## UNITED STATES ENVIRONMENTAL PROTECTION AGENCY WASHINGTON, 

## D.C. 20460

OFFICE OF CHEMICAL SAFETY AND POLLUTION PREVENTION



## MEMORANDUM Date: January 28, 2014

SUBJECT: Dicrotophos: Benchmark Dose Analysis of Acute, Subchronic, and Chronic Studies to Support Derivation of Points of Departure.

DP Barcode: D414900 

## Registration No.: NA 

## PC Code: 035201 · 

## Decision No.: 454409 

## Petition No.: NA 

## Risk Assessment Type: NA TXR 

## No.: 0056878 

## MRIDNo.: NA 

## Regulatory Action: Registration Review 

## Case No.: 145



## FROM: CAS No.: 141-66-2 

## 40 CFR: 180.299

John Liccione

, Toxicologist/Risk Assessor RABV Elizabeth Holman, Physical Scientist RABII Health Effects Division (7905P)



## THROUGH: Anna Lowit, Ph.D. (;} 

# ;J J I 

## Senior Scientist 

# Vr-tJ**~** Immediate Office Health Effects Division (7905P)



## TO: Elissa Reaves Branch Chief RABIV Health Effects Division (7905P)

Page 1 of438 

# ...

### Page 2 of 438 

## I. CONCLUSIONS

HED performed benchmark dose (BMD) analyses of several acute, subchronic, and chronic studies for dicrotophos in order to support de rivation of points of departure (POD) for dicrotophos as part of the si ngle-chemical registration revi ew risk assessment.

This memo summarizes the approach and pres ents the results of BMD analyses.

II. BACKGROUND

BMD analyses were performed with EPA’s Benc hmark Dose Software (Version 2.2) using an exponential model for continuous data. The data selected for evaluation consisted of decreased brain and red blood cell (RBC) c holinesterase (ChE) activities. For the acute studies, the analyses focused on both pup and adult data from the comparative cholinesterase (CCA) studies that entailed single dose exposur es. Data from the 11-day re peated dosing and gestational dosing in the CCA study were also evaluated. In addition, OPP ev aluated data from subchronic (oral, dermal, inhalation), chroni c, and developmental neurotoxici ty studies which included brain and/or RBC acetylcholinestera se (AChE) data in adult animals. AChE data from OP exposures ranging from approximately 21 days of dosing and l onger are considered to be at steady state.

OPP has used the exponential model for modeling AChE activity for the OP and N -methyl carbamate cumulative risk assessments along with multiple single chemical risk assessments of ChE-inhibiting pesticides. Model runs for Ch E activity were conducted with an appropriate benchmark response level (10%). As such the BMD 10 

## (estimated dose to result in 10% change from background levels) and BMDL 10 

## (the lower 95% confidence level on the BMD 10 

## ) are provided in the output. Statistical (e.g., goodness of fit va lues) and graphical results were used in model evaluation.

III. RESULTS

The results of the single dose and rep eated oral dosing BMD analyses are summarized below in Tables 1 and 2 respectiv ely. Table 3 summarizes the results of the BMD analyses for repeated exposure st udies via the dermal and inhalation routes. Details are included in the appendix. G ood model fit (p>0.1) was obtained for the majority of the analyses, with any excepti ons being noted in these summary tables.Page 3 of 438TABLE 1: Results of BMD Exponential Modeling for Brain and RBC ChE Data on Dicrotophos, Single Dose StudiesDicrotophos/StudySex/ageCompartmentBMD ResultsBMD10 (mg/kg) BMDL10 (mg/kg)MRID 46153205CCA Acute Study –Single DoseMalePND8Brain No reliable fit No reliable fitFemalePND8Brain 0.082 0.071MRID 46153205CCA Acute Study –Single DoseMalePND8RBC 0.10 0.051FemalePND8RBC 0.11 0.059MRID 46153205CCA Acute Study –Single DoseMalePND15Brain 0.079 0.070FemalePND15Brain No reliable fit No reliable fitMRID 46153205CCA Acute Study –Single DoseMalePND15RBC No reliable fit No reliable fitFemalePND15RBC 0.10 0.067MRID 46153205CCA Acute Study –Single DoseMalePND22Brain 0.23 0.13FemalePND22Brain No reliable fit No reliable fitMRID 46153205CCA Acute Study –Single DoseMalePND22RBC 0.12 0.088FemalePND22RBC 0.14 0.099MRID 46153206CCA Acute Study –Single DoseAdult MalePND42Brain 0.38 0.22Adult FemalePND42Brain 0.12 0.092Page 4 of 438Dicrotophos/StudySex/ageCompartmentBMD ResultsBMD10 (mg/kg) BMDL10 (mg/kg)MRID 46153206CCA Acute Study –Single DoseAdult MalePND42RBC No reliable fit No reliable fitAdult FemalePND42RBC 0.31 0.20TABLE 2: Results of BMD Exponential Modeling for Brain and RBC ChE Data on Dicrotophos, Repeated Oral DosingStudies Ranging in Duration from 7 days to 2 years.Dicrotophos/StudySex/ageCompartmentBMD ResultsBMD10 (mg/kg/day) BMDL10 (mg/kg/day)MRID 46153204CCA Repeat Study –7 daysAdult MalePND42Brain 0.11 0.054Adult FemalePND42Brain No reliable fit No reliable fitMRID 46153204CCA Repeat Study –7 daysAdult MalePND42RBC 0.072 0.051Adult FemalePND42RBC 0.095 0.069MRID 46153204CCA Repeat Study –7 daysPup MalePND12Brain 0.064 0.032Pup FemalePND12Brain 0.049 0.028MRID 46153204CCA Repeat Study –7 daysPup MalePND12RBC 0.045 0.042Pup FemalePND12RBC 0.024 0.017MRID 46153208CCA Repeat Study –4-WeeksAdult MaleBrain 0.060 0.015Adult FemaleBrain 0.077 0.059Page 5 of 438Dicrotophos/StudySex/ageCompartmentBMD ResultsBMD10 (mg/kg/day) BMDL10 (mg/kg/day)MRID 46153208CCA Repeat Study –4-WeeksAdult MaleRBC 0.12 0.097Adult FemaleRBC 0.17 0.042MRID 46153201DevelopmentalNeurotoxicity inRatsMaternalGestation Day 22(~15D of dosing)Brain0.0280.021MaternalLactation Day 22(~37D of dosing)Brain 0.0230.017MRID 46153201DevelopmentalNeurotoxicity inRatsMaternalGestation Day 22(~15D of dosing)RBC 0.0360.028MaternalLactation Day 22(~37D of dosing)RBC 0.0380.029MRID 46153201DevelopmentalNeurotoxicity inRatsMale FetusGestation Day 22Brain 0.0640.036Female FetusGestation Day 22Brain 0.0940.064MRID 46153201DevelopmentalNeurotoxicity inRatsMale FetusGestation Day 22RBC 0.0660.045Female FetusGestation Day 22RBC 0.0490.033Page 6 of 438Dicrotophos/StudySex/ageCompartmentBMD ResultsBMD10 (mg/kg/day) BMDL10 (mg/kg/day)MRID 43980201SubchronicNeurotoxicity inRatsMale Adult35D of dosingBrain 0.0390.036Female Adult35D of dosingBrain 0.0480.042MRID 43980201SubchronicNeurotoxicity inRatsMale Adult63D of dosingBrain 0.041a0.035aFemale Adult63D of dosingBrain 0.0440.041MRID 43980201SubchronicNeurotoxicity inRatsMale Adult98D of dosingBrain 0.0480.044Female Adult98D of dosingBrain 0.0450.042MRID 43980201SubchronicNeurotoxicity inRatsMale35D of dosingRBC 0.0450.027Female Adult35D of dosingRBC 0.0260.015MRID 43980201SubchronicNeurotoxicity inRatsMale Adult63D of dosingRBC 0.0480.022Female Adult63D of dosingRBC 0.0570.033MRID 43980201SubchronicNeurotoxicity inRatsMale Adult98D of dosingRBC 0.0230.013Female Adult98D of dosingRBC 0.0580.030Page 7 of 438Dicrotophos/StudySex/ageCompartmentBMD ResultsBMD10 (mg/kg/day) BMDL10 (mg/kg/day)MRID 44527802Chronic Toxicity inRats (2 year)Male Adult371D of dosingBrain 0.028a0.025aFemale Adult371D of dosingBrain 0.047b0.037bMRID 44527802Chronic Toxicity inRats (2 year)Male Adult700D of dosingBrain 0.0270.022Female Adult735D of dosingBrain 0.042c0.035cMRID 44527802Chronic Toxicity inRats (2 year)Male371D of dosingRBC 0.039d0.034dFemale371D of dosingRBC 0.040 0.016MRID 44527802Chronic Toxicity inRats (2 year)Male Adult700D of dosingRBC No reliable fit No reliable fitFemale Adult735D of dosingRBC 0.053d0.042daDue to dose spacing issues, adequate statistical model fit (p>0.1) were not obtained for these specific analyses for Brain ChE. However, they are listed here forcharacterization purposes, as these results are similar in magnitude to those obtained using the more robust techniques used inthe 2006 CRA (which included theresults of both the subchronic neurotoxicity and chronic toxicity brain ChE data).bp=0.077 for model fit; based on visual inspection and taking intoaccount the observed dose spacing issues, this model fit is adequate.cp=0.0896 for model fit; based on visual inspection and taking into account the observed dose spacing issues, this model fit isadequate.dDropped high dose in order to improve model fit.Page 8 of 438TABLE 3: Results of BMD Exponential Modeling for Brain and RBC ChE Data on Dicrotophos, Repeated Exposure Studiesvia the dermal and inhalation routes.Dicrotophos/StudySex/ageCompartmentBMD ResultsBMD10 BMDL10MRID 4648450128-Day DermalToxicity StudyMale Brain No reliable fit(p=0.062; 3.67 mg/kg/day)No reliable fit(p=0.062; 2.41 mg/kg/day)Female Brain 3.35 mg/kg/day 2.14 mg/kg/dayMRID 4648450128-Day DermalToxicity StudyMale RBC 3.15 mg/kg/day 2.12 mg/kg/dayFemale RBC 3.65 mg/kg/day 2.54 mg/kg/dayMRID 4814670228-Day InhalationToxicity StudyMale RBC 0.42 ug/L/day 0.34 ug/L/dayFemale RBC 0.71 ug/L/day 0.49 ug/L/dayMRID 4814670228-Day InhalationToxicity StudyMale Brain 0.68 ug/L/day 0.61 ug/L/dayFemale Brain No reliable fit(p=0.01; 0.67 ug/L/day)No reliable fit(p=0.01; 0.62 ug/L/day)

### Page 9 of 438 

### Table 4 - Summary of Points of Departure for Dicrotophos

Exposure Scenario Point of Departure (BMDL 10 

### ) BMD 10 

### , Study and Toxicological Effects

Acute Dietary (all populations) BMDL 10 

### = 0.07mg/kg/day BMD 10 

### = 0.08 mg/kg/day CCA Study (MRID 

## 4615205 ) in the rat – PND 8, 15, & 22

Inhibition of brain ChE in male and female rat pups.

Steady-State Dietary (all populations) BMDL 10 

### = 0.03 mg/kg/day BMD 10 

### = 0.04 mg/kg/day Subchronic and chronic toxicity studies (OP CRA 2002, 2006; MRID’s 

## 43980201 and 44527802 )

Inhibition of brain ChE in adult rat

Incidental Oral

Single Day BMDL 10 

### = 0.07mg/kg/day BMD 10 

### = 0.08 mg/kg/day CCA Study (MRID 

## 4615205 ) in the rat – PND 8, 15, & 22

Inhibition of brain ChE in male and female rat pups.

Incidental Oral

Steady State BMDL 10 

### = 0.03 mg/kg/day BMD 10 

### = 0.04 mg/kg/day Subchronic and chronic toxicity studies (OP CRA 2002, 2006; MRID’s 

## 43980201 and 44527802 )

Inhibition of brain ChE in adult rat

Dermal Single Day & Steady State (21 days and longer) 

## BMDL 10 

## = 2.1 mg/kg/day BMD 10 

## = 3.3 mg/kg/day 28-day dermal toxicity study (MRID 46484501) in rats

Inhibition of brain ChE in adult rat.

Inhalation Single Day & Steady State (21 days and longer) 

## BMDL 10 

## = 0.00062 mg/L/day BMD 10 

## = 0.00067 mg/L/day 28-day inhalation toxicity study (MRID 48146702)

Inhibition of brain ChE in adult rat. Point of Departure (PoD) = A data point or an estimated point that is derived from observed dose-response data and used to mark the beginning of extrapolation to determine risk associated with lower en vironmentally relevant human exposures.

### Page 10 of 438 

# APPENDIX



#  Single Dose CCA Studies

Male PND8 Brain ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) Gnuplot Plotting File: Fri Feb 04 12:50:23 2011

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -4.24116 -4.24116 -4.24116 -4.24116 rho 1.39237 1.39237 1.39237 1.39237 a 1.16793 1.17334 3.5595 3.5595 b 0.287108 0.0424821 0.951872 0.951872 c -- -- 0.171239 0.171239 d -- 2 -- 1

### Page 11 of 438 Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -2.37628 -3.64813 -3.97477 -3.97477 rho -0.278081 3.73979 1.29258 1.29258 a 3.25821 2.40915 3.25782 3.25782 b 1.30244 0.26755 1.84681 1.84681 c -- -- 0.200697 0.200697 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 3.39 0.28 0.1 5 2.83 0.23 0.3 5 1.95 0.17 1 5 1.14 0.2 5 5 0.64 0.07

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 3.258 0.2586 1.139 0.1 2.86 0.2634 -0.2574 0.3 2.204 0.2731 -2.083 1 0.8858 0.31 1.834 5 0.004839 0.6396 2.221 3 0 2.409 0.8353 2.626 0.1 2.346 0.7946 1.363 0.3 2.223 0.7189 -0.8502 1 1.844 0.5065 -3.106 5 0.6322 0.06847 0.2532 4 0 3.258 0.294 1.005 0.1 2.819 0.2678 0.09426 0.3 2.15 0.2248 -1.991 1 1.065 0.1427 1.182 5 0.6541 0.1042 -0.3024 5 0 3.258 0.294 1.005 0.1 2.819 0.2678 0.09426 0.3 2.15 0.2248 -1.991 1 1.065 0.1427 1.182 5 0.6541 0.1042 -0.3024

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i)

### Page 12 of 438 Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 30.21049 6 -48.42098 A2 34.20578 10 -48.41155 A3 32.84948 7 -51.69896 R -13.40698 2 30.81397 2 15.51429 4 -23.02857 3 8.007102 4 -8.014203 4 28.71519 5 -47.43038 5 28.71519 5 -47.43038

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 95.23 8 < 0.0001 Test 2 7.991 4 0.09192 Test 3 2.713 3 0.4381 Test 4 34.67 3 < 0.0001 Test 5a 49.68 3 < 0.0001 Test 5b -15.01 0 N/A Test 6a 8.269 2 0.01601 Test 6b 26.4 1 < 0.0001 Test 7a 8.269 2 0.01601 Test 7b 41.42 1 < 0.0001 Test 7c -9.415e-012 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

### Page 13 of 438 The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0808944 0.0667684 3 0.393797 0.345824 4 0.0723714 0.054721 5 0.0723714 0.054721

### Page 14 of 438 0.5

1 1.5

2 2.5

3 3.5

4

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:50 02/04 2011 BMD BMDL Exponential

0.5

1 1.5

2 2.5

3 3.5

4

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:50 02/04 2011 BMD BMDL Exponential

### Page 15 of 438 0.5

1 1.5

2 2.5

3 3.5

4

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:50 02/04 2011 BMD BMDL Exponential

0.5

1 1.5

2 2.5

3 3.5

4

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

12:50 02/04 2011 BMD BMDL Exponential

### Page 16 of 438 

## Female PND8 Brain ChE Inhibition

====================================== ============================

== Exponential Model. (Ver sion: 1.7; Date: 12/10/2009) Input Data File: C:/D ocuments and Settings/jliccion/My Documents/BMDS220/Data/exp_U ntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 10:49:05 2012

====================================== ============================

==

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~

The form of the re sponse function by Model: Model 2: Y[ dose] = a * exp{sign * b * dose} Model 3: Y[dos e] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the medi an response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nes ted within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(l nalpha +rho *ln(Y[dose])) The variance is to be modeled as Va r(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of reco rds with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

### Page 17 of 438 

## Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha -4.62596 -4 .62596 -4. 62596 -4.62596 rho 1.28187 1.28187 1.28187 1.28187

a 1.20084 1.19122 3.3495 3.3495 b 0.284194 0.0414891 0.92414 6 0.924146

c -- -- 0.187661 0.187661 d -- 2 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha -2.6115 -4 .17284 -4 .48155 -4.48155 rho -0.385145 4.25018 1. 67832 1.67832

a 3.19908 2.41313 3.223 3.223 b 1.11329 0.260689 1.64398 1.64398

c -- -- 0.204218 0.204218 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 3. 19 0.26 0.1 5 2. 99 0.09 0.3 5 2. 09 0.26 1 5 1. 17 0.18 5 5 0. 66 0.05

Estimate d Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ --------- - --------- ---------------- 2 0 3.199 0.2166 -0.0937 0.1 2.862 0.2213 1.293 0.3 2.291 0.231 -1.943 1 1.051 0.2684 0.9929 5 0.01223 0.6327 2.289

### Page 18 of 438 

## 3 0 2.413 0.807 2.152 0.1 2.351 0.7636 1.871 0.3 2.232 0.6835 -0.4633 1 1.859 0.4638 -3.324 5 0.6554 0.05057 0.2038 4 0 3.223 0.284 -0.2598 0.1 2.834 0.255 1.366 0.3 2.224 0.2081 -1.445 1 1.154 0.1199 0.3031 5 0.6589 0.07495 0.03322 5 0 3.223 0.284 -0.2598 0.1 2.834 0.255 1.366 0.3 2.224 0.2081 -1.445 1 1.154 0.1199 0.3031 5 0.6589 0.07495 0.03322

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalp ha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ---------------- - ---- ------------ A1 31.96788 6 -51.93576 A2 39.35241 10 -58.70482 A3 34.17331 7 -54.34662 R -12.57319 2 29.14637 2 18.88165 4 -29.76331 3 10.58704 4 -13.17409 4 32.03381 5 -54.06762 5 32.03381 5 -54.06762

### Page 19 of 438 

## Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does respon se and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Mode l 2 fit the data? (A3 vs. 2)

Test 5a: Does Mode l 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 be tter than Model 2? (3 vs. 2)

Test 6a: Does Mode l 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 be tter than Model 2? (4 vs. 2)

Test 7a: Does Mode l 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 be tter than Model 3? (5 vs. 3) Test 7c: Is Model 5 be tter than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ---------- -------------- ------ -------------- Test 1 103.9 8 < 0.0001 Test 2 14.77 4 0.005205 Test 3 10.36 3 0.01575 Test 4 30.58 3 < 0.0001 Test 5a 47.17 3 < 0.0001 Test 5b - 16.59 0 N/A Test 6a 4.279 2 0.1177 Test 6b 26.3 1 < 0.0001 Test 7a 4.279 2 0.1177 Test 7b 42.89 1 < 0.0001 Test 7c -9.948e-013 0 N/A

The p-value for Test 1 is less than .05. T here appears to be a difference between res ponse and/or variances among the dose levels, it seems appr opriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous

### Page 20 of 438 

## variance model appears to be appropriate.

The p-value for Test 3 is less than .1. Y ou may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you ma y want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you ma y want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0946385 0.0805217 3 0.404161 0.361863 4 0.0816853 0.0711499

### Page 21 of 438 

## 5 0.0816853 0.0711499

0.5

1 1.5

2 2.5

3 3.5

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

10:49 07/26 2012 BMD BMDL Exponential

### Page 22 of 438 0.5

1 1.5

2 2.5

3 3.5

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:49 07/26 2012 BMD BMDL Exponential

0.5

1 1.5

2 2.5

3 3.5

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

10:49 07/26 2012 BMD BMDL Exponential

### Page 23 of 438 

## Male PND8 RBC ChE Inhibition

!!! Warning: optimum may not have been found for Model 3 !!! !!! Bad completion code in maximum likelihood optimization routine !!! !!! Try choosing different initial values !!!

Warning: optimum may not have been found form model 2 (BMDL). Bad process completion in Optimization routine.

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 10:27:49 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 0.5

1 1.5

2 2.5

3 3.5

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:49 07/26 2012 BMD BMDL Exponential

### Page 24 of 438 Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -9.80736 -9.80736 -9.80736 -9.80736 rho 2.82591 2.82591 2.82591 2.82591 a 1841.35 1841.35 3235.05 3235.05 b 0.120565 0.120565 0.717396 0.717396 c -- -- 0.431288 0.431288 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -53.1618 -9.80736 0.618694 6.88865 rho 8.55125 2.82591 1.51144 0.685424 a 2422.14 1841.35 3060.4 2997.3 b 0.101286 0.120565 1.99301 3.31104 c -- -- 0.466057 0.51585 d -- 1 -- 16.8193

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2911 547 0.1 5 3081 551 0.3 5 2146 435 1 5 1624 608 5 5 1465 98

Estimated Values of Interest

### Page 25 of 438 Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2422 842.6 1.297 0.1 2398 806.9 1.893 0.3 2350 740 -0.6154 1 2189 546.5 -2.311 5 1460 96.66 0.1229 3 0 0 0 1.#IO 0.1 0 0 1.#IO 0.3 0 0 1.#IO 1 0 0 1.#IO 5 0 0 1.#IO 4 0 3060 587 -0.5691 0.1 2765 543.6 1.299 0.3 2325 476.9 -0.8393 1 1649 367.8 -0.1521 5 1426 329.7 0.2618 5 0 2997 486.8 -0.3964 0.1 2997 486.8 0.3845 0.3 2140 433.7 0.03031 1 1546 388 0.4486 5 1546 388 -0.4677

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -164.2631 6 340.5261 A2 -158.1441 10 336.2881 A3 -163.6662 7 341.3325 R -179.1511 2 362.3021 2 -167.0551 4 342.1101 3 -277.7852 4 563.5703 4 -165.2316 5 340.4631 5 -164.3516 6 340.7031

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1)

### Page 26 of 438 Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 42.01 8 < 0.0001 Test 2 12.24 4 0.01567 Test 3 11.04 3 0.01149 Test 4 6.778 3 0.07933 Test 5a 228.2 3 < 0.0001 Test 5b -221.5 0 N/A Test 6a 3.131 2 0.209 Test 6b 3.647 1 0.05617 Test 7a 1.371 1 0.2417 Test 7b 226.9 2 < 0.0001 Test 7c 1.76 1 0.1846

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does

### Page 27 of 438 not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 1.04023 0.817659 3 0.87389 Bad completion 4 0.104052 0.0512996 5 0.276846 0.0684109

1000 1500 2000 2500 3000 3500 4000

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

10:27 07/26 2012 BMD BMDL Exponential

### Page 28 of 438 1000 1500 2000 2500 3000 3500 4000

-1 0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:27 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500 4000

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

10:27 07/26 2012 BMD BMDL Exponential

### Page 29 of 438 

## Female PND8 RBC ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 10:10:05 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5. 1000 1500 2000 2500 3000 3500 4000

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:27 07/26 2012 BMD BMDL Exponential

### Page 30 of 438 Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 12.5207 12.5207 12.5207 12.5207 rho(S) 0 0 0 0 a 2008.24 2008.24 3503.85 3503.85 b 0.124795 0.124795 0.714306 0.714306 c -- -- 0.42946 0.42946 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 13.0488 13.0488 12.6189 12.546 rho 0 0 0 0 a 2892.16 2892.16 3339.12 3245 b 0.159702 0.159702 1.87191 3.25902 c -- -- 0.462783 0.516333 d -- 1 -- 16.726

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 3153 486 0.1 5 3337 531 0.3 5 2466 722 1 5 1771 495 5 5 1580 654

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2892 681.6 0.8557 0.1 2846 681.6 1.61

### Page 31 of 438 0.3 2757 681.6 -0.9543 1 2465 681.6 -2.278 5 1301 681.6 0.9138 3 0 2892 681.6 0.8557 0.1 2846 681.6 1.61 0.3 2757 681.6 -0.9543 1 2465 681.6 -2.278 5 1301 681.6 0.9138 4 0 3339 549.7 -0.757 0.1 3033 549.7 1.237 0.3 2568 549.7 -0.4162 1 1821 549.7 -0.2043 5 1545 549.7 0.1406 5 0 3245 530.1 -0.3881 0.1 3245 530.1 0.3881 0.3 2466 530.1 -1.255e-008 1 1676 530.1 0.4029 5 1676 530.1 -0.4029

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -169.0087 6 350.0174 A2 -168.364 10 356.728 A3 -169.0087 6 350.0174 R -181.986 2 367.972 2 -175.6102 3 357.2203 3 -175.6102 3 357.2203 4 -170.2361 4 348.4721 5 -169.3256 5 348.6512

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3)

### Page 32 of 438 Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 27.24 8 0.0006416 Test 2 1.289 4 0.8632 Test 3 1.289 4 0.8632 Test 4 13.2 3 0.004218 Test 5a 13.2 3 0.004218 Test 5b -5.19e-011 0 N/A Test 6a 2.455 2 0.2931 Test 6b 10.75 1 0.001044 Test 7a 0.6338 1 0.426 Test 7b 12.57 2 0.001865 Test 7c 1.821 1 0.1772

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

### Page 33 of 438 Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.659733 0.381932 3 0.659731 0.381932 4 0.110034 0.0586555 5 0.281148 0.076904

500 1000 1500 2000 2500 3000 3500 4000

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

10:10 07/26 2012 BMD BMDL Exponential

### Page 34 of 438 500 1000 1500 2000 2500 3000 3500 4000

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:10 07/26 2012 BMD BMDL Exponential

500 1000 1500 2000 2500 3000 3500 4000

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

10:10 07/26 2012 BMD BMDL Exponential

### Page 35 of 438 

## Male PND15 Brain ChE Inhibition

====================================== ============================

== Exponential Model. (Ver sion: 1.7; Date: 12/10/2009) Input Data File: C:/Usepa/BMDS212/ Data/exp_Testrunscontinuous_Setting.(d) Gnuplot Plotting File: Fri Feb 04 13:02:01 2011

====================================== ============================

==

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~

The form of the re sponse function by Model: Model 2: Y[ dose] = a * exp{sign * b * dose} Model 3: Y[dos e] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 500 1000 1500 2000 2500 3000 3500 4000

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:10 07/26 2012 BMD BMDL Exponential

