10,000 500 3/100 (3) 10/100b (10) 2/100 (2) 1/100 (1)

VI 50,000 2,500 3/100 (3) 10/99b (10) 3/100 (3) 1/100 (1)

VII 100,000 5,000 4/100 (4) 15/100b (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.

Source: Soffritti et al. (2006).

1 In their conclusions, the EFSA review panel took note of negative results of 2-year

2 carcinogenic studies of aspartame (Ishii et et al., 1981; Ishii, 1981; NTP, 2003) and of the 

3 findings of a recent epidemiological study carried out by the US National Cancer Institute (NCI, 

4 2006).

5 In an effort to further clarify these issues, Soffritti et al. (2007) reported the results of 

6 another lifetime study of aspartame in which 95 controls and 70 Sprague-Dawley rats/sex/group 

7 were exposed, first in utero, then via the diet, to aspartame at concentrations of 0, 400, or 

8 2,000 ppm (mg/kg) of feed. The authors assumed an average food consumption of 20 g/day and 

9 an average body weight (males and females) of 400 g, thereby deriving average target doses of 0, 

10 40, and 200 mg/kg-day. Soffritti et al. (2007) began administering the aspartame-supplemented 

11 feed to the dams on GD12; and offspring received feed containing aspartame at the appropriate 

12 concentration from weaning until natural death. Animals were observed three times daily 

13 Monday–Friday, and twice daily on Saturdays, Sundays, and holidays. This regimen was both to 

14 monitor clinical signs and to reduce the possibility of decedents undergoing autolysis before 

15 discovery. As described by the authors, all deceased animals were refrigerated, then necropsied 

16 no more than 19 hours after discovery.

17 Food and drinking water consumption was monitored once/day. Beginning at 6 weeks of 

18 age, individual body weights were recorded once a week for 13 weeks, then every 2 weeks until 

19 natural death. All animals were examined grossly every 2 weeks. After necropsy, tissues and 

20 organs were sampled for histopathological processing and microscopic examination (including
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1 skin and subcutaneous tissue, mammary gland, brain, pituitary, Zymbal’s gland, salivary gland, 

2 Harderian gland, cranium, tongue, thyroid, parathyroid, pharynx, larynx, thymus and mediastinal 

3 lymph nodes, trachea, lung and main stem bronchi, heart, diaphragm, liver, spleen, pancreas, 

4 kidney, adrenal gland, esophagus, stomach, intestine, urinary bladder, prostate, vagina, gonads, 

5 interscapular brown fat pads, subcutaneous and mesenteric lymph nodes), as were all

6 pathological lesions identified on gross necropsy. 

7 There were no differences in food and water consumption or in body weights among the 

8 dose groups. As illustrated graphically by the authors, there was little change in overall survival 

9 rates. Discussion of the histopathological findings focused exclusively on the cancer outcomes. 

10 The incidence of total malignant tumors was increased significantly in high-dose males 

11 compared to controls (p < 0.01). The slight increase in the incidence of total malignant tumors in 

12 females was not statistically significant (Table 4-29). With regard to the incidence of type- or 

13 site-specific neoplasms, Soffritti et al. (2007) reported statistically significant increases 

14 (calculated using the Cox regression model) in combined lymphomas and leukemias in both 

15 sexes of Sprague-Dawley rats. In males, the most frequently observed histiotypes were 

16 lymphoblastic lymphomas involving the lung and mediastinal peripheral nodes, while in females, 

17 the most commonly observed lesions were lymphocytic lymphomas and lympho-immunoblastic

18 lymphomas involving the thymus, spleen, lung and peripheral nodes. There was also an increase 

19 in the incidence of mammary gland carcinomas in female Sprague-Dawley rats. The incidences 

20 of total malignant, mammary, and lymphocytic and leukocytic tumors, in comparison to 

21 concurrent and the range of historical controls for combined lymphomas and leukemias and 

22 mammary gland tumors observed at the ERF, are shown in Table 4-29.

Table 4-29. 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%
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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.

Source: Soffritti et al. (2007).

1 With regard to target organs and tissues susceptible to aspartame carcinogenicity, Soffritti 

2 et al. (2007) drew attention to the similar outcome of these results to those reported by Soffritti 

3 et al. (2006, 2005). The authors suggested that the increased incidence in combined lymphomas 

4 and leukemias in female Sprague-Dawley rats as compared to the earlier study was likely due to 

5 the earlier exposure to aspartame experienced by the animals in the Soffritti et al. (2007) study 

6 (prenatal and postnatal versus postnatal only). The authors provided a direct comparison of the 

7 incidence of lymphomas/leukemias between the studies, as summarized in Table 4-30.

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 preand postnatally or postnatally only

Dose (mg/kg-day) Percent with lymphomas/leukemias

Pre-and postnatal exposurea Postnatal exposure onlyb

0 12.6 8.7

20 17.1 20.0

100 31.4 18.7

Source: a Soffritti et al. (2007); b Soffritti et al. (2006, 2005).

8 An EFSA (2009) review of the Sofftitti et al. (2007) study notes that the ratio between the 

9 incidence in the low-dose group and the incidence in the concurrent control in Table 4-30 is 

10 considerably lower in the animals exposed prenatally (1.4:1) compared to those exposed 

11 postnatally (2.3:1). The ratio in the groups exposed to 100 mg/kg bw/day relative to the 

12 respective concurrent controls is only slightly higher in animals exposed prenatally (2.5:1) 

13 compared to those exposed postnatally (2.2:1). EFSA also notes that the incidence of 

14 lymphomas and leukemias in aspartame-receiving males of the Soffritti et al. (2007) study was 

15 within the range of historical controls for these responses in Sprague-Dawley rats at the ERF. 

16 For example, the percent incidence of combined lymphomas and leukemias in males exposed 

17 pre- and postnatally to 100 mg/kg-day aspartame (17.1%) was within the range of historical 

18 controls for this response (8–31%, with an overall mean of 20.6%). Soffritti et al. (2007) 

19 acknowledge this fact, but reason that comparisons of potentially compound-associated 

20 incidences of tumor formation to incidences in concurrent controls provide a more scientifically 
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1 valid indicator of the tumorigenic impact of a chemical under investigation than comparisons to 

historical control data. 57 2 Furthermore, the incidence of combined lymphomas and leukemias in 

3 the female rats in the high-dose group is well above the historical control range. Therefore, 

4 Soffritti et al. (2007) concluded that their second experiment confirmed the carcinogenic 

5 potential of aspartame in Sprague-Dawley rats observed in Soffritti et al. (2006, 2005). The high 

6 and variable incidence of this tumor type in ERF controls remains a concern. However, the 

7 results provide support for studies suggesting similar effects from methanol (Soffritti et al., 

8 2002a) since methanol is one of the degradation products of aspartame and appears to have 

9 carcinogenic potential at some of the same target organs and tissues.

10 4.6.5.2.3. MTBE. In an experiment that also may be relevant to the carcinogenicity of 

11 methanol, scientists at the ERF carried out a cancer bioassay on MTBE, in which the compound 

12 was administered to 60 Sprague-Dawley rats/sex/group by gavage in olive oil at 0, 250, and 

13 1,000 mg/kg-day, 4 days/week, for 104 weeks (Belpoggi et al., 1995). Doses adjusted to daily 

14 dose were 0, 143, and 571 mg/kg-day. This experiment and its findings may relate to the 

15 carcinogenicity of methanol, since methanol is one of several metabolites of MTBE (ATSDR, 

16 1997). At the end of the exposure period, the animals were allowed to live out their “natural”

17 life, the last animal dying 166 weeks after the start of the experiment (at 174 weeks of age).

18 Mean daily feed and drinking water consumption were determined weekly for the first 

19 13 weeks of the experiment, then every 2 weeks until 112 weeks of age. Individual body weights 

20 were measured according to the same protocol, then every 8 weeks until the end of the 

21 experiment. All animals were examined for gross lesions weekly for the first 13 weeks, then 

22 every 2 weeks until term. All animals were examined grossly at death, then histopathologically

23 examined for a full suite of organs and tissues.

24 As described by the authors, there were no differences among the groups in body weight 

25 and clinical signs of toxicity. Survival was dose-dependently reduced in female rats after 

26 16 weeks of exposure. Paradoxically, survival was improved in high-dose males compared to 

27 controls after 80 weeks. Although there were no noncarcinogenic effects of MTBE reported, a 

28 number of benign and malignant tumors were identified, including tumor types that were not 

29 observed in the ERF methanol study such as an increased incidence of testicular Leydig tumors 

30 in high-dose males and as determined by the authors, as well as a dose-related statistically 

31 significant increase in lymphomas and leukemias in females. The incidences of these tumors 

32 compared to the initial number of animals exposed and compared to those at risk at the time of 

33 the first observed tumor formation are shown in Table 4-31.

 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. 
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Table 4-31. Incidence of Leydig cell testicular tumors and combined lymphomas and 

leukemias in Sprague-Dawley rats exposed to MTBE via gavage for 104 weeks

Durationadjusted dose

Leydig cell tumors Combined lymphomas and leukemias

Number of males Number of males Number of females

Affected Initial At 

riskc

Affected Initial At 

Riskd

Affected Initial At 

Riske

0 2 60 26 10 60 59 2 60 58

143 2 60 25 9 60 59 6b 60 51

571 11a 60 32 7 60 58 12b 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 first leukemia was observed. e Alive female rats at 56 weeks of age, when first leukemia was observed.

Source: Belpoggi et al. (1995).

1 The possible contribution of the metabolite methanol to the reported responses cannot be 

2 quantified. It is also possible that the parent compound and/or one or more of MTBE’s other 

3 metabolites (e.g., tertiary butanol or formaldehyde) may be etiologically linked to the formation 

4 of the identified neoplasms (Blancato et al., 2007).

5 4.6.5.2.4. Formaldehyde. Scientists at the ERF have carried out two long-term experiments on 

6 the potential carcinogenicity of formaldehyde, which is itself a metabolite of methanol, 

7 aspartame and MTBE. While the tumorigenic effects at the portal-of-entry (such as in the oral 

8 cavity and GI tract, for oral studies) may lack relevance to the possible effects of metabolites

9 formed in situ following methanol exposure, systemic neoplasms such as lymphomas and 

10 leukemias have been described for formaldehyde as well (Soffritti et al., 2002b, 1989). This 

11 suggests that formaldehyde metabolized from methanol, aspartame and MTBE may be 

12 etiologically important in the formation of lymphomas and leukemias in animals exposed to 

13 these compounds. 

14 In the first formaldehyde study (designated BT 7001; Soffritti et al., 1989), 50 Sprague15 Dawley rats/sex/group (starting at 7 weeks of age) were exposed to formaldehyde in drinking 

16 water at concentrations of 10, 50, 500, 1,000, and 1,500 mg/L for 104 weeks. Another 50 

17 Sprague-Dawley rats/sex received methanol in drinking water at 15 mg/L, and 100 rats/sex 

18 received water only, as controls. Body weight and water and food consumption were monitored 

19 weekly for the first 13 weeks, then every 2 weeks thereafter. All animals were allowed to live 

20 out their “natural” life, at which point they were subjected to necropsy and a complete 

21 histopathological examination. 

22 The final results of the BT 7001 Soffritti et al. (1989) experiment were reported by 

23 Soffriti et al. (2002b). Water consumption was reduced compared to controls in high-dose males 
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1 and in females at the three highest doses. However, there appeared to be no evidence of 

2 compound-related body weight changes, clinical signs of toxicity among the groups, nor

3 nononcogenic histopathological effects of formaldehyde. The authors noted statistically 

4 significant increases in the incidence of tumor-bearing males at 1500 ppm (p<0.01) and in total 

5 malignant tumors in females at 100, 1000 and 1500 ppm (p<0.01) and in males at 500 ppm 

6 (p<0.05) and 1500 ppm (p<0.01). They reported statistically significant increases in malignant 

7 mammary tumors in females at 100 ppm (p<0.01) and 1500 ppm (p<0.05), and in testicular 

8 interstitial cell adenomas in the 1000 ppm males (p<0.05). They also noted sporadic incidences 

9 in the treatment groups only (primarily at the highest dose) of leiomyosarcomas of the stomach 

10 and intestine considered to be very rare for the ERF rat colony. As for methanol and the other 

11 compounds discussed in this section, they reported increases in the number of 

12 hemolymphoreticular tumors for both sexes. The incidence of hemolymphoreticular neoplasms 

13 among the dose groups is shown in Table 4-32.

Table 4-32. Incidence of hemolymphoreticular neoplasms on Sprague-Dawley rats 

exposed to formaldehyde in drinking water for 104 weeks

Concentration 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 13/50b 8/50

500 12/50a 7/50

1,000 11/50a 11/50a

1,500 23/50b 10/50b

a

p < 0.05 using the χ2 test; bp < 0.01 using the χ2 test. 

Source: Soffritti et al. (2002b).

14

15 Soffritti et al. (1989) also described the results of another experiment (BT 7005) in which 

16 approximately 20 Sprague-Dawley rats/sex/group were exposed to either regular drinking water 

17 or 2,500 mg/L formaldehyde, beginning at 25 weeks of age for 104 weeks. These animals were 

18 allowed to mate and approximately 40–60 of the F0 pups were likewise exposed to 0 or 

19 2,500 ppm formaldehyde in drinking water (after weaning) for 104 weeks. As before, parents 

20 and progeny lived out their normal life span but then were subjected to a complete 

21 histopathological examination. Incidence of leukemias in exposed breeders and offspring is

22 shown in Table 4-33. The authors considered this data to indicate a “slight” increase in 

23 leukemias in breeders at 2,500 ppm, but the changes did not achieve statistical significance.

24
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Table 4-33. Incidence of leukemias in breeder and offspring Sprague-Dawley rats 

exposed to formaldehyde in drinking water 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 NONCANCER EFFECTS

4.7.1. Summary of Key Studies in Methanol Toxicity

1 A substantial body of information exists on the toxicological consequences to humans 

2 who consume or are acutely exposed to large amounts of methanol. Neurological and 

3 immunological effects have been noted in adult human subjects acutely exposed to as low as 

200 ppm (262 mg/m3 4 ) methanol (Mann et al., 2002; Chuwers et al., 1995). Nasal irritation 

effects have been reported by adult workers exposed to 459 ppm (601 mg/m3 5 ) methanol. Frank 

6 effects such as blurred vision and bilateral or unilateral blindness, coma, convulsions/tremors, 

7 nausea, headache, abdominal pain, diminished motor skills, acidosis, and dyspnea begin to occur 

8 as blood levels approach 200 mg methanol/L, and 800 mg/L appears to be the threshold for 

9 lethality. Data for subchronic, chronic or in utero human exposures are very limited. 

10 Determinations regarding longer term effects of methanol are based primarily on animal studies. 

11 An end-point-by-end-point survey of the primary effects of methanol in experimental 

12 animals is given in the following paragraphs. Tabular summaries of the principal toxicological 

13 studies that have examined the impacts of methanol when experimental animals were exposed to 

14 methanol via the oral or inhalation routes are provided in Tables 4-34 and 4-35. Most studies 

15 focused on developmental and reproductive effects. A large number of the available studies were 

16 performed by routes of exposure (e.g., i.p.) that are less relevant to the assessment. The data are 

17 summarized separate sections that address oral exposure (Section 4.7.1.1) and inhalation 

18 exposure (Section 4.7.1.2).

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) Effect Reference

Rat

Sprague-Dawley 

30/sex/group

0, 100, 500, and 

2,500 mg/kg-day for 

13 wk

500 2,500 Reduction of brain 

weights, increase in the 

serum activity of ALT 

and AP. Increased liver 

weights

U.S. EPA 

(1986c)
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Species, strain, 

number/sex Dose/duration NOAEL(mg/kg-day)

LOAEL

(mg/kg-day) Effect Reference

Rat

Sprague-Dawley 

100/sex/group

0, 500, 5,000, or 

20,000 ppm (v/v) in 

drinking water, for 

104 wk. Doses were 

approx. 0, 46.6, 466, 

and 1,872 mg/kg-day 

(male) and 0, 52.9, 

529, and 2101 mg/kgday (female)

466–529 1,872–2,101 Increased incidence of 

ear ducta 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 days/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/kgday on either GD15–

GD17 or GD17–

GD19.

NA 2,500 Neurobehavioral 

deficits (such as 

homing behavior, 

suckling ability

Infurna and 

Weiss (1986)

Mouse

CD-1

8 pregnant 

females and 4

controls

4 g/kg-day in 2 daily 

doses on GD6-GD15

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 pathologists 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 hr/day, for 

up to 14 days

ND ND Clinical signs of toxicity, CNS 

changes, including degeneration 

of the 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 

hr/day, 5 days/wk for 

up to 6 wk

NA 200 Transient reduction in plasma 

testosterone levels

Cameron et al. 

(1984)
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Species, strain, 

number/sex Dose/duration NOAEL(ppm)

LOAEL

(ppm) Effect Reference

Rat

Sprague-Dawley 

5 males/ group

0, or 200 ppm, 

6 hr/day, for either 1 

or 7 days

NA 200 Transient reduction in plasma 

testosterone levels

Cameron et al. 

(1985)

Rat

Sprague-Dawley 

5/sex/group

0, 500, 2,000, or 

5,000 ppm, 

5 days/wk for 4 wk

5,000 NA No compound-related effects

Andrews et al. 

Monkey (1987)

M. fascicularis

3/sex/group

0, 500, 2,000, or 

5,000 ppm, 

5 days/wk for 4 wk

5,000 NA No compound-related effects

Rat

Sprague-Dawley 

10/sex/group

0, 300, or 3,000 ppm, 

6 hr/day, 5 days/wk 

for 4 wk

NA 300 Reduction in size of thyroid 

follicles

Poon et al. 

(1994)

Rat

Sprague-Dawley 

15/sex/group

0 or 2,500 ppm, 6 

hr/day, 5 days/wk 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 hr/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 hr/day, for 12 mo

NA NA No compound-related effects

Mouse

B6C3F1

30/sex/group

0, 10, 100, or 

1000 ppm, 20 hr/day, 

for 12 mo

NA NA No clear-cut compound-related 

effects

Mouse

B6C3F1

52–53/sex/group

0, 10, 100, or 

1,000 ppm, 

20 hr/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 hr/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 toxicity studies

Rat

Sprague-Dawley 

15/pregnant

females/group

0, 5,000, 10,000, or 

20,000 ppm, 7 hr/day 

on either GD1–GD19 

or GD7–GD15.

5,000 10,000 Reduced fetal body weight, 

increased incidence of visceral 

and skeletal abnormalities, 

including rudimentary and extra 

cervical ribs

Nelson et al. 

(1985)

Rat

Sprague-Dawley 

36/pregnant 

females/group

0, 200, 1,000, or 

5000 ppm, 

22.7 hr/day, on GD7–

GD17

1,000 5,000 Late-term resorptions, reduced 

fetal viability, increased 

frequency of fetal 

malformations, variations and 

delayed ossifications.

NEDO (1987)
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Species, strain, 

number/sex Dose/duration NOAEL(ppm)

LOAEL

(ppm) Effect Reference

Rat

Sprague-Dawley 

F1 and F2 

generations of a 

two-generation 

study

0, 10, 100, or 

1000 ppm, 20 hr/day; 

F1- birth to end of 

mating (M) or 

weaning (F); F2-

birth to 8 wks

100 1,000 Reduced weight of brain, 

pituitary, and thymus at 8, 16 

and 24 wk postnatal in F1 and at 

8 wk in F2

Rat

Sprague-Dawley

Follow-up study of 

brain weights in F1

generation of 10–

14/sex/group in F1

generation 

0, 500, 1,000, and 

2,000 ppm; GD0 

through F1 generation

500 1,000 Reduced brain weight at 3 wk 

and 6 wk (males only). Reduced 

brain and cerebrum weight at 8 

wk (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 hr/day on GD6–

GD15.

1,000 2,000 Increased incidence of extra 

cervical 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–GD13 

or on a single day 

during GD5–GD9

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 hr/day on GD7–

GD19

NA 15,000 Reduced pup weight Stanton et al. 

(1995)

Rat

Long-Evans

10–12 pregnant 

females/group

0 or 4,500 ppm from 

GD10 to PND21.

NA 4,500 Subtle cognitive deficits Weiss et al. 

(1996)

Monkey

M. fascicularis 

12 monkeys/group

0, 200, 600, or 

1800 ppm, 2.5 

hr/day, 7 days/wk, 

during premating, 

mating and gestation

ND NDa Shortened period of gestation; 

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,

1999b)

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/m3). However, 

because of uncertainties associated with these results, including clinical intervention and the lack of a doseresponse, EPA was not able to identify a definitive NOAEL or LOAEL from this study.

4.7.1.1. Oral

1 There have been very few subchronic, chronic, or in utero experimental studies of oral 

2 methanol toxicity. In one such experiment, an EPA-sponsored 90-day gavage study in Sprague3 Dawley rats suggested a possible effect of the compound on the liver (U.S. EPA, 1986c). In the 

4 absence of gross or histopathologic evidence of toxicity, fluctuations on some clinical chemistry 
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1 markers of liver biochemistry and increases in liver weights at the highest administered dose 

2 (2,500 mg/kg-day) justify the selection of the mid-dose level (500 mg/kg-day) as a NOAEL for 

3 this effect under the operative experimental conditions. That the bolus effect may have been 

4 important in the induction of those few effects that were apparent in the subchronic study is 

5 suggested by the outcome of lifetime drinking water study of methanol that was carried out in 

6 Sprague-Dawley rats by Soffritti et al. (2002a). According to the authors, no noncancer 

7 toxicological effects of methanol were observed at drinking water concentrations of up to 

8 20,000 ppm (v/v). Based on default assumptions on drinking water consumption and body 

9 weight gain assumptions, the high concentration was equivalent to a dose of 1,780 mg/kg-day in 

10 males and 2,177 mg/kg-day in females. In the stated absence of any changes to parameters 

11 reflective of liver toxicity in the Soffritti et al. (2002a) study, the slight impacts to the liver 

12 observed in the subchronic study at 2,500 mg/kg-day suggest the latter dose to be a minimal 

13 LOAEL. Logically, the true but unknown threshold would at the high end of the range from 500 

14 (the default NOAEL) to 2,500 mg/kg-day for liver toxicity via oral gavage.

15 Two studies have pointed to the likelihood that oral exposure to methanol is associated 

16 with developmental neurotoxicity or developmental deficits. When Infurna and Weiss (1986) 

17 exposed pregnant Long-Evans rats to 2% methanol in drinking water (providing a dose of 

18 approximately 2,500 mg/kg-day), they observed no reproductive or developmental sequelae 

19 other than from 2 tests within a battery of fetal behavioral tests (deficits in suckling ability and 

20 homing behavior). In the oral section of the Rogers et al. (1993a) study, such teratological 

21 effects as cleft palate and exencephaly and skeletal malformations were observed in fetuses of 

22 pregnant female mice exposed to daily gavage doses of 4,000 mg/kg methanol during GD6–

23 GD15. Likewise, an increase in totally resorbed litters and a decrease in the number of live 

24 fetuses/litter appear likely to have been an effect of the compound. Similar skeletal 

25 malformations were observed by Rogers and Mole (1997), Rogers et al. (1993a), and Nelson 

26 et al. (1985) following inhalation exposure.

4.7.1.2. Inhalation

27 Some clinical signs, gross pathology, and histopathological effects of methanol have been 

28 seen in experimental animals including adult nonhuman primates exposed to methanol vapor. 

29 Results from an unpublished study (NEDO, 1987) of M. fascicularis monkeys, chronically 

30 exposed to concentrations as low as 10 ppm for up to 29 months, resulted in histopathological 

31 effects in the liver, kidney, brain and peripheral nervous system. These results were generally

32 reported as subtle and do not support a robust dose response over the range of exposure levels 

33 used. Confidence in the methanol-induced findings of effects in adult nonhuman primates is 

34 limited because this study utilized a small number (2–3) of animals/dose level/time of sacrifice 

35 and inadequately reporting of results (i.e., lack of clear documentation of a concurrent control 
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1 group). In addition, the monkeys used in this study were all wild-caught. All of these concerns 

2 limit the study’s utility in derivation of an RfC.

3 A number of studies have examined the potential toxicity of methanol to the male 

4 reproductive system (Lee et al., 1991; Cameron et al., 1985, 1984). The data from Cameron 

5 et al. (1985, 1984) showed a transient but not necessarily dose-related decrease in serum 

6 testosterone levels of male Sprague-Dawley rats. Lee et al. (1991) reported the appearance of 

7 testicular lesions in 18-month-old male Long-Evans rats that were exposed to methanol for 

8 13 weeks and maintained on folate-deficient diets. Taken together, the Lee et al. (1991) and 

9 Cameron et al. (1985, 1984) study results could indicate chemically-related strain on the rat 

10 system as it attempts to maintain hormone homeostasis. However, the available data are 

11 insufficient to definitively characterize methanol as a toxicant to the male reproductive system. 

12 When Sprague-Dawley rats were exposed to methanol, 6 hours/day for 4 weeks, there 

13 were some signs of irritation to the eyes and nose. Mild changes to the upper respiratory tract 

14 were also described in Sprague-Dawley rats that were exposed for 4 weeks to up to 300 ppm 

15 methanol (Poon et al., 1995). Other possible effects of methanol in rats included a reduction in 

16 size of thyroid follicles (Poon et al., 1994), panlobular vacuolation of the liver, and a decrease in 

17 spleen weight (Poon et al., 1995). NEDO (1987) reported dose-related increases in moderate 

18 fatty degeneration in hepatocytes of male mice exposed via inhalation for 12 months, but this 

19 finding was not observed in the NEDO (1987) 18-month mouse inhalation study. Nodes 

20 reported in the liver of mice from the 18-month study may have been precancerous, but the 

21 18-month study duration was not of sufficient duration to make a determination. 

22 One of the most definitive and quantifiable toxicological impacts of methanol when 

23 administered to experimental animals via inhalation is related to the induction of developmental 

24 abnormalities in fetuses exposed to the compound in utero. Developmental effects have been 

25 demonstrated in a number of species, including monkeys, but particularly rats and mice. Most 

26 developmental teratological effects appear to be more severe in the latter species. For example, 

27 in the study of Rogers et al. (1993a) in which pregnant female CD-1 mice were exposed to 

28 methanol vapors on GD6–GD15 at a range of concentrations, reproductive and fetal effects 

29 included an increase in the number of resorbed litters, a reduction in the number of live pups, and 

30 increased incidence of exencephaly, cleft palate, and the number of cervical ribs. While the 

31 biological significance of the cervical rib effect has been the subject of much debate (See 

32 discussion of Chernoff and Rogers [2004] in Section 5), it appears to be the most sensitive 

indicator of developmental toxicity from this study, with a NOAEL of 1,000 ppm (1,310 mg/m3 33 ). 

34 In rats, however, the most sensitive developmental effect, as reported in the NEDO (1987) two35 generation inhalation studies, was a postnatal reduction in brain weight at 3, 6 and 8 weeks

36 postnatally, which was significantly lower than controls when pups and their dams were exposed 
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to 1,000 ppm (1,310 mg/m3 1 ) during gestation and throughout lactation. The NOAEL reported in 

this study was 500 ppm (655 mg/m3 2 ).

3 Rogers and Mole (1997) addressed the question of which period of gestation was most 

4 critical for the adverse developmental effects of methanol in CD-1 rats. Such malformations and 

5 anomalies as cleft palate, exencephaly, and a range of skeletal defects, appeared to be induced 

6 with a greater incidence when the dams were exposed on or around GD6. These findings were 

7 taken to indicate that methanol is most toxic to embryos during gastrulation and in the early 

8 stages of organogenesis. However, NEDO (1987) gestation-only and two-generation studies 

9 showed that significant reductions in brain weight were observed at a lower exposure levels 

10 when pups and their dams were exposed during lactation as well as gestation, indicating that 

11 exposure during the later stages of organogenesis, including postnatal development, can 

12 significantly contribute to the severity of the effects in this late-developing organ system. 

13 In comparing the toxicity (NOAELs and LOAELs) for the onset of developmental effects 

14 in mice and rats exposed in utero, there is suggestive evidence from the above studies that mice 

15 may be more susceptible to methanol than rats. Supporting evidence for this proposition has 

16 come from in vitro studies in which rat and mouse embryos were exposed to methanol in culture 

17 (Andrews et al., 1993). Further evidence for species-by-species variations in the susceptibility of 

18 experimental animals to methanol during organogenesis has come from experiments on monkeys 

19 (Burbacher et al., 2004a, 2004b, 1999a,1999b). In these studies, exposure of monkeys to 

20 methanol during premating, mating, and throughout gestation resulted in a shorter period of 

gestation in dams exposed to as low as 200 ppm (263 mg/m3 21 ). The shortened gestation period 

22 was largely the result of C-sections performed in the methanol-exposure groups “in response to 

23 signs of possible difficulty in the maintenance of pregnancy,” including vaginal bleeding. 

24 Though statistically significant, the finding of a shortened gestation length may be of limited 

25 biological significance. Gestational age, birth weight and infant size observations in all exposure

26 groups were within normal ranges for M. fascicularis monkeys, and vaginal bleeding 1–4 days 

27 prior to delivery of a healthy infant does not necessarily imply a risk to the fetus (as cited in 

28 CERHR, 2004). An ultrasound examination could have substantiated fetal or placental problems

29 arising from presumptive pregnancy duress (see Section 4.3.2). As discussed in Section 4.4.2, 

30 there is also evidence from this study that methanol caused neurobehavioral effects in exposed 

31 monkey infants that may be related to the gestational exposure. However, the data are not 

32 conclusive,and a dose-response trend is not robust. There is insufficient evidence to determine if 

33 the primate fetus is more or less sensitive than rodents to methanol teratogenesis. Several other 

34 uncertainties contributed to decreased confidence in the use of this primate in quantitative

35 estimates of risk. These included: a mixture of wild- and colony-derived monkey mothers used 

36 in the study; the use of a cohort design necessitated by the complexity of this study also 
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1 seemingly resulted in limitations in power to detect effects (e.g., Fagan test results for controls); 

2 and no apparent adjustment in statistical analysis for results from the neurobehavioral battery of 

3 tests employed leading to concern about inflation of type 1 error. Because of the uncertainties 

4 associated with these results, including the fact that the decrease in gestational length was not 

5 exacerbated with increasing methanol exposure, EPA was not able to identify a definitive 

6 NOAEL or LOAEL from this study. This study does support the weight of evidence for 

7 developmental neurotoxicity in the hazard characterization of low-level methanol exposure.

8 Weiss et al. (1996) and Stanton et al. (1995) evaluated the developmental and 

9 developmental neurotoxicological effects of methanol exposure on pregnant female Long-Evans 

rats and their progeny. In the former study, exposure of dams to 15,000 ppm (19,656 mg/m3 10 ), 

11 7 hours/day on GD7–GD19 resulted in reduced weight gain in pups, but produced little other 

12 evidence of adverse developmental effects. The authors subjected the pups to a number of 

13 neurobehavioral tests that gave little if any indication of compound-related changes. This study,

14 while using high exposure levels, was limited in its power to detect effects due to the small 

15 number of animals used. In the Weiss et al. (1996) study, exposure of pregnant female LongEvans rats to 0 or 4,500 (0 and 5,897 mg/m3 16 ) methanol from GD6 to PND21 likewise provided 

17 fluctuating and inconsistent results in a number of neurobehavioral tests that did not necessarily 

18 indicate any compound-related impacts. The finding of this study indicated subtle cognitive 

19 defects not on the learning of an operant task but in the reversal learning. This study also 

20 reported exposure-related changes in neurodevelopmental markers of NCAMs on PND4.

21 NCAMs are a family of glycoproteins that is needed for migration, axonal outgrowth, and 

22 establishment of the pattern for mature neuronal function.

23 Taking all of these findings into consideration reinforces the conclusion that the most 

24 appropriate endpoints for use in the derivation of an RfC for methanol are associated with 

25 developmental neurotoxicity and developmental toxicity. Among an array of findings indicating 

26 developmental neurotoxicity and developmental malformations and anomalies that have been 

27 observed in the fetuses and pups of exposed dams, an increase in the incidence of cervical ribs of 

28 gestationally exposed mice (Rogers et al., 1993a) and a decrease in the brain weights of 

29 gestationally and lactationally exposed rats (NEDO, 1987) appear to be the most robust and most

30 sensitive effects. 

4.8. NONCANCER MOA INFORMATION

31 A review by Jacobsen and McMartin (1986) has provided a comprehensive summary of 

32 the mechanism by which methanol brings about its acute toxic effects. Overwhelmingly, the 

33 evidence points to methanol poisoning being a consequence of formate accumulation. This 

34 compound is formed from formaldehyde under the action of ADH3. Formaldehyde itself is 
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1 formed from methanol under the action of ADH1. Evidence for the involvement of formate 

2 comes from the delay in the onset of harmful symptoms, detection of formate in the blood 

3 stream, and the profound acidosis that develops 12–24 hours after exposure to methanol. 

4 Treatments for methanol poisoning include the i.v. administration of buffer to correct the 

5 acidosis, hemodialysis to remove methanol from the blood stream, and i.v. administration of 

6 either ethanol or fomepizole to inhibit the activity of ADH1. Therapies to increase endogenous 

7 levels of folate may enhance the activity of THF synthetase, an enzyme that catalyzes the 

8 oxidation of formate to CO2. Jacobsen and McMartin (1986) have drawn attention to the 

9 accumulation of lactate in advanced stages of severe methanol poisoning, a possible consequence 

10 of formate inhibition of mitochondrial respiration and tissue hypoxia. The additional decrease in 

11 blood pH is likely to enhance the nonionic diffusion of formic acid across cell membranes, with 

12 resulting CNS-depression, hypotension, and further lactate production.

13 Jacobsen and McMartin (1986) summarized a body of evidence that also points to the 

14 formate-related acidosis as the etiologically important factor in ocular damage. The hypothesis 

15 suggests that ocular toxicity is due to the inhibition of cytochrome oxidase in the optic nerve by 

16 formate. This would cause inhibition of ATP formation and consequent disruption of optic nerve 

17 function.

18 While it is well established that the toxic consequences of acute methanol poisoning arise 

19 from the action of formate, there is less certainty on how the toxicological impacts of longer20 term exposure to lower levels of methanol are brought about. For example, since developmental 

21 effects in experimental animals appear to be significant adverse effects associated with in utero 

22 methanol exposure, it is important to determine potential MOAs for how these specific effects 

23 are brought about.

24 As described in Section 4.6.1, data from experiments carried out by Dorman et al. (1995),

25 formate is not the probable proximate teratogen in pregnant CD-1 mice exposed to high 

26 concentrations of methanol vapor. This conclusion is based on the fact that there appeared to be 

27 little, if any, accumulation of formate in the blood of methanol-exposed mice, and exencephaly 

28 did not occur until formate levels were grossly elevated. Another line of argument is based on 

29 the observation that treatment of pregnant mice with a high oral dose of formate did not induce 

30 neural tube closure defects at media concentrations comparable to those observed in uterine 

31 decidual swelling after maternal exposure to methanol. Lastly, methanol- but not formate32 induced neural tube closure defects in mouse embryos in vitro at media concentrations 

33 comparable to the levels of methanol detected in blood after a teratogenic exposure.

34 Harris et. al (Hansen et al., 2005; Harris et al., 2004, 2003) carried out a series of 

35 physiological and biochemical experiments on mouse and rat embryos exposed to methanol, 

36 formaldehyde and formate, concluding that the etiologically important substance for embryo 
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1 dysmorphogenesis and embryolethality was likely to be formaldehyde rather than the parent 

2 compound or formate. Specific activities for enzymes involved in methanol metabolism were 

3 determined in rat and mouse embryos during the organogenesis period of 8–25 somites (Harris 

4 et al., 2003). The experiment was based on the concept that differences in the metabolism of 

5 methanol to formaldehyde and formic acid by the enzymes ADH1, ADH3, and CAT may 

6 contribute to hypothesized differences in species sensitivity that were apparent in toxicological 

7 studies. A key finding was that the activity of ADH3 (converting formaldehyde to formate) was 

8 lower in mouse VYS than that of rats throughout organogenesis, consistent with the greater 

9 sensitivity of the mouse to the developmental effects of methanol exposure. Another study 

10 (Harris et al., 2004) which showed that the inhibition of GSH synthesis increases the 

11 developmental toxicity of methanol also lends support to this hypothesis because ADH3-

12 mediated metabolism of formaldehyde is the only enzyme involved in methanol clearance that is 

13 GSH-dependent. These findings provide inferential evidence for the proposition that 

14 formaldehyde may be the ultimate teratogen through diminished ADH3 activity. This concept is 

15 further supported by the demonstration that the LOAELs for the embryotoxic effects of 

16 formaldehyde in rat and mouse embryos were much lower than those for formate and methanol 

17 (Hansen et al., 2005). Taking findings from both sets of experiments together, Harris et. al. 

18 (Hansen et al., 2005; Harris et al., 2004, 2003) concluded that the demonstrable lower capacity of 

19 mouse embryos to transform formaldehyde to formate (by ADH3) could explain the increased 

20 susceptibility of mouse versus rat embryos to the toxic effects of methanol. 

21 While studies such as those by Harris et al. (2004, 2003) and Dorman et al. (1995, 1994) 

22 strongly suggest that formate is not the metabolite responsible for methanol’s teratogenic effects, 

23 there are still questions regarding the relative involvement of methanol versus formaldehyde. In 

24 vitro evidence suggests that formaldehyde is the more embryotoxic moiety, but methanol would 

25 likely play a prominent role, at least in terms of transport to the target tissue. The high reactivity 

26 of formaldehyde would limit its unbound and unaltered transport as free formaldehyde from 

27 maternal to fetal blood (Thrasher and Kilburn, 2001), and the capacity for the metabolism of 

28 methanol to formaldehyde is likely lower in the fetus and neonate versus adults (see discussion 

29 in Section 3.3)

30 In humans, metabolism of methanol occurs primarily through ADH1, whereas in rodents 

31 methanol clearance involves primarily CAT, as well as ADH1. There are no known studies that 

32 compare enzyme activities of human ADH1 and rodent CAT. Assuming that relative expression 

33 and activity of ADH1 is comparable across species, rodents are expected to clear methanol more 

34 rapidly than humans due to involvement of CAT. In fact, even among rodents the metabolism of 

35 methanol may be quite different, as one study has demonstrated that the rate of methanol 

36 oxidation in mice is twice the rate in rats, as well as nonhuman primates (Mannering et al., 
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1 1969). Despite a faster rate of methanol metabolism, mice have consistently shown higher blood 

2 methanol levels than rats following exposure to equivalent concentrations (Tables 3-4 and 3-5). 

3 A faster respiration rate and increased fraction of absorption by mice is thought to be the reason 

4 for the higher blood methanol levels compared to rats (Perkins et al., 1995a). Using the exposure 

5 conditions of Horton et al. (1992) for rats, when the respiration rate scaling coefficient (QPC) 

6 was increased from the rat value of 16.4 to the mouse value of 25.4 while holding all other 

7 parameters constant, peak blood concentrations were predicted by the PBPK model to increase 

8 by 1.4-fold at 200 ppm and 1.8-fold at 2,000 ppm (where metabolism is becoming saturated). 

9 Because smaller species generally have faster breathing rates than larger species (in the PBPK 

10 model, the respiration rate/BW is 3 times slower in humans versus rats and almost 10 times 

11 slower versus mice), humans would be expected to accumulate less methanol than rats or mice 

12 inhaling equivalent concentrations and given the same metabolism rate. However, Horton et al. 

13 (1992) measured a blood concentration in rats exposed to 200 ppm methanol of about 3.7 mg/L 

14 after 6 hours of exposure while Sedevic et al. (1981) measured around 5.5 mg/L in human 

15 volunteers after 6 hours of exposure to 231 ppm. Correcting for the higher exposure, human 

16 blood concentrations would be around 4.8 mg/L if exposed at 200 ppm. Simulations with the 

17 mouse model predict a blood level of 5.7 mg/L after 6 hours of exposure to 200 ppm, only 20% 

18 higher than this interpolated human value. Thus the slower inhalation rate in humans is offset by 

19 the slower metabolic rate, leading to equivalent blood concentrations. (If the same rate of 

20 metabolism/BW as mice is used, human blood concentrations are predicted to decrease by 

21 approximately fivefold.). These differences are considered in Section 5 for the characterization 

22 of human and rodent PBPK models used for the derivation of human equivalent concentrations 

23 (HECs).

4.9. EVALUATION OF CARCINOGENICITY

4.9.1. Summary of Overall Weight-of-Evidence 

24 Under the current Guidelines for Carcinogen Risk Assessment (U.S. EPA 2005a, 2005b), 

25 methanol is likely to be carcinogenic to humans by all routes of exposure based on dose26 dependent trends in multiple tumors in both sexes of two strains of rats, by inhalation and oral 

27 routes of exposure and increases in malignant lymphoma in both sexes of Swiss mice by oral 

28 exposure. Specifically, EPA’s analysis of the Soffritti et al. (2002a) lifespan study of Sprague29 Dawley rats exposed to methanol in drinking water for 104 weeks indicates a statistically 

significant increase in the incidence of lymphoma58 30 in lung and other organs at the two highest 

 58 Combining lymphoblastic lymphomas, lymphocytic lymphomas, lympho-immunoblastic lymphomas and/or

lymphoblastic leukemias as malignant lymphomas but excluding myeloid leukemias, histocytic sarcomas and 

monocytic leukemia as tumors of different origin (Cruzan, 2009; Hailey, 2004; McConnell et al., 1986).
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1 doses for males and across all doses for females (Fisher’s exact, p < 0.05) and a statistically 

2 significant increase in relatively rare hepatocellular carcinomas in males compared to historical 

controls (n=407)59 3 (Fisher’s exact p < 0.05 for all doses and p < 0.01 for the high-dose group). 

4 Statistically significant increases in the incidence of malignant lymphomas relative to historical 

5 controls (Fisher’s exact, p < 0.05) have also been observed in another rodent species, Swiss 

6 mice, following similar mg/kg-day exposures to methanol in drinking water for life (Apaja, 

7 1980). The only available chronic inhalation studies of methanol (NEDO, 1985/2008a, 2008b)

8 reported slight but statistically significant tumor responses in F344 rats at 24 months, and no 

9 evidence of carcinogenicity in B6C3F1 mice at 18 months. EPA’s analysis of the NEDO 

10 (1985/2008b) inhalation study of F344 rats indicates a dose-response trend (Cochrane-Armitage 

11 p < 0.05) and an increased incidence over concurrent controls at the high dose (Fisher’s exact 

12 p < 0.05) of pulmonary adenomas/adenocarcinomas in male rats. This analysis also indicates a 

13 statistically significant dose-response trend (Cochrane-Armitage p < 0.05) and a statistically 

14 significant increased incidence over NTP historical controls at the high-dose (Fisher’s exact 

15 p < 0.05) of pheochromocytomas in female rats.

16 This WOE conclusion is supported by the results of other studies performed by ERF that 

17 have shown tumorigenic responses similar to that of methanol in male and female Sprague18 Dawley rats exposed to formaldehyde (via drinking water), a metabolite of methanol, and to 

19 aspartame (via feed) and MTBE (via olive oil gavage), substances that hydrolyze to release 

20 methanol and formaldehyde. Confidence in the designation of methanol as a likely human

21 carcinogen is strengthened by the fact that methanol is metabolized to formaldehyde, a chemical 

22 that has been classified as carcinogenic to humans (group 1) (IARC, 2004). As discussed below 

23 and in Section 5.4.3, there are uncertainties in the interpretation of these findings. All of the key 

24 studies have design and reporting limitations. EPA has reanalyzed the reported data from both 

25 the ERF (Soffritti et al., 2002a) and NEDO (1987, 1985/2008a, 2008b) studies. In reassessing 

26 the ERF study data, EPA decided to combine only those lymphomas considered to have 

27 originated from the same cell type. In the case of the NEDO data, the significance of the tumor 

28 findings were incompletely reported in the original NEDO (1987) summary. Hence, EPA used

29 translations of the original, detailed Japanese study reports provided by NEDO and the Methanol 

30 Institute (NEDO, 1985/2008a,2008b) and reanalyzed the individual animal data. 

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

31 Evidence of the carcinogenic potential of methanol arises from drinking water studies in 

32 Sprague-Dawley rats (Soffritti et al., 2002a) and in Eppley Swiss Webster mice (Apaja, 1980),

 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/foundation.asp).
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1 and an inhalation study in F344 rats (NEDO, 1985/2008b), with no information available in 

2 humans. As is described in Section 4.2.1.3 (Table 4-2), Soffritti et al. (2002a) reported a number 

3 of tumors in methanol-exposed Sprague-Dawley rats. EPA reanalyzed the tumor findings from 

4 this study using individual animal pathology available from the ERF website (see Section 

5.4.1.1).

60 5 As indicated above, the increase in a relatively rare hepatocellular carcinoma in males 

6 compared to historical controls (Fisher’s exact p < 0.05 for all doses and p < 0.01 for the high7 dose group) is potentially related to methanol dosing. A significant increase in the incidence of

8 ear duct carcinoma was also reported by Soffritti et al. (2002a). However, the high incidence for 

9 this tumor in controls of the Soffritti et al. (2002a) study relative to other studies of Sprague10 Dawley rats (Cruzan, 2009) and the results of an NTP evaluation of pathology slides from 

11 another bioassay (EFSA, 2006; Hailey, 2004) raise questions about the ear duct pathological 

determinations of Soffritti et al. (2002a).

61 12 

13 As is described in Section 4.2.1.3 (Table 4-3), Apaja (1980) found an increase in 

14 malignant lymphomas in mid-dose (p = 0.06) and high-dose (p < 0.05) female and mid-dose 

15 (p < 0.05) male Eppley Swiss Webster mice exposed for life via drinking water. The lack of a 

16 concurrent unexposed control data limit the confidence that can be placed on the relevance of the 

17 increased lymphoma responses noted in this study. However, while controls were not 

18 concurrent, they were from proximate (within 3 years) generations of the same mouse colony, 

19 lymphomas were evaluated via the same classification criteria and, in the case of the Hinderer 

20 (1979) controls, the histopathological analysis was performed by the same author (Apaja, 1980). 

21 In addition, this is a late developing tumor, as noted by the author, suggesting the possibility of a 

22 higher tumor response in the females of all exposure groups had their survival not been 

23 significantly lower than untreated historical controls (see dose-response analysis in Appendix E). 

24 Further, additional support for these study results comes from the fact that, as described above, 

25 Soffritti et al. (2002a) subsequently reported an increase in lymphomas following similar levels 

26 of mg/kg-day methanol drinking water doses to Sprague-Dawley rats, another rodent species 

27 with a high spontaneous lymphoma rate.

28 Chronic inhalation bioassays have been conducted in monkeys, mice, and F344 rats 

29 (NEDO, 1987, 1985/2008a; 1985/2008b). No exposure-related carcinogenic responses were 

30 observed in the monkey or mouse studies. As is described in Section 4.2.2.3, individual tumor 

31 responses from the rat study were not significantly increased over concurrent controls, but the 

 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 allowed 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).
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1 response in the high-dose (1,000 ppm) group for pulmonary adenomas/adenocarcinomas in male 

2 rats was increased over concurrent controls (Fisher’s exact p < 0.05), and the dose-response for 

3 both pulmonary adenomas/adenocarcinomas in male rats and pheochromocytomas in female rats 

4 represent increasing trends (Cochran-Armitage trend test p < 05). Further, the high-dose 

5 responses for both of these tumor types were elevated (p < 0.05) over historical control 

6 incidences within their respective sex and strain. As can be seen from Table 4-5, the severity and 

7 combined incidence of effects reported in the alveolar epithelium of male rat lungs (epithethial 

8 swelling, adenomatosis, pulmonary adenoma and pulmonary adenocarcinoma) and the adrenal 

9 glands of female rats (hyperplasia and pheochromocytoma) were increased over controls and 

10 lower exposure groups. This pathology and the appearance of a rare adenocarcinoma in the 

11 high-dose group are suggestive of a progressive effect associated with methanol exposure. The 

12 increased pheochromocytoma response in female rats is considered to be potentially treatment

13 related because this is a historically rare tumor type for female F344 rats (NTP, 2007, 1999; 

Haseman et al., 1998)62 14 and because, when viewed in conjunction with the increased medullary 

15 hyperplasia observed in the mid-exposure (100 ppm) group females, it is indicative of a 

16 proliferative change with increasing methanol exposure.

17 Additional support for the designation of methanol as a likely carcinogen is provided by 

18 the fact that methanol is metabolized to formaldehyde, which has been associated with increased 

19 incidences of lymphoma and leukemia in humans (IARC, 2004). Furthermore, lymphomas 

20 similar to those noted in Sprague-Dawley rats following exposure to methanol in drinking water 

21 and following a similar dose-response pattern were noted in a bioassay for formaldehyde in 

22 drinking water conducted by the same laboratory (Soffritti et al., 2002b, 1989) (Section 4.9.3). 

23 These shared endpoints suggests that the carcinogenic effects of methanol may result from its 

24 conversion to formaldehyde, though the moiety and MOA responsible for methanol-associated 

25 tumor formation have not been identified.

26 Significant increases in the incidence of lymphoreticular tumors have also been reported 

27 for other chemicals that convert in the body to methanol and/or formaldehyde including 

28 aspartame (Soffritti et al., 2007, 2006, 2005) and MTBE (Belpoggi et al., 1997, 1995). In 

29 contrast, no such tumors have been reported in a similar study conducted with a structurally 

30 similar alcohol, ethanol (Soffritti et al., 2002a). In addition, epidemiological studies have 

31 associated formaldehyde exposure with increases in the incidence of related 

32 lymphohematopoietic tumors. While lymphomas are a rare finding in chronic laboratory 

33 bioassays, NCI (Hauptmann et al., 2003) and NIOSH (Pinkerton et al., 2004) have reported 

34 increased lymphohematopoietic cancer risk, principally leukemia, in humans from occupational 

 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.
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exposure to formaldehyde.

63 1 The similarities in tumor response across these chemicals, as well 

2 as a similar shape in the dose-response curve, supports the hypothesis that the common 

3 carcinogenic moiety for these compounds is the generation or presence of formaldehyde. The 

4 dose-response analysis discussed in Section 5 provides additional evidence supporting a role for 

5 the formaldehyde metabolite of methanol. When “total metabolites in blood” predicted by a 

6 PBPK model was used as the dose metric, model fit to the dose-response data was significantly 

7 improved.

8 As discussed in Section 4.6.5.2, there are challenges relative to the interpretation of the 

9 observed lymphoreticular tumors because there is no indication that ERF used specific pathogen10 free (SPF) rats (Schoeb et al., 2009), and the protocol for the studies conducted by the ERF 

11 (Soffritti et al., 2002c) is different from 2-year bioassays conducted by NTP and NEDO. A 

12 distinct characteristic of the protocol for long-term bioassays conducted by the ERF is to 

13 maintain animals until spontaneous death, rather than sacrificing them at the end of exposure at 

14 104 weeks. This difference in protocol may have an impact on the tumors observed compared to 

15 a 2-year bioassay (Melnick et al., 2007). The ERF methanol and ethanol studies (Soffritti et al., 

16 2002a), as well as the aspartame studies (Soffritti et al., 2007, 2006) described in Section 4.6.5.2, 

17 employed a large number of animals (100 or more per dose group) compared to a typical (e.g.,

18 NTP) cancer bioassay. In addition, the Sprague-Dawley rats used by ERF appear to have 

19 increased sensitivity to certain lymphoma responses relative to F344 rats that have been typically 

used in NTP studies (Caldwell et al., 2008).

64 20 According to Soffritti et al. (2007, 2006), the 

21 overall incidence of lymphomas/leukemias in ERF studies is 13.3% (range, 4.0–25.0%) in 

22 female historical controls (2,274 rats) and 20.6% (range, 8.0–30.9%) in male historical controls 

23 (2,265 rats). This background rate is considered to be high relative to other tumor types and 

24 relative to the background rate for this tumor type in Sprague-Dawley rats from other 

laboratories (Cruzan, 2009; EFSA, 2006),

65 25 However, it is in a range that can be considered 

26 reasonable for studies that employ a large number of animals (Caldwell et al., 2009; Leakey et 

27 al., 2003). These characteristics of ERF studies (i.e., lifetime observation, large number of 

28 animals, and test strain sensitive to endpoint but with a relatively low control background rate 

29 and mortality) may give them the sensitivity needed to detect a chemically related lymphoma 

30 response. 

 63 IARC (2004) concluded that there was sufficient epidemiological evidence 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 mononuclear 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).
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1 Other aspects of ERF studies may impede their ability to reliably detect a chemically 

2 related response (EFSA, 2009, 2006). Chronic inflammatory responses have been reported in 

3 test animals of some ERF studies (EFSA, 2009, 2006), which may be the result of infections in 

4 test animals resulting from a bioassay design that does not employ SPF rats (Schoeb et al., 2009) 

5 and allows the rats to live out their “natural life span” in the absence of disease barriers (e.g.,

6 fully enclosed cages). In fact, the ERF has acknowledged that the primary cause of spontaneous 

7 death in their rats is respiratory infection (Caldwell et al., 2008; Ramazzini Foundation, 2006;

8 Soffritti et al., 2006). Cruzan (2009) has suggested that respiratory infections in test animals of 

9 the Soffritti et al. (2002a) methanol study were not specific to older rats, as findings of lung 

10 pathology were reported as often in rats dying prior to 18 months as in rats dying at or after 

24 months.66 11

12 In their reviews of the recently published ERF studies on aspartame (Soffritti et al., 2007,

13 2006), the European Food Safety Authority (EFSA) have suggested that the increased incidence 

14 of lymphomas/leukemias reported in treated rats was related to chronic respiratory disease in the 

15 rat colony (EFSA, 2009, 2006), which they suggest was caused by a Mycoplasma pulmonis

16 (M. pulmonis) infection. EFSA felt that the increased incidence of these tumors was unrelated to 

17 aspartame, given the high background incidence of chronic inflammatory changes such as 

18 bronchopneumonia in the lungs of treated and untreated rats, and the concern that such tumors

19 might arise as a result of abundant lymphoid hyperplasia in the lungs of rats suffering from 

20 chronic respiratory disease. The scientific evidence to support the EFSA opinion that 

21 lymphomas/leukemias can result from chronic infection is limited (Schoeb et al., 2009; Caldwell 

22 et al., 2008). Epithelial hyperplasias and lymphoid accumulations are commonly found in the 

23 larynx and trachea of rats infected with M. pulmonis, but induction of lymphoma has not been 

24 noted (Everitt and Richter, 1990; Lindsey et al., 1985). Further, the lung, not the larynx or 

25 trachea, has been reported as the site of respiratory tract hemolymphoreticular tumors in ERF 

studies of MTBE (Belpoggi et al., 1998, 1995) and methanol (Soffritti et al., 2002a).67 26 In their 

27 review of the molecular biology and pathogenicity of M. pulmonis, Razin et al. (1998) note that 

28 further study is needed before any conclusion can be reached regarding a relationship between 

29 M. pulmonis and neoplasia. In addition, if the increased incidence of lymphoreticular tumors in 

30 the ERF methanol study was strictly the consequence of an incipient respiratory infection in the 

31 ERF rat colony, one would expect this to be a common finding across ERF studies. However, as 

32 discussed in Section 4.6.5.2, of the 200 compounds tested by ERF, only 8, which includes 

 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 above 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).
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1 methanol, have been associated with an increased incidence of hemolymphoreticular tumors. 

2 Further, the chemicals for which hemolymphoreticular tumors have been reported have chemical 

characteristics or physical properties in common,

68 3 consistent with the hypothesis that the 

4 increased response is chemical-related.

5 While evidence for a causal association between respiratory infections and lymphomas is 

6 limited, there is evidence that respiratory infections may have confounded the interpretation of 

7 lung lesions in the ERF studies. Schoeb et al. (2009) state that lymphomas illustrated in two 

8 ERF studies (Figure 10 of Soffritti et al., 2005 and Figures 1-5 of Belpoggi et al., 1999) do not 

9 demonstrate the lymphoma type, cellular morphology, and organ distribution typical of 

10 lymphoma in rats, but are consistent with “lymphocyte and plasma cell accumulation in the lung 

11 that is characteristic of M. pulmonis disease.” They suggest that, because M. pulmonis disease 

12 can be exacerbated by chemical treatment, a plausible alternative explanation for the dose-related 

13 response reported in the MTBE, aspartame and methanol ERF bioassays is that the studies were 

14 confounded by M. pulmonis disease and that lesions of the disease were interpreted as 

15 lymphoma. However, several ERF lymphoma diagnoses in multiple rat organ systems, including 

16 the lung, have been confirmed by an independent panel of six NIEHS pathologists (Hailey, 

17 2004). Further, 60% of the lymphoma incidences reported in the ERF methanol study involved 

18 organ systems other than the lungs (Schoeb et al., 2009). The incidence of “lung-only” 

19 lymphomas is evenly distributed across the control and dose groups of the methanol study such 

20 that removing “lung-only” lympho-immunoblastic lymphomas from consideration (i.e., using 

21 only lymphomas from other organ systems) does not significantly alter the dose-response for this 

22 lesion (see Section 5.4.3.2). 

23 Based on the NEDO (1987) summary report, IPCS (1997) concluded that “no evidence of 

24 carcinogenicity was found in either species [F344 rats and B6C3F1 mice].” This determination 

25 was made based on Fisher’s exact test results which indicated that the reported high-dose

26 pulmonary adenoma response in male rats and the high-dose pheochromocytoma response in 

27 female rats were not statistically significant. However, IPCS did not have translations of the 

28 original NEDO mouse and rat chronic studies (NEDO, 1985/2008a; 1985/2008b), which 

29 provided additional detail for EPA’s analysis and reported combined lung adenoma and 

30 adenocarcinoma results for high-dose male rats. In addition, IPCS did not consider trend test 

31 results or historical tumor data for F344 rats, both of which indicate a positive result for lung 

32 adenoma/adenocarcinoma (males) and pheochromocytomas (females) from the NEDO rat study.

 68 Methanol, formaldehyde, aspartame, and MTBE, have common metabolites (e.g., formaldehyde); DIPE, TAME, 

methanol, and MTBE are all gasoline-oxygenate additives.
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4.9.3. MOA Information

1 As discussed in Section 4.6.5.1, the results of genotoxicity/mutagenicity studies have 

2 been largely negative, irrespective of the presence or absence of metabolic activation (an S9 

3 microsomal fraction). Studies that investigate the MOA for methanol, particularly with respect 

4 to its developmental effects, have been discussed extensively in Sections 4.6. and 4.8. Studies 

5 such as those by Harris et al. (2004, 2003) suggest that formaldehyde is the proximate teratogen 

6 and provide evidence in support of that hypothesis. It is reasonable to hypothesize that the 

7 highly reactive molecule, formaldehyde, has a role in the carcinogenicity of methanol, given the 

8 ability of formaldehyde to bind to proteins and DNA, induce DNA-protein cross-links, and 

9 possibly participate in reactions leading to free radical formation and the formation of lipid 

10 peroxidation products. As discussed in Section 4.6.3, evidence of oxidative stress following 

11 methanol exposure has been reported in several organ systems. Studies of Wistar rats suggest 

12 that methanol exposure can cause the production of free radical formation, lipid peroxidation, 

13 and protein modifications in the liver (Skrzydlewska et al., 2005) and brain (Rajamani et al., 

14 2006), and adversely impact the oxidant/antioxidant balance in the brain (Dudka, 2006) and 

15 lymphoid organs (Parthasarathy et al., 2006b).

16 As discussed in Section 4.6.5.2, ERF studies of a number of compounds that have 

17 formaldehyde as a metabolic product have been reported to cause lymphomas in 

18 Sprague-Dawley rats. As described in Section 4.6.5.2.4, the ERF has conducted a formaldehyde 

19 drinking water study (Soffritti et al., 2002b, 1989) that is comparable in its design to the 

20 methanol drinking water study of Soffritti et al. (2002a). The mg/kg-day doses of metabolized 

21 methanol in Sprague-Dawley rats from the ERF methanol study estimated from the PBPK model 

22 described in Section 3.4 and mg/kg-day doses of formaldehyde reported in the ERF 

23 formaldehyde study were plotted together versus the hemolymphoreticular neoplasm incidences 

24 in their respective studies (Figure 4-1). Separate linear models were fit to the male and female 

25 rat data from these studies. The model fits shown in Figure 4-1 demonstrate that when 

26 metabolized methanol is used as the dose metric for the methanol study data, the dose-response 

27 data from these two studies can be adequately fit by two separate linear dose-response functions 

for the combined male (R2 = 0.6722) and combined female (R2 28 = 0.779) responses. Even if it is 

29 true that formaldehyde is the common moiety responsible for these tumors, one would not expect 

30 this approach to result in perfect dose-response alignment because the metabolized methanol 

31 estimate is not an accurate representation of formaldehyde distribution, and formaldehyde from 

32 methanol administration would not be expected to distribute the same as orally administered 

33 formaldehyde. However, the similarities in the dose-response data for male and female rats from 

34 these studies are consistent with the hypothesis that formaldehyde is key to methanol’s 

35 carcinogenic MOA. 
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y = 0.0258Ln(x) + 0.0889

R2

 = 0.7592

y = 0.0492Ln(x) + 0.0932

R2

 = 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/d

Incidence 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. Hemolymphoreticular neoplasms in male and female Sprague-Dawley rats 

in formaldehyde and methanol drinking water studies versus mg formaldehyde/kg/day

or mg metabolized methanol/kg/day (predicted by EPA PBPK model).

Source: Soffritti et al. (2002b).

1 As discussed above, methanol is metabolized to formaldehyde, which is deemed to be 

2 carcinogenic to humans (IARC, 2004) by both the oral and inhalation routes, and there are 

3 readily apparent similarities between the dose-response data from oral studies of rats exposed to 

4 formaldehyde and methanol. In addition, the dose-response model fit for the lymphoma 

5 response observed in the Soffritti et al. (2002a) study is improved when predicted total 

6 metabolites is used as the dose-metric (Section 5.4.1.2). However, the database of information 

7 available concerning methanol’s carcinogenic MOA is limited, and the extent to which the parent 

8 or a metabolite such as formaldehyde is responsible for the carcinogenic effects observed in the 

9 studies conducted by Soffritti et al. (2002a) or NEDO (1987, 1985/2008b) is not clear. 

4.10. SUSCEPTIBLE POPULATIONS AND LIFE STAGES

4.10.1. Possible Childhood Susceptibility

10 Studies in animals have identified the fetus as being more sensitive than adults to the 

11 toxic effects of methanol; the greatest susceptibility occurs during gastrulation and early 

12 organogenesis (CERHR, 2004). Table 4-21 summarizes some of the data regarding the relative 

13 ontogeny of CAT, ADH1, and ADH3 in humans and mice. Human fetuses have limited ability to 

14 metabolize methanol as ADH1 activity in 2-month-old and 4–5 month-old fetuses is 3–4% and 
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1 10% of adult activity, respectively (Pikkarainen and Raiha, 1967). ADH1 activity in 9–22 week 

2 old fetal livers was found to be 30% of adult activity (Smith et al., 1971). Likewise, ADH1 

3 activity is ~20–50% of adult activity during infancy (Smith et al., 1971; Pikkarainen and Raiha, 

4 1967). Activity continues to increase until reaching adult levels at 5 years of age (Pikkarainen 

5 and Raiha, 1967). However, no difference between blood methanol levels in 1-year-old infants 

6 and adults was observed following ingesting the same doses of aspartame, which releases 10% 

7 methanol by weight during metabolism (Stegink et al., 1983). Given that the exposure was 

8 aspartame as opposed to methanol, it is difficult to draw any conclusions from this study vis-à9 vis ontogeny data and potential influences of age differences in aspartame disposition. With 

10 regard to inhalation exposure, increased breathing rates relative to adults may result in higher 

11 blood methanol levels in children compared to adults (CERHR, 2004). It is also possible that 

12 metabolic variations resulting in increased methanol blood levels in pregnant women could 

13 increase the fetus’ risk from exposure to methanol. In all, unresolved issues regarding the 

14 identification of the toxic moiety increase the uncertainty with regards to the extent and 

15 pathologic basis for early life susceptibility to methanol exposure.

16 The prevalence of folic acid deficiency has decreased since the United States and Canada 

17 introduced a mandatory folic acid food fortification program in November 1998. However, 

18 folate deficiency is still a concern among pregnant and lactating women, and factors such as 

19 smoking, a poor quality diet, alcohol intake, and folic antagonist medications can enhance 

20 deficiency (CERHR, 2004). Folate deficiency could affect a pregnant woman’s ability to clear 

21 formate, which has also been demonstrated to produce developmental toxicity in rodent in in 

22 vitro studies at high-doses (Dorman et al., 1995). It is not known if folate-deficient humans have 

23 higher levels of blood formate than individuals with adequate folate levels. A limited study in 

24 folate-deficient monkeys demonstrated no formate accumulation following an endotracheal 

25 exposure of anesthetized monkeys to 900 ppm methanol for 2 hours (Dorman et al., 1994). The 

26 situation is obscured by the fact that folic acid deficiency during pregnancy by itself is thought to 

27 contribute to the development of severe congenital malformations (Pitkin, 2007).

4.10.2. Possible Gender Differences

28 There is limited information on potential differences in susceptibility to the toxic effects 

29 of methanol according to gender. However, one study reported a higher background blood 

30 methanol level in human females versus males (Batterman and Franzblau, 1997). In rodents, 

31 fetuses exposed in utero were found to be the most sensitive subpopulation. One study suggested 

32 a possible increased sensitivity of male versus female rat fetuses and pups. When rats were 

33 exposed to methanol pre- and postnatally, 6- and 8-week-old male progeny had significantly 

34 lower brain weights at 1,000 ppm, compared to those in females that demonstrated the same 
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1 effect only at 2,000 ppm (NEDO, 1987). In general, there is little evidence for substantial 

2 disparity in the level or degree of toxic response to methanol in male versus female experimental 

3 animals or humans. However, it is possible that the compound-related deficits in fetal brain 

4 weight that were evident in the pups of F1 generation Sprague-Dawley rats exposed to methanol 

5 in the NEDO (1987) study may reflect a threshold neurotoxicological response to methanol. It is 

6 currently unknown whether higher levels of exposure would result in brain sequelae comparable 

7 to those observed in acutely exposed humans.

4.10.3. Genetic Susceptibility

8 Polymorphisms in enzymes involved in methanol metabolism may affect the sensitivity 

9 of some individuals to methanol. For example, as discussed in Chapter 3, data summarized in 

10 reviews by Agarwal (2001), Burnell et al. (1989), Bosron and Li (1986), and Pietruszko (1980) 

11 discuss genetic polymorphisms for ADH. Class I ADH, the primary ADH in human liver, is a 

12 dimer composed of randomly associated polypeptide units encoded by three genetic loci 

13 (ADH1A, ADH1B, and ADH1C). Polymorphisms are observed at the ADH1B (ADH1B*2, 

14 ADH1B*3) and ADH1C (ADH1C*2) loci. The ADH1B*2 phenotype is estimated to occur in 

15 ~15% of Caucasians of European descent, 85% of Asians, and less that 5% of African 

16 Americans. Fifteen percent of African Americans have the ADH1B*3 phenotype, while it is 

17 found in less than 5% of Caucasian Europeans and Asians. The only reported polymorphisms in 

18 ADH3 occur in the promoter region, one of which reduces the transcriptional activity in vitro

19 nearly twofold (Hedberg et al., 2001). While polymorphisms in ADH3 are described in more 

20 than one report (Cichoz-Lach et al., 2007; Hedberg et al., 2001), the functional consequence(s) 

21 for these polymorphisms remains unclear.
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5. DOSE-RESPONSE ASSESSMENT AND CHARACTERIZATION

5.1. INHALATION RfC FOR EFFECTS OTHER THAN CANCER69

1 In general, the RfC is an estimate of a daily exposure of the human population (including 

2 susceptible subgroups) that is likely to be without an appreciable risk of adverse health effects 

3 over a lifetime. It is derived from a POD, generally the statistical lower confidence limit on the 

4 BMCL or BMDL, with uncertainty/variability factors applied to reflect limitations of the data 

5 used. The inhalation RfC considers toxic effects for both the respiratory system (portal-of-entry) 

6 effects and systems peripheral to the respiratory system (extra-respiratory or systemic effects). It 

is generally expressed in mg/m3 7 . EPA performed an IRIS assessment of methanol in 1991 and 

8 determined that the database was inadequate for derivation of an RfC. While some limitations 

9 still exist in the database (see Sections 5.1.3.2 and 5.3), the experimental toxicity database has 

10 expanded and newer methods and models have been developed to analyze the results. In this 

11 update, the PBPK model, described in Section 3.4, was developed by EPA and is used to estimate 

12 HECs and HEDs from inhalation study data for the derivation of both the RfC and RfD. In both 

13 cases, the use of a PBPK model replaces part of the UF adjustments traditionally used for 

14 species-to-species extrapolation.

