### Sielken & Associates Consulting, Inc. CEB was able to reproduce the BMD analysis from the Sielken report and to provide BMD estimates and calculate the associ ated FQPA factors.

Background: For both the red blood cell (RBC) and brain compartments, Sielken combined male and female test subjects to analyze both a dult and post-natal day 11 (PND11) rats. Sielken performed two different statistical procedures to determine whet her or not it was appropriate to combine male and female AChE values. The results from the firs t procedure, the two-sample t-test, are listed in Tables 5 and 6 of their submission. These results indicate that the difference between adult male and female AChE levels in the brain compartmen t for the 0.3 mg/kg dose group are statistically significant at the alpha=0.05 signi ficance level. The second pr ocedure employed by Sielken to decide whether or not to combine sexes was a li kelihood ratio test. The results of likelihood ratio tests are listed in Tables 7 and 8 of their submission. For the adu lt brain AChE data, the p-values for the likelihood ratio tests are significant for both the BMDS exponential model 3 (p value = 0.0276) and model 5 (p value = 0.0259) (with a cons tant variance model). For the adult RBC AChE data, the p-value for the likelihood ratio test is signif icant for the BMDS exponential model 5 (p value = 0.0180) (with a non-constant variance model). We conclude from the likelihood ratio test that there is significant di fference in model parameters between sexes for adult RBC data under non-constant variance assump tion. However – despite the fact that the likelihood ratio test indicated that there is a significant difference in adult RBC AChE inhibition values between the sexes -- Sielken did comb ine male and female samples for both RBC and brain AChE in adult and PND11 rats. Exponent ial model-5 under non-constant variance model fitted the data adequately for different organs an d different age groups using this combined (sex) sample. Sielken calculated the FQPA factor for br ain AChE in rats which is the ratio of the adult BMD 10 

### to the PND11 BMD 10, 

### is 4.3 in the combined sample. The FQPA factor for RBC AChE in rats is 3.9 in the combined sample.

Analysis: CEB conducted a confirmatory BMD analysis of the brain and RBC AChE data for adult and PND11 rats using the AChE values from th e Sielken report using EPA’s BMDS version 2.1.1 ( 1 )



### . Dose-response relationships were modeled usi ng the exponential family of models in BMDS. The Akaike Information Criterion (AIC), BMD 10 

### , and BMDL 10 

### values from the four exponential models for adult and PND11 rats are provided in Tables 1 and 2. The mi nimum AIC was used to select the best model. When combining male and female AChE data, CEB was able to duplicate the exact same BMD 10 

### values estimated by Sielken for the adult and pup brain data and the pup RBC data. CEB decided not to combine male and female samples for adult RBC data based on the likelihood ratio test results provided by Sielken & Associates ( MRID 47994306 

### ). In addition, CEB calculated BMD 10 

### values for each gender separately. FQPA factors for single-sex and combined samples are provided in Table 3. The values for the FQPA factors varied between 3.3 and 4.8 depending on the compartment of intere st and whether or not the genders were combined.

1 http://www.epa.gov/ncea/bmds/

Page 2 of 42

### Table 1: AIC, BMD 10 

### , and BMDL 10 

### Values for Adult AChE Levels. Detailed computer output is provided in appendix. All calc ulations are based on non-constant variance assumption.

Target Organ Sex Exponential Model BMD 10 BMDL 10 AIC Comments

2 0.0535 0.0484 926.600
3 0.0535 0.0484 926.600
4 0.0482 0.0408 927.175 Brain Male

5 0.0555 0.0443 927.125

2 0.00531 0.00448 714.798 Optimum may not have been found for model 2

3 0.00790 Bad completion 16298.190 Optimum may not have been found for model 3
4 0.00399 0.00353 705.471 RBC Male

5 0.0228 0.0153 679.582
2 0.0468 0.0426 940.775
3 0.0615 0.0498 937.553
4 0.0468 0.0424 940.775 Brain Female

5 0.0672 0.0530 938.210
2 0.00674 0.00558 736.726
3 0.0141 0.00619 736.555
4 0.00575 0.00465 734.135 RBC Female

5 0.0242 0.0144 722.055
2 0.0502 0.0469 1862.988
3 0.0559 0.0481 1863.330
4 0.0502 0.0458 1862.988 Brain Combined

5 0.0607 0.0517 1862.335

2 0.00599 0.00527 1448.372 Optimum may not have been found for model 2

3 0.00917 Bad completion 10518.420 Optimum may not have been found for model 3

4 0.00494 0.00449 1437.768 RBC Combined

5 0.0223 0.0161 1404.951

Page 3 of 42

### Table 2: AIC, BMD 10 

### , and BMDL 10 

### Values for PND11 AChE Leve ls. Detailed computer output is provided in appendix. All calculations ar e based on non-constant variance assumption.