### Page 36 of 438 

## Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the medi an response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nes ted within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(l nalpha +rho *ln(Y[dose])) The variance is to be modeled as Va r(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of reco rds with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha -5.073 -5.073 -5.073 -5.073 rho 2.39751 2.39751 2.39751 2.39751

a 1.5058 1.36944 4.9245 4.9245 b 0.316827 0.04148 0. 982532 0.982532

c -- -- 0.148915 0.148915 d -- 2 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha -5.17757 -5.17758 -4.77837 -4.77837 rho 4.88475 4.88475 2.37197 2.37197

a 3.18867 3.18867 4.43386 4.43386

### Page 37 of 438 

## b 0.28475 0.28475 1.63515 1.63515

c -- -- 0.172746 0.172746 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 4. 69 0.38 0.1 5 3. 73 0.6 0.3 5 2. 95 0.18 1 5 1. 48 0.25 5 5 0. 77 0.04

Estimate d Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ --------- - --------- ---------------- 2 0 3.189 1.276 2.632 0.1 3.099 1.19 1.185 0.3 2.928 1.035 0.04839 1 2.399 0.6363 -3.228 5 0.7679 0.0394 0.1213 3 0 3.189 1.276 2.632 0.1 3.099 1.19 1.185 0.3 2.928 1.035 0.04839 1 2.399 0.6363 -3.228 5 0.7679 0.0394 0.1213 4 0 4.434 0.5364 1.068 0.1 3.881 0.4579 -0.7352 0.3 3.012 0.3391 -0.4074 1 1.481 0. 1461 -0.01373 5 0.767 0.06695 0.1014 5 0 4.434 0.5364 1.068 0.1 3.881 0.4579 -0.7352 0.3 3.012 0.3391 -0.4074 1 1.481 0. 1461 -0.01373 5 0.767 0.06695 0.1014

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij)

### Page 38 of 438 

## Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalp ha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ---------------- - ---- ------------ A1 16.77385 6 -21.54771 A2 29.28119 10 -38.56237 A3 23.94041 7 -33.88081 R -22.09697 2 48.19393 2 3.669795 4 0.6604104 3 3.669795 4 0.6604104 4 23.06442 5 -36.12885 5 23.06442 5 -36.12885

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does respon se and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Mode l 2 fit the data? (A3 vs. 2)

Test 5a: Does Mode l 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 be tter than Model 2? (3 vs. 2)

Test 6a: Does Mode l 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 be tter than Model 2? (4 vs. 2)

Test 7a: Does Mode l 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 be tter than Model 3? (5 vs. 3)

### Page 39 of 438 

## Test 7c: Is Model 5 be tter than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ---------- -------------- ------ -------------- Test 1 102.8 8 < 0.0001 Test 2 25.01 4 < 0.0001 Test 3 10.68 3 0.01358 Test 4 40.54 3 < 0.0001 Test 5a 40.54 3 < 0.0001 Test 5b -1.863e-012 0 N/A Test 6a 1.752 2 0.4165 Test 6b 38.79 1 < 0.0001 Test 7a 1.752 2 0.4165 Test 7b 38.79 1 < 0.0001 Test 7c 0 0 N/A

The p-value for Test 1 is less than .05. T here appears to be a difference between res ponse and/or variances among the dose levels, it seems appr opriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. Y ou may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you ma y want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you ma y want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems

### Page 40 of 438 

## to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.370011 0.335325 3 0.370011 0.335325 4 0.0787916 0.0696334 5 0.0787916 0.0696334

### Page 41 of 438 1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

13:02 02/04 2011 BMD BMDL Exponential

1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

13:02 02/04 2011 BMD BMDL Exponential

### Page 42 of 438 1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

13:02 02/04 2011 BMD BMDL Exponential

1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

13:02 02/04 2011 BMD BMDL Exponential

### Page 43 of 438 

## Female PND15 Brain ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 12:45:32 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -2.93219 -2.93219 -2.93219 -2.93219 rho(S) 0 0 0 0 a 1.45976 1.45976 5.145 5.145 b 0.306168 0.306168 1.00146 1.00146 c -- -- 0.142533 0.142533 d -- 1 -- 1

(S) = Specified

### Page 44 of 438 Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -1.19866 -1.19866 -2.08331 -2.08331 rho 0 0 0 0 a 4.40562 4.40562 4.65255 4.65255 b 1.41217 1.41217 2.83215 2.83215 c -- -- 0.206291 0.206291 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 4.9 0.3 0.1 5 3.31 0.45 0.3 5 2.69 0.1 1 5 1.4 0.17 5 5 0.77 0.04

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.406 0.5492 2.013 0.1 3.825 0.5492 -2.099 0.3 2.884 0.5492 -0.7905 1 1.073 0.5492 1.33 5 0.00378 0.5492 3.12 3 0 4.406 0.5492 2.013 0.1 3.825 0.5492 -2.099 0.3 2.884 0.5492 -0.7905 1 1.073 0.5492 1.33 5 0.00378 0.5492 3.12 4 0 4.653 0.3529 1.568 0.1 3.742 0.3529 -2.736 0.3 2.539 0.3529 0.9589 1 1.177 0.3529 1.412 5 0.9598 0.3529 -1.203 5 0 4.653 0.3529 1.568 0.1 3.742 0.3529 -2.736 0.3 2.539 0.3529 0.9589 1 1.177 0.3529 1.412 5 0.9598 0.3529 -1.203

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

### Page 45 of 438 Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 24.15243 6 -36.30486 A2 36.76879 10 -53.53757 A3 24.15243 6 -36.30486 R -22.1916 2 48.3832 2 2.483245 3 1.033511 3 2.483245 3 1.033511 4 13.54142 4 -19.08285 5 13.54142 4 -19.08285

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 117.9 8 < 0.0001 Test 2 25.23 4 < 0.0001 Test 3 25.23 4 < 0.0001 Test 4 43.34 3 < 0.0001 Test 5a 43.34 3 < 0.0001 Test 5b -7.461e-014 0 N/A Test 6a 21.22 2 < 0.0001 Test 6b 22.12 1 < 0.0001 Test 7a 21.22 2 < 0.0001 Test 7b 22.12 1 < 0.0001 Test 7c -1.727e-012 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. Consider running

### Page 46 of 438 a non-homogeneous variance model.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0746088 0.0565782 3 0.0746088 0.0565782 4 0.0475484 0.0368912 5 0.0475484 0.0368912

### Page 47 of 438 1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:45 07/26 2012 BMD BMDL Exponential

1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:45 07/26 2012 BMD BMDL Exponential

### Page 48 of 438 1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:45 07/26 2012 BMD BMDL Exponential

1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

12:45 07/26 2012 BMD BMDL Exponential

### Page 49 of 438 

## Male PND15 RBC ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 12:36:59 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 1.44121 1.44121 1.44121 1.44121 rho 1.27633 1.27633 1.27633 1.27633 a 1624.28 1624.28 3307.5 3307.5 b 0.152367 0.152367 0.765486 0.765486 c -- -- 0.337185 0.337185 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

### Page 50 of 438 -------- ------- ------- ------- ------- lnalpha -14.0995 -14.0995 -9.83321 -9.83319 rho 3.44246 3.44245 2.81245 2.81245 a 2408.97 2408.97 2753.77 2753.77 b 0.147786 0.147786 1.27794 1.27794 c -- -- 0.429066 0.429066 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 3150 285 0.1 5 2361 304 0.3 5 2006 527 1 5 1716 143 5 5 1171 184

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2409 574.4 2.885 0.1 2374 560 -0.05044 0.3 2305 532.2 -1.254 1 2078 445.4 -1.817 5 1151 161 0.2835 3 0 2409 574.4 2.885 0.1 2374 560 -0.05044 0.3 2305 532.2 -1.254 1 2078 445.4 -1.817 5 1151 161 0.2835 4 0 2754 503.6 1.759 0.1 2565 455.8 -1.002 0.3 2253 379.8 -1.455 1 1620 238.7 0.9031 5 1184 153.7 -0.1919 5 0 2754 503.6 1.759 0.1 2565 455.8 -1.002 0.3 2253 379.8 -1.455 1 1620 238.7 0.9031 5 1184 153.7 -0.1919

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

### Page 51 of 438 Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -153.7633 6 319.5267 A2 -148.7832 10 317.5664 A3 -153.0341 7 320.0682 R -176.9844 2 357.9688 2 -163.1947 4 334.3894 3 -163.1947 4 334.3894 4 -156.4676 5 322.9352 5 -156.4676 5 322.9352

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 56.4 8 < 0.0001 Test 2 9.96 4 0.0411 Test 3 8.502 3 0.0367 Test 4 20.32 3 0.0001456 Test 5a 20.32 3 0.0001456 Test 5b -3.428e-011 0 N/A Test 6a 6.867 2 0.03227 Test 6b 13.45 1 0.0002445 Test 7a 6.867 2 0.03227 Test 7b 13.45 1 0.0002445 Test 7c -1.012e-011 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

### Page 52 of 438 The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.712927 0.573232 3 0.712926 0.573232 4 0.150676 0.0933513 5 0.150676 0.0933513

### Page 53 of 438 1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:37 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:37 07/26 2012 BMD BMDL Exponential

### Page 54 of 438 1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:37 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

12:37 07/26 2012 BMD BMDL Exponential

### Page 55 of 438 

## Female PND15 RBC ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 12:24:12 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.8959 11.8959 11.8959 11.8959 rho -0.129436 -0.129436 -0.129436 -0.129436 a 1685.7 1685.7 3106.95 3106.95 b 0.125024 0.125024 0.732633 0.732633 c -- -- 0.404623 0.404623 d -- 1 -- 1

Parameter Estimates by Model

### Page 56 of 438 Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 18.115 18.115 -3.45217 -3.45217 rho -0.834683 -0.834683 1.92669 1.92669 a 2716.6 2716.6 2809.3 2809.3 b 0.605165 0.605165 2.02783 2.02783 c -- -- 0.470593 0.470593 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2959 143 0.1 5 2318 505 0.3 5 2203 205 1 5 1519 246 5 5 1320 194

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2717 316.6 1.712 0.1 2557 324.7 -1.647 0.3 2266 341.5 -0.4098 1 1483 407.5 0.1963 5 131.8 1119 2.374 3 0 2717 316.6 1.712 0.1 2557 324.7 -1.647 0.3 2266 341.5 -0.4098 1 1483 407.5 0.1963 5 131.8 1119 2.374 4 0 2809 373.7 0.8957 0.1 2536 338.7 -1.441 0.3 2131 286.4 0.5583 1 1518 206.5 0.0131 5 1322 180.8 -0.02589 5 0 2809 373.7 0.8957 0.1 2536 338.7 -1.441 0.3 2131 286.4 0.5583 1 1518 206.5 0.0131 5 1322 180.8 -0.02589

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

### Page 57 of 438 Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -151.3127 6 314.6255 A2 -146.1287 10 312.2574 A3 -150.9284 7 315.8567 R -174.1578 2 352.3157 2 -164.5195 4 337.0391 3 -164.5195 4 337.0391 4 -152.1701 5 314.3402 5 -152.1701 5 314.3402

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 56.06 8 < 0.0001 Test 2 10.37 4 0.03466 Test 3 9.599 3 0.0223 Test 4 27.18 3 < 0.0001 Test 5a 27.18 3 < 0.0001 Test 5b -3.411e-013 0 N/A Test 6a 2.483 2 0.2889 Test 6b 24.7 1 < 0.0001 Test 7a 2.483 2 0.2889 Test 7b 24.7 1 < 0.0001 Test 7c -5.684e-014 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to

### Page 58 of 438 consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.174102 0.120854 3 0.174102 0.120854 4 0.10324 0.0667855 5 0.10324 0.0667855

### Page 59 of 438 1000 1500 2000 2500 3000

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:24 07/26 2012 BMD BMDL Exponential

### Page 60 of 438 1000 1500 2000 2500 3000

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:24 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:24 07/26 2012 BMD BMDL Exponential

### Page 61 of 438 

## Male PND22 Brain ChE Inhibition

================================ ==================================== Exponential Model. (Ver sion: 1.7; Date: 12/10/2009) Input Data File: C:/D ocuments and Settings/jliccion/My Documents/BMDS220/Data/exp_U ntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 12:00:50 2012

================================ ====================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~

The form of the re sponse function by Model: Model 2: Y[ dose] = a * exp{sign * b * dose} Model 3: Y[dos e] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the medi an response for exposure = dose; 1000 1500 2000 2500 3000

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

12:24 07/26 2012 BMD BMDL Exponential

### Page 62 of 438 

## sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nes ted within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(l nalpha +rho *ln(Y[dose])) The variance is to be modeled as Va r(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of reco rds with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha -4.35434 -4 .35434 -4. 35434 -4.35434 rho 1.6561 1.6561 1.6561 1.6561

a 2.15781 2.15781 5.439 5.439 b 0.283673 0.283673 0.889601 0.889601

c -- -- 0.203119 0.203119 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha -3.36896 -3.36896 -4.15291 -4.26284 rho 2.12957 2.12957 1.75732 1.69146

a 4.53515 4.53515 5.37254 5.08566 b 0.283097 0.283097 1.254 1.17339

c -- -- 0.213796 0.229139 d -- 1 -- 1.49786

### Page 63 of 438 

## Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 5. 18 0.31 0.1 5 4. 79 0.69 0.3 5 4. 38 0.48 1 5 2. 28 0.11 5 5 1. 16 0.18

Estimate d Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ --------- - --------- ---------------- 2 0 4.535 0.928 1.554 0.1 4.409 0.9005 0.9472 0.3 4.166 0.8478 0.5647 1 3.417 0.6865 -3.703 5 1.101 0.2056 0.6399 3 0 4.535 0.928 1.554 0.1 4.409 0.9005 0.9472 0.3 4.166 0.8478 0.5647 1 3.417 0.6865 -3.703 5 1.101 0.2056 0.6399 4 0 5.373 0.5493 -0.7838 0.1 4.875 0.5043 -0.3757 0.3 4.048 0.4283 1.732 1 2.354 0.266 -0.6218 5 1.157 0. 1425 0.05304 5 0 5.086 0.4696 0.4492 0.1 4.931 0.4574 -0.6868 0.3 4.345 0.4111 0.1891 1 2.266 0.237 0.1359 5 1.165 0. 1351 -0.08818

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij)

### Page 64 of 438 

## Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalp ha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ---------------- - ---- ------------ A1 12.48147 6 -12.96295 A2 21.28074 10 -22.56148 A3 17.06903 7 -20.13805 R -24.33356 2 52.66712 2 -0.9869933 4 9.973987 3 -0.9869933 4 9.973987 4 14.52238 5 -19.04476 5 16.84384 6 -21.68767

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does respon se and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Mode l 2 fit the data? (A3 vs. 2)

Test 5a: Does Mode l 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 be tter than Model 2? (3 vs. 2)

Test 6a: Does Mode l 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 be tter than Model 2? (4 vs. 2)

Test 7a: Does Mode l 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 be tter than Model 3? (5 vs. 3) Test 7c: Is Model 5 be tter than Model 4? (5 vs. 4)

### Page 65 of 438 

## Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ---------- -------------- ------ -------------- Test 1 91.23 8 < 0.0001 Test 2 17.6 4 0.001478 Test 3 8.423 3 0.03803 Test 4 36.11 3 < 0.0001 Test 5a 36.11 3 < 0.0001 Test 5b -4.103e-013 0 N/A Test 6a 5.093 2 0.07834 Test 6b 31.02 1 < 0.0001 Test 7a 0.4504 1 0.5022 Test 7b 35.66 2 < 0.0001 Test 7c 4.643 1 0.03118

The p-value for Test 1 is less than .05. T here appears to be a difference between res ponse and/or variances among the dose levels, it seems appr opriate to model the data.

The p-value for Test 2 is less than .1 . A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. Y ou may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you ma y want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you ma y want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you ma y want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears

### Page 66 of 438 

## to fit the data better than Model 3.

The p-value for Test 7c is less than .05. Model 5 appears to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.372171 0.0900431 3 0.372171 0.0900431 4 0.108486 0.0924896 5 0.228194 0.130349

### Page 67 of 438 1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:00 07/26 2012 BMD BMDL Exponential

1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:00 07/26 2012 BMD BMDL Exponential

### Page 68 of 438 1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:00 07/26 2012 BMD BMDL Exponential

1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

12:00 07/26 2012 BMD BMDL Exponential

### Page 69 of 438 

## Female PND22 Brain ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 11:05:57 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -2.49442 -2.49442 -2.49442 -2.49442 rho(S) 0 0 0 0 a 2.3037 2.3037 5.7015 5.7015 b 0.249996 0.249996 0.869475 0.869475 c -- -- 0.222164 0.222164 d -- 1 -- 1

(S) = Specified

### Page 70 of 438 Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -0.992616 -0.992616 -2.14826 -2.14826 rho 0 0 0 0 a 4.8018 4.8018 5.19109 5.19109 b 0.44012 0.44012 1.2623 1.2623 c -- -- 0.259981 0.259981 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 5.43 0.44 0.1 5 4.41 0.26 0.3 5 4.02 0.31 1 5 2.51 0.35 5 5 1.33 0.19

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.802 0.6088 2.307 0.1 4.595 0.6088 -0.6797 0.3 4.208 0.6088 -0.69 1 3.092 0.6088 -2.138 5 0.5317 0.6088 2.932 3 0 4.802 0.6088 2.307 0.1 4.595 0.6088 -0.6797 0.3 4.208 0.6088 -0.69 1 3.092 0.6088 -2.138 5 0.5317 0.6088 2.932 4 0 5.191 0.3416 1.564 0.1 4.736 0.3416 -2.131 0.3 3.98 0.3416 0.2613 1 2.437 0.3416 0.4796 5 1.357 0.3416 -0.1738 5 0 5.191 0.3416 1.564 0.1 4.736 0.3416 -2.131 0.3 3.98 0.3416 0.2613 1 2.437 0.3416 0.4796 5 1.357 0.3416 -0.1738

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

### Page 71 of 438 Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 18.6803 6 -25.36059 A2 20.53825 10 -21.07649 A3 18.6803 6 -25.36059 R -22.26118 2 48.52236 2 -0.09230161 3 6.184603 3 -0.09230161 3 6.184603 4 14.35323 4 -20.70647 5 14.35323 4 -20.70647

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 85.6 8 < 0.0001 Test 2 3.716 4 0.4458 Test 3 3.716 4 0.4458 Test 4 37.55 3 < 0.0001 Test 5a 37.55 3 < 0.0001 Test 5b -6.807e-013 0 N/A Test 6a 8.654 2 0.01321 Test 6b 28.89 1 < 0.0001 Test 7a 8.654 2 0.01321 Test 7b 28.89 1 < 0.0001 Test 7c 1.066e-014 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

### Page 72 of 438 The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.239391 0.145676 3 0.239391 0.145676 4 0.11501 0.0940254 5 0.11501 0.0940254

### Page 73 of 438 1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

11:05 07/26 2012 BMD BMDL Exponential

1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

11:05 07/26 2012 BMD BMDL Exponential

### Page 74 of 438 1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

11:05 07/26 2012 BMD BMDL Exponential

1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

11:05 07/26 2012 BMD BMDL Exponential

### Page 75 of 438 

## Male PND22 RBC ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 10:20:48 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -8.69805 -8.69805 -8.69805 -8.69805 rho 2.53353 2.53353 2.53353 2.53353 a 1747.61 1747.61 2991.45 2991.45 b 0.126561 0.126561 0.700467 0.700467 c -- -- 0.43298 0.43298 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 76 of 438 lnalpha -17.1985 122.086 -8.3531 -8.63381 rho 3.79153 -14.2486 2.47439 2.50916 a 2412.33 2137.6 2873.87 2827.29 b 0.118937 0.0660624 1.81398 1.74328 c -- -- 0.472054 0.482053 d -- 18 -- 1.14866

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2849 384 0.1 5 2584 227 0.3 5 2312 231 1 5 1583 113 5 5 1360 152

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2412 476.1 2.051 0.1 2384 465.5 0.9617 0.3 2328 445 -0.07927 1 2142 380 -3.288 5 1331 154.2 0.4209 3 0 2138 613 2.595 0.1 2138 613 1.628 0.3 2138 613 0.6362 1 2138 613 -2.023 5 2138 613 -2.837 4 0 2874 291.7 -0.1907 0.1 2622 260.4 -0.3277 0.3 2237 214 0.7828 1 1604 141.8 -0.3303 5 1357 115.3 0.06212 5 0 2827 285.2 0.1702 0.1 2643 262.1 -0.5037 0.3 2274 217 0.3953 1 1583 137.8 -0.00604 5 1363 114.2 -0.05698

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

### Page 77 of 438 Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -146.7362 6 305.4724 A2 -142.5571 10 305.1142 A3 -143.5228 7 301.0455 R -172.9575 2 349.9149 2 -159.4253 4 326.8506 3 -172.9575 5 355.9149 4 -144.0265 5 298.053 5 -143.8297 6 299.6594

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 60.8 8 < 0.0001 Test 2 8.358 4 0.07931 Test 3 1.931 3 0.5868 Test 4 31.81 3 < 0.0001 Test 5a 58.87 2 < 0.0001 Test 5b -27.06 1 N/A Test 6a 1.007 2 0.6043 Test 6b 30.8 1 < 0.0001 Test 7a 0.6139 1 0.4333 Test 7b 58.26 1 < 0.0001 Test 7c 0.3936 1 0.5304

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately

### Page 78 of 438 describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.885852 0.664886 3 13.3583 0.129834 4 0.115766 0.0880934 5 0.150183 0.0900362

### Page 79 of 438 1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

10:20 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 2 4 6 8 10 12 14Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:20 07/26 2012 BMD BMDL Exponential

### Page 80 of 438 1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

10:20 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:20 07/26 2012 BMD BMDL Exponential

### Page 81 of 438 

## Female PND22 RBC ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 09:57:47 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -2.57572 -2.57572 -2.57572 -2.57572 rho 1.74944 1.74944 1.74944 1.74944 a 1742.99 1742.99 3179.4 3179.4 b 0.13348 0.13348 0.720441 0.720441 c -- -- 0.395702 0.395702 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

### Page 82 of 438 -------- ------- ------- ------- ------- lnalpha -17.9803 -17.9803 -7.3403 -7.3403 rho 3.88486 3.88486 2.37494 2.37494 a 2446.5 2446.5 2840.74 2840.74 b 0.126594 0.126594 1.49896 1.49896 c -- -- 0.467836 0.467836 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 3028 319 0.1 5 2438 302 0.3 5 2280 176 1 5 1700 203 5 5 1321 147

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2447 476 2.731 0.1 2416 464.5 0.1072 0.3 2355 442.2 -0.3809 1 2156 372.3 -2.737 5 1299 139.2 0.3515 3 0 2447 476 2.731 0.1 2416 464.5 0.1072 0.3 2355 442.2 -0.3809 1 2156 372.3 -2.737 5 1299 139.2 0.3515 4 0 2841 321.3 1.303 0.1 2630 293.2 -1.466 0.3 2293 249.2 -0.1187 1 1667 170.6 0.4369 5 1330 130.5 -0.1516 5 0 2841 321.3 1.303 0.1 2630 293.2 -1.466 0.3 2293 249.2 -0.1187 1 1667 170.6 0.4369 5 1330 130.5 -0.1516

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

### Page 83 of 438 Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -146.6727 6 305.3454 A2 -144.4594 10 308.9188 A3 -145.0517 7 304.1035 R -173.6765 2 351.3529 2 -158.7688 4 325.5376 3 -158.7688 4 325.5376 4 -147.4096 5 304.8191 5 -147.4096 5 304.8191

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 58.43 8 < 0.0001 Test 2 4.427 4 0.3513 Test 3 1.185 3 0.7567 Test 4 27.43 3 < 0.0001 Test 5a 27.43 3 < 0.0001 Test 5b -5.684e-013 0 N/A Test 6a 4.716 2 0.09462 Test 6b 22.72 1 < 0.0001 Test 7a 4.716 2 0.09462 Test 7b 22.72 1 < 0.0001 Test 7c -5.684e-014 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. Consider running a homogeneous model.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

### Page 84 of 438 The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.832268 0.669998 3 0.832268 0.669998 4 0.138861 0.0988325 5 0.138861 0.0988325 p = 0.095

### Page 85 of 438 1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

09:57 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

09:57 07/26 2012 BMD BMDL Exponential

### Page 86 of 438 1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

09:57 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

09:57 07/26 2012 BMD BMDL Exponential

### Page 87 of 438 

## Male PND42 Brain ChE Inhibition – MRID 46153206

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) Gnuplot Plotting File: Fri Feb 04 12:21:44 2011

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -5.2133 -5.2133 -5.2133 -5.2133 rho 2.06307 2.06307 2.06307 2.06307 a 2.3455 2.3455 5.2395 5.2395 b 0.276743 0.276743 0.402157 0.402157 c -- -- 0.118332 0.118332 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -5.50474 -5.52216 -5.50474 -5.49807

### Page 88 of 438 rho 2.38374 2.36844 2.38374 2.32664 a 4.95468 4.85725 4.95468 4.86095 b 0.276943 0.256636 0.276943 2.79331 c -- -- 0 0.255042 d -- 1.24955 -- 14.8291

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 4.77 0.27 0.1 5 4.99 0.32 0.3 5 4.57 0.6 5 5 1.24 0.09

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.955 0.4296 -0.9613 0.1 4.819 0.4156 0.9182 0.3 4.56 0.3891 0.0594 5 1.241 0.08246 -0.01693 3 0 4.857 0.4109 -0.4749 0.1 4.808 0.4059 1.005 0.3 4.664 0.3916 -0.5367 5 1.24 0.08155 0.006601 4 0 4.955 0.4296 -0.9613 0.1 4.819 0.4156 0.9182 0.3 4.56 0.3891 0.0594 5 1.241 0.08246 -0.01693 5 0 4.861 0.4027 -0.505 0.1 4.861 0.4027 0.7166 0.3 4.607 0.3783 -0.2182 5 1.24 0.08216 0.006919

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 12.19347 5 -14.38695 A2 19.06913 8 -22.13826 A3 16.85227 6 -21.70453 R -19.06644 2 42.13288

### Page 89 of 438 2 15.81191 4 -23.62381 3 16.17594 5 -22.35187 4 15.81191 4 -23.62381 5 16.45069 6 -20.90139

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 76.27 6 < 0.0001 Test 2 13.75 3 0.003264 Test 3 4.434 2 0.109 Test 4 2.081 2 0.3533 Test 5a 1.353 1 0.2448 Test 5b 0.7281 1 0.3935 Test 6a 2.081 2 0.3533 Test 6b -2.132e-014 0 N/A Test 7a 0.8031 0 N/A Test 7b 0.5495 1 0.4585 Test 7c 1.278 2 0.5279

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

The p-value for Test 5b is greater than .05. Model 3 does

### Page 90 of 438 not seem to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.380441 0.362614 3 0.643489 0.365455 4 0.380441 0.219588 5 0.314162 0.261081

### Page 91 of 438 1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:21 02/04 2011 BMD BMDL Exponential

1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:21 02/04 2011 BMD BMDL Exponential

### Page 92 of 438 1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:21 02/04 2011 BMD BMDL Exponential

1 2 3 4 5

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

12:21 02/04 2011 BMD BMDL Exponential

### Page 93 of 438 

## Female PND42 Brain ChE Inhibition – MRID 46153206

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 09:48:06 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -6.32337 -6.32337 -6.32337 -6.32337 rho 2.59245 2.59245 2.59245 2.59245 a 2.33303 2.33303 5.481 5.481 b 0.270154 0.270154 0.855633 0.855633 c -- -- 0.218938 0.218938 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -6.63517 -6.63517 -6.62712 -6.62712

### Page 94 of 438 rho 3.34293 3.34293 2.88877 2.88877 a 4.83428 4.83428 5.2242 5.2242 b 0.269108 0.269108 1.17157 1.17157 c -- -- 0.239079 0.239079 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 5.22 0.63 0.1 5 4.76 0.19 0.3 5 4.07 0.24 5 5 1.26 0.06

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.834 0.5047 1.709 0.1 4.706 0.4825 0.2506 0.3 4.459 0.441 -1.974 5 1.259 0.05325 0.0486 3 0 4.834 0.5047 1.709 0.1 4.706 0.4825 0.2506 0.3 4.459 0.441 -1.974 5 1.259 0.05325 0.0486 4 0 5.224 0.3963 -0.02371 0.1 4.785 0.3491 -0.1584 0.3 4.046 0.274 0.1945 5 1.26 0.05083 -0.01568 5 0 5.224 0.3963 -0.02371 0.1 4.785 0.3491 -0.1584 0.3 4.046 0.274 0.1945 5 1.26 0.05083 -0.01568

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 13.14253 5 -16.28506 A2 24.0479 8 -32.09581 A3 21.34811 6 -30.69621 R -19.01668 2 42.03336

### Page 95 of 438 2 15.8208 4 -23.6416 3 15.8208 4 -23.6416 4 21.26005 5 -32.52009 5 21.26005 5 -32.52009

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 86.13 6 < 0.0001 Test 2 21.81 3 < 0.0001 Test 3 5.4 2 0.06722 Test 4 11.05 2 0.003977 Test 5a 11.05 2 0.003977 Test 5b -3.908e-014 0 N/A Test 6a 0.1761 1 0.6747 Test 6b 10.88 1 0.0009729 Test 7a 0.1761 1 0.6747 Test 7b 10.88 1 0.0009729 Test 7c 0 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0.