Additionally, this assessment uses the BMD method in its derivation of the POD.70 15 The 

16 suitability of these methods to derive a POD is dependent on the nature of the toxicity database 

17 for a specific chemical. Details of the BMD analyses are found in Appendix C. The use of the 

18 BMD approach for determining the POD improves the assessment by including consideration of 

19 shape of the dose-response curve, independence from experimental doses, and estimation of the 

20 uncertainty pertaining to the calculated dose response. However, the methanol database still has 

21 limitations and uncertainties associated with it, in particular, those uncertainties associated with 

22 human variability, animal-to-human differences, and limitations in the database influence 

23 derivation of the RfC.

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

5.1.1.1. Key Inhalation Studies

24 While a substantial body of information exists on the toxicological consequences to 

25 humans exposed to large amounts of methanol, no human studies exist that would allow for 

 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 and PK models 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% increase in the incidence of 

a particular lesion or a 10% decrease in body weight gain (see Section 5.1.2.2).
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1 quantification of subchronic, chronic, or in utero effects of methanol exposure. Table 4-35

2 summarizes available experimental animal inhalation studies of methanol. Several of these 

3 studies, including the monkey chronic (NEDO, 1987) and developmental (Burbacher et al., 

4 2004a, 2004b, 1999a, 1999b) studies, the male rat reproductive studies (Lee et al., 1991; 

5 Cameron et al., 1985, 1984), and the 4-week rat studies (Poon et al., 1994), are lacking in key 

6 attributes (e.g., documented dose response, documented controls, and duration of exposure) 

7 necessary for their direct use in the quantification of a chronic RfC. These studies will be 

8 considered in this chapter for their contributions to the overall RfC uncertainty. Several 

9 inhalation reproductive or developmental studies were adequately documented and are of 

10 appropriate size and design for quantification and derivation of an RfC. These studies are 

11 considered for use in the derivation of an RfC and are summarized below. 

5.1.1.2. Selection of Critical Effect(s)

12 Developmental effects have been assessed in a number of toxicological studies of 

13 monkeys, rats, and mice. The findings of Rogers and Mole (1997) indicate that methanol is toxic 

14 to mouse embryos in the early stages of organogenesis, on or around GD7. In the study of 

15 Rogers et al. (1993a), in which pregnant female CD-1 mice were exposed to methanol vapors 

16 (1,000, 2,000, and 5,000 ppm) on GD6–GD15, reproductive and fetal effects included an 

17 increase in the number of resorbed litters, a reduction in the number of live pups, and increased 

18 incidences of exencephaly, cleft palate, and the number of cervical ribs. They reported a 

NOAEL for cervical rib malformations at 1,000 ppm (1,310 mg/m3 19 ) and a LOAEL of 2,000 ppm 

(2,620 mg/m3 20 , 49.6% per litter versus 28.0% per litter in the control group). Increased incidence 

21 of cervical ribs was also observed in the rat organogenesis study (NEDO, 1987) in the 5,000 ppm 

22 dose group (65.2% per litter versus 0% in the control group), indicating that the endpoint is 

23 significant across species.

24 The biological significance of the cervical rib endpoint within the regulatory arena has 

25 been the subject of much debate (Chernoff and Rogers, 2004). Previous studies have classified 

26 this endpoint as either a malformation (birth defect of major importance) or a variation 

27 (morphological alternation of minor significance). There is evidence that incidence of 

28 supernumerary ribs (including cervical ribs) is not just the addition of extraneous, single ribs but 

29 rather is related to a general alteration in the development and architecture of the axial skeleton 

30 as a whole. In CD-1 mice exposed during gestation to various types of stress, food and water 

31 deprivation, and the herbicide dinoseb, supernumerary ribs were consistently associated with 

32 increases in length of the 13th rib (Branch et al., 1996). This relationship was present in all fetal 

33 ages examined in the study. The authors concluded that these findings are consistent with 

34 supernumerary ribs being one manifestation of a basic alteration in the differentiation of the 

35 thoraco-lumbar border of the axial skeleton. The biological significance of this endpoint is 

36 further strengthened by the association of supernumerary ribs with adverse health effects in 
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1 humans. The most common effect produced by the presence of cervical ribs is thoracic outlet 

2 disease (Nguyen et al., 1997; Fernandez Noda et al., 1996; Henderson, 1914). Thoracic outlet 

3 disease is characterized by numbness and/or pain in the shoulder, arm, or hands. Vascular effects 

4 associated with this syndrome include cerebral and distal embolism (Bearn et al., 1993; Connell 

5 et al., 1980; Short, 1975), while neurological symptoms include extreme pain, migraine, and 

6 symptoms similar to Parkinson’s (Evans, 1999; Saxton et al., 1999; Fernandez Noda et al., 1996). 

7 Schumacher et al. (1992) observed 242 rib anomalies in 218 children with tumors (21.8%) and 

8 11 (5.5%) in children without malignancy, a statistically significant (p < 0.001) difference that 

9 indicates a strong association between the presence of cervical ribs and childhood cancers. 

10 Specific cancers associated with statistically significant increases in anomalous ribs included 

11 leukemia, brain, tumor, neuroblastoma, soft tissue sarcoma, and Wilm’s tumor.

12 A number of rat studies have confirmed the toxicity of methanol to embryos during 

13 organogenesis (Weiss et al., 1996; NEDO, 1987; Nelson et al., 1985). NEDO (1987) reported 

14 reduced brain, pituitary, and thymus weights in F1 and F2 generation Sprague-Dawley rats at 

15 1,000 ppm methanol. In a follow-up study of the F1 generation brain weight effects, NEDO 

16 (1987) reported decreased brain, cerebellum, and cerebrum weights in F1 males exposed at 

17 1,000 ppm methanol from GD0 through the F1 generation. The exposure levels used in these 

18 studies are difficult to interpret because dams were exposed prior to gestation, and dams and 

19 pups were exposed during gestation and lactation. However, it is clear that postnatal exposure 

20 increases the severity of brain weight reduction. In another experiment in which NEDO (1987) 

21 exposed rats only during organogenesis (GD7–GD17), the observed decreases in brain weights in 

22 offspring at 8 weeks of age were less severe than in the studies for which exposure was 

23 continued postnatally. This finding is not unexpected, given that the brain undergoes tremendous 

24 growth beginning early in gestation and continuing in the postnatal period. Rats are considered 

25 altricial (i.e., born at relatively underdeveloped stages), and many of their neurogenic events 

26 occur postnatally (Clancy et al., 2007). Brain effects from postnatal exposure are also relevant to 

27 humans given that, in humans, gross measures of brain growth increase for at least 2–3 years 

28 after birth, with the growth rate peaking approximately 4 months after birth (Rice and Barone, 

29 2000). 

30 A change in brain weight is considered to be a biologically significant effect (U.S. EPA, 

31 1998). This is true regardless of changes in body weight because brain weight is generally 

32 protected during malnutrition or weight loss, unlike many other organs or tissues 

33 (U.S. EPA, 1998). Thus, change in absolute brain weight is an appropriate measure of effects on 

34 this critical organ system. Decreases in brain weight have been associated with simultaneous 

35 deficits in neurobehavioral and cognitive parameters in animals exposed during gestation to 

36 various solvents, including toluene and ethanol (Gibson, 2000; Coleman et al., 1999; Hass, 
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1 1995). NEDO (1987) reports that brain, cerebellum, and cerebrum weights decrease in a dose2 dependant manner in male rats exposed to methanol throughout gestation and the F1 generation. 

3 Developmental neurobehavioral effects associated with methanol inhalation exposure 

4 have been investigated in monkeys. Burbacher et al. (2004a, 2004b, 1999a, 1999b) exposed M. 

fascicularis monkeys to 0, 262, 786, and 2,359 mg/m3 5 methanol, 2.5 hours/day, 7 days/week 

6 during premating/mating and throughout gestation (approximately 168 days). In these studies, 

exposure of monkeys to up to 1,800 ppm (2,359 mg/m3 7 ) methanol during premating, mating, and 

8 throughout gestation resulted in no changes in reproductive parameters other than a shorter 

9 period of gestation in all exposure groups that did not appear to be dose related. The shortened 

10 gestation period was largely the result of C-sections performed in the methanol exposure groups 

11 “in response to signs of possible difficulty in the maintenance of pregnancy,” including vaginal 

12 bleeding. As discussed in Section 4.7.1.2, though statistically significant, the shortened gestation 

13 finding may be of limited biological significance given questions concerning its relation to the 

14 methanol exposure. Developmental parameters, such as fetal crown-rump length and head 

15 circumference, were unaffected, but there appeared to be neurotoxicological deficits in methanolexposed pups. VDR was significantly reduced in the 786 mg/m3 16 group for males and the 2,359 

mg/m3 17 group for both sexes. However, a dose-response trend for this endpoint was only 

18 exhibited for females. In fact, this is the only effect reported in the Burbacher et al. (2004a, 

19 2004b, 1999a, 1999b) studies for which a significant dose-response trend is evident. As 

20 discussed in Section 4.4.2, confidence may have been increased by statistical analyses to adjust 

21 for multiple testing (CERHR, 2004). Yet it is worth noting that the dose-response trend for VDR 

22 in females remained significant with (p = 0.009) and without (p = 0.0265) an adjustment for the 

23 shortened gestational periods, and it is a measure of functional deficits in sensorimotor 

24 development that is consistent with early developmental CNS effects (brain weight changes 

25 discussed above) that have been observed in rats. 

26 Another test, the Fagan test of infant intelligence, indicated small but not significant

27 deficits of performance (time spent looking at novel faces versus familiar faces) in treated 

28 monkeys. Although not statistically significant and not quantifiable, the results of this test are 

29 also important when considered in conjunction with the brain weight changes noted in the NEDO 

30 (1987) rat study. As discussed in Section 4.7.1.2, the monkey data are not conclusive, and there 

31 is insufficient evidence to determine if the primate fetus is more or less sensitive than rodents to 

32 methanol teratogenesis. Taken together, however, the NEDO (1987) rat study and the Burbacher 

33 et al. (2004a, 2004b, 1999a, 1999b) monkey study suggest that prenatal exposure to methanol 

34 can result in adverse effects on developmental neurology pathology and function, which can be 

35 exacerbated by continued postnatal exposure. 

36 A number of studies described in Section 4.3.2 and summarized in Section 4.7.1.2 have 

37 examined the potential toxicity of methanol to the male reproductive system (Lee et al., 1991; 
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1 Cameron et al., 1985, 1984). Some of the observed effects, including a transient decrease in 

2 testosterone levels, could be the result of chemically related strain on the rat system as it attempts 

3 to maintain hormone homeostasis. However, the data are insufficient to definitively characterize 

4 methanol as a toxicant to the male reproductive system.

5 The studies considered for use in the derivation of an RfC are summarized in Table 5-1. 

6 As discussed in Sections 5.1.3.1 and 5.3, there is uncertainty associated with the selection of an 

7 effect endpoint from the methanol database for use in the derivation of an RfC. Taking into 

8 account the limitations of the studies available for quantification purposes, decreased brain 

9 weight at 6 weeks in male Sprague-Dawley rats exposed throughout gestation and the postnatal 

10 period (NEDO, 1987) was chosen as the critical effect for the purposes of this dose-response 

11 assessment as it can be reliably quantified and represents both a sensitive organ system and a key 

12 period of development. RfC derivations utilizing alternative endpoints (e.g., cervical rib effects 

13 in mice and delayed sensorimotor development in monkeys) and alternative methods (e.g., use of 

14 different BMRLs) are summarized in Appendix C and in Section 5.1.3.1.

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

study

Rat

SpragueDawley 

M,F Not specified -

F1 and F2 

generation

F1- Birth to end 

of mating (M) 

or weaning (F);

F2-birth to 8 

wk

Reduced weight of 

brain, pituitary, and 

thymus at 8, 16, and 

24 wk postnatal in F1

and at 8 wk in F2

100 1,000

NEDO (1987) 

Follow-up study 

of F1 generation

10–14/ sex/

group- F1

generation

GD0 through 

F1 generation

Reduced brain weight 

at 3 wk and 6 wk 

(males only). 

Reduced brain and 

cerebrum weight at 8 

wk (males only) 

500 1,000

NEDO (1987) 

Teratology study 

Rat

SpragueDawley 

M,F 10–12/sex/ 

group

GD7–GD17 Reduced brain, 

pituitary, thyroid, 

thymus, and testis 

weights at 8 wk

postnatal.

1,000 5,000

Nelson et al. 

(1985)

Rat

SpragueDawley 

F 15 pregnant 

dams/group

GD1–GD19 or 

GD7–GD15

Reduced fetal body 

weight, increased 

incidence of visceral 

and skeletal 

abnormalities, 

including rudimentary 

and extra cervical ribs

5,000 10,000
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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/

group

GD6-15 Increased incidence 

of extra cervical ribs, 

cleft palate, 

exencephaly; reduced 

fetal weight and pup 

survival, delayed 

ossification

1,000 2,000

Burbacher et al. 

(20004a, 2004b, 

1999a, 1999b)

M. 

fascicularis

12 pregnant 

monkeys/group

2.5 hr/day, 

7 days/wk, 

during 

premating, 

mating and 

gestation

Shortened period of 

gestation; may be 

related to exposure 

(no dose response), 

neurotox. deficits 

including reduced 

performance in the 

VDR test

- -

b

a

Animals were dosed 20–21 hr/day. NS = Not Specified b

Gestational exposure resulted in a shorter period of gestation in dams exposed to as low as 200 ppm (263 mg/m3). 

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 Analysis for the POD—Application of PBPK and BMD Models 

1 Potential PODs for the RfC derivation, described here and in Appendix C, have been 

2 calculated via the use of monkey, rat and mouse PBPK models, described in Section 3.4. First, 

3 the doses used in an experimental bioassay were converted to an internal dose metric that is most 

4 appropriate for the endpoint being assessed. The PBPK models are capable of calculating 

5 several measures of dose for methanol, including the following:

6  Cmax – The peak concentration of methanol in the blood during the exposure period;

7  AUC – Area under the curve, which represents the cumulative product of 

8 concentration and time for methanol in the blood; and

9  Total metabolism – The production of metabolites of methanol, namely formaldehyde 

10 and formate.

11 As described in Section 3.4, the focus of model development is on obtaining accurate 

12 predictions of increased body burdens over background. The PBPK models do not describe or 

13 account for background levels of methanol, formaldehyde or formate.

14 Although there remains uncertainty surrounding the identification of the proximate 

15 teratogen of importance (methanol, formaldehyde, or formate), the dose metric chosen for 

16 derivation of an RfC was based on blood methanol levels. This decision was primarily based on 

17 evidence that the toxic moiety is not likely to be the formate metabolite of methanol (CERHR, 

18 2004) and evidence that levels of the formaldehyde metabolite following methanol maternal 

19 and/or neonate exposure would be much lower in the fetus and neonate than in adults. While 

20 recent in vitro evidence indicates that formaldehyde is more embryotoxic than methanol and 

21 formate, the high reactivity of formaldehyde would limit its unbound and unaltered transport as 
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1 free formaldehyde from maternal to fetal blood (Thrasher and Kilburn, 2001), and the capacity 

2 for the metabolism of methanol to formaldehyde is likely lower in the fetus and neonate versus 

3 adults (see discussion in Section 3.3). Thus, even if formaldehyde is identified as the proximate

4 teratogen, methanol would likely play a prominent role, at least in terms of transport to the target 

5 tissue. Further discussions of methanol metabolism, dose metric selection, and MOA issues are 

6 covered in Sections 3.3, 4.6, 4.8 and 4.9.2.

7 A BMDL was then derived in terms of the internal dose metric utilized. Finally, the 

8 BMDL values were converted to HECs via the use of a PBPK model parameterized for humans. 

9 The next section describes the rationale for and application of the benchmark modeling 

10 methodology for the RfC derivation.

5.1.2.1. Application of the BMD/BMDL Approach

11 Several developments over the past few years impact the derivation of the RfC: 1) EPA 

12 has developed draft BMD assessment methods (U.S. EPA, 2000b, 1995) and supporting software 

13 (Appendix C) to improve upon the previous NOAEL/LOAEL approach; 2) MOA studies have 

14 been carried out that can give more insight into methanol toxicity; and 3) EPA has refined PBPK 

15 models for methanol on the basis of the work of Ward et al. (1997) (see Section 3.4. for

16 description of the EPA model). The EPA PBPK model provides estimates of HECs from rodent 

17 exposures that are supported by pharmacokinetic information available for rodents and humans. 

18 The following sections describe how the BMD/BMDL approach, along with the EPA PBPK 

19 model, is used to obtain a POD for use in the derivation of an RfC for methanol in accordance 

20 with current draft BMD technical guidance (U.S. EPA, 2000b). 

21 The BMD approach attempts to fit models to the dose-response data for a given endpoint.

22 It has the advantage of taking more of the dose-response data into account when determining the 

23 POD, as well as estimating the dose for which an effect may have a specific probability of 

24 occurring. The BMD approach also accounts, in part, for the quality of the study (e.g., study 

25 size) by estimating a BMDL, the 95% lower bound confidence limit on the BMD. The BMDL is 

26 closer to the BMD (higher) for large studies and further away from the BMD (lower) for small 

27 studies. Because the BMDL approach will account, in part, for a study’s power, dose spacing,

28 and the steepness of the dose-response curve, it is generally preferred over the NOAEL 

29 approach.

30 When possible, all experimental data points are included in this assessment to ensure 

31 adequate fit of a BMD model and derivation of a BMDL. A summary of the POD values 

32 determined by BMD analysis for the critical endpoint (as well as other considered endpoints)

33 (see Appendix C for modeling results), application of UFs, and conversion to HECs using the 

34 BMD and PBPK approach, is included in Section 5.1.3.1.
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1 Use of the BMD approach has uncertainty associated with it. An element of the BMD 

approach is the use of several models to determine which best fits the data.71 2 In the absence of 

3 an established MOA or a theoretical basis for why one model should be used over another, model 

4 selection is based on best fit to the experimental data selection. Model fit was determined by 

statistics (AIC and χ

2 5 residuals of individual dose groups) and visual inspection recommended by

EPA (2000b)72 6

7 The PBPK model developed by EPA for methanol (described in Section 3.4) was applied 

8 for the estimation of methanol blood levels in the exposed dams (NEDO, 1987). When using 

9 PBPK models, it is very important to determine what estimate of internal dose (i.e., dose metric) 

10 can serve as the most appropriate dose metric for the health effects under consideration. 

11 The results of NEDO (1987), shown in Table 4-8, indicate that there is not a cumulative 

12 effect of ongoing exposure on brain-weight decrements in rats exposed postnatally; i.e., the dose

13 response in terms of percent of control is about the same at 3 weeks postnatal as at 8 weeks 

14 postnatal in rats exposed throughout gestation and the F1 generation. However, there does 

15 appear to be a greater brain-weight effect in rats exposed postnatally versus rats exposed only 

16 during organogenesis (GD7–GD17). In male rats exposed during organogenesis only, there is no 

17 statistically significant decrease in brain weight at 8 weeks after birth at the 1,000 ppm exposure 

18 level. Conversely, in male rats exposed to the same level of methanol throughout gestation and 

19 the F1 generation, there was an approximately 5% decrease in brain weights (statistically 

20 significant at the p < 0.01 level). The fact that male rats exposed to 5,000 ppm methanol only 

21 during organogenesis experienced a decrease in brain weight of 10% at 8 weeks postnatal 

22 indicates that postnatal exposure is not necessary for the observation of persistent postnatal 

23 effects. However, the fact that this decrease was less than the 13% decrease observed in male 

24 rats exposed to 2,000 ppm methanol throughout gestation and the 8 week postnatal period 

25 indicates that both exposure concentration and duration are important components of the ability 

26 of methanol to cause this effect. The extent to which the observation of the increased effect is 

27 due to a cumulative effect in rats exposed postnatally versus recovery in rats for which exposure 

28 was discontinued at birth is not clear. 

29 The fact that brain weight is susceptible to both the level and duration of exposure 

30 suggests that a dose metric that incorporates a time component would be the most appropriate 

31 metric to use. For these reasons, and because it is more typically used in internal-dose-based 

 71USEPA’s BMDS 2.1 was used for this assessment as it provides data management tools for running 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 toxicological data. Results 

from all models include a reiteration of the model formula and model run options chosen by the user, goodness-of-fit 

information, the BMD, and the estimate of the lower-bound confidence limit on the BMD (BMDL). 72Akaike’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.
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1 assessments and better reflects total exposure within a given day, daily AUC (measured for 

2 22 hours exposure/day) was chosen as the most appropriate dose metric for modeling the effects 

3 of methanol exposure on brain weights in rats exposed throughout gestation and continuing into 

4 the F1 generation.

5 Application of the EPA methanol PBPK model (described in Section 3.4) to the NEDO 

6 (1987) study, in which developing rats were exposed during gestation and the postnatal period,

7 presents complications that need to be discussed. The neonatal rats in this study were exposed to 

8 methanol gestationally before parturition as well as lactationally and inhalationally after 

9 parturition. The PBPK model developed by EPA only estimates internal dose metrics for 

10 methanol exposure in NP adult mice and rats. Experimental data indicate that inhalation-route 

11 blood methanol kinetics in NP mice and pregnant mice on GD6–GD10 are similar (Dorman 

12 et al., 1995; Perkins et al., 1995a,1995b; Rogers et al., 1993a, 1993b). In addition, experimental 

13 data indicate that the maternal blood:fetal partition coefficient for mice is approximately 1 (see 

14 Section 3.4.1.2). Assuming that these findings apply for rats, the data indicate that PBPK 

15 estimates of PK and blood dose metrics for NP rats are better predictors of fetal exposure during 

16 gestation than would be obtained from default extrapolations from external exposure 

17 concentrations. However, as is discussed to a greater extent in Section 5.3, the additional routes 

18 of exposure presented to the pups in this study (lactation and inhalation) present uncertainties 

19 that suggest the average blood levels in pups in the NEDO (1987) report might be greater than 

20 those of the dam. The assumption made in this assessment is that, if such differences exist 

21 between human mothers and their offspring, they are not expected to be significantly greater than 

22 that which has been postulated for rats. Thus, the PBPK model-estimated adult blood methanol 

23 level is considered to be an appropriate dose metric for the purpose of this analysis and HEC 

24 derivation. 

5.1.2.2. BMD Approach Applied to Brain Weight Data in Rats

25 The NEDO (1987) study reported decreases in brain weights in developing rats exposed 

26 during gestation only (GD7–GD17) or during gestation and the postnatal period, up to 8 weeks. 

27 Because of the biological significance of decreases in brain weight as an endpoint in the 

28 developing rat and because this endpoint was not evaluated in other peer-reviewed studies, BMD 

29 analysis was performed using these data. For the purposes of deriving an RfC for methanol from 

30 developmental endpoints using the BMD method and rat data, decreases in brain weight at 

31 6 weeks of age in the more sensitive gender, males, exposed throughout gestation and continuing 

32 into the F1 generation (both through lactation and inhalation routes) were utilized. Decreases in 

33 brain weight at 6 weeks (gestation and postnatal exposure), rather than those seen at 3 and 8

34 weeks, were chosen as the basis for the RfC derivation because they resulted in lower estimated 

35 BMDs and BMDLs. Decreased brain weights in male rats at 8 weeks age after gestation-only 
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1 exposure were not utilized because they were less severe at the same dose level (1,000 ppm) 

2 compared to gestation and postnatal exposure.

3 The first step in the current BMD analysis is to convert the inhalation doses, given 

4 as ppm values from the studies, to an internal dose metric using the EPA PBPK model (see 

5 Section 3.4). For decreased brain weight in male rats, AUC of methanol in blood (hr × mg/L) is 

6 chosen as the appropriate internal dose metric for the reasons discussed in Section 5.1.2.1. 

7 Predicted AUC values for methanol in the blood of rats are summarized in Table 5-2. These 

8 AUC values are then used as the dose metric for the BMD analysis of decreased brain weight in 

male rats.73 9 The full details of this analysis are reported in Appendix C. More details concerning 

10 the PBPK modeling were presented in Section 3.4.

Table 5-2. The EPA PBPK model estimates of methanol blood levels (AUC) in rats 

following inhalation exposures

Exposure level (ppm) Methanol in blood 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 hr/day exposures for 5 days and calculated for the last 24 hours of that 

period.

11 The current draft BMD technical guidance (U.S. EPA, 2000b) suggests that, in the 

12 absence of knowledge as to what level of response to consider adverse, a change in the mean 

13 equal to one S.D. from the control mean can be used as a BMR for continuous endpoints. 

14 However, it has been suggested that other BMRs, such as 5% change relative to estimated 

15 control mean, are also appropriate when performing BMD analyses on fetal weight change as a 

16 developmental endpoint (Kavlock et al., 1995). Therefore, both a one S.D. change from the 

17 control mean and a 5% change relative to estimated control mean were considered (see Appendix 

18 C for RfC derivations using alternative BMRs). For this endpoint, a one S.D. change from the 

19 control mean returned the lowest BMDL estimates and was considered the most suitable BMR 

20 for use in the RfC derivation. All models were fit using restrictions and option settings 

21 suggested in the draft EPA BMD technical guidance document (U.S. EPA, 2000b). 

22 A summary of the results most relevant to the development of a POD using the BMD 

23 approach (BMD, BMDL, and model fit statistics) for decreased brain weight at 6 weeks in male 

24 rats exposed to methanol throughout gestation and continuing into the F1 generation is provided 

25 in Table 5-3. BMDL values in Table 5-3 represent the 95% lower-bound confidence limit on the 

26 AUC estimated to result in a mean that is one S.D. from the control mean. There is a 2.5-fold 

 73All 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>.
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1 range of BMDL estimates from adequately fitting models, indicating considerable model 

2 dependence. In addition, the fit of the Hill and more complex Exponential models is better than 

3 the other models in the dose region of interest as indicated by a lower scaled residual at the dose 

4 group closest to the BMD (0.09 versus -0.67 or -0.77) and visual inspection. In accordance with 

5 draft EPA BMD Technical Guidance (EPA, 2000b), the BMDL from the Hill model (bolded), is 

6 selected as the most approriate basis for an RfC derivation because it results in the lowest BMDL 

7 from among a broad range of BMDLs and provides a superior fit in the low dose region nearest 

8 the BMD. The Hill model dose-response curve for decreased brain weight in male rats is 

9 presented in Figure 5-1, with response plotted against the chosen internal dose metric of AUC of 

10 methanol in rats. The BMDL1SD was determined to be 90.9 hr × mg/L using the 95% lower 

11 confidence limit of the dose-response curve expressed in terms of the AUC for methanol in 

12 blood.

Table 5-3. Comparison of benchmark dose modeling results for decreased brain weight 

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

Model BMD1SD (AUC,

hr × mg/L) a

BMDL1SD

(AUC,

hr × mg/L)

a

p-value AICc Scaled residuald

Linear 278.30 225.30 0.5376 -203.84 -0.77

2nd degree polynomial 278.30 225.30 0.5376 -203.84 -0.77

3rd degree polynomial 278.30 225.30 0.5376 -203.84 -0.77

Power 278.30 225.30 0.5376 -203.84 -0.77

Hillb 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

The 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. EPA, 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 interest 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-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 value should cause one to question model fit in this region.

Source: NEDO (1987).
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 1.45

 1.5

 1.55

 1.6

 1.65

 1.7

 1.75

 1.8

 1.85

 0 200 400 600 800 1000

Mean Response

dose

Hill Model with 0.95 Confidence Level

09:05 08/24 2009

BMDL BMD

 

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

2 Once the BMDL1SD was obtained in units of hr × mg/L, it was used to derive a chronic 

3 RfC. The first step is to calculate the HEC using the PBPK model described in Appendix B. An 

4 algebraic equation is provided (Equation 1 of Appendix B) that describes the relationship 

5 between predicted methanol AUC and the human equivalent inhalation exposure concentration 

6 (HEC) in ppm.

7 BMDLHEC (ppm) = 0.0224*BMDL1SD+(1334*BMDL1SD)/(794+ BMDL1SD)

8 BMDLHEC (ppm) = 0.0224*90.9+(1334*90.9)/(794+ 90.9) = 139 ppm

Next, because RfCs are typically expressed in units of mg/m3 9 , the HEC value in ppm was 

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m3 10 :

HEC (mg/m3

) = 1.31 × 139 ppm = 182 mg/m3 11
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5.1.3. RfC Derivation – Including Application of Uncertainty Factors 

5.1.3.1. Comparison Between Endpoints and BMDL Modeling Approaches

1 A summary of the PODs for the various developmental endpoints and BMD modeling 

approaches considered for the derivation of an RfC, along with the UFs applied74 2 and the 

3 conversion to an HEC, are presented in Table 5-4 and graphically compared in Figure 5-2 (see 

4 Appendix C for details). Information is presented that compares the use of different endpoints

5 (i.e., cervical rib, decreased brain weight, and increased latency of VDR) and different methods 

6 (i.e., different BMR levels) for estimating the POD. These comparisons are presented to inform 

7 the analysis of uncertainty surrounding these choices. Each approach considered for the 

8 determination of the POD has strengths and limitations, but when considered together for 

9 comparative purposes they allow for a more informed determination for the POD for the 

10 methanol RfC.

11 A 10% extra risk BMR is adequate for most traditional bioassays using 50 animals per 

12 dose group. A smaller BMR of 5% extra risk can sometimes be justified for developmental 

13 studies (e.g., Rogers et al., 1993a), because they generally involve a larger number of subjects.

14 Reference values estimated for cervical rib incidence in mice using Cmax as the dose metric were 

13.6 and 10.4 mg/m3 15 using BMDL10 and BMDL05 PODs, respectively (see Appendix D for 

16 discussion of choice of Cmax as the appropriate dose metric for incidence of cervical rib in mice). 

17 The reference value estimated for alterations in sensorimotor development and performance as

measured by the VDR test in female monkeys using AUC as the dose metric was 1.7 mg/m3 18

19 using the BMDLSD as the POD. As discussed in Section 4.4.2, confidence in this endpoint is 

20 reduced by a marginal dose-response trend in one sex (females) and a limited sample size. 

21 Although the VDR test demonstrates that prenatal and continuing postnatal exposure to methanol 

22 can result in neurotoxicity, the use of such statistically borderline results is not warranted in the 

23 derivation of the RfC, given the availability of better dose-response data in other species.

24 Decreases in brain weight at 6 weeks of age in male rats exposed during gestation and 

25 throughout the F1 generation using AUC as the dose metric yield the reference values of 1.8 and 

2.4 mg/m3 26 for BMRs of one S.D. from the control mean and 5% change relative to control mean, 

27 respectively. Because decreases in brain weight in male rats at 6 weeks postbirth resulted in a 

28 clear dose response and returned RfC estimates lower than or approximate to the other endpoints

29 considered, it was chosen as the critical endpoint. One S.D. from the control mean was chosen 

30 as the appropriate level of response (BMR) for the calculation of the RfC because it is the 

31 standard recommended by EPA’s draft technical guidance (U.S. EPA, 2000b) and yields a lower 

32 BMDL than 5% relative deviance for this data set. Thus, the RfC is:

 74 The rationale for the selection of these UFs is discussed later in Section 5.1.3.
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RfC = PODHEC ÷ UF = 182 mg/m3 ÷ 100 = 2 mg/m3 1 (rounded to one significant figure)

Table 5-4. Summary of PODs for critical endpoints, application of UFs and conversion 

to HEC values using BMD and PBPK modeling 

Rogers et al. (1993a)

Burbacher et al. 

(1999a,1999b) NEDO (1987)

BMDL10 mouse 

cervical rib Cmax

BMDL05 mouse 

cervical rib Cmax

BMDL1SD

female monkey 

VDRa AUC 

BMDL05

rat brain wt. b

AUC 

BMDL1SD

rat brain wt. 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/m3)

c 1360 1036 165 240 182

UFH

d 10 10 10 10 10

UFA

e 3 3 3 3 3

UFD 3 3 3 3 3

UFS 1 1 1 1 1

UFL 1 1 1 1 1

UFTOTAL 100 100 100 100 100

RfC (mg/m3) 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, inhalation) 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 ppm. This equation can also be used to estimate model predictions for HECs 

from Cmax values because Cmax values and AUC values were estimated at steady-state for constant 24 hours 

exposures (i.e., AUC = 24 x Cmax). The ppm HEC estimate is then converted to mg/m3 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.
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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)1SD

Concentration (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)1SD

Concentration (mg/m3

)

POD Interspecies Extrapolation Interspecies Extrapolation Inter-individual Variation individual Variation Database Database RfC

Figure 5-2. PODs (in mg/m3) for selected endpoints with corresponding applied UFs

(chosen RfC value is circled) 

5.1.3.2. Application of UFs

1 UFs are applied to the POD, identified from the rodent data, to account for recognized 

2 uncertainties in extrapolation from experimental conditions to the assumed human scenario (i.e.,

3 chronic exposure over a lifetime). A composite UF of 100-fold (10-fold for interindividual 

4 variation, 3-fold for residual toxicodynamic differences associated with animal-to-human 

5 extrapolation, and 3-fold for database uncertainty) was applied to the POD for the derivation of 

6 the RfC, as described below.

7 5.1.3.2.1. Interindividual Variation UFH. A factor of 10 was applied to account for variation in 

8 sensitivity within the human population (UFH). The UF of 10 is commonly considered to be 

9 appropriate in the absence of convincing data to the contrary. The data from which to determine 

10 the potential extent of variation in how humans respond to chronic exposure to methanol are 

11 limited, given the complex nature of the developmental endpoint employed and uncertainties 

12 surrounding the importance of metabolism to the observed teratogenic effects. Susceptibility to 

13 methanol is likely to involve intrinsic and extrinsic factors. Some factors may include alteration 

14 of the body burden of methanol or its metabolites, sensitization of an individual to methanol 
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1 effects, or augmentation of underlying conditions or changes in processes that share common 

2 features with methanol effects. Additionally, inherent differences in an individual’s genetic 

3 make-up, diet, gender, age, or disease state may affect the pharmacokinetics and 

4 pharmacodynamics of methanol, influencing susceptibility intrinsically. Co-exposure to a 

5 pollutant that alters metabolism or clearance or that adds to background levels of metabolites 

6 may also affect the pharmacokinetics and pharmacodynamics of methanol, influencing 

7 susceptibility extrinsically (see Section 4.9). The determination of the UF for human variation is 

8 supported by several types of information, including information concerning background levels 

9 of methanol in humans, variation in pharmacokinetics revealed through human studies and from 

10 PBPK modeling, variation of methanol metabolism in human tissues, and information on 

11 physiologic factors (including gender and age), or acquired factors (including diet and 

12 environment) that may affect methanol exposure and toxicity. 

13 In using the AUC of methanol in blood as the dose metric for derivation of health 

14 benchmarks for methanol, the assumption is made that concentrations of methanol in blood over 

15 time are related to its toxicity, either through the actions of the parent or it subsequent 

16 metabolism. However, the formation of methanol’s metabolites has been shown in humans to be 

17 carried out by enzymes that are inducible, highly variable in activity, polymorphic, and to also be 

18 involved in the metabolism of other drugs and environmental pollutants. Hence, differences in 

19 the metabolism of methanol that are specific for target tissue, gender, age, route of 

20 administration, and prior exposure to other environmental chemicals may give a different pattern 

21 of methanol toxicity if metabolism is required for that toxicity. Eighty-five percent of Asians 

22 carry an atypical phenotype of ADH that may affect their ability to metabolize methanol 

23 (Agarwal, 2001; Bosron and Li, 1986; Pietruszko, 1980). Also, polymorphisms in ADH3 

24 occurring in the promoter region reduce the transcriptional activity in vitro nearly twofold, 

25 although no studies have reported differences in ADH3 enzyme activity in humans (Hedberg 

26 et al., 2001).

27 Although data on the specific potential for increased susceptibility to methanol are 

28 lacking, there is information on PK and pharmacodynamic factors suggesting that children may 

29 have differential susceptibility to methanol toxicity (see Section 4.10.1). Thus, there is 

30 uncertainty in children’s responses to methanol that should be taken into consideration for 

31 derivation of the UF for human variation that is not available from either measured human data 

32 or PBPK modeling analyses. The enzyme primarily responsible for metabolism of methanol in 

33 humans, ADH, has been reported to be reduced in activity in newborns. Differences in 

34 pharmacokinetics include potentially greater pollutant intake due to greater ventilation rates, 

35 activity, and greater intake of liquids in children. In terms of differences in susceptibility to 

36 methanol due to pharmacodynamic considerations, the substantial anatomical, physiologic, and 

37 biochemical changes that occur during infancy, childhood, and puberty suggest that there are 
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1 developmental periods in which the endocrine, reproductive, immune, audiovisual, nervous, and 

2 other organ systems may be especially sensitive. 

3 There are some limited data from short-term exposure studies in humans and animal 

4 experiments that suggest differential susceptibility to methanol on the basis of gender. Gender 

5 can provide not only different potential targets for methanol toxicity but also differences in 

6 methanol pharmacokinetics and pharmacodynamics. NEDO (1987) reported that in rats exposed 

7 to methanol pre- and postnatally, 6- and 8-week-old male progeny had significantly lower brain 

8 weights at 1,000 ppm, whereas females only showed decreases at 2,000 ppm. In general, gender9 related differences in distribution and clearance of methanol may result from the greater muscle 

10 mass, larger body size, decreased body fat, and increased volumes of distribution in males 

11 compared to females. 

12 5.1.3.2.2. Animal-to-Human Extrapolation UFA. A factor of 3 was applied to account for 

13 uncertainties in extrapolating from rodents to humans. Application of a full UF of 10 would 

14 depend on two areas of uncertainty: toxicokinetic and toxicodynamic uncertainty. In this 

15 assessment, the toxicokinetic component is largely addressed by the determination of a HEC 

16 through the use of PBPK modeling. Given the chosen dose metric (AUC for methanol blood), 

17 uncertainties in the PBPK modeling of methanol are not expected to be greater for one species 

18 than another. The analysis of parameter uncertainty for the PBPK modeling performed for 

19 human, mouse, and rat data gave similar results as to how well the model fit the available data. 

20 Thus, the human and rodent PBPK model performed similarly using this dose metric for 

21 comparisons between species. As discussed in Section 5.3 below, uncertainty does exist 

22 regarding the relation of maternal blood levels estimated by the model to fetal and neonatal 

23 blood levels that would be obtained under the (gestational, postnatal and lactational) exposure 

24 scenario employed in the critical study. However, at environmentally relevant exposure levels, it 

25 is assumed that the ratio of the difference in blood concentrations between a human infant and 

26 mother would be similar to and not significantly greater than the difference between a rat dam 

27 and its fetus. Key parameters and factors which determine the ratio of fetal or neonatal human 

28 versus mother methanol blood levels either do not change significantly with age (partition 

29 coefficients, relative blood flows) or scale in a way that is common across species 

30 (allometrically). For this reason and because EPA has confidence in the ability of the PBPK 

31 model to accurately predict adult blood levels of methanol, the PK uncertainty is reduced and a 

32 value of 1 was applied. Rodent-to-human pharmacodynamic uncertainty is covered by a factor 

33 of 3, as is the practice for deriving RfCs (U.S. EPA, 1994b). Therefore, a factor of 3 is used for 

34 interspecies uncertainty.

35 5.1.3.2.3. Database UFD. A database UF of 3 was applied to account for deficiencies in the 

36 toxicity database. The database for methanol toxicity is quite extensive: there are chronic and 
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1 developmental toxicity studies in rats, mice, and monkeys, a two-generation reproductive 

2 toxicity study in rats, and neurotoxicity and immunotoxicity studies. However, there is 

3 uncertainty regarding which test species is most relevant to humans. In addition, limitations of 

4 the developmental toxicity database employed in this assessment include gaps in testing and 

5 imperfect study design, reporting, and analyses. Developmental studies were conducted at levels 

6 inducing maternal toxicity, a full developmental neurotoxicity test (DNT) in rodents has not been 

7 performed and is warranted given the critical effect of decreased brain weight, there are no 

8 chronic oral studies in mice, and chronic and developmental studies in monkeys were generally 

9 inadequate for quantification purposes, for reasons discussed in Section 5.1.1.1. Problems of 

10 interpretation of developmental and reproductive studies also arise given the dose spacing 

11 between lowest and next highest level. For these reasons, an UF of 3 was applied to account for 

12 deficiencies in the database.

13 5.1.3.2.4. Extrapolation from Subchronic to Chronic and LOAEL-to NOAEL 

14 Extrapolation UFs. A UF was not necessary to account for extrapolation from less than chronic 

15 results because developmental toxicity (cervical rib and decreased brain weight) was used as the 

16 critical effect. The developmental period is recognized as a susceptible lifestage where exposure 

17 during certain time windows is more relevant to the induction of developmental effects than 

18 lifetime exposure (U.S. EPA, 1991).

19 A UF for LOAEL-to-NOAEL extrapolation was not applied because BMD analysis was 

20 used to determine the POD, and this factor was addressed as one of the considerations in 

21 selecting the BMR. In this case, a BMR of one S.D. from the control mean in the critical effect 

22 was selected based on the assumption that it represents a minimum biologically significant 

23 change.

5.1.4. Previous RfC Assessment

24 The health effects data for methanol were assessed for the IRIS database in 1991 and 

25 were determined to be inadequate for derivation of an RfC.

5.2. ORAL RfD

26 In general, the RfD is an estimate of a daily exposure to the human population (including 

27 susceptible subgroups) that is likely to be without an appreciable risk of adverse health effects 

28 over a lifetime. It is derived from a POD, generally the statistical lower confidence limit on the 

29 BMDL, with uncertainty/variability factors applied to reflect limitations of the data used. The 

30 RfD is expressed in terms of mg/kg-day of exposure to an agent and is derived by a similar 

31 methodology as is the RfC. Ideally, studies with the greatest duration of exposure and conducted 

32 via the oral route of exposure give the most confidence for derivation of an RfD. For methanol, 

33 the oral database is currently more limited than the inhalation database. With the development of 
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1 PBPK models for methanol, the inhalation database has been used to help bridge data gaps in the 

2 oral database to derive an RfD.

5.2.1. Choice of Principal Study and Critical Effect–with Rationale and Justification

3 No studies have been reported in which humans have been exposed subchronically or 

4 chronically to methanol by the oral route of exposure and thus, would be suitable for derivation 

5 of an oral RfD. Data exist regarding effects from oral exposure in experimental animals, but 

6 they are more limited than data from the inhalation route of exposure (see Sections 4.2, 4.3, and 

7 4.4).

8 Only 2 oral studies of 90-days duration or longer in animals have been reported (Soffritti 

9 et al., 2002a; U.S. EPA, 1986c) for methanol. EPA (1986c) reported that there were no 

10 differences in body weight gain, food consumption, or gross or microscopic evaluations in 

11 Sprague-Dawley rats gavaged with 100, 500, or 2,500 mg/kg-day versus control animals. Liver 

12 weights in both male and female rats were increased, although not significantly, at the 2,500 

13 mg/kg-day dose level, suggesting a treatment-related response despite the absence of 

14 histopathologic lesions in the liver. Brain weights of high-dose group males and females were 

15 significantly less than control animals at terminal (90 days) sacrifice. The data were not reported 

16 in adequate detail for dose-response modeling and BMD estimation. Based primarily on the 

17 qualitative findings presented in this study, the 500 mg/kg-day dose was deemed to be a 

NOAEL.

75 18

19 The only lifetime oral study available was conducted by Soffiritti et al. (2002a) in 

20 Sprague-Dawley rats exposed to 0, 500, 5,000, 20,000 ppm (v/v) methanol, provided ad libitum 

21 in drinking water. Based on default, time-weighted average body weight estimates for Sprague22 Dawley rats (U.S. EPA, 1988), average daily doses of 0, 46.6, 466, and 1,872 mg/kg-day for 

23 males and 0, 52.9, 529, 2,101 mg/kg-day for females were reported by the study authors. All rats 

24 were exposed for up to 104 weeks, and then maintained until natural death. The authors report 

25 no substantial changes in survival nor was there any pattern of compound-related clinical signs 

26 of toxicity. The authors did not report noncancer lesions, and there were no reported compound27 related signs of gross pathology or histopathologic lesions indicative of noncancer toxicological 

28 effects in response to methanol.

29 Five oral studies investigated the reproductive and developmental effects of methanol in 

30 rodents (Aziz et al., 2002; Fu et al., 1996; Sakanashi et al., 1996; Rogers et al., 1993a; Infurna 

31 and Weiss, 1986), including three studies that investigated the influence of FAD diets on the 

32 effects of methanol exposures (Aziz et al., 2002; Fu et al., 1996; Sakanashi et al., 1996). Infurna 

33 and Weiss (1986) exposed pregnant Long-Evans rats to 2,500 mg/kg-day in drinking water on 

34 either GD15–GD17 or GD17–GD19. Litter size, pup birth weight, pup postnatal weight gain, 

 75 EPA (1986c) did not report details required for a BMD analysis such as standard deviations for mean responses.
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1 postnatal mortality, and day of eye opening were not different in treated animals versus controls. 

2 Mean latency for nipple attachment and homing behavior (ability to detect home nesting 

3 material) were different in both methanol treated groups. These differences were significantly 

4 different from controls. Rogers et al. (1993a) exposed pregnant CD-1 mice via gavage to 4 g/kg5 day methanol, given in 2 equal daily doses. Incidence of cleft palate and exencephaly was 

6 increased following maternal exposure to methanol. Also, an increase in totally resorbed litters 

7 and a decrease in the number of live fetuses per litter were observed.

8 Aziz et al. (2002), Fu et al. (1996), and Sakanashi et al. (1996) investigated the role of 

9 folic acid in methanol-induced developmental neurotoxicity. Like Rogers et al. (1993a), the 

10 former 2 studies observed that an oral gavage dose of 4–5 g/kg-day during GD6–GD15 or GD6–

11 GD10 resulted in an increase in cleft palate in mice fed sufficient folic acid diets, as well as an 

12 increase in resorptions and a decrease in live fetuses per litter. Fu et al. (1996) also observed an 

13 increase in exencephaly in the FAS group. Both studies found that an approximately 50% 

14 reduction in maternal liver folate concentration resulted in an increase in the percentage of litters 

15 affected by cleft palate (as much as threefold) and an increase in the percentage of litters affected 

16 by exencephaly (as much as 10-fold). Aziz et al. (2002) exposed rat dams throughout their 

17 lactation period to 0, 1, 2, or 4% v/v methanol via the drinking water, equivalent to 

approximately 480, 960 and 1,920 mg/kg-day.76 18 Pups were exposed to methanol via lactation 

19 from PND1–PND21. Methanol treatment at 2% and 4% was associated with significant 

20 increases in activity (measured as distance traveled in a spontaneous locomotor activity test) in 

21 the FAS group (13 and 39%, respectively) and most notably, in the FAD group (33 and 66%, 

22 respectively) when compared to their respective controls. At PND45, the CAR in FAD rats 

23 exposed to 2% and 4% methanol was significantly decreased by 48% and 52%, respectively, 

24 relative to nonexposed controls. In the FAS group, the CAR was only significantly decreased in 

25 the 4% methanol-exposed animals and only by 22% as compared to their respective controls. 

5.2.1.1. Expansion of the Oral Database by Route-to-Route Extrapolation

26 Given the oral database limitations, including the limited reporting of noncancer findings 

27 in the subchronic (U.S. EPA, 1986c) and chronic studies (Soffritti et al., 2002a) of rats and the 

28 high-dose levels used in the two rodent developmental studies, EPA has derived an RfD by using 

29 relevant inhalation data and route-to-route extrapolation with the aid of the EPA PBPK model 

30 (see Sections 3.4 and 5.1). Several other factors support use of route-to-route extrapolation for 

31 methanol. The limited data for oral administration indicate similar effects as reported via 

32 inhalation exposure (e.g., the brain and fetal skeletal system are targets of toxicity). Methanol 

33 has been shown to be rapidly and well-absorbed by both the oral and inhalation routes of 

 76 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 60 mL/kg-day = 0.48 g/kg-day = 480 mg/kg-day.
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1 exposure (CERHR, 2004; Kavet and Nauss, 1990). Once absorbed, methanol distributes rapidly 

2 to all organs and tissues according to water content, regardless of route of exposure. 

3 As with the species-to-species extrapolation used in the development of the RfC, the dose 

4 metric used for species-to-species and route-to-route extrapolation of inhalation data to oral data 

5 is the AUC of methanol in blood. Simulations for human oral methanol exposure were 

6 conducted using the model parameters as previously described for human inhalation exposures, 

7 with human oral kinetic/absorption parameters from Sultatos et al. (2004) (i.e., KAS = 0.2, 

8 KSI = 3.17, and KAI = 3.28). Human oral exposures were assumed to occur during six drinking 

9 episodes during the day, at times 0, 3, 5, 8, 11, and 15 hours from the first ingestion of the day. 

10 For example, if first ingestion occurred at 7 am, these would be at 7 am, 10 am, 12 noon, 3 pm, 6 

11 pm, and 10 pm. Each ingestion event was treated as occurring over 3 minutes, during which the 

12 corresponding fraction of the daily dose was infused into the stomach lumen compartment. The 

13 fraction of the total ingested methanol simulated at each of these times was 25%, 10%, 25%, 

14 10%, 25%, and 5%, respectively. Six days of exposure were simulated to allow for any 

15 accumulation (visual inspection of plots showed this to be finished by the 2nd or 3rd day), and 

16 the results for the last 24 hours were used. Dividing the exposure into more and smaller episodes 

17 would decrease the estimated peak concentration but have little effect on AUC. This dose metric 

18 was used for dose-response modeling to derive the POD, expressed as a BMDL. The BMDL 

19 was then back-calculated using the EPA PBPK model to obtain an equivalent oral drinking water 

20 dose in terms of mg/kg-day.

5.2.2. RfD Derivation–Including Application of UFs

5.2.1.2. Consideration of Inhalation Data

21 Inhalation studies considered for derivation of the RfC are used to supplement the oral 

22 database using the route-to-route extrapolation, as previously described. BMD approaches were 

23 applied to the existing inhalation database, and the EPA PBPK model was used for species-to24 species extrapolations. The rationale and approach for determining the RfC is described above 

25 (Section 5.1), and the data used to support the derivation of the RfC were extrapolated using the

26 EPA PBPK model to provide an oral equivalent POD.

5.2.1.3. Selection of Critical Effect(s) from Inhalation Data

27 Methanol-induced effects on the brain in rats (weight decrease) and fetal axial skeletal 

28 system in mice (cervical ribs and cleft palate) were consistently observed at lower levels, than 

29 other targets, in the oral and inhalation databases. Analysis of inhalation developmental toxicity 

30 studies shows lower BMDLs for decreased male brain weight in rats exposed throughout 

31 gestation and the F1 generation (NEDO, 1987) than BMDLs associated with the fetal axial 

32 skeletal system in mice. Therefore, as was noted in Section 5.1.3.1, the BMDL for decreases in 
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1 brain weight in male rats is chosen to serve as the basis for the route-to-route extrapolation and 

2 calculation of the RfD.

5.2.1.4. Selection of the POD 

3 The BMDL chosen for the RfC is used to determine the POD for the RfD. This value is 

4 based on a developmental toxicity dataset that includes in utero and postnatal exposures and is 

5 below the range of estimates for other developmental datasets consisting of exposure only 

6 throughout organogenesis. The neonatal brain is the target organ chosen for derivation of the 

7 RfC. The BMDL for the RfC (AUC of 90.9 hr × mg/L methanol in blood) is converted using the 

EPA model to a human equivalent oral exposure of 38.5 mg/kg-day.77 8

5.2.2. RfD Derivation–Application of UFs

9 In an approach consistent with the RfC derivation, UFs are applied to the oral POD of 

10 38.5 mg/kg-day to address interspecies extrapolation, intraspecies variability, and database 

11 uncertainties for the RfD. Because the same dataset, endpoint, and PBPK model used to derive 

12 the RfC were also used to calculate the oral POD, the total UF of 100 is applied to the BMDL of 

13 38.5 mg/kg-day to yield an RfD of 1.12 mg/kg-day for methanol.

14 RfD = 38.5 mg/kg-day ÷ 100 = 0.4 mg/kg-day (rounded to one significant figure)

5.2.3. Previous RfD Assessment

15 The previous IRIS assessment for methanol included an RfD of 0.5 mg/kg-day that was 

16 derived from a EPA (1986c) subchronic oral study in which Sprague-Dawley rats (30/sex/dose) 

17 were gavaged daily with 0, 100, 500, or 2,500 mg/kg-day of methanol. There were no 

18 differences between dosed animals and controls in body weight gain, food consumption, gross or 

19 microscopic evaluations. Elevated levels of SGPT, serum alkaline phosphatase (SAP), and 

20 increased but not statistically significant liver weights in both male and female rats suggest 

21 possible treatment-related effects in rats dosed with 2,500 mg methanol/kg-day, despite the 

22 absence of supportive histopathologic lesions in the liver. Brain weights of both high-dose group 

23 males and females were significantly less than those of the control group. Based on these 

24 findings, 500 mg/kg-day of methanol was considered a NOAEL in this rat study. Application of 

25 a 1,000-fold UF (interspecies extrapolation, susceptible human subpopulations, and subchronic 

26 to chronic extrapolation) yielded an RfD of 0.5 mg/kg-day. 

 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 HED in mg/kg-day.
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5.3. UNCERTANTIES IN THE INHALATION RfC AND ORAL RfD

1 The following is a more extensive discussion of the uncertainties associated with the RfC 

2 and RfD for methanol beyond that which is addressed quantitatively in Sections 5.1.2, 5.1.3, and 

3 5.2.2. A summary of these uncertainties is presented in Table 5-5. 

Table 5-5. Summary of uncertainties in methanol noncancer 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 biological 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 Cmax increased 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.

Choice 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-tohuman 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 blood 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 

bioassay 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 reliable for quantification.

Human population 

variability

RfC could ↓ or ↑ if 

another value of the 

UF was used

10-fold uncertainty 

factor applied to derive 

the RfC/RfD values

10-fold UF is applied because of limited 

data on human variability or potential 

susceptible subpopulations, particularly 

pregnant mothers and their neonates. 
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5.3.1. Choice of Endpoint

1 The impact of endpoint selection on the derivation of the RfC and RfD was discussed in 

Sections 5.1.3.1 and 5.2.2.2. Potential RfC values considered ranged from 1.7 to 13.6 mg/m3 2 , 

3 depending on whether neurobehavioral function in male monkeys, brain weight decrease in male 

4 rats, or cervical ribs incidence in mice was chosen as the critical effect for derivation of the POD, 

5 with the former endpoint representing the lower end of the RfC range. The use of other 

6 endpoints, particularly pre-term births identified in the Burbacher et al. (2004a, 2004b, 1999a,

7 1999b) monkey study, would potentially result in lower reference values, but significant 

8 uncertainties associated with those studies preclude their use as the basis for an RfC.

9 Burbacher et al. (2004a, 2004b, 1999a, 1999b) exposed M. fascicularis monkeys to 0, 

262, 786, and 2,359 mg/m3 10 methanol 2.5 hours/day, 7 days/week during premating/mating and 

11 throughout gestation (approximately 168 days). They observed a slight but statistically 

12 significant gestation period shortening in all exposure groups that was largely due to C-sections 

13 performed in the methanol exposure groups “in response to signs of possible difficulty in the 

14 maintenance of pregnancy,” including vaginal bleeding. As discussed in Sections 4.3.2 and 

15 5.1.1.2, there are questions concerning this effect and its relationship to methanol exposure. An 

16 ultrasound was not done to confirm the existence of real fetal or placental problems. 

17 Neurobehavioral function was assessed in infants during the first 9 months of life. Two tests out 

18 of nine, returned positive results possibly related to methanol exposure. VDR performance was 

reduced in all treated male infants, and was significantly reduced in the 2,359 mg/m3 19 group for 

both sexes and the 786 mg/m3 20 group for males. However, an overall dose-response trend for this 

21 endpoint was only observed in females. As discussed in Section 4.4.2, confidence in this 

22 endpoint may have been increased by statistical analyses to adjust for multiple testing (CERHR, 

23 2004), but it is a measure of functional deficits in sensorimotor development that is consistent 

24 with early developmental CNS effects (brain weight changes discussed above) that have been 

25 observed in rats. The Fagan test of infant intelligence indicated small but not significant deficits 

26 of performance (time spent looking a novel faces versus familiar faces) in treated infants. 

27 Although these results indicate that prenatal and continuing postnatal exposure to methanol can 

28 result in neurotoxicity to the offspring, especially when considered in conjunction with the gross 

29 morphological effects noted in NEDO (1987), the use of such statistically borderline results is 

30 not warranted in the derivation of the RfC, given the availability of better dose-response data in 

31 other species.

32 NEDO (1987) also examined the chronic neurotoxicity of methanol in M. fascicularis

monkeys exposed to 13.1, 131, or 1,310 mg/m3 33 for up to 29 months. Multiple effects were noted 

at 131 mg/ m3 34 , including slight myocardial effects (negative changes in the T wave on an EKG), 

35 degeneration of the inside nucleus of the thalamus, and abnormal pathology within the cerebral 

white tissue in the brain. The results support the identification of 13.1 mg/m3 36 as the NOAEL for 
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1 neurotoxic effects in monkeys exposed chronically to inhaled methanol. However, as discussed 

2 in Section 4.2.2.3, there exists significant uncertainty in the interpretation of these results and 

3 their utility in deriving an RfC for methanol. These uncertainties include lack of appropriate 

4 control group data, limited nature of the reporting of the neurotoxic effects observed, and use of 

5 wild-caught monkeys in the study. Thus, while the NEDO (1987) study suggests that monkeys 

6 may be a more sensitive species to the neurotoxic effects of chronic methanol exposure than 

7 rodents, the substantial deficits in the reporting of data preclude the quantification of data from 

8 this study for the derivation of an RfC.

9 The increased incidence of cervical ribs was identified as a biologically significant, 

10 potential co-critical effect based on the findings of Rogers et al. (1993a). Mice were exposed to 

11 1,000, 2,000, or 5,000 ppm, and incidence of cervical ribs was statistically increased at 

12 2,000 ppm. However, given that the reference values for the increased incidence of cervical ribs

13 are estimated to be approximately five times higher than the reference values calculated using 

14 decreases in brain weight in male rats (NEDO, 1987), decreased brain weight was chosen as the

15 basis for the derivation of the RfC. 

5.3.2. Choice of Dose Metric

16 A recent review of the reproductive and developmental toxicity of methanol by a panel of 

17 experts concluded that methanol, not its metabolite formate, is likely to be the proximate 

18 teratogen and that blood methanol level is a useful biomarker of exposure (CERHR, 2002; 

19 Dorman et al., 1995). The CERHR Expert Panel based their assessment of potential methanol 

20 toxicity on an assessment of circulating blood levels (CERHR, 2002). In contrast to the 

21 conclusions of the NTP-CERHR panel, in vitro data from Harris et al. (2004, 2003) suggest that 

22 the etiologically important substance for embryo dysmorphogenesis and embryolethality was 

23 likely to be formaldehyde rather than the parent compound or formate. Although there remains 

24 uncertainty surrounding the identification of the proximate teratogen of importance (methanol, 

25 formaldehyde, or formate), the dose metric chosen for derivation of an RfC was based on blood 

26 methanol levels. This decision was primarily based on evidence that the toxic moiety is not 

27 likely to be the formate metabolite of methanol (CERHR, 2004), and evidence that levels of the 

28 formaldehyde metabolite following methanol maternal and/or neonate exposure would be lower 

29 in the fetus and neonate than in adults. While recent in vitro evidence indicates that 

30 formaldehyde is more embryotoxic than methanol and formate, the high reactivity of 

31 formaldehyde would limit its unbound and unaltered transport as free formaldehyde from 

32 maternal to fetal blood (Thrasher and Kilburn, 2001) (see discussion in Section 3.3). Thus, even 

33 if formaldehyde is ultimately identified as the proximate teratogen, methanol would likely play a 

34 prominent role, at least in terms of transport to the target tissue. Further discussions of methanol 

35 metabolism, dose metric selection, and MOA issues are in Sections 3.3, 4.6, 4.8 and 4.9.2. 
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1 There exists some concern in using the F1 generation NEDO (1987) rat study as the basis 

2 from which to derive the RfC. This concern mainly arises from issues related to the low 

3 confidence that the PBPK model is accurately predicting dose metrics for neonates exposed 

4 through multiple and simultaneous routes. The PBPK model was structured to predict internal 

5 dose metrics for adult NP animals and was optimized using adult metabolic and physiological 

6 parameters. Young animals have very different metabolic and physiological profiles than adults 

7 (enzyme activities, respiration rates, etc.). This fact, coupled with multiple routes of exposure, 

8 make it likely that the PBPK did not accurately predict the internal dose metrics for the offspring. 

9 Stern et al. (1996) reported that when rat pups and dams were exposed together during lactation 

10 to 4,500 ppm methanol in air, methanol blood levels in pups from GD6–PND21 were 

11 approximately 2.25 times greater than those of dams. This discrepancy persisted until PND48,

12 when postnatal exposure continued to PND52. It is logical to assume that similar differences in 

13 blood methanol levels would also be observed in the NEDO (1987) F1 study, as the exposure 

14 scenario is similar to that of Stern et al. (1996). Differences between pup and dam blood 

15 methanol levels might be expected to be slightly greater than twofold in the NEDO (1987) F1

16 study as the exposure was continuous (versus 6 hours/day in the Stern et al. [1996] paper) and 

17 lasted for a longer duration (~64 days versus 37). Under a similar scenario, human newborns 

18 may experience higher blood levels than their mothers as a result of breast feeding. As has been 

19 discussed in Chapter 3, children have a limited capacity to metabolize methanol via ADH;

20 however, there is some evidence that human infants are able to efficiently eliminate methanol at 

21 high-exposure levels, possibly via CAT (Tran et al., 2007). At environmentally relevant 

22 exposure levels, it is assumed that the ratio of the difference in blood concentrations between 

23 infant and mother would not be significantly greater than the twofold difference that has been 

observed in rats.78 24 For this reason and because EPA has confidence in the ability of the PBPK 

25 model to accurately predict adult blood levels of methanol, the maternal blood methanol levels 

26 for the estimation of HECs from the NEDO (1987) study were used as the dose metric.

5.3.3. Choice of Model for BMDL Derivations

27 The Hill model adequately fit the dataset (goodness-of-fit p-value = 0.84). Data points 

28 were well predicted near the BMD (scaled residual = 0.09) (see Figure 5-1). There is a 2.5-fold 

29 range of BMDL estimates from adequately fitting models, indicating considerable model 

30 dependence. The BMDL from the Hill model was selected, in accordance with EPA BMD 

31 Technical Guidance (EPA, 2000b), because it results in the lowest BMDL from among a broad 

32 range of BMDLs and provides a superior fit in the low dose region nearest the BMD.

 78 Key parameters and factors which determine the ratio of fetal or neonatal human versus mother methanol blood 

levels either do not change significantly with age (partition coefficients, relative blood flows) or scale in a way that 

is common across species (allometrically).
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5.3.4. Choice of Animal-to-Human Extrapolation Method

1 A PBPK model developed by the EPA, adapted from Ward et al. (1997), was used to 

2 extrapolate animal-to-human concentrations. An AUC blood level of methanol (90.9 hr x mg/L) 

3 associated with a one S.D. change from the control mean for brain weights in rats was estimated 

4 using the rat PBPK model. Then the human PBPK model was used to convert back to a human 

equivalent exposure concentration or a BMCLHEC/1SD of 182 mg/m3 5 . If no PBPK models were 

available, a BMCLHEC/1SD of 424 mg/m3 would have been derived by adjusting the 556.5 mg/m3 6

7 BMCL1SD for external exposure concentration for duration and the animal-to-human standard

8 adjustment factor for systemic effects (the ratio of animal and human blood:air partition 

9 coefficients). This value is approximately 2-fold higher than the value derived using the PBPK 

10 model. However, as discussed above, use of PBPK-estimated maternal blood methanol levels 

11 for the estimation of HECs allows for the use of data-derived extrapolations rather than standard

12 methods for extrapolations from external exposure levels.

13 As discussed in Section 3.4, the PBPK models do not describe or account for background 

14 levels of methanol, formaldehyde or formate, and background levels were subtracted from the 

15 reported data before use in model fitting or validation (if not already subtracted by study 

16 authors), as described below. This approach was taken because the relationship between 

17 background doses and background responses is not known, because the primary purpose of this 

18 assessment is for the determination of noncancer and cancer risk associated with increases in the 

19 levels of methanol or its metabolites (e.g., formate, formaldehyde) over background, and because 

20 the subtraction of background levels is not expected to have a significant impact on PBPK model 

21 parameter estimates as background levels of methanol and its metabolites are low relative to 

22 exposure levels used in methanol bioassays. 

5.3.5. Route-to-Route Extrapolation 

23 To estimate an oral dose POD for decrease in brain weight in rats, a route-to-route 

24 extrapolation was performed on the inhalation exposure POD used to derive the RfC. One way 

25 to characterize the uncertainty associated with this approach is to compare risk levels (BMDL 

26 values) using the dose metric, AUC methanol, for developmental decreases in brain weight 

27 derived from 1) an existing oral subchronic study and 2) from a model estimating this metric 

28 from an existing inhalation subchronic study. There are currently no oral developmental studies 

29 investigating decreases in brain weight available to compare to the risk values estimated using 

30 the second procedure. However, the fact that the oral BMDL of 38.5 mg/kg-day estimated in this 

31 assessment from the NEDO (1987) inhalation study of neonate rats via a PBPK model is lower 

32 than the NOAEL of 500 mg/kg-day identified in EPA (1986c) methanol study of adult rats is 

33 consistent with other studies which suggest that fetal/neonatal organisms are a sensitive 

34 subpopulation.
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5.3.6. Statistical Uncertainty at the POD

1 There is uncertainty in the selection of the BMR level. For decreased brain weight in 

2 rats, no established standard exists, so a BMR of one S.D. change from the control mean was 

3 used. Parameter uncertainty can be assessed through CIs. Each description of parameter 

4 uncertainty assumes that the underlying model and associated assumptions are valid. For the 

5 Hill model applied to the data for decreased brain weight in rats, there is a degree of uncertainty 

6 at the one S.D. level (the POD for derivation of the RfC), with the 95% one-sided lower 

7 confidence limit (BMDL) being ~50% below the maximum likelihood estimate of the BMD.

5.3.7. Choice of Bioassay

8 The NEDO (1987) study was used for development of the RfC and RfD because it 

9 resulted in the lowest BMDL. It was also a well-designed study, conducted in a relevant species 

10 with an adequate number of animals per dose group, and with examination of appropriate 

11 developmental toxicological endpoints. Developmental (Burbacher et al., 2004a, 2004b, 1999a,

12 1999b) and chronic studies (NEDO, 1987) of methanol have been performed in monkeys. As 

13 discussed above in Section 5.3.1 and other sections of this assessment, while the monkey may be 

14 a sensitive species for use in the determination of human risk, reporting deficits and study 

15 uncertainties preclude their use in the derivation of an RfC.

5.3.8. Choice of Species/Gender

16 The RfC and RfD were based on decreased brain weight at 6 weeks postbirth in male rats 

17 (the gender most sensitive to this effect) (NEDO, 1987). This decrease in brain weight also 

18 occurs in female rats; however, if the decreased brain weight in female rats had been used, higher 

19 RfC and RfD values would have been derived (approximately 66% higher than the male derived 

20 values). 

5.3.9. Human Population Variability 

21 The extent of interindividual variation of methanol metabolism in humans has not been 

22 well characterized. As discussed in Section 4.9, there are a number of issues that may lead to 

23 sensitive human subpopulations. Potentially sensitive subpopulations would include individuals 

24 with polymorphisms in the enzymes involved in the metabolism of methanol and individuals 

25 with significant folate deficiencies. Sensitive lifestages would include children and neonates, as 

26 they have increased respiration rates compared to adults, which may increase their methanol 

27 blood levels compared to adults. Also, children have been shown to have decreased ADH 

28 activity relative to adults, thus decreasing their ability to metabolize and eliminate methanol. As 

29 demonstrated by these examples, there exists considerable uncertainty pertaining to human 
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1 population variability in methanol metabolism, which provides justification for the 10-fold 

2 intraspecies UF used to derive the RfC and RfD. 

5.4. 5.4. CANCER ASSESSMENT

5.4.1. Oral Exposure

5.4.1.1. Choice of Study/Data—with Rationale and Justification

3 No human data exist that would allow for quantification of the cancer risk of chronic 

4 methanol exposure. Table 4-34 summarizes the available experimental animal oral exposure 

5 studies of methanol. The Soffritti et al. (2002a) and Apaja (1980) oral studies report effects that 

6 show a statistically significant increase in incidence of cancer endpoints in the treated groups 

7 versus the control group (pair-wise comparison). As detailed in Section 4.2.1.3, Soffritti et al. 

8 (2002a) exposed Sprague-Dawley rats via drinking water to 500–20,000 ppm methanol for 

9 104 weeks. Exposure ended at 104 weeks, but the animals were not euthanized and were 

10 followed until their natural death. Increased lymphoma responses in multiple organs of male and 

11 female rats were the only carcinogenic effects reported in the Soffritti et al. (2002a) methanol 

12 drinking water study that are considered dose related and quantifiable. Hepatocellular 

13 carcinomas observed in male rats are considered potentially dose related (relative to historical 

14 controls) but are not quantifiable due to the lack of a statistically significant dose-response trend. 

15 Significant increases reported for head and ear duct carcinomas in male rats were not used 

16 because NTP pathologists interpreted a majority of these ear duct responses as being 

17 hyperplastic, not carcinogenic, in nature (EFSA, 2006; Hailey, 2004). Apaja (1980) observed 

18 significant increases in malignant lymphomas relative to untreated, historical controls in Swiss 

19 mice exposed to methanol in drinking water for life. Due to the lack of a concurrent control, the 

20 Apaja (1980) study was not considered adequate for derivation of an oral slope factor, but a 

21 quantitative analysis of the dose-response data from this study is included in Appendix E for 

22 comparison purposes.

5.4.1.2. Dose-Response Data

23 The tumor incidence data selected for modeling were the lympho-immunoblastic

24 lymphomas and the combined lympho-immunoblastic, lymphoblastic and lymphocytic 

25 lymphomas in both male and female rats of the Soffritti et al (2002a) study. These lymphomas 

26 were combined at the recommendation of NTP pathologists due to their similar histological 

27 origin. The incidence of histiocytic sarcomas and myeloid leukemias was not significantly 

28 increased in either sex, and the data for these tumors was not combined with the lymphoblastic 

29 lymphomas because they are of a different cell line and the combination is not typically 

30 evaluated either for statistical significance or dose-response modeling (Hailey, 2004; McConnell, 

31 et al., 1986). Table 5-6 gives the lymphoma incidence data from the study which differs slightly 
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1 from the data reported in Soffritti et al. (2002a) in the incidence of lympho-immunoblastic

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 animals 

examined

Lymphoma lymphoimmunoblasticAll lymphomas 

combined

Female rats

0 0 100 9 9

500 66.0 100 17 19a

5,000 624.1 100 19a 20a

20,000 2,177 100 21a 22b

Male rats

0 0 100 16 17

500 53.2 100 24 27

5,000 524 100 28a 29a

20,000 1,780 99 37b 38b

Statistically significant by Fisher’s Exact test: ap < 0.05, bp < 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 Method

3 As with the extrapolations used in the development of the RfC and RfD, the PBPK model 

4 was used for species-to-species extrapolation of the doses to be used in the cancer dose-response 

5 analysis. Three dose metrics were considered for use in the dose-response analysis: total 

6 metabolized methanol; maximum blood concentration of the parent (Cmax); and area under the 

7 blood concentration time curve (AUC) for the parent. Internal dose estimates (above 

8 background) corresponding to the administered doses from the animal bioassay were determined 

9 for each of these metrics with the PBPK model (see Appendix E, Table E-5). To help inform the 

10 selection of the most appropriate dose metric, dose-response analyses were performed using 

11 these PBPK model results to assess which dose metric best corresponded to the observed 

12 incidence data in Table 5-6 (see Appendix E, Tables E-6 and E-7). Figures 5-3, 5-4, and 5-5 

13 show the fit of the multistage model to the all lymphoma incidence data for female and males, 

14 using each dose metric as the dose input.

 79 EPA obtained detailed, individual animal data via an interagency agreement with NIEHS which supported the 

development 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 

histopathological origin and confirm the tumor incidences reported in the Soffriti et al. (2000a) paper. 
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Figure 5-3. All lymphomas versus methanol metabolized (mg/day) for female and male 

rats

Figure 5-4. All lymphomas versus Cmax (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 100000

Fraction 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 50 100 150 200

Fraction 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 80 100 120 140

Fraction Affected

dose

Multistage Cancer Model

22:46 07/23 2009

Multistage Cancer

Female rats (p = 0.18)

 0.05

 0.1

 0.15

 0.2

 0.25

 0.3

 0 20000 40000 60000 80000 100000

Fraction Affected

dose

Multistage Cancer Model

22:41 07/23 2009

Multistage Cancer

Female rats (p = 0.075)

 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 4500

Fraction Affected

dose

Multistage Cancer Model

22:38 07/23 2009

Multistage Cancer

Male rats (p = 0.15)

 0.05

 0.1

 0.15

 0.2

 0.25

 0.3

 0 1000 2000 3000 4000 5000

Fraction Affected

dose

Multistage Cancer Model

22:35 07/23 2009

Multistage Cancer

Female rats (p = 0.077)
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1 The dose-response modeling suggests that total metabolized methanol is a better dose 

2 metric than the parent compound metrics as indicated by improved model fit to responses 

3 reported by Soffritti et al. (2002a) for both lympho-immunoblastic lymphoma (see Appendix E, 

4 Table E-7) and all lymphoma (see Figures 5-3 to 5-5 and Appendix E, Table E-7), and also for 

5 malignant lymphoma responses reported by Apaja (1989) (see Appendix E, Table E-17). Chi6 square p values for the total metabolite dose metric ranged from 0.18 to 0.55 and were 

7 consistently higher than for the other dose metrics. This could be an indication of the importance 

8 of metabolite formation, which is likely to be more rapid at low doses, to the carcinogenic 

9 response. The total metabolized methanol dose metric was selected as the dose metric for use in 

10 the dose-response assessment to derive the POD because it provided the best fit to the response 

11 data. The estimated BMDL for the total methanol metabolized dose metric was then back12 calculated using the EPA PBPK model to obtain a human equivalent oral drinking water dose in 

13 terms of mg/kg-day (see Appendix E, Table E-8).

14 Multistage and multistage Weibull time-to-tumor models were applied to the lymphoma 

15 data obtained from ERF for the Sofffritti et al. (2002a) drinking water study and considered for 

16 determining the POD to be used in the derivation of the oral cancer slope factor (see Appendix E, 

17 Table 3-8). Appendix E gives the details and justification for the various approaches used. As 

18 described in Appendix E, time-to-tumor modeling and multistage quantal modeling gave similar 

19 results, and the tumor responses modeled did not exhibit significant time dependence on dose. 

20 The EPA multistage cancer model fit the response data adequately and was used to derive the 

21 oral cancer slope factor (CSF) (see Appendix E, Tables E-7, E-8, and Figure E-10).

22 BMDs and BMDLs were estimated for the combined lymphomas in male and female rats. 

23 The BMR selected was the standard value of 10% extra risk recommended for dichotomous 

models (U.S. EPA, 2000b).

80 24 The 95% one-sided lower confidence limit defined the BMDL. 

25 The dose terms in the fitting were set equal to the estimated total metabolized doses derived 

26 using the PBPK model for methanol for each of the administered doses in the bioassay. 

27 Application of the multistage model to the incidence data for all lymphomas in male rats 

28 (Table 5-6) resulted in the BMD and BMDL10 values presented in Table 5-7. The results for the 

29 male rat were used in the derivation of the CSF because the female data for this endpoint yielded 

30 slightly higher values (see Appendix E, Tables E-7 and E-8). Assuming that metabolized 

31 methanol distributes in the body according to body weight to the 3⁄4 power, the male rat mg-day 

BMDL10 was converted to a human mg-day BMDL10.

81 32 The human PBPK model (Appendix B) 

33 was then used to convert this human mg-day value for total methanol metabolized back to a 

 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 multiplying by (BWhuman)

3⁄4/(BWrat)3⁄4 =(70 kg)3⁄4/(0.33 kg )3⁄4= 

55.6
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1 human equivalent methanol oral dose HED(BMDL10) of 51.5 mg/kg-day for lymphomas in the 

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 

BMDL10

(mg/kg-day)

Oral CSF

(mg/kg-day)

-1

BMD10

(mg/d)

Rat BMDL10

(mg/d)

Estimated human

BMDL10 (mg/d)

101.7 63.9 3,553 51.5 1.9E-03

Source: Soffritti et al. (2002a).

3 In the case of methanol, there is no information to inform the MOA for carcinogenicity. 

4 As recommended in the Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), “when 

5 the weight of evidence evaluation of all available data is insufficient to establish the MOA for a 

6 tumor site and when scientifically plausible based on the available data, linear extrapolation is 

7 used as a default approach.” Accordingly, for the derivation of a quantitative estimate of cancer 

8 risk for ingested methanol, a linear extrapolation was performed to determine the CSF.

5.4.1.4. Oral Slope Factor

9 The oral slope factor was derived based on a linear extrapolation from this POD 

10 (BMDL10/HED of 55.4 mg/kg-day for lymphomas in the male rat) to the estimated background 

11 response level: 

0.1/ HED(BMDL10) = 0.1/51.5 mg/kg-day = 2E-03 (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

13 No human data exist that would allow for quantification of the cancer risk associated with

14 chronic methanol exposure. Table 4-35 summarizes the available experimental animal inhalation 

15 exposure studies of methanol. The NEDO (1987, 1985/2008b) 24-month rat study is the only 

16 inhalation bioassay available that reports an increase in incidence of any cancer endpoints (see 

17 Section 4.2.2.3). This NEDO (1987, 1985/2008b) study was of high quality and was based on 

18 standard OECD guidelines (OECD, 2007). F344 rats were exposed for 104 weeks to air 

19 concentrations of 0, 10, 100 and 1,000 ppm methanol. Rats were sacrificed and necropsied at the 

 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-d
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1 end of the 104-week exposure period. The NEDO (1987, 1985/2008b) study reports increased 

2 pulmonary adenomas/adenocarcinomas and pheochromocytomas in high-dose (1,000 ppm) male 

3 and female rats, respectively. The combined incidence of pulmonary adenomas and 

4 adenocarcinomas was significantly increased in the high-dose males (see Tables 4-4 and 5-8),

5 and both tumor types were considerably elevated at the high-dose over historical control 

6 incidences within their respective sex and strain (see discussion in Section 4.2.2.3). As shown in 

7 Table 4-5, the severity and combined incidence of potential precursor effects in the alveolar 

8 epithelium of male rat lungs (epithethial swelling, adenomatosis, pulmonary adenoma, and 

9 pulmonary adenocarcinoma) and the adrenal glands of female rats (hyperplasia and 

10 pheochromocytoma) were increased in the higher exposure groups compared with the controls 

11 and lower exposure groups. While the incidence of male rat pulmonary adenomas was also high 

12 in the lowest (10 ppm) exposure group, the appearance of a rare adenocarcinoma in the high13 dose group is suggestive of a progressive effect associated with methanol exposure. While the 

14 increased pheochromocytoma response in female rats is not statistically increased over controls, 

15 it is considered to be potentially treatment related because this is a historically rare tumor type 

for female F344 rats (NTP, 2007, 1999; Haseman et al., 1998),

83 16 and when viewed in conjunction 

17 with the increased medullary hyperplasia observed in the mid-exposure (100 ppm) group 

18 females, it is suggestive of a proliferative change with increasing methanol exposure.

5.4.2.2. Dose-Response Data

19 The tumor incidence data selected for modeling are the NEDO (1987, 1985/2008b) 

20 reported incidences of adenoma/adenocarcinoma in male rats and pheochromocytoma in female 

21 rats. These data are presented in Table 5-8.

Table 5-8. Incidence data for tumor responses in male and female F344 rats

Dose (ppm) Number of animals affected/number examined

Pheochromocytoma Pulmonary adenoma/adenocarcinoma

Female rats

0 2/50 2/52

10 3/51 0/19

100 2/49 0/20

1000 7/51a,b 0/52

Male rats

0 7/52 1/52

10 2/16 5/50

100 2/10 2/52

1000 4/51 7/52b,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.
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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 Adjustments and Extrapolation Method

1 As with the extrapolations used in the development of the RfC and RfD, the PBPK model 

2 was used for species-to-species extrapolation of the doses to be used in the cancer dose-response 

3 analysis. Three dose metrics were considered for use in the dose-response analysis: total 

4 metabolized methanol, maximum blood concentration of the parent (Cmax), and area under the 

5 blood concentration time curve (AUC) for the parent. Each of the dose metrics corresponding to 

6 the administered dose from the animal bioassay was determined with the PBPK model (see 

7 Appendix E, Table E-10). To help inform the selection of the most appropriate dose metric, 

8 dose-response analyses were performed using these PBPK model results to assess which dose 

9 metric best corresponded to the observed incidence data in Table 5-8 (see Table E-11). All of 

10 the dose metrics resulted in similar fit to the incidence data for both endpoints, with the total 

11 metabolites metric providing a slightly improved fit to the female pheochromocytoma response 

12 data.

13 Unlike the oral data discussed in Section 5.4.1.2, dose-response modeling of the 

14 inhalation data from NEDO (1987, 1985/2008b) does not suggest the use of any one dose metric 

15 over the other. However, since the pheochromocytoma response likely involves systemically 

16 distributed, metabolized methanol, and to be consistent with the oral CSF, analysis the total 

17 methanol metabolized dose metric is selected as the dose metric for use in the dose-response 

18 assessment to derive the inhalation POD. The estimated BMDL for the methanol metabolized 

19 dose metric was then back-calculated using the EPA PBPK model to obtain a human equivalent 

20 air exposure concentration in terms of mg/m3 (see Table E-13).

21 The EPA multistage model was applied to the data in Table 5-8 obtained from the NEDO 

22 (1987, 1985/2008b) inhalation study and considered for determining POD to be used in the 

23 derivation of the inhalation cancer unit risk (Table E-11). Appendix E gives the details and 

24 justification for the various approaches used. As described in Appendix E, time-to-tumor and 

25 quantal modeling gave similar results, and the tumor responses modeled did not exhibit 

26 significant time dependence on dose. The EPA multistage cancer model fit the response data 

27 adequately and was used to derive the IUR (Tables E-11, E-12, and Figure E-13).

28 BMDs and BMDLs were estimated for tumor responses in male and female rats as shown 

29 in Table 5-8. The BMR selected was the standard value of 10% extra risk recommended for 
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quantal models (U.S. EPA, 2000b).

84 1 The 95% one-sided lower confidence limit defined the 

2 BMDL. The dose terms in the fitting were set equal to the estimated total metabolized doses 

3 derived using the PBPK model for methanol for each of the administered doses in the bioassay. 

4 Application of the multistage model to the incidence data for pheochromocytomas in 

5 female rats (Table 5-8) resulted in the BMD and BMDL10 values presented in Table 5-9. The 

6 results for the female rat were used because the female data for pheochromocytoma yielded 

7 slightly lower BMDL values (Tables E-11 and E-13). Assuming that metabolized methanol 

8 distributes in the body according to body weight to the 3⁄4 power, the female rat mg-day BMDL10

was converted to a human mg-day BMDL10.

85 9 The human PBPK model (Appendix B) was then 

10 used to convert this human mg-day value for total methanol metabolized back to a human 

equivalent methanol inhalation concentration, HEC(BMCL10), of 81.9 mg/m3 or 81,900 μg/m3 11

for pheochromocytomas in the female rat (see Appendix E, Table E-13).86 12

Table 5-9. BMD results and IUR using pheochromocytoma in female rats

Amount metabolized (mg/day)

Human equivalent 

BMCL10

(mg/m3

)

IUR

(μg/m3

)

-1

BMD10

(mg/day)

BMDL10 

(mg/day)

Estimated human

BMDL10 (mg/day)

29.5 14.6 971 81.9 1.2E-06

Source: NEDO (1987, 1985/2008b).

5.4.2.4. IUR

The IUR in terms of (μg/m3) -1 13 was then derived based on a linear extrapolation from this 

14 POD to the estimated background response level:

0.1/ HEC(BMCL10) = 0.1/(81,900 μg/m3) = 1E-06 (μg/m3)

-1 15

(rounded to one significant figure)

5.4.3. Uncertainties in Cancer Risk Assessment

16 The following is a discussion of the uncertainties associated with the cancer potency 

17 estimate for methanol beyond that which can be addressed with the quantitative approach 

18 applied. A summary of these uncertainties is presented in Table 5-10.

 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 (BWhuman)

3⁄4/(BWrat)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/m3

:

HED = (14.69 x 971 mg/d)/(19282.9 - 971 mg/d) + (0.063554 x 971 mg/d) = 81.9 mg/ m3
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Table 5-10. 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 chronic 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 Soffritti 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 

determination 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 and EFSA recommend that only 

lymphomas of the same cellular origin be 

combined for dose-response analysis. With 

respect to lung and adrenal tumors, examination of 

concurrent alveolar and adrenal noncancer 

hyperplastic endpoints supports a proliferative 

change 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 tissue 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 Section 4.9. concerning human 

studies of methanol metabolite formaldehyde).

Choice of 

endpoint for 

POD derivation

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/gender

CSF and IUR would 

be ↓ if based on 

another gender

CSF and IUR are 

based on the most 

sensitive and reliably 

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 model 

for POD 

derivation

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 multistage 

model provides adequate fit to the data, which is 

not improved by a time-to-tumor modeling.

Choice of 

animal-to-human 

extrapolation 

method

Traditional method 

could ↑ the 

HEC(BMCL10) 

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

1 The protocols used at the laboratories that have performed cancer bioassays of methanol, 

2 particularly those of the ERF, differ from the more commonly used (e.g., NTP) protocols. The 

3 unique features of the ERF study design and their implications to a methanol cancer risk 
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1 assessment are discussed in Section 4.9.2. Separate from these experimental design issues are 

2 considerations relative to the quality of the cancer bioassays and any associate uncertainties. 

3 The number of animals per dose group in ERF studies is often higher than the 50 animals 

4 per sex per dose group typically used in EPA and NTP studies, increasing the statistical power of 

5 the ERF cancer bioassay. However, ERF sometimes shares controls across concurrent studies 

6 (Cruzan, 2009; Belpoggi et al., 1995). In contrast, EPA requires (U.S. EPA, 1998) and NTP 

7 generally uses (Melnick et al., 2007) concurrent, matched controls for each carcinogen bioassay. 

8 The use shared controls does not necessarily compromise a study, but the use of a concurrent, 

9 matched control is generally preferred as a means of further avoiding confounding factors and 

10 increasing the reliability of a study regarding the interpretation of findings in treated animals.

11 The published report of the methanol bioassay (Soffritti et al., 2002a) indicates that the 

12 experiment was performed according to good laboratory practice (GLP) and standard operating 

13 procedures (SOP) of the ERF. Further, an independent review of ERF (Huff, 2002) suggests that 

14 quality control procedures associated with GLP were in place. However, questions have been 

15 raised about the quality of studies at the ERF by European Food Safety Authority (EFSA, 2006) 

16 in regards to the aspartame bioassays conducted by the ERF (Soffritti et al., 2006); by extension 

17 this EFSA report has raised issues for consideration in regards to methanol. EFSA (2006) has 

18 suggested that an inspection by the Italian GLP compliance monitoring authority (Ministry of 

Health) necessary to confirm GLP had not been conducted.87 19 The EFSA (2006) report also 

20 identifies specific deviations from OECD guidelines (OECD, 2007), including a lack of a 

21 complete analysis of the test substance, no clear information on the stability of the substance, a 

22 lack of clinical observations or macroscopic changes, a lack of hematological assays, a lack of 

23 serology (e.g., to confirm the presence of infection) and limited histopathology reports. While 

24 these details may be recorded internally by the ERF as part of their standard protocol, because 

25 there is no documentation of these details available for consideration, there remains some 

26 uncertainty regarding the level at which they were performed. There is limited evidence, 

27 however, that these factors had a significant impact on the adequacy of the study for assessing 

28 carcinogenic potential (see Sections 4.2.1.3, 4.9.2 and 5.4.3.2).

29 EFSA (2006) also expresses concern over the possibility of compromised pathological 

30 diagnosis in the ERF aspartame study (Soffritti et al., 2006) due to extensive autolysis. ERF 

31 performs pathological examinations on “dying animals undergoing necropsy” (Soffritti et al., 

32 2002a). This creates difficulties in pathological examinations associated with cell autolysis that 

33 can occur when pathology slides are prepared after natural death. The NTP (Hailey, 2004) 

34 commented on the increased prevalence of autolysis in slides from the ERF (Soffritti et al., 2006) 

35 aspartame study. EPA conducted a detailed analysis of the individual animal tissue data obtained 

 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 obtaining this certification.
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1 from ERF for their chronic methanol, MTBE, formaldehyde, and aspartame studies, and 

2 determined that autolysis and other causes of tissue loss did not substantially impact tissue 

3 denominators. For most of the tissues evaluated there were more than 96 (individual animal) 

4 samples available for microscopic evaluation, and for all sites and dose groups, denominators 

5 were larger than for routine NTP bioassays (i.e., >50). Thus, missing tissues does not appear to 

6 have been a serious problem in the methanol study. While this analysis does not completely rule 

7 out the possibility that pathology slides and diagnoses were impacted by autolysis, it does 

8 indicate that this possibility would be offset by the large group size (response denominator) 

9 employed. Further, even if autolysis was a confounding factor, its presence would not negate 

10 positive cancer findings as autolysis would tend to decrease, not increase, the power to observe 

11 an effect. 

12 There were no differences in survival among the methanol dose groups of the Soffritti et 

13 al. (2002a) study. However, Cruzan (2009) has suggested that “While the survival at 104 weeks 

14 was within the normal, but widespread, range for Sprague-Dawley rats, there was significant 

15 early mortality among all groups, including the controls” and that “the control group from an 

16 inhalation study (Cruzan et al., 1998) had much better survival through 104 weeks than seen in 

17 the RF methanol study.” Yet, according to Table 12 of the Cruzan (2009) article, 104-week 

18 survival in male (~40%) and female (~50%) control rats of the Cruzan et al. (1998) study was 

19 not discernibly different from the 104 week survival of male (~40%) and female (~50%) control 

20 rats of the Soffritti et al. (2002a) methanol study (see Appendix E, Figures E-1 and E-2). 

21 Survival of male and female Sprague-Dawley rats in the Soffritti et al. (2002a) study at 

22 104 weeks was greater than 40% in all but the female 5,000 ppm group. Further, at the NTP 

23 (NTP, 2006), the 104-week survival of 353 control female Sprague-Dawley rats was 41.5% 

24 (range of 28.3%–51% in 7 studies) using NTP’s new diet and corn oil gavage, indicating that 

25 survival in the Soffritti et al. (2002a) methanol study was not low. 

26 Studies such as the Soffritti et al. (2002a) methanol study that allow test animals to live a 

27 full life span can be difficult to interpret due to the need to distinguish between age-related and 

28 chemical-related effects. Full life-span studies may have advantages. Huff et al. (2009) note that 

29 “studies truncated after 2 years of exposure do not allow sufficient latency periods for late30 developing tumors, such as the 80% of all human cancers that occur after 60 years of age.” 

31 Several recent publications have noted deficiencies with the 2-year study design used at the NTP 

32 and have recommended extending the duration of rodent studies to increase the sensitivity of 

33 their bioassays (Huff et al., 2009; Huff et al., 2007; Maronpot et al., 2004; Bucher et al., 2002). 

34 While arguments have been documented related to the possible confounding influence of 

35 infection and autolysis on the results obtained from the ERF, available evidence does not indicate 

36 that these factors significantly influenced the observed lymphoma/leukemia response in the 

37 methanol or other bioassays conducted at ERF. In addition, for the purposes of this assessment 
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1 and at the request of the EPA, the ERF and NEDO have provided additional study details beyond 

2 that which is normally available from published journal articles, including quality assurance 

3 reports and individual animal data. Based on a review of this information, consideration of the 

4 issues, and absent additional data to the contrary, EPA has determined that both studies were 

5 sufficient for use in the assessment of risk from methanol exposure.

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

6 There are a number of uncertainties regarding the interpretation of both the lymphoma 

7 response in male Sprague-Dawley rats (Soffritti et al., 2002a) that forms the basis of the oral 

8 CSF and the pheochromocytoma response in F344 rats (NEDO, 1985/2008b) that forms the basis 

9 for the inhalation CSF.

10 There is also a wide range in the background incidence of hemolymphoreticular tumors 

11 reported in control groups of ERF studies. Between 1984 and 1997, incidence rates of 

12 hemolymphoreticular neoplasms in control rats at ERF increased by 38% among male rats and 

13 decreased by 12% among female rats (Caldwell et al., 2008). Soffritti et al. (2007, 2006) reports 

14 that among 2,265 untreated males and 2,274 untreated females the average incidence of 

15 lymphomas and leukemias is 20.6% (range, 8.0-30.9%) in males and 13.3% (range, 4.0-25%) in 

16 females. Caldwell et al. (2008) noted that for the incidences of these lesions for the ERF colony 

17 are relatively low and stable across studies. EFSA (2006) and Cruzan (2009) consider it to be 

18 high relative to other tumor types and relative to the background rate for this tumor type in 

19 Sprague-Dawley rats from other laboratories (see Section 4.9.2 for further discussion).

20 In the ERF bioassays, including methanol, hemolymphoreticular neoplasms were divided 

21 into specific histological types (lymphoblastic lymphoma, lymphoblastic leukemia, lymphocytic 

22 lymphoma, lympho-immunoblastic lymphoma, myeloid leukemia, histocytic sarcoma, and 

23 monocytic leukemia) for identification purposes. Upon examining slides from the aspartame 

24 study conducted by the ERF, a PWG of the NTP at the NIEHS (Hailey, 2004) found that “The 

25 diagnoses of lymphatic and histocytic neoplasms in the cases reviewed were generally 

26 confirmed. The NTP does not routinely subdivide lymphomas into specific histological types as 

27 was done by the ERF, however the PWG accepted their more specific diagnosis if the lesion was 

28 considered to be consistent with a neoplasm of lymphocytic, histocytic, monocytic, and/or 

29 myeloid origin.” The NTP PWG also noted that while lymphoblastic lymphomas, lymphocytic 

30 lymphomas, lympho-immunoblastic lymphomas and lymphoblastic leukemias as malignant 

31 lymphomas can be combined, myeloid leukemias, histocytic sarcomas and monocytic leukemia 

32 should be treated as separate malignancies and not combined with the other lymphomas for 

33 statistical evaluation since they are of different cellular origin (Hailey, 2004). Other researchers 

34 have also noted this distinction between myeloid leukemias and histiocytic sarcomas and other 

35 lymphomas (McConnell et al., 1986). To decrease the uncertainty in the combination of tumors 

36 relied upon for dose-response modeling, the current dose-response modeling conducted for 
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1 methanol did not include myeloid leukemia, histocytic sarcoma, monocytic leukemia, alone or in 

2 combination with lymphoblastic lymphoma, lymphoblastic leukemia, lymphocytic lymphoma, 

3 and lympho-immunoblastic lymphoma.

4 As expressed by EFSA (2006) and others (Cruzan, 2009; Schoeb et al., 2009), there is 

5 concern that the lympho-immunoblastic lymphoma response in the ERF aspartame study 

6 (Soffritti et al., 2006, 2005) was caused by or confused with sequalae of a M. pulmonis infection. 

7 Infection of the ERF colony with M. pulmonis has not been confirmed (Caldwell et al., 2008)

8 and, as discussed in Section 4.9.2, a link between M. pulmonis infection and induction of 

9 lymphoma in rats has not been established in the literature. As noted in Section 4.9.2, there is 

10 evidence suggesting that respiratory infections may have confounded the interpretation of lung 

11 lesions in ERF studies. Lymphoma illustrations in 2 ERF studies (Figure 10 of Soffritti et al. 

12 [2005] and Figures 1-5 of Belpoggi et al. [1999]), suggest that ERF MTBE and aspartame 

13 bioassays may have been confounded by a respiratory infection such as M. pulmonis and that 

14 lesions associated with this infection may have been interpreted as lymphoma (Schoeb et al., 

15 2009). However, other ERF lymphoma diagnoses in multiple rat organ systems, including the 

16 lung, have been confirmed by an independent working group panel of six NIEHS pathologists

17 (Hailey, 2004). In addition, the incidence of “lung-only” lympho-immunoblastic lymphomas 

18 was evenly distributed across the control and 0, 500 5000, 20,000 ppm dose groups for male (9, 

19 10, 14 and 13) and female (3, 5, 6 and 7) rats of the Soffritti et al. (2002a) methanol study 

20 (Schoeb et al., 2009). Consequently, removing “lung-only” lympho-immunoblastic lymphomas 

21 from consideration and using only lymphomas from organ systems not likely to be confounded 

22 by a respiratory infection (i.e., subtracting the lung-only incidence from lympho-immunoblastic 

23 lymphomas reported in Table 5-6) still results in significant dose-response curves for this lesion 

24 with p values of 0.20 and 0.42 for males and females, respectively (see Figure 5-6). The 

25 BMDL10 estimates of 99 (males) and 110 (females) mg metabolized methanol/day are about 50%

26 higher than metabolized methanol BMDL10 estimates for this endpoint when “lung-only” 

27 responses are included (Appendix E, Table E-7).

28

29
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1

Figure 5-6. Lympho-immunoblastic Lymphoma minus “lung-only” response in rats of 

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

2 For increases in 2 other tumor types (ear duct and head/oral cavity tumors) reported in the 

3 ERF methanol study (Table 4-2), an independent review of the 75 pathology slides from the ERF 

4 aspartame study has suggested differences in interpretation. After reviewing these slides, the 

5 NTP PWG noted that “about half” of hyperplastic and neoplastic lesions in the ear duct or oral 

6 cavity were more severely classified by ERF study pathologists, compared with diagnosis from 

7 the PWG (EFSA, 2006). Though a similar review has not been conducted of the Soffritti et al. 

8 (2002a) ERF methanol bioassay results, there is uncertainty regarding the ERF interpretation of 

9 these lesions. For this reason, these lesions were not considered in the derivation of the oral 

10 CSF. 

11 Another uncertainty that confounds the interpretation of some ERF studies is the 

12 possibility of litter effects in ERF test groups. Bucher (2002) has reported that ERF does not

13 randomize the assignment of animals to treatment groups, but generally “assigns all animals 

14 from a given litter to the same treatment group, recording which litter each animal came from.”

15 This approach would make it more difficult to distinguish the relative contributions of the 

16 chemical and genetics to the effect of concern. In response to an EPA query regarding this matter

17 (Knowles, 2008), ERF has stated that “the assignment of test animals to dose groups will vary in 

18 ERF studies according to the experimental protocol and aims of the research” and “in the case of 

19 experiments in which exposure begins at 6-8 weeks of age (for example BT960, methanol), 

20 randomization is performed so as to have no more than one female and one male from each litter 

21 in each experimental group.” For other experiments in which exposure begins during prenatal 

life, 88 22 “randomization is performed on the breeders,” but the offspring are not randomized across 

23 dose groups in order to “..simulate as much as possible the human situation in which all 

24 descendents are part of a population.”

 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 

cancer 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 50 100 150 200

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

08:35 09/04 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

 0.05

 0.1

 0.15

 0.2

 0 50 100 150 200

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

08:40 09/04 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

Males (p= 0.20) Females (p= 0.42)
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1 There is uncertainty regarding the pheochromocytoma response observed in the NEDO 

2 (1985/2008b) study with respect to both its relation to exposure and to its pathology. As 

3 discussed in Section 4.2.2.3, the incidence of pheochromocytomas in female rats exhibited a 

4 dose-response trend (Cochrane-Armitage p < 0.05). While the incidence of 13.7% (7/51) in the 

5 high-dose group was significantly elevated (p < 0.05) over NTP historical controls, it was not 

6 significantly elevated over the concurrent control rate of 4% (2/50). Much of the uncertainty is 

7 inherent in difficulties associated with the characterization of this lesion. According to NTP 

8 (2000), the primary criterion used to distinguish pheochromocytomas from nonneoplastic adrenal 

9 medullary hyperplasia, the presence of mild-to-moderate compression of the adjacent tissue, can 

10 be a difficult determination. Further, while NEDO (1985/2008b) reported adrenal effects as 

11 “hyperplasia of medullary cells” and “pheochromocytoma,” NTP pathologists categorize 

12 pheochromocytomas into three types: benign, complex and malignant (NTP, 2007, 1999). This 

13 is an important distinction as complex and malignant pheochromocytomas are a rare tumor type, 

14 occurring spontaneously in female F344 rats at rates ranging from 0.1% to 0.7% (NTP, 2007, 

15 1999; Haseman et al., 1998) and with cell proliferation activity that is much higher than benign 

16 pheochromocytomas (Pace et al., 2002). Thus, severity of the pheochromocytoma response 

17 reported by NEDO (1985/2008b) is uncertain, potentially ranging from mischaracterized 

18 hyperplasia to highly proliferative and potentially metastatic malignancies. Finally, the NEDO 

19 study was a two-year study, and these lesions, which include diffuse hyperplasia, nodular 

20 hyperplasia, and pheochromocytoma, progress with age. Thus, it is possible that a life-span

21 study would have detected a more severe carcinogenic response (e.g., progression of the mid22 dose group hyperplastic responses as reported in Table 4-5). 

5.4.3.3. Consistency across Chronic Bioassays for Methanol

23 The observation of a lymphoma response in rats (Soffritti et al., 2002a) and mice (Apaja, 

24 1980), along with reported associations between lymphomas and human exposure to methanol’s

25 metabolite, formaldehyde (see Section 4.9), contributes significantly to the cancer weight-of26 evidence determination. This was the only carcinogenic response that was observed in more than 

27 one animal bioassay. However, tissue concordance across species, strains and routes of exposure 

28 is not assumed, and a lack of such concordance does not negate the validity of individual 

29 findings nor the potential relevance of such findings to humans. 

30 Besides the Soffritti et al. (2002a) drinking water study of rats (2 years) reported by ERF, 

31 the only other chronic studies available are the Apaja (1980) lifespan drinking water study study 

32 in Swiss mice which did not include a concurrent untreated control group, and those reported in 

33 the Japanese NEDO (1987, 1985/2008a, 2008b) study of monkeys (7–29 months), mice 

34 (18 months), and F344 rats (2 years). None of the NEDO studies involved lifetime evaluations,

35 and only the rat study can be said to cover a significant portion of the test species life span. The 

36 only organ system that exhibited an increase in effects in both inhalation and drinking water 
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1 studies was the testes. High-dose rats of the Soffritti et al. (2002a) methanol study exhibited an 

2 increase in testicular interstitial cell adenomas, and the incidence of testicular hyperplasia was 

3 increased in high-dose rats of the NEDO (1987, 1985/2008b) study. However, neither effect was 

4 statistically increased over controls, and both effects were well within their historical control 

5 ranges. An increase in lymphomas was the only carcinogenic effect reported in the Soffritti et al. 

6 (2002a) and Apaja (1980) drinking water studies that is considered dose related and quantifiable, 

7 and male pulmonary adenoma/adenocarcinoma and female pheochromocytoma were the only 

8 carcinogenic effects from the NEDO (1987, 1985/2008b) inhalation study that are considered 

9 exposure related and quantifiable.

10 As discussed in Section 4.9.2, there are several differences between the NEDO (1987,

11 1985/2008b) and Soffritti et al. (2002a) studies conducted in rats which limit their direct 

12 comparison and may explain some of the differences in response. These include route of 

13 exposure, lifetime evaluation period, and strain of species used. Pulmonary adenomas observed 

14 in the NEDO inhalation study could be portal-of-entry effects associated with the inhalation 

15 route of exposure. Differences in systemic effects observed in the two studies such as the 

16 pheochromocytoma response in the NEDO (1987, 1985/2008b) study and the lymphoma 

17 responses observed in the Soffritti et al. (2002a) study are systemic responses and differences 

18 would not be expected based on route of exposure. Differences in the evaluation period between 

19 the two studies may have contributed to the lack of endpoint concordance. In the Soffritti et al. 

20 (2002a) study, the animals were administered methanol for 104 weeks and then followed until 

21 the completion of their natural lifetime. The average life span for these animals was 94 and 96 

22 weeks for males and females, respectively, with animals living as long as 153 weeks (female). 

23 However, this does not explain the difference in lymphoma response between the studies as 

24 many of the lympho-immunoblastic lymphomas (most common type observed) were observed 

25 prior to 104 weeks (control – 5/9; 500 ppm – 7/17; 5,000 ppm – 13/19; 20,000 ppm – 11/21). The 

26 NEDO (1987, 1985/2008b) study was conducted in F344 rats versus the Sprague-Dawley rats 

27 used in the Soffritti et al. (2002a) ERF study. More importantly, the background rates of selected 

28 types of lymphomas and leukemias are very different between the two strains of rats. In the 

29 F344 rat, there is a high background rate of mononuclear cell leukemia, while there is a much 

30 lower background rate of this leukemia type in the Sprague-Dawley rat (Caldwell et al., 2008). 

31 The types of lymphoma reported in the Sprague-Dawley rat by Soffritti et al. (2002a) following 

32 methanol administration are rarely diagnosed in the F344 rat. Thus, the strain difference 

33 between the two studies is a likely explanation for the fact that lymphomas were only increased 

34 in the Soffritti et al. (2002a) rat study. NTP (2007, 1999) reports do not suggest a significant 

35 difference between F344 and Sprague-Dawley rats with respect to pulmonary adenomas and 

36 pheochromocytomas. Another possible explanation for this difference includes a different 

37 profile of circulating metabolites associated with oral first-pass liver metabolism.
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5.4.3.4. Choice of Endpoint for POD Derivation

1 Keeping in mind the aforementioned uncertainties associated with the interpretation of 

2 the Soffritti et al. (2002a) and NEDO (1985/2008b) study results, the choice of tumors to use for 

3 the derivation of the oral and inhalation CSFs was made on the basis of the appearance of a dose4 related increase in response rates for the selected tumor categories. Analysis of lymphomas used 

5 a pair-wise statistical comparison (Fisher’s Exact test) and a trend test (Cochran Armitage trend 

6 test) to determine whether the slope of that trend was statistically significantly greater than 0. 

7 The Fisher’s Exact result for the male rat high-dose group and the results of the Cochran 

8 Armitage Trend Test were both p < 0.01. The decision not to include the liver tumors in the 

9 dose-response analysis was made based on a lack of dose response according to pair-wise and a 

10 trend test results versus concurrent controls This is not to say that the slight increase in the 

11 incidence of this tumor type observed in all dose groups of male rats was not related to methanol 

12 exposure (this is a relatively rare Sprague-Dawley rat tumor), only that the increase was not 

13 statistically significant and did not contribute significantly to the overall tumor response.

14 As discussed in Sections 4.2.2.3 and 4.9.2, the high-dose incidences for pulmonary 

15 adenomas/adenocarcinomas were increased over concurrent controls (p < 0.05). While the high16 dose incidences of pheochromocytomas in the NEDO (1985/2008b) study were not statistically 

17 increased over concurrent controls, the dose-response for both tumor types represents increasing 

18 trends (Cochran Armitage trend test; p < 0.05), and in both cases, the high-dose response 

19 incidences were considerably elevated over historical control incidences (p < 0.05) within their 

20 respective sex and strain. Further, both tumor responses are accompanied by proliferative 

21 changes (e.g., hyperplastic responses) in their respective cell types that suggest tumor 

22 progression. 

5.4.3.5. Choice of Species/Gender 

23 The oral CSF was based on male rat lymphomas rather than female rat lymphomas. The 

24 inhalation IUR was based on female rat pheochromocytomas rather than male rat 

25 adenoma/carcinomas. In both cases, the gender that exhibited the steeper dose response and the 

26 higher risk estimate was chosen. 

27 Both the CSF and IUR were based on rat studies. Use of the Apaja (1989) mouse data 

28 would have resulted in a 5-fold higher, but less reliable oral CSF due to a high level of 

29 uncertainty associated with the Apaja (1989) study, which contained limited experimental detail

30 and did not include a concurrent control group (see Section 4.2.1.3).

5.4.3.6. Choice of Model for POD Derivation

31 The multistage-cancer model contained in EPA’s BMDS version 2.1 was used to derive 

32 both the CSF and IUR estimates for methanol. When no biologically-based cancer model exists 

33 and evidence for a nonlinear cancer MOA is lacking, as is the case for methanol, the preference 
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1 within the EPA’s IRIS program is for the use of a multistage model. There is uncertainty 

2 associated with whether the multistage model is the most appropriate choice, but in the absence 

3 of a biologically based model, dose-response modeling is largely a curve-fitting exercise, and the

4 multistage model is sufficiently flexible for most cancer bioassay data. In the case of the oral 

5 CSF, individual animal response data was obtained from the authors of the principal study 

6 (Sofffritti et al., 2002a) and a multistage-Weibull time-to-tumor model was applied to determine 

7 whether the lifespan study design of the study had an appreciable impact on the dose-response 

8 analysis. As described in Appendix E, time-to-tumor modeling and multistage quantal modeling 

9 gave similar results, and the tumor responses modeled did not exhibit significant time 

10 dependence on dose. 

11

5.4.3.7. Choice of Dose Metric 

12 The total methanol metabolized was selected over AUC or the Cmax as the most 

13 appropriate dose metric for derivation of the oral cancer slope factor and inhalation unit risk

14 primarily because it provided the best fit to response data, particularly lymphoma incidence from

15 Soffritti et al. (2002a) (see Figures 5-3 through 5-5 and Table E-7 of Appendix E) and Apaja 

16 (1989) (see Table E-17 of Appendix E). Also, lymphomas and respiratory effects have been 

17 observed in studies conducted with formaldehyde, and lymphomas have been observed in 

18 chronic bioassays conducted with other compounds that convert to formaldehyde (i.e., MTBE

19 and aspartame). As discussed in Section 4.9.3, metabolites of methanol, particularly 

20 formaldehyde, may play a role in the MOA.

21 In considering the dose-response relationship for methanol-induced carcinogenesis, a key 

22 factor is the saturation of metabolism since metabolic transformation to formaldehyde and 

23 generation of oxidative stress are considered likely candidates in the mode of action. Cruzan

24 (2009) indicates that saturation occurs at dose of “600 mg/kg,” but saturation depends on the 

25 dose rate, not the total administered dose. For example, if 600 mg/kg is given in a single bolus, 

26 the internal concentration immediately following that bolus could well be high enough to 

27 saturate metabolism, while the same total dose ingested over the course of a day might not. 

28 To aid in interpretation of the Soffretti et al. (2002a) bioassay, water ingestion in rats was 

29 assumed to shift between nocturnal (high activity) and diurnal (low activity) periods, each lasting 

30 12 hours. Rats were assumed to consume 20% of their daily water ingestion during the diurnal 

31 period and 80% during the nocturnal period. Ingestion in each period was assumed to occur in 

32 “bouts” which were treated as periods of continuous (zero-order) infusion to the stomach. 

33 During the nocturnal period each bout was assumed to last 45 minutes, followed by 45 minutes

34 without ingestion (overall period is 1.5 hours) and during the diurnal period the bout was 

35 assumed to last on 3 hours followed by 2.5 hours without ingestion (overall period is 3 hours). 
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1 Given this exposure pattern, the total amount metabolized per day (after periodicity is 

2 reached) in a 420 g rat (average weight in Soffretti et al., 2002a) was estimated using the PBPK 

3 model, with the results shown in Figure 5-7. The amount increases almost linearly with 

4 exposure until ~ 400 mg/kg/d, but continues to increase above that point, becoming almost 

5 completely saturated by 2,000 mg/kg/d. This pattern occurs in part because of the circadian 

6 ingestion pattern. The more rapid ingestion rate during the dark cycle leads to the highest 

7 internal concentrations and hence the initial metabolic saturation during that part of the day. But 

8 the lower ingestion (light) period, internal concentrations drop, allowing for an exposure range 

9 (400-1600 mg/kg/d) where nocturnal metabolism is saturated but diurnal metabolism is not.

10

Figure 5-7. Total amount metabolized per day (after periodicity is reached) in a 420 g 

rat

11 While, based on this exposure-dose pattern, one might expect a similar exposure12 response relationship, this pattern does not include detoxification mechanisms. If such 

13 mechanisms also saturate, then it is possible for the slower increase in total metabolism above 

14 400 mg/kg/d to result in a significant increase in effect, though full metabolic saturation at 

15 ~2,000 mg/kg/d would still be expected to result in a maximal effect at that exposure level.

5.4.3.8. Choice of Animal-to-Human Extrapolation Method

16 A PBPK model was used to extrapolate animal-to-human concentrations. The estimated 

17 methanol metabolized for each dose administered to the animals in NEDO (1985/2008b) and 

18 Soffritti et al. (2002a) were determined using the animal PBPK model, and then the BMDL10

19 determined by the methods described previously (Section 5.4.2.1). Assuming that metabolized 

20 methanol distributes in the body according to body weight to the 3⁄4 power, the rat mg-day 

21 BMDL10 was converted to a human mg-day BMDL10. The human PBPK model (Appendix B) 
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1 was then used to convert this human mg-day values for total methanol metabolized back to a 

2 human equivalent methanol oral dose HED(BMDL10) of 51.5 mg/kg-day for lymphomas in the 

3 male rat, and a human equivalent methanol inhalation concentration HEC(BMCL10) of 

81.9 mg/m3 4 for pheochromocytomas in the female rat. If traditional dosimetry assumptions are 

5 used, the HED(BMDL10) and HEC(BMCL10) estimates would have been approximately 4-fold 

6 higher than the value derived using the PBPK model.

7 As discussed in Sections 3.4 and 5.3.4, 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 was taken because the relationship between 

11 background doses and background responses is not known, because the primary purpose of this 

12 assessment is for the determination of noncancer and cancer risk associated with increases in the 

13 levels of methanol or its metabolites (e.g., formate, formaldehyde) over background, and because 

14 the subtraction of background levels is not expected to have a significant impact on PBPK model 

15 parameter estimates as background levels of methanol and its metabolites are low relative to 

16 exposure levels used in methanol bioassays. Further, while it is possible that background levels 

17 of methanol or its metabolites contribute to background responses for some adverse effects, the 

18 results of dose-response modeling of cancer endpoints using “background dose” models suggest 

19 that this contribution is relatively small (see discussion in Appendix E, Section E.4).

20

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

21 As discussed in Sections 4.9.2, there is human evidence for the association of lymphomas 

22 with a metabolite of methanol, formaldehyde. However, there is no information available in the 

23 literature regarding the observation of cancer in humans following chronic administration of 

24 methanol. The only observations in animals were noted in the chronic studies of methanol 

25 conducted by Apaja (1980), Soffritti et al. (2002a) and NEDO (1985/2008b) and there is 

26 uncertainty associated with the interpretation of the tumor responses reported in these studies. 

27 As a consequence, the overall WOE, while convincing, is not strong. 
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6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF HAZARD AND DOSE 

RESPONSE

6.1. HUMAN HAZARD POTENTIAL

1 Methanol is the smallest member of the family of aliphatic alcohols. Also known as 

2 methyl alcohol or wood alcohol, among other synonyms, it is a colorless, very volatile, and 

3 flammable liquid that is widely used as a solvent in many commercial and consumer products. It 

4 is freely miscible with water and other short-chain aliphatic alcohols but has little tendency to 

5 distribute into lipophilic media. Methanol can be formed in the mammalian organism as a 

6 metabolic byproduct and can be ingested with foodstuffs, such as fruits or vegetables. A 

7 potential for human exposure exists today in the form of the artificial sweetener, aspartame, 

8 which is a methyl ester of the dipeptide aspartyl-phenylalanine. Methanol is the major anti9 freeze constituent of windshield washer fluid. Its use as a fuel additive for internal combustion 

10 engines is, as yet, limited by its corrosive properties.

11 Because of its very low oil:water partition coefficient, methanol is taken up efficiently by 

12 the lung or the intestinal tract and distributes freely in body water without any tendency to 

13 accumulate in fatty tissues. It can be metabolized completely to CO2, but may also, as a regular 

14 byproduct of metabolism, enter the C1-pool and become incorporated into biomolecules. Animal 

15 studies indicate that blood methanol levels increase with the breathing rate and that metabolism 

16 becomes saturated at high exposure levels. Because of its volatility it can also be excreted 

17 unchanged via urine or exhaled air. 

18 The acute toxicity in laboratory animals in response to high levels of exposure results 

19 from CNS depression. NEDO (1987) reported that methanol blood levels around 5,000 mg/L 

20 were necessary to cause clinical signs and CNS changes in monkeys. In humans, however, acute 

21 toxicity is caused by metabolic acidosis that appears to affect predominantly the nervous system,

22 with potentially lasting effects such as blindness, Parkinson-like symptoms, and cognitive 

23 impairment. These effects can be observed in humans when blood methanol levels exceed 

24 200 mg/L. The species differences in toxicity from acute exposures appear to be the result of a 

25 limited ability of humans to metabolize formic acid. 

26 Despite the existence of many case reports on acute human exposures, the knowledge 

27 base for long-term, low-level exposure of humans to methanol is limited. The current TLV for 

methanol is 200 ppm (262 mg/m3 28 ) (ACGIH, 2000). Controlled experiments with human 

29 volunteers indicate that only minor neurobehavioral changes occur following 4-hour exposure to 

30 this concentration. A limited study on self-reported health effects in 66 persons exposed to 

31 methanol at levels that came close to or exceeded the NIOSH short-term ceiling of 800 ppm 

(1048 mg/m3 32 ), in comparison with an age-matched group of 66 less or not exposed persons, 

33 suggested a statistically significant increase in the incidence of CNS-related symptoms, such as 

34 dizziness, nausea, headache, and blurred vision (Frederick et al., 1984). Impaired vision and 
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1 nasal irritation were observed in a study of 33 methanol-exposed workers (Kawai et al., 1991).

2 None of the case reports or human studies have investigated cancer as a potential outcome of 

3 methanol exposure. 

4 A number of reproductive, developmental, subchronic and chronic exposure duration 

5 studies have been conducted in mice, rats, and monkeys. This summary will focus primarily on 

6 reproductive and developmental toxicity, and cancer as the main endpoints of concern. Sections 

7 4.7, 5.1.1 and 5.2.1 contain more extensive summaries that consider the dose-related effects that 

8 have been observed in other organ systems following subchronic or chronic exposure. 

9 Although there is no evidence in humans, methanol has shown to be a reproductive and 

10 developmental toxicant in several animal studies. No studies have been reported in which 

11 humans have been exposed subchronically or chronically to methanol by the oral route of 

12 exposure, and thus would be suitable for derivation of an oral RfD. Data exist regarding effects 

13 from oral exposure in experimental animals, but they are more limited than data from the 

14 inhalation route of exposure (see Sections 4.2, 4.3, and 4.4). Two oral studies in rats (Soffritti 

15 et al., 2002a; U.S. EPA, 1986), one oral study in mice (Apaja, 1980) and several inhalation 

16 studies in monkeys, rats and mice (NEDO, 1987, 1985/2008a, 2008b) of 90-days duration or 

17 longer have been reported. While some noncancer effects of methanol exposure were noted in 

18 these studies, principally in the liver and brain, they were either not quantifiable due to study 

19 limitations or occurred at high doses relative to reproductive/developmental effects. As 

20 discussed below, the results of inhalation reproductive/developmental toxicity studies in rats 

21 (NEDO, 1987), mice (Rogers et al., 1993a), and monkeys (Burbacher et al., 2004a, 2004b, 

22 1999a, 1999b) are the principal considerations for both the RfD and RfC values derived in this 

23 assessment. 

24 A larger number of studies have used the inhalation route to assess the potential of 

25 reproductive or developmental toxicity of methanol in mice, rats, and monkeys, with 

26 concentrations ranging from 200 to 20,000 ppm (blood levels reaching as high as 8.65 mg/mL). 

27 To sum up the findings, rat dams survived even the highest doses without gross signs of toxicity, 

28 but their offspring were severely affected (Nelson et al., 1985). Two more inhalation studies, 

29 Rogers et al. (1993a,1993b) and Rogers and Mole (1997), confirmed that methanol causes 

30 exencephaly and cleft palate in mice, the most sensitive days being GD6 and GD7 (i.e., early 

31 organogenesis). These severe malformations were observed at exposure concentrations of 

32 5,000 ppm or above. Nelson et al. (1985) and Rogers et al. (1993a) also observed an increased 

33 occurrence of ossification disturbances and skeletal anomalies at methanol concentrations 

34 ≥ 2,000 ppm, of which cervical ribs in mouse fetuses is considered the critical effect for toxicity 

35 value derivation in this review. A study conducted in pregnant cynomolgus monkeys that were 

36 exposed to 200–600 ppm methanol for 2.5 hours/day throughout premating, mating, and 

37 gestation showed no signs of maternal or fetal toxicity. The potential compound-related effects 
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1 noted were a shortening of the gestation period by less than 5% and developmental neurotoxicity, 

2 particularly delayed sensorimotor development (Burbacher et al., 2004a, 2004b, 1999a, 1999b).

3 While all of the above studies were conducted with exposure durations of 7 hours/day or 

4 less, NEDO (1987) conducted a series of developmental/reproductive studies in rats that used 

5 exposure times of 20 hours/day or more at concentrations between 500 and 5,000 ppm. A two6 generation study by these researchers that exposed the dams throughout pregnancy and the pups 

7 through 8 weeks of age, demonstrated dose-dependent reductions in brain weights that forms the 

8 basis for the RfC derived in this review. 

9 Carcinogenic effects following methanol exposure were observed in a chronic drinking 

10 water study in Eppley Swiss Webster mice (Apaja, 1980) and two chronic rat studies: a drinking 

11 water study of Sprague-Dawley rats (Soffritti et al., 2002a) and an inhalation study of F344 rats 

12 (NEDO, 1985/2008b). Following administration via drinking water, both Apaja (1980) and 

13 Soffritti et al. (2002a) observed positive dose-response trends for increases in the incidence of 

14 lymphomas in both test animal genders. Soffritti et al. (2002a) characterized the lymphomas in 

15 their study as lymphoreticular, principally lympho-immunoblastic. EPA re-analyzed the 

16 lymphoma data from the Soffritti et al. (2002a) study for quantification purposes, combining 

17 only tumors of the same cell type origin. There was a slight increase in hepatocellular

18 carcinomas in male rats of all exposure groups of this study that was not statistically elevated 

19 over controls in any group, but potentially this tumor is related to methanol exposure given the 

20 low historical background rate for this tumor in this rat strain. As discussed in Section 5.4.1.1, 

21 the other tumor increases reported by Soffritti et al. (2002a) are not quantifiable or were

22 considered hyperplastic rather than carcinogenic following a review by NTP pathologists (EFSA, 

23 2006; Hailey, 2004). No tumor responses were significantly increased over controls in the 

24 chronic inhalation bioassays performed by NEDO (1987) in monkeys, and mice, but the high25 dose incidences for pulmonary adenomas/adenocarcinomas in male rats was elevated over 

26 concurrent controls and pheochromocytomas in female rats were significantly elevated over 

27 historical control incidences for these tumor types within their respective sex and strain. The 

28 dose response for both of these tumor types represents increasing trends (Cochran Armitage 

29 trend test; p < 0.05). Further, both tumor responses are accompanied by proliferative changes 

30 (e.g., hyperplastic responses) in their respective cell types. 

6.2. DOSE RESPONSE

6.2.1. Noncancer/Inhalation

31 Clearly defined toxic endpoints at moderate exposure levels have been observed only in 

32 reproductive and developmental toxicity studies. Three endpoints from developmental toxicity 

33 studies were considered for derivation of the RfC: formation of cervical ribs in CD-1 mice 

34 exposed to methanol during organogenesis (Rogers et al., 1993a), deficits in sensorimotor 
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1 development as measured by VDR tests administered to monkeys exposed to methanol 

2 (Burbacher et al., 2004a, 2004b, 1999a, 1999b), and reduced brain weights in rats exposed to 

3 methanol from early gestation through 8 weeks of postnatal life (NEDO, 1987). For the purpose 

4 of comparability and to better illustrate methodological uncertainty, reference values were 

5 derived for all of these endpoints using a BMD modeling approach which evaluated several 

6 models and various measures of risk. In the present review, mostly because of a paucity of 

7 adequate long-term or developmental oral studies and the existence of several inhalation studies 

8 that examined sensitive subpopulations (pregnant mothers, developing fetuses and neonates) in 

9 various species, it was decided to use the critical effect from an inhalation study to derive an 

10 RfD. Thus, the criteria and rationales on which the RfC assessment is based also form the basis 

11 for the RfD derivation.

12 The Rogers et al. (1993a) inhalation study is a multidose developmental study that was 

13 considered for use in the derivation of a reference value. The exposure concentrations in this 

14 study were 0, 1,000, 2,000, and 5,000 ppm administered for 7 hours/day on GD7–GD17. The 

15 BMD evaluation, based on the nested log-logistic model of BMDS version 2.1, produced 

16 BMD/BMDL values in terms of internal peak blood methanol (Cmax). PBPK modeling was used 

17 to convert the internal animal dose metrics to HECs, and a UF of 100 was applied to yield RfCs 

of 10.4 mg/m3 and 13.6 mg/m3 18 for 5 and 10% extra risk, respectively.

19 Reproductive and developmental neurobehavioral effects observed in monkeys following 

20 methanol inhalation exposure (Burbacher et al., 2004a, 2004b, 1999a, 1999b ) were also 

21 considered for use in the derivation of a reference value. M. fascicularis monkeys were exposed 

to 0, 262, 786, and 2,359 mg/m3 22 methanol 2.5 hours/day, 7 days/week during premating/mating 

23 and throughout gestation (approximately 168 days). Delayed sensorimotor development as 

24 measured by a VDR test was the only effect in this study that exhibited a dose-response and is a 

25 measure of a functional deficit that is consistent with early developmental CNS effects (e.g.,

26 brain weight changes) that have been observed in rats (NEDO, 1987). Though there is 

27 uncertainty associated with this effect and its relation to methanol exposure, a BMD analysis was 

28 performed for comparative purposes. BMD/BMDL values for the VDR endpoint were estimated 

29 using AUCs derived from a monkey PBPK model of blood methanol data reported in the 

30 Burbacher et al. (1999a) study. A human methanol PBPK modeling was then used to convert the 

31 internal AUC BMDL to an HEC, and a UF of 100 was applied to yield a reference value estimate 

of 1.7 mg/m3 32 .

33 Reduced brain weight was evaluated based on the results of a two-generation study by 

34 NEDO (1987) in which fetal rats and their dams were exposed from the first day of gestation 

35 until 8 weeks of age, and brain weights were determined at 3, 6, and 8 weeks of age. To obtain 

36 reference value estimates from these studies, a rat PBPK model was used to predict PODs in 

37 terms of internal doses, which were divided by UFs and converted to HEC reference values via a 
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1 human PBPK model (see Table 5-4). BMD modeling was executed using two different BMRs, 

2 one S.D. (as is usual with continuous data) and 5% relative (to control response) risk. The 

resulting reference value estimates were 2.4 and 1.8 mg/m3 3 (5% relative risk and 1 S.D.,

4 respectively) for reduced brain weight at 6 weeks of age following gestational and postnatal 

5 exposure. 

6 Despite the variety of approaches, different critical effects, and different data sources, all 

7 reference value estimates fell within a narrow range. The reference value associated with the 

8 BMD estimate of the dose corresponding to a one S.D. decrease in brain weight in male rats at 6 

9 weeks post-birth observed in the NEDO (1987) developmental toxicity study is considered most 

10 suitable for derivation of the methanol chronic RfC due to the relevance of the exposure 

11 scenario/study design (see Sections 5.1.2.2 and 5.3), and endpoint (see Section 5.3) to the 

12 potential for developmental effects in neonatal humans, the relative robustness of the dose13 response data and because it resulted in one of the lowest reference values of the BMD 

14 derivations (see Table 5-4). Thus, the proposed chronic RfC for exposure to methanol is 2

mg/m3 15 , an evaluation that includes a UFH of 10 for intraspecies variability, a UFA of 3 to address 

16 the pharmacodynamic component of interspecies variability, and a UFD of 3 for database 

17 uncertainty. 

18 The confidence in this RfC is medium to high. Confidence in the NEDO (1987) 

19 developmental studies is medium to high. While there are issues with the lack of reporting 

20 detail, the critical effect (brain weight reduction) has been reproduced in an oral study of adult 

21 rats (U.S. EPA, 1986c), and the exposure regimen involving pre- and postnatal exposures 

22 addresses a potentially sensitive human subpopulation. Confidence in the database is medium. 

23 Despite the fact that skeletal and brain effects have been demonstrated and corroborated in 

24 multiple animal studies in rats, mice, and monkeys, some study results were not quantifiable, 

25 there is uncertainty regarding which is the most relevant test species, and there is limited data 

26 regarding reproductive or developmental toxicity of methanol in humans. There is also 

27 uncertainty regarding the potential active agent—the parent compound, methanol, formaldehyde, 

28 or formic acid. There are deficiencies in our knowledge of the metabolic pathways of methanol 

29 in the human fetus during early organogenesis, when the critical effects can be induced in 

30 animals. Thus, the medium-to-high confidence in the critical study and the medium confidence 

31 in the database together warrant an overall confidence descriptor of medium to high.

6.2.2. Noncancer/Oral

32 There is a paucity of scientific data regarding the outcomes of chronic oral exposure to 

33 methanol. No data exist for long-term methanol exposure of humans. A subchronic (90-day) 

34 oral study in Sprague-Dawley rats reported brain and liver weight changes, with some evidence 

35 for minor liver damage at 2,500 mg/kg-day that was not supported by histopathologic findings 
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1 (U.S. EPA, 1986c). Liver necrosis was reported in Eppley Swiss Webster mice that consumed 

2 approximately 2000 mg/kg-day (Apaja, 1980). In the only other study that administered 

3 methanol chronically to animals by the oral route, Soffritti et al. (2002a) reported that, overall, 

4 there was no pattern of compound-related clinical signs of toxicity in Sprague-Dawley rats 

5 exposed to up to approximately 2,000 mg/kg-day. The authors further reported that there were 

6 no compound-related signs of gross pathology nor histopathologic lesions indicative of 

7 noncancer toxicological effects in response to methanol; however, they did not provide any 

8 detailed data to illustrate these findings.

9 As discussed above and in Section 5.1.1, reproductive and developmental effects are 

10 considered the most sensitive and quantifiable effects reported in studies of methanol. Oral 

11 reproductive and developmental studies employed single doses that were too high to be of use. 

12 In the absence of suitable reproductive or developmental data from oral exposure studies, it was 

13 decided to conduct a route-to-route extrapolation and to use the critical effect from the inhalation 

14 study (brain weight) to derive an RfD. Thus, the POD (in terms of AUC methanol in blood) used 

15 for the derivation of the RfC was also used for the derivation of the RfD. This POD was divided 

16 by a UF of 100, and a human PBPK model was used to obtain an RfD value of 0.4 mg/kg-day. 

17 As for the RfC, the 100-fold UF includes a UFH of 10 for intraspecies variability, a UFA of 3 to 

18 address pharmacodynamic uncertainty, and a UFD of 3 for database uncertainty.

19 The confidence in the RfD is medium to high. Despite the relatively high confidence in 

20 the critical studies, all limitations to confidence as presented for the RfC also apply to the RfD. 

21 Confidence in the RfD is slightly lower than for the RfC due to the lack of adequate oral studies 

22 for the RfD derivation, necessitating a route-to-route extrapolation. 

6.2.3. Cancer/Oral and Inhalation

23 Under the current Guidelines for Carcinogen Risk Assessment (U.S. EPA 2005a, 2005b), 

24 methanol fulfils the criteria to be described as likely to be a human carcinogen by all routes of 

25 exposure. This descriptor is based principally on findings of dose-related, statistically significant 

26 increases in the incidence of: lymphoreticular tumors in lifetime studies of both sexes of Eppley 

27 Swiss Webster mice (Apaja, 1980) and both sexes of Sprague-Dawley rats (Soffritt et al., 2002a),

28 a slight but significant (compared to historical controls) increase in relatively rare hepatocellular 

29 carcinomas in male Sprague-Dawley rats following oral exposure (Soffritti et al., 2002a), and 

30 dose-related occurrences of pulmonary adenomas/adenocarcinomas and pheochromocytomas in 

31 F344 rats by inhalation exposure (NEDO, 1985/2008b). This determination is supported by the 

32 results of other studies that have shown tumorigenic responses similar to those observed by 

33 Soffritti et al. (2002a) in rats exposed to formaldehyde, a metabolite of methanol, and to the 

34 metabolic precursors of methanol and formaldehyde, aspartame and MTBE. In addition, 

35 epidemiological studies have associated exposure to formaldehyde with increases in the 
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1 incidence of both leukemias and lymphomas (IARC, 2004). However, the key studies, Soffritti 

2 et al. (2002a), NEDO (1985/2008b) and Apaja (1980), have associated uncertainties (see below 

3 and discussions in Sections 4.9.2 and 5.4.3) that reduce confidence in the chosen descriptor. 

4 The statistically significant increase in the incidence of lymphoreticular tumors observed 

5 in the Soffritti et al. (2002a) drinking water study of Sprague-Dawley rats was used in the 

6 determination of the POD for estimating the methanol oral CSF. A PBPK model was developed,

7 and several model predictions of internal dose metrics were considered for use in the dose8 response analysis and derivation of the human equivalent dose. Methanol metabolized was 

9 selected as the dose metric best suited for derivation of the oral POD because of its superior fit to 

10 the response data and consistency with the hypothesis that formaldehyde may be the 

11 carcinogenic agent associated with methanol exposure. The EPA multistage cancer model was 

12 used to derive a BMDL10 for the male rat in terms of mg methanol metabolized/day. Assuming 

13 that metabolized methanol distributes in the body according to body weight to the 3⁄4 power, the 

14 rat BMDL10 of 63.9 mg-day was converted to a human BMDL10 of 3,553 mg-day. The human 

15 PBPK model was then used to convert this human mg-day value for total methanol metabolized 

16 back to a human equivalent methanol oral dose HED(BMDL10) of 51.5 mg/kg-day for 

lymphomas in the male rat. The oral CSF of 2E-03 (mg/kg-day)-1 17 was then derived based on a 

18 linear extrapolation from this POD to estimated background levels.

19 Pulmonary adenomas/adenocarcinomas in male F344 rats and pheochromocytomas in 

20 female F344 rats observed in the chronic inhalation study of NEDO (1985/2008b) were 

21 considered in the determination of the POD for estimating the inhalation CSF. In this case, all 

22 dose metrics estimated by the PBPK model provided a similar acceptable fit to the tumor 

23 response data. Methanol metabolized was selected as the dose metric for derivation of the 

24 inhalation POD for consistency with the approach used for the derivation of the oral POD and 

25 with the hypothesis that formaldehyde may be the carcinogenic agent associated with methanol 

26 exposure. As for the oral POD, the EPA multistage cancer model was used to derive a BMDL10 

27 for the rat in terms of mg methanol metabolized/day. Assuming that metabolized methanol 

28 distributes in the body according to body weight to the 3⁄4 power, the rat BMDL10 of 15 mg-day 

29 was converted to a human BMDL10 of 971 mg-day. The human PBPK model was then used to 

30 convert this human mg-day value for total methanol metabolized back to a human equivalent 

methanol inhalation concentration HEC(BMCL10) of 81,900 μg/m3 31 for pheochromocytomas in 

the female rat. The inhalation cancer unit risk of 1E-06 (μg/m3)

-1 32 was then derived based on a 

33 linear extrapolation from this POD to estimated background levels.

34 Section 5.4.3 of this assessment documents several uncertainties with the quantification 

35 of cancer risk. The main uncertainties can be grouped into issues related to study quality, the 

36 interpretation of study results, and the consistency of the results with other laboratories. Other 

37 uncertainties discussed in Section 5.4.3 include the choice of tumor endpoint, the choice of dose-
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1 response model, the PBPK model and dose metric used for the animal to human extrapolations,

2 and the human relevance of the carcinogenic responses in rats and mice.
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36 U.S. EPA (Environmental Protection Agency). (1997) Guidance on cumulative risk 

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34 U.S. EPA (Environmental Protection Agency). (2005b) Supplemental guidance for 

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36 Washington, DC; EPA/630/R-03/003F. Available from: http://www.epa.gov/iris/backgr-d.htm. 