### Page 96 of 438 The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.391518 0.375628 3 0.391518 0.375628 4 0.120262 0.0920115 5 0.120262 0.0920115

### Page 97 of 438 1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

09:48 07/26 2012 BMD BMDL Exponential

1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

09:48 07/26 2012 BMD BMDL Exponential

### Page 98 of 438 1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

09:48 07/26 2012 BMD BMDL Exponential

1 2 3 4 5 6

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

09:48 07/26 2012 BMD BMDL Exponential

### Page 99 of 438 

## Male PND42 RBC ChE Inhibition – MRID 46153206

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 09:37:15 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 9.40035 9.40035 9.40035 9.40035 rho(S) 0 0 0 0 a 1857.97 1857.97 2909.55 2909.55 b 0.122373 0.122373 0.634799 0.634799 c -- -- 0.461207 0.461207 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

### Page 100 of 438 Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 10.6164 10.6164 10.1309 9.55907 rho 0 0 0 0 a 2599.01 2599.01 2749.97 2706.5 b 0.125486 0.125486 1.29416 3.18191 c -- -- 0.510418 0.520599 d -- 1 -- 17.2149

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2642 102 0.1 5 2771 145 0.3 5 2237 75 5 5 1409 153

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2599 202 0.4759 0.1 2567 202 2.263 0.3 2503 202 -2.945 5 1388 202 0.235 3 0 2599 202 0.4759 0.1 2567 202 2.263 0.3 2503 202 -2.945 5 1388 202 0.235 4 0 2750 158.5 -1.524 0.1 2587 158.5 2.603 0.3 2317 158.5 -1.126 5 1406 158.5 0.04631 5 0 2707 119 -1.211 0.1 2706 119 1.211 0.3 2237 119 -7.557e-009 5 1409 119 1.727e-009

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -104.0035 5 218.007

### Page 101 of 438 A2 -102.5167 8 221.0335 A3 -104.0035 5 218.007 R -135.9478 2 275.8955 2 -116.1642 3 238.3285 3 -116.1642 3 238.3285 4 -111.3092 4 230.6185 5 -105.5907 5 221.1815

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 66.86 6 < 0.0001 Test 2 2.974 3 0.3957 Test 3 2.974 3 0.3957 Test 4 24.32 2 < 0.0001 Test 5a 24.32 2 < 0.0001 Test 5b -4.547e-013 0 N/A Test 6a 14.61 1 0.0001321 Test 6b 9.71 1 0.001833 Test 7a 3.175 0 N/A Test 7b 21.15 2 < 0.0001 Test 7c 11.44 1 0.00072

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately

### Page 102 of 438 describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is less than .05. Model 5 appears to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.839619 0.6992 3 0.839619 0.6992 4 0.176545 0.122108 5 0.288844 0.215638

### Page 103 of 438 1500 2000 2500 3000

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

09:37 07/26 2012 BMD BMDL Exponential

1500 2000 2500 3000

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

09:37 07/26 2012 BMD BMDL Exponential

### Page 104 of 438 1500 2000 2500 3000

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

09:37 07/26 2012 BMD BMDL Exponential

1500 2000 2500 3000

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

09:37 07/26 2012 BMD BMDL Exponential

### Page 105 of 438 

## Female PND42 RBC ChE I nhibition – MRID 46153206 – CCA Acute Study – Single Dose

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 09:28:44 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 9.21771 9.21771 9.21771 9.21771 rho(S) 0 0 0 0 a 1852.25 1852.25 2571.45 2571.45 b 0.0933434 0.0933434 0.558034 0.558034 c -- -- 0.55481 0.55481 d -- 1 -- 1

(S) = Specified

### Page 106 of 438 Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 9.55953 9.55953 9.23825 9.21771 rho 0 0 0 0 a 2386.35 2386.35 2463.15 2449 b 0.0943021 0.0943021 0.941449 1.44792 c -- -- 0.6045 0.611678 d -- 1 -- 1.51031

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2449 110 0.1 5 2399 138 0.3 5 2214 101 5 5 1498 95

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2386 119.1 1.176 0.1 2364 119.1 0.6581 0.3 2320 119.1 -1.987 5 1489 119.1 0.1648 3 0 2386 119.1 1.176 0.1 2364 119.1 0.6581 0.3 2320 119.1 -1.987 5 1489 119.1 0.1648 4 0 2463 101.4 -0.3121 0.1 2376 101.4 0.5155 0.3 2223 101.4 -0.2084 5 1498 101.4 0.005014 5 0 2449 100.4 -1.101e-008 0.1 2399 100.4 -7.1e-009 0.3 2214 100.4 3.207e-009 5 1498 100.4 -1.132e-008

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

### Page 107 of 438 Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -102.1771 5 214.3543 A2 -101.7522 8 219.5044 A3 -102.1771 5 214.3543 R -129.5198 2 263.0397 2 -105.5953 3 217.1906 3 -105.5953 3 217.1906 4 -102.3825 4 212.765 5 -102.1771 5 214.3543

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 55.54 6 < 0.0001 Test 2 0.8498 3 0.8375 Test 3 0.8498 3 0.8375 Test 4 6.836 2 0.03277 Test 5a 6.836 2 0.03277 Test 5b -1.023e-012 0 N/A Test 6a 0.4108 1 0.5216 Test 6b 6.426 1 0.01125 Test 7a 0 0 N/A Test 7b 6.836 2 0.03277 Test 7c 0.4108 1 0.5216

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately

### Page 108 of 438 describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 1.11727 0.975931 3 1.11727 0.975931 4 0.30961 0.203503 5 0.309665 0.209803

### Page 109 of 438 1400 1600 1800 2000 2200 2400 2600

0 1 2 3 4 5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

09:28 07/26 2012 BMD BMDL Exponential

1400 1600 1800 2000 2200 2400 2600

0 1 2 3 4 5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

09:28 07/26 2012 BMD BMDL Exponential

### Page 110 of 438 1400 1600 1800 2000 2200 2400 2600

0 1 2 3 4 5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

09:28 07/26 2012 BMD BMDL Exponential

1400 1600 1800 2000 2200 2400 2600

0 1 2 3 4 5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

09:28 07/26 2012 BMD BMDL Exponential

### Page 111 of 438 

## Male Adult PND42 Brain ChE Inhibiti on – MRID 46153204 – CCA Repeat Study – 7 days

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 09:06:15 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -0.824905 -0.824905 -0.824905 -0.824905 rho -0.64772 -0.64772 -0.64772 -0.64772 a 4.08651 4.08651 5.502 5.502 b 0.9598 0.9598 6.62714 6.62714 c -- -- 0.579876 0.579876 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

### Page 112 of 438 -------- ------- ------- ------- ------- lnalpha -7.29471 -6.90537 -7.29471 -6.92184 rho 3.98835 3.71141 3.98835 3.71739 a 4.98676 4.89846 4.98676 4.90965 b 0.982501 1.35533 0.982501 10.6441 c -- -- 0 0.682783 d -- 1.5818 -- 12.0615

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 4.57 0.54 0.008 5 4.87 0.97 0.02 5 5.24 0.13 0.08 5 4.75 0.48 0.4 5 3.35 0.34

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.987 0.642 -1.452 0.008 4.948 0.632 -0.275 0.02 4.89 0.6173 1.269 0.08 4.61 0.5489 0.5711 0.4 3.366 0.2932 -0.1236 3 0 4.898 0.604 -1.216 0.008 4.895 0.6032 -0.09134 0.02 4.882 0.6003 1.333 0.08 4.755 0.5716 -0.0187 0.4 3.351 0.2986 -0.007228 4 0 4.987 0.642 -1.452 0.008 4.948 0.632 -0.275 0.02 4.89 0.6173 1.269 0.08 4.61 0.5489 0.5711 0.4 3.366 0.2932 -0.1236 5 0 4.91 0.6045 -1.256 0.008 4.91 0.6045 -0.1467 0.02 4.91 0.6045 1.222 0.08 4.701 0.5576 0.1969 0.4 3.352 0.2974 -0.01677

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

### Page 113 of 438 Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 4.568212 6 2.863576 A2 12.78752 10 -5.575039 A3 5.46186 7 3.07628 R -7.438085 2 18.87617 2 3.555166 4 0.8896673 3 3.940053 5 2.119894 4 3.555166 4 0.8896673 5 4.033916 6 3.932167

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 40.45 8 < 0.0001 Test 2 16.44 4 0.002484 Test 3 14.65 3 0.00214 Test 4 3.813 3 0.2823 Test 5a 3.044 2 0.2183 Test 5b 0.7698 1 0.3803 Test 6a 3.813 3 0.2823 Test 6b 0 0 N/A Test 7a 2.856 1 0.09104 Test 7b 0.1877 1 0.6648 Test 7c 0.9575 2 0.6196

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

### Page 114 of 438 The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

The p-value for Test 5b is greater than .05. Model 3 does not seem to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.107237 0.087504 3 0.177869 0.0903093 4 0.107237 0.0538057 5 0.0866814 0.0634267

### Page 115 of 438 3 3.5

4 4.5

5 5.5

6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

09:06 07/26 2012 BMD BMDL Exponential

3 3.5

4 4.5

5 5.5

6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

09:06 07/26 2012 BMD BMDL Exponential

### Page 116 of 438 3 3.5

4 4.5

5 5.5

6

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dose Exponential Model 4 with 0.95 Confidence Level

09:06 07/26 2012 BMD BMDL Exponential

3 3.5

4 4.5

5 5.5

6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

09:06 07/26 2012 BMD BMDL Exponential

### Page 117 of 438 

## Female Adult PND42 Brain ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 days

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 08:50:38 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -1.76604 -1.76604 -1.76604 -1.76604 rho(S) 0 0 0 0 a 4.1018 4.1018 5.985 5.985 b 1.20825 1.20825 7.34047 7.34047 c -- -- 0.507618 0.507618 d -- 1 -- 1

(S) = Specified

### Page 118 of 438 Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -1.16986 -1.28374 -1.16986 -1.288 rho 0 0 0 0 a 5.24123 5.12329 5.24123 5.13667 b 1.16573 1.78374 1.16573 10.1204 c -- -- 0 0.621025 d -- 2.2111 -- 12.7803

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 5.01 0.45 0.008 5 4.7 0.53 0.02 5 5.7 0.65 0.08 5 5.01 0.3 0.4 5 3.19 0.27

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 5.241 0.5571 -0.928 0.008 5.193 0.5571 -1.977 0.02 5.12 0.5571 2.326 0.08 4.775 0.5571 0.945 0.4 3.288 0.5571 -0.3931 3 0 5.123 0.5263 -0.4813 0.008 5.123 0.5263 -1.797 0.02 5.12 0.5263 2.464 0.08 5.055 0.5263 -0.1895 0.4 3.189 0.5263 0.003803 4 0 5.241 0.5571 -0.928 0.008 5.193 0.5571 -1.977 0.02 5.12 0.5571 2.326 0.08 4.775 0.5571 0.945 0.4 3.288 0.5571 -0.3931 5 0 5.137 0.5252 -0.5393 0.008 5.137 0.5252 -1.859 0.02 5.137 0.5252 2.398 0.08 5.01 0.5252 -5.042e-008 0.4 3.19 0.5252 8.864e-011

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

### Page 119 of 438 Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 9.575562 6 -7.151123 A2 12.17667 10 -4.353339 A3 9.575562 6 -7.151123 R -10.68695 2 25.37389 2 2.123198 3 1.753604 3 3.546697 4 0.9066065 4 2.123198 3 1.753604 5 3.600025 5 2.799951

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 45.73 8 < 0.0001 Test 2 5.202 4 0.2672 Test 3 5.202 4 0.2672 Test 4 14.9 3 0.0019 Test 5a 12.06 2 0.002408 Test 5b 2.847 1 0.09154 Test 6a 14.9 3 0.0019 Test 6b -2.309e-014 0 N/A Test 7a 11.95 1 0.0005462 Test 7b 0.1067 1 0.744 Test 7c 2.954 2 0.2284

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

### Page 120 of 438 The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5b is greater than .05. Model 3 does not seem to fit the data better than Model 2.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0903817 0.0690732 3 0.202611 0.0886113 4 0.0903817 0.058109 5 0.0900742 0.0773327

### Page 121 of 438 2.5

3 3.5

4 4.5

5 5.5

6 6.5

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

08:50 07/26 2012 BMD BMDL Exponential

2.5

3 3.5

4 4.5

5 5.5

6 6.5

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

08:50 07/26 2012 BMD BMDL Exponential

### Page 122 of 438 2.5

3 3.5

4 4.5

5 5.5

6 6.5

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

08:50 07/26 2012 BMD BMDL Exponential

2.5

3 3.5

4 4.5

5 5.5

6 6.5

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

08:50 07/26 2012 BMD BMDL Exponential

### Page 123 of 438 

## Male Adult PND42 RBC ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 days

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 09:19:37 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 10.295 10.295 10.295 10.295 rho(S) 0 0 0 0 a 2078.86 2078.86 3036.6 3036.6 b 1.48194 1.48194 2.71408 2.71408 c -- -- 0.254232 0.254232 d -- 1 -- 1

(S) = Specified

### Page 124 of 438 Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 10.5072 10.4841 10.5072 10.4841 rho 0 0 0 0 a 2805.98 2770.13 2805.98 2770.13 b 1.45783 1.61757 1.45783 1.61757 c -- -- 0 9.11846e-023 d -- 1.24183 -- 1.24183

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2892 120 0.008 5 2637 69 0.02 5 2706 170 0.08 5 2587 189 0.4 5 1544 318

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2806 191.3 1.006 0.008 2773 191.3 -1.595 0.02 2725 191.3 -0.2262 0.08 2497 191.3 1.051 0.4 1566 191.3 -0.259 3 0 2770 189.1 1.441 0.008 2758 189.1 -1.427 0.02 2731 189.1 -0.2994 0.08 2560 189.1 0.3204 0.4 1547 189.1 -0.03928 4 0 2806 191.3 1.006 0.008 2773 191.3 -1.595 0.02 2725 191.3 -0.2262 0.08 2497 191.3 1.051 0.4 1566 191.3 -0.259 5 0 2770 189.1 1.441 0.008 2758 189.1 -1.427 0.02 2731 189.1 -0.2994 0.08 2560 189.1 0.3204 0.4 1547 189.1 -0.03928

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

### Page 125 of 438 Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -141.1878 6 294.3755 A2 -135.5167 10 291.0334 A3 -141.1878 6 294.3755 R -168.1686 2 340.3371 2 -143.84 3 293.6801 3 -143.5513 4 295.1027 4 -143.84 3 293.6801 5 -143.5513 5 297.1027

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 65.3 8 < 0.0001 Test 2 11.34 4 0.02298 Test 3 11.34 4 0.02298 Test 4 5.305 3 0.1508 Test 5a 4.727 2 0.09408 Test 5b 0.5774 1 0.4473 Test 6a 5.305 3 0.1508 Test 6b 5.684e-014 0 N/A Test 7a 4.727 1 0.02969 Test 7b 0 1 1 Test 7c 0.5774 2 0.7492

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. Consider running a non-homogeneous variance model.

### Page 126 of 438 The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5b is greater than .05. Model 3 does not seem to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0722722 0.0615642 3 0.100957 0.0626997 4 0.0722722 0.0510405 5 0.100957 0.0626997

### Page 127 of 438 1000 1500 2000 2500 3000

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

09:19 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

09:19 07/26 2012 BMD BMDL Exponential

### Page 128 of 438 1000 1500 2000 2500 3000

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

09:19 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

09:19 07/26 2012 BMD BMDL Exponential

### Page 129 of 438 

## Female Adult PND42 RBC ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 days

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 08:57:28 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 46.3616 46.3616 46.3616 46.3616 rho -4.68666 -4.68666 -4.68666 -4.68666 a 2159.07 2159.07 2869.65 2869.65 b 1.18628 1.18628 6.72111 6.72111 c -- -- 0.565193 0.565193 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 130 of 438 lnalpha 46.089 42.7945 46.089 42.7945 rho -4.65647 -4.23916 -4.65647 -4.23916 a 2738.2 2721.59 2738.2 2721.59 b 1.10456 1.3007 1.10456 1.3007 c -- -- 0 9.78199e-023 d -- 1.17804 -- 1.17804

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2733 67 0.008 5 2724 129 0.02 5 2645 139 0.08 5 2556 123 0.4 5 1703 307

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2738 101.1 -0.115 0.008 2714 103.2 0.2142 0.02 2678 106.5 -0.701 0.08 2507 124.2 0.8888 0.4 1760 282.9 -0.4528 3 0 2722 102.9 0.248 0.008 2709 103.9 0.3216 0.02 2685 105.9 -0.842 0.08 2539 119.2 0.3238 0.4 1713 274.6 -0.07902 4 0 2738 101.1 -0.115 0.008 2714 103.2 0.2142 0.02 2678 106.5 -0.701 0.08 2507 124.2 0.8888 0.4 1760 282.9 -0.4528 5 0 2722 102.9 0.248 0.008 2709 103.9 0.3216 0.02 2685 105.9 -0.842 0.08 2539 119.2 0.3238 0.4 1713 274.6 -0.07902

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

### Page 131 of 438 Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -138.5584 6 289.1167 A2 -132.4008 10 284.8015 A3 -133.578 7 281.156 R -163.4756 2 330.9513 2 -134.4407 4 276.8814 3 -134.1772 5 278.3544 4 -134.4407 4 276.8814 5 -134.1772 6 280.3544

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 62.15 8 < 0.0001 Test 2 12.32 4 0.01516 Test 3 2.354 3 0.5022 Test 4 1.725 3 0.6313 Test 5a 1.198 2 0.5492 Test 5b 0.527 1 0.4679 Test 6a 1.725 3 0.6313 Test 6b -5.684e-014 0 N/A Test 7a 1.198 1 0.2736 Test 7b -5.684e-014 1 N/A Test 7c 0.527 2 0.7684

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems

### Page 132 of 438 to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

The p-value for Test 5b is greater than .05. Model 3 does not seem to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0953868 0.0756576 3 0.113817 0.0774704 4 0.0953868 0.0692102 5 0.113817 0.0774704

### Page 133 of 438 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

08:57 07/26 2012 BMD BMDL Exponential

1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

08:57 07/26 2012 BMD BMDL Exponential

### Page 134 of 438 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

08:57 07/26 2012 BMD BMDL Exponential

1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

08:57 07/26 2012 BMD BMDL Exponential

### Page 135 of 438 

## Male Pup PND12 Brain Ch E Inhibition – MRID 46153204 – CCA Repeat Study – 7 days

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 08:25:27 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -8.32332 -8.32332 -8.32332 -8.32332 rho 5.39693 5.39693 5.39693 5.39693 a 3.3307 3.3307 5.0925 5.0925 b 1.63255 1.63255 2.97327 2.97327 c -- -- 0.236622 0.236622 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 136 of 438 lnalpha -8.61175 -8.8193 -8.61175 -8.70155 rho 5.70001 5.87358 5.70001 5.76615 a 4.66116 4.46 4.66116 4.58846 b 1.64862 2.42768 1.64862 1.72529 c -- -- 0 0 d -- 16.5321 -- 1.18577

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 4.84 1.34 0.008 5 4.06 0.38 0.02 5 4.85 1.11 0.08 5 4.09 0.95 0.4 5 2.41 0.18

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.661 1.084 0.3688 0.008 4.6 1.044 -1.156 0.02 4.51 0.9871 0.7702 0.08 4.085 0.7446 0.01437 0.4 2.41 0.1656 -0.006172 3 0 4.46 0.9815 0.8658 0.008 4.46 0.9815 -0.9113 0.02 4.46 0.9815 0.8885 0.08 4.46 0.9815 -0.843 0.4 2.41 0.161 1.507e-008 4 0 4.661 1.084 0.3688 0.008 4.6 1.044 -1.156 0.02 4.51 0.9871 0.7702 0.08 4.085 0.7446 0.01437 0.4 2.41 0.1656 -0.006172 5 0 4.588 1.043 0.5395 0.008 4.56 1.024 -1.092 0.02 4.505 0.9886 0.7814 0.08 4.17 0.7916 -0.227 0.4 2.409 0.1628 0.008265

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

### Page 137 of 438 Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -7.251392 6 26.50278 A2 1.972525 10 16.05495 A3 0.4520246 7 13.09595 R -17.12902 2 38.25804 2 -2.591413 4 13.18283 3 -2.993934 5 15.98787 4 -2.591413 4 13.18283 5 -2.523869 5 15.04774

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 38.2 8 < 0.0001 Test 2 18.45 4 0.001009 Test 3 3.041 3 0.3853 Test 4 6.087 3 0.1075 Test 5a 6.892 2 0.03187 Test 5b -0.805 1 N/A Test 6a 6.087 3 0.1075 Test 6b -1.332e-014 0 N/A Test 7a 5.952 2 0.051 Test 7b 0.9401 0 N/A Test 7c 0.1351 1 0.7132

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems

### Page 138 of 438 to adequately describe the data.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 7b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0639084 0.0553471 3 0.359494 0.0542734 4 0.0639084 0.0315121 5 0.0868813 0.032075

### Page 139 of 438 2 3 4 5 6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

08:25 07/26 2012 BMD BMDL Exponential

2 3 4 5 6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

08:25 07/26 2012 BMD BMDL Exponential

### Page 140 of 438 2 3 4 5 6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

08:25 07/26 2012 BMD BMDL Exponential

2 3 4 5 6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

08:25 07/26 2012 BMD BMDL Exponential

### Page 141 of 438 

## Female Pup PND12 Brain ChE Inhibiti on – MRID 46153204 – CCA Repeat Study – 7 days

!!! Warning: optimum may not have been found for Model 3 !!! !!! Bad completion code in maximum likelihood optimization routine !!! !!! Try choosing different initial values !!!

Warning: optimum may not have been found form model 2 (BMDL). Bad process completion in Optimization routine.

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 08:10:18 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -4.96504 -4.96504 -4.96504 -4.96504 rho 2.50125 2.50125 2.50125 2.50125 a 2.88042 2.88042 4.683 4.683 b 2.124 2.124 3.44388 3.44388 c -- -- 0.201794

### Page 142 of 438 0.201794 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -5.4287 -4.96504 -5.4287 -5.50603 rho 3.01672 2.50125 3.01672 3.04617 a 4.44123 2.88042 4.44123 4.3599 b 2.13199 2.124 2.13199 11.3907 c -- -- 0 0.434166 d -- 1 -- 13.0978

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 4.46 0.41 0.008 5 4.14 0.46 0.02 5 4.41 1.07 0.08 5 3.79 0.29 0.4 5 1.89 0.2

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.441 0.6278 0.06684 0.008 4.366 0.6119 -0.8264 0.02 4.256 0.5887 0.5855 0.08 3.745 0.4854 0.2081 0.4 1.893 0.1735 -0.03801 3 0 0 0 1.#IO 0.008 0 0 1.#IO 0.02 0 0 1.#IO 0.08 0 0 1.#IO 0.4 0 0 1.#IO 4 0 4.441 0.6278 0.06684 0.008 4.366 0.6119 -0.8264 0.02 4.256 0.5887 0.5855 0.08 3.745 0.4854 0.2081 0.4 1.893 0.1735 -0.03801 5 0 4.36 0.6003 0.3729 0.008 4.36 0.6003 -0.8191 0.02 4.36 0.6003 0.1866 0.08 3.728 0.4729 0.2945 0.4 1.893 0.1685 -0.0388

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

### Page 143 of 438 Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 4.15743 6 3.685141 A2 12.5282 10 -5.056394 A3 8.746326 7 -3.492651 R -14.50785 2 33.0157 2 7.305294 4 -6.610589 3 -357.2157 4 722.4313 4 7.305294 4 -6.610589 5 7.805507 6 -3.611014

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 54.07 8 < 0.0001 Test 2 16.74 4 0.00217 Test 3 7.564 3 0.05594 Test 4 2.882 3 0.4102 Test 5a 731.9 3 < 0.0001 Test 5b -729 0 N/A Test 6a 2.882 3 0.4102 Test 6b 1.776e-014 0 N/A Test 7a 1.882 1 0.1701 Test 7b 730 2 < 0.0001 Test 7c 1 2 0.6064

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose

### Page 144 of 438 levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0494189 0.0444033 3 0.0496049 Bad completion 4 0.0494189 0.0284767 5 0.0774737 0.0317548

### Page 145 of 438 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

08:10 07/26 2012 BMD BMDL Exponential

1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

08:10 07/26 2012 BMD BMDL Exponential

### Page 146 of 438 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

08:10 07/26 2012 BMD BMDL Exponential

1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

08:10 07/26 2012 BMD BMDL Exponential

### Page 147 of 438 

## Male Pup PND12 RBC ChE I nhibition – MRID 46153204 – CCA Repeat Study – 7 days

!!! Warning: optimum may not have been found for Model 3 !!! !!! Bad completion code in maximum likelihood optimization routine !!! !!! Try choosing different initial values !!!