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24 Ward, KW; Blumenthal, GM; Welsch, F; et al. (1997) Development of a physiologically 

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28 Weiss, B; Stern, S; Soderholm, SC; et al. (1996) Developmental neurotoxicity of 

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31 Wentzel, P; Rydberg, U; Eriksson, UJ. (2006) Antioxidative treatment diminishes 

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34 Wentzel, P; Eriksson, UJ. (2006) Ethanol-induced fetal dysmorphogenesis in the mouse 

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37 White, LR; Marthinsen, ABL; Richards, RJ; et al. (1983) Biochemical and cytological 

38 studies of rat lung after inhalation of methanol vapour. Toxicol Lett 17:1–5.
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1

2 Wilson, SL; Cradock, MM. (2004) Review: Accounting for prematurity in developmental 

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5 Wu, AH; Kelly, T; McKay, C; et al. (1995) Definitive identification of an exceptionally 

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8

9 Yuan J. (1993) Modeling blood/plasma concentrations in dosed feed and dosed drinking 

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12

13 Zorzano, A; Herrera, E. (1989) Disposition of ethanol and acetaldehyde in late pregnant 

14 rats and their fetuses. Pediatr Res 25(1):102–6.
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APPENDIX A. SUMMARY OF EXTERNAL PEER REVIEW AND PUBLIC 

COMMENTS AND DISPOSITION

1 [Page intentionally left blank]
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APPENDIX B. DEVELOPMENT, CALIBRATION AND APPLICATION OF A 

METHANOL PBPK MODEL

1 This appendix is adapted from a report prepared for the U.S. EPA under contract by the 

2 Center for Biological Monitoring and Modeling, Battelle Northwest Laboratories (WA 09, 

3 Battelle Project No. 48746, January 31, 2007).

B.1. SUMMARY

4 This appendix describes the development, calibration, and approach for application of

5 mouse, rat, and human PBPK models to extrapolate mouse and rat methanol inhalation-route 

6 internal dose metrics to human inhalation exposure concentrations that result in the same internal 

7 dose (HEC). The human oral methanol dose(s) yielding internal dose(s) equivalent to the mouse

8 or rat internal dose at the (HED) is also presented.

9 A PBPK model was developed to describe the blood kinetics of methanol (MeOH) in 

10 mice and humans. The model includes compartments for lung/blood MeOH exchange, liver, fat, 

11 and the rest of the body. To describe blood MeOH kinetics, the model employs two saturable 

12 descriptions of MeOH metabolic clearance in mice and rats, and one first-order metabolic 

13 clearance, and a first-order description of renal clearance (from blood) in humans. Renal 

14 clearance is a minor pathway and does not appreciably affect MeOH blood kinetics.

15 This model is a revision of the model reported by Ward et al. (1997), reflecting 

16 significant simplifications (removal of compartments for placentae, embryo/fetus, and 

17 extraembrionic fluid) and two elaborations (addition of an intestine lumen compartment to the 

18 existing stomach lumen compartment and addition of a bladder compartment which impacts 

19 simulations for human urinary excretion.), while maintaining the ability to describe MeOH blood 

20 kinetics. The model reported here uses a single consistent set of parameters; the Ward et al.

21 model employed a number of data-set specific parameters. Other biokinetic MeOH models that 

22 were considered as starting points for the current model also used varied parameters by dataset to 

23 achieve model fits to the data. For example, the model of Bouchard et al. (2001) used different 

24 respiratory rates and fractional inhalation absorbed for different human exposures.

25 The mouse model was calibrated against inhalation-route blood MeOH kinetic data and 

26 verified using intravenous-route blood MeOH kinetic data. The rat model was calibrated against 

27 low-dose intravenous data and validated with inhalation-route data. The human model was 

28 calibrated against inhalation-route MeOH kinetic data. The models accurately described the 

29 inhalation route pharmacokinetics of MeOH. Mouse model simulations of oral- and i.v.-route 

30 kinetics compare well to some but not all the experimental data.

31 The MeOH HECs predicted by the model (based on 1,000 ppm inhalation exposure in 

32 mice) were >1,000 ppm using either blood AUC or Cmax as the dose metrics. The MeOH HED 
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1 derived by cross-route extrapolation of this inhalation-route HEC was 110 mg/kg-day, based on 

2 MeOH blood AUC following zero order uptake of MeOH (a constant rate of delivery). Because 

3 of the lack of human data from high-dose exposures, it was not possible to calibrate the model 

4 for inhalation exposures above 1,000 ppm or oral exposures above 110 mg/kg-day. However, 

5 because the BMD approach was used to estimate an internal experimental animal dose and UFs

6 were applied to the internal dose, the human model can be used to back-calculate that internal 

7 dose to an RfC and an RfD below 1,000 ppm or 110 mg/kg-day, respectively.

B.2. MODEL DEVELOPMENT

B.2.1. Model Structure

8 This model is a revision of the model reported by Ward et al. (1997), reflecting 

9 significant simplifications and two elaborations, while maintaining the ability to describe MeOH 

10 blood kinetics in mice, rats, and humans (Figure B-1). The kidney, pregnancy and the fetal 

11 compartment have been removed. The kidney was lumped with the body compartment because 

12 the blood:tissue partition coefficients for these tissues were similar. The elaborate time13 dependent descriptions of pregnancy were removed because analysis of the available 

14 pharmacokinetic data indicates that blood MeOH kinetics in NP and pregnant mice are not 

15 different enough to warrant separate descriptions. Because the maternal blood:fetal blood 

16 partition coefficients were near 1, there was no need to explicitly model fetal kinetics; they will 

17 be equivalent to maternal blood kinetics. Further supporting data exist for ethanol, which is 

18 quite similar to MeOH in its partitioning and transport properties. In rats (Zorzano and Herrara, 

19 1989; Guerri and Sanchis 1985), sheep (Brien et al., 1985; Cumming et al., 1984), and guinea 

20 pigs (Clarke et al., 1986), fetal and maternal blood concentrations of ethanol are virtually 

21 superimposable; maternal to fetal blood ratios are very close to 1, including during late gestation. 

22 Also, fetal brain concentrations in guinea pigs (Clarke et al., 1986) were also very similar to the 

23 mothers’.

24 In addition to the absolute maternal-fetal concentration similarity noted above, it is 

25 common practice to use blood concentrations as an appropriate metric for risk extrapolation via 

26 PBPK modeling for effects in various tissues, based on the reasonable expectation that any 

27 tissue:blood differences will be similar in both the test species and humans. For example, even if 

28 the brain:blood ratio was around 1.2 in the mouse or rat, the similar biochemical make-up of 

29 brain tissue and blood in rats and humans leads to the expectation that the brain:blood levels in 

30 humans (which depend on the biochemical make-up) will also be close to 1.2, and so the relative 

31 "error" that might occur by using blood instead of brain concentration in evaluating the dose32 response in rats will be cancelled out by using blood instead of brain concentration in the human. 

33 The fact that measured fetal blood levels are virtually identical to maternal levels for methanol 

34 (and ethanol) tells us that the rate of metabolism in the fetus is not sufficient to significantly 
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1 reduce the fetal concentration versus maternal, and use of a PBPK model to predict maternal 

2 levels will give a better estimate of fetal exposure than use of the applied dose or exposure, 

3 because there are animal-human differences in adult PK of MeOH for which the model accounts, 

4 based on PK data from humans as well as rodents.

Figure B-1. Schematic of the PBPK model used to describe the inhalation, 

5 oral, and i.v. route pharmacokinetics of MeOH. KAS, first-order oral 

6 absorption rate from stomach; KAI, first-order uptake from the intestine; KSI, 

7 first-order transfer between stomach and intestine; Vmax and Km and Vmax2 and 

8 Km2, Michaelis-Menten rate constants for high affinity/low capacity and low 

9 affinity/high capacity metabolic clearance of MeOH; KLL, alternate first-order 

10 rate constant; KBL, rate constant for urinary excretion from bladder. Both 

11 metabolic pathways were used to describe MeOH clearance in the mouse and rat, 

12 while a single pathway describes metabolic clearance in the human.

5 A lung compartment was added to describe delivery of MeOH to blood as a function of 

6 ventilation, partitioning, and blood flow rather than the less standard approach used by Ward 

7 et al. (1997). A term was added to the gas uptake equations to describe the fractional respiratory 

8 bioavailability of MeOH. A fat compartment was included because it is the only tissue with a 

9 tissue:blood partitioning coefficient appreciably different than unity, and the liver is included 

10 because it is the primary site of metabolism. A bladder compartment was added to better 

11 describe the kinetics of human urinary data, where the drop in excretion rate is slower than the 

12 predicted decline in blood methanol and hence rate of metabolite production. Also, to best 

13 describe the observed rat dosimetry after oral exposure while maintaining metabolic parameters 
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1 fit to data from inhalation and IV exposure, a small rate of elimination from the intestine (lumen) 

2 compartment to feces. (The mouse data could be adequately fit with this rate set to zero, 

3 corresponding to 100% absorption; humans were assumed to have zero fecal elimination, like the 

4 mouse.) The final models thus include compartments for fat, liver, the rest of the body, bladder 

5 (only used for humans), and lung. The mouse and rat models describe inhalation, oral, and 

6 intravenous route dosing and the human model describes inhalation and oral route dosing and the 

7 rat model includes a non-zero rate of fecal elimination. Although there is an endogenous 

8 background level of both MeOH and formate (See Section 3.3), the model does not explicitly 

9 describe or account for background levels of MeOH or formate. In this analysis, when non-zero 

10 background levels have been measured (in blood), that background was simply subtracted from 

11 the concentrations measured during exposure. However, a zero-order rate of infusion could be 

12 added to the liver, blood, or stomach compartments to mimic background levels if that was 

13 considered necessary. 

14 MeOH is well absorbed by the inhalation and oral routes, and is readily metabolized to 

15 formaldehyde, which is rapidly converted to formate in both rodents and humans. Although the 

16 enzymes responsible for metabolizing formaldehyde are different in rodents (CAT) and humans 

17 (ALD) the metabolite, formate, is the same, and the metabolic rates are similar (Clary, 2003). 

18 Most of the published rodent kinetic models for MeOH describe the metabolism of MeOH to 

19 formaldehyde as a saturable process but differ in the handling of formate metabolism and 

20 excretion (Bouchard et al., 2001; Fisher et al., 2000; Ward et al., 1997; Horton et al., 1992). 

21 Ward et al. (1997) used one saturable and one first-order pathway for mice, and Horton et al. 

22 (1992) applied two saturable pathways of metabolism to describe MeOH elimination in rats. 

23 Bouchard et al. (2001) employed one metabolic pathway and a second pathway described as 

24 urinary elimination in rats and humans. The need for two saturable metabolic pathways in the 

25 mouse model was confirmed through simulation and optimization. High exposure (>2,000 ppm 

26 MeOH) and low exposure (1,000 ppm MeOH) blood data could not be adequately fit either 

27 visually or by more formal optimization without the second saturable metabolic pathway. The 

28 optimization approach and results are found below and in the Additional Materials at the end of 

29 this appendix. 

30 While the PPK model explicitly describes the concentration of methanol, it only 

31 describes the rate of metabolism or conversion of MeOH to its metabolites. Distribution and 

32 metabolism of formaldehyde is not considered by the model, and this model tracks neither 

33 formate nor formaldehyde. (The data that would be needed to parameterize or validate a specific 

34 description of either of these metabolites is not available). Since the metabolic conversion of 

35 formaldehyde to formate is rapid (< 1 minute) in all species (Kavet and Nauss, 1990), the MeOH 

36 clearance rate may approximate a formate production rate, though this has not been verified. 

37 Thus the rate of MeOH metabolism predicted by the model can be used as a dose metric for

38 either or both of these metabolites, but scaling of that metabolic rate metric to humans would 
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require use of an inter-species scaling factor, (BWhuman/BWrodent )

0.25 1 , to account for the general 

2 expectation of slower clearance of the metabolites in humans.

3 The model was initially coded in acslXtreme v1.4 and updated in acslXtreme v 2.3 

4 (Aegis Technologies, Huntsville, AL). Most procedures used to generate this report, except those 

5 for the optimization, may be run by executing the corresponding .m files. The model code 

6 (acslXtreme .csl file) and supporting .m files are available electronically and as text in the 

7 Additional Materials at the end of this appendix. A key identifying .m files associated with 

8 figures and tables in this report is also provided in the Additional Materials. 

B.2.2. Model Parameters

9 Physiological parameters such as tissue volumes, blood flows, and ventilation rates were 

10 obtained from the open literature (Table B-1). Parameters for blood flow, ventilation, and 

11 metabolic capacity were scaled as a function of body weight raised to the 0.75 power, according 

12 to the methods of Ramsey and Andersen (1984).

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

Mouse Rat

SD | F344 Human Source

Body weight (kg) 0.03a 0.275b 70 Measured/estimated

Tissue volume (% 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 Calculatedc

Flows (L/hr/kg0.75)

Alveolar ventillationd 25.4 16.4 16.5 Perkins et al. 1995a; Brown et al. 1997; 

Cardiac output 25.4 16.4 24.0 U.S. EPA, 2004

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 constantse 1st

order saturable

VmaxC (mg/hr/kg0.75) 19 5.0 0 NA 33.1

Fitted

Km (mg/L) 5.2 6.3 NA NA 23.7

Vmax2C (mg/hr/kg0.75) 3.2 8.4 22.3 NA

Km2 (mg/L) 660 65 100 NA

K1C (BW0.25/hr) NA NA 0.0373 0.0342

KLLC (BW0.25/hr)f NA NA 95.7 NA
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Oral absorption

VmASC (mg/hr/kg0.75) 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/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 0.583h Ward et al., 1997; Fiserova-Bergerova and 

Fat:Blood 0.083 0.083 0.142 Diaz, 1986

Blood:Air 1350i 1350 1626 Horton et al., 1992; Fiserova-Bergerova and 

Diaz, 1986

Body:Blood 0.66 0.66 0.805 Rodent: estimated; human: FiserovaBergerova 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 0.866k 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

Minute 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 available, a mouse QPC (Alveolar Ventilation/BW0.75) of 25.4 was used (average from Perkins et al., 1995a). 

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/kg0.75) e

Vmax, Km, and Vmax2, Km2 represent the two saturable metabolic clearance processes assumed to occur solely in the liver. 

The Vmax used in the model = VmaxC (mg/kg0.75·hr)BW0.75. K1C is the first-order loss from the blood for human 

simulations that represents 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 / BW0.25

f

KLLC – alternate human first-order metabolism rate (used only when VmaxC = Vmax2C = 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

1 Mouse model partition coefficients were used as reported (liver, fat, blood:air) or 

2 estimated (lung, body). The mouse body compartment partition coefficient was set 

3 approximately equal to the measured value for muscle (Ward et al., 1997). The mouse lung 

4 partition coefficient was assumed to be 1.0, similar to the liver partition coefficient. This 

5 parameter has no numerically significant impact on modeled blood dose metrics. 

6 Human partition coefficients were reported by Horton et al. (1992), but were in fact 

7 measured in rat tissues. The reported rat fat partition coefficient was considerably closer to unity 

8 than reported for MeOH or ethanol by other researchers (Ward et al., 1997; Pastino and Conolly, 
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1 2000) and assumed to be in error. Human partition coefficients were obtained from Fiserova2 Bergerova and Diaz (1986).

B.2.3. Mouse Model Calibration

B.2.3.1. Inhalation-Route Calibration

3 For purposes of conducting interspecies extrapolations of MeOH dosimetry, the 

4 inhalation route was the most important route requiring calibration for the mouse model. The 

5 critical endpoint and NOEL, which are the basis for the HEC estimation, are from inhalation6 route studies. The ability to predict blood MeOH concentrations from inhalation exposures was 

7 therefore a priority. Pharmacokinetic data from other routes, i.v. and oral, were used to verify 

8 clearance terms derived by fitting to the inhalation data or to estimate a MeOH oral uptake rate 

9 constants. Holding other parameters constant, the mouse PBPK model was calibrated against 

10 inhalation-route blood pharmacokinetic data (Figure B-2) by fitting five parameters: Michaelis11 Menten constants for one high affinity/low capacity and one low-affinity high-capacity enzyme 

12 and the inhalation fractional availability term. 

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/kg0.75 13 ), the 

average value measured by Perkins et al. (1995a), which is similar to the value of 29 (L/hr/kg0.75 14 ) 

15 reported in Brown et al. (1997). Where ventilation rates were reported for individual exposure 
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1 concentrations by Perkins et al. (1995a), they were used directly in the model and a notation was 

2 made in the figure legend. Reported ventilation rates varied from 592 to 857 L/kg x 8 hr, 

3 depending on exposure concentration (Perkins et al., 1995a). Adjusting these values to 2/3 total 

(for alveolar ventilation) and allometrically scaling by BW0.75 4 , values used in the model for these 

exposures ranged from 20.5 to 29.7 (L/hr/kg0.75 5 ) (See Table B-1). A fractional availability of 

6 73% of alveolar ventilation was visually optimized to best describe the inhalation-route blood 

7 MeOH pharmacokinetic data. This percentage of uptake for inhalation exposures is similar to 

8 values reported for other alcohols in rodents (Teeguarden et al., 2005), but considerably lower 

9 than the value reported by Perkins et al. (1995a) of 126% of alveolar ventilation (85% of total 

10 ventilation). 

11 The calibrated model predicted blood MeOH concentration time-course agreed well with 

12 measured values in adult mice in the inhalation studies of Rogers et al. (1997, 1993) (Figure B13 2), and Perkins et al. (1995a), as well as in NP and early gestation (GD8) mice of Dorman et al.

14 (1995) (Figure B-3). Parameter values used in the calibrated model are given in Table B-1.

Figure B-3. Simulation of inhalation exposures to MeOH in NP mice from Perkins 

15 et al. (1995a) (8-hour exposures) and Dorman et al. (1995), (6-hour exposures). Data 

16 points represent measured blood MeOH concentrations and lines represent PBPK model 

17 simulations. Note: data was obtained using DigitizIt (SharIt! Inc. Greensburg, PA) to 

18 digitize data from Figure 2 of Perkins et al. (1995a) and Figure B-2 from Dorman et al., 

19 (1995). Default ventilation rates (Table B-1) were used to simulate the Dorman data. The 

20 alveolar ventilation rate for each data set from Perkins et al. (1995) was set equal to the 

21 measured value reported in that manuscript. For the 2,500, 5,000, and 10,000 ppm exposure 

22 groups, the alveolar ventilation rates were 29, 24, and 21 (L/hr/kg0.75), respectively. The 

23 cardiac output for these simulations was set equal to the alveolar ventilation rate.
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B.2.3.1. (should be B.2.3.2) Oral-Route Calibration

1 The mouse model was calibrated for the oral route by fitting the rate constants for oral 

2 uptake of MeOH. Calibration of the oral route was not required for interpretation of the critical 

3 toxicology studies. This exercise was undertaken to estimate the rate constants for oral uptake so 

4 it could be used to make dose-route extrapolations for calculating human oral-route exposures 

5 equivalent to mouse exposures at the NOEL. 

6 Ward et al. (1997) described MeOH uptake as the sum of a fast and slow process (two 

7 rate constants), with a fraction of the administered dose attributed to each process. The rate 

8 constants and the fraction of the dose attributed to each process were varied to describe oral9 route blood MeOH kinetics for each GD. For instance, the fraction of the total oral dose 

10 assigned to the fast absorption process varied from 54 to 71%, depending on the data set. An 

11 alternative approach with uptake attributed to stomach and intestine, which allows for greater 

12 flexibility in fitting the data (Staats et al., 1991), was compared to a simpler one utilizing a single 

13 rate of uptake. In both the current model and the model of Ward et al. (1997), orally ingested 

14 MeOH was assumed to be 100 % absorbed.

Initially, a single oral absorption rate constant (KAS, hr-1 15 ) was fitted to oral-route blood 

16 MeOH kinetics reported by Ward et al. (1997, 1995). Using these data, an average KAS 

(0.62 hr-1 17 ) was estimated that provides adequate fits to MeOH blood kinetics following 2,500 

18 mg/kg dose in NP and GD18 mice and 1,500 mg/kg in GD8 mice up to ~8 hours. At later time 

19 points, however, a model using a single oral uptake rate constant consistently under predicts 

20 blood concentrations of MeOH (results not shown). Fits were improved by using the two 

21 compartment GI tract model (Figure B-4). However, when fitting the oral data in rats, it was 

22 found that the fits were significantly improved if the uptake from the stomach was treated as a 

saturable process. Vmax (VMASC) was scaled as BW0.75 23 , as is done for other Vmaxs, and the Km 

(KMASC) was scaled as BW1 24 to reflect that the variable used is the total amount in the stomach, 

whose volume is expected to scale with BW1 25 . For the mouse, model fits were not significantly 

26 improved when KMASC was allowed to vary (change from the value fitted to rats, 1830 mg/kg), 

27 so it was kept at the rat value.

28 Using the two-compartment oral absorption model and adjusting only the absorption 

29 parameters resulted in a good fit to the lower oral dose (1,500 mg/kg) (Dorman et al., 1995), but 

30 consistently under-prediction of the 2,500 mg/kg oral dosing blood levels (Ward et al., 1997). 

31 When the metabolic constants (VmaxC values) were decreased, the data from the higher dose 

32 were fit, but the fit of the data for the 1,500 mg/kg dose was lost (see Additional Materials, 

33 Figure B-19). Also, when using the lower clearance required to fit the data of Ward et al. (1997), 

34 the inhalation data of Rogers et al. (1993) could no longer be fit by the model (see Additional 

35 Materials, Figure B-20). The two-compartment GI tract approach (with parameters that better fit 

36 the low dose data) was retained in the model and used for all final mouse oral route simulations.

37
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Figure B-4. Oral exposures to MeOH in pregnant mice on GD8 (Dorman et al., 1995) 

1 or NP and GD18. Data points represent measured blood concentrations and lines represent 

2 PBPK model estimations for NP mice.

Source: Ward et al., (1997).

B.2.3.1. (B2.3.3) Intravenous Route Simulation

1 The parameterization of MeOH clearance (high-and-low affinity metabolic pathways) 

2 was verified by simulation of data sets describing the intravenous-route pharmacokinetics of 

3 MeOH. MeOH blood kinetics data in NP mice are only available for a single i.v. dose of 

4 2,500 mg/kg (Ward et al., 1997). MeOH blood kinetics are also reported in GD18 mice 

5 following administration of a broader range of doses: 100, 500, and 2,500 mg/kg. Because 

6 MeOH kinetics appear similar for NP and pregnant mice after administration of 2,500 mg/kg 

7 prior to 20 hours, the model is expected to fit data for both pregnant and NP mice using the same 

8 set of parameters, and hence, data for both life stages were used to verify metabolic clearance of 

9 MeOH.

10 Initial blood concentrations of MeOH following i.v. administration were not proportional 

11 to administered dose in the data from Ward et al. (1997). Initial concentrations were 

12 approximately 1.5-fold lower in the 100 mg/kg dose group than expected if a dose-independent 

13 volume of distribution (VD) is assumed (Figure B-5A). Initial blood concentrations were, 

14 however, proportional to administered dose between 2,500 and 500 mg/kg. Two possible 

15 explanations were then considered:

16 1) the VD, which is not impacted by any other PBPK parameters and is only determined by 

17 the biochemical partitioning properties of MeOH, is twofold lower at 100 mg/kg than at 

18 the higher concentrations, while the VD at 500 and 2,500 mg/kg are exactly as predicted 

19 by the PBPK model without adjustment; or
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1 2) a dilution error occurred during preparation of the 100 mg/kg dosing solution used by 

2 Ward et al. (1997).

3 Figure B-5. Mouse intravenous route MeOH blood kinetics. A) MeOH was 

4 infused over 1.5 minutes into female CD-1 mice at target doses of 100 (circles), 

5 500 (triangles) or 2,500 (squares) mg/kg. Mice were NP, GD9 or GD18 at the 

6 time of dosing. Data points represent measured blood concentrations and lines 

7 represent PBPK model simulations. B) Comparison of the 100 mg/kg dose data 

8 (points) and PBPK model simulations assuming a 100 mg/kg dose as reported 

9 (solid line) or to a presumed 200 mg/kg dose (dashed lines). Note: the 24-hour 

10 time point data from the 500 mg/kg NP and GD 9 mice are below the reported 

11 detection limit (2 μg/ml) and so are not shown.

Source: Adapted from Ward et al. (1997).

3 To account for this unexpected nonproportionality, Ward et al. (1997) used higher 

4 partition coefficients for placenta and embryonic fluid and lower Vmax for the metabolism of 

A

B
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1 MeOH for the 100 and 500 mg/kg doses than for the 2,500 mg/kg dose. These adjustments to 

2 partition coefficients effectively change the volume of distribution. However, the PBPK model 

3 obtained with measured partition coefficients and otherwise calibrated to inhalation data, as 

4 described above, was capable of simulating both the 500 and 2,500 mg/kg data without adjust5 ment or varying parameters between those 2 doses. Further, the data at the nominal dose of 100 

6 mg/kg could also be adequately fit without other parameter adjustment simply by simulating a 

7 dose of 200 mg/kg (dotted line, Figure B-5B). If the relative difference in the VD at 100 mg/kg 

8 was not a round number and/or the apparent value at 500 mg/kg was intermediate between 100 

9 and 2,500 mg/kg, the possibility of a dose-related variation in this or other parameters would be 

10 given more weight. Also it seems unlikely that the volumes of distribution would be different in 

11 these animals solely based on differing exposure concentrations. But the far simpler and more 

12 likely explanation for the observed pattern of VD seems to be a dosing error (2). Therefore, a 

13 single set of parameters was retained which describe the 2,500 and 500 mg/kg doses rather than 

14 adjust parameters to fit a data set (the 100 mg/kg dose group) that appeared inconsistent and may 

15 be the result of a simple experimental error rather than attributable to a dose dependence. 

16 Thus, high- (2,500 mg/kg) and mid-dose (500 mg/kg) intravenous-route pharmacokinetic 

17 data were used to validate the parameters calibrated from the inhalation studies for the metabolic 

18 clearance of MeOH. Metabolic constants reasonably predict blood MeOH kinetics following a 

19 2,500 mg/kg dose in NP animals until 12 hours postexposure, but under predict blood MeOH in 

20 GD9 and GD18 mice at 8 hours of exposure and beyond, and under-predict levels in both NP and 

21 pregnant mice at 15 hours and beyond. At this high-dose, where blood kinetics of MeOH were 

22 reported in NP, GD9, and GD18 mice, the data for the GD18 mice was inconsistent with the 

23 GD9 and NP animals. The GD9 data at 12 hours appears inconsistent with the NP data, but then 

24 the 2 are nearly identical again at 15 hours, so it is not clear if that difference at 12 hours is real

25 or just due to experimental variability. Blood levels of MeOH were ~500 mg/L in GD18 mice at 

26 24 hours, but were nondetectable after 18 hours in the other groups (detection limit 2 mg/L). 

27 Blood concentrations were accurately predicted following administration of 500 mg/kg MeOH 

28 (Figure B-5A). The model predictions did not match the 100 mg/kg data unless one assumed an 

29 error in dose preparation, as described above (Figure B-5B). The calibration of the MeOH 

30 PBPK model is consistent with both the available inhalation and oral-route data. 

B.2.3.1. (B2.3.4)Total Methanol Metabolic Clearance

31 Quantifying production of formaldehyde following MeOH exposure for use as an 

32 alternative dose metric is of particular interest because formaldehyde is also undergoing toxicity 

33 assessment. However, it is important to understand that because the model was developed to 

34 describe blood MeOH kinetics, metabolism of MeOH to neither formaldehyde nor formate is 

35 specifically described; the model tracks neither of these metabolites. While the metabolic 

36 clearance of MeOH described by the model may be presumed to equate with formaldehyde 
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1 production, this metabolic flux simply leaves the computational model system without specific 

2 attribtution. Since the metabolic conversion of formaldehyde to formate is rapid in all species 

3 (< 1 minute) (Kavet and Nauss, 1990), the MeOH clearance rate may approximate a formate 

4 production as well as a formaldehyde production rate, though this has not been verified.

5 Thus, production of formaldehyde or formate following exposure to MeOH can only be 

6 estimated by summing the total amount of MeOH cleared by metabolic processes. If used, this 

7 metric of formaldehyde or formate dose should be adjusted by an inter-species scaling factor 

(SF = [BWhuman/BWrodent]

0.25 8 ) to adjust for expected species differences in the clearance of these 

9 two metabolites (this is a default factor based on the generally accepted assumption that total 

metabolism scales as BW0.75 and hence clearance per BW scales as 1/BW0.25 10 ). The rate of 

11 MeOH clearance may roughly be equated to the total amount of metabolites produced. Values of 

12 total MeOH clearance as a function of exposure in mice and humans are presented in the 

13 Additonal Materials (Tables 7-9).

B.2.3.1. (B.2.3.5)Formal Optimization of Mouse Model Parameters

14 Formal optimization of five parameters (inhalation fractional availability and the Vmax

15 and Kms for high and low affinity MeOH metabolism) was attempted using optimization 

16 routines in acslXtreme v2.01.1.2. Under the best circumstances, formal optimizations offer the 

17 benefit of repeatability and confirmation that global optima have not been significantly missed 

18 by user-guided visual optimization. Incorporating judgments regarding the value of specific data 

19 sets, while possible when visually fitting, is more difficult when using optimization routines. 

20 This is an important distinction between these approaches for this modeling exercise.

21 The mouse inhalation route NOEL was less than 1,000 ppm MeOH. The model is 

22 calibrated against inhalation-route data because of the importance of this exposure route in the 

23 assessment. Unfortunately, the vast majority of the MeOH data came from much higher 

24 exposure concentrations. As expected, various attempts at formal optimization lead to improved 

25 fits for some but never all data sets. This is to be expected when there is significant variability in 

26 the underlying data. Various data-weighting schemes were included to improve overall 

27 optimization while maintaining a good fit to the lowest concentration (1,000 ppm) data. In the 

28 end, formal optimization provided no significant improvement over the fractional availability 

29 and metabolic parameter values obtained by visual optimization, so these were retained in the 

30 final version of the model.

31 Further details on the approach and results from the formal optimization are found in the 

32 Additional Materials in outline format with supporting figures. More complete documentation 

33 was not developed because the products of the optimizations were not used in the final model. 

34 The documentation is intended only to demonstrate that appropriate optimizations were 

35 conducted and what the results of those optimizations were.
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B.2.4. Mouse Model Sensitivity Analysis

1 An evaluation of the importance of selected parameters on mouse model estimates of 

2 blood MeOH AUC was performed by conducting a sensitivity analysis using the subroutines 

3 within acslXtreme. Files for reproducing the sensitivity analysis are available in the model as 

4 described in the additional materials. The analysis was conducted by measuring the change in 

5 model output corresponding to a 1% change in a given model parameter when all other 

6 parameters were held fixed. A normalized sensitivity coefficient of 1 indicates that there is a 

7 one-to-one relationship between the fractional change in the parameter and model output; values 

8 close to zero indicate a small effect on model output. A positive value for the normalized 

9 sensitivity coefficient indicates that the output and the corresponding model parameter are 

10 directly related while a negative value indicates they are inversely related.

11 Sensitivity analyses were conducted for the inhalation and oral routes. The 

12 inhalation-route analysis was conducted under the exposure conditions of Rogers and Mole 

13 (1997) and Rogers et al. (1993), 7-hour inhalation exposures at the NOEL concentration of 

14 1,000 ppm. The oral route sensitivity analysis was conducted for an oral dose of 1,000 mg/kg.

15 Parameters with sensitivity coefficients less than 0.1 are not reported. The parameters 

16 with the largest sensitivity coefficients for the inhalation route at 1,000 ppm were pulmonary 

17 ventilation, VmaxC, and partitioning to the body compartment (Figures B-6 [metabolism] and B-7 

18 [flows and partition coefficients]). MeOH AUC was also sensitive to KM2 and VmaxC. The 

19 sensitivity coefficient for pulmonary ventilation increases from 1 to ~1.75 during the exposure 

20 period as metabolism begins to saturate. The sensitivity coefficient is 1 for concentrations 100 

21 ppm MeOH or less or when hepatic clearance is nonlimiting.

22 Oral-route mouse blood MeOH AUC was sensitive to the rate constants for uptake. 

23 Blood AUC was most sensitive to the first-order rate constant for uptake from the stomach, KAS, 

24 during the first hour after exposure, becoming less important over time (Figure B-8). Blood 

25 MeOH AUC was also modestly sensitive to KAI, and KSI, the rate constants for uptake from the 

26 intestine and transfer rates between compartments, respectively.

27
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-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 

1 parameters. Sensitivity coefficients calculated for an exposure of 1,000 ppm MeOH are 

2 reported for blood MeOH AUC. Note: Km, Vmax refer to the high-affinity, low-capacity 

3 pathway and Km2, Vmax2 refer to the low-affinity, high-capacity pathway.

-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 ventilation), and partitioning to the body (PR) 

1 compartment are reported for blood MeOH AUC. Sensitivity coefficients calculated for 

2 an exposure to 1,000 ppm MeOH. 
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-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

1 Figure B-8. Mouse model sensitivity coefficients for oral exposures to MeOH. The 

2 sensitivity of blood MeOH AUC to oral absorption rate constants (KAS: stomach; KAI: 

3 intestine; KSI: transfer between compartments) is reported. 

B.2.5. Mouse Drinking Water Ingestion Pattern

1 To simulate exposures of mice via drinking water under bioassay conditions, an ingestion 

2 pattern first used by Keys et al. (2004) , based on data from Yuan (1993) was used. The pattern 

3 specifies a fraction of percent of total daily ingestion consumed in each half-hour interval. The 

4 first interval was shifted to correspond to the beginning of the active (dark) period, for 

5 consistency with patterns used for humans and rats. A Table function was used in acslXtreme to 

6 interpolate an instantaneous rate between the measured (30-min) values, with normalization so 

7 that the 24-hour integral equals 100%. The daily pattern is shown in Figure B-9A and the 

8 resulting blood concentration for a mouse exposed for 6 days per week (2100 mg/kg) is shown in 

9 Figure B-9B.

10

11
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1

2

Figure B-9. Mouse daily drinking water 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 obtain a rate of 

addition of methanol into the stomach lumen compartment (mg/h).

Source: Yuan (1993); Keys et al. (2004)
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B.2.6. Rat Model Calibration

1 The model was initially calibrated-to-fit data from intravenous, inhalation, and oral 

2 exposures in Sprague-Dawley (SD) rats using the 100 and 2500 mg/kg intravenous (IV) data 

3 provided in the command file of Ward et al. (1997). Holding other parameters constant, the rat 

4 PBPK model was calibrated against the Ward et al. (1997) IV-route blood pharmacokinetic data 

5 (Figure B-10) by fitting Michaelis-Menten constants for one high affinity/low capacity and one 

6 low-affinity, high-capacity enzyme, using the optimization routines in acslXtreme v2.3. Also 

7 shown for comparison in Figure B-10A are the 100 mg/kg IV data of Horton et al. (1992), 

8 obtained using Fischer 344 (F344) rats (data extracted from figures using DigitizIt), with a model 

9 simulation (heavy red line) which differs from that for the SD rat only due to the predicted effect 

10 of know body weight differences. While the fit to the Ward et al. data for SD rats is excellent, 

11 especially for the lower dose, the rate of clearance is over-predicted for the F344 rat when 

12 parameters fit to SD rat data are used. The 100 mg/kg IV data, with an alternate simulation for 

13 the F344 rat obtained with distinct parameters (see below) is expanded in Figur B-10B, 

14 emphasizing the difference in clearance between the two strains.

15 We then attempted to fit the model to the inhalation data of Horton et al. (1992) by 

16 adjusting only the inhalation fractional uptake (FRACIN). The results, shown in Figure B-11A, 

17 are clearly poor. While the model does match the uptake portion of the inhalation data for the 

18 1200 and 2000 ppm exposures, it under-predicts the peak concentration reached at 200 ppm. 

19 Further, the post-exposure clearance predicted by the model is much more rapid than indicated 

20 by the data, as occurred with the IV kinetics (Figure B-10). (Since the peak concentration for the 

21 2000 ppm inhalation exposure actually occurred at 7 hr, we also simulated a 7-hr exposure, 

22 shown by the thin black line. The result indicates that the data are more consistent with and 

23 better predicted by the longer exposure duration, but clearance is still over-predicted post24 exposure.) Therefore we concluded that the data show a clear strain difference in metabolism, 

25 and should support at least a partially independent set of parameters for SD and F344 rats.

26 We then combined the 100 mg/kg IV and inhalation data of Horton et al. (for F344 rats) 

27 and attempted to simultaneously identify the four metabolic parameters (Vmax and Km for two 

28 pathways) and FRACIN. However when this was done the resulting values for the two Km’s 

29 were ~ 90 ± 50 mg/L and 70 ± 40 mg/L (Km and Km2, respectively), which are clearly 

30 indistinguishable from a statistical standpoint. If instead the Km’s were fixed at the more distinct 

31 values identified from the SD rat IV data (6.3 and 65 mg/L), the optimization routine tended to 

32 set the Vmax associated with the lower Km to zero. Thus the F344 rat data of Horton et al. 

33 (1992) appear to be most consistent with a single metabolic pathway, even though the observed 

34 concentrations spanned almost 2 orders of magnitude. Therefore those data (including the 100 

35 mg/kg IV data) were simultaneously fit by adjusting a single Vmax and Km, along with the 
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1 inhalation fraction, FRACIN, with the second metabolic pathway set to zero (Figures B-10B and 

2 B-11B).

3 Figure B-10. NP rat i.v.-route methanol blood kinetics. MeOH was infused into: female 

4 Sprague-Dawley rats (275 g) at target doses of 100 (open squares and thin black 

5 line) or 2,500 (filled squares and heavy blue line) mg/kg; or (filled diamonds and 

6 heavy red lines) male F-344 rats (220 g) at target doses of 100 mg/kg. Data points 

7 represent measured blood concentrations and lines represent PBPK model 

8 simulations with (A) metabolic parameters fit to the Sprague-Dawley rat data or (B) 

9 metabolic parameters fit to F-344 data (see text for further details).

Source: Ward et al.(1997; squares); Horton et al. (1992; diamonds).
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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 obtaine 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)
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1 Model simulations of the F344 rat data with the F344-specific parameters are shown in 

2 Figure B-10B (heavy red line) and Figure B-11B. Unfortunately we were unable to 

3 simultaneously fit the inhalation data for all exposure levels, although a wide range of metabolic 

4 saturation (Km) values were tested. We could obtain a better fit of the high-concentration data 

5 by constraining FRACIN to a higher value, for example, but then the fits to the lower 

6 concentration data were compromised (not shown). Examining Figure 2 of Horton et al. (1992), 

7 the experimental variability (indicated by the error bars) on the 2000 ppm data was much larger 

8 than the 200 or 1200 ppm data, and as indicated by the simulations in Figure B-11 here, there is 

9 at least the appearance that the exposure was actually for 7 hr instead of 6 hr. (To be clear, the 

10 2000 ppm data were used in the optimization with the duration of inhalation set to 6 hr, but the 

11 routine selected parameters which only poorly fit those data.) Since our greatest concern is in 

12 predicting dosimetry at lower exposure levels, near to the points of departure, we decided to 

13 retain the fits shown here. The corresponding parameters are listed in Table B-1. The fractional 

14 absorption (20%) was lower than that estimated for mice (66.5%), but Perkins et al. (1995) also 

15 found lower fractional absorption of inhaled methanol in rats vs. mice.

16 Finally, first-order oral absorption parameters were first fit to the lower dose (100 mg/kg) 

17 oral absorption data reported by Ward et al. (1997), using the optimization routines in 

18 acslXtreme v2.3 (Figure B-12, heavy/solid lines). (Since the animals used were SD rats, the SD19 specific metabolic parameters were used.) While the fit to the low-concentration data was quite 

20 good (Figure B-12, lower panel), the fit to the 2500 mg/kg data (Figure B-12, upper panel) 

21 exhibited a much faster and higher peak than shown by the data. Even when the model was fit to 

22 both the high- and low-concentration data simultaneously, the fit to the high-concentration data 

23 could not be significantly improved without completely degrading the low-concentration fit (not 

24 shown). Also note that the 2500 mg/kg linear simulation completely over-estimates all the data 

25 points; i.e., the area-under-the-curve for this dose is higher than indicated by the data, indicating 

26 that the assumption of 100% absorption is not valid. Therefore, an alternative model using a 

27 saturable (Michaelis-Menten) equation for absorption from the stomach and fecal elimination 

28 (linear term) from the intestine was considered (thin lines) and found to significantly improve the 

29 high-concentration simulation, with a nearly identical fit the low-concentration data. While 

30 methanol absorption is not known to be regulated by transporters or other processes that would 

31 give rise to rate saturation, it is clear form the discrepancy between the linear model and the 2500 

32 mg/kg data that uptake is slower than predicted by such a model and its use would lead to an 

33 over-prediction of internal concentrations. Therefore parameters for the saturable uptake model 

34 are reported in Table B-1 and the KMASC applied to mice and humans. Note that since the 

35 saturation constant corresponds to a fairly large dose (620 mg/kg), the model is still effectively 

36 linear at low- to moderate dose rates.
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Figure B-12. Model fits to data sets from oral exposures to 100 (squares) or 2,500 

1 (diamonds) mg/kg MeOH in female Sprague-Dawley rat (Expanded scale in lower 

2 panel). Symbols are concentrations obtained from the command file. The thick lines 

3 represent PBPK model fits using a linear (first-order) equation for absorption from 

4 the stomach compartment with no fecal elimination, while the thin lines use a 

5 Michaelis-Menton equation with a small fraction eliminated in the feces. All other 

6 GI rates, including absorption from the intestine, are first order.

Source: Ward et al.(1997).
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B.2.7. Rat Model Simulations

1 A range of adverse developmental effects was noted in rat pups exposed to methanol 

2 throughout embryogenesis (NEDO, 1987). SD rats were exposed in utero over different periods 

3 of pregnancy and as neonates via inhalation or in drinking water. Inhalation exposures to 

4 methanol were carried out for 18–22 hours, depending on the exposure group. Simulations of 

5 predicted Cmax, AUC, and total metabolized from 22-hour exposures to 500, 1,000, and 5,000 

6 ppm MeOH are shown in Figure B-13. Simulations of oral exposures of SD rats to 65.9, 624.1, 

7 or 2,177 mg/kg-day (500, 5,000, 20,000 ppm in drinking water), daily dose estimations from the 

8 study of Soffritti et al. (2002), based on measured water consumption, kindly provided by 

9 Cynthia Van Landingham, Environ International, Ruston, Louisiana, are shown in Figure B-13. 

10 Although the exposures in these studies are to rats over long periods and in some cases exposures 

11 of the newborn pups, the model simulations are to NP adult rats only, using the dose-group 

12 specific average body weights of 0.33-0.34 kg BW from the study of Soffritti et al. (2002) and do 

13 not take into account changes is body weight or composition. These simulated values are 

14 presumed to be a better surrogate for and predictor of target-tissue concentrations in developing 

15 rats, and the corresponding estimated human concentrations a better predictor of developmental 

16 risk in humans than would be obtained using the applied concentration or dose and default 

17 extrapolations. The logic here is simply that the ratio of actual target tissue concentration (in the 

18 developing rat pup or human) to the simulated concentration in the NP adult is expected to be the 

19 same in both species and hence, that proportionality drops out in calculating a HEC.

20 Figure B-13 depicts simulations run to determine internal doses for 22 hours/day 

21 inhalation exposures at 500, 1,000, or 2,000 ppm. Simulation results for continuous inhalation 

22 exposures are shown for contrast. The simulations show that for all but the highest dose (2,000 

23 ppm) steady state is reached within 22 hours, and that “periodicity,” where the concentration 

24 time course is the same for each subsequent day, is reached by the 2nd day of exposure. At 

25 2,000 ppm, however, steady state is not reached until after 48 hours for the continous exposure. 

26 Therefore, the Cmax, 24-hour AUC and amount metabolized per day (AMET) were by simulating 

27 22 hours/day exposures for 5 days and calculating values of AUC and AMET over the last day 

28 (24 hours) of that period.

29
B- 24 DRAFT – DO NOT CITE OR QUOTE

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)

Cmax

(mg/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 

1 2,000 ppm MeOH. Rat BW was set to 0.33 kg. Simulations are shown for both 

2 continuous (thin, dashed/dotted lines in plot) and 22 hours/day exposures (thick, solid lines 

3 in plot). Cmax, AUC, and amount metabolized (AMET) are determined from the 22 

4 hour/day simulations, run for a total of 5 days (120 hours), with the AUC and AMET 

5 calculated for the last 24 hours of the simulation.

1

2 Figure B-14 depicts simulations run to mimic a single oral exposure, treated as a 

3 continuous infusion for 12 hours (assuming 12-hour period when rats are awake and active). 

4 Total AUC and AMET and AUC24 and AMET24 for the first 24 hours after start of exposure 

5 were calculated.
B- 25 DRAFT – DO NOT CITE OR QUOTE

1

2

Figure B-14. Simulated rat oral exposures of Sprague-Dawley rats to 65.9, 624.2, or 

3 2,177 mg MeOH/kg/day. Dosing was simulated as a 12-hour, zero-order infusion to the 

4 liver compartment. The AUC and total amount metabolized are given for a period 

5 sufficient for the MeOH to clear (84 hours), and the AUC24 and AMET24 values represent 

6 just the first 24 hours of exposure. (Results shown for illustrative purposes. Dosimetry used 

7 in assessment was simulated using a more realistic water ingestion pattern.)

3 To simulate ingestion of methanol in drinking water by rats under bioassay conditions, an 

4 ingestion pattern based on the observations of Spiteri (1982) and Peng et al. (1990). While mice 

5 ingest water in frequent, small bouts (Gannon et al., 1992) that are reasonably described as a 

6 continuous delivery to the stomach, rats exhibit clear periods of ingestion alternating with 

7 periods where no ingestion occurs (Spiteri, 1982; Peng et al., 1990). Based on those data a 

8 reasonable representation of rat water ingestion can be described as serious of pulses. During the 

9 dark/active period of each day (first 12 hr) each bout of drinking was assumed to last 45 min 

10 followed by 45 min without ingestion (total of 8 bouts). During the light/inactive period (next 12 

11 hr) drinking bouts were assumed to last only 30 min followed by 2.5 hr (150 min) without 

12 drinking (4 bouts). An equal amount was assumed to be consumed in each bout within the dark 

13 period, likewise within each light-period bout, with the respective amounts adjusted such that 

14 80% of the total ingestion occurs during the dark and 20% during the light (Burwell et al., 1992). 

15 The resulting absorption pattern is shown in Figure B-15A and a simulated blood concentration 

16 time-curve (for 50 mg/kg/day dosing) is shown in Figure B-15B.

Exposure 

dose 

(mg/kg·day)

Body 

weight (kg)

Cmax

(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 45,138 347.4 138.4
B- 26 DRAFT – DO NOT CITE OR QUOTE

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 multiplying 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

4 The mouse model was scaled to human body weight (70 kg or study-specific average), 

5 using human tissue compartment volumes and blood flows, and calibrated to fit the human 

6 inhalation-exposure data available from the open literature, which comprised data from four 

7 publications (Ernstgard et al., 2005; Batterman et al., 1998; Osterloh et al., 1996; Sedivec et al., 

8 1981). 

9 A first-order rate of loss of MeOH from the blood, K1C, and a first-order bladder 

10 compartment time constant, KBL, were used to provide an estimate of urinary MeOH 
B- 27 DRAFT – DO NOT CITE OR QUOTE

1 elimination. The inhalation-route urinary MeOH kinetic data described by Sedivec et al. (1981) 

2 (Figure B-16) were used to inform these parameters. The urine MeOH concentration data 

3 reported by the authors were converted to amount in urine by assuming 0.5 mL/hr/kg total 

4 urinary output (Horton et al., 1992). Sedivec et al. (1981) measured a fractional uptake of 

5 57.7%, based on total amount inhaled. Since the PBPK model uses alveolar rather than total 

6 ventilation and this is typically assumed to be 2/3 of total ventilation the fractional uptake of 

7 Sedivec et al. (1981) was corrected by dividing by 2/3 to obtain a value for FRACIN of 0.8655. 

8 The resulting values of K1C and KBL, shown in Table B-1, differ somewhat depending on 

9 whether first-order or saturable liver metabolism is used. These are only calibrated against a 

10 small data set and should be considered an estimate. Urine is a minor route of MeOH clearance 

11 with little impact on blood MeOH kinetics.

12 Although the high-doses used in the mouse studies warrant the use of a second metabolic 

13 pathway with a high Km, the human exposure data all represent lower concentrations and may 

14 not require or allow for accurate calibration of a second metabolic pathway. Horton et al. (1992) 

15 employed two sets of metabolic rate constants to describe human MeOH disposition, similar to 

16 the description used for rats and mice, but in vitro studies using monkey tissues with non-MeOH 

17 substrates were used as justification for this approach. Although Bouchard et al. (2001)

18 described their metabolism using Michaelis-Menten metabolism, Starr and Festa (2003) reduced 

19 that to an effective first-order equation and showed adequate fits. Perkins et al. (1995) estimated 

20 a Km of 320 ± 1273 mg/L (mean ± S.E.) by fitting a one-compartment model to data from a 

21 single oral poisoning to an estimated dose. In addition to the extremely high standard error, the 

22 large standard error for the associated Vmax (93 ± 87 mg/kg/hr) indicates that the set of 

23 Michaelis-Menten constants was not uniquely identifiable using this data. Other Michaelis24 Menten constants that have been used to describe MeOH metabolism in various models for 

25 primates are given in Table B-2. Because the Km calculated by Perkins et al. (1995) from the 

26 high-dose oral exposure is 320 mg/L, while the highest observed concentration in the data sets 

27 considered here is 14 mg/L (Batterman et al., 1998), forcing the model to use this higher Km 

28 would simply result in fits that are effectively indistinguishable from the linear model. A simple, 

29 linear model is preferred over the use of a Km value that high.
B- 28 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 B-16. Urinary MeOH elimination concentration (upper panel) and cumulative 

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

2 Data points in lower panel represent estimated total urinary MeOH elimination from 

3 humans exposed to 78 (diamonds), 157 (triangles), and 231 (circles) ppm MeOH for 8 

4 hours, and lines represent PBPK model simulations. Solid lines are model results with the 

5 saturable equation for hepatic metabolism while dashed lines show results for liner 

6 metabolism. Data digitized from Sedivec et al. (1981) and provided for modeling by the 

7 EPA.

Source: Sedivec et al. (1981).
B- 29 DRAFT – DO NOT CITE OR QUOTE

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 monkey: 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 ± 8.7a (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 Vmax, 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 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 the remaining ones. For more detail, see 

text and Table B-3.

1 To estimate both the Michaelis-Menten and first-order rates, all human data under 

2 nonworking conditions (Batterman et al., 1998; Osterloh et al., 1996; Sedivec et al., 1981) were 

3 used. Before discussing the parameter estimation, however, adjustments were made to one of 

4 these data sets (Osterloh et al., 1996). Batterman et al., (1998) and Sedivec et al. (1981) both 

5 subtracted background levels before reporting their results. However, Osterloh et al. (1996) 

6 measured and reported (plotted) blood methanol in nonexposed controls (data shown in 

7 Figure B-17). The data for Osterloh et al. (1996) clearly show a time-dependent trend which is 

8 close to linear, and a linear regression is also included. However, the blood concentration 

9 (average) in the exposed group of that study was ~1.2 mg/L, whereas the data and regression in 

10 Figure B-17 indicate a value of ~ 0.9 mg/L. Therefore, the exposure data for Osterloh et al 

11 (1996) were corrected by subtracting time-zero value for the exposed group plus a time12 dependent factor obtained by multiplying the slope of this regression (0.093 mg/L-hr) by the 

13 measurement time.

14 The metabolic (first-order or saturable) and urinary elimination constants were 

15 numerically fit to the nonworking human data sets while holding the value for FRACIN at 

16 0.8655 (estimated from the results of Sedivec et al. as described above) and holding the 

ventilation rate constant at 16.5 L/hr/kg0.75 and QPC at 24 L/hr/kg0.75 17 (values used by EPA 

18 [2000d] for modeling the inhalation-route kinetics vinyl chloride). Other human-specific 

19 physiological parameters were set as reported in Table B-1. 
B- 30 DRAFT – DO NOT CITE OR QUOTE

Control blood concentrations

(Osterloh et al., 1996)

y = 0.093x + 0.9094

R2

 = 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); Osterloh et al. (1996). 

1 Either (a) the set of VmaxC, Km, K1C, and KBL were simultaneously varied while fitting 

2 the entire data set or (b) KLLC, K1C, and KBL were so varied and fit. Thus the two model fits 

3 are separated by a single degree of freedom (one additional parameter in case [a]). Statistical 

4 results given in Tables B-2 and B-3 are from these global fitting exercises. Final fitted 

5 parameters that have been used in the model for the risk assessment are given in Table B-1. The 

resulting fits of the two parameterizations (1st 6 order or optimized Km/Vmax) are shown in 

7 Figures B-16 and B-18.

8 Use of a first-order rate has the advantage of resulting in one fewer variable in the model 

9 and results in an adequate fit to the data, but the saturable model clearly fits some of the data 

10 better (Figures B-16 and B-18). To discriminate the goodness-of-fit resulting from the inclusion 

11 of an additional variable necessary to describe saturable metabolism versus using a single first12 order rate, a likelihood ratio test was performed. Models are considered to be nested when the 

13 basic model structures are identical except for the addition of complexity, such as the added 

14 metabolic rate. Under these conditions, the likelihood ratio can be used to statistically compare 

15 the relative ability of the two different metabolism scenarios to describe the same data, as 

16 described by Collins et al. (1999). The hypothesis that one metabolic description is better than 

17 another is calculated using the likelihood functions evaluated at the maximum likelihood 

18 estimates. Since the parameters are optimized in the model using the maximum LLF, the 

19 resultant LLF is used for the statistical comparison of the models. The equation states that two 

times the log of the likelihood ratio follows a χ2 20 distribution with r degrees of freedom:

2[log( (model1) / (model 2))] 2[log (model1) log (model 2)] 2 λ λ λ λ χ r 21 − = − − ≅
B- 31 DRAFT – DO NOT CITE OR QUOTE

1 The likelihood ratio test states that if twice the difference between the maximum LLF of the two 

different descriptions of metabolism is greater than the χ2 2 distribution, then the model fit has 

3 been improved (Devore, 1995; Collins et al., 1999; Steiner et al., 1990).

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 

4 Michaelis-Menten kinetics to describe the metabolism of MeOH in humans. The rate 

5 constants used for each simulation are given in Table B-3.

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 value S.D. Correlation matrix LLF

Michaelis-Menten (optimized) -0.994 -24.1

Km 23.8 8.8
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VmaxC 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 discrete 

and not from sets of data obtained over time and therefore a true S.D. would be a higher value.

1 At greater than a 99.95% confidence level, using 2 metabolic rate constants (Km and 

2 VmaxC) is preferred over utilizing a single rate constant (Table B-4). While the correlation 

3 coefficients (Table B-3) indicate that Vmax and Km are highly correlated, that is not unexpected, 

4 and the S.D.s (Table B-3) indicate that each is reasonably bounded. If the data were 

5 indistinguishable from a linear system, Km in particular would not be so bounded from above, 

6 since the Michaels-Menten model becomes indistinguishable from a linear model as VmaxC and 

7 Km tend to infinity. Moreover, the internal dose candidate POD, for example the BMDL10 for 

8 the inhalation-induced brain-weight changes from NEDO (1987), with methanol blood AUC as 

9 the metric, is 374.67 mg-hr/L, which corresponds to an average blood concentration of 15.6 

10 mg/L. Therefore the Michaelis-Menten metabolism rate equation appears to be sufficiently 

11 supported by the existing data, and its use is expected to improve the accuracy of the HEC 

12 calculations, since those are being conducted in a concentration range in which the nonlinearity 

13 has an impact. 

Table B-4. Comparison of LLF for Michaelis-Menten and first-order metabolism

LLF (logλ) for M-M LLF (logλ) for 1st

order

LLF

1st versus M-M a 2 χ r (99% confidence) b 2 χ r (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[log λ(model1) − log λ(model 2)] b

significance level at r = 1 degree of freedom.

14 While the use of Michaelis-Menten kinetics might allow predictions across a wide 

15 exposure range (into the nonlinear region), extrapolation above 1,000 ppm is not suggested since 

16 the highest human exposure data are for 800 ppm. Extrapolations to higher concentrations are 

17 potentially misleading since the nonlinearity in the exposure-internal-dose relationship for 

18 humans is uncertain above this point. The use of a BMD or internally applied UFs should place 

19 the exposure concentrations within the range of the model.

20 The data from Ernstgard et al. (2005) was used to assess the use of the first-order 

21 metabolic rate constant to a dataset collected under conditions of light work. Historical measures 

of QPC (52.6 L/hr/kg0.75) and QCC (26 L/hr/kg0.75 22 ) for individuals exposed under conditions of 

50 w of work from that laboratory (52.6 L/hr/kg0.7 23 ) (Ernstgard, personal communication; Corley 
B- 33 DRAFT – DO NOT CITE OR QUOTE

1 et al., 1994; Johanson et al., 1986) were used for the 2-hour exposure period (Figure B-19). 

2 Otherwise, there were no changes in the model parameters (no fitting to these data). The results 

3 are remarkably good, given the lack of parameter adjustment to data collected in a different 

4 laboratory, using different human subjects than those to which the model was calibrated.

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

5 Figure B-19. Inhalation exposures to MeOH in human volunteers. Data points 

6 represent measured blood MeOH concentrations from humans (4 males and 4 females) 

7 exposed to 100 ppm (open symbols) or 200 ppm (filled symbols) for 2 hours during light 

8 physical activity. Solid lines represent PBPK model simulations with no fitting of model 

9 parameters. For the first 2 hours, a QPC of 52.6 L/hr/kg0.75 (Johansen et al. 1986), and a 

10 QCC of 26 L/hr/kg0.75 (Corley et al., 1994) were used by the model.

Source: Ernstgard et al.(2005).

B.2.8.1. (Should be B.2.8.2) Oral Route

5 There were no human data available for calibration or validation of the oral route for the 

6 human model. In the absence of data to estimate rate constants for oral uptake, the ‘humanset.m’ 

7 file which sets parameters for human simulations applies the KMAS for the mouse with the other 

8 absorption parameters set to match those identified for ethanol in humans by Sultatos et al. 

9 (2004); VmASC was set such that for a 70-kg person, VMAS/KMAS matches the first-order 

uptake constant of Sultatos et al. (2004) (0.21 hr-1 10 ). While Sultatos et al. include a term for 

11 ethanol metabolism in the stomach, no such term is included here and the rate of fecal 

12 elimination is set to zero, corresponding to 100% absorption. However zero-order ingestion, a 

13 continuous infusion at a constant rate into the stomach lumen equal to the daily dose/24 hours, 

14 was assumed for all human simulations. Since absorption was assumed to be 100% of 

15 administered MeOH, at steady state the rate of uptake from the stomach and intestine 

16 compartments (combined) must equal the rate of infusion to the stomach. Since Cmax is driven by 

17 the oral absorption rate, which was assumed rather than fitted and verified, Cmax was not used as 

18 a dose metric for human oral route simulations. AUC, which is less dependent on rate of uptake, 
B- 34 DRAFT – DO NOT CITE OR QUOTE

1 was used as the dose metric and for estimation of HEDs. Since the AUC was computed for a 

2 continuous oral exposure, its value is just 24-hours times the steady-state blood concentration at 

3 a given oral uptake rate.

B.2.8.1. (B.2.8.3) Inhalation Route HECs and Oral Route HEDs

4 The atmospheric MeOH concentration resulting in a human daily average blood MeOH 

5 AUC (hr×mg/L) or Cmax (mg/L) equal to that occurring in experimental animals following 

6 exposure at the POD concentration is termed the HEC. Similarly, the oral dose (rate) resulting in 

7 human daily average blood MeOH AUC (hr×mg/L) equivalent to that occurring in an 

8 experimental animal at the POD concentration is termed the HED. 

9 To determine the HEC for specific exposures in mice, the mouse PBPK model is first 

10 used to determine the daily blood MeOH 24-hours AUC and Cmax associated with 7 hour/day 

11 inhalation exposures. Mice were exposed each day for 10 days, so the full 10-day exposure was 

12 simulated and an average 24-hours AUC calculated over that time, so no other duration 

13 adjustment was needed. The human AUC was determined for the last 24 hours of a continuous 

14 1,000-hour exposure, to assure steady state was achieved. The human Cmax was determined at 

15 steady state and so is equivalent to the steady state blood MeOH concentration. Results are given 

16 in Table B-5 and for inhalation shown in Figure B-20.

17 For example, for a 1,000 ppm exposure this resulted in model-predicted peak blood of 

18 133 mg/L and an AUC of 770 (hr×mg/L). The human model can then be used to determine the 

19 human MeOH exposure concentration leading to the same daily average AUC or Cmax under

20 continuous exposure conditions. Based on AUC, the HEC of the 1,000-ppm exposure is 

21 684 ppm, while based on peak (human steady-state) concentration, the HEC is predicted to be 

22 1110 ppm. The parameters used in the human model for these simulations are listed in Table B-1 

23 for saturable kinetics.

24 The HED was calculated by using the human model to find the oral dose (mg/kg-day) that 

25 gave a blood MeOH AUC equivalent to the mouse AUC following an exposure at the POD.

26 Zero-order absorption was assumed. For example, the human oral exposure equivalent to a 

27 1,000-ppm inhalation exposure in mice (i.e., with an AUC of 770 mg-hr/L) is 163 mg/kg-day. 

28 Since a 200 mg/kg-day oral exposure gave a human AUC of 1,320 mg-hr/L, which falls between 

29 the values predicted for inhalation exposures at 800 ppm (1,090 mg-hr/L) and 1,000 ppm (2,090 

30 mg-hr/L), this oral exposure rate was taken to be the upper end for the model to accurately 

31 estimate an HED.
B- 35 DRAFT – DO NOT CITE OR QUOTE

Table B-5. PBPK model predicted Cmax and 24-hour AUC for mice and humans 

exposed to MeOH

Exposure 

concentration 

(ppm)

Inhalation Route Oral Route

Mousea Humanb Human

AUC

(mg-hr/L) Cmax (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 6130 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 considered unreliable above 1,000 ppm (inhalation) or 200 mg/kgday (oral), but are 

included for comparison

1 Again, since the available human exposure data is to, at most, 800 ppm, the model could 

2 not be calibrated for higher exposures that approximate most of the mouse and rat exposure 

3 concentrations. The AUC in humans for similar exposure levels is ~3 times greater than in the 

4 mouse, primarily because human exposure estimates are expected to result from 24-hour 

5 exposures and the mice were exposed for 7 hours.
B- 36 DRAFT – DO NOT CITE OR QUOTE

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). Cmax for human exposures is equal to the steady-state 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 BW0.75 to 

reflect cross-species scaling (Human simulation results above 1,000 ppm are not 

considered reliable but are shown for comparison).

1 While the PBPK computational code can be used in the future to derive HECs or HEDs 

2 for other exposures, an alternative approach was developed that allows non-PBPK model users to 

3 estimate MeOH HECs and HEDs from benchmark doses in the form of AUCs. This approach 

4 uses algebraic equations describing the relationship between predicted MeOH 24-hour AUC or 

5 total amount metabolized in the liver (MET, mg [per day]) (constant 24-hour exposure) and the 

6 inhalation exposure level (i.e., an HEC in ppm) (Equations 1, 1b, 3 or 3b) or oral exposure rate 

7 (i.e., an HED in mg/kg-day) (Equations 2, 2b, 4 or 4b). To use the equations to derive an HEC or 

8 HED, the target human AUC is applied to the appropriate equation. Since these relationships are 

9 for continuous exposures, blood concentration is constant, and hence extrapolation for a Cmax is 

10 obtained by simply using AUC = 24*Cmax.
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794 AUC

1334 AUC HEC (ppm ) 0.0224 AUC 1000

+

× 1 < = × + Equation 1a

 UF 

 





+

× = × +

794 AUC

1334 AUC 2 RfC (ppm) 0.0224 AUC Equation 1b

569 AUC

275.4 AUC HED (mg/kg/day ) 0.0058 AUC 200

+

× 3 < = × + Equation 2a

 UF 

 





+

× = × +

569 AUC

275.4 AUC 4 RfD (mg/kg/day) 0.0058 AUC Equation 2b

(19282.9 MET)

14.69 MET HEC (ppm ) 0.063554 MET 1000 −

× 5 < = × + Equation 3a

( ) UF 















−

× = × +

19282.9 MET

14.69 MET 6 RfC (ppm) 0.063554 MET Equation 3b

(19282.9 MET)

3.3 MET HED (mg/kg/day ) 0.014287 MET 200 −× 7 < = × + Equation 4a

( ) UF 















−

× = × +

19282.9 MET

3.3 MET 8 RfD (mg/kg/day) 0.014287 MET Equation 4b

B.2.9. Conclusions and Discussion

9 Mouse, rat, and human MeOH PBPK models have been developed and calibrated to data 

10 in the open literature. The model simplifies the structure used by Ward et al. (1997) while adding 

11 specific refinements (e.g., a standard lung compartment and a two-compartment GI tract). 

12 Although the endpoints of concern are developmental effects which occur during in utero 

13 and (to a lesser extent) lactational exposure, it is not necessary for a MeOH PBPK model to 

14 specifically describe pregnancy (i.e., specify a fetal/gestational/conceptus compartment) and 

15 lactation in order for it to provide better cross-species extrapolation of risk than default methods. 

16 Representation of the unique physiology of pregnancy and the fetus/conceptus would be 

17 necessary if MeOH pharmacokinetics differed significantly during pregnancy or if the observed 

18 partitioning of MeOH into the fetus/conceptus versus the mother showed a concentration ratio 

19 significantly greater than or less than 1. MeOH pharmacokinetics GD6–GD10 in the mouse, are 

20 not different from NP mice (Pollack and Brouwer, 1996), and the maternal blood:fetus/conceptus 

21 partition coefficient is reported to be near 1 (Ward et al., 1997; Horton et al., 1992). At GD18 in 

22 the mouse, maternal blood levels are only modestly different from those in NP animals (see 

23 Figures B-4 and B-5 for examples), and in general the PBPK model simulations for the NP 

24 animal match the pregnancy data as well as the NP data. Likewise maternal blood kinetics in 

25 monkeys differs little from those in NP animals (see Section 3.4.7). Further, in both mice and 

26 monkeys, to the extent that late-pregnancy blood levels differ from NP for a given exposure, they 

27 are higher; i.e., the difference between model predictions and actual concentrations is in the same 

28 direction. These data support the assumption that the ratio of actual target-tissue methanol 

29 concentration to (predicted) NP maternal blood concentrations will be about the same across 
B- 38 DRAFT – DO NOT CITE OR QUOTE

1 species, and hence that using NP maternal blood levels in place of fetal concentrations will not 

2 lead to a systematic error when extrapolating risks. Thus, a full representation of pregnancy and 

3 the fetal/conceptus compartment appears to be unnecessary. 

4 While lactational exposure is less direct than fetal exposure and blood or target-tissue 

5 levels in the breast-feeding infant or pup are likely to differ more from maternal levels, the 

6 health-effects data indicate that most of the effects of concern are due to fetal exposure, with 

7 only a small influence due to postbirth exposures. Separating out the contribution of postbirth 

8 exposure from pre-birth exposure to a given endpoint in a way that would allow the risk to be 

9 estimated from estimates of both exposure levels would be extremely difficult, even if one had a 

10 lactation/child PBPK model that allowed for prediction of blood (or target-tissue) levels in the 

11 offspring. And one would still expect the target-tissue concentrations in the offspring to be 

12 closely related to maternal blood levels (which depend on ambient exposure and determine the 

13 amount delivered through breast milk), with the relationship between maternal levels and those 

14 in the offspring being similar across species.

15 Therefore, the development of a lactation/child PBPK model appears not to be supported, 

16 given the minimal change that is likely to result in risk extrapolations and use of (NP) maternal 

17 blood levels as a measure of risk in the offspring is still considered preferable over use of default 

18 extrapolation methods. In particular, the existing human data allow for accurate predictions of 

19 maternal blood levels, which depend strongly on the rate of maternal methanol clearance. 

20 Failing to use the existing data (via PBPK modeling) on human methanol clearance (versus that 

21 in other species) would be to ignore this very important determinant of exposure to breast-fed 

22 infants. And since bottle-fed infants do not receive methanol from their mothers, they are 

23 expected to have lower or, at most, similar overall exposures for a given ambient concentration 

24 than the breast-fed infant, so that use of maternal blood levels for risk estimation should also be 

25 adequately protective for that group.

26 During model development, several inconsistencies between experimental blood MeOH 

27 kinetic data embedded in the Ward et al. model (1995) and the published figures first reporting 

28 these data were discovered. Therefore, data were digitized from the published literature when a 

29 figure was available, and the digitized data was compared to the provided data. When the 

30 digitized data and the data embedded in the computational files (i.e., provided to Battelle under 

31 contract from the EPA) were within 3% of each other, the provided data was used; when the 

32 difference was greater than 3%, the digitized data was used. Often, using the published figures 

33 as a data source resulted in substantial improvements of the fit to the data in the cases where the 

34 published figures were different from the embedded data. 

35 The final MeOH PBPK model fits well inhalation-route blood kinetic data from separate 

36 laboratories in rodents and humans. Intravenous-route blood MeOH kinetic data in NP mice 

37 were only available for a single i.v. dose of 2,500 mg/kg, but were available for GD18 mice 

38 following administration of a broader range of doses: 100, 500, and 2,500 mg/kg. Up to 
B- 39 DRAFT – DO NOT CITE OR QUOTE

1 20 hours postexposure, blood MeOH kinetics appear similar for NP and pregnant mice after 

2 administration of 2,500 mg/kg. The intravenous pharmacokinetic data in GD18 mice showed an 

3 unexpected dose-dependent nonlinearity in initial blood concentrations, suggesting either a dose 

4 dependence on the volume distribution, which is unlikely, or some source of experimental 

5 variability. To account for this nonlinearity, Ward et al. (1997) used dose-specific partition 

6 coefficients for placenta and embryonic fluid and Vmax for the metabolism of MeOH. The 

7 current model uses a consistent set of parameters that are not varied by dose and therefore does 

8 not fit these 100 mg/kg dose intravenous data. The model does fit the 500 and 2,500 mg/kg 

9 doses, and if a presumed i.v. dose of 200 mg/kg (twice the reported 100 mg/kg) is employed, is 

10 able to predict initial blood concentrations for the lowest dose data, as expected. The i.v. data 

11 from the Ward et al. (1995) model does match the corresponding published figures. 

12 The model fits to the mouse oral-route MeOH kinetic data using a consistent set of 

13 parameters (Figure B-4) are reasonably good but not as good as fits to the inhalation data. The 

14 model consistently underpredicts the amount of blood MeOH reported in two studies (Ward 

15 et al., 1997, 1995). Ward et al. (1997) utilized a different Vmax for each oral absorption data set. 

16 In the report by Ward et al. (1997) the GD18 and the GD8 data from Dorman et al. (1995) were 

17 both fit using a Vmax of ~80 mg/kg/hr (body weights were not listed, the model assumed that 

18 GD8 and GD18 mice were both 30 g; Ward et al. (1997) did not scaled by body weight), but 

19 lower partition coefficients for placenta (1.63 versus 3.28) and embryonic fluid (0.0037 versus 

20 0.77). The current model adequately fits the oral pharmacokinetic data using a single set of 

21 parameters that is not varied by dose or source of data.

22 The fits of the rat model to the limited dataset readily available were quite good. The low23 dose exposures of all routes were emphasized in model optimization since they were the doses 

24 most relevant to risk assessment. Based on a rat inhalation exposure to 500 ppm, the human HEC 

25 would be 301 ppm (by applying an AUC of 226 [Figure B-12] to Equation 1).

26 The mouse, rat, and human models fit multiple datasets from multiple research groups 

27 using consistent parameters that are representative of each species, but are not varied within 

28 species. Using the model, it will be possible to ascertain chronic human exposure concentrations 

29 that are likely to be without an appreciable risk of deleterious effects.

B.3. ADDITIONAL MATERIAL

30 • Results from Optimizations

31 • acslXtreme Program (.csl) File (Electronic Attachment)

32 • acslXtreme procedure (.cmd) file

33 • Key to .m files for reproducing the results in this report

34 • Code for .m files
B- 40 DRAFT – DO NOT CITE OR QUOTE

1 • Personal communication from Lena Ernstgard regarding human exposures reported in the 

2 Ernstgard and Johanson, 2005 SOT poster

3 • Personal communication from Dr. Rogers regarding mouse exposures to MeOH

4 • Data and simulations for MeOH Metabolic Clearance/Total Metabolites Produced

5 • Multiple daily oral dosing for humans

B.3.1. Results From Optimizations

B.3.1.1. Approach for and Results of the Optimization of Metabolic Parameters and 

Inhalation Route Fractional Availability in Mice

6 The approach and results are presented below in outline format with supporting figures. More 

7 complete documentation was not developed because the products of the optimizations were not 

8 used in the final model. The documentation here is intended only to demonstrate that appropriate 

9 optimizations were conducted and what the results of those optimizations were.

10 1. The Vmax for the low affinity pathway was set to 0 and the remaining VmaxC, Km, and 

11 fractional availability were optimized using inhalation data only.

12 a. The optimizer was unable to find a value for Km that was greater than 0.

13 b. The resulting metabolic parameters essentially represented a zero order loss process.

14 2. The Vmax for the low affinity pathway was set to 0 and the remaining VmaxC and Km were 

15 optimized using all (oral, intravenous and inhalation) data.

16 a. The optimized single Km, 135 mg/L, was equal to the average of the 2 original Kms.

17 b. Fits to the MeOH blood levels following inhalation exposures > 2,000 ppm are slightly 

18 improved, but the model fits to the 1,000 ppm exposure concentration overpredict 

19 reported values by 20%.

20 3. Parameters for both metabolic pathways were optimized using all (oral, intravenous and

21 inhalation) data.

22 a. The fit to the high-dose intravenous data from Ward et al. (1997) (2,500 mg/kg) was 

23 improved (Figure B-21). 

24 b. The fit to the high-dose oral data, also from Ward et al. (1997), (2,500 mg/kg) was 

25 improved (Figure B-22). 

26 c. The fit to the mid-dose i.v. data (500 mg/kg) dose was not as good as using the visually 

27 fit parameters (Figure B-21)

28 d. The fit to the low-dose oral data (1,500 mg/kg) was not as good when the visually fit 

29 parameters were used (Figure B-22). The low-dose data was from Dorman et al. (1995).
B- 41 DRAFT – DO NOT CITE OR QUOTE

1 e. Neither set of parameters resulted in an adequate fit to the low-dose intravenous data 

2 (100 mg/kg; Figure B-21).

3 f. Fits to the inhalation data following exposures to < 5,000 ppm MeOH were substantially 

4 worse than when using the visually fit parameters (Figure B-23)

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 

5 parameter optimizations. Solid blue lines - visually optimized; dashed red lines -

6 clearance parameters (Km, Km2, VmaxC, Vmax2C) optimized using all inhalation data 

7 sets; dash/dot green lines - clearance parameters optimized using all data sets (inhalation, 

8 oral, and intravenous).

Source: Ward et al. (1997).
B- 42 DRAFT – DO NOT CITE OR QUOTE

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 different clearance and uptake 

1 parameter optimizations. Solid blue lines - visually optimized; dashed red lines -

2 clearance parameters (Km, Km2, VmaxC, Vmax2C) optimized using all inhalation data sets; 

3 dash/dot green lines - clearance parameters optimized using all data sets (inhalation, oral, 

4 and intravenous).

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 

1 parameter optimizations. Dotted black lines - model optimized fractional inhalation, solid 

2 blue lines - visually optimized; dashed red lines - clearance parameters (Km, Km2, VmaxC, 

3 Vmax2C) optimized using all inhalation data sets; dash/dot green lines - clearance 

4 parameters optimized using all data sets (inhalation, oral, and intravenous).

Source: Rogers et al. (1997).
B- 43 DRAFT – DO NOT CITE OR QUOTE

B.3.1.1. [B3.1.2.] Conclusion

1 Under the best circumstances, formal optimizations offer the benefit of repeatability and 

2 confirmation that global optima have not been missed by user-guided visual optimization. 

3 Incorporating judgments regarding the value of specific data sets while easy when visually 

4 fitting, is difficult at best when using optimization routines. This is an important distinction 

5 between these approaches for this modeling exercise.

6 The mouse NOEL was 1,000 ppm MeOH. Fitting the blood MeOH concentration data at 

7 this exposure drove our modeling exercises because of the importance of this exposure group in 

8 the risk assessment. Unfortunately, the vast majority of the blood MeOH data came from much 

9 higher exposures. As expected, our various attempts at optimization led to fits that were better 

10 for some, but never all, data sets. This is to be expected when there is clearly significant 

11 variability in the underlying data. Various data weighting schemes were included to improve 

12 overall optimization while maintaining a good fit to the 1,000 ppm data. In the end, optimization 

13 offered no significant improvement over the fractional uptake and metabolic parameter values 

14 obtained by visual optimization, so these were retained in the final version of the model.

B.3.2. acslXtreme Program (.csl) File

15 PROGRAM MeOH -- PBPK Model for Methanol

16 PROGRAM MeOH -- PBPK Model for Methanol

17 ! Based on MeOH Model by Ward et al 1997 with these revisions:

18 ! TS Poet, P Hinderliter and J Teeguarden, 

19 ! Center for Biological Monitoring and Modeling 4/16/05

20 ! Pacific Northwest National Laboratory

21 ! Model contains inhalation, iv, and oral (multiple patterns).

22 ! 1) Removed fetal compartment and other tissues that could be lumped

23 ! based on similarity of partition coefficients or did not need to be

24 ! specified directly (Bone, mammary tissue) for the modeling purposes here.

25 ! 2) Changed day to hr.

26 ! 3) Flows (scaled to BW or BW**0.75), Metabolism (BW**0.75) and 

27 ! tissue volumes (BW) are scaled in the model.

28 ! Final has somach and intestine compartments which provide fast and

29 ! slow absorption rates, respectively.

30 ! 4) Bladder compartment (for human simulations) added by Paul

31 ! Schlosser, U.S. EPA, Oct. 2008

32 ! 5) "Sipping" drinking water exposure code for rats, to match data 

33 ! from Peng et al. (1990) 

34 ! 6) Time-variable drinking pattern for mice from Keys et al. (2004)

35 ! added by Paul Schlosser, U.S. EPA, Aug. 2009

36 ! Version is final version used for simulations

37 ! ************ MODEL UNITS *****************

38 ! Concentration, mg/L

39 ! Mass of Chemical, mg

40 ! Volume, L

41 ! Flow, L/hr

42 ! Body Weight Kg

43 !==========================72 Character Line==========================!

44 INITIAL

45 ! Initialize some Variables before start
B- 44 DRAFT – DO NOT CITE OR QUOTE

1 Integer IDS, MULTE

2 REAL DRT(6), DRP(6) !store drink water times, percents in array!

3

4 CONSTANT BW = 0.030 ! Body weight (kg)

5 CONSTANT QPC = 15. ! Alveolar ventilation (L/hr/kg**0.75)

6

7 ! Blood Flows (fraction of cardiac output)

8 CONSTANT QCC = 15.0 ! Cardiac output (L/hr/kg**0.75)

9 CONSTANT QFC = 0.05 ! Fat

10 CONSTANT QLC = 0.25 ! Liver

11 ! Blood flow to rest of body Calculated by Flow Balance!

12 QRC = 1.0 - (QFC + QLC)

13 QC = QCC*BW**0.75

14 QP = QPC*BW**0.75

15

16 ! Tissue Volumes for mice (fraction of body weight)

17 CONSTANT VAC = 0.0123 ! Arterial blood

18 CONSTANT VFC = 0.07 ! Fat

19 CONSTANT VLC = 0.055 ! Liver

20 CONSTANT VLuC = 0.0073 ! Lung tissue

21 CONSTANT VVBC = 0.0368 ! Venous blood

22 VRC = 0.91 - (VAC+VFC+VLC+VLuC+VVBC)

23

24 ! Partition Coefficients (Mouse values from Ward et al used as default)

25 CONSTANT PB = 1350 ! MeOH Blood:Air; Use Horton value!

26 CONSTANT PF = 0.08 ! MeOH Fat:Blood

27 CONSTANT PL = 1.1 ! MeOH Liver:Blood

28 CONSTANT PLU = 1.0 ! MeOH Lung:Blood, compartment for dosing only

29 CONSTANT PR = 0.8 ! MeOH Rest of body:Blood

30

31 ! Hepatic Metabolism of MeOH

32 CONSTANT KM = 45.0 ! mg/L

33 CONSTANT VMAXC = 15.0 ! mg/hr/BW**0.75

34 VMAX = VMAXC*BW**0.75 ! mg/hr

35 CONSTANT VMAX2C = 15.0! 2nd saturable pathway

36 VMAX2 = VMAX2C*BW**0.75

37 CONSTANT KM2= 45.0

38 CONSTANT KLLC = 0.0 ! First-order metabolism

39 ! Set VMAXC = VMAX2C = 0, when KLLC > 0

40 KLL = KLLC/BW**0.25

41

42 ! MeOH Clearance from Blood!

43 CONSTANT K1C = 0.01 ! First-order clearance, BW**0.25/hr

44 K1 = K1C/BW**0.25 ! Scaled blood elimination, hr-1

45 ! This lumped term was used in the WARD model an accounted for

46 ! renal elimination and "additional" non-hepatic metabolism of

47 ! MeOH associated only with high dose i.v. data. 

48 ! A 1st-order term should not be used to represent two processes 

49 ! with different dose-dependencies.

50 ! This has not been used for mouse data (set=0), but was used to

51 ! approximate human urinary data!

52

53 ! Bladder compartment added (Paul Schlosser, U.S. EPA, 10/2008)

54 CONSTANT KBL=0.0 ! Bladder constant, 1/hr

55

56 ! Fractional Absorption of MeOH

57 CONSTANT FRACin = 0.85 ! Inhalation, value from Perkins et al
B- 45 DRAFT – DO NOT CITE OR QUOTE

1 CONSTANT KFEC = 0.0 ! Fecal elimination constant, 1/hr

2 ! KFEC determines oral bioavailability

3

4 ! Molecular Weight of MeOH

5 CONSTANT MWMe = 32.0 ! mol wt, g/mol!

6

7 ! Closed Chamber Parameters

8 CONSTANT VChC = 100.0 ! Volume of closed chamber (L)

9 CONSTANT Rats = 0.0 ! Number of rats in chamber

10 CONSTANT kLoss = 0.0 ! Chamber loss rate /hr

11 ! Set RATS = 0.0 and KLOSS = 0.0 for open chamber

12

13 ! Blood Flows (L/hr)

14 QF = QFC*QC ! Fat

15 QL = QLC*QC ! Liver

16 QR = QRC*QC ! Rest of Body

17

18 ! Tissue Volumes (mL)

19 VAB = VAC*BW! Arterial blood volume

20 VF = VFC*BW ! Fat

21 VL = VLC*BW ! Liver

22 VLu = VLuC*BW ! Lung

23 VR = VRC*BW ! Rest of the body

24 VVB = VVBC*BW ! Venous blood

25 VBL = VAB + VVB ! Total blood

26

27 !----------Timing commands------------!

28 CONSTANT TCHNG = 6.0 ! End of exposure!

29 CONSTANT TSTOP = 24.0 ! End of experiment/simulation!

30 CONSTANT POINTS = 1000.0 ! No. points for simulation output!

31 CONSTANT REST = 100000.0 ! End of work period for human exercise

32 CONSTANT WORK = 100000.0 ! Start of work period for human exercise

33 SCHEDULE DS1.AT.REST ! Change from work to rest conditions

34 SCHEDULE DS2.AT.WORK ! Change from rest to work conditions

35 ! Human Rest/Work (changes in blood-flow fractions to fat/liver not currently used0

36 CONSTANT QPCHR=15.0, QCCHR=15.0, QLCHR=0.25, QFCHR=0.05 ! Rest

37 CONSTANT QPCHW=52.0, QCCHW=26.0, QLCHW=0.16, QFCHW=0.06 ! Work

38

39 !---------Simulation Control------------!

40 ! Exposure Conditions Based on User Defined Initial Amounts of

41 ! Chemical (mg)

42 CONSTANT CONCppm = 0.0 ! Air Concentration in ppm

43 VCh = VChC-(Rats*BW) ! Volume of Occupied Chamber

44 CONCmg = CONCppm*MWMe/24451 ! Convert ppm to mg/Liter!

45 ACHO = CONCmg*VCH ! Init Amt in Chamber, mg!

46

47 ! Oral dosing

48 CONSTANT KAS = 0.1 ! 1st order oral abs, hr-1

49 CONSTANT KMASC = 550 ! Saturable oral abs Kmasc [=] mg/kg

50 KMAS = KMASC*BW

51 CONSTANT VASC = 1740 ! Saturable oral ab VmaxC, mg/hr/kg^0.75

52 VAS = VASC*BW**0.75 ! Saturable oral ab Vmax, mg/hr

53 CONSTANT KAI = 0.1 ! 1st order oral abs from intestine, hr-1

54 CONSTANT KSI = 0.5 ! 1st order transfer stom to intes hr-1

55 CONSTANT DOSE = 0.0 ! Oral dose in mg/kg BW

56 CONSTANT ODS = 0.0 ! Switch for zero order oral uptake

57 ! (Set to 1 for zero order, set to 0 for first order)
B- 46 DRAFT – DO NOT CITE OR QUOTE

1 ODOSE = DOSE*BW*(1.0-ODS) ! Convert mg/kg to mg total (oral)

2 RAOZ = DOSE*BW*ODS/24.0 ! mg/hr for zero order dosing

3

4 ! Daily dose for steady drinking water by "sipping" (by rats)

5 CONSTANT DWDOSE = 0 ! mg/kg/d by periodic sipping

6 CONSTANT PER1 = 1.5 ! Period between sipping episodes (hr) during dark

7 ! "Between" means from the start of 1 to the start of the next episode

8 CONSTANT DUR1 = 0.75 ! Duration of sipping episodes during dark (hr)

9 CONSTANT PER2 = 3.0 ! Period during light (hr) between sipping episodes

10 CONSTANT DUR2 = 0.5 ! Duration of sipping episodes during light (hr)

11 CONSTANT FNIGHT = 0.8 ! Fraction of drinking during night

12 constant days = 7.0 ! days/week of oral exposure

13 constant metd = 7.0 ! number of days at end over which AUCBF and AMETF

14 ! are averaged

15 tmetf = metd*24.0

16 dayon=24.0*days

17 ! Night sipping rate (mg/h) during episodes

18 DWRNIGHT = DWDOSE*BW*FNIGHT*PER1/(12.0*DUR1)

19 ! Day sipping rate (mg/h) during episodes

20 DWRDAY = DWDOSE*BW*(1-FNIGHT)*PER2/(12.0*DUR2)

21 IDOSE=0

22 ! Above assumes 12-hr each for day/night

23

24 ! Drinking Table from Deborah Keys for mice, as used in

25 ! A quantitative description of suicide inhibition of dichloroacetic acid in rats and mice.

26 ! Keys DA, Schultz IR, Mahle DA, Fisher JW.

27 ! Toxicol Sci. 2004 Dec;82(2):381-93.

28 ! Based on data of Yuan, J. (1993). Modeling blood/plasma concentrations in dosed feed and dosed

29 ! drinking water toxicology studies. Toxicol. Appl. Pharmacol. 119, 131-141.

30 constant rdrink = 1.0 ! Default for use of sipping w/ DWDOSE abopve

31 ! set rdrink = 0.0 to use pattern below

32 table mdrinkp,1,49 / 0., .5, 1., 1.5, 2., 2.5, 3., 3.5, &

33 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, &

34 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, &

35 12., 12.5, 13., 13.5, 14., 14.5, 15., 15.5, &

36 16., 16.5, 17., 17.5, 18., 18.5, 19., 19.5, &

37 20., 20.5, 21., 21.5, 22., 22.5, 23., 23.5, 24.0, &

38 0.12 , 0.9, 1.6, 1.8, 1.9, 2.9, 4.0, 4.5, 4.9, 4.9, &

39 4.8, 4.4, 4.0, 5.0, 5.9, 5.3, 4.5, 3.9, &

40 3.2, 3.0, 2.7, 2.5, 2.3, 2.3, 2.3, 1.9, &

41 1.4, 1.4, 1.3, 1.3, 1.3, 1.1, 0.8, 0.8, &

42 0.8, 0.6, 0.5, 0.7, 0.8, 0.6, 0.4, 0.2, &

43 0.05, 0.08, 0.14, 0.07, 0.06, 0.08, 0.12 /

44

45 ! Larger bolus dosing

46 CONSTANT DRDOSE=0.0 ! Total dose by drinking water in boluses, mg/kg day

47 ! Times for multiple oral drinks/day *after* 0

48 ! Must be ascending, 0 <= times < 24 hr

49 ! CONSTANT DRT=0, 2, 4, 6, 8, 10 ! Rat values

50 Constant DRT = 0.0, 3.0, 5.0, 8.0, 11.0, 15.0 ! Human values

51 ! DRTIME(1) assumed = 0 and not used

52 ! Fraction consumed by drinking at those times

53 CONSTANT DRP = 0.25, 0.1, 0.25, 0.1, 0.25, 0.05

54

55 !Total oral bolus dose; initial value given at t=0 via initial condition

56 TODOSE = DRP(1)*DRDOSE*BW*(1.0-ODS) + ODOSE

57
B- 47 DRAFT – DO NOT CITE OR QUOTE

1 ! IV dosing

2 CONSTANT IVDOSE = 0.0 ! IV dose, mg/kg

3 CONSTANT TINF = 0.025 ! Length of exposure (hrs), default = 1.5 min (bolus)

4 ! 1.5 min reported by Ward and Pollack, DMD 1996

5 TIV = IVDOSE*BW ! Expected amt infused, mg

6 IV1 = TIV/TINF ! Rate of infusion, mg/hg

7

8 ! For I.V. Runs, control step size if necessary by changing MaxT, not POINTs or CINT

9 MAXT = 1.0 ! Maximum Step Size, Hours

10 !IF (IVDOSE.GE.1.0E-4) MAXT = 1.0E-4

11

12 ! Liver infusion

13 CONSTANT LIVR0 = 0.0 ! Zero-order liver total, mg/kg/day

14 RLIV0 = LIVR0*BW/TCHNG ! Rate in mg/hr

15

16 !--------Dose Scheduling-----------------

17 CONSTANT MULTE=0 ! Default is *no* repeated dosing/inhalation

18 CIZONE = 1.0 ! Start with inhalation on

19 IVZONE = 1.0 ! Start with IV on

20 SCHEDULE OFF.AT.TCHNG ! Turn off exposure at TCHNG

21 DAY = 0;

22 NEWDAY = 0; IDS = 2 ! First dose given as initial condition

23 IF (MULTE) SCHEDULE ORALDOSE.AT.DRT(2)

24 ALGORITHM IALG = 2 ! Gear algorithm

25 END ! END OF INITIAL

26

27 DYNAMIC

28

29 DERIVATIVE

30 !************************* MeOH *************************

31 IVR = IVZONE*IV1 ! IV dosing; IVR = ate of infusion, mg/hg

32 ! Oral Dosing 

33 DWING = ( (DWRNIGHT*PULSE(0.0,PER1,DUR1)*PULSE(0.0,24.0,12.0) + &

34 DWRDAY*PULSE(0.0,PER2,DUR2)*(1-PULSE(0.0,24.0,12.0)) )*rdrink + &

35 (1-rdrink)*mdrinkp(mod(T,24.0))*0.02*DWDOSE*BW )*PULSE(0.0,168,dayon)

36 RAS = KAS*STOM + VAS*STOM/(KMAS+STOM)

37 RSTOM = DWING + RAOZ - RAS - KSI*STOM ! Change in stomach (mg/hr)

38 RINT = KSI*STOM - RFEC - KAI*AINTEST ! Change in intestines (mg/hr)

39 RLZ = RLIV0*CIZONE ! Zero-order to liver

40 RAO = RAS + KAI*AINTEST + RLZ ! Oral absorption (mg/hr)

41 RFEC = KFEC*AINTEST

42 FEC = INTEG(RFEC, 0.0)

43 STOM = INTEG(RSTOM, TODOSE) ! Amt in stomach (mg)

44 AINTEST = INTEG(RINT, 0.0) ! Amt in intestines (mg)

45 OralDoseCheck = INTEG(RAO, 0.0)

46

47 ! Arterial Blood 

48 RAAB = QC*(CVLU - CAB)

49 AAB = INTEG(RAAB, 0.0) ! Amount, mg

50 CAB = AAB/VAB ! Concentration, mg/L

51 AAUCB = INTEG(CAB, 0.0) ! AUC, hr*mg/L

52

53 ! Fat 

54 RF = QF*(CAB - CVF)

55 AF = INTEG(RF, 0.0) ! Amount, mg

56 CF = AF/VF ! Concentration, mg/L

57 CVF = CF/PF ! AUC, hr*mg/L
B- 48 DRAFT – DO NOT CITE OR QUOTE

1

2 ! Liver 

3 RAL = QL*(CAB - CVL) + RAO - RMETL - RMETL2 - RMETL3

4 AL = INTEG(RAL, 0.0) ! Amount, mg

5 CL = AL/VL ! Concentration, mg/L

6 CVL = CL/PL ! Concentration, mg/L

7 AUCL = INTEG(CL, 0.0)! AUC, hr*mg/L

8

9 ! Liver Metabolism 

10 RMETL = VMAX*CVL/(KM + CVL)

11 METL = INTEG(RMETL, 0.0)

12 RMETL2 = VMAX2*CVL/(KM2 + CVL)

13 METL2 = INTEG(RMETL2, 0.0)

14 RMETL3 = KLL*CVL

15 METL3 = INTEG(RMETL3, 0.0)

16

17 ! Total Amount Metabolized (Formate and Formaldehyde)

18 ! Does not include K1C for human MeOH excretion estimate

19 AMET = METL + METL2 + METL3

20 AMET24 = AMET*24.0/TSTOP

21 ! Total amount metabolized in last tmetf hr of exposure, averaged per day

22 AMETF = INTEG((RMETL+RMETL2+RMETL3)*PULSE(TSTOP-tmetf,TSTOP,tmetf),0.0)* & 

23 24.0/tmetf ! (tmetf = 24.0*metd)

24 ! Chamber concentration (mg/L)

25 RACh = (Rats*QP*CLEx) - (FRACin*Rats*QP*CCh) - (kLoss*ACh)

26 ACh = INTEG(RACh, AChO)

27

28 ! The following calculation yields an air concentration equal to the 

29 ! closed chamber value if a closed chamber run is in place and a 

30 ! specified constant air concentration if an open chamber run is in place

31 CCh = ACh*Cizone/VCh

32 CCPPM = CCh*24451/MWMe

33 CLoss = INTEG(kLoss*ACh, 0.0)

34

35 ! Lungs

36 RALu = QP*(FRACin*CCh - CLEx) + QC*(CVB - CVLu)

37 ALu = INTEG(RALu, 0.0)

38 CLu = ALu/VLu ! Concentration, mg/L

39 CVLu = CLu/PLu ! Exiting Concentration, mg/L

40

41 ! Amount Inhaled 

42 RInh = FRACin*QP*CCh

43 AInh = INTEG(RInh, 0.0) ! mg per rat

44 AInhC = AInh*Rats ! mg for a group of rats

45

46 ! Amount Exhaled 

47 CLEx = CVB/PB ! Concentration, mg/L

48 RAEx = QP*CLEx

49 AEx = INTEG(RAEx, 0.0)*PULSE(0,TCHNG,TSTOP) ! Amount, mg per rat

50 AExC = AEx*Rats ! Amount, mg, for a group of rats

51 AxF = INTEG(RAEx*PULSE(TCHNG,24,24), 0.0) ! Amount exhaled post-exposure

52

53 ! Rest of Body

54 RAR = QR*(CAB - CVR)

55 AR = INTEG(RAR, 0.0) ! Amount, mg

56 CR = AR/VR ! Concentration, mg/L

57 CVR = CR/PR ! Exiting Venous Concentration, mg/L
B- 49 DRAFT – DO NOT CITE OR QUOTE

1 AUCR = INTEG(CR, 0.0) ! AUC, hr*mg/L

2

3 ! Venous Blood (mg)

4 RURB = K1*CVB*VVB ! Lumped Clearance from Blood

5 RAVB = QF*CVF + QL*CVL + QR*CVR + IVR - QC*CVB - RURB

6 AVB = INTEG(RAVB, 0.0) ! Amount, mg

7 CVB = AVB / VVB ! Concentration, mg/L

8 AUCB = INTEG(CVB, 0.0) ! AUC, hr*mg/L (total over entire exposure)

9 AUCBB = AUCB*24.0/TSTOP ! Average over exposure, hr*mg/(L*day)

10 AUCBF = INTEG(CVB*PULSE(TSTOP-tmetf,TSTOP,tmetf),0)*24.0/tmetf 

11 ! AUCBF = Last tmetf AUC averaged/day (tmetf = 24.0*metd)

12 ! For "steady state" AUC in blood over a day, set exposures to 

13 ! several weeks to reach "periodicity", then use AUCBF w/ metd = 7

14

15 ! Bladder compartment, added by PS, U.S. EPA, 10/2008

16 RBL = KBL*ABL ! Rate of clearance from bladder (mg/hr)

17 ABL = INTEG((RURB-RBL),0.0) ! Amount in bladder (mg)

18 RUR= RBL/(BW*0.5e-3)! Urine concentration = rate/[BW*(0.5e-3 L/h/kg BW)]

19 URB = INTEG(RBL, 0.0) ! Amount cleared to urine, mg

20 URBF = INTEG(RURB*PULSE(TSTOP-tmetf,TSTOP,tmetf),0)*24.0/tmetf 

21 ! Amount cleared to urine in last tmetf averaged/day (tmetf = 24.0*metd)

22

23 !************************* Mass Balance **************************

24 Tbody = AAB + AF + AL + ALU + AR + AVB + ABL + STOM + AINTEST

25 MetabORClrd = METB + METL + METL2 + METL3 + AEX + FEC

26 TMass = Tbody + MetabORClrd

27 TDose = AinH + INTEG(IVR+DWING+RAOZ+RLZ,0.0) + TODOSE

28 MassBal=100*(TDose-TMass)/(TMass+1e-12)

29 !compare to TIV, ODOSE, or AINHC

30 ! Check Blood Flows

31 QTOT = QF + QL + QR

32 QRECOV = 100.0*QTOT/QC

33 END ! End of Derivative

34 TERMT(T.GE.TStop)

35

36 !-------Exposure Control------------

37 DISCRETE ORALDOSE ! Stom is amount in stomach

38 IDOSE = DRP(IDS)*DRDOSE*BW

39 STOM = STOM + IDOSE ! Drinking percent

40 TODOSE = TODOSE + IDOSE

41 IF (IDS.EQ.1) THEN

42 STOM = STOM + ODOSE

43 TODOSE = TODOSE + ODOSE

44 ENDIF

45 IDS = IDS+1

46 IF (IDS.EQ.7) THEN ! For 6 doses

47 IDS = 1

48 NEWDAY = NEWDAY + 24

49 SCHEDULE ORALDOSE.AT.NEWDAY ! Go to start of the next day

50 ELSE

51 SCHEDULE ORALDOSE.AT.(NEWDAY+DRT(IDS)) ! Go to next drink time

52 ENDIF

53 END ! OF DISCRETE ORALDOSE

54

55 DISCRETE OFF ! Turn INHAL exposure off

56 CIZONE = 0.0

57 IVZONE = 0.0
B- 50 DRAFT – DO NOT CITE OR QUOTE

1 DAY=DAY+1

2 IF (MULTE) SCHEDULE ON.AT.(DAY*24.0)

3 END ! OF DISCRETE OFF

4

5 DISCRETE ON

6 CIZONE=1.0

7 SCHEDULE OFF.AT.(T+TCHNG)

8 END ! OF DISCRETE ON

9

10 DISCRETE DS1 ! Human at rest

11 ! Equations scheduled for change during simulation repeated here

12 QC = QCCHR*BW**0.75

13 QP = QPCHR*BW**0.75

14 QF = QFC*QC ! QFCHR*QC ! Equations for alternate flow fractions

15 QL = QLC*QC ! QLCHR*QC ! But QFC and QLC taken to be 'at rest' values

16 QRC = 1.0 - (QFC + QLC)

17 QR = QRC*QC

18 END ! OF DISCRETE DS1

19

20 DISCRETE DS2 ! Human at work (50W)

21 ! Equations scheduled for change during simulation repeated here

22 QC = QCCHW*BW**0.75

23 QP = QPCHW*BW**0.75

24 QF = QFC*QC ! QFCHW*QC ! Equations for alternate flow fractions

25 QL = QLC*QC ! QLCHW*QC ! But don't seem to work (fit data) well

26 QRC = 1.0 - (QFC + QLC)

27 QR = QRC*QC

28 END ! OF DISCRETE DS2

29

30 END ! End of Dynamic

31 END ! End of Program

B.3.3. acslXtreme procedure (.cmd) file

32 ! File MEOHCBMMfinal.CMD - FOR PBPK MODEL FOR METHANOL

33 ! taken from .cmd file from Ward et al., Edited by KWW - 06/02/96

34 ! Developed for this (CBMM) model - 4/15/15

35 ! Final with Digitized Data - 5/25/05

36 ! Final Version has fast and slow rates of oral absorption

37 ! Version 4 is final version used for simulations

38 ! Final Version 1.10.06

39 ! Beyond this comment, this file is left "as is" for archival purposes. But most if not all of

40 ! the functions and data sets defined here are replicated and/or replaced in the .m files below. 

41 ! Only use these when there is no corresponding .m file.

42 ! – Paul Schlosser, U.S. EPA, Oct. 2008

43 !------------------------------------------------------------

44 PREPARE T,CVB,MetB

45

46 ! Procedural blocks for general mouse/rat data

47 PROCED CDMICE ! Anatomic/physiologic data for mice

48 SET BW=0.03, TSTOP=1.5

49 SET IVDose=0, DOSE=0, CONCppm=0

50 SET PL=1.06, PF=0.083, PR=0.66, PB=1350
B- 51 DRAFT – DO NOT CITE OR QUOTE

1 SET QPC=25.4,QCC=25.4,fracin=0.73

2 SET QLC = 0.25,QFC=0.05

3 SET KM=12,VmaxC=14.3,KLC=0.0,KAS=2

4 SET Vmax2c=19,km2=210,KAI=0.22,KSI=1.1

5 SET VAC = 0.0123,VFC = 0.07,VLC = 0.055

6 SET VLuC = 0.0073, VVBC = 0.0368 

7 !Volumes from Brown et al

8 !Mouse QPC avg from Brown 29, 24 used in Corley et al and others

9 !AVG of measured vent rates by Perkins et al 25.4 L/hr/kg^0.75

10 !Blood volume 4.9% total. As per Brown 25:75 split art:ven

11 !Metab originally from Ward et al - KldC for mice =0

12 END

13

14 PROCED HUMAN

15 SET BW=70

16 SET IVDose=0, DOSE=0, CONCppm=0

17 SET PL=1.06, PR=0.66, fracin=0.75

18 SET VFC=0.214, VLC=0.026,VLUC=0.008

19 SET VAC= 0.0198,VVBC=0.0593

20 SET QPC=18.5, QCC=18.5, QLC=0.227, QFC=0.052

21 SET KM=12,VmaxC=11,KLC=0.044,KAS=2.0

22 SET KAI=0.22,KSI=1.1

23 SET PB = 1626, PF=0.14

24 SET Vmax2c=0

25 !Volumes from Brown et al

26 !QPC from Brown, upper end 13.4 L/hr/kg^0.75

27 !Need higher for data, 15 L/hr/kg^0.75 used in several published human models

28 !Blood volume 7.9% total. As per Brown 25:75 split art:ven

29 !Frac absorbed from Ernstgard SOT poster + personal communication

30 !Human Partition Coef. equal to mice. Horton et al. used rat

31 !Except Human Partition Coef blood and fat - from Fiserova-Bergerova and Diaz, 1986

32 ! - but rat values are inconsistent with expected fat partitioning for an alcohol like this

33 ! - for example Pastino and Conolly EtOH model, fat PC =0.1

34 END

35

36 PROCED SDRAT !Anatomic/physiologic data for rats

37 SET BW=0.3, TSTOP=1.5

38 SET IVDose=0, DOSE=0, CONCppm=0

39 SET PL=1.6, PF=0.1, PR=1.3

40 SET KM=45,VmaxC=15,KLC=0.1,KAS=5

41 SET VAC = 0.0185, VFC=0.07, VLC= 0.034, VLuC=0.005, VVBC=0.0555

42 !Volumes from Brown et al

43 !PC from horton et al., PF reduced to 0.1 from Horton's 1.1

44 !Blood volume 7.4% total. As per Brown 25:75 split art:ven

45 !Metab originally from Ward et al - KldC for mice =0

46 !Rat model not calibrated

47 END

48

49 PROCED PREG
B- 52 DRAFT – DO NOT CITE OR QUOTE

1 !For GD 18 mice, BW increased as estimated from Roger's et al

2 !Increased VFC as per Corley CRT development review 

3 !This just to give a WAG as to how data might change from BW and different volume of 

4 distribution

5 !Not invoked for any PROCs below as the default

6 !Liver to 140% of NP 

7 SET BW = 0.055, VFC=0.08,VLC=0.11,VVBC=0.05

8 END

9

10 PROCED CLEARIT

11 SET IVDose=0, DOSE=0, CONCppm=0

12 END

13

14 PROCED SHOWIT

15 display Vmaxc,km,klc,pb,pf,pr,pl,kas,fracin

16 END

17

18 !Procedural blocks for all non-pregnant mouse data

19 ! IV

20 PROCED MWARDIV25

21 !Ward et al., TAP 1997

22 !Figure 2, data from Ward model cmd

23 !Data was checked via digitizit - within +/-5% of cmd file

24 CLEARIT

25 CDMICE

26 SET TSTOP=24.0

27 SET IVDOSE=2500., tchng=0.025

28 END

29

30 PROCED PMWARDIV25

31 PLOT /D=MWARDIV25, CVB

32 END

33

34 DATA MWARDIV25(T,CVB)

35 0.08 4481.8

36 0.25 4132.2

37 0.5 3888

38 1.00 3164.8

39 2.0 2303.5

40 4.00 1921.5

41 6 1883.8

42 8 1620

43 12 838

44 18 454.7

45 24 NaN

46 END

47

48 PROCED MWARD95IV25

49 !Ward et al., FAT 1995
B- 53 DRAFT – DO NOT CITE OR QUOTE

1 !Figure 2

2 !Data via digitizit

3 CLEARIT

4 CDMICE

5 SET TSTOP=24.0

6 SET IVDOSE=2500., tchng=0.025

7 END

8

9 PROCED PMWARD95IV25

10 PLOT /D=MWARD95IV25, CVB

11 END

12

13 DATA MWARD95IV25(T,CVB)

14 0.53 3299.60

15 1.06 3244.54

16 1.54 3190.71

17 3.07 2803.13

18 4.07 2544.36

19 5.02 2237.77

20 6.02 2063.59

21 7.02 1873.10

22 8.02 1521.92

23 9.03 1670.30

24 10.03 1423.12

25 END

26

27 ! Procs for pegnant IV below: MWARDGD9IV25, MWARDGD18IV25, MWARDGD18IV5, 

28 MWARDGD18IV1

29 ! Oral

30 PROCED MWARDPO25

31 !Ward et al., FAT 1995

32 !Figure 2, data from Ward model cmd

33 !Data was checked via digitizit - within +/-5% of cmd file

34 CLEARIT

35 CDMICE

36 SET TSTOP=24, DOSE=2500

37 END

38

39 PROCED PMWARDPO25

40 PLOT /D=MWARDPO25, CVB

41 END

42

43 DATA MWARDPO25(T,CVB)

44 0.504 2370

45 0.96 2645

46 1.44 2705

47 1.992 2719

48 2.208 2781

49 3 2704
B- 54 DRAFT – DO NOT CITE OR QUOTE

1 4.008 2370

2 4.992 2617

3 6 2516

4 7.008 2635

5 7.992 2213

6 9 2370

7 10.008 2028

8 10.992 1916

9 12 1347

10 13.008 1467

11 13.992 1354

12 15 1175

13 16.008 864.3

14 16.992 745.2

15 18 422.4

16 19.01 428

17 21 243

18 24 136

19 END

20

21 !Procs for pregnant Oral below: MDORGD8PO15, MWARDGD18PO25

22 !Inhalation

23 ! QPC set to measured as in Perkins et al., FAT, 1995 for each concentration

24

25 PROCED MPERKIN25

26 !Perkins et al., FAT, 1995

27 !Fig. 2 data in Ward cmd file

28 CLEARIT

29 CDMICE

30 SET TSTOP=24, CONCppm=2500, vchc=5000

31 SET QPC = 29., QCC=29.

32 SET TCHNG=8

33 END

34

35 PROCED PMPERKIN25

36 PLOT /D=MPERKIN25, CVB

37 END

38

39 !This data from DigitizIt

40 DATA MPERKIN25(T,CVB)

41 2.0 414.0

42 4.0 453.0

43 6.0 586.0

44 8.25 694.0

45 12 282.0

46 16 0.6

47 END

48

49 !This data from cmd file
B- 55 DRAFT – DO NOT CITE OR QUOTE

1 !DATA MPERKIN25(T,CVB)

2 !1.99 386.49

3 !4.01 617.57

4 !6.00 816.22

5 !8.26 970.27

6 !12.00 393.24

7 !16.0 13.51

8 END

9

10 PROCED MPERKIN50

11 !Perkins et al., FAT, 1995

12 !Fig. 2, data in Ward cmd file

13 !Data in command file higher than appears in figure

14 CLEARIT

15 CDMICE

16 SET TSTOP=24, CONCppm=5000, vchc=5000

17 SET TCHNG=8, qpc=24.,qcc=24.

18 END

19

20 PROCED PMPERKIN50

21 PLOT /D=MPERKIN50, CVB

22 END

23

24 !this from Digitizit, Fig 2 Perkins et al

25 DATA MPERKIN50(T,CVB)

26 1 644.00

27 2 877.00

28 3 1340.00

29 4 1450.00

30 6 2040.00

31 8.25 2290.0

32 12 1410.0

33 16 583.0

34 20 271.0

35 24 9.7

36 END

37

38 !This data from cmd file

39 !DATA MPERKIN50(T,CVB)

40 !1.0 906.76

41 !2.0 1202.7

42 !3.0 1828.38

43 !4.0 1986.49

44 !6.0 2800

45 !8.3 3125.68

46 !12.0 1914.86

47 !16.0 806.76

48 !20.0 367.57

49 !24.0 10.81
B- 56 DRAFT – DO NOT CITE OR QUOTE

1 !END

2

3 PROCED MPERKIN100

4 !Perkins et al., FAT, 1995

5 !Fig. 2 data in Ward cmd file

6 !Note, Table 6 in Ward paper - max value of 3260 +/- 151

7 CLEARIT

8 CDMICE

9 SET TCHNG=8, CONCppm=1,0000, tstop=36,vchc=5000

10 SET QPC=21,qcc=21

11 END

12

13 PROCED PMPERKIN100

14 PLOT /D=MPERKIN100, CVB

15 END

16

17 !this from Digitizit, Fig 2 Perkins et al

18 DATA MPERKIN100(T,CVB)

19 2.0 2080.0

20 4.0 2530.0

21 6.0 3350.0

22 8.25 3350.0

23 12 2370.0

24 16 1830.0

25 20 1080.0

26 24 591.0

27 28 44.6

28 END

29

30 !DATA MPERKIN100(T,CVB)

31 !This from original cmd file

32 !2.0 2809.46

33 !4.0 3405.4

34 !6.0 4528.38

35 !8.3 4524.32

36 !12.0 3212.16

37 !16.0 2456.76

38 !20.0 1439.19

39 !24.0 798.65

40 !28.0 55.4

41 !END

42

43 ! Procs for Preg mouse Inhalaiton date below: MDOR8IN10,MDOR8IN15

44 !and:MROGGD7IN10, MROGGD6IN1, MROGGD6IN2, MROGGD6IN5, 

45 MROGGD6IN10

46

47 !pregnant mice

48 ! IV

49 PROCED MWARDGD9IV25
B- 57 DRAFT – DO NOT CITE OR QUOTE

1 !Ward et al., DMD, 1996

2 !Not used in the manuscript, only in cmd file

3 CLEARIT

4 CDMICE

5 SET TSTOP=24

6 SET IVDOSE=2500., TINF=0.025

7 END

8

9 PROCED PMWARDGD9IV25

10 PLOT /D=MWARDGD9IV25, CVB

11 END

12

13 DATA MWARDGD9IV25(T,CVB)

14 0.0833 4606.2

15 0.25 4079.5

16 0.5 3489.3

17 1 2939.6

18 2 3447.6

19 4 2605.0

20 6 2690.5

21 8 2574.9

22 12 1506.1

23 18 498.6

24 24. NaN

25 END

26

27 PROCED PROCED MWARDGD18IV25

28 !Ward et al., DMD, 1996

29 !Note, Table 6 in Ward paper - max value of 3521+/- 492

30 CLEARIT

31 CDMICE

32 SET TSTOP=24

33 SET IVDOSE=2500., TINF=0.025

34 END

35

36 PROCED PMWARDGD18IV25

37 PLOT /D=MWARDGD18IV25, CVB

38 END

39

40 DATA MWARDGD18IV25(T,CVB)

41 0.0833 4250.0

42 0.25 3445.1

43 0.5 2936.8

44 1.0 2470.5

45 2.0 2528.1

46 4.0 2292.3

47 6.0 2269.4

48 8.0 2057.0

49 12 1805.9
B- 58 DRAFT – DO NOT CITE OR QUOTE

1 18 1482.2

2 24.0 496.1

3 END

4

5 PROCED MWARDGD18IV5 

6 !Ward et al., DMD, 1996

7 !Note, Table 6 in Ward paper - max value of 868.8 +/- 53.9

8 CLEARIT

9 CDMICE

10 SET TSTOP=6

11 SET IVDOSE=500., TINF=0.025

12 END

13

14 PROCED PMWARDGD18IV5

15 PLOT /D=MWARDGD18IV5, CVB

16 END

17

18 DATA MWARDGD18IV5(T,CVB)

19 0.25 854.7

20 0.5 720.2

21 1.0 624.1

22 2.0 453.2

23 3.0 307.6

24 4.0 217.7

25 4.5 202.6

26 END

27

28 PROCED MWARDGD18IV1

29 !Ward et al., DMD, 1996

30 !Ward Proc GD8, but must be 18 as per PBPK manuscript

31 !Note, Table 6 in Ward paper - max value of 252 +/- 12.9

32 !table matches file

33 CLEARIT

34 CDMICE

35 SET TSTOP=4

36 SET IVDOSE=100.

37

38 PROCED PMWARDGD18IV1

39 PLOT /D=MWARDGD18IV1, CVB

40 END

41

42 DATA MWARDGD18IV1(T,CVB)

43 0.25 252

44 0.52 242.2

45 1.0 222.7

46 2 176.4

47 3 134.2

48 3.5 94.41

49 END
B- 59 DRAFT – DO NOT CITE OR QUOTE

1

2 ! Oral

3

4 PROCED MDORGD8PO15

5 !Ward et al., cmd file

6 !Note, Table 6 in Ward paper - max value of 1610 +/- 704

7 !Table and file match w/in round off

8 !Data must be from Dorman

9 !Dorman Teratology, 1995, Fig. 1

10 !within error for Digitiz data the same

11 CLEARIT

12 CDMICE

13 SET TSTOP=24, DOSE=1500

14 END

15

16 PROCED PMDORGD8PO15

17 PLOT /D=MDORGD8PO15, CVB

18 END

19

20 DATA MDORGD8PO15(T,CVB)

21 1 1609.6

22 2 1331.2

23 4 1241.6

24 8 707.2

25 16 160.0

26 24 38.4

27 END

28

29 PROCED MWARDGD18PO25

30 !Ward et al., DMD, 1996

31 !Note, Table 6 in Ward paper - max value of 3205 +/- 291

32 CLEARIT

33 CDMICE

34 SET TSTOP=24, DOSE=2500

35 END

36

37 PROCED PMWARDGD18PO25

38 PLOT /D=MWARDGD18PO25, CVB

39 END

40

41 !from cmd file, replaced with digitized

42 !DATA MWARDGD18PO25(T,CVB)

43 !0.25 2770.

44 !0.5 3299.

45 !1 3336.

46 !2 3502.

47 !4 3217.

48 !6 2999.

49 !10 2036.
B- 60 DRAFT – DO NOT CITE OR QUOTE

1 !12 1832.

2 !15 949.1

3 !18 403.5

4 !21 40.47

5 !24. 16.03

6 !END

7

8 !Digitizit data

9 DATA MWARDGD18PO25(T,CVB)

10 0.5 2024

11 1 2554

12 2 3193

13 4 3002

14 6 2933

15 10 1976

16 12 1922

17 15 1339

18 18 1033

19 21 832

20 24 580

21 END

22

23 !Inhalation

24

25 PROCED MDOR8IN10

26 ! Ward et al., TAP 1997

27 !Note, Table 6 in Ward paper - max value of 2080 +/- 800

28 !Fig 7? Table 6 attributes to Dorman

29 !Digitizit of Dorman Fig 2 matches cmd file

30 !actual exposure ppm 9900

31 CLEARIT

32 CDMICE

33 SET TCHNG=6, CONCppm=9900, tstop=36

34 END

35

36 PROCED PMDOR8IN10

37 PLOT /D=MDOR8IN10, CVB

38 END

39

40 DATA MDOR8IN10(T,CVB)

41 1 771.2

42 2 1017.6

43 4 1788.8

44 6 2076.8

45 8 2281.6

46 16 1152.0

47 24 268.8

48 END

49
B- 61 DRAFT – DO NOT CITE OR QUOTE

1 PROCED MDOR8IN15

2 ! Ward et al., TAP 1997

3 !Note, Table 6 in Ward paper - max value of 7136 +/- 736

4 !Fig 7? Table 6 attributes to Dorman

5 !Digitizit of Dorman Fig 2 matches cmd file

6 CLEARIT

7 CDMICE

8 SET TCHNG=6, CONCppm=15000, tstop=36

9 SET vchc=5000000000

10 END

11

12 PROCED PMDOR8IN15

13 PLOT /D=MDOR8IN15, CVB

14 END

15

16 DATA MDOR8IN15(T,CVB)

17 1 1475.2

18 2 2486.4

19 4 4588.8

20 6 7123.2

21 8 5888.0

22 16 3456.0

23 24 1446.4

24 END

25

26 !Files above provided in cmd file from Ward PBPK model, TAP 1997

27 !Files below added for this evaluation,

28 !sources described in proc files and in notebook

29

30 PROCED MROGGD7IN10

31 ! Rogers et al., Teratology, 1997

32 ! Actual Values kindly Provided by Rogers

33 CLEARIT

34 CDMICE

35 SET TCHNG=7, CONCppm=1,0000, tstop=36

36 SET vchc=500000000,bw=0.032

37 END

38

39 PROCED PMROGGD7IN10

40 PLOT /D=MROGGD7IN10, CVB

41 END

42

43 DATA MROGGD7IN10(T,CVB)

44 1 930

45 4 2800

46 6 3360

47 7 3990

48 7.5 3980

49 8 4120
B- 62 DRAFT – DO NOT CITE OR QUOTE

1 9 3270

2 12 2630

3 16 1690

4 26 60

5 END

6

7 PROCED MROGGD6IN1

8 CLEARIT

9 !Rogers et al., Teratology, 1993

10 !Rogers data from GD 6 and 10

11 !In Table 2

12 CDMICE

13 SET TCHNG=7, CONCppm=1,000, tstop=36,vchc=500000000,bw=0.032

14 END

15

16 PROCED PMROGGD6IN1

17 PLOT /D=MROGGD6IN1, CVB

18 END

19

20 DATA MROGGD6IN1(T,CVB)

21 7 63

22 7 131

23 END

24

25

26 PROCED MROGGD6IN2

27 ! Rogers et al., Teratology, 1993

28 !Rogers data from GD 6 and 10

29 !In Table 2

30 CLEARIT

31 CDMICE

32 SET TCHNG=7, CONCppm=2000, tstop=36, vchc=500000000,bw=0.032

33 END

34

35 PROCED PMROGGD6IN2

36 PLOT /D=MROGGD6IN2, CVB

37 END

38

39 DATA MROGGD6IN2(T,CVB)

40 7 487

41 7 641

42 END

43

44 PROCED MROGGD6IN5

45 ! Rogers et al., Teratology, 1993

46 !Rogers data from GD 6 and 10

47 !In Table 2

48 CLEARIT

49 CDMICE
B- 63 DRAFT – DO NOT CITE OR QUOTE

1 SET TCHNG=7, CONCppm=5000, tstop=36,vchc=500000000,bw=0.032

2 END

3

4 PROCED PMROGGD6IN5

5 PLOT /D=MROGGD6IN5, CVB

6 END

7

8 DATA MROGGD6IN5(T,CVB)

9 7 2126

10 7 1593

11 END

12

13 PROCED MROGGD6IN10

14 ! Rogers et al., Teratology, 1993

15 !Rogers data from GD 6, 10, 15

16 !In Table 2

17 CLEARIT

18 CDMICE

19 SET TCHNG=7, CONCppm=1,0000, tstop=36,vchc=500000000,bw=0.032

20 END

21

22 PROCED PMROGGD6IN10

23 PLOT /D=MROGGD6IN10, CVB

24 END

25

26 DATA MROGGD6IN10(T,CVB)

27 7 4653

28 7 4304

29 7 3655

30 END

31

32 !Human inhalation dta

33

34 PROCED HJOHIN1

35 !Ernstgard et al. SOT poster 200 ppm human

36 !Digitized from Fig 2

37 !Also personal communication - Ernstgard

38 !QPC from Johanson et al. Scand J. Work Env. 86 =52.6

39 !If Assume value = alveolar. similar to Astrand '83 value of 56 L/hr/kr^0.75

40 !Fracin - 50% of total (from poster) ~76%

41 !QCC from Corley et al TAP 129, 1994

42 CLEARIT

43 HUMAN

44 SET TCHNG=2, CONCppm=100, tstop=16

45 SET QPC=52.6,qcc=26,vchc=500000000

46 END

47

48 PROCED PHJOHIN1

49 PLOT /D=HJOHIN1, CVB
B- 64 DRAFT – DO NOT CITE OR QUOTE

1 END

2

3 DATA HJOHIN1(T,CVB)

4 0.20 0.87

5 0.46 1.50

6 0.97 2.31

7 1.46 3.24

8 1.91 3.65

9 2.17 3.52

10 2.50 2.55

11 2.91 2.23

12 3.51 1.59

13 4.01 1.72

14 5.02 0.41

15 6.00 0.50

16 9.24 0.12

17 END

18

19 PROCED HJOHIN2

20 !Ernstgard et al. SOT poster 200 ppm human

21 !Digitized from Fig 2

22 !Also personal communication - Ernstgard

23 !QPC from Johanson et al. Scand J. Work Env. 86 =52.6

24 !If Assume value = alveolar. similar to Astrand '83 value of 56 L/hr/kr^0.75

25 !Fracin - 50% of total (from poster) ~75%

26 !QCC from Corley et al TAP 129, 1994

27 CLEARIT

28 HUMAN

29 SET TCHNG=2, CONCppm=200, tstop=16

30 SET QPC=52.6,qcc=26,vchc=500000000

31 END

32

33 PROCED PHJOHIN2

34 PLOT /D=HJOHIN2, CVB

35 END

36

37 DATA HJOHIN2(T,CVB)

38 0.22 1.63

39 0.49 2.92

40 0.92 4.76

41 1.47 6.30

42 1.90 7.65

43 2.16 6.20

44 2.47 5.49

45 2.91 4.96

46 3.50 3.64

47 4.00 3.43

48 4.99 1.94

49 5.97 1.03
B- 65 DRAFT – DO NOT CITE OR QUOTE

1 8.90 0.21

2 END

3

4 PROCED HOSTERIN2

5 ! Osterloh et al., JOEM 1996

6 ! Digitized data provided by EPA

7 ! Subtracted background from exposure blood levels

8 CLEARIT

9 HUMAN

10 SET TCHNG=4, CONCppm=200, tstop=16

11 SET vchc=500000000, BW=78.2

12 END

13

14 PROCED PHOSTERIN2

15 PLOT /D=HOSTERIN2, CVB

16 END

17

18 DATA HOSTERIN2(T,CVB)

19 0.05 0.54

20 0.25 1.39

21 0.50 1.82

22 0.75 2.28

23 1.00 2.42

24 1.50 2.94

25 2.00 3.37

26 2.50 3.90

27 3.00 4.21

28 3.50 4.61

29 4.00 4.82

30 5.00 2.99

31 6.00 2.30

32 7.00 1.40

33 7.95 1.07

34 END

35

36 PROCED HBATIN82

37 !Batterman et al., Int Arch Occ Health 1998

38 !Digitized Data

39 CLEARIT

40 HUMAN

41 SET TCHNG=2, CONCppm=800, tstop=16

42 SET vchc=500000000

43 END

44

45 PROCED PHBATIN82

46 PLOT /D=HBATIN82, CVB

47 END

48

49 DATA HBATIN82(T,CVB)
B- 66 DRAFT – DO NOT CITE OR QUOTE

1 2.223 13.658

2 2.495 13.282

3 2.742 11.928

4 3.230 9.456

5 4.231 6.197

6 5.247 3.953

7 6.262 2.325

8 7.251 1.551

9 8.216 1.176

10 END

11

12 PROCED HBATIN81

13 !Batterman et al., Int Arch Occ Health 1998

14 !Digitized Data

15 CLEARIT

16 HUMAN

17 SET TCHNG=1, CONCppm=800, tstop=16

18 SET vchc=500000000

19 END

20

21 PROCED PHBATIN81

22 PLOT /D=HBATIN81, CVB

23 END

24

25 DATA HBATIN81(T,CVB)

26 1.096 6.477

27 1.398 6.136

28 1.644 5.345

29 2.143 4.270

30 3.178 2.661

31 4.188 1.307

32 5.199 0.732

33 6.266 0.552

34 7.292 0.356

35 8.209 0.093

36 END

37

38 PROCED HBATIN830

39 !Batterman et al., Int Arch Occ Health 1998

40 !Digitized Data

41 !body weight not provided

42 CLEARIT

43 HUMAN

44 SET TCHNG=0.5, CONCppm=800, tstop=16

45 SET vchc=500000000

46 END

47

48 PROCED PHBATIN830

49 PLOT /D=HBATIN830, CVB
B- 67 DRAFT – DO NOT CITE OR QUOTE

1 END

2

3 DATA HBATIN830(T,CVB)

4 0.579 4.608

5 0.857 4.685

6 1.137 4.870

7 1.650 3.452

8 2.650 2.082

9 3.662 0.910

10 4.693 0.316

11 5.713 0.320

12 6.643 0.292

13 7.696 0.547

14 END

15

16 PROCED HSEDIN231

17 !Sedivec et al., Int Arch Occ Health 1981

18 !Digitized Data

19 !Note, urine volumes not given, these are estimates

20 !urine production of 0.75 mg/hr, this for info purposes only!!!

21 CLEARIT

22 HUMAN

23 SET TCHNG=8, CONCppm=231, tstop=24

24 SET vchc=500000000

25 END

26

27 PROCED PHSEDIN231

28 PLOT /D=HSEDIN231, Metb

29 END

30

31 DATA HSEDIN231(T,Metb)

32 0.043 0.0042

33 2.174 0.33

34 4.478 0.87

35 6.478 1.46

36 8.522 2.15

37 10.348 2.63

38 12.130 2.91

39 14.044 3.07

40 18.870 3.32

41 23.696 3.52

42 END

43

44 PROCED HSEDIN157

45 !Sedivec et al., Int Arch Occ Health 1981

46 !Digitized Data

47 !Note, urine volumes not given, these are estimates

48 !urine production of 0.75 mg/hr, this for info purposes only!!!CLEARIT

49 HUMAN
B- 68 DRAFT – DO NOT CITE OR QUOTE

1 SET TCHNG=8, CONCppm=157, tstop=24

2 SET vchc=500000000

3 END

4

5 PROCED PHSEDIN157

6 PLOT /D=HSEDIN157, Metb

7 END

8

9 DATA HSEDIN157(T,Metb)

10 0.126 0.0038

11 2.204 0.228

12 4.242 0.576

13 6.196 0.975

14 8.326 1.47

15 10.163 1.81

16 12.094 2.00

17 14.016 2.12

18 18.8966 2.34

19 23.776 2.53

20 END

21

22 PROCED HSEDIN78

23 !Sedivec et al., Int Arch Occ Health 1981

24 !Digitized Data

25 !Note, urine volumes not given, these are estimates

26 !urine production of 0.75 mg/hr, this for info purposes only!!!

27 CLEARIT

28 HUMAN

29 SET TCHNG=8, CONCppm=78, tstop=24

30 SET vchc=500000000

31 END

32

33 PROCED PHSEDIN78

34 PLOT /D=HSEDIN78, Metb

35 END

36

37 DATA HSEDIN78(T,Metb)

38 0.03 0.013

39 2.06 0.189

40 3.96 0.397

41 6.09 0.652

42 8.09 0.820

43 10.11 0.933

44 11.93 1.02

45 13.92 1.09

46 18.89 1.27

47 END

48

49 !AUC, Cmax estimation procedures
B- 69 DRAFT – DO NOT CITE OR QUOTE

1 Proced mousin

2 !To determine AUC for 7 hr exposure in mice

3 CLEARIT

4 CDMICE

5 SET TCHNG=7, tstop=24

6 SET vchc=50000000000

7 SET CONCppm=1

8 start /nc

9 d concppm,AUCB,amet,cvb

10 SET CONCppm=5

11 start /nc

12 d concppm,AUCB,amet,cvb

13 SET CONCppm=10

14 start /nc

15 d concppm,AUCB,amet,cvb

16 SET CONCppm=25

17 start /nc

18 d concppm,AUCB,amet,cvb

19 SET CONCppm=50

20 start /nc

21 d concppm,AUCB,amet,cvb

22 SET CONCppm=75

23 start /nc

24 d concppm,AUCB,amet,cvb

25 SET CONCppm=100

26 start /nc

27 d concppm,AUCB,amet,cvb

28 SET CONCppm=175

29 start /nc

30 d concppm,AUCB,amet,cvb

31 SET CONCppm=208.3

32 start /nc

33 d concppm,AUCB,amet,cvb

34 SET CONCppm=250

35 start /nc

36 d concppm,AUCB,amet,cvb

37 SET CONCppm=325

38 start /nc

39 d concppm,AUCB,amet,cvb

40 SET CONCppm=500

41 start /nc

42 d concppm,AUCB,amet,cvb

43 SET CONCppm=750

44 start /nc

45 d concppm,AUCB,amet,cvb

46 SET CONCppm=1,000

47 start /nc

48 d concppm,AUCB,amet,cvb

49 SET CONCppm=2000
B- 70 DRAFT – DO NOT CITE OR QUOTE

1 start /nc

2 d concppm,AUCB,amet,cvb

3 SET CONCppm=2500

4 start /nc

5 d concppm,AUCB,amet,cvb

6 SET CONCppm=5000

7 start /nc

8 d concppm,AUCB,amet,cvb

9 SET CONCppm=1,0000

10 start /nc

11 d concppm,AUCB,amet,cvb

12 SET CONCppm=50000

13 start /nc

14 d concppm,AUCB,amet,cvb

15 END

16

17 Proced mousinC

18 !To determine 7 hr Cmax, note - not at SS

19 CLEARIT

20 CDMICE

21 SET TCHNG=7, tstop=7,VCHC=50000000000

22 SET CONCppm=1

23 start /nc

24 d conc ppm,cvb

25 SET CONCppm=10

26 start /nc

27 d concppm,CVB 

28 SET CONCppm=50

29 start /nc

30 d concppm,CVB 

31 SET CONCppm=100

32 start /nc

33 d concppm,CVB

34 SET CONCppm=250

35 start /nc

36 d concppm,CVB 

37 SET CONCppm=500

38 start /nc

39 d concppm,CVB 

40 SET CONCppm=1,000

41 start /nc

42 d concppm,CVB

43 SET CONCppm=2000

44 start /nc

45 d concppm,CVB 

46 SET CONCppm=2500

47 start /nc

48 d concppm,CVB 

49 SET CONCppm=5000
B- 71 DRAFT – DO NOT CITE OR QUOTE

1 start /nc

2 d concppm,CVB 

3 SET CONCppm=1,0000

4 start /nc

5 d concppm,CVB 

6 SET CONCppm=50000

7 start /nc

8 d concppm,CVB 

9 END

10

11 Proced humin

12 ! To determine 24 hr AUC,Cmax at SS for human

13 CLEARIT

14 human

15 SET TCHNG=360, tstop=1,000

16 SET vchc=5000000000, points=48

17 SET Concppm=1

18 Start /nc

19 d concppm,aucBb,cvb

20 SET CONCppm=10

21 start /nc

22 d concppm,aucBb,cvb

23 SET CONCppm=50

24 start /nc

25 d concppm,aucBb,cvb

26 SET CONCppm=100

27 start /nc

28 d concppm,aucBb,cvb

29 SET CONCppm=250

30 start /nc

31 d concppm,aucBb,cvb

32 SET CONCppm=500

33 start /nc

34 d concppm,aucBb,cvb

35 SET CONCppm=625

36 start /nc

37 d concppm,aucBb,cvb

38 SET CONCppm=750

39 start /nc

40 d concppm,aucBb,cvb

41 SET CONCppm=875

42 start /nc

43 d concppm,aucBb,cvb

44 SET CONCppm=1,000

45 start /nc

46 d concppm,aucBb,cvb

47 SET CONCppm=2000

48 start /nc

49 d concppm,aucBb,cvb
B- 72 DRAFT – DO NOT CITE OR QUOTE

1 SET CONCppm=2500

2 start /nc

3 d concppm,aucBb,cvb

4 SET CONCppm=5000

5 start /nc

6 d concppm,aucBb,cvb

7 SET CONCppm=1,0000

8 start /nc

9 d concppm,aucBb,cvb

10 SET CONCppm=50000

11 start /nc

12 d concppm,aucBb,cvb

13 END

14

15 Proced humor

16 ! To determine 24 hr AUC

17 ! Oral exposure

18 CLEARIT

19 human

20 SET TCHNG=1,000, tstop=1,000

21 SET vchc=5000000000, points=48

22 SET dose=0.1

23 SET ODS=1

24 start /nc

25 d dose,aucBb

26 SET dose=1

27 Start /nc

28 d dose,aucBb

29 SET dose=5

30 start /nc

31 d dose,aucBb

32 SET dose=10

33 start /nc

34 d dose,aucBb

35 SET dose=50

36 start /nc

37 d dose,aucBb

38 SET dose=100

39 start /nc

40 d dose,aucBb

41 SET dose=250

42 start /nc

43 d dose,aucBb

44 SET dose=350

45 start /nc

46 d dose,aucBb

47 SET dose=500

48 start /nc

49 d dose,aucBb
B- 73 DRAFT – DO NOT CITE OR QUOTE

1 SET dose=750

2 start /nc

3 d dose,aucBb

4 SET dose=1,000

5 start /nc

6 d dose,aucBb

7 SET dose=2500

8 start /nc

9 d dose,aucBb

10 SET dose=5000

11 start /nc

12 d dose,aucBb

13 S ODS=0

14 END

15

16 !Procedural blocks for all non-pregnant rat data

17 !Not calibrated!!!!!!