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 08:39:43 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -11.204 -11.204 -11.204 -11.204 rho 2.83836 2.83836 2.83836 2.83836 a 1966.78 1966.78 3319.05 3319.05 b 2.34368 2.34368 3.68739 3.68739 c -- -- 0.187554 0.187554

### Page 148 of 438 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -13.0404 -11.204 -12.8334 -12.772 rho 3.06382 2.83836 3.0367 3.02866 a 3163.42 1966.78 3184.98 3167.55 b 2.3357 2.34368 3.02504 4.53849 c -- -- 0.131671 0.299528 d -- 1 -- 1.17643

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 3161 315 0.008 5 3148 441 0.02 5 3046 206 0.08 5 2565 355 0.4 5 1245 86

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 3163 339 -0.01598 0.008 3105 329.5 0.2928 0.02 3019 315.6 0.191 0.08 2624 254.6 -0.5204 0.4 1243 81.03 0.06038 3 0 0 0 1.#IO 0.008 0 0 1.#IO 0.02 0 0 1.#IO 0.08 0 0 1.#IO 0.4 0 0 1.#IO 4 0 3185 340.6 -0.1574 0.008 3119 329.9 0.1976 0.02 3023 314.6 0.1662 0.08 2591 248.9 -0.2293 0.4 1244 81.72 0.02589 5 0 3168 337.2 -0.04347 0.008 3123 330 0.1685 0.02 3040 316.7 0.04582 0.08 2586 248.1 -0.1938 0.4 1244 81.88 0.02595

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij)

### Page 149 of 438 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -152.8372 6 317.6744 A2 -147.1905 10 314.381 A3 -148.9075 7 311.8151 R -178.8974 2 361.7948 2 -149.0642 4 306.1284 3 -926.4701 4 1860.94 4 -149.0035 5 308.007 5 -148.9843 6 309.9687

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 63.41 8 < 0.0001 Test 2 11.29 4 0.02346 Test 3 3.434 3 0.3294 Test 4 0.3133 3 0.9575 Test 5a 1555 3 < 0.0001 Test 5b -1555 0 N/A Test 6a 0.1919 2 0.9085 Test 6b 0.1214 1 0.7275 Test 7a 0.1536 1 0.6951 Test 7b 1555 2 < 0.0001 Test 7c 0.03831 1 0.8448

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

### Page 150 of 438 The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0451088 0.0418769 3 0.0449551 0.00137288 4 0.0404466 0.0266464 5 0.0449313 0.0267158

### Page 151 of 438 1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

08:39 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

08:39 07/26 2012 BMD BMDL Exponential

### Page 152 of 438 1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

08:39 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

08:39 07/26 2012 BMD BMDL Exponential

### Page 153 of 438 

## Female Pup PND12 RBC ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 days

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 08:17:15 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.1801 11.1801 11.1801 11.1801 rho(S) 0 0 0 0 a 1921.56 1921.56 3427.2 3427.2 b 2.2818 2.2818 9.0659 9.0659 c -- -- 0.347361 0.347361 d -- 1 -- 1

(S) = Specified

### Page 154 of 438 Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.4766 11.4766 11.3104 11.2855 rho 0 0 0 0 a 3093.99 3093.99 3198.93 3152.37 b 2.47298 2.47298 6.86053 8.62251 c -- -- 0.347938 0.394135 d -- 1 -- 1.36772

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 3066 307 0.008 5 3264 326 0.02 5 2906 429 0.08 5 2297 214 0.4 5 1250 133

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 3094 310.5 -0.2016 0.008 3033 310.5 1.661 0.02 2945 310.5 -0.2786 0.08 2539 310.5 -1.74 0.4 1151 310.5 0.7159 3 0 3094 310.5 -0.2016 0.008 3033 310.5 1.661 0.02 2945 310.5 -0.2786 0.08 2539 310.5 -1.74 0.4 1151 310.5 0.7159 4 0 3199 285.8 -1.04 0.008 3088 285.8 1.381 0.02 2931 285.8 -0.1995 0.08 2318 285.8 -0.1634 0.4 1247 285.8 0.02229 5 0 3152 282.2 -0.6843 0.008 3104 282.2 1.27 0.02 2987 282.2 -0.6446 0.08 2289 282.2 0.06497 0.4 1251 282.2 -0.006065

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

### Page 155 of 438 Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -152.251 6 316.502 A2 -148.8683 10 317.7367 A3 -152.251 6 316.502 R -178.871 2 361.742 2 -155.9578 3 317.9155 3 -155.9578 3 317.9155 4 -153.8804 4 315.7608 5 -153.5684 5 317.1367

Additive constant for all log-likelihoods = -22.97. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 60.01 8 < 0.0001 Test 2 6.765 4 0.1488 Test 3 6.765 4 0.1488 Test 4 7.413 3 0.05982 Test 5a 7.413 3 0.05982 Test 5b 0 0 N/A Test 6a 3.259 2 0.196 Test 6b 4.155 1 0.04152 Test 7a 2.635 1 0.1046 Test 7b 4.779 2 0.09169 Test 7c 0.6241 1 0.4295

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

### Page 156 of 438 The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0426047 0.0344945 3 0.0426047 0.0344945 4 0.0242662 0.0168301 5 0.0331546 0.0175827

### Page 157 of 438 1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

08:17 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

08:17 07/26 2012 BMD BMDL Exponential

### Page 158 of 438 1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

08:17 07/26 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

08:17 07/26 2012 BMD BMDL Exponential

### Page 159 of 438 

## Male Adult Brain ChE Inhibition – MRID 46153208 – CCA Repeat Study – 4 weeks

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 14:06:38 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -3.94117 -3.94117 -3.94117 -3.94117 rho 1.98884 1.98884 1.98884 1.98884 a 3.41909 3.41909 5.334 5.334 b 1.74036 1.74036 8.08458 8.08458 c -- -- 0.441016 0.441016 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -5.2548 -5.28939 -5.2548 -5.28939 rho 3.07692 3.08263 3.07692 3.08263

### Page 160 of 438 a 4.9674 4.88667 4.9674 4.88667 b 1.745 2.44432 1.745 3.14325 c -- -- 0 0.498516 d -- 16.9716 -- 6.35018

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 4.72 0.87 0.008 5 5.08 0.3 0.02 5 4.86 1.26 0.4 5 2.47 0.32

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.967 0.8509 -0.6501 0.008 4.899 0.8329 0.4872 0.02 4.797 0.8065 0.1746 0.4 2.472 0.2908 -0.01285 3 0 4.887 0.8192 -0.4549 0.008 4.887 0.8192 0.5277 0.02 4.887 0.8192 -0.07279 0.4 2.47 0.2862 -2.551e-008 4 0 4.967 0.8509 -0.6501 0.008 4.899 0.8329 0.4872 0.02 4.797 0.8065 0.1746 0.4 2.472 0.2908 -0.01285 5 0 4.887 0.8192 -0.4549 0.008 4.887 0.8192 0.5277 0.02 4.887 0.8192 -0.07278 0.4 2.47 0.2862 -1.148e-008

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -3.21505 5 16.4301 A2 3.489223 8 9.021555 A3 -0.550366 6 13.10073 R -14.81746 2 33.63492 2 -1.026699 4 10.0534

### Page 161 of 438 3 -0.7543121 5 11.50862 4 -1.026699 4 10.0534 5 -0.7543122 6 13.50862

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 36.61 6 < 0.0001 Test 2 13.41 3 0.003831 Test 3 8.079 2 0.0176 Test 4 0.9527 2 0.6211 Test 5a 0.4079 1 0.523 Test 5b 0.5448 1 0.4605 Test 6a 0.9527 2 0.6211 Test 6b 6.217e-015 0 N/A Test 7a 0.4079 0 N/A Test 7b -1.39e-007 1 N/A Test 7c 0.5448 2 0.7616

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

The p-value for Test 5b is greater than .05. Model 3 does not seem to fit the data better than Model 2.

### Page 162 of 438 The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0603784 0.0511959 3 0.358308 0.0521715 4 0.0603784 0.0152893 5 0.25108 0.0175685

### Page 163 of 438 2 3 4 5 6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

14:06 07/26 2012 BMD BMDL Exponential

2 3 4 5 6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

14:06 07/26 2012 BMD BMDL Exponential

### Page 164 of 438 2 3 4 5 6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

14:06 07/26 2012 BMD BMDL Exponential

2 3 4 5 6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

14:06 07/26 2012 BMD BMDL Exponential

### Page 165 of 438 

## Female Adult Brain ChE I nhibition – MRID 46153208 – CCA Repeat Study – 4 weeks

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 13:38:51 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -2.58909 -2.58909 -2.58909 -2.58909 rho 0.993649 0.993649 0.993649 0.993649 a 3.48202 3.48202 5.25 5.25 b 1.36299 1.36299 7.62073 7.62073 c -- -- 0.49161 0.49161 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 166 of 438 lnalpha -2.93057 -2.93057 -3.23987 -3.23987 rho 1.36971 1.36971 1.50693 1.50693 a 4.66926 4.66926 4.96171 4.96171 b 1.3702 1.3702 20.16 20.16 c -- -- 0.548498 0.548498 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 5 0.95 0.008 5 4.49 0.65 0.02 5 4.33 0.28 0.4 5 2.71 0.52

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.669 0.6637 1.114 0.008 4.618 0.6587 -0.4357 0.02 4.543 0.6514 -0.7313 0.4 2.699 0.456 0.05341 3 0 4.669 0.6637 1.114 0.008 4.618 0.6587 -0.4357 0.02 4.543 0.6514 -0.7313 0.4 2.699 0.456 0.05341 4 0 4.962 0.6616 0.1294 0.008 4.628 0.6278 -0.4916 0.02 4.218 0.5855 0.4264 0.4 2.722 0.4209 -0.06476 5 0 4.962 0.6616 0.1294 0.008 4.628 0.6278 -0.4916 0.02 4.218 0.5855 0.4264 0.4 2.722 0.4209 -0.06476

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 0.9434142 5 8.113172 A2 4.276277 8 7.447445 A3 1.599232 6 8.801536

### Page 167 of 438 R -10.68106 2 25.36212 2 0.20663 4 7.58674 3 0.20663 4 7.58674 4 1.396782 5 7.206435 5 1.396782 5 7.206435

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 29.91 6 < 0.0001 Test 2 6.666 3 0.08335 Test 3 5.354 2 0.06877 Test 4 2.785 2 0.2484 Test 5a 2.785 2 0.2484 Test 5b -2.421e-013 0 N/A Test 6a 0.4049 1 0.5246 Test 6b 2.38 1 0.1229 Test 7a 0.4049 1 0.5246 Test 7b 2.38 1 0.1229 Test 7c 1.776e-015 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

### Page 168 of 438 Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0768945 0.0587043 Note: model 2 is in line with other data 3 0.0768945 0.0587043 4 0.0124188 0.0058289 5 0.0124188 0.0058289

### Page 169 of 438 2 2.5

3 3.5

4 4.5

5 5.5

6 6.5

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

13:38 07/26 2012 BMD BMDL Exponential

2 2.5

3 3.5

4 4.5

5 5.5

6 6.5

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

13:38 07/26 2012 BMD BMDL Exponential

### Page 170 of 438 2 2.5

3 3.5

4 4.5

5 5.5

6 6.5

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

13:38 07/26 2012 BMD BMDL Exponential

2 2.5

3 3.5

4 4.5

5 5.5

6 6.5

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

13:38 07/26 2012 BMD BMDL Exponential

### Page 171 of 438 

## Male Adult RBC ChE Inhibition – MR ID 46153208 – CCA Repeat Study – 4 weeks

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 14:24:32 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 25.9833 25.9833 25.9833 25.9833 rho -2.1644 -2.1644 -2.1644 -2.1644 a 1767.69 1767.69 2276.4 2276.4 b 0.866793 0.866793 6.19176 6.19176 c -- -- 0.630486 0.630486 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 6.1897 6.1897 12.7833 17.5797 rho 0.485225 0.485225 -0.401355 -1.04105

### Page 172 of 438 a 2129.55 2129.55 2190.71 2167.98 b 0.871327 0.871327 13.9059 36.672 c -- -- 0.686638 0.695881 d -- 1 -- 3.96345

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2168 135 0.008 5 2160 56 0.02 5 2005 201 0.4 5 1507 143

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2130 141.8 0.6065 0.008 2115 141.5 0.7148 0.02 2093 141.2 -1.39 0.4 1503 130.3 0.0708 3 0 2130 141.8 0.6065 0.008 2115 141.5 0.7148 0.02 2093 141.2 -1.39 0.4 1503 130.3 0.0708 4 0 2191 127.5 -0.3983 0.008 2118 128.4 0.7242 0.02 2024 129.5 -0.3286 0.4 1507 137.4 0.002292 5 0 2168 120.5 0.0002958 0.008 2163 120.6 -0.05368 0.02 2001 125.6 0.0767 0.4 1509 145.5 -0.02549

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -107.0761 5 224.1521 A2 -103.7524 8 223.5049 A3 -106.9872 6 225.9744 R -123.9714 2 251.9428 2 -108.6306 4 225.2612

### Page 173 of 438 3 -108.6306 4 225.2612 4 -107.4493 5 224.8986 5 -106.9872 6 225.9744

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 40.44 6 < 0.0001 Test 2 6.647 3 0.08403 Test 3 6.47 2 0.03937 Test 4 3.287 2 0.1933 Test 5a 3.287 2 0.1933 Test 5b -2.842e-014 0 N/A Test 6a 0.9242 1 0.3364 Test 6b 2.363 1 0.1243 Test 7a -1.481e-011 0 N/A Test 7b 3.287 2 0.1933 Test 7c 0.9242 1 0.3364

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

### Page 174 of 438 The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.12092 0.0972113 Note: model 2 is in line with other data 3 0.12092 0.0972113 4 0.0276407 0.0138321 5 0.0216227 0.0154075

### Page 175 of 438 1400 1600 1800 2000 2200 2400

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

14:24 07/26 2012 BMD BMDL Exponential

1400 1600 1800 2000 2200 2400

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

14:24 07/26 2012 BMD BMDL Exponential

### Page 176 of 438 1400 1600 1800 2000 2200 2400

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

14:24 07/26 2012 BMD BMDL Exponential

1400 1600 1800 2000 2200 2400

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

14:24 07/26 2012 BMD BMDL Exponential

### Page 177 of 438 

## Female Adult RBC ChE Inhibition – MR ID 46153208 – CCA Repeat Study – 4 weeks

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Thu Jul 26 13:55:06 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 10.4112 10.4112 10.4112 10.4112 rho(S) 0 0 0 0 a 2041.96 2041.96 2522.1 2522.1 b 0.617849 0.617849 5.55669 5.55669 c -- -- 0.686125 0.686125 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

### Page 178 of 438 Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 10.5153 10.4857 10.5153 10.4857 rho 0 0 0 0 a 2330.92 2319.33 2330.92 2319.33 b 0.615354 1.97119 0.615354 3.01794 c -- -- 0 0.779574 d -- 5.93393 -- 7.42855

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2274 321 0.008 5 2282 158 0.02 5 2402 143 0.4 5 1817 133

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2331 192 -0.6628 0.008 2319 192 -0.4363 0.02 2302 192 1.16 0.4 1822 192 -0.06215 3 0 2319 189.2 -0.5358 0.008 2319 189.2 -0.4412 0.02 2319 189.2 0.977 0.4 1817 189.2 -5.485e-008 4 0 2331 192 -0.6628 0.008 2319 192 -0.4363 0.02 2302 192 1.16 0.4 1822 192 -0.06215 5 0 2319 189.2 -0.5358 0.008 2319 189.2 -0.4412 0.02 2319 189.2 0.977 0.4 1817 189.2 3.486e-007

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------

### Page 179 of 438 A1 -114.1117 5 238.2233 A2 -111.2047 8 238.4094 A3 -114.1117 5 238.2233 R -123.2795 2 250.5591 2 -115.1533 3 236.3065 3 -114.8568 4 237.7137 4 -115.1533 3 236.3065 5 -114.8568 5 239.7137

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 24.15 6 0.0004902 Test 2 5.814 3 0.121 Test 3 5.814 3 0.121 Test 4 2.083 2 0.3529 Test 5a 1.49 1 0.2222 Test 5b 0.5929 1 0.4413 Test 6a 2.083 2 0.3529 Test 6b 0 0 N/A Test 7a 1.49 0 N/A Test 7b 2.381e-007 1 0.9996 Test 7c 0.5929 2 0.7435

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

### Page 180 of 438 The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

The p-value for Test 5b is greater than .05. Model 3 does not seem to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.171219 0.124387 3 0.347192 0.128842 4 0.171219 0.0419733 5 0.309645 0.0212892

### Page 181 of 438 1600 1800 2000 2200 2400 2600

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

13:55 07/26 2012 BMD BMDL Exponential

1600 1800 2000 2200 2400 2600

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

13:55 07/26 2012 BMD BMDL Exponential

### Page 182 of 438 1600 1800 2000 2200 2400 2600

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

13:55 07/26 2012 BMD BMDL Exponential

1600 1800 2000 2200 2400 2600

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

13:55 07/26 2012 BMD BMDL Exponential

### Page 183 of 438 

#  Developmental Neurotoxicity Study

MRID 46153201 - Developmental Neurotoxicity in Rats – Maternal Brain, Gestation Day 22

========================= ==================== ==================

===== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrot ophos/exp_Dicrotophos DNT Mat Brain GD22 NHV_Setting.(d) Gnuplot Plotting File: Tue Sep 25 15:00:58 2012

========================= ==================== ==================

=====

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~

~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008

### Page 184 of 438 

## Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -3.20581 -3.20581 -3.20581 -3.20581 rho 2.13666 2.13666 2.13666 2.13666 a 1.70351 1.70351 5.019 5.019 b 1.45984 1.45984 4.42973 4.42973 c -- -- 0.195448 0.195448 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -3.30468 -3.30468 -3.42465 -3.43268 rho 2.40575 2.40575 2.16818 2.15031 a 4.25643 4.25643 4.92726 4.74926 b 1.44382 1.44382 4.74643 4.79315 c -- -- 0.201992 0.21699 d -- 1 -- 1.43371

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 4.78 0.99 0.05 5 4.26 1.06 0.2 5 2.49 0.51 1 5 1.03 0.21

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 4.256 1.094 1.07 0.05 3.96 1.003 0.6688

### Page 185 of 438 

## 0.2 3.189 0.7731 -2.021 1 1.005 0.1927 0.2945 3 0 4.256 1.094 1.07 0.05 3.96 1.003 0.6688 0.2 3.189 0.7731 -2.021 1 1.005 0.1927 0.2945 4 0 4.927 1.017 -0.3239 0.05 4.097 0.8323 0.4391 0.2 2.517 0.4908 -0.1231 1 1.029 0.1862 0.007129 5 0 4.749 0.9596 0.07163 0.05 4.299 0.8622 -0.1019 0.2 2.481 0.4775 0.04026 1 1.031 0.1857 -0.01006

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -2.693171 5 15.38634 A2 3.160304 8 9.679392 A3 3.062163 6 5.875674 R -19.88139 2 43.76277 2 -0.9442328 4 9.888466 3 -0.9442328 4 9.888466 4 2.797887 5 4.404226 5 3.062163 6 5.875674

### Page 186 of 438 

## Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 46.08 6 < 0.0001 Test 2 11.71 3 0.008458 Test 3 0.1963 2 0.9065 Test 4 8.013 2 0.0182 Test 5a 8.013 2 0.0182 Test 5b -1.329e-011 0 N/A Test 6a 0.5286 1 0.4672 Test 6b 7.484 1 0.006224 Test 7a -2.961e-012 0 N/A Test 7b 8.013 2 0.0182 Test 7c 0.5286 1 0.4672

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

### Page 187 of 438 

## The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0729736 0.0618696 3 0.0729736 0.0618696 4 0.0282081 0.0207607 5 0.0520527 0.0213844

### Page 188 of 438 1 2 3 4 5 6

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

15:01 09/25 2012 BMD BMDL Exponential

1 2 3 4 5 6

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

15:01 09/25 2012 BMD BMDL Exponential

### Page 189 of 438 1 2 3 4 5 6

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

15:01 09/25 2012 BMD BMDL Exponential

1 2 3 4 5 6

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

15:01 09/25 2012 BMD BMDL Exponential

### Page 190 of 438 

## MRID 46153201 - Developmental Neurotoxicity in Rats – Maternal Brain, Lactation Day 22

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrot ophos/exp_Dicrotophos DNT Mat Brain LD22 NHV_Setting.(d) Gnuplot Plotting File: Tue Sep 25 15:12:12 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 191 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -2.78329 -2.78329 -2.78329 -2.78329 rho 1.82372 1.82372 1.82372 1.82372 a 1.5864 1.5864 5.565 5.565 b 1.49419 1.49419 4.69765 4.69765 c -- -- 0.164292 0.164292 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -2.97927 -3.04436 -3.02506 -3.02506 rho 2.46029 2.89388 1.94883 1.94883 a 4.08839 3.70667 4.9458 4.9458 b 1.47961 1.03448 5.78575 5.78575 c -- -- 0.191911 0.191911 d -- 8.87425 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 5.3 1.07 0.05 5 3.55 0.82 0.2 5 2.27 0.56 1 5 0.96 0.23

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 4.088 1.275 2.126 0.05 3.797 1.164 -0.4743 0.2 3.041 0.8856 -1.947 1 0.931 0.2065 0.3137 3 0 3.707 1.453 2.452 0.05 3.707 1.453 -0.2411 0.2 3.707 1.453 -2.211 1 0.96 0.2057 5.165e-007 4 0 4.946 1.046 0.7571

### Page 192 of 438 

## 0.05 3.942 0.8386 -1.045 0.2 2.206 0.4763 0.3022 1 0.9614 0.2121 -0.01507 5 0 4.946 1.046 0.7571 0.05 3.942 0.8386 -1.045 0.2 2.206 0.4763 0.3022 1 0.9614 0.2121 -0.01507

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -1.716286 5 13.43257 A2 3.132869 8 9.734261 A3 3.081416 6 5.837168 R -21.01312 2 46.02624 2 -3.475981 4 14.95196 3 -7.696264 5 25.39253 4 2.117408 5 5.765184 5 2.117408 5 5.765184

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 193 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 48.29 6 < 0.0001 Test 2 9.698 3 0.02131 Test 3 0.1029 2 0.9498 Test 4 13.11 2 0.00142 Test 5a 21.56 1 < 0.0001 Test 5b -8.441 1 N/A Test 6a 1.928 1 0.165 Test 6b 11.19 1 0.0008238 Test 7a 1.928 1 0.165 Test 7b 19.63 0 N/A Test 7c 0 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 194 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

Degrees of freedom for Test 7b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0712083 0.0586767 3 0.750151 0.0149042 4 0.0228324 0.0172106 5 0.0228324 0.0172106

### Page 195 of 438 1 2 3 4 5 6 7

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

15:12 09/25 2012 BMD BMDL Exponential

1 2 3 4 5 6 7

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

15:12 09/25 2012 BMD BMDL Exponential

### Page 196 of 438 1 2 3 4 5 6 7

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

15:12 09/25 2012 BMD BMDL Exponential

1 2 3 4 5 6 7

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

15:12 09/25 2012 BMD BMDL Exponential

### Page 197 of 438 

## MRID 46153201 - Developmental Neurotoxicity in Rats – Maternal RBC, Gestation Day 22

================== ================== ================== ============== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicro tophos/exp_Dicrotophos DNT Mat RBC GD22 NHV_Setting.(d) Gnuplot Plotting File: Tue Sep 25 15:24:05 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 198 of 438 

## -------- ------- ------- ------- ------- lnalpha 16.9485 16.9485 16.9485 16.9485 rho -0.923565 -0.923565 -0.923565 -0.923565 a 1555.6 69.289 2722.65 2722.65 b 0.62262 -2.0097 3.33194 3.33194 c -- -- 0.448443 0.448443 d -- 2 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 24.0537 16.9485 12.4658 13.3588 rho -1.81995 -0.923565 -0.307124 -0.436023 a 2591.98 69.289 2626.47 2591.53 b 2.15278 -2.0097 5.89745 6.08096 c -- -- 0.481125 0.49383 d -- 2 -- 1.31261

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2593 218 0.05 5 2342 79 0.2 5 1638 120 1 5 1282 226

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2592 130.9 0.01741 0.05 2327 144.3 0.225 0.2 1685 193.7 -0.5447 1 301.1 928.2 2.363 3 0 0 1.176e+146 4.929e-143 0.05 0 1.176e+146 4.452e-143 0.2 0 1.176e+146 3.114e-143 1 0 1.176e+146 2.437e-143 4 0 2626 152 -0.4924 0.05 2278 155.3 0.9148

### Page 199 of 438 

## 0.2 1683 162.8 -0.6133 1 1267 170 0.192 5 0 2592 143.4 0.02289 0.05 2344 146.6 -0.02341 0.2 1640 158.5 -0.0255 1 1280 167.3 0.02934

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -110.7946 5 231.5893 A2 -107.5784 8 231.1568 A3 -110.6941 6 233.3881 R -136.1596 2 276.3192 2 -119.7293 4 247.4586 3 -215.0441 5 440.0882 4 -111.4873 5 232.9747 5 -110.6941 6 233.3881

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R)

### Page 200 of 438 

## Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 57.16 6 < 0.0001 Test 2 6.432 3 0.09236 Test 3 6.231 2 0.04435 Test 4 18.07 2 0.0001191 Test 5a 208.7 1 < 0.0001 Test 5b -190.6 1 N/A Test 6a 1.587 1 0.2078 Test 6b 16.48 1 < 0.0001 Test 7a -1.805e-007 0 N/A Test 7b 208.7 1 < 0.0001 Test 7c 1.587 1 0.2078

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately

### Page 201 of 438 

## describe the data; you may want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0489417 0.0396102 3 -0.161513 Bad completion 4 0.0363023 0.0276139 5 0.0519051 0.0305501