18 !Procs from Ward CMD file

19

20 PROCED WARDIV25

21 CLEARIT

22 SDRAT

23 SET TSTOP=48.

24 SET IVDOSE=2500.

25 END

26

27 PROCED PWARDIV25

28 PLOT /D=WARDIV25, CVB

29 END

30

31 DATA WARDIV25(T,CVB)

32 0.072 4849

33 0.168 3926

34 0.24 2965

35 0.504 2836

36 1.008 3248

37 1.992 2589

38 3 2619

39 4.008 2514

40 7.008 2315

41 19.992 1495

42 22.992 1272

43 24 1214

44 25.992 982

45 28.008 957

46 30 860

47 37.992 238

48 39 200

49 40.008 150
B- 74 DRAFT – DO NOT CITE OR QUOTE

1 40.992 167

2 43.008 77

3 END

4

5 PROCED WARDIV1

6 CLEARIT

7 SDRAT

8 SET TSTOP=8

9 SET IVDOSE=100., tchng=0.016

10 END

11

12 PROCED PWARDIV1

13 PLOT /D=RG0IV1, CVB

14 END

15

16 DATA WARDIV1 (T,CVB)

17 0.072 141.7

18 0.168 121.8

19 0.24 111.6

20 0.504 99.7

21 0.744 97.4

22 1.008 86.3

23 1.488 80.3

24 1.992 58

25 3 44.4

26 4.008 22.8

27 4.992 10.9

28 6 3.8

29 7.008 1.4

30 END

31

32 PROCED WARDPO25

33 CLEARIT

34 cdmice

35 SET BW=0.3

36 SET TSTOP=48

37 SET DOSE=2500

38 END

39

40 PROCED PWARDPO25

41 PLOT /D=WARDPO25, CVB

42 END

43

44 DATA WARDPO25(T,CVB)

45 0.072 862.7

46 0.168 1243

47 0.24 1356

48 0.504 1621

49 1.008 1641
B- 75 DRAFT – DO NOT CITE OR QUOTE

1 1.992 1611

2 3 1869

3 4.008 1896

4 7.008 2181

5 24 1365

6 25.992 1081

7 28.008 921

8 30 958.4

9 31.008 969.8

10 45 42.9

11 46.008 27.1

12 46.992 16.4

13 48 23.9

14 49.008 41.9

15 49.992 13.1

16 52.008 2.3

17 52.992 1

18 END

19

20 PROCED WARDPO1

21 CLEARIT

22 cdmice

23 SET BW=0.3

24 SET DOSE=100, tstop=8

25 END

26

27 PROCED PWARDPO1

28 PLOT /D=WARDPO1, CVB

29 END

30

31 DATA WARDPO1(T,CVB)

32 0.072 85.5

33 0.168 95.6

34 0.24 95.5

35 0.504 91.1

36 0.744 86.6

37 1.008 80.6

38 1.488 71.3

39 1.992 61.1

40 3 45.1

41 4.008 27.4

42 4.992 16.4

43 6 8.9

44 7.008 4.2

45 END
B- 76 DRAFT – DO NOT CITE OR QUOTE

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

1 Found in the Runtime Files Folder

2 CDmice.m Sets parameters for (CD) mouse simulations

3 Rogers-mouse-inhal.m Figure B-2 - Simulations of mouse inhalation exposures from GD 6, 

4 7, 8 and 10 mice from Rogers et al., 1993. 

5 PerkinsDorm-mouse-inh.m Figure B-3 - Simulations of inhalation exposures to MeOH in NP 

6 mice from Perkins et al. 1995 (8 hr exposures) and GD 8 mice from 

7 Dorman et al. 1995 (6 hr exposures)

8 Ward_mouse_GD18.m Figure B-4 - Oral exposures to MeOH in pregnant and non-pregnant 

9 mice Data from Dorman et al., 1995 and Ward et al., 1997

10 Ward-mouse-iv.m Figure B-5 - Simulations of mouse IV exposures to MeOH from 

11 Ward et al., 1997

12 Apaja-mouse-drink.m Calculates internal doses for mice in Apaja (1980)

13

14 SDrat.m Sets parameters for Sprague-Dawley (SD) rat simulations

15 F344rat.m Sets parameters for F344 rat simulations

16 Ward-rat-iv.m Figure B-10 – Simulations rat IV exposures from Ward et al., 1997 and 

17 Horton et al., 1992

18 Horton-rat-inhal.m Figure B-11 – Simulations rat inhalation exposures from Horton et al., 1992

19 Ward-rat-oral.m Figure B-12 – Simulations rat oral exposures from Ward et al., 1997

20 Nedo-rat-inhal-devpmt-rat.m Figure B-13 – Simulations rat inhalation (bioassay) exposures 

21 (200, 500, 1,000, 2000, & 5000 ppm)

22 Nedo-rat-inhal-cancer.m Simulations for NEDO F344 rat cancer inhalation study

23 rat-infu-sims.m Figure B-14 – Simulations rat "oral" exposures (bioassay doses, but using 

24 liver infusion; for illustration only)

25

26 humanset.m Sets human MeOH PBPK parameters

27 Sedivec_human_inh.m Figure B-16 - Simulation of human urinary MeOH elimination 

28 following Inhalation exposures from Sedivec et al. 1981

29 Batterman_human_inh.m Figure B-17 (upper panel) - Simulations of human inhalation exposure 

30 data of Batterman et al. 1998

31 Osterloh_human_inh.m Figure B-17 (lower panel) - Simulations of human inhalation exposure 

32 data of Osterloh et al. 1996

33 Ernstgard_human_inh.m Figure B-18 - Simulations of human inhalation exposures to MeOH 

34 from Ernstgard et al. 2005

35

36 mouse_inh_sim.m Produces data for Table B-5, mouse inhalation exposures

37 human_inh_sim.m Produces data for Table B-5, human inhalation expsoures

38 human_oral_sim.m Produces data for Table B-5, human oral exposures

39 human_drink_compare.m Figure B-24 and Table B-9 (altenate drinking pattern comparison)

40

41 Found in the Sensitivity Analysis Files Folder

42 Fig_B-6 Sensitivity of the mouse model to metabolic parameters (e.g., Km and Vmax) 

43 for the inhalation route

44 Fig_B-7 Sensitivity of the mouse model to flow parameters (e.g., blood flow to liver) 

45 and to the rest-of-body partition coefficient for the inhalation route
B- 77 DRAFT – DO NOT CITE OR QUOTE

1 Fig_B-8 Sensitivity analysis of the rat model to oral absorption parameters for a bolus 

2 oral exposure (1,000 mg/kg)

B.3.4.1. [B.3.4.2.] Code for .m files

3 % File CDmice.m

4 % Sets parameters for mouse simulations, MeOH PBPK model

5 CONCPPM=10; WESITG=0; WEDITG=0; CINT-0.1;

6 start @nocallback

7 BW=0.03; TSTOP=24; TCHNG=7; REST=20000; WORK=20000;

8 IVDOSE=0; DOSE=0; DRDOSE=0; RATS=0; KLOSS=0; LIVR0=0;

9 PL=1.06; PF=0.083; PR=0.66; PLU=1; PB=1350;

10 QPC=25.4; QCC=25.4; FRACIN=0.73; KFEC=0;

11 QLC=0.25; QFC=0.05;

12 KM=12; VMAXC=14.3; K1C=0.0; KAS=0.0; KLLC=0;

13 VMAX2C=19; KM2=210; KAI=0.5; KSI=5.0;

14 VAC=0.0123; VFC=0.07; VLC=0.055; VLUC=0.0073; VVBC=0.0368; 

15 CONCPPM=0.0; IVDOSE=0.0; DOSE=0.0; DWDOSE=0; MULTE=0; RDRINK=1;

16 % Volumes from Brown et al

17 % Mouse QPC avg from Brown 29; 24 used in Corley et al and others

18 % AVG of measured vent rates by Perkins et al 25.4 L/hr/kg^0.75

19 % Blood volume 4.9% total. As per Brown 25:75 split art:ven

20 % Metab originally from Ward et al - KldC for mice = 0

21

22 % use mouseINH_fit-params.m % File contents copied below

23 % Updated parameters as obtained by Paul Schlosser, U.S. EPA

24 % August 11, 2009 [this file updated]

25

26 % Values generated through parameter estimation script 'mouseINH_fit.m'

27 VMAX2C = 3.222500e+00; KM2 = 660; VMAXC = 19; KM = 5.2; FRACIN = 6.650939e-01;

28

29 % Values generated through parameter estimation script 'mouseor_fit.m'

30 VASC = 1.833246e+03; KSI = 2.2; KAI = 0.33; MASC = 620;

31 _____________________________________________________________________________________________

32 % File Rogers_mouse_inhal.m (Figure B-2)

33 % Produces MeOH PBPK figures for Rogers' mouse inhalation exposures

34 % Variables in the plot command are case sensitive

35 use CDmice

36 % set mouse parameters

37 %------- DATA BLOCKS 

38 % These data blocks taken directly from MeOH CBMMv3.cmd

39 % Data for are T (hours), CV (mg/L)

40 % semicolons (";") creates a new line in a data file

41

42 % Rogers et al., Teratology, 1997

43 D7IN10 = [1, 930; 4, 2800; 6, 3360; 7, 3990; 7.5, 3980; 

44 8, 4120; 9, 3270; 12, 2630; 16, 1690; 26, 60];

45

46 %Rogers et al., Teratology, 1993

47 D6IN1 = [7, 63; 7, 131]; D6IN2 = [7, 487; 7, 641];

48 D6IN5 = [7, 2126; 7, 1593]; D6IN7p5 = [7, 2801; 7, 3455];

49 D6IN10 = [7, 4653; 7, 4304]; D6IN15 = [7, 7720; 7, 7394];

50 

51 %-----RUN MODEL

52 RATS=0.0; KLOSS=0.0; % -> open chamber

53 TCHNG=7; CONCPPM=10000; TSTOP=27.0; MULTE=0; BW=0.032;

54 CINT=TSTOP/1000; cs=[]; prepare @clear T CVB
B- 78 DRAFT – DO NOT CITE OR QUOTE

1 for CONCPPM=[1, 2, 5, 7.5, 10, 15]*1000

2 start @nocallback

3 cs=[cs,_cvb]; 

4 % Since TSTOP & CINT not changing, assume _t also the same.

5 end

6

7 %-----PLOT COMMANDS

8 % The rogers.aps file will retain changes made using the plot 

9 % editor as long as the editor is called by clicking the

10 % words EDIT PLOT PROPERTIES not the little icon in the

11 % properties dialogue box

12 plot(_t,cs(:,1), _t,cs(:,2), _t,cs(:,3), _t,cs(:,4), _t,cs(:,5), _t,cs(:,6), ...

13 D6IN1(:,1),D6IN1(:,2),D6IN2(:,1),D6IN2(:,2),D6IN5(:,1),D6IN5(:,2), ...

14 D6IN7p5(:,1),D6IN7p5(:,2),D6IN10(:,1),D6IN10(:,2), ...

15 D7IN10(:,1),D7IN10(:,2),D6IN15(:,1),D6IN15(:,2), 'rogers.aps')

16

17 %-----WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL

18 % Cannot save data with different # of rows to the same table.

19 cs=[_t,cs];

20 save cs @file='Rogersplotdata.csv' @format=ASCII @separator=ascii

21 _____________________________________________________________________________________________

22 % File: PerkinsDorm-mouse-inh.m (Figure B-3)

23 % Produces MeOH PBPK simulations Perkins 1995 inhalation exposures,

24 % and Ward 1997 (pregnant) and Dorman 1995 for comparison)

25 % Includes all nonpregnant and "early" GD (<GD 10) sets

26 % GD18 not included

27

28 %------- DATA BLOCKS 

29 % These data blocks taken directly from MeOH CBMMv3.cmd

30 % Data for are T (hours), CV (mg/L)

31 % Perkins et al, FAT, 1995

32 Perk25 =[2, 414; 4, 453; 6, 586; 

33 8.25, 694; 12, 282; 16 0.6];

34 %Perkins et al, FAT, 1995

35 Perk50= [1, 666; 2, 905; 3, 1370; 

36 4, 1480; 6, 2090; 8.25, 2310; 

37 12, 1420; 16, 597; 20, 276; 24, 36.2];

38 %Perkins et al, FAT, 1995

39 Perk100=[2, 2080.0; 4, 2530; 6, 3350;

40 8.25, 3350; 12, 2370; 16, 1830;

41 20, 1080; 24, 591; 28, 44.6];

42 %Ward et al., TAP 1997

43 Dor815=[1, 1475.2; 2, 2486.4; 4, 4588.8; 

44 6, 7123.2; 8, 5888; 16, 3456; 24, 1446.4];

45

46 %table 6, TAP 1997

47 %estimate all Cmax at end of exposure

48 %this is to compare model fits to published values that may be different from cmd file

49 %the last value (2300) is not in table, it is estimated from figure in Perkins et al from 5000 ppm exposure

50 % 8 3250 - non pregnant mouse 10,000 ppm

51 % 6 7136 - GD 8 mouse 15,000 ppm

52 % 8 2300 - non preg mouse 15,000 ppm

53

54 %-----RUN MODEL

55 use CDmice

56 RATS=0; KLOSS=0; % -> open chamber

57 MULTE=0; TSTOP=24; CONCPPM=2500; QPC = 29; QCC=29; TCHNG=8;
B- 79 DRAFT – DO NOT CITE OR QUOTE

1 start @nocallback

2 Cs25 = _cvb; Ts25 = _t;

3 CONCPPM=5000; QPC=24; QCC=24; start @nocallback

4 Cs50 = _cvb; Ts50 = _t;

5 CONCPPM=10000; TSTOP=36; QPC=21; QCC=21; start @nocallback

6 Cs100 = _cvb; Ts100 = _t;

7 use CDmice

8 RATS=0; KLOSS=0; % -> open chamber

9 TCHNG=6; CONCPPM=15000; TSTOP=36; start @nocallback

10

11 %-----PLOT COMMANDS

12 % The .aps file will retain changes made using the plot 

13 % editor as long as the editor is called by clicking the

14 % words EDIT PLOT PROPERTIES not the little icon in the

15 % properties dialogue box

16 plot(Ts25, Cs25, Ts50, Cs50, Ts100, Cs100,_t, _cvb, ...

17 Perk25(:,1), Perk25(:,2), Perk50(:,1), Perk50(:,2), ...

18 Perk100(:,1), Perk100(:,2),Dor815(:,1), Dor815(:,2), 'inhalation.aps')

19

20 %-----WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL

21 % Can't save data with different # of rows to the same table.

22 mytable1 = [Ts25, Cs25, Ts50, Cs50, Ts100, Cs100,_t, _cvb];

23 save mytable1 @file='PerkinDormanplotdata.csv' @format=ASCII @separtor=comma

24 _____________________________________________________________________________________________

25 % File WardGD18.m

26 % Creates Figure B-4, including Ward et al 1997 NP and GD 18 mouse data

27 % and Dorman et al 1995 GD 8 mouse data.

28 CDmice

29 TSTOP=25; DOSE=1500; CONCPPM=0; MULTE=0;

30 prepare @clear T CVB

31 start @nocallback

32 T1=_t;P1=_cvb;

33 DOSE=2500; start @nocallback

34 D15=[1, 1609.6; 2, 1331.2; 4, 1241.6;

35 8, 707.2; 16, 160; 24, 38.4];

36 D25a=[0.5, 2370; 0.96, 2645; 1.44, 2705; 2, 2719; 

37 2.2, 2781; 3, 2704; 4, 2370; 5, 2617; 6, 2516; 

38 7, 2635; 8, 2213; 9, 2370; 10, 2028; 11, 1916; 

39 12, 1347; 13, 1467; 14, 1354; 15, 1175; 16, 864.3; 

40 17, 745.2; 18, 422.4; 19, 428; 21, 243; 24, 136];

41 D25b=[0.5, 2024; 1, 2554; 2, 3193; 4, 3002; 6, 2933; 

42 10, 1976; 12, 1922; 15, 1339; 18, 1033; 21, 832; 24, 580];

43

44 plot(D15(:,1),D15(:,2),D25a(:,1),D25a(:,2),D25b(:,1),D25b(:,2),...

45 T1,P1,_t,_cvb,"wardgd18plot.aps")

46 % File Ward-mouse-iv.m

47 % M File for reproducing MeOH PBPK Figure B-5 For WARD iv mouse exposures

48 % (also Ward Pregnant Includes all nonpregnant and Pregnant)

49 _____________________________________________________________________________________________

50 %------- DATA BLOCKS 

51 %Taken directly from MeOH CBMMv3.cmd, values are [T (hours), CV (mg/L)]

52 %Ward et al, FAT 1995

53 NPIV25=[0.08, 4481.8; 0.25, 4132.2; 0.5, 3888; 1, 3164.8; 2, 2303.5; 

54 4, 1921.5; 6, 1883.8; 8, 1620; 12, 838; 18, 454.7; 24, 1.41];

55 %PROCED MWARDGD8IV25

56 GD8IV25=[0.0833, 4606.2; 0.25, 4079.5; 0.5, 3489.3; 1, 2939.6; 2, 3447.6; 

57 4, 2605.0; 6, 2690.5; 8, 2574.9; 12, 1506.1; 18, 498.6; 24, 0.554];
B- 80 DRAFT – DO NOT CITE OR QUOTE

1 %!Ward et al., DMD, 1996

2 GD18IV25=[0.0833, 4250.0; 0.25, 3445.1; 0.5, 2936.8; 1, 2470.5; 2, 2528.1; 

3 4, 2292.3; 6, 2269.4; 8, 2057.0; 12, 1805.9; 18, 1482.2; 24.0, 496.1];

4 %Ward et al., DMD, 1996

5 GD18IV5=[0.25, 854.7; 0.5, 720.2; 1, 624.1; 

6 2, 453.2; 3, 307.6; 4, 217.7; 4.5, 202.6];

7 %Ward et al., DMD, 1996

8 GD18IV1=[0.25, 252; 0.52, 242.2; 1, 222.7; 2, 176.4; 3, 134.2; 3.5, 94.41]; 

9

10 %-----RUN MODEL

11 use CDMICE

12 TSTOP=24.0; IVDOSE=2500; TCHNG=0.025; start @nocallback

13 CVs25 = _cvb; Ts25 = _t; TSTOP=6; IVDOSE=500; start @nocallback

14 CVs5 = _cvb; Ts5 = _t; TSTOP=4; IVDOSE=100; start @nocallback

15 CVs1 = _cvb; Ts1 = _t; IVDOSE=200; start @nocallback

16

17 %-----PLOT COMMANDS

18 % The .aps file will retain changes made using the plot 

19 % editor as long as the editor is called by clicking the

20 % words EDIT PLOT PROPERTIES not the little icon in the

21 % properties dialogue box

22 plot(Ts25, CVs25, Ts5, CVs5, Ts1, CVs1, NPIV25(:,1), NPIV25(:,2),...

23 GD8IV25(:,1), GD8IV25(:,2), GD18IV25(:,1), GD18IV25(:,2), ...

24 GD18IV5(:,1), GD18IV5(:,2), GD18IV1(:,1), GD18IV1(:,2), 'iv.aps')

25 plot(_t, _cvb, Ts1, CVs1, GD18IV1(:,1), GD18IV1(:,2), 'ivb.aps')

26

27 %-----WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL

28 % Cant save data with different # of rows to the same table.

29 mytable1 = [Ts25, CVs25, Ts5, CVs5, _t, _cvb, Ts1, CVs1];

30 save mytable1 @file='WardIV.csv' @format=ASCII @separator=comma

31 _____________________________________________________________________________________________

32 % File Apaja-mouse-drink.m

33 % Calculates internal doses for mice in Apaja (1980)

34 use CDmice

35 DWDOSE=1; start @nocallback

36 DWDS=[0.045, 550; 0.045, 970; 0.045, 1800;

37 0.040, 560; 0.040, 1000; 0.040. 2100];

38 % Above are BWs and doses for males, then females, from Apaja (1980)

39 ODS=1; TSTOP=24*3*7; MULTE=1; DAYS=6.0; simres=[]; LIVR0=0; 

40 PER1=1.5; DUR1=0.75; PER2=3.0; DUR2=0.5; FNIGHT=0.8; CINT=0.01;

41 prepare @clear T CVB STOM

42 for RDRINK =0 %[1, 0] % 0 -> mouse drinking pattern

43 for i=1:length(DWDS)

44 BW=DWDS(i,1); DWDOSE=DWDS(i,2); start @nocallback

45 simres=[simres;[TDOSE*(24/TSTOP)/BW,BW,AUCBF,max(_cvb),AMF]];

46 end

47 plot(_t,_cvb)

48 end

49 simres=[simres(:,1)*7/6, simres];

50 simres/100 % Print values to screen (/100)

51 TDOSE*(24/TSTOP)/BW % Check that final total dose/day is correct

52 save simres @file='Apaja_mouse_drink_sims.csv' @format=ascii @separator=comma

53 _____________________________________________________________________________________________

54 % File SDrat.m

55 % Sets parameters for rat simulations, MeOH PBPK model

56 CONCPPM=10; WESITG=0; WEDITG=0; TSTOP=24; TCHNG=6; MULTE=0;

57 REST=20000; WORK=20000;
B- 81 DRAFT – DO NOT CITE OR QUOTE

1 start @nocallback

2 BW=0.275; TSTOP=24; FRACIN=0.2;

3 IVDOSE=0; DOSE=0; CONCPPM=0; DRDOSE=0; DWDOSE=0; ODS=0; LIVR0=0;

4 QCC=16.4; QPC=16.4; QFC=0.07; QLC=0.25;

5 PL=1.06; PF=0.083; PR=0.66; PB=1350;

6 KM=6.3; VMAXC=5.0; VMAX2C=8.4; KM2=65;

7 KLLC=0.0; K1C=0.0; 

8 VAC=0.0185; VFC=0.07; VLC=0.037; VLUC=0.005; VVBC=0.0443;

9

10 KAS=10.9; KSI=6.8; KAI=0.039; KFEC=0.0; VASC=0; 

11 % Just above are linear absorption params fit to 100 mg/kg

12 % oral data, w/ no fecal elimination

13

14 KAS=0.0; % Below are for saturable uptake model

15 % Values generated through parameter estimation script 'ratoral_fit.m'

16 KSI = 7.4; KAI = 0.051; KFEC = 0.029; VASC = 5.573125e+03; KMASC = 620;

17 _____________________________________________________________________________________________

18 % File F344rat.m: parameters specific to F344 rat

19 % Created by Paul Schlosser, U.S. EPA, Aug. 2009

20 use SDrat

21 VMAXC=0; VMAX2C=22.3; KM2=100;

22 _____________________________________________________________________________________________

23 % File: Ward-rat-iv.m

24 % Creates Figure B-9; rat MeOH PBPK model, to simulate

25 % Ward '97 rat iv 2500 & 100 mg/kg (BW=275, SD)

26 % and Horton '92 iv 100 mg/kg (BW=100, F344)

27 rwi25 =[0.072, 4849; 0.168, 3926; 0.24, 2965; 0.5, 2836; 1, 3248; 

28 2, 2589; 3, 2619; 4, 2514; 7, 2315; 20, 1495; 23, 1272; 24, 1214; 

29 26, 982; 28, 957; 30, 860; 38, 238; 39, 200; 40, 150; 41, 167; 43, 77];

30

31 % ward '97 rat iv 100 mg/kg (BW 275, SD)

32 rwi1=[0.072, 141.7; 0.168, 121.8; 0.24, 111.6; 0.5, 99.7; 0.744, 97.4; 

33 1, 86.3; 1.488, 80.3; 2, 58; 3, 44.4; 4, 22.8; 5, 10.9; 6, 3.8];

34

35 % horton '92 rat iv 100 mg/kg (BW 220, F344)

36 rhi1=[0.24, 91.13; 0.52, 80.14; 0.90, 70.29; 1.38, 61.22; 

37 1.86, 60.63; 2.79, 39.40; 4.30, 26.05; 5.81, 12.51];

38

39 use SDrat

40 prepare @clear T CVB

41 TSTOP=48; IVDOSE=2500; TCHNG=0.016; BW=0.275; CINT=0.1; start @nocallback

42 Twi25 = _t; Cwi25 = _cvb;

43 IVDOSE=100; start @nocallback

44 Twi1 = _t; Cwi1 = _cvb;

45 BW=0.22; start @nocallback

46

47 plot(Twi25, Cwi25, Twi1, Cwi1, _t, _cvb, rwi25(:,1), rwi25(:,2), ...

48 rwi1(:,1), rwi1(:,2), rhi1(:,1), rhi1(:,2), 'rwi2500.aps')

49

50 use F344rat

51 IVDOSE=100; BW=0.22; TCHNG=0.016; CINT=0.1; start @nocallback

52 plot(Twi25, Cwi25, Twi1, Cwi1, _t, _cvb, rwi25(:,1), rwi25(:,2), ...

53 rwi1(:,1), rwi1(:,2), rhi1(:,1), rhi1(:,2), 'rwi100.aps')

54 _____________________________________________________________________________________________

55 % File: Horton-rat-inhal.m

56 % MeOH PBPK model rat simulations for Horton '92 rat inhalation data

57 % Creates Figure B-10
B- 82 DRAFT – DO NOT CITE OR QUOTE

1 hi20=[0.46, 18.70; 1, 23.76; 3, 59.73; 6, 80.12

2 7, 83.25; 9, 53.49; 12, 16.54; 15, 0.91];

3 hi12=[0.46, 4.89; 1, 8.02; 3, 20.57; 6, 26.63; 

4 7, 16.12; 8, 9.28; 9, 5.23; 10.5, 2.93; 12, 0.98];

5 hi2=[0.48, 1.2; 3, 3.1; 6, 3.7; 6.47, 2.7; 

6 7, 2.0; 8, 1.6; 9, 1.2];

7

8 use F344rat

9 prepare @clear T CVB

10 TSTOP=16; CONCPPM=2000; TCHNG=6; BW=0.22; CINT=0.1; start @nocallback

11 t20 = _t; c20 = _cvb;

12 CONCPPM=1200; TSTOP=13; start @nocallback

13 t12 = _t; c12 = _cvb;

14 CONCPPM=200; TSTOP=10; start @nocallback

15 t2 = _t; c2 = _cvb;

16 TSTOP=16; CONCPPM=2000; TCHNG=7; start @nocallback

17

18 plot(t20, c20, t12, c12, t2, c2, hi20(:,1), hi20(:,2), ...

19 hi12(:,1), hi12(:,2), hi2(:,1), hi2(:,2), _t, _cvb, 'hi2000.aps')

20 _____________________________________________________________________________________________

21 % File: Ward-rat-oral.m

22 % MeOH PBPK model rat simulations for Ward '97 rat oral data

23 % Creates Figre B-11

24 use SDRat

25 KAS=10.9; KSI=6.8; KAI=0.039; KFEC=0.0; VASC=0; 

26 BW=0.3; ODS=0; prepare @clear T CVB

27 DOSE=100; TSTOP=10; start @nocallback

28 t1=_t;c1=_cvb;

29 DOSE=2500; TSTOP=36; start @nocallback

30 t2=_t;c2=_cvb;

31

32 KAS=0; use ratoral_fit-params

33 DOSE=100; TSTOP=10; start @nocallback

34 t1A=_t;c1A=_cvb;

35 DOSE=2500; TSTOP=36; start @nocallback

36 t2A=_t;c2A=_cvb;

37

38 d1=[0.072, 85.5; 0.168, 95.6; 0.24, 95.5; 0.504, 91.1; 

39 0.744, 86.6; 1.008, 80.6; 1.488, 71.3; 1.992, 61.1; 

40 3, 45.1; 4.008, 27.4; 4.992, 16.4; 6, 8.9; 7.008, 4.2];

41

42 d25=[0.072, 862.7; 0.168, 1243; 0.24, 1356; 0.504, 1621;

43 1.008, 1641; 1.992, 1611; 3, 1869; 4.008, 1896; 7.008, 2181;

44 24, 1365; 25.992, 1081; 28.008, 921; 30, 958.4; 31.008, 969.8];

45

46 plot(t2,c2, t2A,c2A, d25(:,1),d25(:,2), ...

47 t1,c1, t1A,c1A, d1(:,1),d1(:,2),"wardratoralplot.aps")

48 plot(t2,c2, t2A,c2A, d25(:,1),d25(:,2), ...

49 t1,c1, t1A,c1A, d1(:,1),d1(:,2),"wardratoralplotb.aps")

50 _____________________________________________________________________________________________

51 % File: Nedo-rat-inhal-devpmt.m

52 % MeOH PBPK model rat simulations for rat inhalation exposures

53 % 200, 500, 1000, 2000, and 5000 ppm

54 % Internal doses for NEDO developmental inhalation exposures, Sprague-Dawley rats

55 % Creates Figure B-13 ('simres' is tabulated results)

56 use SDRat

57 prepare @clear T CVB
B- 83 DRAFT – DO NOT CITE OR QUOTE

1

2 simres=[]; ts=[]; cs=[]; ts2=[]; cs2=[]; TSTOP=24*2*7; CINT=1;

3 for CONCPPM=[200, 500, 1000, 2000, 5000]

4 TCHNG=22; MULTE=1; start @nocallback

5 res=[CONCPPM,max(_cvb),AUCBF,AMF];

6 ts=[ts,_t]; cs=[cs,_cvb];

7 TCHNG=TSTOP; MULTE=0; %CONCPPM=22*cp/24; 

8 start @nocallback

9 simres=[simres;[res,CONCPPM,max(_cvb),AUCBF,AMF]];

10 ts2=[ts2,_t]; cs2=[cs2,_cvb];

11 end

12

13 simres

14 plot(ts(:,2),cs(:,2),ts(:,3),cs(:,3),ts(:,4),cs(:,4), ...

15 ts2(:,2),cs2(:,2),ts2(:,3),cs2(:,3),ts2(:,4),cs2(:,4), ...

16 [24 24],[0,95], 'fig13.aps')

17 save simres @file='Nedo_devpomt_rat_inhal_sims.csv' @format=ascii @separator=comma

18 cs=[ts(:,1),cs,cs2]; save cs @file='Fig13_sims.csv' @format=ascii @separator=comma

19 _____________________________________________________________________________________________

20 % File Nedo-rat-inhal-cancer.m

21 % Simulations for NEDO F344 rat cancer inhalation study

22 use F344rat

23 TCHNG=22.7; TSTOP=5*7*24; MULTE=1;

24 res=[]; CONCPPM=200; prepare @clear T CVB

25 start @nocallback

26 TCHNG=19.5; ODS=1; cppm=[0,10,100,1000];

27 bwm=[422.1, 418.3, 417.7, 410.0];

28 bwf=[268.7, 270.6, 267.0, 264.9];

29 for sdose=[0,25.4863]

30 for i=1:length(cppm)

31 CONCPPM=cppm(i);

32 BW=bwm(i)/1000;

33 DOSE=sdose/(BW^0.25);

34 start @nocallback

35 res=[res;[CONCPPM,TCHNG,BW,AUCBF,max(_cvb),AMF]]

36 BW=bwf(i)/1000;

37 DOSE=sdose/(BW^0.25);

38 start @nocallback

39 res=[res;[CONCPPM,TCHNG,BW,AUCBF,max(_cvb),AMF]]

40 end

41 end

42

43 save res @file='Nedo_rat_cancer_sims.csv' @format=ascii @separator=comma

44 _____________________________________________________________________________________________

45 % File: rat-infu-sims.m

46 % MeOH PBPK model rat simulations for zero-order liver infusions

47 % Creates Figre B-14

48 use SDRat

49 lv0=[0.33, 65.9; 0.33, 624.1; 0.34, 2177;

50 0.49, 53.2; 0.50, 524;

51 0.54 1780];

52 % Above are BWs and doses from Soffretti et al. 2002a

53 prepare @clear T CVB

54 TCHNG=12; MULTE=0; simres=[]; ts=[]; cs=[];

55 for i=1:3

56 BW=lv0(i,1); LIVR0=lv0(i,2); TSTOP=24; start @nocallback

57 res=[LIVR0,BW,max(_cvb),0,AUCB,0,AMET];
B- 84 DRAFT – DO NOT CITE OR QUOTE

1 TSTOP=84; start @nocallback

2 res(4)=AUCB; res(6)=AMET; simres=[simres;res];

3 ts=[ts,_t]; cs=[cs,_cvb];

4 end

5 simres/100

6 plot(ts(:,1),cs(:,1),ts(:,2),cs(:,2),ts(:,3),cs(:,3),'fig14b.aps')

7 save simres @file='rat_liver-infusion_sims.csv' @format=ascii @separator=comma

8 _____________________________________________________________________________________________

9 % File: humanset.m

10 % Sets parameters for human simulations. Expects the user to define

11 % metabf = "linear" to use 1st-order metabolism parameters; otherwise

12 % metabf set to "non-linear" and Michaelis-Menten parameters used.

13 BW = 70; FRACIN = 0.8655; IVDOSE=0; DOSE=0; CONCPPM=0; LIVR0=0;

14 PB = 1626; PL = 0.583; %1.06; 

15 PF = 0.142; PR = 0.805; %0.66; 

16 PLU=1.07; %1.0;

17 VFC = 0.214; VLC = 0.026; VLUC = 0.008; 

18 VAC = 0.0198; VVBC = 0.0593; 

19 QPC = 18.5; QCC = 18.5; QLC = 0.227; QFC = 0.052; 

20 KM = 12.76; VMAXC = 0; %VMAXC=11.72; %low KM optimum

21 KM2 = 460; VMAX2C = 0; %VMAX2C=304.5; %high KM optimum

22 KLLC = 60.7; %linear liver metabolism optimum

23 K1C = 0.0397; KAI = 0.22; KSI = 1.1; KAS = 2.0; 

24 RATS=0; KLOSS=0; % constant exposure/no chamber losses

25 QPC=24.0; QCC=16.5; REST=3000; WORK=3000;

26

27 % Below are optimal values for Michaelis-Menten liver metabolism

28 K1C = 0.0342; KLLC = 0.0; KBL=0.612;

29 KM = 23.7; VMAXC = 33.1; 

30

31 % Mouse oral uptake KMASC; others set to match ethanol values 

32 % for humans from Sultatos et al. (2004), with VASC set so that 

33 % VASC/KMAS = 0.21/h, the Sultatos et al. 1st-order constant, 

34 % and KFEC = 0 corresponding to assumed 100% absorption.

35 VASC = 377; KSI = 3.17; KAI = 3.28; KMASC = 620; KFEC=0;

36

37 exist metabf; % check if metabf defined

38 if ~ans % If not...

39 metabf = "non-linear"

40 end

41 if metabf=="linear"

42 % Below are optimal values for 1st-order liver metabolism

43 K1C = 0.0373; KLLC = 95.7; KBL=0.564; VMAXC=0.0;

44 % Below are 'no bladder values; uncomment next line to use

45 %K1C = 0.0278; KLLC = 70.3; KBL=1000.0; VMAXC=0.0;

46 else metabf="non-linear";

47 end

48 disp(['Simulation for ',ctot(metabf),' human kinetics']);

49 _____________________________________________________________________________________________

50 % File: Sedivec_human_inh.m

51 % Creates MeOH PBPK Figure B-16

52 % For human inhalation exposures, w/ data of Sedivec et al

53

54 %------- DATA BLOCKS 

55 % These data blocks taken directly from MeOH CBMMv3.cmd

56 % Data are T (hours), CV (mg/L), cumulative urinary clearance (mg)

57 % Rounded to 3-4 sig figs
B- 85 DRAFT – DO NOT CITE OR QUOTE

1 % Sedivec et al., Int Arch Occ Health 1981, urine

2 HS231 = [2, 3.338, 0.1168; 4, 5.776, 0.4358; 6, 7.371, 0.8960; 

3 8, 8.581, 1.454; 10, 6.576, 1.985; 12, 3.243, 2.328; 

4 14, 1.32 , 2.488; 19, 0.333, 2.632; 24, 0, 2.661];

5

6 HS157 = [2, 2.185, 0.0765; 4, 3.941, 0.291; 6, 4.896, 0.600; 

7 8, 5.708, 0.971; 10, 4.360, 1.324; 12, 1.738, 1.537; 

8 14, 0.776, 1.625; 19, 0.231, 1.713; 24, 0, 1.733];

9

10 HS78 = [2, 0.881, 0.0308; 4, 1.648, 0.1193; 6, 2.285, 0.2570; 

11 8, 2.551, 0.4263; 10, 1.515, 0.5686; 12, 0.708, 0.6464; 

12 14, 0.430, 0.6863; 19, 0.094, 0.7321; 24, 0, 0.7403];

13

14 %-----RUN MODEL

15 use humanset

16 TCHNG=8; TSTOP=24; CONCPPM=231; 

17 prepare @clear T RUR METB

18 start @nocallback

19 ur1 = _metb; t1 = _t; cu1=_rur;, % Save time series for urine MeOHc

20 CONCPPM=157; start @nocallback

21 ur2 = _metb; t2 = _t; cu2= _rur;

22 CONCPPM=78; start @nocallback

23

24 %-----PLOT COMMANDS

25 plot(t1,ur1,t2,ur2,_t,_metb,HS231(:,1),HS231(:,3),...

26 HS157(:,1),HS157(:,3),HS78(:,1),HS78(:,3), 'sedivic.aps')

27 plot(t1,cu1,t2,cu2,_t,_rur,HS231(:,1),HS231(:,2),...

28 HS157(:,1),HS157(:,2),HS78(:,1),HS78(:,2), 'sedivic2.aps')

29

30 %-----WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL

31 % Cant save data with different # of rows to the same table.

32 mytable1 = [t1,ur1,cu1,t2,ur2,cu2,_t,_metb,_rur];

33 eval(['save mytable1 @file=Sedv_fit_KLLC.',num2str(round(KLLC)),'.csv @format=ascii 

34 @separator=comma']);

35 _____________________________________________________________________________________________

36 % File: Batterman_human_inh.m

37 % Creates MeOH PBPK Figure B-17 (upper panel)

38 % For human inhalation exposures of Batterman et al 1998

39

40 %These data blocks taken directly from MeOH CBMMv3.cmd

41 %Data are T (hours), CV (mg/L)

42 % Batterman et al., Int Arch Occ Health 1998

43 HB82=[2, 13.6; 2.25, 13.4; 2.5, 12; 3, 9.6;

44 4, 6.4; 5, 4.1; 6, 2.6; 7, 1.8; 8, 1.4];

45 HB81=[1, 6.5; 1.25, 6.2; 1.5, 5.4; 2, 4.3; 3, 2.8; 

46 4, 1.5; 5, 0.94; 6, 0.72; 7, 0.52; 8, 0.23];

47 HB830=[0.5, 4.6; 0.75, 4.7; 1, 4.9; 1.5, 3.5; 2.5, 2.2; 

48 3.5, 1; 4.5, 0.52; 5.5, 0.51; 6.5, 0.47; 7.5, 0.68];

49

50 use humanset

51 prepare @clear T CVB

52 TCHNG=2; CONCPPM=800; TSTOP=16; start @nocallback

53 t2=_t; c2=_cvb; TCHNG=1; start @nocallback

54 t1=_t; c1=_cvb; TCHNG=0.5; start @nocallback

55 t30=_t; c30=_cvb;

56

57 %-----PLOT COMMANDS
B- 86 DRAFT – DO NOT CITE OR QUOTE

1 plot(t2,c2, t1,c1, t30,c30, HB82(:,1),HB82(:,2), ...

2 HB81(:,1),HB81(:,2),HB830(:,1),HB830(:,2), 'batterman.aps')

3

4 %-----WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL

5 % Cant save data with different # of rows to the same table.

6 le=1:min([length(t1),length(t2),length(t30)]);

7 mytable1 = [t2(le),c2(le),t1(le),c1(le),t30(le),c30(le)];

8 eval(['save mytable1 @file=Batter_fit_KLLC.',num2str(round(KLLC)),'.csv ' ...

9 '@format=ascii @separator=comma']);

10 _____________________________________________________________________________________________

11 % File: Osterloh_human_inh.m

12 % Creates Fig B-17 (lower panel)

13 % Data from Osterloh et al., JOEM 1996

14 % Digitized data provided by EPA (dat1)

15 % Subtracted background from exposure blood levels

16 % by Paul Schlosser, U.S. EPA

17 use humanset

18 BW=78.2;

19 dat1=[0.05, 0.54; 0.25, 1.39; 0.50, 1.82; 0.75, 2.28; 1, 2.42; 

20 1.5, 2.94; 2, 3.37; 2.5, 3.90; 3, 4.21; 3.5, 4.61; 

21 4, 4.82; 5, 2.99; 6, 2.30; 7, 1.40; 8, 1.07];

22 dat=[0.25, 1.183; 0.5, 1.526; 0.75, 1.948; 1, 2.073; 1.5, 2.741; 

23 2, 3.118; 2.5, 3.495; 3, 3.998; 3.5, 4.181; 4, 4.48; 

24 5, 2.790; 6, 1.943; 7, 1.106; 8, 0.687];

25

26 prepare @clear T CVB

27 TCHNG=4; CONCPPM=200; TSTOP=16; start @nocallback

28 plot(_t,_cvb,dat(:,1),dat(:,2), 'osterloh.aps')

29 mytable1=[_t,_cvb];

30 eval(['save mytable1 @file=Oster_fit_KLLC.',num2str(round(KLLC)),'.txt @format=ascii']);

31 _____________________________________________________________________________________________

32 % File: Ernstgard_human_inh.m

33 % Creates MeOH PBPK Figure B-18, w/ data of Ernstgard et al 2005a,b

34 % For human inhalation exposures w/ exercise

35 %------- DATA BLOCKS 

36 %These data blocks taken directly from MeOH CBMMv3.cmd

37 %Data are T (hours), CV (mg/L)

38 % Ernstgard et al. SOT poster, 100 ppm & 200 ppm human

39 ern1 =[0.20, 0.87; 0.46, 1.50; 0.97, 2.31; 1.46, 3.24; 

40 1.91, 3.65; 2.17, 3.52; 2.50, 2.55; 2.91, 2.23; 3.51, 1.59;

41 4.01, 1.72; 5.02, 0.41; 6.00, 0.50; 9.24, 0.12];

42 ern2 =[0.22, 1.63; 0.49, 2.92; 0.92, 4.76; 1.47, 6.30; 

43 1.90, 7.65; 2.16, 6.20; 2.47, 5.49; 2.91, 4.96; 3.50, 3.64; 

44 4.00, 3.43; 4.99, 1.94; 5.97, 1.03; 8.90, 0.21];

45

46 %-----RUN MODEL

47 use humanset

48 QPCHR=QPC; QCCHR=QCC; REST=2.0; WORK=0.0;

49 TCHNG=2.0; CONCPPM=100; TSTOP=10.0; QPCHW=52.6; QCCHW=26.0; 

50 %FRACIN=0.9509; %FRACIN=0.9324;

51 prepare T CVB QP QC

52 start @nocallback, 

53 cv1 = _cvb; t1 = _t; CONCPPM=200; start @nocallback

54

55 %-----PLOT COMMANDS

56 plot(t1, cv1, _t, _cvb,...

57 ern1(:,1),ern1(:,2),ern2(:,1),ern2(:,2), 'ernstgard.aps')
B- 87 DRAFT – DO NOT CITE OR QUOTE

1 %-----WRITE OUT DATA TO A TEXT FILE FOR IMPORTING INTO EXCEL

2 % Cant save data with different # of rows to the same table.

3 mytable1 = [t1, cv1, _t, _cvb];

4 eval(['save mytable1 @file=Ernst_nofit_KLLC.',num2str(round(KLLC)),'.csv ' ...

5 '@format=ascii @separator=comma']);

6 _____________________________________________________________________________________________

7 % File: mouse_inh_sim.m

8 % Runs simulations for Table B-5, mouse internal-dose calculations 

9 % from inhalation exposure, over the concentration range specified

10 % in the 'for' statement below.

11 % Results saved to file 'MouseInhalSims.csv'.

12 use CDMice

13 BW=0.03; TCHNG=7; % 7 hr/day exposures

14 TSTOP=240; MULTE=1; % Run for 10 days; multi-day exposure 'on'

15 RATS=0.0; KLOSS=0.0; % -> open chamber

16 prepare @clear T CVB

17 CONCPPM=10000; CINT=0.02; start @nocallback

18 % plot(_t,_cvb) % uncomment to see/check that periodicity reached by TSTOP

19 inhres=[]; CINT=0.2;

20 for CONCPPM=[1, 10, 50, 100, 250, 500, 1000, 2000, 5000, 10000]

21 start @nocallback

22 inhres=[inhres;[CONCPPM, AUCBB, max(_cvb), AMET24/(BW^0.75)]];

23 end

24 save inhres @file=MouseInhalSims.csv @format=ASCII @separator=comma

25 _____________________________________________________________________________________________

26 % File: human_inh_sim.m

27 % Runs simulations for Table B-5, human internal-dose calculations 

28 % from inhalation exposure, over the concentration range specified

29 % in the 'for' statement below.

30 % Results saved to file 'HumanInhSims_KLLC.#.csv', where # is

31 % value of KLLC used (0 if non-linear/Michaelis-Menten kinetics).

32 % If metab="linear", 1st order kinetics used; otherwise non-linear.

33 use humanset

34 WESITG=0; WEDITG=0; simres=[]; MULTE=0; CINT=1.0; RATS=0.0; KLOSS=0.0;

35 CONCPPM=0; TSTOP=1000; TCHNG=1000; DOSE=0; DWDOSE=0; ODS=1;

36 prepare @clear T CVB STOM

37 start @nocallback

38 for CONCPPM=[1, 10, 50, 100, 250, 500, 1000, 2000, 5000, 10000]

39 start @nocallback

40 simres=[simres;[CONCPPM,AUCBF,max(_cvb),AMF/(BW^0.75)]]

41 end

42 disp(['Simulation for ',ctot(metabf),' human kinetics']);

43 eval(['save simres @file=HumanInhSims_KLLC.',num2str(round(KLLC)), ...

44 '.csv @format=ascii @separator=comma']);

45 _____________________________________________________________________________________________

46 % File: human_oral_sim.m

47 % Runs simulations for Table B-5, human internal-dose calculations from 

48 % oral exposure, over the exposure range specified in the 'for' statement below.

49 % Results saved to file 'Hum_DW_Sims_KLLC.#.csv', where # is value of KLLC

50 % used (0 if non-linear/Michaelis-Menten kinetics).

51 % If metab="linear", 1st order kinetics used; otherwise non-linear.

52 use humanset

53 WESITG=0; WEDITG=0; MULTE=0; CINT=0.1; RATS=0.0; KLOSS=0.0;

54 CONCPPM=0; TSTOP=1000; DWDOSE=0; DOSE=1; ODS=1; DRDOSE=0;

55 prepare @clear T CVB STOM

56 start @nocallback

57 simres=[];
B- 88 DRAFT – DO NOT CITE OR QUOTE

1 for DOSE=[0.1, 1, 10, 50, 100, 250, 500, 1000, 2000, 5000]

2 start @nocallback

3 simres=[simres;[DOSE,AUCBF,max(_cvb)],AMF];

4 end

5 disp(['Simulation for ',ctot(metabf),' human kinetics']);

6 eval(['save simres @file=Hum_DW_Sims_KLLC.',num2str(round(KLLC)),'.csv ' ...

7 '@format=ascii @separator=comma']);

8 _____________________________________________________________________________________________

9 % file human_drink_compare.m

10 % creates Figure B-24 and Table B-9

11 % Created by Paul Schlosser, U.S. EPA, 8/26/09

12 use humanset

13 WESITG=0; WEDITG=0; MULTE=0; CINT=0.1; TSTOP=48; DOSE=0.1; ODS=1; DRDOSE=0;

14 prepare @clear T CVB

15 start @nocallback

16 T1=_t; C1=_cvb; DOSE=0; LIVR0=0.1; TCHNG=12; MULTE=1; start @nocallback

17 T2=_t; C2=_cvb; LIVR0=0; ODS=0; DOSE=0.1; start @nocallback

18 T3=_t; C3=_cvb; DOSE=0; DRDOSE=0.1; start @nocallback

19 plot(T1,C1,T2,C2,T3,C3,_t,_cvb,'humoralsim.aps')

20

21 tbl=[]; metd = 1.0; 

22 for dse=[0.1, 1.0, 10, 100, 250, 500]

23 row=[]; LIVR0=0; DOSE=0; DRDOSE=dse; start @nocallback

24 row=[dse,max(_cvb),AUCBF,AMETF];

25 LIVR0=dse; DOSE=0; DRDOSE=0; start @nocallback

26 row=[row,max(_cvb),AUCBF,AMETF];

27 LIVR0=0; DOSE=dse; DRDOSE=0; start @nocallback

28 tbl=[tbl;[row,max(_cvb),AUCBF,AMETF]];

29 end

30 tbl

31

B.3.5. Personal Communication from Lena Ernstgard Regarding Human Exposures 

Reported in the Ernstgard and Johanson, 2005 SOT Poster

32 From: Lena Ernstgård [Lena.Ernstgard@imm.ki.se]

33 Sent: Wednesday, March 23, 2005 12:39 AM

34 To: Poet, Torka S

35 Subject: RE: Human MeOH poster

36 Hi,

37 We measured the ventilation rate and they ought to be similar to those reported by Dr. Johanson at the same 

38 workload.

39 Sincerly,

40 Lena Ernstgård 

41

42 At 18:41 2005-03-22, you wrote:

43

44 Thank you very much. Your net uptake is what we thought. Did you measure ventilation rates?

45

46 Thanks again,

47 Torka

48

49 Torka Poet, PhD

50 Center for Biological Monitoring and Modeling 

51 Pacific Northwest National Laboratories

52 902 Battelle Blvd. 

53 P.O. Box 999, MSIN P7-59
B- 89 DRAFT – DO NOT CITE OR QUOTE

1 Richland, WA 99352

2 ph: (509)376-7740

3 fax: (509)376-9449

4 e-mail: Torka.poet@pnl.gov

5 (Express Mail Delivery: 790 Sixth Street, Zip Code 99354) 

6

7 From: Lena Ernstgård [mailto:Lena.Ernstgard@imm.ki.se] 

8 Sent: Sunday, March 20, 2005 11:21 PM

9 To: Poet, Torka S

10 Subject: Re: Human MeOH poster

11 Hi,

12 The manuscript has not been submitted yet, but it will be soon I hope. I will save your mail and send you a 

13 copy as soon as possible. 

14 When I say % of net uptake, i mean the relative uptake. It is calculated as: conc in exposure chamber -

15 (minus) exhaled conc / (divided by) conc in exposure chamber. I hope you understand how we have done.

16 Sincerly,

17 Lena Ernstgård

B.3.6. Personal Communication from Dr. Rogers Regarding Mouse Exposures

18 Jeff Gift, Ph.D.

19 National Center for Environmental Assessment EPA (B243-01) RTP, NC 27711

20 919-541-4828

21 919-541-0245 (fax)

22 gift.jeff@epa.gov

23

24 ----- Forwarded by Jeff Gift/RTP/USEPA/US on 04/04/2005 04:31 PM -----

25 John Rogers 

26 To: Jeff Gift/RTP/USEPA/US@EPA 

27 04/04/2005 03:50 PM 

28 Subject: Re: report(Document link: Jeff Gift) 

29

30 Hi Jeff:

31 It's easier just to give you the numbers from that figure in tabular form:

32

33 Time (hr)

34 (exposure Blood [MeOH] SEM

35 ends @ 7 hr) (mg/ml)

36 1 0.93 0.05

37 4 2.80 0.20

38 6 3.36 0.08

39 7 3.99 0.13

40 7.5 3.98 0.21

41 8 4.12 0.07

42 9 3.27 0.16

43 12 2.63 0.21

44 16 1.69 0.08

45 26 0.06 0.02

46

47 Whew! Surprised I could find these numbers that fast, I get a little worried when someone asks 

48 for 12 year old data, I'm not as organized as I'd like to be,to say the least.

49
B- 90 DRAFT – DO NOT CITE OR QUOTE

1 John

2

3 John M. Rogers, Ph.D.

4 Chief, Developmental Biology Branch (MD-67) Reproductive Toxicology Division National 

5 Health and Environmental Effects Research Laboratory Research Triangle Park, NC 27711

6 T: (919)541-5177

7 F: (919)541-4017

8 e-mail: rogers.john@epa.gov

B.3.7. Total MeOH Metabolic Clearance/Metabolites Produced

Table B-6. Mouse total MeOH metabolic clearance/metabolites produced 

following inhalation exposuresa

Exposure concentration 

(ppm) AUC (mg/L-hr) Cmax (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 6.01E-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 7.28E+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-hour period during which mice were exposed for 7 hours to MeOH according to the conditions of 

the dose-response study.
B- 91 DRAFT – DO NOT CITE OR QUOTE

Table B-7. Human total MeOH metabolic clearance/metabolites produced 

from inhalation exposuresa

Exposure concentration (ppm) AUC 

(mg/L-hr) Cmax (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 exposuresa

Exposure concentration (mg/kg-day) AUC (mg/L-hr) Total 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.
B- 92 DRAFT – DO NOT CITE OR QUOTE

B.3.8. Multiple Daily Oral Dosing for Humans

1 Current mode simulations of oral exposures to humans use a constant rate of infusion to 

2 the stomach lumen. This approach results in a steady rate of absorption from the stomach equal 

3 to the exposure rate irregardeless of the oral uptake rate constants (assumed equal to the mouse), 

4 hence avoids the difficulty that independent values of these constants are not available for 

5 humans due to a lack of human oral PK data.. A more likely drinking scenario was tested by 

6 using additional code within the model to simulate a 6-times/day drinking schedule, over the 

7 course of 15 h (see code below). The schedule is still an approximation, as it assumes 6 episodes 

8 of drinking, each considered to be a bolus. Specifically, it was assumed that humans drank at 0, 

9 3, 5, 8, 11, and 15 hours from the first ingestion of each day, with the respective fractions of daily 

10 consumption being 25, 10, 25, 10, 25, and 5% at those times. The predicted blood 

11 concentrations resulting from simulations of six daily boluses, once/day boluses, 12 h/d infusion 

12 (zero order), or constant (zero order) are shown in Figure B-24 for a total dose of 0.1 mg/kg. 

13 Table B-9 shows PBPK model predicted Cmax, AUC, and Amet (for the last 24 hours of repeated 

14 exposures) for humans exposed to MeOH via six daily boluses, 12 h/d infusion, or a single daily 

15 gavage.

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

comparing the first few days for four exposure scenarios: continuous (zeroorder) infusion; 12 h/d infusion, a single daily bolus, and a pattern of six boluses 

per day (see text).
B- 93 DRAFT – DO NOT CITE OR QUOTE

Table B-9. Repeated daily oral dosing of humans with MeOH*

Dose 

(mg/kg)

Six daily boluses 12 h/d infusion Single-daily bolus

Cmax

(mg/L)

AUC 

(mg-h/L)

Amet 

(mg)

Cmax

(mg/L)

AUC 

(mg-h/L)

Amet 

(mg)

Cmax

(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 33.4 109 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 h
C- 1 DRAFT – DO NOT CITE OR QUOTE

APPENDIX C. RfC DERIVATION OPTIONS

C.1. RFC DERIVATIONS USING THE NEDO METHANOL REPORT (NEDO, 1987)

1 The BMD approach was utilized in the derivation of potential chronic inhalation 

2 reference values. In the application of the BMD approach, continuous models in the EPA’s 

3 BMDS, version 2.1, were fit to datasets for decreased brain weight in male rats exposed 

4 throughout gestation and the postnatal period to 6 weeks and male rats exposed during gestation 

5 on days 7–17 only (NEDO, 1987). Although there remains uncertainty surrounding the 

6 identification of the proximate teratogen of importance (methanol, formaldehyde, or formate), 

7 the dose metrics chosen for the derivation of RfCs were based on blood methanol levels. This 

8 decision was primarily based on evidence that the toxic moiety is not likely to be the formate 

9 metabolite of methanol (CERHR, 2004), and evidence that levels of the formaldehyde metabolite 

10 following methanol maternal and/or neonate exposure would be much lower in the fetus and 

11 neonate than in adults. While recent in vitro evidence indicates that formaldehyde is more 

12 embryotoxic than methanol and formate, the high reactivity of formaldehyde would significantly 

13 limit its transport from maternal to fetal blood, and the capacity for the metabolism of methanol 

14 to formaldehyde is lower in the fetus and neonate versus adults. Further discussions of methanol 

15 metabolism, dose metric selection and MOA issues are covered in Sections 3.3, 4.5 and 4.6. 

C.1.1. Decreased Brain Weight in Male Rats Exposed throughout Gestation and into the 

Postnatal Period (NEDO, 1987)

16 The results of NEDO (1987), shown in Table 4-14, indicate that there is not a cumulative 

17 effect of ongoing exposure on brain-weight decrements in rats exposed postnatally; i.e., the dose

18 response in terms of percent of control is about the same at 3 weeks postnatal as at 8 weeks 

19 postnatal in rats exposed throughout gestation and the F1 generation. However, there does 

20 appear to be a greater brain-weight effect in rats exposed postnatally versus rats exposed only 

21 during organogenesis (GD7–GD17). In male rats exposed during organogenesis only, there is no 

22 statistically significant decrease in brain weight at 8 week after birth at the 1,000 ppm exposure 

23 level. Conversely, in male rats exposed to the same level of methanol throughout gestation and 

24 the F1 generation, there was an approximately a 5% decrease in brain weights (statistically 

25 significant at the p < 0.01 level). The extent to which this observation is due to recovery in rats 

26 for which exposure was discontinued at birth versus a cumulative effect in rats exposed 

27 postnatally is not clear. The fact that male rats exposed to 5,000 ppm methanol only during 

28 organogenesis experienced a decrease of brain weight of 10% at 8 weeks postnatal indicates that 

29 postnatal exposure is not necessary for the observation of persistent postnatal effects. However, 

30 the fact that this decrease was less than the 13% decrease observed in male rats exposed to 
C- 2 DRAFT – DO NOT CITE OR QUOTE

1 2,000 ppm methanol throughout gestation, and the 8 week postnatal period indicates that the 

2 absence of postnatal exposure allows for some measure of recovery. 

3 It appears that once methanol exposure is discontinued, continuous biological processes 

4 that are disrupted by exposure, manifesting as decreased brain weight, undergo some recovery 

5 and brain weights begin to return to normal values. This indicates that brain weight is 

6 susceptible to both the level and duration of exposure. Therefore, a dose metric that incorporates 

7 a time component would be the most appropriate metric to use. For these reasons and because it 

8 is more typically used in internal-dose-based assessments and better reflects total exposure 

9 within a given day, daily AUC (measured for 22-hour exposure/day) was chosen as the most 

10 appropriate dose metric for modeling the effects of methanol exposure on brain weights in rats 

11 exposed throughout gestation and continuing into the F1 generation.

12 Application of the EPA methanol PBPK model (described in Section 3.4) to the NEDO 

13 (1987) study in which developing rats were exposed during gestation and the postnatal period

14 presents complications that need to be discussed. The neonatal rats in this study were exposed to 

15 methanol gestationally before parturition, as well as lactationally and inhalationally after 

16 parturition. The PBPK model developed by the EPA only estimates internal dose metrics for 

17 methanol exposure in NP adult mice and rats. Experimental data indicate that inhalation-route 

18 blood methanol kinetics in NP mice and pregnant mice on GD6–GD10 are similar (Dorman 

19 et al., 1995; Perkins et al., 1995a,b; Rogers et al., 1993a,b). In addition, experimental data 

20 indicate that the maternal blood:fetal partition coefficient for mice is approximately 1 (see 

21 Section 3.4.1.2). Assuming that these findings apply for rats, they indicate that pharmacokinetic 

22 and blood dose metrics for NP rats are appropriate surrogates for fetal exposure during early 

23 gestation. However, as is discussed to a greater extent in Section 5.3, the additional routes of 

24 exposure presented to the pups in this study (lactation and inhalation) present uncertainties that 

25 make it reasonable to assume that average blood levels in pups in the NEDO report are also 

26 greater than those of the dam. However, it is also reasonable to assume that any differences seen 

27 between the pups and dams would also be seen in mothers and human offspring. Therefore, the 

28 presumed differences between pup and dam blood methanol levels are deemed relatively 

29 inconsequential, and the PBPK model-estimated adult blood methanol levels are assumed to be 

30 appropriate dose metrics for the purpose of this analysis.

31 The first step in the current analysis is to convert the inhalation doses, given as ppm 

32 values from the studies, to an internal dose surrogate or dose metric using the EPA PBPK model 

33 (see Section 3.4). Predicted AUC values for methanol in the blood of rats and humans are 

34 summarized in Table C-1.

35
C- 3 DRAFT – DO NOT CITE OR QUOTE

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

following inhalation exposures

Exposure level (ppm) Methanol in blood AUC (hr × mg/L)a in rats

500 79.2

1,000 226.7

2000 967.8

a

AUC values were obtained by simulating 22 hour/day exposures for 5 days and calculated for the last 24 hours of 

that period.

1 The current BMD technical guidance (U.S. EPA, 2000b) suggests that in the absence of 

2 knowledge as to what level of response to consider adverse, a change in the mean equal to 

3 1 control S.D. from the control mean can be used as a BMR for continuous endpoints. However, 

4 it has been suggested that other BMRs, such as 5% change relative to estimated control mean, 

5 are also appropriate when performing BMD analyses on fetal weight change as a developmental 

6 endpoint (Kavlock et al., 1995). Therefore, in this assessment, both a 1 control mean S.D.

7 change and a 5% change relative to estimated control mean were considered. All models were fit 

8 using restrictions and option settings suggested in the EPA BMD technical guidance document 

9 (U.S. EPA, 2000b).

C.1.2. BMD Approach with a BMR of 1 Control Mean S.D. – Gestation and into the 

Postnatal Period (NEDO, 1987)

10 A summary of the results most relevant to the development of a POD using the BMD 

11 approach (BMD, BMDL, and model fit statistics) for decreased brain weight at 6 weeks in male 

12 rats exposed to methanol throughout gestation and continuing into the F1 generation, with a 

13 BMR of 1 control mean S.D, is provided in Table C-2. The 6 week male brain weight responses 

14 were chosen because they resulted in lower BMD and BMDL estimates than male responses at 3

15 and 8 weeks and female responses at any time point (data not shown). Model fit and was 

determined by statistics (AIC and χ

2 16 residuals of individual dose groups) and visual inspection, 

17 as recommended by EPA (2000b). There is a 2.5-fold range of BMDL estimates from adequately 

18 fitting models, indicating considerable model dependence. In addition, the fit of the Hill and 

19 more complex Exponential models is better than the other models in the dose region of interest 

20 as indicated by a lower scaled residual at the dose group closest to the BMD (0.09 versus -0.67 

21 or -0.77) and visual inspection. In accordance with EPA BMD Technical Guidance (EPA, 

22 2000b), the BMDL from the Hill model (bolded), is selected as the most approriate basis for an 

23 RfC derivation because it results in the lowest BMDL from among a broad range of BMDLs and 

24 provides a superior fit in the low dose region nearest the BMD. The BMDL1SD was determined 

25 to be 90.9 hr × mg/L using the 95% lower confidence limit of the dose-response curve expressed 

26 in terms of the AUC for methanol in blood.
C- 4 DRAFT – DO NOT CITE OR QUOTE

Table C-2. Comparison of BMD1SD results for decreased brain weight in male rats at 6 

weeks of age using modeled AUC of methanol as a dose metric

Model BMD1SD (AUC,

hr × mg/L) a

BMDL1SD

(AUC,

hr × mg/L)

a

p-value AICc Scaled residuald

Linear 278.30 225.30 0.5376 -203.84 -0.77

2nd degree polynomial 278.30 225.30 0.5376 -203.84 -0.77

3rd degree polynomial 278.30 225.30 0.5376 -203.84 -0.77

Power 278.30 225.30 0.5376 -203.84 -0.77

Hillb 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

The 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. 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, 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 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 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 ==================================================================== 

2 Hill Model. (Version: 2.14; Date: 06/26/2008) 

3 Input Data File: C:\Usepa\BMDS21\Data\Methanol\NEDO\Gest-F1\hilm-6wk-brwHil4 Restrict.(d) 

5 Gnuplot Plotting File: C:\Usepa\BMDS21\Data\Methanol\NEDO\Gest-F1\hilm-6wk6 brwHil-Restrict.plt

7 Mon Aug 24 13:08:09 2009

8 ==================================================================== 

9

10 BMDS Model Run 

11 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

12

13 The form of the response function is: 

14

15 Y[dose] = intercept + v*dose^n/(k^n + dose^n)

16

17

18 Dependent variable = Mean

19 Independent variable = Dose

20 rho is set to 0

21 v is set to -0.5

22 Power parameter restricted to be greater than 1

23 A constant variance model is fit

24

25 Total number of dose groups = 4

26 Total number of records with missing values = 0

27 Maximum number of iterations = 250

28 Relative Function Convergence has been set to: 1e-008
C- 5 DRAFT – DO NOT CITE OR QUOTE

1 Parameter Convergence has been set to: 1e-008

2

3

4

5 Default Initial Parameter Values 

6 alpha = 0.00539333

7 rho = 0 Specified

8 intercept = 1.78

9 v = -0.5 Specified

10 n = 1.2699

11 k = 924.206

12

13

14 Asymptotic Correlation Matrix of Parameter Estimates

15

16 ( *** The model parameter(s) -rho -v 

17 have been estimated at a boundary point, or have been specified by 

18 the user,

19 and do not appear in the correlation matrix )

20

21 alpha intercept n k

22

23 alpha 1 1.7e-008 -1.7e-008 -2.9e-009

24

25 intercept 1.7e-008 1 -0.56 -0.73

26

27 n -1.7e-008 -0.56 1 0.15

28

29 k -2.9e-009 -0.73 0.15 1

30

31

32

33 Parameter Estimates

34

35 95.0% Wald Confidence Interval

36 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit

37 alpha 0.00496114 0.0010023 0.00299667 0.00692562

38 intercept 1.77806 0.0193152 1.7402 1.81591

39 n 1.07212 0.217805 0.645228 1.49901

40 k 915.874 179.818 563.437 1268.31

41

42

43

44 Table of Data and Estimated Values of Interest

45

46 Dose N Obs Mean Est Mean Obs Std Dev Est Std Dev Scaled Res.

47 ------ --- -------- -------- ----------- ----------- ----------

48

49 0 12 1.78 1.78 0.07 0.0704 0.0956

50 79.2 12 1.74 1.74 0.09 0.0704 -0.21

51 226.7 11 1.69 1.69 0.06 0.0704 0.159

52 967.8 14 1.52 1.52 0.07 0.0704 -0.0355

53

54

55

56 Model Descriptions for likelihoods calculated

57

58

59 Model A1: Yij = Mu(i) + e(ij)

60 Var{e(ij)} = Sigma^2

61

62 Model A2: Yij = Mu(i) + e(ij)

63 Var{e(ij)} = Sigma(i)^2

64

65 Model A3: Yij = Mu(i) + e(ij)

66 Var{e(ij)} = Sigma^2

67 Model A3 uses any fixed variance parameters that
C- 6 DRAFT – DO NOT CITE OR QUOTE

1 were specified by the user

2

3 Model R: Yi = Mu + e(i)

4 Var{e(i)} = Sigma^2

5

6

7 Likelihoods of Interest

8

9 Model Log(likelihood) # Param's AIC

10 A1 105.539862 5 -201.079724

11 A2 106.570724 8 -197.141449

12 A3 105.539862 5 -201.079724

13 fitted 105.499930 4 -202.999859

14 R 77.428662 2 -150.857324

15

16

17 Explanation of Tests 

18

19 Test 1: Do responses and/or variances differ among Dose levels? 

20 (A2 vs. R)

21 Test 2: Are Variances Homogeneous? (A1 vs A2)

22 Test 3: Are variances adequately modeled? (A2 vs. A3)

23 Test 4: Does the Model for the Mean Fit? (A3 vs. fitted)

24 (Note: When rho=0 the results of Test 3 and Test 2 will be the same.)

25

26 Tests of Interest 

27

28 Test -2*log(Likelihood Ratio) Test df p-value 

29

30 Test 1 58.2841 6 <.0001

31 Test 2 2.06173 3 0.5597

32 Test 3 2.06173 3 0.5597

33 Test 4 0.0798644 1 0.7775

34

35 The p-value for Test 1 is less than .05. There appears to be a

36 difference between response and/or variances among the dose levels

37 It seems appropriate to model the data

38

39 The p-value for Test 2 is greater than .1. A homogeneous variance 

40 model appears to be appropriate here

41

42

43 The p-value for Test 3 is greater than .1. The modeled variance appears 

44 to be appropriate here

45

46 The p-value for Test 4 is greater than .1. The model chosen seems 

47 to adequately describe the data

48

49

50 Benchmark Dose Computation

51

52 Specified effect = 1

53

54 Risk Type = Estimated standard deviations from the control mean 

55

56 Confidence level = 0.95

57

58 BMD = 169.597

59
C- 7 DRAFT – DO NOT CITE OR QUOTE

 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 1000

Mean Response

dose

Hill Model with 0.95 Confidence Level

09:05 08/24 2009

BMDL BMD

 

Hill

Figure C-1. Hill model, 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).

1 Once the BMDL1SD was obtained in units of hr × mg/L, it was used to derive a chronic 

2 inhalation reference value. The first step is to calculate the HEC using the PBPK model 

3 described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B) that 

4 describes the relationship between predicted methanol AUC and the human equivalent inhalation 

5 exposure concentration (HEC) in ppm.

6 BMDLHEC (ppm)= 0.0224*BMDL1SD+(1334*BMDL1SD)/(794+ BMDL1SD)

7 BMDLHEC (ppm)= 0.0224*374.7+(1334*374.7)/(794+ 90.9) = 139 ppm

 Next, because RfCs are typically expressed in units of mg/m3 8 , the HEC value in ppm 

was converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m3 9 :
C- 8 DRAFT – DO NOT CITE OR QUOTE

HEC (mg/m3) = 1.31 × 139 ppm = 182 mg/m3 1

2 Finally, this HEC value was divided by a composite 100-fold UF (3 for uncertainty 

3 associated with animal to human differences, 10 for consideration of human variability, and 3 for 

4 database deficiencies) to obtain the chronic inhalation reference value:

RfC (mg/m3) = 182 mg/m3 ÷ 100 = 1.8 mg/m3 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)

6 A summary of the results most relevant to the development of a POD using the BMD 

7 approach (BMD, BMDL, and model fit statistics) for decreased brain weight at 6 weeks in male 

8 rats exposed to methanol throughout gestation and continuing into the F1 generation, with a 

9 BMR of 0.05 change relative to estimated control mean, is provided in Table C-3. The 6 week 

10 male brain weight responses were chosen because they resulted in lower BMD and BMDL

11 estimates than male responses at 3 and 8 weeks and female responses at any time point (data not 

shown). Model fit was determined by statistics (AIC and χ

2 12 residuals of individual dose groups) 

13 and visual inspection, as recommended by U.S. EPA (2000b). There is a 2.4-fold range of 

14 BMDL estimates from adequately fitting models, indicating considerable model dependence. In 

15 addition, the fit of the Hill and more complex Exponential models is better than the other models

16 in the dose region of interest as indicated by a lower scaled residual at the dose group closest to 

17 the BMD (0.09 versus -0.67 or -0.77) and visual inspection. In accordance with EPA BMD 

18 Technical Guidance (EPA, 2000b), the BMDL from the Hill model (bolded), is selected as the 

19 most approriate basis for an RfC derivation because it results in the lowest BMDL from among a 

20 broad range of BMDLs and provides a superior fit in the low dose region nearest the BMD. The 

21 BMDL05 was determined to be 123.9 hr × mg/L, using the 95% lower confidence limit of the 

22 dose-response curve expressed in terms of the AUC for methanol in blood.
C- 9 DRAFT – DO NOT CITE OR QUOTE

Table C-3. Comparison of BMD05 results for decreased brain weight in male rats at 6 

weeks of age using modeled AUC of methanol as a dose metric

Model BMD05 (AUC,

hr × mg/L)

a

BMDL05 (AUC,

hr × mg/L)

a p-value AICc Scaled Residuald

Linearb 344.49 297.92 0.5376 -203.84 -0.77

2nd degree polynomial 344.49 297.92 0.5376 -203.84 -0.77

3rd degree polynomial 344.49 297.92 0.5376 -203.84 -0.77

Power 344.49 297.92 0.5376 -203.84 -0.77

Hill 223.18 123.87 0.8366 -203.04 -0.09

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

The 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 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, 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 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 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 ==================================================================== 

2 Hill Model. (Version: 2.14; Date: 06/26/2008) 

3 Input Data File: C:\Usepa\BMDS21\Data\Methanol\NEDO\Gest-F1\hilm-6wk-brwHil4 Restrict.(d) 

5 Gnuplot Plotting File: C:\Usepa\BMDS21\Data\Methanol\NEDO\Gest-F1\hilm-6wk6 brwHil-Restrict.plt

7 Mon Aug 24 09:40:25 2009

8 ==================================================================== 

9

10 BMDS Model Run 

11 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

12

13 The form of the response function is: 

14

15 Y[dose] = intercept + v*dose^n/(k^n + dose^n)

16

17

18 Dependent variable = Mean

19 Independent variable = Dose

20 rho is set to 0

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

28 Parameter Convergence has been set to: 1e-008

29
C- 10 DRAFT – DO NOT CITE OR QUOTE

1

2

3 Default Initial Parameter Values 

4 alpha = 0.00539333

5 rho = 0 Specified

6 intercept = 1.78

7 v = -0.26

8 n = 1.08282

9 k = 401.076

10

11

12 Asymptotic Correlation Matrix of Parameter Estimates

13

14 ( *** The model parameter(s) -rho -n 

15 have been estimated at a boundary point, or have been specified by 

16 the user,

17 and do not appear in the correlation matrix )

18

19 alpha intercept v k

20

21 alpha 1 4e-009 -1.2e-008 6.2e-009

22

23 intercept 4e-009 1 0.54 -0.64

24

25 v -1.2e-008 0.54 1 -0.99

26

27 k 6.2e-009 -0.64 -0.99 1

28

29

30

31 Parameter Estimates

32

33 95.0% Wald Confidence Interval

34 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit

35 alpha 0.00495736 0.00100154 0.00299438 0.00692034

36 intercept 1.77823 0.0184949 1.74198 1.81448

37 v -0.600699 0.340055 -1.2672 0.0657974

38 n 1 NA

39 k 1284.65 1317.9 -1298.39 3867.68

40

41 NA - Indicates that this parameter has hit a bound

42 implied by some inequality constraint and thus

43 has no standard error.

44

45

46

47 Table of Data and Estimated Values of Interest

48

49 Dose N Obs Mean Est Mean Obs Std Dev Est Std Dev Scaled Res.

50 ------ --- -------- -------- ----------- ----------- ----------

51

52 0 12 1.78 1.78 0.07 0.0704 0.0872

53 79.2 12 1.74 1.74 0.09 0.0704 -0.165

54 226.7 11 1.69 1.69 0.06 0.0704 0.0884

55 967.8 14 1.52 1.52 0.07 0.0704 -0.00677

56

57

58

59 Model Descriptions for likelihoods calculated

60

61

62 Model A1: Yij = Mu(i) + e(ij)

63 Var{e(ij)} = Sigma^2

64

65 Model A2: Yij = Mu(i) + e(ij)

66 Var{e(ij)} = Sigma(i)^2

67
C- 11 DRAFT – DO NOT CITE OR QUOTE

1 Model A3: Yij = Mu(i) + e(ij)

2 Var{e(ij)} = Sigma^2

3 Model A3 uses any fixed variance parameters that

4 were specified by the user

5

6 Model R: Yi = Mu + e(i)

7 Var{e(i)} = Sigma^2

8

9

10 Likelihoods of Interest

11

12 Model Log(likelihood) # Param's AIC

13 A1 105.539862 5 -201.079724

14 A2 106.570724 8 -197.141449

15 A3 105.539862 5 -201.079724

16 fitted 105.518584 4 -203.037168

17 R 77.428662 2 -150.857324

18

19

20 Explanation of Tests 

21

22 Test 1: Do responses and/or variances differ among Dose levels? 

23 (A2 vs. R)

24 Test 2: Are Variances Homogeneous? (A1 vs A2)

25 Test 3: Are variances adequately modeled? (A2 vs. A3)

26 Test 4: Does the Model for the Mean Fit? (A3 vs. fitted)

27 (Note: When rho=0 the results of Test 3 and Test 2 will be the same.)

28

29 Tests of Interest 

30

31 Test -2*log(Likelihood Ratio) Test df p-value 

32

33 Test 1 58.2841 6 <.0001

34 Test 2 2.06173 3 0.5597

35 Test 3 2.06173 3 0.5597

36 Test 4 0.0425562 1 0.8366

37

38 The p-value for Test 1 is less than .05. There appears to be a

39 difference between response and/or variances among the dose levels

40 It seems appropriate to model the data

41

42 The p-value for Test 2 is greater than .1. A homogeneous variance 

43 model appears to be appropriate here

44

45

46 The p-value for Test 3 is greater than .1. The modeled variance appears 

47 to be appropriate here

48

49 The p-value for Test 4 is greater than .1. The model chosen seems 

50 to adequately describe the data

51

52

53 Benchmark Dose Computation

54

55 Specified effect = 0.05

56

57 Risk Type = Relative risk 

58

59 Confidence level = 0.95

60

61 BMD = 223.178

62
C- 12 DRAFT – DO NOT CITE OR QUOTE

 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 1000

Mean Response

dose

Hill Model with 0.95 Confidence Level

09:40 08/24 2009

BMDL BMD

 

Hill

Figure C-2. Hill model, BMR of 0.05 relative risk - Decreased Brain weight in male 

rats at 6 weeks age versus AUC, F1 Generation inhalational study.

Source: NEDO (1987).

1 Once the BMDL05 was obtained in units of hr × mg/L, it was used to derive a chronic 

2 inhalation reference value. The first step is to calculate the HEC using the PBPK model 

3 described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B) that 

4 describes the relationship between predicted methanol AUC and the human equivalent inhalation 

5 exposure concentration (HEC) in ppm. 

6 BMDLHEC (ppm)= 0.0224*BMDL05+(1334*BMDL05)/(794+ BMDL05)

7 BMDLHEC (ppm)= 0.0224*503.0 + ((1334*123.9)/(794+ 123.9)) = 183 ppm

 Next, because RfCs are typically expressed in units of mg/m3 8 , the HEC value in 

ppm was converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m3 9 :
C- 13 DRAFT – DO NOT CITE OR QUOTE

HEC (mg/m3) = 1.31 × 183 ppm = 240 mg/m3 1

2 Finally, this HEC value was divided by a composite 100-fold UF (3 for uncertainty 

3 associated with animal to human differences, 10 for consideration of human variability, and 3 for 

4 database deficiencies) to obtain the chronic inhalation reference value:

RfC (mg/m3) = 240 mg/m3 ÷ 100 = 2.4 mg/m3 5

C.2. DECREASED BRAIN WEIGHT IN MALE RATS EXPOSED DURING 

GESTATION ONLY (GD7–GD17) (NEDO, 1987)

6 Cmax, as calculated by the EPA’s PBPK model, was selected as the dose metric for this 

7 exposure scenario, in concordance with the choice of this dose metric for the increased incidence 

8 of cervical rib in mice in the Rogers et al. study (1993). Exposures occurred only during the 

9 major period of organogenesis in both studies. As there is evidence that Cmax is a better predictor 

10 of response than AUC for incidence of cervical rib (see Appendix D), it was assumed appropriate 

11 to consider Cmax the better predictor for decreased brain weight as well. 

12 The first step in the current analysis is to convert the inhalation doses, given as ppm 

13 values from the studies, to an internal dose surrogate or dose metric using the EPA PBPK model 

14 (see Section 3.4). Predicted Cmax values for methanol in the blood of rats are summarized in 

15 Table C-4.

Table C-4. EPA’s PBPK model estimates of methanol blood levels (Cmax) in rats 

following inhalation exposures

Exposure level (ppm) Methanol in lood Cmax (mg /L)a in rats

200 1.2

1,000 10.6

5000 630.5

a

Cmax values were obtained by simulating 22 hr/day exposures

16 The current BMD technical guidance (U.S. EPA, 2000b) suggests that in the absence of 

17 knowledge as to what level of response to consider adverse, a change in the mean equal to 

18 1 control S.D. from the control mean can be used as a BMR for continuous endpoints. However, 

19 it has been suggested that other BMRs, such as 5% change relative to estimated control mean, 

20 are also appropriate when performing BMD analyses on fetal weight change as a developmental 

21 endpoint (Kavlock et al., 1995). Therefore, in this assessment, both a 1 control mean S.D.

22 change and a 5% change relative to estimated control mean were considered. All models were fit 

23 using restrictions and option settings suggested in the EPA’s BMD technical guidance document 

24 (U.S. EPA, 2000b).
C- 14 DRAFT – DO NOT CITE OR QUOTE

C.2.1. BMD Approach with a BMR of 1 Control Mean S.D. (GD7–GD17) (NEDO, 1987)

1 A summary of the results most relevant to the development of a POD using the BMD 

2 approach (BMD, BMDL, and model fit statistics) for decreased brain weight at 8 weeks in male 

3 rats exposed to methanol during gestation from days 7–17, with a BMR of 1 control mean S.D, is 

4 provided in Table C-5. Male brain weight responses were chosen because they resulted in lower 

5 BMD and BMDL estimates than female responses (data not shown). Model fit was determined 

by statistics (AIC and χ

2 6 residuals of individual dose groups) and visual inspection, as

7 recommended by EPA (2000b). The polynomial and power models reduced to linear form and 

8 returned identical modeling results. In contrast, the more complex Hill and Exponential4 

9 models, which estimate a response “plateau” or asymptote, returned similar, markedly nonlinear 

10 results. This is because these models approximated the response “plateau” to be near the 

11 maximum drop in brain weight observed in the study (approximately 10% at the high dose), 

resulting in a distinctly “L” shaped dose-response curve.

89 12 In this case, the only PBPK model 

13 estimated Cmax dose that is associated with a significant response over controls, the high-dose, is 

14 60-fold higher than the mid-dose Cmax estimate. Thus, there are many plausible curve shapes 

15 and,consequently, a wide range of BMDL estimates. Per EPA (2000b) guidance and to err on the 

16 side of public health protection, the lowest BMDL1SD of 10.26 mg methanol/L in blood estimated 

17 from adequate and plausible models was chosen for use in the RfC derivation. However, it 

18 should be noted that there is a great deal of uncertainty and model dependence associated with 

19 these dose-response data.

Table C-5. Comparison of BMD1SD results for decreased brain weight in male rats at 8 

weeks of age using modeled Cmax of methanol as a dose metric

Model BMD1SD (Cmax, 

mg/L)a

BMDL1SD (Cmax,

mg/L)

a p-value AICc Scaled residuald

Linear 207.18 135.22 0.7881 -173.12 -0.43

2nd 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

Hillb 43.08 10.26 0.9602 -171.59 -0.10

Exponential 2 199.98 127.55 0.9494 -173.13 -0.42

Exponential 3 199.98 127.55 0.9494 -173.13 -0.42

Exponential 4b 39.53 10.26 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. 
C- 15 DRAFT – DO NOT CITE OR QUOTE

a

The BMDL is the 95% lower confidence limit on the Cmax 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, 2000b). b

Per EPA (2000b) guidance and to err on the side of public health protection, the lowest BMDL1SD of 10.26 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 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 value should cause one to question model fit in this region.

Source: NEDO (1987) 

1 ==================================================================== 

2 Hill Model. (Version: 2.14; Date: 06/26/2008) 

3 Input Data File: C:\Usepa\BMDS21\Data\Methanol\NEDO\Gest-only\hilm-8wk4 brwHil-Restrict.(d) 

5 Gnuplot Plotting File: C:\Usepa\BMDS21\Data\Methanol\NEDO\Gest-only\hilm6 8wk-brwHil-Restrict.plt

7 Tue Aug 25 12:40:30 2009

8 ==================================================================== 

9

10 BMDS Model Run 

11 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

12

13 The form of the response function is: 

14

15 Y[dose] = intercept + v*dose^n/(k^n + dose^n)

16

17

18 Dependent variable = Mean

19 Independent variable = Dose

20 Power parameter restricted to be greater than 1

21 The variance is to be modeled as Var(i) = exp(lalpha + rho * ln(mean(i)))

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

29

30

31 Default Initial Parameter Values 

32 lalpha = -4.68678

33 rho = 0

34 intercept = 2

35 v = -0.19

36 n = 0.861776

37 k = 303.331

38

39

40 Asymptotic Correlation Matrix of Parameter Estimates

41

42 ( *** The model parameter(s) -n 

43 have been estimated at a boundary point, or have been specified by the user,

44 and do not appear in the correlation matrix )

45

46 lalpha rho intercept v k

47

48 lalpha 1 -1 -0.083 0.6 -0.18

49

50 rho -1 1 0.096 -0.6 0.18

51
C- 16 DRAFT – DO NOT CITE OR QUOTE

1 intercept -0.083 0.096 1 0.19 -0.55

2

3 v 0.6 -0.6 0.19 1 -0.73

4

5 k -0.18 0.18 -0.55 -0.73 1

6

7

8

9 Parameter Estimates

10

11 95.0% Wald Confidence Interval

12 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit

13 lalpha 7.03732 4.98399 -2.73112 16.8058

14 rho -18.1432 7.32604 -32.502 -3.78448

15 intercept 2.0068 0.0134454 1.98045 2.03316

16 v -0.232906 0.0881494 -0.405676 -0.0601362

17 n 1 NA

18 k 121.949 194.687 -259.631 503.529

19

20 NA - Indicates that this parameter has hit a bound

21 implied by some inequality constraint and thus

22 has no standard error.

23

24

25

26 Table of Data and Estimated Values of Interest

27

28 Dose N Obs Mean Est Mean Obs Std Dev Est Std Dev Scaled Res.

29 ------ --- -------- -------- ----------- ----------- ----------

30

31 0 11 2 2.01 0.047 0.0608 -0.371

32 1.2 11 2.01 2 0.075 0.0614 0.295

33 10.6 12 1.99 1.99 0.072 0.0662 0.0954

34 630.5 10 1.81 1.81 0.161 0.154 -0.0338

35

36

37

38 Model Descriptions for likelihoods calculated

39

40

41 Model A1: Yij = Mu(i) + e(ij)

42 Var{e(ij)} = Sigma^2

43

44 Model A2: Yij = Mu(i) + e(ij)

45 Var{e(ij)} = Sigma(i)^2

46

47 Model A3: Yij = Mu(i) + e(ij)

48 Var{e(ij)} = exp(lalpha + rho*ln(Mu(i)))

49 Model A3 uses any fixed variance parameters that

50 were specified by the user

51

52 Model R: Yi = Mu + e(i)

53 Var{e(i)} = Sigma^2

54

55

56 Likelihoods of Interest

57

58 Model Log(likelihood) # Param's AIC

59 A1 83.205960 5 -156.411920

60 A2 92.060485 8 -168.120970

61 A3 90.797178 6 -169.594356

62 fitted 90.795933 5 -171.591867

63 R 70.761857 2 -137.523714

64

65

66 Explanation of Tests 

67
C- 17 DRAFT – DO NOT CITE OR QUOTE

1 Test 1: Do responses and/or variances differ among Dose levels? 

2 (A2 vs. R)

3 Test 2: Are Variances Homogeneous? (A1 vs A2)

4 Test 3: Are variances adequately modeled? (A2 vs. A3)

5 Test 4: Does the Model for the Mean Fit? (A3 vs. fitted)

6 (Note: When rho=0 the results of Test 3 and Test 2 will be the same.)

7

8 Tests of Interest 

9

10 Test -2*log(Likelihood Ratio) Test df p-value 

11

12 Test 1 42.5973 6 <.0001

13 Test 2 17.7091 3 0.000505

14 Test 3 2.52661 2 0.2827

15 Test 4 0.00248896 1 0.9602

16

17 The p-value for Test 1 is less than .05. There appears to be a

18 difference between response and/or variances among the dose levels

19 It seems appropriate to model the data

20

21 The p-value for Test 2 is less than .1. A non-homogeneous variance 

22 model appears to be appropriate

23

24 The p-value for Test 3 is greater than .1. The modeled variance appears 

25 to be appropriate here

26

27 The p-value for Test 4 is greater than .1. The model chosen seems 

28 to adequately describe the data

29

30

31 Benchmark Dose Computation

32

33 Specified effect = 1

34

35 Risk Type = Estimated standard deviations from the control mean 

36

37 Confidence level = 0.95

38

39 BMD = 43.0842

40

41 BMDL = 10.2551
C- 18 DRAFT – DO NOT CITE OR QUOTE

 1.7

 1.75

 1.8

 1.85

 1.9

 1.95

 2

 2.05

 0 100 200 300 400 500 600

Mean Response

dose

Hill Model with 0.95 Confidence Level

12:40 08/25 2009

BMDL BMD

 

Hill

Figure C-3. Hill model, BMR of 1 Control Mean S.D. - Decreased Brain weight in 

male rats at 8 weeks age versus Cmax, Gestation only inhalational study.

Source: NEDO (1987).

1 Once the BMDL1SD was obtained in units of mg/L, it was used to derive a chronic 

2 inhalation reference value. The first step is to calculate the HEC using the PBPK model 

3 described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B) that 

4 describes the relationship between predicted methanol AUC and the human equivalent inhalation 

5 exposure concentration (HEC) in ppm. This equation can also be used to estimate model 

6 predictions for HECs from Cmax values because Cmax values and AUC values, were estimated at 

7 steady-state for constant 24-hour exposures (i.e., AUC = 24 x Cmax).

8 BMDLHEC (ppm) = 0.0224*BMDL1SD*24+(1334*BMDL1SD*24)/(794+ BMDL1SD*24)

9 BMDLHEC (ppm) = 0.0224*10.3*24 + ((1334*10.3*24)/(794+ 10.3*24)) = 322 ppm
C- 19 DRAFT – DO NOT CITE OR QUOTE

Next, because RfCs are typically expressed in units of mg/m3 1 , the HEC value in ppm was 

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m3 2 :

HEC (mg/m3

) = 1.31 × 322 ppm = 422 mg/m3 3

4 Finally, this HEC value was divided by a composite 100-fold 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:

RfC (mg/m3

) = 422 mg/m3 ÷ 100 = 4.2 mg/m3 7

C.2.2. BMD Approach with a BMR of 0.05 Change Relative to Control Mean (GD7–GD17) 

(NEDO, 1987)

8 A summary of the results most relevant to the development of a POD using the BMD 

9 approach (BMD, BMDL, and model fit statistics) for decreased brain weight at 8 weeks in male 

10 rats exposed to methanol during gestation from days 7 to 17, with a BMR of 0.05 change relative 

11 to estimated control mean, is provided in Table C-6. Model fit was determined by statistics (AIC 

and χ

2 12 residuals of individual dose groups) and visual inspection, as recommended by EPA 

13 (2000b). Modeling considerations and uncertainties for this dataset were discussed in C.2.1 and, 

14 as was done for the BMR of 1 S.D., the lowest BMDL05 of 21.07 mg methanol/L in blood 

15 estimated from adequate and plausible models was chosen for use in the RfC derivation.
C- 20 DRAFT – DO NOT CITE OR QUOTE

Table C-6. Comparison of BMD05 modeling results for decreased brain weight in male 

rats at 8 weeks of age using modeled Cmax of methanol as a common dose metric

Model BMD05 (Cmax, 

mg/L)a

BMDL05 (Cmax, 

mg/L)a p-value AICc Scaled residuald

Linearb 328.84 226.08 0.7881 -173.12 0.02

2nd degree polynomial 328.84 226.08 0.7881 -173.12 0.02

3rd degree polynomial 328.84 226.08 0.7881 -173.12 0.02

Power 328.84 226.08 0.9446 -173.12 0.02

Hillb 92.30 Not reported 0.9602 -171.59 0.10

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 4b 76.36 21.07 Not reported -171.59 0.10

a

The BMDL is the 95% lower confidence limit on the Cmax estimated to decrease brain 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 BMDL05 of 21.07 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 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 value should cause one to question model fit in this region.

Source: NEDO (1987).

1 ==================================================================== 

2 Exponential Model. (Version: 1.61; Date: 7/24/2009) 

3 Input Data File: C:\Usepa\BMDS21\Data\Methanol\NEDO\Gest-only\expm-8wk4 brwSetting.(d) 

5 Gnuplot Plotting File: 

6 Tue Aug 25 14:15:15 2009

7 ==================================================================== 

8

9 BMDS Model Run 

10 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

11

12 The form of the response function by Model: 

13 Model 2: Y[dose] = a * exp{sign * b * dose}

14 Model 3: Y[dose] = a * exp{sign * (b * dose)^d}

15 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}]

16 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

17

18 Note: Y[dose] is the median response for exposure = dose;

19 sign = +1 for increasing trend in data;

20 sign = -1 for decreasing trend.

21

22 Model 2 is nested within Models 3 and 4.

23 Model 3 is nested within Model 5.

24 Model 4 is nested within Model 5.

25

26

27 Dependent variable = Mean

28 Independent variable = Dose

29 Data are assumed to be distributed: normally

30 Variance Model: exp(lnalpha +rho *ln(Y[dose]))

31 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)
C- 21 DRAFT – DO NOT CITE OR QUOTE

1

2 Total number of dose groups = 4

3 Total number of records with missing values = 0

4 Maximum number of iterations = 250

5 Relative Function Convergence has been set to: 1e-008

6 Parameter Convergence has been set to: 1e-008

7

8 MLE solution provided: Exact

9

10

11 Initial Parameter Values

12

13 Variable Model 2 Model 3 Model 4 Model 5

14 -------- ------- ------- ------- -------

15 lnalpha NC NC 7.32457 NC

16 rho NC NC -18.5236 NC

17 a NC NC 2.1105 NC

18 b NC NC 0.00239093 NC

19 c NC NC 0.816778 NC

20 d NC NC -- NC

21

22

23

24 Parameter Estimates by Model

25

26 Variable Model 2 Model 3 Model 4 Model 5

27 -------- ------- ------- ------- -------

28 lnalpha NC NC 7.03418 NC

29 rho NC NC -18.1386 NC

30 a NC NC 2.00677 NC

31 b NC NC 0.00941775 NC

32 c NC NC 0.902498 NC

33 d NC NC -- NC

34

35 NC = No Convergence

36

37

38 Table of Stats From Input Data

39

40 Dose N Obs Mean Obs Std Dev

41 ----- --- ---------- -------------

42 0 11 2 0.047

43 1.2 11 2.01 0.075

44 10.6 12 1.99 0.072

45 630.5 10 1.81 0.161

46

47

48 Estimated Values of Interest

49

50 Model Dose Est Mean Est Std Scaled Residual

51 ------- ------ ---------- --------- ----------------

52 4 0 2.007 0.06082 -0.3692

53 1.2 2.005 0.06142 0.2932

54 10.6 1.988 0.06617 0.09527

55 630.5 1.812 0.1538 -0.03335

56

57

58

59 Other models for which likelihoods are calculated:

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

67 Model A3: Yij = Mu(i) + e(ij)
C- 22 DRAFT – DO NOT CITE OR QUOTE

1 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

2

3 Model R: Yij = Mu + e(i)

4 Var{e(ij)} = Sigma^2

5

6

7 Likelihoods of Interest

8

9 Model Log(likelihood) DF AIC

10 ------- ----------------- ---- ------------

11 A1 83.20596 5 -156.4119

12 A2 92.06049 8 -168.121

13 A3 90.61606 6 -169.2321

14 R 70.76186 2 -137.5237

15 4 90.79579 5 -171.5916

16

17

18 Additive constant for all log-likelihoods = -40.43. This constant added to the

19 above values gives the log-likelihood including the term that does not

20 depend on the model parameters.

21

22

23 Explanation of Tests

24

25 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R)

26 Test 2: Are Variances Homogeneous? (A2 vs. A1)

27 Test 3: Are variances adequately modeled? (A2 vs. A3)

28

29 Test 6a: Does Model 4 fit the data? (A3 vs 4)

30

31

32 Tests of Interest

33

34 Test -2*log(Likelihood Ratio) D. F. p-value

35 -------- ------------------------ ------ --------------

36 Test 1 42.6 6 < 0.0001

37 Test 2 17.71 3 0.000505

38 Test 3 2.889 2 0.2359

39 Test 6a -0.3595 1 N/A

40

41

42 The p-value for Test 1 is less than .05. There appears to be a

43 difference between response and/or variances among the dose

44 levels, it seems appropriate to model the data.

45

46 The p-value for Test 2 is less than .1. A non-homogeneous

47 variance model appears to be appropriate.

48

49 The p-value for Test 3 is greater than .1. The modeled

50 variance appears to be appropriate here.

51

52 The p-value for Test 6a is less than .1. Model 4 may not adequately

53 describe the data; you may want to consider another model.

54

55

56 Benchmark Dose Computations:

57

58 Specified Effect = 0.050000

59

60 Risk Type = Relative deviation

61

62 Confidence Level = 0.950000

63

64

65 BMD and BMDL by Model

66

67 Model BMD BMDL
C- 23 DRAFT – DO NOT CITE OR QUOTE

1 ------- ------------ ----------

2 2 0 0 Not computed

3 3 0 0 Not computed

4 4 76.3561 21.0664

5 5 0 0 Not computed

 1.7

 1.75

 1.8

 1.85

 1.9

 1.95

 2

 2.05

 0 100 200 300 400 500 600

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

14:15 08/25 2009

BMDL BMD

 

Exponential

Figure C-4. Exponential4 model, BMR of 0.05 relative risk - Decreased Brain weight 

in male rats at 8 weeks age versus Cmax, Gestation only inhalational study.

Source: NEDO (1987).

6 Once the BMDL05 was obtained in units of mg/L, it was used to derive a chronic 

7 inhalation reference value. The first step is to calculate the HEC using the PBPK model 

8 described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B) that 

9 describes the relationship between predicted methanol AUC and the human equivalent inhalation 

10 exposure concentration (HEC) in ppm. This equation can also be used to estimate model 

11 predictions for HECs from Cmax values because Cmax values, and AUC values were estimated at 

12 steady-state for constant 24-hour exposures (i.e., AUC = 24 x Cmax).

13 BMDLHEC (ppm) = 0.0224*BMDL05*24+(1334*BMDL05*24)/(794+ BMDL05*24)

14 BMDLHEC (ppm) = 0.0224*21.07*24 + ((1334*21.07*24)/(794+ 21.07*24)) = 530 ppm
C- 24 DRAFT – DO NOT CITE OR QUOTE

Next, because RfCs are typically expressed in units of mg/m3 1 , the HEC value in ppm was 

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m3 2 :

HEC (mg/m3) = 1.31 × 530 ppm = 695 mg/m3 3

4 Finally, this HEC value was divided by a composite 100-fold 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:

RfC (mg/m3) = 695 mg/m3 ÷ 100 = 7.0 mg/m3 7

C.3. RfC DERIVATIONS USING ROGERS ET AL. (1993) 

8 For the purposes of deriving an RfC for methanol from developmental endpoints using 

9 the BMD method and mouse data, cervical rib incidence data were evaluated from Rogers et al. 

10 (1993). In this paper, Rogers et al. (1993) also utilized a BMD methodology, examining the 

11 dosimetric threshold for cervical ribs and other developmental impacts by applying a log-logistic 

12 maximum likelihood model to the dose-response data. Using air exposure concentrations (ppm) 

13 as their dose metric, a value for the lower 95% confidence limit on the benchmark dose for 5% 

additional risk in mice was 305 ppm (400 mg/m3 14 ), using the log-logistic model. Although the 

15 teratology portion of the NEDO study (1987) also reported increases in cervical rib incidence in 

16 Sprague-Dawley rats, the Rogers et al. (1993) study was chosen for dose-response modeling 

17 because effects were seen at lower doses, it was peer-reviewed and published in the open 

18 literature, and data on individual animals were available for a more statistically robust analysis 

19 utilizing nested models available in BMDS.

20 The first step in the current BMD analysis is to convert the inhalation doses, given as 

21 ppm values from the studies, to an internal dose surrogate or dose metric using the EPA’s PBPK 

22 model (see Section 3.4). For cervical rib malformations, Cmax of methanol in blood (mg/L) is 

23 chosen as the appropriate internal dose metric metric (see Appendix D for further explanation).

24 Predicted Cmax values for methanol in the blood of mice are summarized in Table C-7.

Table C-7. EPA’s PBPK model estimates of methanol blood levels (Cmax) in mice 

following inhalation exposures

Exposure concentration (ppm) Methanol in blood Cmax (mg/L)a in mice

1 0.0216

10 0.218

50 1.14

100 2.46

250 7.83
C- 25 DRAFT – DO NOT CITE OR QUOTE

Exposure concentration (ppm) Methanol in blood Cmax (mg/L)a in mice

500 26.4

1,000 134

a

Rounded to three significant figures.

1 These Cmax values are then used as the dose metric for the BMD analysis of cervical rib 

2 incidence.

3 A 10% BMR level is the value typically calculated for comparisons across chemicals and 

4 endpoints for dichotomous responses because this level is near the low end of the observable 

5 range for many types of toxicity studies. However, reproductive and developmental studies 

6 having a nested design often have a greater sensitivity, and a 5% BMR is typically appropriate 

7 for determination of a POD (U.S. EPA, 2000b; Allen et al., 1994). Rogers et al. (1993) utilized a 

8 5% added risk for the BMR in the original study. This assessment utilizes both a 10% and 5% 

extra risk level as a BMR for the determination of a POD.

90 9 The nested suite of models available 

10 in BMDS was used to model the cervical rib data. In general, data from developmental toxicity 

11 studies are best modeled using nested models, as these models account for any intralitter 

12 correlation (i.e., the tendency of littermates to respond similarly to one another relative to other 

13 litters in a dose group). All models were fit using restrictions and option settings suggested in 

14 the EPA’s BMD technical guidance document (U.S. EPA, 2000b).

C.3.1. BMD Approach with a BMR of 0.10 Extra Risk (Rogers et al., 1993)

15 A summary of the results most relevant to the development of a POD using the BMD 

16 approach (BMD, BMDL, and model fit statistics) for increased incidence of cervical rib in mice 

17 exposed to methanol during gestation from days 6 to 15, with a BMR of 0.10 extra risk, is 

provided in Table C-8. Model fit was determined by statistics (AIC and χ

2 18 residuals of individual 

19 dose groups) and visual inspection, as recommended by U.S. EPA (2000b). The best model fit to 

20 these data (from visual inspection and comparison of AIC values) was obtained using the Nested 

21 Logistic (NLogistic) model. The BMDL10 was determined to be 94.3 mg/L using the 95% lower 

22 confidence limit of the dose-response curve expressed in terms of the Cmax for methanol in blood.

Table C-8. Comparison of BMD modeling results for cervical rib incidence in mice 

using modeled Cmax of methanol as a common dose metric

Model BMD10

(Cmax, mg/L)a

BMDL10 (Cmax, 

mg/L)a p-value AICc Scaled residuald

NLogisticb 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 design 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. 
C- 26 DRAFT – DO NOT CITE OR QUOTE

Model BMD10

(Cmax, mg/L)a

BMDL10 (Cmax, 

mg/L)a p-value AICc Scaled residuald

Rai and Van Ryzin 221.509 110.754 0.163 1051.65 0.661

a

Daily Cmax 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 Cmax for a 10% extra risk (dichotomous endpoints) 

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 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).

1 ==================================================================== 

2 NLogistic Model. 

3 (Version: 2.13; Date: 02/20/2007) 

4 Input Data File: U:\Methanol\BMDS\CervicalRib\Cmax\NLog_Cmax_10_default.(d) 

5 Wed Nov 07 15:45:40 2007

6 ==================================================================== 

7

8 BMD Method for RfC: Incidence of Cervical Rib in Mice versus Cmax Methanol, GD 6-15 

9 inhalational study (Rogers,et al., 1993)

10 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

11 The probability function is: 

12

13 Prob. = alpha + theta1*Rij + [1 - alpha - theta1*Rij]/ 

14 

15 [1+exp(-beta-theta2*Rij-rho*log(Dose))],

16

17 where Rij is the litter specific covariate.

18

19 Restrict Power rho >= 1. 

20

21 Total number of observations = 166

22 Total number of records with missing values = 0

23 Total number of parameters in model = 9

24 Total number of specified parameters = 0

25

26 Maximum number of iterations = 250

27 Relative Function Convergence has been set to: 1e-008

28 Parameter Convergence has been set to: 1e-008

29

30 Default Initial Parameter Values 

31 alpha = 0.297863

32 beta = -7.94313

33 theta1 = 0

34 theta2 = 0

35 rho = 1.09876

36 phi1 = 0.213134

37 phi2 = 0.309556

38 phi3 = 0.220142

39 phi4 = 0.370587

40

41 Parameter Estimates

42

43 Variable Estimate Std. Err. 

44 alpha 0.102434 *

45 beta -4.80338 *

46 theta1 0.0325457 *

47 theta2 -0.436115 *
C- 27 DRAFT – DO NOT CITE OR QUOTE

1 rho 1 *

2 phi1 0.200733 *

3 phi2 0.307656 *

4 phi3 0.212754 *

5 phi4 0.368426 *

6

7 * - Indicates that this value is not calculated.

8

9 Log-likelihood: -515.422 AIC: 1046.84

10

11 Litter Data

12

13 Lit.-Spec. Litter Scaled

14 Dose Cov. Est._Prob. Size Expected Observed Residual

15 --------------------------------------------------------------------------

16 0.0000 1.0000 0.135 1 0.135 0 -0.3950

17 0.0000 1.0000 0.135 1 0.135 0 -0.3950

18 0.0000 2.0000 0.168 2 0.335 0 -0.5790

19 0.0000 2.0000 0.168 2 0.335 1 1.1490

20 0.0000 2.0000 0.168 2 0.335 0 -0.5790

21 0.0000 2.0000 0.168 2 0.335 2 2.8770

22 0.0000 2.0000 0.168 2 0.335 0 -0.5790

23 0.0000 3.0000 0.200 3 0.600 0 -0.7317

24 0.0000 3.0000 0.200 3 0.600 0 -0.7317

25 0.0000 3.0000 0.200 3 0.600 1 0.4874

26 0.0000 3.0000 0.200 3 0.600 1 0.4874

27 0.0000 4.0000 0.233 4 0.930 0 -0.8699

28 0.0000 4.0000 0.233 4 0.930 1 0.0650

29 0.0000 4.0000 0.233 4 0.930 0 -0.8699

30 0.0000 4.0000 0.233 4 0.930 1 0.0650

31 0.0000 5.0000 0.265 5 1.326 0 -1.0004

32 0.0000 5.0000 0.265 5 1.326 1 -0.2458

33 0.0000 5.0000 0.265 5 1.326 3 1.2632

34 0.0000 5.0000 0.265 5 1.326 1 -0.2458

35 0.0000 5.0000 0.265 5 1.326 0 -1.0004

36 0.0000 5.0000 0.265 5 1.326 1 -0.2458

37 0.0000 5.0000 0.265 5 1.326 1 -0.2458

38 0.0000 5.0000 0.265 5 1.326 0 -1.0004

39 0.0000 5.0000 0.265 5 1.326 0 -1.0004

40 0.0000 5.0000 0.265 5 1.326 1 -0.2458

41 0.0000 6.0000 0.298 6 1.786 3 0.7656

42 0.0000 6.0000 0.298 6 1.786 6 2.6578

43 0.0000 6.0000 0.298 6 1.786 1 -0.4959

44 0.0000 6.0000 0.298 6 1.786 0 -1.1267

45 0.0000 6.0000 0.298 6 1.786 0 -1.1267

46 0.0000 6.0000 0.298 6 1.786 1 -0.4959

47 0.0000 6.0000 0.298 6 1.786 2 0.1348

48 0.0000 6.0000 0.298 6 1.786 0 -1.1267

49 0.0000 6.0000 0.298 6 1.786 2 0.1348

50 0.0000 6.0000 0.298 6 1.786 3 0.7656

51 0.0000 6.0000 0.298 6 1.786 5 2.0271

52 0.0000 6.0000 0.298 6 1.786 0 -1.1267

53 0.0000 6.0000 0.298 6 1.786 3 0.7656

54 0.0000 6.0000 0.298 6 1.786 3 0.7656

55 0.0000 6.0000 0.298 6 1.786 3 0.7656

56 0.0000 6.0000 0.298 6 1.786 5 2.0271

57 0.0000 7.0000 0.330 7 2.312 0 -1.2513

58 0.0000 7.0000 0.330 7 2.312 1 -0.7100

59 0.0000 7.0000 0.330 7 2.312 2 -0.1688

60 0.0000 7.0000 0.330 7 2.312 3 0.3725

61 0.0000 7.0000 0.330 7 2.312 2 -0.1688

62 0.0000 7.0000 0.330 7 2.312 3 0.3725

63 0.0000 7.0000 0.330 7 2.312 5 1.4551

64 0.0000 7.0000 0.330 7 2.312 0 -1.2513

65 0.0000 7.0000 0.330 7 2.312 2 -0.1688

66 0.0000 7.0000 0.330 7 2.312 5 1.4551

67 0.0000 7.0000 0.330 7 2.312 1 -0.7100
C- 28 DRAFT – DO NOT CITE OR QUOTE

1 0.0000 7.0000 0.330 7 2.312 2 -0.1688

2 0.0000 7.0000 0.330 7 2.312 1 -0.7100

3 0.0000 8.0000 0.363 8 2.902 1 -0.9020

4 0.0000 8.0000 0.363 8 2.902 4 0.5204

5 0.0000 8.0000 0.363 8 2.902 3 0.0463

6 0.0000 8.0000 0.363 8 2.902 8 2.4170

7 0.0000 8.0000 0.363 8 2.902 2 -0.4279

8

9 134.0000 1.0000 0.494 1 0.494 0 -0.9887

10 134.0000 1.0000 0.494 1 0.494 0 -0.9887

11 134.0000 2.0000 0.430 2 0.859 0 -1.0732

12 134.0000 2.0000 0.430 2 0.859 2 1.4251

13 134.0000 3.0000 0.383 3 1.150 3 1.7287

14 134.0000 3.0000 0.383 3 1.150 1 -0.1400

15 134.0000 3.0000 0.383 3 1.150 2 0.7944

16 134.0000 3.0000 0.383 3 1.150 1 -0.1400

17 134.0000 4.0000 0.356 4 1.425 3 1.1858

18 134.0000 4.0000 0.356 4 1.425 0 -1.0729

19 134.0000 5.0000 0.346 5 1.732 0 -1.0898

20 134.0000 5.0000 0.346 5 1.732 4 1.4275

21 134.0000 5.0000 0.346 5 1.732 0 -1.0898

22 134.0000 5.0000 0.346 5 1.732 1 -0.4604

23 134.0000 5.0000 0.346 5 1.732 0 -1.0898

24 134.0000 6.0000 0.350 6 2.099 3 0.4839

25 134.0000 6.0000 0.350 6 2.099 2 -0.0534

26 134.0000 7.0000 0.363 7 2.543 3 0.2128

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 2 -0.2530

31 134.0000 7.0000 0.363 7 2.543 0 -1.1847

32 134.0000 8.0000 0.383 8 3.068 2 -0.4373

33 134.0000 8.0000 0.383 8 3.068 0 -1.2562

34 134.0000 8.0000 0.383 8 3.068 8 2.0195

35

36 526.0000 2.0000 0.703 2 1.406 2 0.8346

37 526.0000 3.0000 0.631 3 1.892 3 1.1101

38 526.0000 4.0000 0.562 4 2.250 2 -0.1967

39 526.0000 4.0000 0.562 4 2.250 1 -0.9842

40 526.0000 5.0000 0.506 5 2.530 3 0.3091

41 526.0000 5.0000 0.506 5 2.530 5 1.6241

42 526.0000 5.0000 0.506 5 2.530 3 0.3091

43 526.0000 5.0000 0.506 5 2.530 1 -1.0058

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 3 0.1162

47 526.0000 6.0000 0.466 6 2.796 5 1.2556

48 526.0000 6.0000 0.466 6 2.796 6 1.8253

49 526.0000 6.0000 0.466 6 2.796 5 1.2556

50 526.0000 6.0000 0.466 6 2.796 2 -0.4534

51 526.0000 6.0000 0.466 6 2.796 0 -1.5928

52 526.0000 6.0000 0.466 6 2.796 2 -0.4534

53 526.0000 6.0000 0.466 6 2.796 0 -1.5928

54 526.0000 6.0000 0.466 6 2.796 5 1.2556

55 526.0000 6.0000 0.466 6 2.796 4 0.6859

56 526.0000 6.0000 0.466 6 2.796 3 0.1162

57 526.0000 6.0000 0.466 6 2.796 2 -0.4534

58 526.0000 6.0000 0.466 6 2.796 4 0.6859

59 526.0000 6.0000 0.466 6 2.796 2 -0.4534

60 526.0000 7.0000 0.444 7 3.105 0 -1.5658

61 526.0000 7.0000 0.444 7 3.105 4 0.4511

62 526.0000 7.0000 0.444 7 3.105 5 0.9554

63 526.0000 7.0000 0.444 7 3.105 1 -1.0615

64 526.0000 7.0000 0.444 7 3.105 4 0.4511

65 526.0000 7.0000 0.444 7 3.105 1 -1.0615

66 526.0000 7.0000 0.444 7 3.105 5 0.9554

67 526.0000 7.0000 0.444 7 3.105 3 -0.0531
C- 29 DRAFT – DO NOT CITE OR QUOTE

1 526.0000 7.0000 0.444 7 3.105 4 0.4511

2 526.0000 7.0000 0.444 7 3.105 1 -1.0615

3 526.0000 7.0000 0.444 7 3.105 3 -0.0531

4 526.0000 7.0000 0.444 7 3.105 3 -0.0531

5 526.0000 8.0000 0.437 8 3.496 0 -1.5793

6 526.0000 8.0000 0.437 8 3.496 7 1.5832

7 526.0000 8.0000 0.437 8 3.496 5 0.6796

8 526.0000 9.0000 0.443 9 3.985 0 -1.6270

9 526.0000 9.0000 0.443 9 3.985 6 0.8225

10

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 1.0000 0.926 1 0.926 1 0.2834

14 2005.0000 2.0000 0.894 2 1.789 1 -1.5502

15 2005.0000 2.0000 0.894 2 1.789 2 0.4157

16 2005.0000 3.0000 0.853 3 2.559 3 0.5454

17 2005.0000 3.0000 0.853 3 2.559 1 -1.9294

18 2005.0000 3.0000 0.853 3 2.559 1 -1.9294

19 2005.0000 3.0000 0.853 3 2.559 3 0.5454

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 4 0.6851

23 2005.0000 4.0000 0.802 4 3.208 2 -1.0440

24 2005.0000 4.0000 0.802 4 3.208 3 -0.1795

25 2005.0000 4.0000 0.802 4 3.208 4 0.6851

26 2005.0000 4.0000 0.802 4 3.208 4 0.6851

27 2005.0000 5.0000 0.743 5 3.714 1 -1.7660

28 2005.0000 5.0000 0.743 5 3.714 3 -0.4648

29 2005.0000 5.0000 0.743 5 3.714 5 0.8364

30 2005.0000 5.0000 0.743 5 3.714 5 0.8364

31 2005.0000 5.0000 0.743 5 3.714 4 0.1858

32 2005.0000 5.0000 0.743 5 3.714 4 0.1858

33 2005.0000 6.0000 0.681 6 4.086 6 0.9945

34 2005.0000 6.0000 0.681 6 4.086 2 -1.0836

35 2005.0000 6.0000 0.681 6 4.086 4 -0.0445

36 2005.0000 6.0000 0.681 6 4.086 5 0.4750

37 2005.0000 6.0000 0.681 6 4.086 6 0.9945

38 2005.0000 6.0000 0.681 6 4.086 5 0.4750

39 2005.0000 6.0000 0.681 6 4.086 4 -0.0445

40 2005.0000 6.0000 0.681 6 4.086 5 0.4750

41 2005.0000 6.0000 0.681 6 4.086 3 -0.5641

42 2005.0000 6.0000 0.681 6 4.086 6 0.9945

43 2005.0000 6.0000 0.681 6 4.086 0 -2.1227

44 2005.0000 6.0000 0.681 6 4.086 0 -2.1227

45 2005.0000 7.0000 0.623 7 4.361 7 1.1486

46 2005.0000 7.0000 0.623 7 4.361 5 0.2781

47 2005.0000 7.0000 0.623 7 4.361 5 0.2781

48 2005.0000 7.0000 0.623 7 4.361 7 1.1486

49 2005.0000 7.0000 0.623 7 4.361 6 0.7133

50 2005.0000 8.0000 0.576 8 4.606 0 -1.7419

51

52 Combine litters with adjacent levels of the litter-specific covariate

53 within dose groups until the expected count exceeds 3.0, to help improve

54 the fit of the X^2 statistic to chi-square.

55 

56 Grouped Data

57 Mean Scaled

58 Dose Lit.-Spec. Cov. Expected Observed Residual

59 -------------------------------------------------------------

60 0.0000 1.0000 0.270 0 -0.5586

61 0.0000 2.0000 1.675 3 1.0237

62 0.0000 3.0000 2.401 2 -0.2443

63 0.0000 4.0000 3.722 2 -0.8049

64 0.0000 5.0000 3.977 4 0.0098

65 0.0000 5.0000 3.977 2 -0.8614

66 0.0000 5.0000 3.977 1 -1.2970

67 0.0000 5.0000 1.326 1 -0.2458
C- 30 DRAFT – DO NOT CITE OR QUOTE

1 0.0000 6.0000 3.573 9 2.4207

2 0.0000 6.0000 3.573 1 -1.1474

3 0.0000 6.0000 3.573 1 -1.1474

4 0.0000 6.0000 3.573 2 -0.7013

5 0.0000 6.0000 3.573 5 0.6367

6 0.0000 6.0000 3.573 5 0.6367

7 0.0000 6.0000 3.573 6 1.0827

8 0.0000 6.0000 3.573 8 1.9747

9 0.0000 7.0000 4.624 1 -1.3869

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 5 0.1441

13 0.0000 7.0000 4.624 7 0.9096

14 0.0000 7.0000 4.624 3 -0.6214

15 0.0000 7.0000 2.312 1 -0.7100

16 0.0000 8.0000 5.805 5 -0.2698

17 0.0000 8.0000 5.805 11 1.7418

18 0.0000 8.0000 2.902 2 -0.4279

19

20 134.0000 1.0000 0.989 0 -1.3982

21 134.0000 2.0000 1.718 2 0.2488

22 134.0000 3.0000 3.449 6 1.3759

23 134.0000 3.0000 1.150 1 -0.1400

24 134.0000 4.0000 2.850 3 0.0799

25 134.0000 5.0000 3.463 4 0.2388

26 134.0000 5.0000 3.463 1 -1.0962

27 134.0000 5.0000 1.732 0 -1.0898

28 134.0000 6.0000 4.199 5 0.3044

29 134.0000 7.0000 5.086 5 -0.0284

30 134.0000 7.0000 5.086 4 -0.3578

31 134.0000 7.0000 5.086 2 -1.0166

32 134.0000 8.0000 3.068 2 -0.4373

33 134.0000 8.0000 3.068 0 -1.2562

34 134.0000 8.0000 3.068 8 2.0195

35

36 526.0000 2.0000 1.406 2 0.8346

37 526.0000 3.0000 1.892 3 1.1101

38 526.0000 4.0000 4.500 3 -0.8351

39 526.0000 5.0000 5.060 8 1.3670

40 526.0000 5.0000 5.060 4 -0.4926

41 526.0000 6.0000 5.592 6 0.1644

42 526.0000 6.0000 5.592 8 0.9700

43 526.0000 6.0000 5.592 11 2.1785

44 526.0000 6.0000 5.592 2 -1.4469

45 526.0000 6.0000 5.592 2 -1.4469

46 526.0000 6.0000 5.592 9 1.3729

47 526.0000 6.0000 5.592 5 -0.2384

48 526.0000 6.0000 5.592 6 0.1644

49 526.0000 7.0000 3.105 0 -1.5658

50 526.0000 7.0000 3.105 4 0.4511

51 526.0000 7.0000 3.105 5 0.9554

52 526.0000 7.0000 3.105 1 -1.0615

53 526.0000 7.0000 3.105 4 0.4511

54 526.0000 7.0000 3.105 1 -1.0615

55 526.0000 7.0000 3.105 5 0.9554

56 526.0000 7.0000 3.105 3 -0.0531

57 526.0000 7.0000 3.105 4 0.4511

58 526.0000 7.0000 3.105 1 -1.0615

59 526.0000 7.0000 3.105 3 -0.0531

60 526.0000 7.0000 3.105 3 -0.0531

61 526.0000 8.0000 3.496 0 -1.5793

62 526.0000 8.0000 3.496 7 1.5832

63 526.0000 8.0000 3.496 5 0.6796

64 526.0000 9.0000 3.985 0 -1.6270

65 526.0000 9.0000 3.985 6 0.8225

66

67 2005.0000 1.0000 2.777 3 0.4909
C- 31 DRAFT – DO NOT CITE OR QUOTE

1 2005.0000 2.0000 3.577 3 -0.8022

2 2005.0000 3.0000 5.118 4 -0.9786

3 2005.0000 3.0000 5.118 4 -0.9786

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 4 0.6851

7 2005.0000 4.0000 3.208 2 -1.0440

8 2005.0000 4.0000 3.208 3 -0.1795

9 2005.0000 4.0000 3.208 4 0.6851

10 2005.0000 4.0000 3.208 4 0.6851

11 2005.0000 5.0000 3.714 1 -1.7660

12 2005.0000 5.0000 3.714 3 -0.4648

13 2005.0000 5.0000 3.714 5 0.8364

14 2005.0000 5.0000 3.714 5 0.8364

15 2005.0000 5.0000 3.714 4 0.1858

16 2005.0000 5.0000 3.714 4 0.1858

17 2005.0000 6.0000 4.086 6 0.9945

18 2005.0000 6.0000 4.086 2 -1.0836

19 2005.0000 6.0000 4.086 4 -0.0445

20 2005.0000 6.0000 4.086 5 0.4750

21 2005.0000 6.0000 4.086 6 0.9945

22 2005.0000 6.0000 4.086 5 0.4750

23 2005.0000 6.0000 4.086 4 -0.0445

24 2005.0000 6.0000 4.086 5 0.4750

25 2005.0000 6.0000 4.086 3 -0.5641

26 2005.0000 6.0000 4.086 6 0.9945

27 2005.0000 6.0000 4.086 0 -2.1227

28 2005.0000 6.0000 4.086 0 -2.1227

29 2005.0000 7.0000 4.361 7 1.1486

30 2005.0000 7.0000 4.361 5 0.2781

31 2005.0000 7.0000 4.361 5 0.2781

32 2005.0000 7.0000 4.361 7 1.1486

33 2005.0000 7.0000 4.361 6 0.7133

34 2005.0000 8.0000 4.606 0 -1.7419

35

36 Chi-square = 105.13 DF = 98 P-value = 0.2930

37

38 To calculate the BMD and BMDL, the litter specific covariate is fixed

39 at the mean litter specific covariate of all the data: 5.379518

40 ==================================================================== 
C- 32 DRAFT – DO NOT CITE OR QUOTE

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 2000

Fraction 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 Cmax Methanol, GD 6-15 inhalational study.

Source: Rogers et al. (1993).

1 Once the BMDL10 was obtained in units of mg/L, it was used to derive a chronic 

2 inhalation reference value. The first step is to calculate the HEC using the PBPK model 

3 described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B) that 

4 describes the relationship between predicted methanol AUC and the human equivalent inhalation 

5 exposure concentration (HEC) in ppm. This equation can also be used to estimate model 

6 predictions for HECs from Cmax values because Cmax values and AUC values were estimated at 

7 steady-state for constant 24-hour exposures (i.e., AUC = 24 x Cmax).

8 BMDLHEC (ppm) = 0.0224*BMDL10*24+(1334*BMDL10*24)/(794+ BMDL10*24)

9 BMDLHEC (ppm) = 0.0224*94.3*24 + ((1334*94.3*24)/(794+ 94.3*24)) = 1038 ppm

Next, because RfCs are typically expressed in units of mg/m3 10 , the HEC value in ppm was 

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m3 11 :

HEC (mg/m3

) = 1.31 × 1038 ppm = 1360 mg/m3 12
C- 33 DRAFT – DO NOT CITE OR QUOTE

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/m3

) = 1360 mg/m3 ÷ 100 = 13.6 mg/m3 5

C.3.2. BMD Approach with a BMR of 0.05 Extra Risk (Rogers et al., 1993)

6 A summary of the results most relevant to the development of a POD using the BMD 

7 approach (BMD, BMDL, and model fit statistics) for increased incidence of cervical rib in mice 

8 exposed to methanol during gestation from days 6 to 15, with a BMR of 0.05 extra risk, is 

provided in Table C-9. Model fit was determined by statistics (AIC and χ

2 9 residuals of individual 

10 dose groups) and visual inspection, as recommended by U.S. EPA (2000b). The best model fit to 

11 these data (from visual inspection and comparison of AIC values) was obtained using the 

12 NLogistic model. The BMDL05 was determined to be 44.7 mg/L using the 95% lower 

13 confidence limit of the dose-response curve expressed in terms of the Cmax for methanol in blood.

Table C-9. Comparison of BMD modeling results for cervical rib incidence in mice 

using modeled Cmax of methanol as a common dose metric

Model BMD05

(Cmax, mg/L)a

BMDL05 

(Cmax, mg/L)a p-value AICc Scaled residuald

NLogisticb 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 Cmax 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 Cmax for a 5% extra risk 

(dichotomous endpoints) 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 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 ==================================================================== 

15 NLogistic Model. 

16 (Version: 2.13; Date: 02/20/2007) 

17 Input Data File: U:\Methanol\BMDS\CervicalRib\Cmax\NLog_Cmax_10_default.(d) 

18 Wed Nov 07 15:45:40 2007

19 ==================================================================== 
C- 34 DRAFT – DO NOT CITE OR QUOTE

1 BMD Method for RfC: Incidence of Cervical Rib in Mice versus Cmax Methanol, GD 6-15 

2 inhalational study (Rogers, et al., 1993)

3 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

4 The probability function is: 

5

6 Prob. = alpha + theta1*Rij + [1 - alpha - theta1*Rij]/ 

7

8 [1+exp(-beta-theta2*Rij-rho*log(Dose))],

9

10 where Rij is the litter specific covariate.

11

12 Restrict Power rho >= 1. 

13

14 Total number of observations = 166

15 Total number of records with missing values = 0

16 Total number of parameters in model = 9

17 Total number of specified parameters = 0

18

19 Maximum number of iterations = 250

20 Relative Function Convergence has been set to: 1e-008

21 Parameter Convergence has been set to: 1e-008

22

23 Default Initial Parameter Values 

24 alpha = 0.297863

25 beta = -7.94313

26 theta1 = 0

27 theta2 = 0

28 rho = 1.09876

29 phi1 = 0.213134

30 phi2 = 0.309556

31 phi3 = 0.220142

32 phi4 = 0.370587

33

34 Parameter Estimates

35

36 Variable Estimate Std. Err. 

37 alpha 0.102434 *

38 beta -4.80338 *

39 theta1 0.0325457 *

40 theta2 -0.436115 *

41 rho 1 *

42 phi1 0.200733 *

43 phi2 0.307656 *

44 phi3 0.212754 *

45 phi4 0.368426 *

46

47 * - Indicates that this value is not calculated.

48

49 Log-likelihood: -515.422 AIC: 1046.84

50 Litter Data

51

52 Lit.-Spec. Litter Scaled

53 Dose Cov. Est._Prob. Size Expected Observed Residual

54 --------------------------------------------------------------------------

55 0.0000 1.0000 0.135 1 0.135 0 -0.3950

56 0.0000 1.0000 0.135 1 0.135 0 -0.3950

57 0.0000 2.0000 0.168 2 0.335 0 -0.5790

58 0.0000 2.0000 0.168 2 0.335 1 1.1490

59 0.0000 2.0000 0.168 2 0.335 0 -0.5790

60 0.0000 2.0000 0.168 2 0.335 2 2.8770

61 0.0000 2.0000 0.168 2 0.335 0 -0.5790

62 0.0000 3.0000 0.200 3 0.600 0 -0.7317

63 0.0000 3.0000 0.200 3 0.600 0 -0.7317

64 0.0000 3.0000 0.200 3 0.600 1 0.4874

65 0.0000 3.0000 0.200 3 0.600 1 0.4874

66 0.0000 4.0000 0.233 4 0.930 0 -0.8699
C- 35 DRAFT – DO NOT CITE OR QUOTE

1 0.0000 4.0000 0.233 4 0.930 1 0.0650

2 0.0000 4.0000 0.233 4 0.930 0 -0.8699

3 0.0000 4.0000 0.233 4 0.930 1 0.0650

4 0.0000 5.0000 0.265 5 1.326 0 -1.0004

5 0.0000 5.0000 0.265 5 1.326 1 -0.2458

6 0.0000 5.0000 0.265 5 1.326 3 1.2632

7 0.0000 5.0000 0.265 5 1.326 1 -0.2458

8 0.0000 5.0000 0.265 5 1.326 0 -1.0004

9 0.0000 5.0000 0.265 5 1.326 1 -0.2458

10 0.0000 5.0000 0.265 5 1.326 1 -0.2458

11 0.0000 5.0000 0.265 5 1.326 0 -1.0004

12 0.0000 5.0000 0.265 5 1.326 0 -1.0004

13 0.0000 5.0000 0.265 5 1.326 1 -0.2458

14 0.0000 6.0000 0.298 6 1.786 3 0.7656

15 0.0000 6.0000 0.298 6 1.786 6 2.6578

16 0.0000 6.0000 0.298 6 1.786 1 -0.4959

17 0.0000 6.0000 0.298 6 1.786 0 -1.1267

18 0.0000 6.0000 0.298 6 1.786 0 -1.1267

19 0.0000 6.0000 0.298 6 1.786 1 -0.4959

20 0.0000 6.0000 0.298 6 1.786 2 0.1348

21 0.0000 6.0000 0.298 6 1.786 0 -1.1267

22 0.0000 6.0000 0.298 6 1.786 2 0.1348

23 0.0000 6.0000 0.298 6 1.786 3 0.7656

24 0.0000 6.0000 0.298 6 1.786 5 2.0271

25 0.0000 6.0000 0.298 6 1.786 0 -1.1267

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 3 0.7656

29 0.0000 6.0000 0.298 6 1.786 5 2.0271

30 0.0000 7.0000 0.330 7 2.312 0 -1.2513

31 0.0000 7.0000 0.330 7 2.312 1 -0.7100

32 0.0000 7.0000 0.330 7 2.312 2 -0.1688

33 0.0000 7.0000 0.330 7 2.312 3 0.3725

34 0.0000 7.0000 0.330 7 2.312 2 -0.1688

35 0.0000 7.0000 0.330 7 2.312 3 0.3725

36 0.0000 7.0000 0.330 7 2.312 5 1.4551

37 0.0000 7.0000 0.330 7 2.312 0 -1.2513

38 0.0000 7.0000 0.330 7 2.312 2 -0.1688

39 0.0000 7.0000 0.330 7 2.312 5 1.4551

40 0.0000 7.0000 0.330 7 2.312 1 -0.7100

41 0.0000 7.0000 0.330 7 2.312 2 -0.1688

42 0.0000 7.0000 0.330 7 2.312 1 -0.7100

43 0.0000 8.0000 0.363 8 2.902 1 -0.9020

44 0.0000 8.0000 0.363 8 2.902 4 0.5204

45 0.0000 8.0000 0.363 8 2.902 3 0.0463

46 0.0000 8.0000 0.363 8 2.902 8 2.4170

47 0.0000 8.0000 0.363 8 2.902 2 -0.4279

48

49 134.0000 1.0000 0.494 1 0.494 0 -0.9887

50 134.0000 1.0000 0.494 1 0.494 0 -0.9887

51 134.0000 2.0000 0.430 2 0.859 0 -1.0732

52 134.0000 2.0000 0.430 2 0.859 2 1.4251

53 134.0000 3.0000 0.383 3 1.150 3 1.7287

54 134.0000 3.0000 0.383 3 1.150 1 -0.1400

55 134.0000 3.0000 0.383 3 1.150 2 0.7944

56 134.0000 3.0000 0.383 3 1.150 1 -0.1400

57 134.0000 4.0000 0.356 4 1.425 3 1.1858

58 134.0000 4.0000 0.356 4 1.425 0 -1.0729

59 134.0000 5.0000 0.346 5 1.732 0 -1.0898

60 134.0000 5.0000 0.346 5 1.732 4 1.4275

61 134.0000 5.0000 0.346 5 1.732 0 -1.0898

62 134.0000 5.0000 0.346 5 1.732 1 -0.4604

63 134.0000 5.0000 0.346 5 1.732 0 -1.0898

64 134.0000 6.0000 0.350 6 2.099 3 0.4839

65 134.0000 6.0000 0.350 6 2.099 2 -0.0534

66 134.0000 7.0000 0.363 7 2.543 3 0.2128

67 134.0000 7.0000 0.363 7 2.543 2 -0.2530
C- 36 DRAFT – DO NOT CITE OR QUOTE

1 134.0000 7.0000 0.363 7 2.543 2 -0.2530

2 134.0000 7.0000 0.363 7 2.543 2 -0.2530

3 134.0000 7.0000 0.363 7 2.543 2 -0.2530

4 134.0000 7.0000 0.363 7 2.543 0 -1.1847

5 134.0000 8.0000 0.383 8 3.068 2 -0.4373

6 134.0000 8.0000 0.383 8 3.068 0 -1.2562

7 134.0000 8.0000 0.383 8 3.068 8 2.0195

8

9 526.0000 2.0000 0.703 2 1.406 2 0.8346

10 526.0000 3.0000 0.631 3 1.892 3 1.1101

11 526.0000 4.0000 0.562 4 2.250 2 -0.1967

12 526.0000 4.0000 0.562 4 2.250 1 -0.9842

13 526.0000 5.0000 0.506 5 2.530 3 0.3091

14 526.0000 5.0000 0.506 5 2.530 5 1.6241

15 526.0000 5.0000 0.506 5 2.530 3 0.3091

16 526.0000 5.0000 0.506 5 2.530 1 -1.0058

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 3 0.1162

20 526.0000 6.0000 0.466 6 2.796 5 1.2556

21 526.0000 6.0000 0.466 6 2.796 6 1.8253

22 526.0000 6.0000 0.466 6 2.796 5 1.2556

23 526.0000 6.0000 0.466 6 2.796 2 -0.4534

24 526.0000 6.0000 0.466 6 2.796 0 -1.5928

25 526.0000 6.0000 0.466 6 2.796 2 -0.4534

26 526.0000 6.0000 0.466 6 2.796 0 -1.5928

27 526.0000 6.0000 0.466 6 2.796 5 1.2556

28 526.0000 6.0000 0.466 6 2.796 4 0.6859

29 526.0000 6.0000 0.466 6 2.796 3 0.1162

30 526.0000 6.0000 0.466 6 2.796 2 -0.4534

31 526.0000 6.0000 0.466 6 2.796 4 0.6859

32 526.0000 6.0000 0.466 6 2.796 2 -0.4534

33 526.0000 7.0000 0.444 7 3.105 0 -1.5658

34 526.0000 7.0000 0.444 7 3.105 4 0.4511

35 526.0000 7.0000 0.444 7 3.105 5 0.9554

36 526.0000 7.0000 0.444 7 3.105 1 -1.0615

37 526.0000 7.0000 0.444 7 3.105 4 0.4511

38 526.0000 7.0000 0.444 7 3.105 1 -1.0615

39 526.0000 7.0000 0.444 7 3.105 5 0.9554

40 526.0000 7.0000 0.444 7 3.105 3 -0.0531

41 526.0000 7.0000 0.444 7 3.105 4 0.4511

42 526.0000 7.0000 0.444 7 3.105 1 -1.0615

43 526.0000 7.0000 0.444 7 3.105 3 -0.0531

44 526.0000 7.0000 0.444 7 3.105 3 -0.0531

45 526.0000 8.0000 0.437 8 3.496 0 -1.5793

46 526.0000 8.0000 0.437 8 3.496 7 1.5832

47 526.0000 8.0000 0.437 8 3.496 5 0.6796

48 526.0000 9.0000 0.443 9 3.985 0 -1.6270

49 526.0000 9.0000 0.443 9 3.985 6 0.8225

50

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 1.0000 0.926 1 0.926 1 0.2834

54 2005.0000 2.0000 0.894 2 1.789 1 -1.5502

55 2005.0000 2.0000 0.894 2 1.789 2 0.4157

56 2005.0000 3.0000 0.853 3 2.559 3 0.5454

57 2005.0000 3.0000 0.853 3 2.559 1 -1.9294

58 2005.0000 3.0000 0.853 3 2.559 1 -1.9294

59 2005.0000 3.0000 0.853 3 2.559 3 0.5454

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 4 0.6851

63 2005.0000 4.0000 0.802 4 3.208 2 -1.0440

64 2005.0000 4.0000 0.802 4 3.208 3 -0.1795

65 2005.0000 4.0000 0.802 4 3.208 4 0.6851

66 2005.0000 4.0000 0.802 4 3.208 4 0.6851

67 2005.0000 5.0000 0.743 5 3.714 1 -1.7660
C- 37 DRAFT – DO NOT CITE OR QUOTE

1 2005.0000 5.0000 0.743 5 3.714 3 -0.4648

2 2005.0000 5.0000 0.743 5 3.714 5 0.8364

3 2005.0000 5.0000 0.743 5 3.714 5 0.8364

4 2005.0000 5.0000 0.743 5 3.714 4 0.1858

5 2005.0000 5.0000 0.743 5 3.714 4 0.1858

6 2005.0000 6.0000 0.681 6 4.086 6 0.9945

7 2005.0000 6.0000 0.681 6 4.086 2 -1.0836

8 2005.0000 6.0000 0.681 6 4.086 4 -0.0445

9 2005.0000 6.0000 0.681 6 4.086 5 0.4750

10 2005.0000 6.0000 0.681 6 4.086 6 0.9945

11 2005.0000 6.0000 0.681 6 4.086 5 0.4750

12 2005.0000 6.0000 0.681 6 4.086 4 -0.0445

13 2005.0000 6.0000 0.681 6 4.086 5 0.4750

14 2005.0000 6.0000 0.681 6 4.086 3 -0.5641

15 2005.0000 6.0000 0.681 6 4.086 6 0.9945

16 2005.0000 6.0000 0.681 6 4.086 0 -2.1227

17 2005.0000 6.0000 0.681 6 4.086 0 -2.1227

18 2005.0000 7.0000 0.623 7 4.361 7 1.1486

19 2005.0000 7.0000 0.623 7 4.361 5 0.2781

20 2005.0000 7.0000 0.623 7 4.361 5 0.2781

21 2005.0000 7.0000 0.623 7 4.361 7 1.1486

22 2005.0000 7.0000 0.623 7 4.361 6 0.7133

23 2005.0000 8.0000 0.576 8 4.606 0 -1.7419

24

25 Combine litters with adjacent levels of the litter-specific covariate

26 within dose groups until the expected count exceeds 3.0, to help improve

27 the fit of the X^2 statistic to chi-square.