### Page 202 of 438 1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

15:24 09/25 2012 BMD BMDL Exponential

0 500 1000 1500 2000 2500 3000

-1 -0.5 0 0.5 1Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

15:24 09/25 2012 BMD BMDL Exponential

### Page 203 of 438 1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

15:24 09/25 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

15:24 09/25 2012 BMD BMDL Exponential

### Page 204 of 438 

## MRID 46153201 - Developmental Neurotoxicity in Rats – Maternal RBC, Lactation Day 22

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/e xp_Dicrotophos DNT Mat RBC LD22_Setting.(d) Gnuplot Plotting File: Fri Aug 17 12:36:01 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 205 of 438 

## -------- ------- ------- ------- ------- lnalpha 9.87791 9.87791 9.87791 9.87791 rho(S) 0 0 0 0 a 1592.21 1592.21 2753.1 2753.1 b 0.592857 0.592857 3.28434 3.28434 c -- -- 0.456282 0.456282 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.1405 11.1405 9.88416 9.88416 rho 0 0 0 0 a 2363.74 2363.74 2612.64 2612.64 b 0.678249 0.678249 5.94524 5.94524 c -- -- 0.50467 0.50467 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2622 185 0.05 5 2263 140 0.2 5 1723 195 1 5 1319 75

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2364 262.5 2.2 0.05 2285 262.5 -0.1868 0.2 2064 262.5 -2.904 1 1200 262.5 1.017 3 0 2364 262.5 2.2 0.05 2285 262.5 -0.1868 0.2 2064 262.5 -2.904 1 1200 262.5 1.017

### Page 206 of 438 

## 4 0 2613 140.1 0.1494 0.05 2280 140.1 -0.2691 0.2 1713 140.1 0.1661 1 1322 140.1 -0.04645 5 0 2613 140.1 0.1494 0.05 2280 140.1 -0.2691 0.2 1713 140.1 0.1661 1 1322 140.1 -0.04645

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -108.7791 5 227.5583 A2 -106.531 8 229.062 A3 -108.7791 5 227.5583 R -134.9985 2 273.997 2 -121.4049 3 248.8099 3 -121.4049 3 248.8099 4 -108.8416 4 225.6832 5 -108.8416 4 225.6832

Additive constant for all log-likelihoods = -18.38. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 207 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 56.94 6 < 0.0001 Test 2 4.496 3 0.2126 Test 3 4.496 3 0.2126 Test 4 25.25 2 < 0.0001 Test 5a 25.25 2 < 0.0001 Test 5b -3.979e-013 0 N/A Test 6a 0.1249 1 0.7238 Test 6b 25.13 1 < 0.0001 Test 7a 0.1249 1 0.7238 Test 7b 25.13 1 < 0.0001 Test 7c -1.137e-013 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 208 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.155342 0.119381 3 0.155342 0.119381 4 0.0379301 0.0292592 5 0.0379301 0.0292592

### Page 209 of 438 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:36 08/17 2012 BMD BMDL Exponential

1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:36 08/17 2012 BMD BMDL Exponential

### Page 210 of 438 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:36 08/17 2012 BMD BMDL Exponential

1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

12:36 08/17 2012 BMD BMDL Exponential

### Page 211 of 438 

## MRID 46153201 - Developmental Neurotoxicity in Rats – Male Fetus Gestation Day 22, Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrot ophos/exp_Dicrotophos DNT Fet Brain M GD22 NHV_Setting.(d) Gnuplot Plotting File: Tue Sep 25 15:51:15 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 212 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -3.90785 -3.90785 -3.90785 -3.90785 rho 3.41373 3.41373 3.41373 3.41373 a 1.04679 1.06393 1.8375 1.8375 b 0.750205 0.602732 3.44021 3.44021 c -- -- 0.399093 0.399093 d -- 2 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -4.125 -4.08459 -4.05885 -4.05885 rho 4.25023 4.8 3.54227 3.54227 a 1.56895 1.49333 1.6798 1.6798 b 0.71878 0.965201 3.02726 3.02726 c -- -- 0.428293 0.428293 d -- 11.6299 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 1.75 0.34 0.05 5 1.51 0.25 0.2 5 1.22 0.28 1 4 0.77 0.08

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 1.569 0.3311 1.223 0.05 1.514 0.3068 -0.02595 0.2 1.359 0.2439 -1.273 1 0.7646 0.07187 0.1497 3 0 1.493 0.3396 1.69 0.05 1.493 0.3396 0.1097 0.2 1.493 0.3396 -1.8 1 0.77 0.06928 3.084e-008 4 0 1.68 0.3293 0.4767

### Page 213 of 438 

## 0.05 1.545 0.2839 -0.2749 0.2 1.244 0.1933 -0.2733 1 0.766 0.08195 0.0983 5 0 1.68 0.3293 0.4767 0.05 1.545 0.2839 -0.2749 0.2 1.244 0.1933 -0.2733 1 0.766 0.08195 0.0983

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 18.05219 5 -26.10438 A2 21.5422 8 -27.08441 A3 20.77383 6 -29.54765 R 6.841334 2 -9.682669 2 19.52083 4 -31.04166 3 17.3763 5 -24.7526 4 20.57197 5 -31.14395 5 20.57197 5 -31.14395

Additive constant for all log-likelihoods = -17.46. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 214 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 29.4 6 < 0.0001 Test 2 6.98 3 0.07254 Test 3 1.537 2 0.4638 Test 4 2.506 2 0.2856 Test 5a 6.795 1 0.009141 Test 5b -4.289 1 N/A Test 6a 0.4037 1 0.5252 Test 6b 2.102 1 0.1471 Test 7a 0.4037 1 0.5252 Test 7b 6.391 0 N/A Test 7c 0 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

### Page 215 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

Degrees of freedom for Test 7b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.146582 0.122038 3 0.853782 0.0997103 4 0.0635125 0.0361406 5 0.0635125 0.0361406

### Page 216 of 438 0.6 0.8

1 1.2 1.4 1.6 1.8

2 2.2

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

15:51 09/25 2012 BMD BMDL Exponential

0.6 0.8

1 1.2 1.4 1.6 1.8

2 2.2

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

15:51 09/25 2012 BMD BMDL Exponential

### Page 217 of 438 0.6 0.8

1 1.2 1.4 1.6 1.8

2 2.2

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

15:51 09/25 2012 BMD BMDL Exponential

0.6 0.8

1 1.2 1.4 1.6 1.8

2 2.2

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

15:51 09/25 2012 BMD BMDL Exponential

### Page 218 of 438 

## MRID 46153201 - Developmental Neurotoxicity in Rats – Female Fetus Gestation Day 22, Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrot ophos/exp_Dicrotophos DNT Fet Brain F GD22 NHV_Setting.(d) Gnuplot Plotting File: Tue Sep 25 16:17:06 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 219 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -5.84249 -5.84249 -5.84249 -5.84249 rho 5.75815 5.75815 5.75815 5.75815 a 0.983047 1.02456 1.6485 1.6485 b 0.723412 0.609064 3.3292 3.3292 c -- -- 0.415963 0.415963 d -- 2 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -5.97293 -5.97293 -6.02656 -6.02656 rho 6.58288 6.58288 6.15482 6.15482 a 1.45092 1.45092 1.50478 1.50478 b 0.701409 0.701409 1.87726 1.87726 c -- -- 0.384227 0.384227 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 1.57 0.18 0.05 5 1.36 0.13 0.2 5 1.22 0.11 1 4 0.72 0.02

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 1.451 0.1718 1.55 0.05 1.401 0.1531 -0.5977 0.2 1.261 0.1083 -0.847 1 0.7195 0.01708 0.05961 3 0 1.451 0.1718 1.55 0.05 1.401 0.1531 -0.5977 0.2 1.261 0.1083 -0.847 1 0.7195 0.01708 0.05962 4 0 1.505 0.1728 0.8441

### Page 220 of 438 

## 0.05 1.422 0.1451 -0.9518 0.2 1.215 0.0894 0.1318 1 0.72 0.01787 0.005117 5 0 1.505 0.1728 0.8441 0.05 1.422 0.1451 -0.9518 0.2 1.215 0.0894 0.1318 1 0.72 0.01787 0.005117

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 31.76435 5 -53.5287 A2 38.2085 8 -60.41701 A3 38.05597 6 -64.11194 R 12.14416 2 -20.28833 2 36.08699 4 -64.17398 3 36.08699 4 -64.17398 4 37.1013 5 -64.20259 5 37.1013 5 -64.20259

Additive constant for all log-likelihoods = -17.46. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 221 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 52.13 6 < 0.0001 Test 2 12.89 3 0.004884 Test 3 0.3051 2 0.8585 Test 4 3.938 2 0.1396 Test 5a 3.938 2 0.1396 Test 5b -1.14e-011 0 N/A Test 6a 1.909 1 0.167 Test 6b 2.029 1 0.1544 Test 7a 1.909 1 0.167 Test 7b 2.029 1 0.1544 Test 7c 0 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

### Page 222 of 438 

## The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.150213 0.139435 3 0.150213 0.139435 4 0.0943991 0.0643789 5 0.0943991 0.0643789

### Page 223 of 438 0.6 0.8

1 1.2 1.4 1.6 1.8

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

16:17 09/25 2012 BMD BMDL Exponential

0.6 0.8

1 1.2 1.4 1.6 1.8

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

16:17 09/25 2012 BMD BMDL Exponential

### Page 224 of 438 0.6 0.8

1 1.2 1.4 1.6 1.8

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

16:17 09/25 2012 BMD BMDL Exponential

0.6 0.8

1 1.2 1.4 1.6 1.8

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

16:17 09/25 2012 BMD BMDL Exponential

### Page 225 of 438 

## MRID 46153201 - Developmental Neurotoxicity in Rats – Male Fetus Gestation Day 22, RBC

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicro tophos/exp_Dicrotophos DNT Fet RBC M GD22 NHV_Setting.(d) Gnuplot Plotting File: Tue Sep 25 16:53:37 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 226 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 1.03839 1.03839 1.03839 1.03839 rho 1.22269 1.22269 1.22269 1.22269 a 1702.34 1702.34 2673.3 2673.3 b 0.63661 0.63661 3.15295 3.15295 c -- -- 0.467052 0.467052 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -11.0332 -20.4514 -7.02355 -7.68872 rho 2.86511 4.15186 2.29724 2.37931 a 2416.4 2297.33 2576.99 2524.11 b 0.625986 0.962514 3.30124 3.98937 c -- -- 0.488945 0.519213 d -- 15.1312 -- 1.67697

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2546 112 0.05 5 2423 351 0.2 5 1923 190 1 4 1311 124

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2416 282.3 1.026 0.05 2342 269.9 0.6714 0.2 2132 236 -1.981 1 1292 115.2 0.3277 3 0 2297 344.1 1.616 0.05 2297 344.1 0.8166 0.2 2297 344.1 -2.432 1 1311 107.4 -8.301e-009 4 0 2577 247.1 -0.2804

### Page 227 of 438 

## 0.05 2377 225.2 0.4607 0.2 1941 178.4 -0.2196 1 1309 113.5 0.04367 5 0 2524 238.6 0.2051 0.05 2445 229.8 -0.2189 0.2 1922 172.6 0.007321 1 1311 109.4 0.007334

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -109.831 5 229.6619 A2 -105.6637 8 227.3275 A3 -108.5976 6 229.1951 R -127.8621 2 259.7243 2 -112.01 4 232.0201 3 -115.82 5 241.6401 4 -108.9808 5 227.9616 5 -108.5976 6 229.1951

Additive constant for all log-likelihoods = -17.46. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 228 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 44.4 6 < 0.0001 Test 2 8.334 3 0.03958 Test 3 5.868 2 0.05319 Test 4 6.825 2 0.03296 Test 5a 14.44 1 0.0001443 Test 5b -7.62 1 N/A Test 6a 0.7665 1 0.3813 Test 6b 6.058 1 0.01384 Test 7a -2.842e-014 0 N/A Test 7b 14.44 1 0.0001443 Test 7c 0.7665 1 0.3813

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 229 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.168311 0.136826 3 0.895371 0.0659445 4 0.0659603 0.0451428 5 0.105207 0.0477208

### Page 230 of 438 1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

16:53 09/25 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

16:53 09/25 2012 BMD BMDL Exponential

### Page 231 of 438 1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

16:53 09/25 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

16:53 09/25 2012 BMD BMDL Exponential

### Page 232 of 438 

## MRID 46153201 - Developmental Neurotoxicity in Rats – Female Fetus Gestation Day 22, RBC

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicro tophos/exp_Dicrotophos DNT Fet RBC F GD22 NHV_Setting.(d) Gnuplot Plotting File: Tue Sep 25 16:37:34 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 233 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 5.29309 5.29309 5.29309 5.29309 rho 0.641199 0.641199 0.641199 0.641199 a 1738.64 1738.64 2649.15 2649.15 b 0.535125 0.535125 3.04573 3.04573 c -- -- 0.508339 0.508339 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -20.0566 -20.0566 -14.7374 -14.6371 rho 4.10424 4.10424 3.33968 3.3265 a 2323.16 2323.16 2572.87 2568.95 b 0.509266 0.509266 5.09487 5.10144 c -- -- 0.547597 0.549011 d -- 1 -- 1.02477

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2523 455 0.05 5 2362 50 0.2 5 1825 207 1 4 1414 142

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2323 356.7 1.253 0.05 2265 338.6 0.6422 0.2 2098 289.5 -2.11 1 1396 125.5 0.2858 3 0 2323 356.7 1.253 0.05 2265 338.6 0.6422 0.2 2098 289.5 -2.11 1 1396 125.5 0.2858 4 0 2573 312.4 -0.357

### Page 234 of 438 

## 0.05 2311 261.1 0.4358 0.2 1829 176.7 -0.05122 1 1416 115.2 -0.03518 5 0 2569 311.1 -0.3303 0.05 2316 261.8 0.395 0.2 1828 176.6 -0.0357 1 1416 115.5 -0.03627

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -113.4178 5 236.8357 A2 -103.8996 8 223.7991 A3 -110.9046 6 233.8092 R -127.1543 2 258.3086 2 -115.6768 4 239.3536 3 -115.6768 4 239.3536 4 -110.906 5 231.812 5 -110.9046 6 233.8092

Additive constant for all log-likelihoods = -17.46. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 235 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 46.51 6 < 0.0001 Test 2 19.04 3 0.0002687 Test 3 14.01 2 0.0009073 Test 4 9.544 2 0.008462 Test 5a 9.544 2 0.008462 Test 5b -7.105e-013 0 N/A Test 6a 0.002752 1 0.9582 Test 6b 9.542 1 0.002009 Test 7a -1.137e-013 0 N/A Test 7b 9.544 2 0.008462 Test 7c 0.002752 1 0.9582

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 236 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.206887 0.162064 3 0.206887 0.162064 4 0.0490294 0.0326672 5 0.0508117 0.0326739

### Page 237 of 438 1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

16:37 09/25 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

16:37 09/25 2012 BMD BMDL Exponential

### Page 238 of 438 1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

16:37 09/25 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000

0 0.2 0.4 0.6 0.8 1Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

16:37 09/25 2012 BMD BMDL Exponential

### Page 239 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 35 D of dosing – Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp _Dicrotophos Brain M 35 D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 09:52:07 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 240 of 438 

## lnalpha -5.35004 -5.35004 -5.35004 -5.35004 rho 2.13496 2.13496 2.13496 2.13496 a 2.56227 2.56227 11.55 11.55 b 1.02749 1.02749 2.58311 2.58311 c -- -- 0.0997732 0.0997732 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -5.7115 -5.7115 -5.52084 -5.52084 rho 3.53687 3.53687 2.1434 2.1434 a 8.70498 8.70498 10.972 10.972 b 0.974451 0.974451 3.07872 3.07872 c -- -- 0.108508 0.108508 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 11 0.95 0.04 6 9.82 0.68 0.39 6 4.13 0.36 2.03 6 1.21 0.08

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 8.705 2.64 2.129 0.04 8.372 2.465 1.439 0.39 5.953 1.348 -3.311 2.03 1.204 0.07988 0.1799 3 0 8.705 2.64 2.129 0.04 8.372 2.465 1.439 0.39 5.953 1.348 -3.311 2.03 1.204 0.07988 0.1799 4 0 10.97 0.8242 0.08333 0.04 9.839 0.7333 -0.06216 0.39 4.135 0.2896 -0.03883

### Page 241 of 438 

## 2.03 1.209 0.07757 0.0179 5 0 10.97 0.8242 0.08333 0.04 9.839 0.7333 -0.06216 0.39 4.135 0.2896 -0.03883 2.03 1.209 0.07757 0.0179

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 1.950612 5 6.098776 A2 14.09387 8 -12.18775 A3 13.80876 6 -15.61753 R -45.66171 2 95.32343 2 -9.867837 4 27.73567 3 -9.867837 4 27.73567 4 13.79643 5 -17.59285 5 13.79643 5 -17.59285

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1)

### Page 242 of 438 

## Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 119.5 6 < 0.0001 Test 2 24.29 3 < 0.0001 Test 3 0.5702 2 0.7519 Test 4 47.35 2 < 0.0001 Test 5a 47.35 2 < 0.0001 Test 5b 7.105e-015 0 N/A Test 6a 0.02468 1 0.8752 Test 6b 47.33 1 < 0.0001 Test 7a 0.02468 1 0.8752 Test 7b 47.33 1 < 0.0001 Test 7c -7.105e-015 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

### Page 243 of 438 

## Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.108123 0.101367 3 0.108123 0.101367 4 0.0386449 0.0360282 5 0.0386449 0.0360282

### Page 244 of 438 

## Neurotoxicity – Male Adult Brain – 35 D – Assuming Non-Homogeneous Variance

2 4 6 8 10 12

0 0.5 1 1.5 2Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

09:52 09/28 2012 BMD BMDL Exponential

2 4 6 8 10 12

0 0.5 1 1.5 2Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

09:52 09/28 2012 BMD BMDL Exponential

### Page 245 of 438 2 4 6 8 10 12

0 0.5 1 1.5 2Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

09:52 09/28 2012 BMD BMDL Exponential

2 4 6 8 10 12

0 0.5 1 1.5 2Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

09:52 09/28 2012 BMD BMDL Exponential

### Page 246 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 35 D of dosing - Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp _Dicrotophos Brain F 35D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 10:21:01 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 247 of 438 

## lnalpha -2.55932 -2.55932 -2.55932 -2.55932 rho 1.07 1.07 1.07 1.07 a 2.31083 2.31083 10.143 10.143 b 0.863059 0.863059 2.1879 2.1879 c -- -- 0.102346 0.102346 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -0.25934 -2.11997 -2.75269 -2.75269 rho -0.0920517 1.50676 1.11283 1.11283 a 9.41977 8.34087 9.46199 9.46199 b 1.99174 0.909034 2.5039 2.5039 c -- -- 0.113337 0.113337 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 9.66 1.07 0.04 6 8.44 0.78 0.45 6 3.82 0.54 2.38 6 1.09 0.3

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 9.42 0.7922 0.7428 0.04 8.698 0.7951 -0.7961 0.45 3.844 0.8256 -0.07136 2.38 0.08229 0.9854 2.505 3 0 8.341 1.713 1.887 0.04 8.043 1.666 0.5835 0.45 5.541 1.258 -3.349 2.38 0.9586 0.3356 0.9595 4 0 9.462 0.8817 0.5501 0.04 8.662 0.8394 -0.6491 0.45 3.791 0.53 0.1326

### Page 248 of 438 

## 2.38 1.094 0.2654 -0.03736 5 0 9.462 0.8817 0.5501 0.04 8.662 0.8394 -0.6491 0.45 3.791 0.53 0.1326 2.38 1.094 0.2654 -0.03736

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -2.277647 5 14.55529 A2 2.193629 8 11.61274 A3 1.990205 6 8.019589 R -42.24709 2 88.49418 2 -7.989047 4 23.97809 3 -13.12035 4 34.2407 4 1.572602 5 6.854796 5 1.572602 5 6.854796

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1)

### Page 249 of 438 

## Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 88.88 6 < 0.0001 Test 2 8.943 3 0.03006 Test 3 0.4068 2 0.8159 Test 4 19.96 2 < 0.0001 Test 5a 30.22 2 < 0.0001 Test 5b -10.26 0 N/A Test 6a 0.8352 1 0.3608 Test 6b 19.12 1 < 0.0001 Test 7a 0.8352 1 0.3608 Test 7b 29.39 1 < 0.0001 Test 7c 5.329e-015 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

### Page 250 of 438 

## Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0528987 0.044071 3 0.115904 0.0340592 4 0.0477914 0.0418788 5 0.0477914 0.0418788

### Page 251 of 438 0 2 4 6 8 10

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

10:21 09/28 2012 BMD BMDL Exponential

0 2 4 6 8 10

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:21 09/28 2012 BMD BMDL Exponential

### Page 252 of 438 0 2 4 6 8 10

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dose Exponential Model 4 with 0.95 Confidence Level

10:21 09/28 2012 BMD BMDL Exponential

0 2 4 6 8 10

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:21 09/28 2012 BMD BMDL Exponential

### Page 253 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 63 D of dosing - Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/e xp_Dicrotophos Brain M 63D_Setting.(d) Gnuplot Plotting File: Wed Aug 15 17:30:00 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 254 of 438 

## -------- ------- ------- ------- ------- lnalpha -1.49368 -1.49368 -1.49368 -1.49368 rho(S) 0 0 0 0 a 2.66662 2.66662 11.172 11.172 b 1.01047 1.01047 2.57269 2.57269 c -- -- 0.100592 0.100592 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -0.38777 -0.387771 -0.944247 -0.944247 rho 0 0 0 0 a 10.1109 10.1109 10.1943 10.1943 b 2.23756 2.23756 2.93089 2.93089 c -- -- 0.119181 0.119181 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 10.64 0.76 0.04 5 8.53 0.54 0.39 6 4.24 0.3 2.03 5 1.18 0.33

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 10.11 0.8238 1.573 0.04 9.245 0.8238 -1.942 0.39 4.225 0.8238 0.04529 2.03 0.1077 0.8238 2.911 3 0 10.11 0.8238 1.573 0.04 9.245 0.8238 -1.942 0.39 4.225 0.8238 0.04529 2.03 0.1077 0.8238 2.911

### Page 255 of 438 

## 4 0 10.19 0.6237 1.751 0.04 9.201 0.6237 -2.406 0.39 4.078 0.6237 0.6364 2.03 1.238 0.6237 -0.2093 5 0 10.19 0.6237 1.751 0.04 9.201 0.6237 -2.406 0.39 4.078 0.6237 0.6364 2.03 1.238 0.6237 -0.2093

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 5.430448 5 -0.8608967 A2 8.704349 8 -1.408698 A3 5.430448 5 -0.8608967 R -39.71535 2 83.43071 2 -6.734526 3 19.46905 3 -6.734526 3 19.46905 4 -0.6132832 4 9.226566 5 -0.6132832 4 9.226566

Additive constant for all log-likelihoods = -20.22. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 256 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 96.84 6 < 0.0001 Test 2 6.548 3 0.0878 Test 3 6.548 3 0.0878 Test 4 24.33 2 < 0.0001 Test 5a 24.33 2 < 0.0001 Test 5b -4.141e-012 0 N/A Test 6a 12.09 1 0.0005076 Test 6b 12.24 1 0.0004671 Test 7a 12.09 1 0.0005076 Test 7b 12.24 1 0.0004671 Test 7c -6.767e-012 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. Consider running a non-homogeneous variance model.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 257 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is le ss than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is le ss than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0470873 0.0394876 3 0.0470873 0.0394876 4 0.0411167 0.0350677 5 0.0411167 0.0350677

### Page 258 of 438 0 2 4 6 8 10 12

0 0.5 1 1.5 2Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

17:17 08/15 2012 BMD BMDL Exponential

0 2 4 6 8 10 12

0 0.5 1 1.5 2Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

17:17 08/15 2012 BMD BMDL Exponential

### Page 259 of 438 0 2 4 6 8 10 12

0 0.5 1 1.5 2Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

17:17 08/15 2012 BMD BMDL Exponential

0 2 4 6 8 10 12

0 0.5 1 1.5 2Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

17:17 08/15 2012 BMD BMDL Exponential

### Page 260 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 63 D of dosing - Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp _Dicrotophos Brain F 63D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 10:26:47 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 261 of 438 

## -------- ------- ------- ------- ------- lnalpha -4.68033 -4.68033 -4.68033 -4.68033 rho 1.87867 1.87867 1.87867 1.87867 a 2.3627 2.3627 11.361 11.361 b 0.913632 0.913632 2.25288 2.25288 c -- -- 0.089697 0.089697 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -4.77973 -5.02263 -4.90013 -4.90013 rho 3.06724 3.47531 1.99599 1.99599 a 8.59347 8.05 10.5797 10.5797 b 0.879352 0.44095 2.65259 2.65259 c -- -- 0.0995164 0.0995164 d -- 14.5433 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 10.82 1.17 0.04 6 9.38 0.56 0.45 6 3.95 0.39 2.38 6 1.07 0.1

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 8.593 2.482 2.198 0.04 8.296 2.351 1.129 0.45 5.785 1.353 -3.323 2.38 1.06 0.1002 0.2477 3 0 8.05 3.043 2.23 0.04 8.05 3.043 1.071 0.45 8.05 3.043 -3.3 2.38 1.07 0.09129 9.564e-010 4 0 10.58 0.9086 0.6479 0.04 9.621 0.8264 -0.7133

### Page 262 of 438 

## 0.45 3.941 0.3391 0.0685 2.38 1.07 0.09233 -0.00313 5 0 10.58 0.9086 0.6479 0.04 9.621 0.8264 -0.7133 0.45 3.941 0.3391 0.0685 2.38 1.07 0.09233 -0.00313

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -0.5237584 5 11.04752 A2 12.18991 8 -8.379818 A3 11.14526 6 -10.29053 R -45.33264 2 94.66529 2 -10.59215 4 29.18429 3 -17.66907 5 45.33813 4 10.50284 5 -11.00568 5 10.50284 5 -11.00568

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R)

### Page 263 of 438 

## Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 115 6 < 0.0001 Test 2 25.43 3 < 0.0001 Test 3 2.089 2 0.3518 Test 4 43.47 2 < 0.0001 Test 5a 57.63 1 < 0.0001 Test 5b -14.15 1 N/A Test 6a 1.285 1 0.257 Test 6b 42.19 1 < 0.0001 Test 7a 1.285 1 0.257 Test 7b 56.34 0 N/A Test 7c -1.219e-012 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately

### Page 264 of 438 

## describe the data; you may want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

Degrees of freedom for Test 7b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.119816 0.111804 3 1.94272 0.0264226 4 0.0443778 0.0409235 5 0.0443778 0.0409235

### Page 265 of 438 0 2 4 6 8 10 12

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

10:26 09/28 2012 BMD BMDL Exponential

0 2 4 6 8 10 12

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:26 09/28 2012 BMD BMDL Exponential

### Page 266 of 438 0 2 4 6 8 10 12

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

10:26 09/28 2012 BMD BMDL Exponential

0 2 4 6 8 10 12

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:26 09/28 2012 BMD BMDL Exponential

### Page 267 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 98 D of dosing - Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp _Dicrotophos Brain M 98 D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 10:14:31 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 268 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -5.05353 -5.05353 -5.05353 -5.05353 rho 1.62388 1.62388 1.62388 1.62388 a 2.71461 2.71461 10.3215 10.3215 b 0.959459 0.959459 2.49046 2.49046 c -- -- 0.115339 0.115339 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -0.746064 -5.80558 -5.07656 -5.07656 rho -0.445126 3.37553 1.72791 1.72791 a 9.58233 7.99941 9.58415 9.58415 b 2.07729 0.917593 2.5519 2.5519 c -- -- 0.125271 0.125271 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 9.83 0.5 0.04 6 8.57 0.34 0.39 6 4.3 0.43 2.03 5 1.25 0.08

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 9.582 0.4164 1.33 0.04 8.818 0.4242 -1.434 0.39 4.262 0.4987 0.1858 2.03 0.1413 1.065 2.329 3 0 7.999 1.834 2.231 0.04 7.711 1.724 1.22 0.39 5.593 1.003 -3.158 2.03 1.242 0.07909 0.2286 4 0 9.584 0.5567 0.9874

### Page 269 of 438 

## 0.04 8.771 0.5157 -0.953 0.39 4.299 0.2785 0.004762 2.03 1.248 0.09565 0.05186 5 0 9.584 0.5567 0.9874 0.04 8.771 0.5157 -0.953 0.39 4.299 0.2785 0.004762 2.03 1.248 0.09565 0.05186

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 12.79932 5 -15.59864 A2 18.8407 8 -21.68141 A3 16.39977 6 -20.79954 R -37.54094 2 79.08187 2 2.38671 4 3.226581 3 -4.63259 4 17.26518 4 15.30633 5 -20.61266 5 15.30633 5 -20.61266

Additive constant for all log-likelihoods = -20.22. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 270 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 112.8 6 < 0.0001 Test 2 12.08 3 0.007105 Test 3 4.882 2 0.08708 Test 4 28.03 2 < 0.0001 Test 5a 42.06 2 < 0.0001 Test 5b -14.04 0 N/A Test 6a 2.187 1 0.1392 Test 6b 25.84 1 < 0.0001 Test 7a 2.187 1 0.1392 Test 7b 39.88 1 < 0.0001 Test 7c 0 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 271 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0507201 0.045494 3 0.114823 0.107812 4 0.0475727 0.0440646 5 0.0475727 0.0440646

### Page 272 of 438 2 4 6 8 10

0 0.5 1 1.5 2Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

10:14 09/28 2012 BMD BMDL Exponential

2 4 6 8 10

0 0.5 1 1.5 2Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:14 09/28 2012 BMD BMDL Exponential

### Page 273 of 438 2 4 6 8 10

0 0.5 1 1.5 2Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

10:14 09/28 2012 BMD BMDL Exponential

2 4 6 8 10

0 0.5 1 1.5 2Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:14 09/28 2012 BMD BMDL Exponential

### Page 274 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 98 D of dosing - Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp _Dicrotophos Brain F 98D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 10:31:24 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 275 of 438 

## -------- ------- ------- ------- ------- lnalpha -4.89038 -4.89038 -4.89038 -4.89038 rho 1.92736 1.92736 1.92736 1.92736 a 2.55186 2.55186 11.256 11.256 b 0.847726 0.847726 2.18707 2.18707 c -- -- 0.104071 0.104071 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -5.27493 -5.27493 -5.11347 -5.11347 rho 3.33392 3.33393 2.02428 2.02428 a 8.37786 8.37786 10.4017 10.4017 b 0.809137 0.809137 2.6591 2.6591 c -- -- 0.116653 0.116653 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 10.72 0.87 0.04 6 9.16 0.66 0.45 6 4 0.38 2.38 6 1.23 0.1

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 8.378 2.474 2.319 0.04 8.111 2.344 1.096 0.45 5.821 1.348 -3.308 2.38 1.221 0.09983 0.2155 3 0 8.378 2.474 2.319 0.04 8.111 2.344 1.096 0.45 5.821 1.348 -3.308 2.38 1.221 0.09983 0.2155 4 0 10.4 0.83 0.9393 0.04 9.475 0.7552 -1.02

### Page 276 of 438 

## 0.45 3.99 0.3147 0.07557 2.38 1.23 0.09562 0.005597 5 0 10.4 0.83 0.9393 0.04 9.475 0.7552 -1.02 0.45 3.99 0.3147 0.07557 2.38 1.23 0.09562 0.005597

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 3.249723 5 3.500554 A2 13.13754 8 -10.27508 A3 12.9266 6 -13.8532 R -44.46281 2 92.92563 2 -10.51323 4 29.02647 3 -10.51323 4 29.02647 4 11.8236 5 -13.6472 5 11.8236 5 -13.6472

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R)

### Page 277 of 438 

## Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 115.2 6 < 0.0001 Test 2 19.78 3 0.0001889 Test 3 0.4219 2 0.8098 Test 4 46.88 2 < 0.0001 Test 5a 46.88 2 < 0.0001 Test 5b -3.421e-011 0 N/A Test 6a 2.206 1 0.1375 Test 6b 44.67 1 < 0.0001 Test 7a 2.206 1 0.1375 Test 7b 44.67 1 < 0.0001 Test 7c -8.917e-013 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately

### Page 278 of 438 

## describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.130213 0.121365 3 0.130213 0.121365 4 0.0451815 0.0417724 5 0.0451815 0.0417724

### Page 279 of 438 2 4 6 8 10 12

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

10:31 09/28 2012 BMD BMDL Exponential

2 4 6 8 10 12

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:31 09/28 2012 BMD BMDL Exponential

### Page 280 of 438 2 4 6 8 10 12

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

10:31 09/28 2012 BMD BMDL Exponential

2 4 6 8 10 12

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:31 09/28 2012 BMD BMDL Exponential

### Page 281 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 35 D of dosing - RBC

================== ================== ================== ============== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos /exp_Dicrotophos RBC M 35D_Setting.(d) Gnuplot Plotting File: Wed Aug 15 18:22:26 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 282 of 438 

## -------- ------- ------- ------- ------- lnalpha 10.4438 10.4438 10.4438 10.4438 rho(S) 0 0 0 0 a 1304.83 1304.83 2186.1 2186.1 b 0.263495 0.263495 1.63727 1.63727 c -- -- 0.486624 0.486624 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.5468 11.5468 10.4752 10.4438 rho 0 0 0 0 a 1878.08 1878.08 2115.78 2082 b 0.322765 0.322765 5.15125 4.94339 c -- -- 0.520209 0.536503 d -- 1 -- 1.30887

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 2082 198 0.04 6 1973 137 0.39 6 1208 224 2.03 6 1117 238

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 1878 321.6 1.553 0.04 1854 321.6 0.9063 0.39 1656 321.6 -3.411 2.03 975.3 321.6 1.079 3 0 1878 321.6 1.553 0.04 1854 321.6 0.9063 0.39 1656 321.6 -3.411 2.03 975.3 321.6 1.079

### Page 283 of 438 

## 4 0 2116 188.2 -0.4396 0.04 1927 188.2 0.6019 0.39 1237 188.2 -0.3748 2.03 1101 188.2 0.2125 5 0 2082 185.3 -4.731e-008 0.04 1973 185.3 -1.347e-008 0.39 1208 185.3 -1.916e-007 2.03 1117 185.3 -9.454e-008

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -137.326 5 284.6519 A2 -136.3651 8 288.7303 A3 -137.326 5 284.6519 R -159.8279 2 323.6558 2 -150.562 3 307.1239 3 -150.562 3 307.1239 4 -137.7023 4 283.4047 5 -137.326 5 284.6519

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 284 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 46.93 6 < 0.0001 Test 2 1.922 3 0.5888 Test 3 1.922 3 0.5888 Test 4 26.47 2 < 0.0001 Test 5a 26.47 2 < 0.0001 Test 5b -6.253e-013 0 N/A Test 6a 0.7528 1 0.3856 Test 6b 25.72 1 < 0.0001 Test 7a 5.684e-014 0 N/A Test 7b 26.47 2 < 0.0001 Test 7c 0.7528 1 0.3856

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 285 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.326431 0.225356 3 0.326431 0.225356 4 0.0453734 0.0268497 5 0.0686448 0.029309

Subchronic Study (MRID 43980201) –Male RBC 35 Day ChEI

### Page 286 of 438 800 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

18:22 08/15 2012 BMD BMDL Exponential

800 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

18:22 08/15 2012 BMD BMDL Exponential

### Page 287 of 438 800 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

18:22 08/15 2012 BMD BMDL Exponential

800 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

18:22 08/15 2012 BMD BMDL Exponential

### Page 288 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 35 D of dosing - RBC

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos /exp_Dicrotophos RBC F 35D_Setting.(d) Gnuplot Plotting File: Wed Aug 15 18:28:54 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 289 of 438 

## -------- ------- ------- ------- ------- lnalpha 10.1611 10.1611 10.1611 10.1611 rho(S) 0 0 0 0 a 1404.69 1404.69 2334.15 2334.15 b 0.202233 0.202233 1.35745 1.35745 c -- -- 0.493705 0.493705 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.3332 11.3332 10.2709 10.2709 rho 0 0 0 0 a 1930.7 1930.7 2215.67 2215.67 b 0.23614 0.23614 10.4606 10.4606 c -- -- 0.579666 0.579666 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 2223 194 0.04 6 1885 220 0.45 6 1372 124 2.38 6 1210 151

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 1931 289.1 2.477 0.04 1913 289.1 -0.2335 0.45 1736 289.1 -3.085 2.38 1101 289.1 0.9269 3 0 1931 289.1 2.477 0.04 1913 289.1 -0.2335 0.45 1736 289.1 -3.085 2.38 1101 289.1 0.9269

### Page 290 of 438 

## 4 0 2216 169.9 0.1057 0.04 1897 169.9 -0.1763 0.45 1293 169.9 1.142 2.38 1284 169.9 -1.072 5 0 2216 169.9 0.1057 0.04 1897 169.9 -0.1763 0.45 1293 169.9 1.142 2.38 1284 169.9 -1.072

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -133.9336 5 277.8673 A2 -132.8064 8 281.6128 A3 -133.9336 5 277.8673 R -157.7928 2 319.5856 2 -147.999 3 301.9979 3 -147.999 3 301.9979 4 -135.251 4 278.502 5 -135.251 4 278.502

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 291 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 49.97 6 < 0.0001 Test 2 2.254 3 0.5213 Test 3 2.254 3 0.5213 Test 4 28.13 2 < 0.0001 Test 5a 28.13 2 < 0.0001 Test 5b -2.274e-013 0 N/A Test 6a 2.635 1 0.1046 Test 6b 25.5 1 < 0.0001 Test 7a 2.635 1 0.1046 Test 7b 25.5 1 < 0.0001 Test 7c 5.684e-014 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 292 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.446179 0.318588 3 0.446179 0.318588 4 0.0259723 0.015175 5 0.0259723 0.015175

### Page 293 of 438 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

18:28 08/15 2012 BMD BMDL Exponential

1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

18:28 08/15 2012 BMD BMDL Exponential

### Page 294 of 438 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

18:28 08/15 2012 BMD BMDL Exponential

1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

18:28 08/15 2012 BMD BMDL Exponential

### Page 295 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 63 D of dosing - RBC

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos /exp_Dicrotophos RBC M 63D_Setting.(d) Gnuplot Plotting File: Thu Aug 16 10:46:01 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 296 of 438 

## -------- ------- ------- ------- ------- lnalpha 10.7728 10.7728 10.7728 10.7728 rho(S) 0 0 0 0 a 1377.45 1377.45 2270.1 2270.1 b 0.260646 0.260646 1.61428 1.61428 c -- -- 0.487497 0.487497 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.645 11.645 10.7728 10.7728 rho 0 0 0 0 a 1931.26 1931.26 2163.18 2162 b 0.32133 0.32133 5.10741 5.10008 c -- -- 0.536847 0.537454 d -- 1 -- 1.00968

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 2162 330 0.04 5 1980 237 0.39 6 1297 190 2.03 5 1162 158

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 1931 337.8 1.673 0.04 1907 337.8 0.4858 0.39 1704 337.8 -2.95 2.03 1006 337.8 1.033 3 0 1931 337.8 1.673 0.04 1907 337.8 0.4858 0.39 1704 337.8 -2.95 2.03 1006 337.8 1.033

### Page 297 of 438 

## 4 0 2163 218.4 -0.01329 0.04 1978 218.4 0.01989 0.39 1298 218.4 -0.01114 2.03 1161 218.4 0.006859 5 0 2162 218.4 1.92e-010 0.04 1980 218.4 8.428e-009 0.39 1297 218.4 -4.705e-009 2.03 1162 218.4 -3.77e-008

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -129.5009 5 269.0019 A2 -127.7203 8 271.4407 A3 -129.5009 5 269.0019 R -146.8995 2 297.799 2 -139.095 3 284.19 3 -139.095 3 284.19 4 -129.5013 4 267.0026 5 -129.5009 5 269.0019

Additive constant for all log-likelihoods = -20.22. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 298 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 38.36 6 < 0.0001 Test 2 3.561 3 0.3129 Test 3 3.561 3 0.3129 Test 4 19.19 2 < 0.0001 Test 5a 19.19 2 < 0.0001 Test 5b 5.684e-014 0 N/A Test 6a 0.0007433 1 0.9782 Test 6b 19.19 1 < 0.0001 Test 7a 0 0 N/A Test 7b 19.19 2 < 0.0001 Test 7c 0.0007433 1 0.9782

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 299 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.327888 0.217228 3 0.327888 0.217228 4 0.0476235 0.0220455 5 0.0484141 0.022048

### Page 300 of 438 1000 1200 1400 1600 1800 2000 2200 2400 2600

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dose Exponential Model 2 with 0.95 Confidence Level

10:46 08/16 2012 BMD BMDL Exponential

1000 1200 1400 1600 1800 2000 2200 2400 2600

0 0.5 1 1.5 2Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:46 08/16 2012 BMD BMDL Exponential

### Page 301 of 438 1000 1200 1400 1600 1800 2000 2200 2400 2600

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dose Exponential Model 4 with 0.95 Confidence Level

10:46 08/16 2012 BMD BMDL Exponential

1000 1200 1400 1600 1800 2000 2200 2400 2600

0 0.5 1 1.5 2Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:46 08/16 2012 BMD BMDL Exponential

### Page 302 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 63 D of dosing - RBC

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/e xp_Dicrotophos RBC F 63D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 11:18:26 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 303 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 18.7242 18.7242 18.7242 18.7242 rho -1.11765 -1.11765 -1.11765 -1.11765 a 1345.69 74.3657 2343.6 2343.6 b 0.255319 -0.616097 1.42005 1.42005 c -- -- 0.452296 0.452296 d -- 2 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 20.2881 -16.3558 14.1275 14.0468 rho -1.28831 3.79399 -0.483572 -0.473011 a 2210.33 1880 2245.09 2234.69 b 1.10865 0.402495 3.856 3.79697 c -- -- 0.49561 0.499659 d -- 15.03 -- 1.07725

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 2232 94 0.04 6 2100 267 0.45 6 1308 266 2.38 6 1113 185

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2210 178.3 0.2977 0.04 2114 183.5 -0.1929 0.45 1342 245.9 -0.3399 2.38 158 975.7 2.398 3 0 1880 456.5 1.889 0.04 1880 456.5 1.18 0.45 1880 456.5 -3.069 2.38 1113 168.9 -3.162e-011 4 0 2245 180.9 -0.1772

### Page 304 of 438 

## 0.04 2083 184.2 0.223 0.45 1312 206 -0.05237 2.38 1113 214.4 0.002226 5 0 2235 181.2 -0.03642 0.04 2097 183.9 0.03843 0.45 1305 205.8 0.03589 2.38 1117 213.5 -0.04142

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -138.7208 5 287.4416 A2 -135.4185 8 286.837 A3 -138.6256 6 289.2512 R -162.2392 2 328.4784 2 -148.7014 4 305.4028 3 -153.0004 5 316.0008 4 -138.6573 5 287.3147 5 -138.6256 6 289.2512

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 305 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 53.64 6 < 0.0001 Test 2 6.605 3 0.08563 Test 3 6.414 2 0.04047 Test 4 20.15 2 < 0.0001 Test 5a 28.75 1 < 0.0001 Test 5b -8.598 1 N/A Test 6a 0.06344 1 0.8011 Test 6b 20.09 1 < 0.0001 Test 7a -4.857e-009 0 N/A Test 7b 28.75 1 < 0.0001 Test 7c 0.06344 1 0.8011

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 306 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0950353 0.0725077 3 2.13902 0.0543219 4 0.0573055 0.0329713 5 0.0653959 0.0332344

### Page 307 of 438 800 1000 1200 1400 1600 1800 2000 2200 2400

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dose Exponential Model 2 with 0.95 Confidence Level

11:18 09/28 2012 BMD BMDL Exponential

800 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

11:18 09/28 2012 BMD BMDL Exponential

### Page 308 of 438 800 1000 1200 1400 1600 1800 2000 2200 2400

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dose Exponential Model 4 with 0.95 Confidence Level

11:18 09/28 2012 BMD BMDL Exponential

800 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

11:18 09/28 2012 BMD BMDL Exponential

### Page 309 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 98 D of dosing - RBC

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos /exp_Dicrotophos RBC M 98D_Setting.(d) Gnuplot Plotting File: Thu Aug 16 10:51:51 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 310 of 438 

## -------- ------- ------- ------- ------- lnalpha 10.8012 10.8012 10.8012 10.8012 rho(S) 0 0 0 0 a 1301.44 1301.44 2475.9 2475.9 b 0.300176 0.300176 1.80553 1.80553 c -- -- 0.416203 0.416203 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 12.0285 12.0285 10.8076 10.8076 rho 0 0 0 0 a 1964.71 1964.71 2351.37 2351.37 b 0.411444 0.411444 9.25845 9.25845 c -- -- 0.470352 0.470352 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2358 230 0.04 6 1957 279 0.39 6 1163 246 2.03 5 1082 210

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 1965 409.2 2.149 0.04 1933 409.2 0.1458 0.39 1673 409.2 -3.055 2.03 852.2 409.2 1.255 3 0 1965 409.2 2.149 0.04 1933 409.2 0.1458 0.39 1673 409.2 -3.055 2.03 852.2 409.2 1.255

### Page 311 of 438 

## 4 0 2351 222.2 0.06673 0.04 1966 222.2 -0.0983 0.39 1140 222.2 0.2575 2.03 1106 222.2 -0.2412 5 0 2351 222.2 0.06673 0.04 1966 222.2 -0.0983 0.39 1140 222.2 0.2575 2.03 1106 222.2 -0.2412

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -129.8135 5 269.6271 A2 -129.5355 8 275.0711 A3 -129.8135 5 269.6271 R -150.6552 2 305.3105 2 -143.3131 3 292.6261 3 -143.3131 3 292.6261 4 -129.8831 4 267.7661 5 -129.8831 4 267.7661

Additive constant for all log-likelihoods = -20.22. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 312 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 42.24 6 < 0.0001 Test 2 0.556 3 0.9064 Test 3 0.556 3 0.9064 Test 4 27 2 < 0.0001 Test 5a 27 2 < 0.0001 Test 5b -1.023e-012 0 N/A Test 6a 0.139 1 0.7092 Test 6b 26.86 1 < 0.0001 Test 7a 0.139 1 0.7092 Test 7b 26.86 1 < 0.0001 Test 7c 5.684e-014 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 313 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.256075 0.155401 3 0.256075 0.155401 4 0.0226006 0.0130755 5 0.0226006 0.0130755

### Page 314 of 438 1000 1500 2000 2500

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dose Exponential Model 2 with 0.95 Confidence Level

10:51 08/16 2012 BMD BMDL Exponential

1000 1500 2000 2500

0 0.5 1 1.5 2Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:51 08/16 2012 BMD BMDL Exponential

### Page 315 of 438 1000 1500 2000 2500

0 0.5 1 1.5 2Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

10:51 08/16 2012 BMD BMDL Exponential

1000 1500 2000 2500

0 0.5 1 1.5 2Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:51 08/16 2012 BMD BMDL Exponential

### Page 316 of 438 

## MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 98 D of dosing - RBC

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos /exp_Dicrotophos RBC F 98D_Setting.(d) Gnuplot Plotting File: Thu Aug 16 11:05:41 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 317 of 438 

## -------- ------- ------- ------- ------- lnalpha 12.1245 12.1245 12.1245 12.1245 rho(S) 0 0 0 0 a 1352.9 1352.9 2732.1 2732.1 b 0.359925 0.359925 1.5662 1.5662 c -- -- 0.356258 0.356258 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 12.5983 12.5983 12.1658 12.1263 rho 0 0 0 0 a 2413.34 2413.34 2657.26 2576 b 0.4809 0.4809 3.02373 2.33836 c -- -- 0.378347 0.396739 d -- 1 -- 7.2391

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 2550 138 0.04 6 2602 896 0.45 6 1388 124 2.38 6 1022 218

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2413 544.1 0.6152 0.04 2367 544.1 1.056 0.45 1944 544.1 -2.502 2.38 768.3 544.1 1.142 3 0 2413 544.1 0.6152 0.04 2367 544.1 1.056 0.45 1944 544.1 -2.502 2.38 768.3 544.1 1.142

### Page 318 of 438 

## 4 0 2657 438.3 -0.5995 0.04 2469 438.3 0.7429 0.45 1429 438.3 -0.2295 2.38 1007 438.3 0.08604 5 0 2576 429.7 -0.1482 0.04 2576 429.7 0.1482 0.45 1388 429.7 7.29e-008 2.38 1022 429.7 3.877e-007

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -157.4935 5 324.9869 A2 -141.392 8 298.7839 A3 -157.4935 5 324.9869 R -173.004 2 350.0079 2 -163.1795 3 332.359 3 -163.1795 3 332.359 4 -157.9892 4 323.9784 5 -157.5155 5 325.0309

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

### Page 319 of 438 

## Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 63.22 6 < 0.0001 Test 2 32.2 3 < 0.0001 Test 3 32.2 3 < 0.0001 Test 4 11.37 2 0.003393 Test 5a 11.37 2 0.003393 Test 5b -5.684e-013 0 N/A Test 6a 0.9915 1 0.3194 Test 6b 10.38 1 0.001273 Test 7a 0.04397 0 N/A Test 7b 11.33 2 0.003468 Test 7c 0.9475 1 0.3304

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. Consider running a non-homogeneous variance model.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

### Page 320 of 438 

## The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Degrees of freedom for Test 7a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.21909 0.129923 3 0.21909 0.129923 4 0.058001 0.0295989 5 0.337773 0.0348488

### Page 321 of 438 1000 1500 2000 2500 3000 3500

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dose Exponential Model 2 with 0.95 Confidence Level

11:05 08/16 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

11:05 08/16 2012 BMD BMDL Exponential

### Page 322 of 438 1000 1500 2000 2500 3000 3500

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

11:05 08/16 2012 BMD BMDL Exponential

1000 1500 2000 2500 3000 3500

0 0.5 1 1.5 2 2.5Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

11:05 08/16 2012 BMD BMDL Exponential

### Page 323 of 438 

## MRID 44527802 - Chronic Toxicity in Rats (2 ye ar) – Male Adult 371D of dosing - Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp _Dicrotophos Brain M 371D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 11:32:09 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 324 of 438 

## lnalpha -1.73281 -1.73281 -1.73281 -1.73281 rho -0.426944 -0.426944 -0.426944 -0.426944 a 3.19814 3.19814 11.235 11.235 b 1.39146 1.39146 3.6419 3.6419 c -- -- 0.106809 0.106809 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -0.282356 -0.282357 -2.69836 -2.69836 rho -0.197562 -0.197562 0.900588 0.900588 a 9.95502 9.95502 9.97461 9.97461 b 3.51045 3.51045 4.39481 4.39481 c -- -- 0.126785 0.126785 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 10.7 0.13 0.02 8 8.63 0.58 0.25 7 4.26 0.29 1.42 5 1.26 0.38

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 9.955 0.692 2.637 0.02 9.28 0.6968 -2.639 0.25 4.139 0.7547 0.4241 1.42 0.0681 1.132 2.354 3 0 9.955 0.692 2.637 0.02 9.28 0.6968 -2.639 0.25 4.139 0.7547 0.4241 1.42 0.0681 1.132 2.354 4 0 9.975 0.7309 2.431 0.02 9.242 0.7062 -2.45 0.25 4.168 0.4934 0.495

### Page 325 of 438 

## 1.42 1.282 0.2901 -0.1665 5 0 9.975 0.7309 2.431 0.02 9.242 0.7062 -2.45 0.25 4.168 0.4934 0.495 1.42 1.282 0.2901 -0.1665

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 12.98972 5 -15.97945 A2 19.28066 8 -22.56132 A3 13.10721 6 -14.21441 R -45.55398 2 95.10797 2 -6.55156 4 21.10312 3 -6.55156 4 21.10312 4 2.796104 5 4.407792 5 2.796104 5 4.407792

Additive constant for all log-likelihoods = -23.89. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1)

### Page 326 of 438 

## Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 129.7 6 < 0.0001 Test 2 12.58 3 0.005634 Test 3 12.35 2 0.002084 Test 4 39.32 2 < 0.0001 Test 5a 39.32 2 < 0.0001 Test 5b -3.245e-011 0 N/A Test 6a 20.62 1 < 0.0001 Test 6b 18.7 1 < 0.0001 Test 7a 20.62 1 < 0.0001 Test 7b 18.7 1 < 0.0001 Test 7c -2.723e-012 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

### Page 327 of 438 

## Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is le ss than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is le ss than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0300134 0.0259369 3 0.0300134 0.0259369 4 0.0276746 0.0247364 5 0.0276746 0.0247364

### Page 328 of 438 0 2 4 6 8 10

0 0.2 0.4 0.6 0.8 1 1.2 1.4Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

11:32 09/28 2012 BMD BMDL Exponential

0 2 4 6 8 10

0 0.2 0.4 0.6 0.8 1 1.2 1.4Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

11:32 09/28 2012 BMD BMDL Exponential

### Page 329 of 438 0 2 4 6 8 10

0 0.2 0.4 0.6 0.8 1 1.2 1.4Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

11:32 09/28 2012 BMD BMDL Exponential

0 2 4 6 8 10

0 0.2 0.4 0.6 0.8 1 1.2 1.4Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

11:32 09/28 2012 BMD BMDL Exponential

### Page 330 of 438 

## MRID 44527802 - Chronic Toxicity in Rats (2 year ) – Female Adult 371D of dosing - Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp_D icrotophos Brain F 371D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 11:46:05 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