28 

29 Grouped Data

30 Mean Scaled

31 Dose Lit.-Spec. Cov. Expected Observed Residual

32 -------------------------------------------------------------

33 0.0000 1.0000 0.270 0 -0.5586

34 0.0000 2.0000 1.675 3 1.0237

35 0.0000 3.0000 2.401 2 -0.2443

36 0.0000 4.0000 3.722 2 -0.8049

37 0.0000 5.0000 3.977 4 0.0098

38 0.0000 5.0000 3.977 2 -0.8614

39 0.0000 5.0000 3.977 1 -1.2970

40 0.0000 5.0000 1.326 1 -0.2458

41 0.0000 6.0000 3.573 9 2.4207

42 0.0000 6.0000 3.573 1 -1.1474

43 0.0000 6.0000 3.573 1 -1.1474

44 0.0000 6.0000 3.573 2 -0.7013

45 0.0000 6.0000 3.573 5 0.6367

46 0.0000 6.0000 3.573 5 0.6367

47 0.0000 6.0000 3.573 6 1.0827

48 0.0000 6.0000 3.573 8 1.9747

49 0.0000 7.0000 4.624 1 -1.3869

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 5 0.1441

53 0.0000 7.0000 4.624 7 0.9096

54 0.0000 7.0000 4.624 3 -0.6214

55 0.0000 7.0000 2.312 1 -0.7100

56 0.0000 8.0000 5.805 5 -0.2698

57 0.0000 8.0000 5.805 11 1.7418

58 0.0000 8.0000 2.902 2 -0.4279

59

60 134.0000 1.0000 0.989 0 -1.3982

61 134.0000 2.0000 1.718 2 0.2488

62 134.0000 3.0000 3.449 6 1.3759

63 134.0000 3.0000 1.150 1 -0.1400

64 134.0000 4.0000 2.850 3 0.0799

65 134.0000 5.0000 3.463 4 0.2388

66 134.0000 5.0000 3.463 1 -1.0962

67 134.0000 5.0000 1.732 0 -1.0898
C- 38 DRAFT – DO NOT CITE OR QUOTE

1 134.0000 6.0000 4.199 5 0.3044

2 134.0000 7.0000 5.086 5 -0.0284

3 134.0000 7.0000 5.086 4 -0.3578

4 134.0000 7.0000 5.086 2 -1.0166

5 134.0000 8.0000 3.068 2 -0.4373

6 134.0000 8.0000 3.068 0 -1.2562

7 134.0000 8.0000 3.068 8 2.0195

8

9 526.0000 2.0000 1.406 2 0.8346

10 526.0000 3.0000 1.892 3 1.1101

11 526.0000 4.0000 4.500 3 -0.8351

12 526.0000 5.0000 5.060 8 1.3670

13 526.0000 5.0000 5.060 4 -0.4926

14 526.0000 6.0000 5.592 6 0.1644

15 526.0000 6.0000 5.592 8 0.9700

16 526.0000 6.0000 5.592 11 2.1785

17 526.0000 6.0000 5.592 2 -1.4469

18 526.0000 6.0000 5.592 2 -1.4469

19 526.0000 6.0000 5.592 9 1.3729

20 526.0000 6.0000 5.592 5 -0.2384

21 526.0000 6.0000 5.592 6 0.1644

22 526.0000 7.0000 3.105 0 -1.5658

23 526.0000 7.0000 3.105 4 0.4511

24 526.0000 7.0000 3.105 5 0.9554

25 526.0000 7.0000 3.105 1 -1.0615

26 526.0000 7.0000 3.105 4 0.4511

27 526.0000 7.0000 3.105 1 -1.0615

28 526.0000 7.0000 3.105 5 0.9554

29 526.0000 7.0000 3.105 3 -0.0531

30 526.0000 7.0000 3.105 4 0.4511

31 526.0000 7.0000 3.105 1 -1.0615

32 526.0000 7.0000 3.105 3 -0.0531

33 526.0000 7.0000 3.105 3 -0.0531

34 526.0000 8.0000 3.496 0 -1.5793

35 526.0000 8.0000 3.496 7 1.5832

36 526.0000 8.0000 3.496 5 0.6796

37 526.0000 9.0000 3.985 0 -1.6270

38 526.0000 9.0000 3.985 6 0.8225

39

40 2005.0000 1.0000 2.777 3 0.4909

41 2005.0000 2.0000 3.577 3 -0.8022

42 2005.0000 3.0000 5.118 4 -0.9786

43 2005.0000 3.0000 5.118 4 -0.9786

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 4 0.6851

47 2005.0000 4.0000 3.208 2 -1.0440

48 2005.0000 4.0000 3.208 3 -0.1795

49 2005.0000 4.0000 3.208 4 0.6851

50 2005.0000 4.0000 3.208 4 0.6851

51 2005.0000 5.0000 3.714 1 -1.7660

52 2005.0000 5.0000 3.714 3 -0.4648

53 2005.0000 5.0000 3.714 5 0.8364

54 2005.0000 5.0000 3.714 5 0.8364

55 2005.0000 5.0000 3.714 4 0.1858

56 2005.0000 5.0000 3.714 4 0.1858

57 2005.0000 6.0000 4.086 6 0.9945

58 2005.0000 6.0000 4.086 2 -1.0836

59 2005.0000 6.0000 4.086 4 -0.0445

60 2005.0000 6.0000 4.086 5 0.4750

61 2005.0000 6.0000 4.086 6 0.9945

62 2005.0000 6.0000 4.086 5 0.4750

63 2005.0000 6.0000 4.086 4 -0.0445

64 2005.0000 6.0000 4.086 5 0.4750

65 2005.0000 6.0000 4.086 3 -0.5641

66 2005.0000 6.0000 4.086 6 0.9945

67 2005.0000 6.0000 4.086 0 -2.1227
C- 39 DRAFT – DO NOT CITE OR QUOTE

1 2005.0000 6.0000 4.086 0 -2.1227

2 2005.0000 7.0000 4.361 7 1.1486

3 2005.0000 7.0000 4.361 5 0.2781

4 2005.0000 7.0000 4.361 5 0.2781

5 2005.0000 7.0000 4.361 7 1.1486

6 2005.0000 7.0000 4.361 6 0.7133

7 2005.0000 8.0000 4.606 0 -1.7419

8

9 Chi-square = 105.13 DF = 98 P-value = 0.2930

10

11 To calculate the BMD and BMDL, the litter specific covariate is fixed

12 at the mean litter specific covariate of all the data: 5.379518

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 2000

Fraction 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 Cmax Methanol, GD 6-15 inhalational study.

Source: Rogers et al. (1993).

14 Once the BMDL05 was obtained in units of mg/L, it was used to derive a chronic 

15 inhalation reference value. The first step is to calculate the HEC using the PBPK model 

16 described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B) that 

17 describes the relationship between predicted methanol AUC and the human equivalent inhalation 

18 exposure concentration (HEC) in ppm. This equation can also be used to estimate model 

19 predictions for HECs from Cmax values because Cmax values and AUC values were estimated at 

20 steady-state for constant 24-hour exposures (i.e., AUC = 24 x Cmax).

21 BMDLHEC (ppm) = 0.0224*BMDL05*24+(1334*BMDL05*24)/(794+ BMDL05*24)
C- 40 DRAFT – DO NOT CITE OR QUOTE

1 BMDLHEC (ppm) = 0.0224*44.7*24 + ((1334*44.7*24)/(794+ 44.7*24)) = 791 ppm

Next, because RfCs are typically expressed in units of mg/m3 2 , the HEC value in ppm was 

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m3 3 :

HEC (mg/m3

) = 1.31 × 791 ppm = 1036 mg/m3 4

5 Finally, this HEC value was divided by a composite 100-fold UF (3 for uncertainty 

6 associated with animal to human differences, 10 for consideration of human variability, and 3 for 

7 database deficiencies) to obtain the chronic inhalation reference value:

RfC (mg/m3) = 1036 mg/m3 ÷ 100 = 10.4 mg/m3 8

C.4. RFC DERIVATIONS USING BURBACHER ET AL. (1999A,B) 

9 The BMD approach was utilized in the derivation of potential chronic inhalation 

10 reference values from effects seen in monkeys due to prenatal methanol exposure. Deficits in 

11 VDR were evaluated from Burbacher et al. (1999a,1999b). In the application of the BMD 

12 approach, continuous models in the EPA’s BMDS, version 1.4.1c, were fit to the dataset for 

13 increased latency in VDR in neonatal monkeys. As the EPA’s PBPK model was not 

14 parameterized for monkeys, external concentration (ppm) was used as the dose metric.

15 The VDR test, which assesses time (from birth) it takes for an infant to grasp for a 

16 brightly colored object containing an applesauce-covered nipple, is a measure of sensorimotor 

17 development. Beginning at 2 weeks after birth, infants were tested 5 times/day, 4 days/week.

18 Performance on that test, measured as age from birth at achievement of test criterion (successful 

19 object retrieval on 8/10 consecutive trials over 2 testing sessions), was reduced in all treated male

20 infants. The times (days after birth) to achieve the criteria for the VDR test were 23.7 ± 4.8 

21 (n = 3), 32.4 ± 4.1 (n = 5), 42.7 ± 8.0 (n = 3), and 40.5 ± 12.5 (n = 2) days for males and 

22 34.2 ± 1.8 (n = 5), 33.0 ± 2.9 (n = 4), 27.6 ± 2.7 (n = 5), and 40.0 ± 4.0 (n = 7) days for females 

23 in the control to 1800 ppm groups, respectively. As discussed in Section 4.3.2, this type of 

24 response data is sometimes adjusted to account for premature births by subtracting time (days) 

25 premature from the time (days from birth) needed to meet the test criteria (Wilson and Cradock, 

26 2004). When this type of adjustment is applied, the times (days after birth or, if shorter, days 

27 after control mean gestation length) to achieve the criteria for VDR test were 22.0 ± 9.54 (n = 3), 

28 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 

29 (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 

30 control to 1800 ppm groups, respectively. When these data were modeled within BMDS 

31 (version 2.1), there was no significant difference between unadjusted responses and/or variances 

32 among the dose levels for males and females combined (p = 0.244), for males only (p = 0.321) 
C- 41 DRAFT – DO NOT CITE OR QUOTE

1 and for males only with the high-dose group excluded (p = 0.182), or for adjusted responses of 

2 males and females combined (p = 0.12), males only (p = 0.448) and males only with the highdose group excluded (p = 0.586).91 3 The only data that offered a significant dose-response trend 

4 was that for unadjusted (p = 0.0265) and adjusted (p = 0.009) female responses, but the model 

5 fits for the adjusted female response data were unacceptable. Only the unadjusted female VDR 

6 response data offered both a dose-response trend and acceptable model fits. The modeling 

7 results for this data set are presented in Table C-10. 

8 The current BMD technical guidance (U.S. EPA, 2000b) suggests that in the absence of 

9 knowledge as to what level of response to consider adverse, a change in the mean equal to 

10 1 control S.D. from the control mean can be used as a BMR for continuous endpoints. A 

11 summary of the results most relevant to the development of a POD using the BMD approach 

12 (BMD, BMDL, and model fit statistics) for increased latency of VDR in female neonatal 

13 monkeys exposed to methanol with a BMR of 1 control mean S.D. is provided in Table C-10.

Model fit was determined by statistics (AIC and χ

2 14 residuals of individual dose groups) and 

15 visual inspection, as recommended by EPA (2000b). The 3rd degree polynomial model returned 

a lower AIC than the other models.

92 16 The BMDL1SD was determined to be 81.7 hr×mg/L, using 

17 the 95% lower confidence limit of the dose-response curve expressed in terms of the ppm of 

18 external methanol concentration.

 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 low- and mid-exposure groups is significant. It was not, so 

nonnegative restriction on the β coefficients of the poly models was retained.
C- 42 DRAFT – DO NOT CITE OR QUOTE

Table C-10. Comparison of BMD modeling results for VDR in female monkeys using 

AUC blood methanol as the dose metric

Model BMD1SD (AUC,

hr × mg/L)

a

BMDL1SD

(AUC,

hr × mg/L)

a

p-value AICc Scaled residuald

Linear 119.058 51.9876 0.1440 110.4492 0.5380

2nd degree polynomial 114.094 59.6412 0.2388 109.43782 0.0994

3rd degree polynomial 120.176 81.6513 0.2718 109.17894 0.0199

Powerb 133.517 63.0615 0.1112 111.11010 0.0000

Hill 132.283 -- NA 113.11010 0.0000

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 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 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: Burbacher et al. (1999a).

1 ==================================================================== 

2 Polynomial Model. 

3 (Version: 2.13; Date: 04/08/2008) 

4 Input Data File: C:\USEPA\BMDS2\Data\Burbacher\PolfemSet.(d) 

5 Gnuplot Plotting File: C:\USEPA\BMDS2\Data\Burbacher\PolfemSet.plt

6 Fri Dec 12 15:30:29 2008

7 ==================================================================== 

8 VDR in female monkeys using AUC blood methanol as the dose metric

9 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

10 The form of the response function is: 

11

12 Y[dose] = beta_0 + beta_1*dose + beta_2*dose^2 + ...

13

14 Dependent variable = F_VDR

15 Independent variable = F_Dose

16 The polynomial coefficients are restricted to be positive

17 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

18

19 Total number of dose groups = 4

20 Total number of records with missing values = 0

21 Maximum number of iterations = 250

22 Relative Function Convergence has been set to: 1e-008

23 Parameter Convergence has been set to: 1e-008

24

25 Default Initial Parameter Values 

26 lalpha = 4.07254

27 rho = 0

28 beta_0 = 34.2

29 beta_1 = 0

30 beta_2 = 0

31 beta_3 = 0

32

33 Asymptotic Correlation Matrix of Parameter Estimates

34

35 ( *** The model parameter(s) -beta_1 -beta_2 
C- 43 DRAFT – DO NOT CITE OR QUOTE

1 have been estimated at a boundary point, or have been specified by 

2 the user,

3 and do not appear in the correlation matrix )

4

5 lalpha rho beta_0 beta_3

6

7 lalpha 1 -1 -0.0076 0.018

8 rho -1 1 0.0076 -0.018

9 beta_0 -0.0076 0.0076 1 -0.37

10 beta_3 0.018 -0.018 -0.37 1

11

12 Parameter Estimates

13 95.0% Wald CI

14 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. 

15 Limit

16 lalpha -13.5062 9.81148 -32.7363 

17 5.72395

18 rho 4.90831 2.77841 -0.537284 

19 10.3539

20 beta_0 31.5013 1.49057 28.5798 

21 34.4228

22 beta_1 8.36431e-025 NA

23 beta_2 0 NA

24 beta_3 3.19775e-006 1.53534e-006 1.88544e-007 6.20695e25 006

26

27 NA - Indicates that this parameter has hit a bound

28 implied by some inequality constraint and thus

29 has no standard error.

30

31 Table of Data and Estimated Values of Interest

32

33 Dose N Obs Mean Est Mean Obs Std Dev Est Std Dev Scaled Res.

34 ------ --- -------- -------- ----------- ----------- ----------

35

36 0 5 34.2 31.5 4.09 5.55 1.09

37 6.73 4 33 31.5 5.83 5.55 0.54

38 28.28 5 27.6 31.6 5.94 5.58 -1.59

39 138.1 7 40 39.9 10.7 9.93 0.0199

40

41 Model Descriptions for likelihoods calculated

42

43 Model A1: Yij = Mu(i) + e(ij)

44 Var{e(ij)} = Sigma^2

45 Model A2: Yij = Mu(i) + e(ij)

46 Var{e(ij)} = Sigma(i)^2

47 Model A3: Yij = Mu(i) + e(ij)

48 Var{e(ij)} = exp(lalpha + rho*ln(Mu(i)))

49 Model A3 uses any fixed variance parameters that

50 were specified by the user

51

52 Model R: Yi = Mu + e(i)

53 Var{e(i)} = Sigma^2

54

55 Likelihoods of Interest

56

57 Model Log(likelihood) # Param's AIC

58 A1 -51.042924 5 112.085848

59 A2 -47.867444 8 111.734888

60 A3 -49.286738 6 110.573475

61 fitted -50.589469 4 109.178938

62 R -55.013527 2 114.027055

63

64 Explanation of Tests 

65

66 Test 1: Do responses and/or variances differ among Dose levels? 

67 (A2 versus R)
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1 Test 2: Are Variances Homogeneous? (A1 vs A2)

2 Test 3: Are variances adequately modeled? (A2 versus A3)

3 Test 4: Does the Model for the Mean Fit? (A3 versus fitted)

4 (Note: When rho=0 the results of Test 3 and Test 2 will be the same.)

5

6 Tests of Interest 

7

8 Test -2*log(Likelihood Ratio) Test df p-value 

9

10 Test 1 14.2922 6 0.02654

11 Test 2 6.35096 3 0.09573

12 Test 3 2.83859 2 0.2419

13 Test 4 2.60546 2 0.2718

14

15 The p-value for Test 1 is less than .05. There appears to be a

16 difference between response and/or variances among the dose levels

17 It seems appropriate to model the data

18

19 The p-value for Test 2 is less than .1. A non-homogeneous variance 

20 model appears to be appropriate

21

22 The p-value for Test 3 is greater than .1. The modeled variance appears 

23 to be appropriate here

24

25 The p-value for Test 4 is greater than .1. The model chosen seems 

26 to adequately describe the data

27

28 

29

Specified 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 80 100 120 140

Mean Response

dose

Polynomial Model with 0.95 Confidence Level

15:30 12/12 2008

BMDL BMD

 

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.
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Source: Burbacher et al. (1999a, 1999b)

1 Once the BMDL1SD was obtained in units of ppm, it was used to derive a chronic 

2 inhalation reference value. The first step is to calculate the HEC using the PBPK model 

3 described in Appendix B. An algebraic equation is provided (Equation 1 of Appendix B) that 

4 describes the relationship between predicted methanol AUC and the human equivalent inhalation 

5 exposure concentration (HEC) in ppm.

6 BMDLHEC (ppm) = 0.0224*BMDL1SD+(1334*BMDL1SD)/(794+ BMDL1SD)

7 BMDLHEC (ppm) = 0.0224*81.7+(1334*81.7)/(794+ 81.7) = 126.3 ppm

Next, because RfCs are typically expressed in units of mg/m3 8 , the HEC value in ppm was 

converted using the conversion factor specific to methanol of 1 ppm = 1.31 mg/m3 9 :

HEC (mg/m3

) = 1.31 × 126.3 ppm = 165 mg/m3 10

11 Finally, this HEC value was divided by a composite 100-fold UF (3 for uncertainty 

12 associated with animal to human differences, 10 for consideration of human variability, and 3 for 

13 database deficiencies) to obtain the chronic inhalation reference value:

RfC (mg/m3

) = 165 mg/m3 ÷ 100 = 1.7 mg/m3 14
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APPENDIX D. RfC DERIVATION – COMPARISON OF DOSE METRICS 

D.1. METHODS

D.1.1. Dose Metric Comparisons

1 Three potential dose metrics were evaluated for possible use in risk extrapolation of 

2 methanol-induced developmental effects: AUC of methanol in the blood; Cmax of methanol in the 

3 blood; and total metabolism of methanol. The latter metric was considered because 

4 developmental effects may be caused by metabolites of methanol, particularly formaldehyde, and 

5 formate. These three metrics were evaluated by determining how well they were able to explain 

6 the variation in response for incidence of cervical ribs (CR) and supernumerary ribs (SNR) in a 

7 concentration-time bioassay by Rogers et al. (1995, raw data obtained from personal 

8 communication). In particular, pregnant CD-1 mice were exposed to 2,000, 5,000, 10,000, or 

9 15,000 ppm methanol for 1, 2, 3, 5, or 7 hours on GD7 and developmental effects evaluated at 

10 GD17. This endpoint was selected because it was the most sensitive of those examined and gave 

11 a reasonable dose-response relationship overall. 

12 Initially, the fraction of pups within each litter carrying either or both CR and SNR was 

13 calculated, and then the average across all litters in each concentration-time combination was 

14 computed. However, as shown in Figure D-1, the resulting data appear to be nonmonotonic, 

15 with the responses from 5-hour exposures exceeding those from 7-hour exposures, and the 

16 responses from 2-hour exposures exceeding those from 3-hour exposures. It was noted that the 

17 study was done with a block-design, where the dams/litters for some concentration-time 

18 combination were divided between multiple blocks and the average CR + SNR incidence in 

19 controls varied from 30–52% among the 8 blocks. Therefore block-control response (percent) 

20 was subtracted from each exposed litter's response (percent) before calculating an average 

21 response among litters in a given concentration-time combination. The resulting data are 

22 presented in Figure D-1.
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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 direct 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 

averages across litters.

Source: Rogers et al. (1995).

1 While the correction for background differences does not completely correct the apparent 

2 nonmonotonic dose, the 2-hour response is now less than or below the 3-hour response at 5,000 

3 and 10,000 ppm, and the strong disparity that appeared between the 5- and 7-hour data at 2,000 

4 ppm is eliminated. Overall, the data show a more consistent dependence on duration of 

5 exposure, except for the response to 3 hours of 15,000 ppm methanol. Therefore these 
D- 3 DRAFT – DO NOT CITE OR QUOTE

1 background-corrected response measures will be used to evaluate the 3 dose metrics, with the 

2 exception that the 3-hour 15,000 ppm data point will be dropped as an outlier. In particular, the 

3 dose-response relationship based on these data will be plotted against each of the dose metrics to 

4 determine which provides the most consistent overall dose-response relationship.

D.2. RESULTS

D.2.1. Dose Metric Comparisons

5 The average incidence of CR plus SNR from the concentration-time developmental 

6 bioassay of Rogers et al. (1995), with block-specific control values subtracted from each litter 

7 average before calculating overall average responses, is plotted in Figure D-2 against three dose 

8 metrics: AUC, Cmax, and total amount metabolized of methanol (The volume units for Cmax and 

9 AUC were adjusted to put all three data sets on approximately the same scale for comparison).

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 each metric is 

the same, only the metric associated with those responses changes.

Source: Rogers et al. (1995).

12 While none of the metrics results are in complete alignment of the dose-response data, 

13 the scatter for the Cmax dose-response (i.e., the range of response values associated with a given 

14 small range of the dose metric – scatter in the y-direction) is quite a bit less than either of the 

-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 (%)
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1 other two metrics. Thus, Cmax appears to be a better predictor of response than AUC or amount 

2 metabolized. Looking at the exposure-response data in Figure D-1, one can see that 2- and 

3 3-hour exposures at 5,000 ppm elicit no increase over control, while 5- and 7-hour exposures at 

4 this level do. 

5 If AUC or amount metabolized were true measures of risk, then one would expect a 

6 graded response, where the 2- and 3-hour exposures were intermediate between controls and 5–

7 7-hour exposures. But the lack of response at those shorter times indicates that the concentration 

8 (Cmax) has not risen high enough in such a short exposure to cause a response, while it has at the 

9 longer durations. From Figure D-2, it appears that a Cmax of 11 mg/cL (1,100 mg/L) is a 

10 NOAEL, with a linear increase in CR + SNR from that level to 38 mg/cL, after which the 

11 response begins to plateau. Note that while the plot is of response above background, the plateau 

12 is effectively at 100% total incidence: the highest points in Figure D-1 are from the 7-hour 

13 exposures at 15,000 ppm, where actual incidence was 98% (30% in controls); and the next 

14 highest points are from the 5-hour 15,000 and 10,000 ppm exposures, where the incidences were 

15 94% and 93%, respectively (32% in controls; both from the same block).
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APPENDIX E. EVALUATION OF THE CANCER POTENCY OF METHANOL

E.1. INTRODUCTION

1 Two studies were selected for the evaluation of the cancer potency of methanol (Soffritti 

2 et al., 2002a, 2000b, 2002c; NEDO, 1987, 1987/2008b). The Soffritti et al. (2002a) study is the 

3 only oral study available with effects that show a statistically significant increase in incidence of 

4 any cancer endpoints in the treated groups versus the concurrent control group (pair-wise 

5 comparison) and is used to derive the POD for deriving an oral cancer slope factor. The NEDO 

6 (1987, 1987/2008b) 24-month rat study is the only inhalation study available with effects that 

7 show a statistically significant increase in incidence of any cancer endpoints and was used to 

8 derive the POD for the inhalation cancer unit risk. A third study, Apaja (1980), reported 

9 statistically significant increases in malignant lymphomas in Eppley Swiss Webster mice over 

10 historical controls (pair-wise comparison) following drinking water exposure to methanol. 

11 Because this study did not involve a concurrent control group it is not used for the derivation of a 

12 cancer oral slope factor, but its dose-response is evaluated here for comparative purposes. 

E.1.1. Oral CSF POD

13 The Soffritti et al. (2002a) study, conducted by the Ramazzini Foundation, presents a 

14 number of challenges if these data are to be used in dose-response modeling to assess the 

15 carcinogenic potency of methanol. One challenge, determining the appropriate HED, is best 

16 addressed using a PBPK model to derive an HED dose that considers the kinetic differences in 

17 humans and the animal model, i.e., species extrapolation. Such a model was developed by the 

18 EPA and is not addressed in this appendix; however, the dose metrics derived from that PBPK 

19 model are used in the modeling of the data.

20 The other major challenge, which is addressed in this appendix, is how to model the 

21 nonstandard protocol by which methanol was tested, as reported in Soffritti et al. (2002a). In 

22 most oncogenicity studies, typified by those conducted by the NTP, animals are dosed for 

23 104 weeks, with a scheduled sacrifice of all surviving animals at the end of treatment. In the 

24 study for methanol reported by Soffritti et al. (2002a), while the animals were treated with 

25 methanol for 104 weeks, animals were not euthanized and examined on a specified schedule but 

26 were followed until their natural death. It is well known that the incidence of background tumors 

27 in a number of organs is different between that seen at a scheduled sacrifice at 104 or 105 weeks 

28 and in the same sex/strain that is followed for a lifetime. A higher background incidence can 

29 increase the difficulty of detecting chemically related responses (Melnick et al., 2007). Further, 

30 performing pathological examinations on tissues collected after natural death can create 
E- 2 DRAFT – DO NOT CITE OR QUOTE

difficulties associated with cell autolysis.93 1 At the same time, the shorter duration of the 2-year 

2 bioassays used at the NTP misses about two thirds of the life span of the rodent, potentially 

3 missing late stage or late appearing chemically related tumor responses (Melnick et al., 2007). 

4 ERF believes that “cutting short an experiment after two years may mask a possible carcinogenic 

5 response,” but ERF further suggests that all chronic cancer studies “should continue until 

6 spontaneous animal death” (Soffritti et al., 2002a). Soffritti et al. (2002a) cite ERF studies of 

7 benzene, xylenes, mancozeb, and vinyl acetate monomer as examples for which carcinogenic 

8 responses were observed after the 2-year treatment period.

9 These data were evaluated using three different approaches and two different dose10 response models (EPA’s multistage cancer and a multistage Weibull time-to-tumor model). 

11 These approaches involved using the administered dose and models that would rely upon the 

12 published data (Option 1), while another would rely on unpublished information that would be 

13 provided by the Ramazzini Foundation (Options 1 and 2), while a third option (Option 3) would 

14 also rely upon this unpublished information but would also incorporate results from PBPK 

15 modeling for methanol developed by the EPA. 

E.1.2. Selection of the Data Modeled and HED

16 The individual animal data from the Ramazzini Foundation study was provided to EPA in 

17 the standard NTP format in which the number of days on study, the tissues examined and the 

18 tumor types found were given for each animal. The tumors with incidences that were 

19 statistically significantly increased or were considered to be rare tumors and considered for dose20 response modeling were the incidence of hepatocellular carcinoma in male rats and the incidence 

21 of hemolymphoreticular neoplasms in both male and female rats. The incidence of lympho22 immunoblastic lymphomas was modeled separately, and the combined incidence of all the 

23 lymphomas was considered for dose-response modeling. Table E-2 provides the incidence of 

24 these neoplasms reported in each dose group. The incidence of histiocytic sarcomas and myeloid 

25 leukemias were not significantly increased in either sex. The incidence of these tumors was not 

26 combined with the lymphoblastic lymphomas because they are of a different cell line and the 

27 combination is not typically evaluated either for statistical significance or dose-response 

28 modeling (McConnell et al., 1986).

29 The drinking water concentrations provided in the Soffritti et al. (2002a) study were 

30 converted to doses in mg/kg-day. Initially, an attempt was made to estimate the dose of methanol 

31 to individual animals for development of an average dose; however, water consumption 

32 information was available only on a cage-by-cage basis. Based on the available information, the 

33 average water consumption for each treatment group was calculated using the available data 

 93 Autolysis may develop in carcasses of animals if they are not processed immediately after death or if the animals 

become severely moribund prior to death. These types of changes can compromise pathological diagnosis and 

subclassification of neoplasms. 
E- 3 DRAFT – DO NOT CITE OR QUOTE

1 reported for weeks 1–104. Although individual body weights were available, the corresponding 

2 intake was not available. The average body weight over the period of dosing for the experiment 

3 (using measurements taken on day 1–day 736) was calculated for each dosed group. A weighted 

4 average was calculated for the body weights using the number of animals for which body 

5 weights were recorded at each time point. The average body weight and the average water 

6 consumption in (mL/day) were used to calculate the mg/kg-day doses. The equation used for 

7 this calculation is:

1000 BodyWeight(kg)

Dose(ppm) WaterConsumption(mL/ day) Dose(mg / kg day)

×

× − = .

8 Table E-1 provides the values used to obtain the mg/kg-day doses, as well as the resulting 

9 mg/kg-day doses. In addition, the average and median times of death were calculated for each 

10 group (both dosed and control), for the only the dosed groups combined (excluding the controls),

11 and for all the groups in the study combined (including control). These values were obtained 

12 using the reported weeks on study for each animal. One male rat (ID # 129) in the 20,000 ppm 

13 group was not examined microscopically and was excluded from the time of death calculations 

14 and all modeling. If this animal was included in the calculations for the average and median

15 times of death, the median time of death for all male rat dosed groups would increase from 97–

16 98 weeks; all of the other average and median times of death that include the 20,000 ppm group 

17 do not change. 

18 In the absence of a kinetic model, extrapolation from animal to human was based on the 

default assumption of body weight3/4. 19 This extrapolation was applied to the animal POD 

20 estimates to obtain the HEDs reported in Table E-1. This extrapolation was calculated using the 

21 average body weight of the dosed animals excluding controls (0.33 kg for the female rats and 

22 0.51 kg for the male rats) over the dosing period of the study (through day 736) and 70 kg for the 

human body weight. The equation used for the body weight3⁄4 23 extrapolation is

1/ 4

















Human Body Weight (kg)

Animal Body Weight (kg)

24 and results in a value of 0.26 for the female rats and 0.29 for the male rats.

E.1.2.1. Dose-Response Modeling

25 Option 1 – Quantal Dose-Response Modeling. Under this option, the standard default 

26 modeling approach outlined in the Cancer Guidelines (U.S. EPA, 2005a) was applied. The 

27 cancer bioassay data were fit using a multistage model, which is the current model preferred by 

28 EPA’s IRIS program for cancer dose-response modeling. The PODs for use in the derivation of 
E- 4 DRAFT – DO NOT CITE OR QUOTE

1 CSFs were calculated using the multistage-cancer model available in the BMDS program 

2 (http://www.epa.gov/NCEA/bmds). 

3 BMDS was used to estimate BMDs and 95% lower bounds on the BMDs or BMDLs 

4 associated with a 10% extra risk (BMDL10). For this assessment, the multistage model was 

5 determinded to be an appropriate model for characterization of the dose-response curve in the 

6 observable range. At this time, the MOA for the tumors observed following exposure to 

7 methanol is not known; therefore, linear extrapolation was conducted to estimate a CSF. 

8 Option 2 – Time-to-Tumor Dose-Response Modeling. This option is similar to 

9 Option 1; however, rather than the use of quantal models, a time-to-tumor model was applied to 

10 the selected datasets. Data for this analysis was provided by the Ramazzini Foundation and can 

11 be obtained from their web site (http://www.ramazzini.it/fondazione/study.asp). The same 

12 assumptions regarding the HED and low-dose extrapolation were applied. Because BMDS does 

13 not include time-to-tumor modeling, the QRISK portion of Statox was relied upon for dose14 response modeling. Statox is an internal EPA program that is used for gathering and analyzing 

15 animal bioassay data and contains the QRISK component for dose-response modeling. The 

16 QRISK component of Statox Version 5.5 fits a multistage Weibull model to the data. The 

17 multistage Weibull model is multistage in dose and Weibull in time and essentially assesses the 

18 probability that a tumor would have been identified at time t. The multistage Weibull model has 

19 the form:

k c k q q d q d q d t t

p d t e ( ) ( ) 0

2

0 1 2 ( , ) 1 − + × + × + × × − = − 

20 with dose (d) and time (t) as the variables. The parameters estimated by fitting the model to the 

21 data are the dose parameters q0 through qk, the induction time (t0) and the power term for 

22 time (c). 

If t

0 23 is interpreted as the time (assumed to be the same for all animals) from when a tumor 

24 is observable (i.e., capable of being detected if the animal were to be sacrificed and a necropsy 

25 performed) to the time the tumor causes the death of the animal, then these models can be 

applied to data on incidental and fatal tumors simultaneously. Note that t and t

0 26 only appear in 

the model in the form of t-t

0. To make this explicit, we write P(d,t) = F(d,t-t0 27 ). The probability 

of an incidental tumor by time t is taken to be F(d,t) (t 28 0 = 0) and the probability of a fatal tumor 

29 by time t is taken to be F(d,t-t0). There are three possible types of incidence contexts for each 

30 animal which contribute separately to the likelihood function for this model. These are:

31 • Censored response – animal died without having the tumor(s) being modeled

32 • Incidental response – the animal died with the tumor(s) but the death was not caused by 

33 the tumor(s) (i.e., the time to death from those tumors would have been later than the 

34 actual death time); and 
E- 5 DRAFT – DO NOT CITE OR QUOTE

1 • Fatal incidence – the tumor(s) being modeled was the cause of death. 

2 The contribution of each animal to the likelihood is then defined for its time of death (t). 

3 The complete likelihood is defined as:

∏ ∏ ∏ = = = = 



















 





∂

∂ −  ×



 



  × − −



 



 −

g

j 1 Incidence(i,j) Fatal

j 0

Incidence(i,j) Incidental

j j 0

Incidence(i,j) Censored

j )

t

F(d , t t )

(1 F(d , t) (F(d , t) F(d , t t ) (

4 where g is the number of dose groups in the study, including the control group, and i varies from 

5 1 to the total number of animals in the study examined for the tumor type(s) being modeled.

6 As with the quantal modeling, the lower bound on a dose at an extra risk of 10% was 

7 estimated. Goodness-of-fit was determined by visually inspecting graphical output of the 

8 modeling. AIC values were also calculated for the time-to-tumor model fit.

9 This option was proposed because time-to-tumor modeling is typically applied to account 

10 for differences in survival among treated and control groups. However, in this case there were 

11 no differences detected in the survival times. Figures E-1 and E-2 are graphs of the proportion 

12 surviving versus the weeks on study for the female rats and male rats, respectively. In addition, 

13 the Life Table program (Thomas et al., 1997) was run on the data. None of the statistical tests in 

14 this program indicated a difference in survival between the control and the dosed groups. 

15 The protocol used by the Ramazzini Foundation was different from that typically 

16 employed in chronic rat bioassays. In typical rodent bioassays, a compound is administered to 

17 the animals for approximately 104 weeks, and the animals sacrificed within a short period (days) 

18 following the end of treatment. In the Ramazzini Foundation bioassays (i.e., for methanol, 

19 formaldehyde, MTBE, and aspartame), the animals were administered compounds for 104 weeks 

20 but were allowed to live until a natural death, which, in some animals, occurred months after the 

21 completion of chemical administration. This can have an impact on the tumor incidence and 

22 therefore, the potential risk of tumor development associated with administration of a given 

23 compound. Time-to-tumor modeling was used in this case to attempt to adjust for the extended 

24 life span of the some of the animals in this study.

25 For time-to-tumor modeling, the POD must consider a specific time as well as a specific 

26 risk level. Since this study was not a standard study with a fixed study length, several 

27 assumptions can be made with regard to the time to be used. For this modeling exercise, Two 

28 different possible approaches were considered. 

29 For the first approach, the model was fit to animal data using all the times reported up to 

30 the last death time or 153 weeks for the female rats and 148 weeks for the male rats. Every 

31 tumor observed was assumed to be a fatal tumor or the cause of death in the animal. While the 

32 animals lived longer than 104 or 105 weeks, the POD was calculated at 105 weeks, since it was 

33 assumed that an animal life span of 148–153 weeks would not correspond to the average 70-year 

34 human life span. 
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1 For the second approach, an attempt was made to simulate what might have occurred if 

2 the study had been a standard 2-year protocol that was terminated at 105 weeks, with all 

3 surviving animals sacrificed at 105 weeks. It was assumed that the tumors discovered in the 

4 animals that survived longer than 105 weeks would have been present and found at necropsy. 

5 Therefore, all tumors in animals that died in weeks 105 and earlier were assumed to be fatal or 

6 the cause of death in the animals, and all tumors that would have been discovered at the necropsy 

7 of animals were assumed to be incidental or not the cause of death. The life span assumed for 

8 this analysis was 105 weeks in the rat and the POD was calculated for a 10% risk to a human at 

9 105 weeks. This approach was conducted mainly for comparative purposes to evaluate the 

10 potential impact on the POD if the study duration was shortened and for a more direct 

11 comparison to the quantal PODs and serves only as bounding exercise for the risk.

12 Option 3 – Dose-Response Modeling using PK Dose Metrics. For the third approach, 

13 PK dose metrics obtained from the PBPK model (Section 3.4) were used as the doses. Both 

14 time-to-tumor modeling and quantal modeling was done. The Statox program was used to 

15 estimate MLEs and lower bounds on dose associated with a 10% extra risk (LED10s), and 

16 BMDS multistage model was used to estimate BMDs and 95% lower bounds on the BMDs or 

17 BMDLs associated with a 10% extra risk (BMDL10). Each of the dose metrics, provided in 

18 Table E-5, were used in this option of the dose-response modeling. 

E.1.2.1. Results for Oral Slope Factor POD

19 Quantal Dose-Response Modeling (Option 1). Quantal dose-response modeling was 

20 conducted using estimated mg/kg-day doses and the incidence of lympho-immunoblastic 

21 neoplasms and the combined lymphomas for the female rat and the hepatocellular carcinomas, 

22 the lympho-immunoblastic neoplasms, and the combined lymphomas for the male rat. The 

23 results of the quantal modeling for this option are given in Table E-3. The multistage model gave 

24 an adequate fit (p value > 0.05) and was able to derive BMDL10 values for all of the lymphoma 

25 data. However, for the male rat hepatocellular carcinomas, the multistage model failed to 

estimate a BMDL10. Human equivalent BMDL10 values94 26 and CSF are also provided in 

27 Table E-3.

28 The POD values calculated from the female rat data ranged from 259–405 mg/kg-day for 

29 the lympho-immunoblastic neoplasms and 251–400 mg/kg-day for the combined lymphomas. 

30 For the HED POD values calculated from the male rat data, the range of values for the liver 

31 hepatocellular carcinomas was 537–567 mg/kg-day; however, for 3 of the models (Multistage, 

32 Quantal Linear and Log-Probit), the BMDS program failed to estimate a BMDL10. The lympho33 immunoblastic endpoint in the male rats produced POD values that ranged from 110–223 mg/kg34 day, and the all lymphomas incidence data gave POD values of 113–227 mg/kg-day. Figures E-3 

 94 Computed from the animal values by multiplying by the body weight3⁄4 animal-to-human extrapolation value (0.26 

for females and 0.29 for males)
E- 7 DRAFT – DO NOT CITE OR QUOTE

1 through E-7 show graphs of the quantal models that give the lowest POD for each of the 

2 endpoints modeled.

3 Time-to-Tumor Dose-Response Modeling (Option 2). Results of the time-to-tumor 

4 modeling using estimated mg/kg-day doses with POD and CSF values for each approach 

5 described above are given in Table E-4. Approach 2 gives smaller POD estimates than 

6 Approach 1. With Approach 2, an artificial end of the study is assumed of 105 weeks and the 

7 designation of approximately half of the total tumors changed from fatal to incidental. This 

8 approach evaluates the potential impact on the POD of terminating the study at 105 weeks, rather 

9 than allowing the animals to live until their natural death, assuming that the same animals 

10 bearing tumors would be “observed” at 105 weeks, rather than at later time points. For 

11 Approach 1, the model was fit to the actual observed weeks-on-study, and a time of 105 weeks 

12 was used in calculating the POD. For the female rat, the PODs based on the lympho13 immunoblastic neoplasms were 349 mg/kg-day for approach 1 and 179 mg/kg-day 

14 forApproach 2. The PODs for the combined lymphomas ranged were 321 and 198 mg/kg-day 

15 for the 2 appraoches. For the PODs calculated from the male rat data, the values were 783 and 

16 612 mg/kg-day for the hepatocellular carcinomas, 174 and 91 mg/kg-day for the lympho17 immunoblastic neoplasms, and 192 and 92 mg/kg-day for the combined lymphomas. Figures E18 3 through E-7 show the modeling results for the time-to-tumor modeling using Approache 1 

19 where the time is fixed at 105 weeks and the doses are allowed to vary. Figures E-8 and E-9 

20 show Kaplan-Meier curves versus the model fit to the combined lymphoma data for the females 

21 and males, respectively. In these graphs each line corresponds to a specific dose, and time is 

22 allowed to vary up to the study end of 153 or 148 weeks. For the male rat combined lymphomas 

23 (Figure E-9), the multistage Weibull predicted values more closely match the Kaplan-Meier at 

24 105 weeks than at the average life span (94 weeks) or the end of study (148 weeks). For the 

25 female rat combined lymphomas, the closest match of the Kaplan-Meier curves to the model 

26 predicted values appears to be around the average life span of 96 weeks.

27 The AIC values for the time-to-tumor modeling are all higher than those for the quantal 

28 modeling. However, since the LLFs on which the AIC are based are different for time-to-tumor 

29 versus quantal models, the AIC values from time-to-tumor models cannot be compared to those 

30 from quantal models.

31 Dose-Response Modeling using PBPK Dose Metrics (Option 3). Both time-to-tumor 

32 and quantal dose-response modeling were conducted using the incidence of lympho33 immunoblastic neoplasms and the combined lymphomas for the female rat and the hepatocellular 

34 carcinomas, the lympho-immunoblastic neoplasms, and the combined lymphomas for the male 

35 rat and the three PBPK dose metrics (blood methanol AUC, peak blood concentration, and the 

36 total metabolized per day) obtained from the EPA’s PBPK model (Table E-5).

37 Only Approach 1 was used for the time-to-tumor dose-response modeling using PBPK 

38 dose metrics, and results are given in Table E-6a; HEDs are provided in Table E-6b.
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1 The results of the multistage quantal modeling are given in Table E-7. Most model runs 

gave an adequate fit to the data by the χ

2 2 Goodness of Fit p-value (e.g., p-values > 0.05),

3 although for male rat hepatocellular carcinomas, the calculations using the AUC or amount 

4 metabolized dose metric were unable to converge for the model fit or the derivation of a BMDL. 

5 A plot of the model fit for male rat combined lymphomas using total methanol metabolized per 

6 day as the dose metric (the endpoint and dose metric used in the derivation of the oral CSF) is

7 shown in Figure E-10; HEDs are provided in Table E-8 where the BMDL was calculated.

E.2. INHALATION ORAL CANCER UNIT RISK POD

8 The NEDO (1987, 1987/2008b) study was conducted using a standard protocol with 

9 exposure for 104 weeks, followed by sacrifice of all animals surviving to 104 weeks. As with 

10 the Soffretti (2002a) study, pharmacokinetic dose metrics for use in the dose-response 

11 assessment were determined for the inhalation exposures using the EPA’s PBPK model.

E.2.1. Selection of the Data Modeled and HED

12 The individual animal data from the NEDO (1987/2008b) study were provided in a 2008 

13 translation of the study from Japanese to English. Although the translation provided the number 

14 of days on study and the neoplastic responses seen in each animal, the translation did not provide 

15 results if a tissue was examined histopathologically with no neoplastic responses. This makes it 

16 difficult to determine which of the individual animals were not examined, although the tables did 

17 indicate that, for some of the animals, selected organs (specifically a few lungs in males and a 

18 few adrenal glands in females) were not examined. Therefore, time-to-tumor analysis could not 

19 be conducted with results from the inhalation data as was done with the oral data. However, 

20 survival analysis of all the data from the NEDO (1987/2008b) study did not indicate that there 

21 were any survival problems (Figures E-11 and E-12). This suggests that a time-to-tumor analysis 

22 is not necessary.

23 The tumors with significantly increased incidence that were considered for dose-response 

24 modeling were the female rat adrenal gland pheochromocytomas and the male rat lung tumors 

25 (papillary adenomas and adenocarcinomas combined or papillary adenomas, adenocarcinomas 

26 and adenomatosis combined); Table E-9 gives the incidence of these tumors.

E.2.1.1. Dose-Response Modeling

27 Quantal Dose-Response Modeling using Pharmacokinetic Dose Metrics. For the 

28 selected endpoints from the NEDO (1987) study, only quantal dose-response modeling using 

29 pharmacokinetic internal dose metrics estimated by the PBPK model (described in Section 3.4 

30 and Appendix B) was conducted. Each of the dose metrics provided in Table E-10 was used in 

31 the BMDS software with the incidence data in Table E-9 to estimate the BMDs and 95% lower 

32 confidence limits (BMDLs) associated with a 10% extra risk (BMDL10).
E- 9 DRAFT – DO NOT CITE OR QUOTE

E.2.2. Results for the IUR POD

1 Quantal dose-response modeling was conducted using the incidence of adrenal gland 

2 phoechromocytomas in female rats and the combined incidence of lung adenomas and 

3 adenocarcinomas or lung adenomas, adenocarcinomas and adenomatosis in male rats using the 

4 pharmacokinetics dose metrics derived from the EPA’s PBPK model. The results of this 

5 modeling are given in Table E-11. The multistage model gave an adequate fit to the data in all 

instances as determined by the χ

2 6 goodness-of-fit p-value (e.g., p-values > 0.05). A plot of the 

7 model fit for female rat pheochromocytomas using total methanol metabolized per day as the 

8 dose metric (the endpoint and dose metric used in the derivation of the IUR) is shown in 

9 Figure E-13. HECs are provided in Table E-12. 

E.3. ANALYSIS OF APAJA (1980) DRINKING WATER STUDY

10 The Apaja (1980) study was similar to the Soffritti et al. (2002a) study in that it was a life

11 span drinking water study. The primary differences are that Apaja (1980) used Eppley Swiss 

12 Webster mice, did not stop exposure at 104 weeks and did not employ an untreated concurrent 

13 control group. Methanol exposure groups of this study served as controls for malonaldehyde 

14 exposed mice. As with the Soffretti (2002a) study, pharmacokinetic dose metrics for use in the 

15 dose-response assessment were determined for the oral exposures using the EPA’s PBPK mouse 

16 model.

E.3.1. Selection of the Data Modeled and HED

17 Individual animal data from the Apaja (1980) study were not available. Therefore, time18 to-tumor analysis could not be conducted. The tumors with significantly increased incidence that 

19 were considered for dose-response modeling were malignant lymphomas in male and female 

20 mice; Table E-13 gives the incidence of these tumors.

E.3.1.1. Dose-Response Modeling

Quantal Dose-Response Modeling using Pharmacokinetic Dose Metrics. A 1st 21 degree 

22 multisage model was used to evaluate the malignant lymphoma response from the Apaja (1980) 

study versus pharmacokinetic dose metrics without background95 23 was conducted. Each of the 

24 dose metrics provided in Table E-14 was used in the BMDS software with the incidence data in 

25 Table E-13 to estimate the BMDs and 95% lower confidence limits (BMDLs) associated with a 

26 10% extra risk (BMDL10).

 95 The assumption of zero background dose is consistent with what was done for the derivation of the oral CSF and 

EPA practice.
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E.3.2. Results for the IUR POD

1 Quantal dose-response modeling was conducted using the incidence of malignant 

2 lymphoma in male and female Eppley Swiss Webster mice using the pharmacokinetics dose 

3 metrics derived from the EPA’s PBPK model. The results of this modeling are given in 

Table E-15. The 1st 4 degree multistage model gave an adequate fit to the data in all instances as 

determined by the χ

2 5 goodness-of-fit p-value (e.g., p-values > 0.05). A plot of the model fits for 

6 male and female mice using total methanol metabolized per day as the dose metric is shown in 

7 Figure E-14. BMDL10 HECs associated with each dose metric are provided in Table E-16. 

E.4. BACKGROUND DOSE ANALYSES

8 The primary purpose of this cancer analysis is for the determination of cancer risk 

9 associated with increases in the levels of methanol or its metabolites (e.g., formate, 

10 formaldehyde) over background. Thus, the PBPK model estimates of internal dose used in the 

11 dose-response analyses described above do not describe or account for background levels of 

12 methanol or its metabolites. However, background levels of methanol may have contributed to 

13 the response levels reported for some of the tumors associated with methanol. If this 

14 contribution is large, it could significantly impact cancer risk estimates.

15 Of the Soffritti et al. (2002a), NEDO (1985/2008b) and Apaja (1989) studies, only the 

16 NEDO study reported methanol blood levels in test animals. Based on background levels of 

17 methanol in the blood of F344 rats reported by NEDO (1987; 1985/2008b) PBPK model 

18 estimates of AUC methanol (mg-h/L), Cmax methanol (mg/L) and total metabolites (mg/day) in 

19 control animals were obtained. AUC methanol, Cmax methanol and methanol metabolite 

20 background levels were estimated to be 97.16 mg-h/L, 4.05 mg/L and 7.79 mg/day for female 

21 F344 rats and 96.02, 4.00 and 10.81 mg/day for male F344 rats, respectively.

22 Available bioassays do not allow for the quantification of the relationship between 

23 background levels and background responses, but the “background dose” Multistage-cancer 

24 model contained in version 2.1 of EPA’s BMDS software can be used to estimate what 

25 background dose would be necessary to explain the dose-response data evaluated in this 

26 assessment if all of the background response were due to background levels of methanol or its 

27 metabolites. The results of dose-response modeling of cancer endpoints evaluated in this 

28 assessment using the Multistage-cancer “background dose” model are shown in Table E-17. 

29 As can be seen from Table E-17, the Multistage-cancer-bgdose model predicts that AUC 

30 methanol, Cmax methanol and methanol metabolite background doses necessary to explain the 

31 dose-response data if all of the background response were due to background levels of methanol 

32 or its metabolites are well above the actual background levels for these metrics predicted by the 

33 PBPK model. In the case of lymphoma responses in Sprague-Dawley rats, the background doses 

34 for all metrics predicted by the Multistage-cancer-bgdose model are 20-2,000 fold higher than 
E-11 DRAFT – DO NOT CITE OR QUOTE

1 the actual background doses predicted in F344 rats by the PBPK model. In the case of female 

2 pheochromocytoma and male lung responses in F344 rats, the background doses predicted by the 

3 Multistage-cancer-bgdose model are 3-5 fold higher than the actual background doses predicted 

4 by the PBPK model. The fact that background dose estimates from the Multistage-cancer-bgdose 

5 model are several-fold and sometimes more than 1,000-fold higher than the PBPK model 

6 predictions suggests that the contribution of background levels of methanol or its metabolites to 

7 the background responses of these tumors is relatively small. 
E-12 DRAFT – DO NOT CITE OR QUOTE

Table E-1. Calculation of mg/kg-day doses

Female Sprague-Dawley Rats Male Sprague-Dawley Rats

Dose (ppm)

Body 

weight 

(kg)

Water 

consump.

(g/day or 

mL/day)

Dose 

(mg/kg 

-day)

Average 

time of 

death 

(wk)

Median 

time of 

death 

(wk)

Body 

weight 

(kg)

Water 

consump.

(g/day or 

mL/day)

Dose 

(mg/kg -

day)

Average 

time of 

death 

(wk)

Median 

time of 

death 

(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

Averaged 

over all 

dosed groups 

(excluding 

control)

0.33 96 99 0.51 94 97

Averaged 

over all 

groups 

(including 

control)

0.33 97 100 0.51 93 96

Source: Soffretti et al. (2002a).

Table E-2. Incidence for neoplasms considered for dose-response modeling

Dose 

(ppm)

Dose 

(mg/kgday)

Number of 

animals 

examined

Heptocellular 

carcinomas

Histiocytic 

sarcoma

Leukemia 

monocytic

Leukemia 

myloid

Lymphoma 

lymphoblastic

Lymphoma 

lympocytic

Lymphoma 

lymphoimmunoblastic

All 

lymphomas 

combined

Female Sprague-Dawley rats

0 0 100 1 3 0 0 9 9

500 66.0 100 2 3 1 1 17 19a

5000 624.1 100 2 3 1 0 19a 20a

20000 2177 100 3 3 1 0 21a 22b

Cochran Armitage Trend Test 0.19 0.5 0.3 0.8 0.04 0.04

Male Sprague-Dawley rats

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 28a 29a

20000 1780 99 3 1 0 1 1 37b 38b

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; bFisher’s Exact p-value < 0.01

Source: Soffretti et al. (2002a).
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Table E-3. Results from multistage (1o) quantal modeling rat data using mg/kg-day 

exposures and default HED derivation method

AIC p-value

Scaled 

residual at 

observed 

dose closest 

to BMD

Animal values Human values

BMD10 BMDL10

Human 

equivalent 

BMDL10a

CSFb

Female 

SpragueDawley rat

All organs 

lymphoimmunoblastic 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 

SpragueDawley rat

Hepatocellular 

carcinoma 72.84 0.38 Faileda

All organs 

lymphoimmunoblastic 455.67 0.34 0.14 714.26 448.43 131.0 7.6E-4

All organs – all 

lymphomas 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 BMDL10.

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 valuesb

AIC Prediction 

time (weeks)

MLE 

(mg/kg-day)

LED10

(mg/kg-day) CSFc

Approach 1 - Model fit to actual death times, dose estimates computed at 105 weeeksa

Female 

SpragueDawley rat

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 

SpragueDawley rat

Hepatocellular carcinoma 105.45 105 4250.7 783.3 1.3E-04

All organs 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 study at 105 Weeksa

Female 

SpragueDawley rat

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 

SpragueDawley rat

Hepatocellular carcinoma 1.16E+05d 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 values are computed by converting the animal doses to HED before modeling by multiplying by the body weight3⁄4 animal-tohuman extrapolation value (0.26 for females and 0.29 for males).

c

Calculated as CSF = 0.1/Human LED10. d

Model failed to optimize.

Source: Soffretti et al. (2002a).
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Table E-5. PBPK model estimated dose-metrics for doses 

Sex Dose (mg/kg-day) Body weight 

(kg)

AUC

(mg-h/L) Peak (mg/L) Amount metabolized(mg/day)

Female

SpragueDawley

66 0.33 66.84 5.96 18.39

624.1 0.33 9543.63 500.59 126.68

2177 0.34 91262.27 4157.98 141.60

Male

SpragueDawley

53.2 0.49 55.76 4.81 21.82

524 0.50 7500.14 395.56 168.85

1780 0.54 80420.25 3629.08 200.03

Source: Soffretti et al. (2002a).

Table E-6a. Results from time-to-tumor modeling of data using PBPK dose metrics

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 LED10 AIC MLE LED10 AIC MLE LED10

Female 

SpragueDawley 

rat

All organs 

lymphoimmunoblastic105

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 

SpragueDawley 

rat

Hepatocellular 

carcinoma 105 110.47 undefined 119186 110.45 undefined 5385.9 109.60 undefined 300.4

All organs 

lymphoimmunoblastic105

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: Soffretti et al. (2002a)

Table E-6b. HEDs from time-to-tumor modeling of data using PBPK dose metrics

Approach 1 - Model fit to actual death times, 

dose estimates computed at 105 weeeks

AUC (mg-h/L) Peak (mg/L) Amount 

Metabolized (mg)

LED10 (mg/kg-day) LED10 (mg/kg-day) LED10 (mg/kg-day)

Female 

SpragueDawley rat

All organs lympho-immunoblastic 647.0 676.2 87.7

All organs all lymphomas 618.3 645.4 79.4

Male 

SpragueDawley rat

Hepatocellular carcinoma 965.4 1023.9 260.0

All organs lympho-immunoblastic 594.0 457.3 62.4

All organs – all lymphomas 657.1 478.1 66.7

Source: Soffretti et al. (2002a)
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Table E-7. Results of Multistage (1o) quantal modeling of data using PBPK dose 

metrics

AUC (mg-h/L) Peak (mg/L) Amount Metabolized (mg/d)

AIC pvalue

Scaled 

residual 

at dose 

nearest 

to BMD

BMD10 BMDL10 AIC pvalue

Scaled 

residual 

at dose 

nearest to 

BMD

BMD10 BMDL10 AIC pvalue

Scaled 

residual 

at dose 

nearest to 

BMD

BMD10 BMDL10

Female Sprague-Dawley rats

All organs lympho-immunoblastic

357.9 0.13 -0.132 112790 49771 359.9 0.13 -1.56 5034.3 2240.3 357.9 0.34 -0.913 139.8 77.2

All organs – all lymphomas

372.78 0.075 -0.128 111621 48789.6 372.17 0.077 -0.145 4982.2 2196.3 370.57 0.18 -0. 002 134.4 74.5

Male Sprague-Dawley rats

Hepatocellular carcinoma

73.21 0.37 Faileda 73.19 0.37 Faileda 72.55 0.36 Faileda

All organs lympho-immunoblastic

457.45 0.14 1.214 36865 21989 457.3 0.15 1.173 1639.0 981.7 455.46 0.36 -0.750 94.4 61.6

All organs – all lymphomas

468.09 0.15 0.878 37822 22195 467.98 0.15 0.838 1686.5 993.1 467.35 0.20 -0.901 101.7 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 

multistage (1o) quantal modeling of data using PBPK dose metrics

AUC (mg-h/L) Peak (mg/L)

Amount metabolized 

(mg/d)

a

HED BMDL10 (mg/kgday)HED BMDL10 

(mg/kg-day)

HED BMDL10

(mg/kg-day)

Female 

SpragueDawley rat

All organs lymphoimmunoblastic 560.96 584.37 74.54

All organs – all 

lymphomas 555.20 578.18 71.92

Male 

SpragueDawley rat

Hepatocellular 

carcinoma N/A N/A N/A

All organs lymphoimmunoblastic 395.99 405.56 49.65

All organs – all 

lymphomas 397.25 407.22 51.50b

a

Total metabolized methanol was selected as the preferred dose metric (see discussion in section 5.4.1.3). Before 

applying human PBPK model to obtain these HED BMDL10 estimates, Table E-7 mg/d values were converted to 

human mg/d by multiplying by either: (BWhuman)

3⁄4/ (BWrat)3⁄4=(70 kg)3⁄4/ (0.33 kg )3⁄4=55.6 for male rats or, 

(BWhuman)

3⁄4/(BWrat)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).
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Table E-9. Incidence for neoplasms considered for dose-response modeling

Dose (ppm) Examined Adrenal gland 

phoechromocytoma

Lung adenoma and 

adenocarcinoma

Lung adenoma, 

adenocarcinoma, 

adenomatosis

Female F344 rats

0 50 2

10 51 3

100 49 2

1,000 51 7

Male F344 rats

0 52 1 5

10 50 5 6

100 52 2 7

1,000 52 7a 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).
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Table E-11. Benchmark results from multistage quantal dose-response modeling data 

using PBPK dose-metrics

Model

AUC (mg-h/L) Peak (mg/L) Amount metabolized (mg/d)

AIC pvalue

Scaled 

residual 

at dose 

nearest

to BMD

BMD10 BMDL10 AIC p-value

Scaled 

residual at 

dose 

nearest to

BMD

BMD10 BMDL10 AIC p-value

Scaled 

residual 

at dose 

nearest

to BMD

BMD10 BMDL10

Female F344 rats

Adrenal glands – pheochromocytoma

Multistage (3o) 101.37 0.88 0.000 442.1 217.0 101.37 0.88 0.000 23.1 11.3 101.37 0.88 0.000 29.5 14.6

Male F344 rats

Lung – adenomas and adenocarcinomas

Multistage (3o) 107.99 0.16 0.000 454.9 230.7 107.99 0.16 0.000 23.8 12.1 107.99 0.16 0.001 42.2 21.6

Lung – adenomas, adenocarcinomas and adenomatosis

Multistage (1o) 168.61 0.89 -0.035 378.9 168.8 168.61 0.89 -0.035 19.8 8.83 168.59 0.90 -0.038 34.8 15.6

Source: NEDO (1987, 1987/2008b).

Table E-12. Application of human PBPK model to derive HECs from BMDL10

estimates in Table E-11 using multistage quantal modeling

AUC (mg-h/L) Peak (mg/L) Amount metabolized (mg/d)

a

HEC BMCL10 

(mg/m3)

HEC BMCL10 

(mg/m3) HEC BMCL10 (mg/m3

)

Female F344 

rat

Adrenal glands -

phoechromocytoma 384.2262 464.79844 81.85b

Male F344 rat

Lung - adenomas and 

adenocarcinomas 401.96305 485.15995 101.27

Lung - adenomas, 

adenocarcinomas and 

adenomatosis 316.20336 386.70859 73.12

a Total metabolized methanol was selected as the preferred dose metric (see discussion in section 5.4.2.3). Before 

applying human PBPK model to obtain these HEC BMCL10 estimates, Table E-7 mg/d values were converted to 

human mg/d by multiplying by either: (BWhuman)

3⁄4/ (BWrat)3⁄4=(70 kg)3⁄4/ (0.33 kg )3⁄4=55.6 for male rats or, 

(BWhuman)

3⁄4/(BWrat)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: NEDO (1985/2008b)

Table E-13. 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 9c

1,000 25 10d
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Dose (ppm) Examined Malignant Lymphoma

Male Swiss Webster mice

Historical untreated controls a 100 8

10 25 1

100 25 6d

1,000 25 4

a

Toth et al. (1977); bHinderer et al. (1979); cp-value = 0.06; d p-values < 0.05 

Source: Apaja (1980)

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

Daily 

Dose 

(mg/kg-d)

Weekly 

Avg. Dose

(mg/kg-d)

Body

Weight

(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 1462.07 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

1800.00 1542.86 0.045 14998.60 1162.69 41.86

Source: Apaja (1980).

Table E-15. Benchmark results from Multistage-cancer dose-response modeling data 

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

Gender

AUC (mg-h/L) Peak (mg/L) Amount metabolized (mg/d)

AIC pvalue

Scaled 

residual 

at dose 

nearest 

to BMD

BMD10 BMDL10 AIC p-value

Scaled 

residual at 

dose 

nearest to 

BMD

BMD10 BMDL10 AIC p-value

Scaled 

residual 

at dose 

nearest 

to BMD

BMD10 BMDL10

Female micea 288.91 0.32 1.354 5808.3 2957.6 288.42 0.43 1.090 428.5 225.1 288.07 0.55 -0.937 22.7 10.1

Male micea 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 (2o) used for Amount metabolized metric

Source: Apaja (1980).

1
E-19 DRAFT – DO NOT CITE OR QUOTE

Table E-16. Application of human PBPK model to derive HEDs from BMDL10

estimates of Table E-15, Multistage (1o) modeling of malignant lymphoma in Swiss 

mice (Apaja, 1980) using PBPK dose metrics

AUC (mg-h/L) Peak (mg/L) Amount metabolized (mg/d)

a

HED BMDL10 (mg/kg-day) HED BMDL10 (mg/kgday)

HED BMDL10

(mg/kg-day)

Female mice 248 281 9.7

Male mice 269 305 14.7

a

Before applying human PBPK model Table 11 mg/day values were converted to human mg/day by multiplying by 

either: (BWhuman)

3⁄4/ (BWrat)3⁄4=(70 kg)3⁄4/ (0.33 kg )3⁄4=55.6 for male rats or, (BWhuman)3⁄4/(BWrat)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 pvalue

Background 

Dose BMD10 BMDL10 AIC pvalue

Background 

Dose BMD10 BMDL10 AIC pvalue

Background 

Dose BMD10 BMDL10

Female Sprague-Dawley rats (Soffritti et al., 2002a)

All organs lympho-immunoblastic

Multistage (1o) 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

Multistage (1o) 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

Male Sprague-Dawley rats (Soffritti et al., 2002a)

Hepatocellular carcinoma

Multistage (1o) 97.52 0.00 Faileda 73.19 0.37 Faileda 72.55 0.36 Faileda

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 lymphomas

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 rats (NEDO, 1985/2008b)

Adrenal glands – pheochromocytoma

Multistage (2o) 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 rats (NEDO, 1985/2008b)

Lung – adenomas and adenocarcinomas

Multistage (1o) 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 (1o) 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 (1o) 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 three times maximum input doses.

Source: NEDO (1987, 1987/2008b).
E-20 DRAFT – DO NOT CITE OR QUOTE

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).
E-21 DRAFT – DO NOT CITE OR QUOTE

0

0.2

0.4

0.6

0.8

1

0 500 1000 1500 2000

Fraction 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 lympho-immunoblastic – Multistage Weibull Model 

– Approach 1.

Source: Soffretti et al. (2002a).

0

0.2

0.4

0.6

0.8

1

0 500 1000 1500 2000

Fraction 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 lymphomas – Multistage Weibull Model – Approach 1.

Source: Soffretti et al. (2002a).

1
E-22 DRAFT – DO NOT CITE OR QUOTE

0

0.2

0.4

0.6

0.8

1

0 200 400 600 800 1000 1200 1400 1600 1800

Risk

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. Male – 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 1800

Fraction 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 lympho-immunoblastic – Multistage Weibull Model –

Approach 1.

Source: Soffretti et al. (2002a).
E-23 DRAFT – DO NOT CITE OR QUOTE

0

0.2

0.4

0.6

0.8

1

0 200 400 600 800 1000 1200 1400 1600 1800

Fraction 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 lymphomas – 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 Weeks

Probability of Response

 2177 mg/kg-day 624.1 mg/kg-day 66 mg/kg-day Control

96 105 weeks

weeks

153

weeks

Figure E-8. Female rats –All lymphomas time-to-tumor model fit and Kaplan Meier 

curves.

Source: Soffretti et al. (2002a).
E-24 DRAFT – DO NOT CITE OR QUOTE

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 Weeks

Probability 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 rats –All lymphomas time-to-tumor model fit and Kaplan Meier 

curves.

Source: Soffretti et al. (2002a).

 0.1

 0.15

 0.2

 0.25

 0.3

 0.35

 0.4

 0.45

 0.5

 0 50 100 150 200

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

08:55 09/03 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

Figure E-10. Male rats- All lymphomas; dose = amount metabolized (mg/day); 1o

multistage model.

Source: Soffretti et al. (2002a).
E-25 DRAFT – DO NOT CITE OR QUOTE

 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).
E-26 DRAFT – DO NOT CITE OR QUOTE

 0

 0.05

 0.1

 0.15

 0.2

 0.25

 0 5 10 15 20 25 30

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

08:58 09/03 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

Figure E-13. Female rats- pheochromocytomas; dose = amount metabolized (mg/d); 3o

multistage model.

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 40

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

09:58 09/03 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

 0

 0.1

 0.2

 0.3

 0.4

 0.5

 0 5 10 15 20 25 30 35 40

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

09:56 09/03 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

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

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

Source: Apaja (1980)