### Page 331 of 438 

## -------- ------- ------- ------- ------- lnalpha -2.23363 -2.23363 -2.23363 -2.23363 rho 1.2928 1.2928 1.2928 1.2928 a 3.16268 3.16268 11.9595 11.9595 b 1.10318 1.10318 2.92122 2.92122 c -- -- 0.11308 0.11308 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -3.01945 -2.23363 -2.5126 -2.5126 rho 2.17165 1.2928 1.74029 1.74029 a 9.30288 3.16268 10.2893 10.2893 b 1.1014 1.10318 2.55838 2.55838 c -- -- 0.124037 0.124037 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 11.39 0.8 0.03 8 8.78 1.77 0.32 8 5.03 1.89 1.74 7 1.42 0.3

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 9.303 2.489 2.371 0.03 9.001 2.402 -0.2597 0.32 6.54 1.698 -2.515 1.74 1.369 0.3107 0.4364 3 0 0 0 1.#IO 0.03 0 0 1.#IO 0.32 0 0 1.#IO 1.74 0 0 1.#IO 4 0 10.29 2.164 1.438 0.03 9.623 2.042 -1.168

### Page 332 of 438 

## 0.32 5.251 1.205 -0.5189 1.74 1.381 0.3771 0.2711 5 0 10.29 2.164 1.438 0.03 9.623 2.042 -1.168 0.32 5.251 1.205 -0.5189 1.74 1.381 0.3771 0.2711

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -23.50406 5 57.00811 A2 -12.80559 8 41.61118 A3 -20.49633 6 52.99266 R -57.95135 2 119.9027 2 -25.85755 4 59.71509 3 -1098.597 4 2205.195 4 -22.05583 5 54.11166 5 -22.05583 5 54.11166

Additive constant for all log-likelihoods = -28.49. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R)

### Page 333 of 438 

## Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 90.29 6 < 0.0001 Test 2 21.4 3 < 0.0001 Test 3 15.38 2 0.000457 Test 4 10.72 2 0.004695 Test 5a 2156 2 < 0.0001 Test 5b -2145 0 N/A Test 6a 3.119 1 0.07738 Test 6b 7.603 1 0.005826 Test 7a 3.119 1 0.07738 Test 7b 2153 1 < 0.0001 Test 7c 7.105e-015 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately

### Page 334 of 438 

## describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is le ss than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is le ss than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0956609 0.084013 3 0.0955059 Bad completion 4 0.0473812 0.0369167 5 0.0473812 0.0369167

### Page 335 of 438 2 4 6 8 10 12

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

11:46 09/28 2012 BMD BMDL Exponential

2 4 6 8 10 12

-1 -0.5 0 0.5 1 1.5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

11:46 09/28 2012 BMD BMDL Exponential

### Page 336 of 438 2 4 6 8 10 12

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

11:46 09/28 2012 BMD BMDL Exponential

2 4 6 8 10 12

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

11:46 09/28 2012 BMD BMDL Exponential

### Page 337 of 438 

## MRID 44527802 - Chronic Toxicity in Rats (2 ye ar) – Male Adult 700D of dosing - Brain

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/e xp_Dicrotophos Brain M 700D_Setting.(d) Gnuplot Plotting File: Thu Aug 16 15:31:49 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 3 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 338 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -1.33784 -1.33784 -1.33784 NC rho(S) 0 0 0 NC a 5.90295 5.90295 9.3765 NC b 3.83083 3.83083 14.5936 NC c -- -- 0.342294 NC d -- 1 -- NC

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -1.20104 -1.20104 -1.33784 NC rho 0 0 0 NC a 8.76465 8.76465 8.93 NC b 3.94725 3.94725 9.89295 NC c -- -- 0.320052 NC d -- 1 -- NC

NC = No Convergence

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 8.93 0.64 0.02 6 7.84 0.48 0.25 3 3.37 0.47

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 8.765 0.5485 0.8526 0.02 8.099 0.5485 -1.158 0.25 3.267 0.5485 0.3248 3 0 8.765 0.5485 0.8526 0.02 8.099 0.5485 -1.158 0.25 3.267 0.5485 0.3248

### Page 339 of 438 

## 4 0 8.93 0.5123 -5.119e-008 0.02 7.84 0.5123 -9.392e-008 0.25 3.37 0.5123 8.996e-008

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 2.871643 4 2.256713 A2 3.428468 6 5.143065 A3 2.871643 4 2.256713 R -20.8416 2 45.6832 2 1.708829 3 2.582342 3 1.708829 3 2.582342 4 2.871643 4 2.256713

Additive constant for all log-likelihoods = -15.62. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

### Page 340 of 438 

## Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 48.54 4 < 0.0001 Test 2 1.114 2 0.573 Test 3 1.114 2 0.573 Test 4 2.326 1 0.1273 Test 5a 2.326 1 0.1273 Test 5b -3.759e-012 0 N/A Test 6a 2.576e-014 0 N/A Test 6b 2.326 1 0.1273

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 6a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

### Page 341 of 438 

## Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0266922 0.0224428 3 0.0266922 0.0224428 4 0.0160799 0.0109029 5 0 0 Not computed

### Page 342 of 438 

## 44527802 Male Brain 700 Days

2 3 4 5 6 7 8 9 10

0 0.05 0.1 0.15 0.2 0.25Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

15:31 08/16 2012 BMD BMDL Exponential

2 3 4 5 6 7 8 9 10

0 0.05 0.1 0.15 0.2 0.25Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

15:31 08/16 2012 BMD BMDL Exponential

### Page 343 of 438 2 3 4 5 6 7 8 9 10

0 0.05 0.1 0.15 0.2 0.25Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

15:31 08/16 2012 BMD BMDL Exponential

### Page 344 of 438 

## MRID 44527802 - Chronic Toxicity in Rats (2 year ) – Female Adult 735D of dosing - Brain

================== ================== ================== ============== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp_D icrotophos Brain F 735D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 11:54:25 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 345 of 438 

## lnalpha -2.69825 -2.69825 -2.69825 -2.69825 rho 1.19931 1.19931 1.19931 1.19931 a 2.13771 2.13771 8.7885 8.7885 b 1.29137 1.29137 3.09918 3.09918 c -- -- 0.084526 0.084526 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -0.326076 -2.69825 -2.25807 -2.25807 rho 0.103578 1.19931 1.28449 1.28449 a 7.92961 2.13771 7.87151 7.87151 b 2.48182 1.29137 2.75747 2.75747 c -- -- 0.0867278 0.0867278 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 8.37 0.7 0.03 7 6.83 0.63 0.32 8 3.55 1.26 1.74 6 0.78 0.17

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 7.93 0.9457 1.317 0.03 7.361 0.9421 -1.49 0.32 3.584 0.9076 -0.1053 1.74 0.1056 0.7562 2.184 3 0 0 0 1.#IO 0.03 0 0 1.#IO 0.32 0 0 1.#IO 1.74 0 0 1.#IO 4 0 7.872 1.217 1.159 0.03 7.301 1.159 -1.074 0.32 3.657 0.7436 -0.4084

### Page 346 of 438 

## 1.74 0.742 0.2669 0.3491 5 0 7.872 1.217 1.159 0.03 7.301 1.159 -1.074 0.32 3.657 0.7436 -0.4084 1.74 0.742 0.2669 0.3491

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -6.814623 5 23.62925 A2 2.525238 8 10.94952 A3 -5.369442 6 22.73888 R -46.0087 2 96.01741 2 -11.18381 4 30.36762 3 -1729.478 4 3466.955 4 -6.80877 5 23.61754 5 -6.80877 5 23.61754

Additive constant for all log-likelihoods = -26.65. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1)

### Page 347 of 438 

## Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 97.07 6 < 0.0001 Test 2 18.68 3 0.0003184 Test 3 15.79 2 0.0003727 Test 4 11.63 2 0.002984 Test 5a 3448 2 < 0.0001 Test 5b -3437 0 N/A Test 6a 2.879 1 0.08976 Test 6b 8.75 1 0.003096 Test 7a 2.879 1 0.08976 Test 7b 3445 1 < 0.0001 Test 7c -8.882e-015 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

### Page 348 of 438 

## Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is le ss than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is le ss than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0424529 0.0340801 3 0.0815883 Bad completion 4 0.042056 0.0345724 5 0.042056 0.0345724

### Page 349 of 438 0 2 4 6 8

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

11:54 09/28 2012 BMD BMDL Exponential

0 2 4 6 8

-1 -0.5 0 0.5 1 1.5Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

11:54 09/28 2012 BMD BMDL Exponential

### Page 350 of 438 0 2 4 6 8

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

11:54 09/28 2012 BMD BMDL Exponential

0 2 4 6 8

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

11:54 09/28 2012 BMD BMDL Exponential

### Page 351 of 438 

## MRID 44527802 - Chronic Toxicity in Rats (2 ye ar) – Male Adult 371D of dosing – RBC – dropped high dose for this analysis

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrot ophos/exp_Dicrotophos RBC M 371D drop HD NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 12:35:39 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 3 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 352 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 15.4749 15.4749 15.4749 NC rho -0.706578 -0.706578 -0.706578 NC a 1508.63 53.5272 2674.35 NC b 2.68357 -1.46949 12.8291 NC c -- -- 0.448707 NC d -- 2 -- NC

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 5.45303 15.4749 8.74185 NC rho 0.659525 -0.706578 0.211036 NC a 2462.72 53.5272 2545.95 NC b 2.7058 -1.46949 11.6043 NC c -- -- 0.465397 NC d -- 2 -- NC

NC = No Convergence

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 6 2547 94 0.02 8 2263 249 0.25 7 1260 169

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2463 200.7 1.029 0.02 2333 197.1 -1.004 0.25 1252 160.5 0.1303 3 0 0 1.98e+112 3.151e-109 0.02 0 1.98e+112 3.233e-109 0.25 0 1.98e+112 1.684e-109 4 0 2546 181 0.01424 0.02 2264 178.8 -0.01644

### Page 353 of 438 

## 0.25 1260 168 0.004953

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -119.0649 4 246.1299 A2 -116.1881 6 244.3761 A3 -119.0482 5 248.0964 R -143.7683 2 291.5365 2 -120.1301 4 248.2601 3 -270.0204 5 550.0409 4 -119.0482 5 248.0964

Additive constant for all log-likelihoods = -19.3. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

### Page 354 of 438 

## Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 55.16 4 < 0.0001 Test 2 5.754 2 0.05631 Test 3 5.72 1 0.01677 Test 4 2.164 1 0.1413 Test 5a 301.9 0 N/A Test 5b -299.8 1 N/A Test 6a -1.596e-010 0 N/A Test 6b 2.164 1 0.1413

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

Degrees of freedom for Test 5a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

Degrees of freedom for Test 6a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

Benchmark Dose Computations:

### Page 355 of 438 

## Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0389388 0.0336212 3 -0.220888 Bad completion 4 0.0178461 0.010819 5 0 0 Not computed

1000 1200 1400 1600 1800 2000 2200 2400 2600 2800

0 0.05 0.1 0.15 0.2 0.25Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:35 09/28 2012 BMD BMDL Exponential

### Page 356 of 438 0 500 1000 1500 2000 2500

-1 -0.8 -0.6 -0.4 -0.2 0 0.2Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:35 09/28 2012 BMD BMDL Exponential

1000 1200 1400 1600 1800 2000 2200 2400 2600 2800

0 0.05 0.1 0.15 0.2 0.25Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:35 09/28 2012 BMD BMDL Exponential

### Page 357 of 438 

## MRID 44527802 - Chronic Toxicity in Rats (2 year ) – Female Adult 371D of dosing – RBC-

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp _Dicrotophos RBC F 371D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 12:46:41 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 358 of 438 

## lnalpha 14.2165 14.2165 14.2165 14.2165 rho -0.445437 -0.445437 -0.445437 -0.445437 a 1531.17 9.86089 2812.95 2812.95 b 0.39272 -0.742496 2.04011 2.04011 c -- -- 0.405268 0.405268 d -- 4 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -11.619 -11.619 8.07627 8.07627 rho 3.12527 3.12527 0.411867 0.411867 a 2249.65 2249.65 2609.32 2609.32 b 0.378668 0.378668 5.2003 5.2003 c -- -- 0.46184 0.46184 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 2679 120 0.03 8 2321 385 0.32 8 1494 337 1.74 7 1197 178

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2250 518.9 2.34 0.03 2224 509.8 0.5369 0.32 1993 429.4 -3.286 1.74 1164 185.3 0.4706 3 0 2250 518.9 2.34 0.03 2224 509.8 0.5368 0.32 1993 429.4 -3.286 1.74 1164 185.3 0.4706 4 0 2609 286.6 0.6876 0.03 2406 281.9 -0.8577 0.32 1471 254.7 0.2554

### Page 359 of 438 

## 1.74 1205 244.5 -0.08933 5 0 2609 286.6 0.6876 0.03 2406 281.9 -0.8577 0.32 1471 254.7 0.2554 1.74 1205 244.5 -0.08933

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -188.0859 5 386.1717 A2 -182.1171 8 380.2342 A3 -188.0761 6 388.1521 R -216.2988 2 436.5976 2 -200.4398 4 408.8795 3 -200.4398 4 408.8795 4 -188.7125 5 387.4251 5 -188.7125 5 387.4251

Additive constant for all log-likelihoods = -28.49. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1)

### Page 360 of 438 

## Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 bett er than Model 3? (5 vs. 3) Test 7c: Is Model 5 bett er than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 68.36 6 < 0.0001 Test 2 11.94 3 0.007601 Test 3 11.92 2 0.002583 Test 4 24.73 2 < 0.0001 Test 5a 24.73 2 < 0.0001 Test 5b -2.331e-012 0 N/A Test 6a 1.273 1 0.2592 Test 6b 23.45 1 < 0.0001 Test 7a 1.273 1 0.2592 Test 7b 23.45 1 < 0.0001 Test 7c 0 0 N/A

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

### Page 361 of 438 

## Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.27824 0.213749 3 0.27824 0.213749 4 0.0395307 0.0159367 5 0.0395307 0.0159367

### Page 362 of 438 1000 1500 2000 2500

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:46 09/28 2012 BMD BMDL Exponential

1000 1500 2000 2500

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:46 09/28 2012 BMD BMDL Exponential

### Page 363 of 438 1000 1500 2000 2500

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:46 09/28 2012 BMD BMDL Exponential

1000 1500 2000 2500

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

12:46 09/28 2012 BMD BMDL Exponential

### Page 364 of 438 

## MRID 44527802 - Chronic Toxicity in Rats (2 year) – Male Adult 700D of dosing - RBC

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp _Dicrotophos RBC M 700D NHV_Setting.(d) Gnuplot Plotting File: Fri Sep 28 12:41:03 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 3 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 365 of 438 

## lnalpha 10.4904 10.4904 10.4904 NC rho 0.128353 0.128353 0.128353 NC a 2092.3 2092.3 3080.7 NC b 2.73147 2.73147 13.0533 NC c -- -- 0.438058 NC d -- 1 -- NC

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 4.57664 4.57663 5.67375 NC rho 0.904493 0.904495 0.727192 NC a 2781.29 2781.29 2936.06 NC b 2.80583 2.80583 21.5306 NC c -- -- 0.481103 NC d -- 1 -- NC

NC = No Convergence

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 2934 360 0.02 6 2407 262 0.25 3 1417 317

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2781 356 1.213 0.02 2630 347.1 -1.57 0.25 1379 259.2 0.253 3 0 2781 356 1.213 0.02 2630 347.1 -1.57 0.25 1379 259.2 0.253 4 0 2936 311.1 -0.01871 0.02 2403 289.2 0.03388 0.25 1420 238.9 -0.01847

### Page 366 of 438 

## Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -105.016 4 218.032 A2 -104.5863 6 221.1726 A3 -104.8524 5 219.7047 R -117.7888 2 239.5777 2 -107.27 4 222.54 3 -107.27 4 222.54 4 -104.8524 5 219.7047

Additive constant for all log-likelihoods = -15.62. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4)

### Page 367 of 438 

## Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 26.41 4 < 0.0001 Test 2 0.8594 2 0.6507 Test 3 0.5321 1 0.4657 Test 4 4.835 1 0.02788 Test 5a 4.835 1 0.02788 Test 5b -4.604e-012 0 N/A Test 6a -2.842e-014 0 N/A Test 6b 4.835 1 0.02788

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. Consider running a homogeneous model.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is le ss than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 6a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

Benchmark Dose Computations:

Specified Effect = 0.100000

### Page 368 of 438 

## Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0375506 0.0244537 3 0.0375506 0.0244537 4 0.00994309 0.00605707 5 0 0 Not computed

500 1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

12:41 09/28 2012 BMD BMDL Exponential

### Page 369 of 438 500 1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

12:41 09/28 2012 BMD BMDL Exponential

500 1000 1500 2000 2500 3000 3500

0 0.05 0.1 0.15 0.2 0.25Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

12:41 09/28 2012 BMD BMDL Exponential

### Page 370 of 438 

## MRID 44527802 - Chronic Toxicity in Rats (2 ye ar) – Female Adult 735D of dosing – RBC – dropped high dose for this analysis

======================== ================== ================== ======== Exponential Model. (V ersion: 1.7; Date: 12/10/2009) Input Data File: F:/dicrotophos/exp _Dicrotophos RBC F 735D drop HD_Setting.(d) Gnuplot Plotting File: Tue Aug 21 18:02:22 2012

======================== ================== ================== ========

BMDS Model Run

~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~
The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dos e] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(ln alpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 3 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Converge nce has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

### Page 371 of 438 

## Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.3837 11.3837 11.3837 NC rho(S) 0 0 0 NC a 1557.33 1557.33 2718.45 NC b 1.95711 1.95711 9.77858 NC c -- -- 0.469105 NC d -- 1 -- NC

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.4546 11.4546 11.3837 NC rho 0 0 0 NC a 2504.58 2504.58 2589 NC b 1.99571 1.99571 10.2157 NC c -- -- 0.498094 NC d -- 1 -- NC

NC = No Convergence

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 2589 255 0.03 7 2246 208 0.32 8 1339 432

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ----- ----- --------- ---------------- 2 0 2505 307.1 0.7774 0.03 2359 307.1 -0.9736 0.32 1322 307.1 0.1523 3 0 2505 307.1 0.7774 0.03 2359 307.1 -0.9736

### Page 372 of 438 

## 0.32 1322 307.1 0.1523 4 0 2589 296.4 3.613e-008 0.03 2246 296.4 1.708e-008 0.32 1339 296.4 2.188e-009

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + l og(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- -- ---------- A1 -142.4122 4 292.8244 A2 -140.1325 6 292.265 A3 -142.4122 4 292.8244 R -159.1432 2 322.2865 2 -143.2283 3 292.4565 3 -143.2283 3 292.4565 4 -142.4122 4 292.8244

Additive constant for all log-likelihoods = -21.14. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or vari ances differ among Dose levels? (A2 vs. R) Test 2: Are Variance s Homogeneous? (A2 vs. A1) Test 3: Are variances ad equately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

### Page 373 of 438 

## Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 bett er than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 bett er than Model 2? (4 vs. 2)

Tests of Interest

Test -2*log(Likelihood Ra tio) D. F. p-value

-------- --- ------------------ --- ------ -------------- Test 1 38.02 4 < 0.0001 Test 2 4.559 2 0.1023 Test 3 4.559 2 0.1023 Test 4 1.632 1 0.2014 Test 5a 1.632 1 0.2014 Test 5b -5.684e-014 0 N/A Test 6a 0 0 N/A Test 6b 1.632 1 0.2014

The p-value for Test 1 is le ss than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 6a are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

### Page 374 of 438 

## Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- --- --------- ---------- 2 0.0527935 0.0419443 3 0.0527935 0.0419443 4 0.0217502 0.0113337 5 0 0 Not computed

### Page 375 of 438 1000 1500 2000 2500

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

18:02 08/21 2012 BMD BMDL Exponential

1000 1500 2000 2500

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

18:02 08/21 2012 BMD BMDL Exponential

### Page 376 of 438 

#  28- Day Dermal Study - MRID 46484501

Male Brain ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) Gnuplot Plotting File: Tue Apr 10 11:56:01 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5. 1000 1500 2000 2500

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

18:02 08/21 2012 BMD BMDL Exponential

### Page 377 of 438 Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -1.61623 -1.61623 -1.61623 -1.61623 rho(S) 0 0 0 0 a 3.4103 3.4103 5.355 5.355 b 0.00976756 0.00976756 0.0433234 0.0433234 c -- -- 0.393046 0.393046 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -1.14855 -1.14855 -1.38383 -1.49766 rho 0 0 0 0 a 4.79786 4.79786 5.10701 4.85581 b 0.0105971 0.0105971 0.0517293 0.0915615 c -- -- 0.42112 0.455124 d -- 1 -- 6.42604

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 5.1 0.42 2 5 4.61 0.42 5 5 4.84 0.55 10 5 3.71 0.65 80 4 2.21 0.39

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4.798 0.5631 1.2 2 4.697 0.5631 -0.3464 5 4.55 0.5631 1.151

### Page 378 of 438 10 4.315 0.5631 -2.404 80 2.055 0.5631 0.5496 3 0 4.798 0.5631 1.2 2 4.697 0.5631 -0.3464 5 4.55 0.5631 1.151 10 4.315 0.5631 -2.404 80 2.055 0.5631 0.5496 4 0 5.107 0.5006 -0.03133 2 4.816 0.5006 -0.9221 5 4.433 0.5006 1.817 10 3.913 0.5006 -0.9069 80 2.198 0.5006 0.04868 5 0 4.856 0.4729 1.155 2 4.856 0.4729 -1.162 5 4.838 0.4729 0.007529 10 3.71 0.4729 -8.731e-005 80 2.21 0.4729 1.064e-005

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 7.394781 6 -2.789563 A2 8.391339 10 3.217322 A3 7.394781 6 -2.789563 R -14.15813 2 32.31627 2 1.782609 3 2.434781 3 1.782609 3 2.434781 4 4.60599 4 -1.21198 5 5.971963 5 -1.943926

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

### Page 379 of 438 Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 45.1 8 < 0.0001 Test 2 1.993 4 0.737 Test 3 1.993 4 0.737 Test 4 11.22 3 0.01057 Test 5a 11.22 3 0.01057 Test 5b -7.994e-015 0 N/A Test 6a 5.578 2 0.0615 Test 6b 5.647 1 0.01749 Test 7a 2.846 1 0.09162 Test 7b 8.379 2 0.01516 Test 7c 2.732 1 0.09836

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you may want to consider another model.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you may want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is greater than .05. Model 5 does not seem to fit the data better than Model 4.

Benchmark Dose Computations:

### Page 380 of 438 Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 9.94238 7.45725 3 9.94238 7.45725 4 3.6661 2.41499 (p = 0.062 – better visual fit than model 5) 5 8.52006 3.50191 (p = 0.092)



### 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

0 10 20 30 40 50 60 70 8Mean Response



### dose Exponential Model 2 with 0.95 Confidence Level

11:56 04/10 2012 BMD BMDL Exponential

### Page 381 of 438 

### 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

0 10 20 30 40 50 60 70 8Mean Response



### dose Exponential Model 3 with 0.95 Confidence Level

11:56 04/10 2012 BMD BMDL Exponential

### Page 382 of 438 

### 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

0 10 20 30 40 50 60 70 8Mean Response



### dose Exponential Model 4 with 0.95 Confidence Level

11:56 04/10 2012 BMD BMDL Exponential

### Page 383 of 438 

## Female Brain ChE Inhibition

====================================== ============================

== Exponential Model. (Ver sion: 1.7; Date: 12/10/2009) Input Data File: C:/Usepa/BMDS212/ Data/exp_Testrunscontinuous_Setting.(d) Gnuplot Plotting File: Tue Apr 10 13:29:49 2012

====================================== ============================

==

BMDS Model Run 1.5

2 2.5

3 3.5

4 4.5

5 5.5

6

0 10 20 30 40 50 60 70 8Mean Response



### dose Exponential Model 5 with 0.95 Confidence Level

11:56 04/10 2012 BMD BMDL Exponential

### Page 384 of 438 

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~

The form of the re sponse function by Model: Model 2: Y[ dose] = a * exp{sign * b * dose} Model 3: Y[dos e] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the medi an response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nes ted within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(l nalpha +rho *ln(Y[dose])) The variance is to be modeled as Va r(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of reco rds with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha -5.68391 -5 .68391 -5. 68391 -5.68391 rho 3.13014 3.13014 3.13014 3.13014

a 3.06133 3.06133 5.3445 5.3445 b 0.0122454 0.0122454 0.0474812 0.0474812

c -- -- 0.313629 0.313629 d -- 1 -- 1

### Page 385 of 438 

## Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha -5.79711 -5.81027 -5.87967 -5.87967 rho 3.53656 3.70235 3.47947 3.47947

a 4.61968 4.4075 4.99158 4.99158 b 0.0121034 0.0124162 0.0491276 0.0491276

c -- -- 0.340431 0.340431 d -- 12.7156 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 5. 09 1.15 2 5 4. 32 0.47 5 5 4. 46 0.97 10 5 3. 76 0.18 80 4 1. 76 0.18

Estimate d Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ --------- - --------- ---------------- 2 0 4.62 0.8249 1.275 2 4.509 0.7903 -0.5353 5 4.348 0.7412 0.3367 10 4.093 0.666 -1.118 80 1.754 0.1489 0.07704 3 0 4.407 0.8528 1.79 2 4.407 0.8528 -0.2294 5 4.407 0.8528 0.1377 10 4.407 0.8528 -1.698 80 1.76 0.1559 1.34e-010 4 0 4.992 0.8669 0.2539 2 4.683 0.776 -1.047 5 4.275 0.6619 0.6265 10 3.714 0.5183 0.1999 80 1.764 0.1419 -0.05563 5 0 4.992 0.8669 0.2539 2 4.683 0.776 -1.047 5 4.275 0.6619 0.6265 10 3.714 0.5183 0.1999

### Page 386 of 438 

## 80 1.764 0.1419 -0.05563

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalp ha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ---------------- - ---- ------------ A1 -1.689579 6 15.37916 A2 9.468585 10 1.06283 A3 4.932064 7 4.135871 R -17.51614 2 39.03229 2 1.288065 4 5.423869 3 -1.380025 5 12.76005 4 3.140973 5 3.718054 5 3.140973 5 3.718054

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does respon se and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Mode l 2 fit the data? (A3 vs. 2)

Test 5a: Does Mode l 3 fit the data? (A3 vs 3)

### Page 387 of 438 

## Test 5b: Is Model 3 be tter than Model 2? (3 vs. 2)

Test 6a: Does Mode l 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 be tter than Model 2? (4 vs. 2)

Test 7a: Does Mode l 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 be tter than Model 3? (5 vs. 3) Test 7c: Is Model 5 be tter than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ---------- -------------- ------ -------------- Test 1 53.97 8 < 0.0001 Test 2 22.32 4 0.0001734 Test 3 9.073 3 0.02834 Test 4 7.288 3 0.06326 Test 5a 12.62 2 0.001814 Test 5b -5 .336 1 N/A Test 6a 3.582 2 0.1668 Test 6b 3.706 1 0.05422 Test 7a 3.582 2 0.1668 Test 7b 9.042 0 N/A Test 7c 3.553e-015 0 N/A

The p-value for Test 1 is less than .05. T here appears to be a difference between res ponse and/or variances among the dose levels, it seems appr opriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. Y ou may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you ma y want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you ma y want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

### Page 388 of 438 

## The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

Degrees of freedom for Test 7b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 8.705 7.80685 3 67.4765 6.76989 4 3.34679 2.14411 5 3.34679 2.14411

### Page 389 of 438 1 2 3 4 5 6 7

0 10 20 30 40 50 60 70 80Mean Response



### dose Exponential Model 2 with 0.95 Confidence Level

13:29 04/10 2012 BMD BMDL Exponential

### Page 390 of 438 1 2 3 4 5 6 7

0 10 20 30 40 50 60 70 80Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

13:29 04/10 2012 BMD BMDL Exponential

### Page 391 of 438 

### 1 2 3 4 5 6 7

0 10 20 30 40 50 60 70 80Mean Response



### dose Exponential Model 4 with 0.95 Confidence Level

13:29 04/10 2012 BMD BMDL Exponential

### Page 392 of 438 

## Male RBC ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) Gnuplot Plotting File: Mon Apr 09 11:59:34 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. 

### 1 2 3 4 5 6 7

0 10 20 30 40 50 60 70 80Mean Response



### dose Exponential Model 5 with 0.95 Confidence Level

13:29 04/10 2012 BMD BMDL Exponential

### Page 393 of 438 Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 18.3126 18.3126 18.3126 18.3126 rho -1.07049 -1.07049 -1.07049 -1.07049 a 1705.62 17.471 2502.15 2502.15 b 0.0065143 -1.01194e-005 0.0389852 0.0389852 c -- -- 0.489864 0.489864 d -- 3 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -0.767797 -124.287 3.91331 3.91331 rho 1.54984 18 0.868281 0.868281 a 2134.93 1939.92 2340.26 2340.26 b 0.00662482 1.01952e-005 0.0799318 0.0799318 c -- -- 0.55037 0.55037 d -- 3.96819 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 2383 78 2 5 2115 314 5 5 2012 243 10 5 1772 114 80 4 1287 196

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2135 259 2.142 2 2107 256.3 0.07131 5 2065 252.4 -0.4727 10 1998 246 -2.055

### Page 394 of 438 80 1257 171.8 0.3535 3 0 1940 399.4 2.48 2 1940 399.4 0.9801 5 1940 399.4 0.4035 10 1940 399.4 -0.94 80 1940 399.4 -3.269 4 0 2340 205.4 0.4654 2 2185 199.3 -0.7831 5 1994 191.5 0.2148 10 1761 181.5 0.1338 80 1290 158.5 -0.03491 5 0 2340 205.4 0.4654 2 2185 199.3 -0.7831 5 1994 191.5 0.2148 10 1761 181.5 0.1338 80 1290 158.5 -0.03491

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -137.2995 6 286.5989 A2 -131.9825 10 283.9649 A3 -137.2605 7 288.521 R -155.7613 2 315.5227 2 -143.2835 4 294.5669 3 -155.7613 5 321.5227 4 -137.6444 5 285.2888 5 -137.6444 5 285.2888

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

### Page 395 of 438 Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 47.56 8 < 0.0001 Test 2 10.63 4 0.031 Test 3 10.56 3 0.01439 Test 4 12.05 3 0.007228 Test 5a 37 2 < 0.0001 Test 5b -24.96 1 N/A Test 6a 0.7678 2 0.6812 Test 6b 11.28 1 0.0007843 Test 7a 0.7678 2 0.6812 Test 7b 36.23 0 N/A Test 7c 0 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5b is less than .05. Model 3 appears to fit the data better than Model 2.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

Degrees of freedom for Test 7b are less than or equal to 0. The Chi-Square test for fit is not valid.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

### Page 396 of 438 Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 15.9039 11.3002 3 55630.7 66.322 4 3.14705 2.12215 5 3.14705 2.12215



### 1000 1200 1400 1600 1800 2000 2200 2400 2600

0 10 20 30 40 50 60 70 8Mean Response



### dose Exponential Model 2 with 0.95 Confidence Level

11:59 04/09 2012 BMD BMDL Exponential

### Page 397 of 438 

### 1000 1200 1400 1600 1800 2000 2200 2400 2600

0 10000 20000 30000 40000 50000Mean Response



### dose Exponential Model 3 with 0.95 Confidence Level

11:59 04/09 2012 BMDL Exponential

### Page 398 of 438 

### 1000 1200 1400 1600 1800 2000 2200 2400 2600

0 10 20 30 40 50 60 70 8Mean Response



### dose Exponential Model 4 with 0.95 Confidence Level

11:59 04/09 2012 BMD BMDL Exponential

### Page 399 of 438 

## Female RBC ChE Inhibition

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) Gnuplot Plotting File: Tue Apr 10 10:43:13 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

### 1000 1200 1400 1600 1800 2000 2200 2400 2600

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### dose Exponential Model 5 with 0.95 Confidence Level

11:59 04/09 2012 BMD BMDL Exponential

### Page 400 of 438 Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 5 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 7.85523 7.85523 7.85523 7.85523 rho 0.480096 0.480096 0.480096 0.480096 a 3433.25 3433.25 5411.7 5411.7 b 0.00969376 0.00969376 0.0433292 0.0433292 c -- -- 0.39192 0.39192 d -- 1 -- 1

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha -3.57205 -3.57205 -0.559726 -0.559726 rho 1.90958 1.90958 1.51063 1.51063 a 4773.54 4773.54 5120.9 5120.9 b 0.00973329 0.00973329 0.0525265 0.0525265 c -- -- 0.426574 0.426574 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 5 5154 183 2 5 4847 736 5 5 4341 503 10 5 3972 348 80 4 2227 297

Estimated Values of Interest

### Page 401 of 438 Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 4774 545.6 1.559 2 4682 535.5 0.691 5 4547 520.8 -0.8836 10 4331 497.2 -1.614 80 2191 259.4 0.2764 3 0 4774 545.6 1.559 2 4682 535.5 0.691 5 4547 520.8 -0.8836 10 4331 497.2 -1.614 80 2191 259.4 0.2764 4 0 5121 478.8 0.1546 2 4828 458 0.09243 5 4443 430.1 -0.5285 10 3921 391.4 0.291 80 2228 255.4 -0.01082 5 0 5121 478.8 0.1546 2 4828 458 0.09243 5 4443 430.1 -0.5285 10 3921 391.4 0.291 80 2228 255.4 -0.01082

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -156.4691 6 324.9381 A2 -151.3857 10 322.7713 A3 -155.6395 7 325.279 R -179.0346 2 362.0691 2 -159.4802 4 326.9604 3 -159.4802 4 326.9604 4 -155.8312 5 321.6624 5 -155.8312 5 321.6624

Additive constant for all log-likelihoods = -22.05. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1)

### Page 402 of 438 Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 55.3 8 < 0.0001 Test 2 10.17 4 0.03771 Test 3 8.508 3 0.03661 Test 4 7.681 3 0.05308 Test 5a 7.681 3 0.05308 Test 5b -6.821e-013 0 N/A Test 6a 0.3834 2 0.8256 Test 6b 7.298 1 0.006903 Test 7a 0.3834 2 0.8256 Test 7b 7.298 1 0.006903 Test 7c 0 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is less than .05. Model 4 appears to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0.

### Page 403 of 438 The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 10.8248 8.91619 3 10.8248 8.91619 4 3.64832 2.54018 5 3.64832 2.54018

### Page 404 of 438 

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### Page 405 of 438 

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### Page 406 of 438 

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### Page 407 of 438 

#  Inhalation Study

Male Brain ChE – 28-day Inhalation – MRID 48146702 !!! Warning: optimum may not have been found for Model 3 !!! !!! Bad completion code in maximum likelihood optimization routine !!! !!! Try choosing different initial values !!!

Warning: optimum may not have been found form model 2 (BMDL). Bad process completion in Optimization routine.

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Documents and Settings/jliccion/My Documents/BMDS220/Data/exp_UntitledData_Setting.(d) Gnuplot Plotting File: Mon Jul 30 14:12:00 2012 

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### Page 408 of 438 ====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 10.4647 10.4647 10.4647 10.4647 rho 0.302693 0.302693 0.302693 0.302693 a 4595.58 4595.58 24486 24486 b 0.977116 0.977116 0.915869 0.505406 c -- -- 6.19129e-005 0.0589647 d -- 1 -- 2

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 77.6412 10.4647 77.6412 9.37824 rho -6.32986 0.302693 -6.32986 0.439104 a 23644.7 4595.58 23644.7 23323.4 b 0.239859 0.977116 0.239859 0.538103 c -- -- 0 0 d -- 1 -- 2.25879

Table of Stats From Input Data

### Page 409 of 438 Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 10 2.332e+004 1495 0.097 10 2.329e+004 874.2 0.73 10 2.067e+004 461.8 2.9 10 1516 582.5

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2.364e+004 1040 -0.9875 0.097 2.31e+004 1119 0.534 0.73 1.985e+004 1810 1.438 2.9 1.179e+004 9399 -3.458 3 0 0 4.026e-045 1.832e+049 0.097 0 4.026e-045 1.83e+049 0.73 0 4.026e-045 1.624e+049 2.9 0 4.026e-045 1.191e+048 4 0 2.364e+004 1040 -0.9875 0.097 2.31e+004 1119 0.534 0.73 1.985e+004 1810 1.438 2.9 1.179e+004 9399 -3.458 5 0 2.332e+004 989.5 -0.01075 0.097 2.329e+004 989.2 -0.01211 0.73 2.066e+004 963.5 0.02627 2.9 1518 543.1 -0.008893

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -291.8297 5 593.6593 A2 -283.7506 8 583.5013 A3 -289.6198 6 591.2396 R -384.9128 2 773.8255 2 -326.1689 4 660.3377 3 -13081.25 4 26170.51 4 -326.1689 4 660.3377 5 -289.6202 5 589.2405

Additive constant for all log-likelihoods = -36.76. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

### Page 410 of 438 Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 202.3 6 < 0.0001 Test 2 16.16 3 0.001052 Test 3 11.74 2 0.002825 Test 4 73.1 2 < 0.0001 Test 5a 2.558e+004 2 < 0.0001 Test 5b -2.551e+004 0 N/A Test 6a 73.1 2 < 0.0001 Test 6b 1.137e-013 0 N/A Test 7a 0.0008484 1 0.9768 Test 7b 2.558e+004 1 < 0.0001 Test 7c 73.1 1 < 0.0001

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. You may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you may want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you may want to consider another model.

Degrees of freedom for Test 6b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 7a is greater than .1. Model 5 seems

### Page 411 of 438 to adequately describe the data.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

The p-value for Test 7c is less than .05. Model 5 appears to fit the data better than Model 4.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.43926 0.3305 3 0.107828 Bad completion 4 0.43926 0.3305 5 0.686213 0.614588

### Page 412 of 438 

### 0 5000 10000 15000 20000 25000

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### dose Exponential Model 2 with 0.95 Confidence Level

14:12 07/30 2012 BMD BMDL Exponential

### Page 413 of 438 

### 0 5000 10000 15000 20000 25000

-1 -0.5 0 0.5 1 1.5 2 2.5Mean Response



### dose Exponential Model 3 with 0.95 Confidence Level

14:12 07/30 2012 BMD BMDL Exponential

### Page 414 of 438 

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### dose Exponential Model 4 with 0.95 Confidence Level

14:12 07/30 2012 BMD BMDL Exponential

### Page 415 of 438 

## Female Brain ChE – 28-da y Inhalation – MRID 48146702

====================================== ============================

== Exponential Model. (Ver sion: 1.7; Date: 12/10/2009) Input Data File: C:/D ocuments and Settings/jliccion/My Documents/BMDS220/Data/exp_U ntitledData_Setting.(d) Gnuplot Plotting File: Mon Jul 30 14:22:33 2012

====================================== ============================

==

BMDS Model Run 0 5000 10000 15000 20000 25000

0 0.5 1 1.5 2 2.5Mean Response



### dose Exponential Model 5 with 0.95 Confidence Level

14:12 07/30 2012 BMD BMDL Exponential

### Page 416 of 438 

## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~

The form of the re sponse function by Model: Model 2: Y[ dose] = a * exp{sign * b * dose} Model 3: Y[dos e] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the medi an response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nes ted within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(l nalpha +rho *ln(Y[dose])) The variance is to be modeled as Va r(i) = exp(lalpha + log(mean(i)) * rho)

Total number of dose groups = 4 Total number of reco rds with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha 1.49669 1.49669 1.49669 1.49669 rho 1.20941 1.20941 1.20941 1.20941

a 17653.1 17653.1 25651.5 25651.5 b 0.156663 0.156663 0.942468 0.942468

c -- -- 0.560257 0.560257 d -- 1 -- 1

### Page 417 of 438 

## Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- -- ----- ------- lnalpha -8.24114 1.49669 -8.11446 -8.11446 rho 2.23232 1.20941 2.21045 2.21045

a 23640.9 17653.1 23955.3 23955.3 b 0.156554 0.156663 0. 453224 0.453224

c -- -- 0.494794 0.494794 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 10 2.443e+004 1667 0.097 10 2.284e+004 613.8 0.73 10 2.068e+004 616 2.9 10 1.509e+004 843.7

Estimate d Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ --------- - --------- ---------------- 2 0 2.364e+004 1236 2.018 0.097 2.328e+004 1216 -1.157 0.73 2.109e+004 1088 -1.185 2.9 1.501e+004 744.9 0.3235 3 0 0.01028 0.1327 5.822e+005 0.097 0.01024 0.1324 5.456e+005 0.73 0.00998 0.1303 5.018e+005 2.9 0.009139 0.1236 3.862e+005 4 0 2.396e+004 1197 1.254 0.097 2.343e+004 1169 -1.61 0.73 2.055e+004 1010 0.4187 2.9 1.51e+004 719.1 -0.06267 5 0 2.396e+004 1197 1.254 0.097 2.343e+004 1169 -1.61 0.73 2.055e+004 1010 0.4187 2.9 1.51e+004 719.1 -0.06267

Other models for which likelihoods are calculated:

### Page 418 of 438 

## Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalp ha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ---------------- - ---- ------------ A1 -295.3979 5 600.7958 A2 -287.8865 8 591.7731 A3 -293.7705 6 599.5409 R -348.2795 2 700.5589 2 -298.286 4 604.5721 3 -1364.391 4 2736.782 4 -296.4755 5 602.9511 5 -296.4755 5 602.9511

Additive constant for all log-likelihoods = -36.76. This constant added to the above values gives the log-likeli hood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does respon se and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Mode l 2 fit the data? (A3 vs. 2)

Test 5a: Does Mode l 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 be tter than Model 2? (3 vs. 2)

Test 6a: Does Mode l 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 be tter than Model 2? (4 vs. 2)

### Page 419 of 438 

## Test 7a: Does Mode l 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 be tter than Model 3? (5 vs. 3) Test 7c: Is Model 5 be tter than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ---------- -------------- ------ -------------- Test 1 120.8 6 < 0.0001 Test 2 15.02 3 0.001797 Test 3 11.77 2 0.002784 Test 4 9.031 2 0.01094 Test 5a 2141 2 < 0.0001 Test 5b -2132 0 N/A Test 6a 5.41 1 0.02002 Test 6b 3.621 1 0.05705 Test 7a 5.41 1 0.02002 Test 7b 2136 1 < 0.0001 Test 7c -1.137e-013 0 N/A

The p-value for Test 1 is less than .05. T here appears to be a difference between res ponse and/or variances among the dose levels, it seems appr opriate to model the data.

The p-value for Test 2 is less than .1. A non-homogeneous variance model appears to be appropriate.

The p-value for Test 3 is less than .1. Y ou may want to consider a different variance model.

The p-value for Test 4 is less than .1. Model 2 may not adequately describe the data; you ma y want to consider another model.

The p-value for Test 5a is less than .1. Model 3 may not adequately describe the data; you ma y want to consider another model.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is less than .1. Model 4 may not adequately describe the data; you ma y want to consider another model.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

### Page 420 of 438 

## The p-value for Test 7a is less than .1. Model 5 may not adequately describe the data; you ma y want to consider another model.

The p-value for Test 7b is less than .05. Model 5 appears to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.672996 0.624367 3 0.672528 0.00931339 4 0.48667 0.381665 5 0.48667 0.381665

### Page 421 of 438 14000 16000 18000 20000 22000 24000 26000

0 0.5 1 1.5 2 2.5 3Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

10:36 08/20 2012 BMD BMDL

Exponential

14000 16000 18000 20000 22000 24000 26000

0 0.5 1 1.5 2 2.5 3Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

10:36 08/20 2012 BMD BMDL Exponential

### Page 422 of 438 14000 16000 18000 20000 22000 24000 26000

0 0.5 1 1.5 2 2.5 3Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

10:36 08/20 2012 BMD BMDL Exponential

### Page 423 of 438 14000 16000 18000 20000 22000 24000 26000

0 0.5 1 1.5 2 2.5 3Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

10:36 08/20 2012 BMD BMDL Exponential

### Page 424 of 438 

## Male RBC ChE – 28-day Inhalation – MRID 48146702

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) Gnuplot Plotting File: Wed Apr 11 13:03:04 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.0663 11.0663 11.0663 11.0663 rho(S) 0 0 0 0 a 1202.55 1202.55 2063.25 2063.25 b 0.246761 0.246761 0.429754 0.429754 c -- -- 0.226827 0.226827 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- -------

### Page 425 of 438 lnalpha 11.0874 11.0874 11.0798 11.0798 rho 0 0 0 0 a 1910.86 1910.86 1928.22 1928.22 b 0.251628 0.251628 0.432846 0.432846 c -- -- 0.281002 0.281002 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 10 1965 290.6 0.097 10 1826 249 0.73 10 1562 338.9 2.9 10 936 151.8

Estimated Values of Interest

Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 1911 255.6 0.6698 0.097 1865 255.6 -0.4798 0.73 1590 255.6 -0.3489 2.9 921.1 255.6 0.1841 3 0 1911 255.6 0.6698 0.097 1865 255.6 -0.4798 0.73 1590 255.6 -0.3489 2.9 921.1 255.6 0.1841 4 0 1928 254.7 0.4568 0.097 1871 254.7 -0.5615 0.73 1553 254.7 0.1166 2.9 937 254.7 -0.01189 5 0 1928 254.7 0.4568 0.097 1871 254.7 -0.5615 0.73 1553 254.7 0.1166 2.9 937 254.7 -0.01189

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -241.3258 5 492.6517 A2 -238.2695 8 492.539 A3 -241.3258 5 492.6517

### Page 426 of 438 R -266.0157 2 536.0313 2 -241.7475 3 489.495 3 -241.7475 3 489.495 4 -241.5965 4 491.1929 5 -241.5965 4 491.1929

Additive constant for all log-likelihoods = -36.76. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 55.49 6 < 0.0001 Test 2 6.113 3 0.1063 Test 3 6.113 3 0.1063 Test 4 0.8433 2 0.656 Test 5a 0.8433 2 0.656 Test 5b 0 0 N/A Test 6a 0.5412 1 0.4619 Test 6b 0.3021 1 0.5826 Test 7a 0.5412 1 0.4619 Test 7b 0.3021 1 0.5826 Test 7c -5.684e-014 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

### Page 427 of 438 Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.418716 0.341868 3 0.418716 0.341868 4 0.345981 0.207226 5 0.345981 0.207226

### Page 428 of 438 

### 800 1000 1200 1400 1600 1800 2000 2200

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### dose Exponential Model 2 with 0.95 Confidence Level

13:03 04/11 2012 BMD BMDL Exponential

### Page 429 of 438 

### 800 1000 1200 1400 1600 1800 2000 2200

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### dose Exponential Model 3 with 0.95 Confidence Level

13:03 04/11 2012 BMD BMDL Exponential

### Page 430 of 438 

### 800 1000 1200 1400 1600 1800 2000 2200

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### dose Exponential Model 4 with 0.95 Confidence Level

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### Page 431 of 438 

## Female RBC ChE – 28-day Inhalation – MRID 48146702

==================================================================== Exponential Model. (Version: 1.7; Date: 12/10/2009) Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) Gnuplot Plotting File: Mon Apr 16 10:04:10 2012

====================================================================

BMDS Model Run

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The form of the response function by Model: Model 2: Y[dose] = a * exp{sign * b * dose} Model 3: Y[dose] = a * exp{sign * (b * dose)^d} Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}]

Note: Y[dose] is the median response for exposure = dose; 

### 800 1000 1200 1400 1600 1800 2000 2200

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### dose Exponential Model 5 with 0.95 Confidence Level

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### Page 432 of 438 sign = +1 for increasing trend in data; sign = -1 for decreasing trend.

Model 2 is nested within Models 3 and 4. Model 3 is nested within Model 5. Model 4 is nested within Model 5.

Dependent variable = Mean Independent variable = Dose Data are assumed to be distributed: normally Variance Model: exp(lnalpha +rho *ln(Y[dose])) rho is set to 0. A constant variance model is fit.

Total number of dose groups = 4 Total number of records with missing values = 0 Maximum number of iterations = 250 Relative Function Convergence has been set to: 1e-008 Parameter Convergence has been set to: 1e-008

MLE solution provided: Exact

Initial Parameter Values

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.9528 11.9528 11.9528 11.9528 rho(S) 0 0 0 0 a 1549.38 1549.38 2233.35 2233.35 b 0.141393 0.141393 0.935475 0.935475 c -- -- 0.5791 0.5791 d -- 1 -- 1

(S) = Specified

Parameter Estimates by Model

Variable Model 2 Model 3 Model 4 Model 5

-------- ------- ------- ------- ------- lnalpha 11.9887 11.9887 11.9602 11.9602 rho 0 0 0 0 a 2027.8 2027.8 2086.01 2086.01 b 0.147815 0.147815 0.922103 0.922103 c -- -- 0.626407 0.626407 d -- 1 -- 1

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 10 2127 425.4 0.097 10 1967 282.5 0.73 10 1718 321.6 2.9 10 1358 570.8

Estimated Values of Interest

### Page 433 of 438 Model Dose Est Mean Est Std Scaled Residual

------- ------ ---------- --------- ---------------- 2 0 2028 401.2 0.782 0.097 1999 401.2 -0.2517 0.73 1820 401.2 -0.8071 2.9 1321 401.2 0.2927 3 0 2028 401.2 0.782 0.097 1999 401.2 -0.2517 0.73 1820 401.2 -0.8071 2.9 1321 401.2 0.2927 4 0 2086 395.5 0.3278 0.097 2019 395.5 -0.4184 0.73 1704 395.5 0.1101 2.9 1360 395.5 -0.0195 5 0 2086 395.5 0.3278 0.097 2019 395.5 -0.4184 0.73 1704 395.5 0.1101 2.9 1360 395.5 -0.0195

Other models for which likelihoods are calculated:

Model A1: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma^2

Model A2: Yij = Mu(i) + e(ij) Var{e(ij)} = Sigma(i)^2

Model A3: Yij = Mu(i) + e(ij) Var{e(ij)} = exp(lalpha + log(mean(i)) * rho)

Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -259.0561 5 528.1123 A2 -256.0633 8 528.1267 A3 -259.0561 5 528.1123 R -267.7197 2 539.4394 2 -259.7748 3 525.5497 3 -259.7748 3 525.5497 4 -259.2042 4 526.4084 5 -259.2042 4 526.4084

Additive constant for all log-likelihoods = -36.76. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Explanation of Tests

Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) Test 2: Are Variances Homogeneous? (A2 vs. A1) Test 3: Are variances adequately modeled? (A2 vs. A3) Test 4: Does Model 2 fit the data? (A3 vs. 2)

### Page 434 of 438 Test 5a: Does Model 3 fit the data? (A3 vs 3) Test 5b: Is Model 3 better than Model 2? (3 vs. 2)

Test 6a: Does Model 4 fit the data? (A3 vs 4) Test 6b: Is Model 4 better than Model 2? (4 vs. 2)

Test 7a: Does Model 5 fit the data? (A3 vs 5) Test 7b: Is Model 5 better than Model 3? (5 vs. 3) Test 7c: Is Model 5 better than Model 4? (5 vs. 4)

Tests of Interest

Test -2*log(Likelihood Ratio) D. F. p-value

-------- ------------------------ ------ -------------- Test 1 23.31 6 0.0006982 Test 2 5.986 3 0.1123 Test 3 5.986 3 0.1123 Test 4 1.437 2 0.4874 Test 5a 1.437 2 0.4874 Test 5b -4.093e-012 0 N/A Test 6a 0.2961 1 0.5863 Test 6b 1.141 1 0.2854 Test 7a 0.2961 1 0.5863 Test 7b 1.141 1 0.2854 Test 7c 0 0 N/A

The p-value for Test 1 is less than .05. There appears to be a difference between response and/or variances among the dose levels, it seems appropriate to model the data.

The p-value for Test 2 is greater than .1. A homogeneous variance model appears to be appropriate here.

The p-value for Test 3 is greater than .1. The modeled variance appears to be appropriate here.

The p-value for Test 4 is greater than .1. Model 2 seems to adequately describe the data.

The p-value for Test 5a is greater than .1. Model 3 seems to adequately describe the data.

Degrees of freedom for Test 5b are less than or equal to 0. The Chi-Square test for fit is not valid.

The p-value for Test 6a is greater than .1. Model 4 seems to adequately describe the data.

The p-value for Test 6b is greater than .05. Model 4 does not seem to fit the data better than Model 2.

The p-value for Test 7a is greater than .1. Model 5 seems to adequately describe the data.

The p-value for Test 7b is greater than .05. Model 5 does not seem to fit the data better than Model 3.

Degrees of freedom for Test 7c are less than or equal to 0. The Chi-Square test for fit is not valid.

### Page 435 of 438 Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.712784 0.491602 3 0.712785 0.491602 4 0.337843 0.113152 5 0.337843 0.113152



### 800 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2 2.5Mean Response



### dose Exponential Model 2 with 0.95 Confidence Level

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### Page 436 of 438 

### 800 1000 1200 1400 1600 1800 2000 2200 2400

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### dose Exponential Model 3 with 0.95 Confidence Level

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### Page 437 of 438 

### 800 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2 2.5Mean Response



### dose Exponential Model 4 with 0.95 Confidence Level

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### Page 438 of 438 

### 800 1000 1200 1400 1600 1800 2000 2200 2400

0 0.5 1 1.5 2 2.5Mean Response



### dose Exponential Model 5 with 0.95 Confidence Level

10:04 04/16 2012 BMD BMDL Exponential