Target Organ Sex Exponential Model BMD 10 

### BMDL 10 

### AIC Comments

2 0.00833 0.00755 771.965 optimum may not have been found form model 2

3 0.790 Bad completion 893.120

4 0.00786 0.00687 773.629 Brain Male

5 0.0143 0.0112 761.629

2 0.00284 0.00236 653.234 optimum may not have been found form model 2

3 0.00381 Bad completion 6181.454 optimum may not have been found form model 3

4 0.00197 0.00174 638.955 RBC Male

5 0.00477 0.00294 633.107
2 0.00926 0.00840 762.469
3 0.0113 0.00877 762.593
4 0.00926 0.00807 762.469 Brain Female

5 0.0136 0.0103 761.157
2 0.00337 0.00274 657.487
3 0.00337 0.00274 657.487
4 0.00244 0.00202 648.741 RBC Female

5 0.00731 0.00387 644.0862
2 0.00876 0.00817 1530.567
3 0.0105 0.00865 1529.881
4 0.00876 0.00771 1530.567 Brain Combined

5 0.0140 0.0116 1517.282
2 0.00309 0.00268 1305.488
3 0.00309 0.00268 1305.488
4 0.00222 0.00195 1283.358 RBC Combined

5 0.00573 0.00387 1272.576

Page 4 of 42

### Table 3: FQPA Factors

Adult PND11 Pups

Source Sex Target Organ BMD 10 

### Exponential Model selected BMD 10 

### Exponential Model selected FQPA Factor

Brain 0.0535 Model

‐ 2 0.0143 Model

‐ 5 3.741 Male RBC 0.0228 Model

‐ 5 0.00477 Model

‐ 5 4.780
Brain 0.0615 Model

‐ 3 0.0136 Model

‐ 5 4.522 CEB
Female RBC 0.0242 Model

‐ 5 0.00731 Model

‐ 5 3.310
Brain 0.0607 Model

‐ 5 0.0140 Model

‐ 5 4.336 Sielken & Associates Combined RBC 0.0223 Model

‐ 5 0.00573 Model

‐ 5 3.892



### References: Sielken, R. (2010) Benchmark Doses for Brain and RBC Acetylcholinesterase in Adult and Pups Exposed to Aldicarb. Project Number: G202152. Unpublished st udy prepared by Sielken, Inc.February 18, 2010. 68 p.p. MRID 47994306.

Page 5 of 42

### Appendix 

# :

Selected BMDS Output: All model parameters were estimated using non-constant variance assumption.

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.

Adult Male Brain :

==================================================================== Exponential Model. (Version: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 14:28:55 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 5.045e+004 1274 0.03 8 4.786e+004 1710 0.05 8 4.581e+004 1427 0.065 8 4.615e+004 1829 0.08 8 4.137e+004 3154 0.15 8 3.682e+004 2517 0.3 8 2.896e+004 2276

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 6 of 42Var{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 -453.1118 8 922.2235 A2 -448.1513 14 924.3026 A3 -451.6047 9 921.2093 R -525.2118 2 1054.424 2 -459.3004 4 926.6009 3 -459.3004 4 926.6009 4 -458.5878 5 927.1755 5 -457.5626 6 927.1253

Additive constant for all log-likelihoods = -51.46. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0535001 0.048422 3 0.0535001 0.048422 4 0.0481827 0.0408124 5 0.0555194 0.0442621

Page 7 of 4225000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

14:28 04/13 2010 BMD BMDL Exponential

25000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

14:28 04/13 2010 BMD BMDL Exponential

25000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

14:28 04/13 2010 BMD BMDL Exponential

Page 8 of 4225000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

14:28 04/13 2010 BMD BMDL Exponential

Adult Female Brain :

==================================================================== Exponential Model. (Version: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 14:44:56 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 5.043e+004 1460 0.03 8 4.784e+004 2819 0.05 8 4.736e+004 2454 0.065 8 4.558e+004 1642 0.08 8 4.379e+004 3036 0.15 8 3.623e+004 2600 0.3 8 2.53e+004 3199

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 9 of 42Var{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 -463.2022 8 942.4044 A2 -459.3958 14 946.7916 A3 -462.2567 9 942.5135 R -534.3943 2 1072.789 2 -466.3873 4 940.7746 3 -463.7764 5 937.5529 4 -466.3873 4 940.7746 5 -463.1051 6 938.2103

Additive constant for all log-likelihoods = -51.46. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0467726 0.0426385 3 0.0614607 0.0498428 4 0.0467726 0.0423962 5 0.0672431 0.0530345

Page 10 of 4220000 25000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

14:44 04/13 2010 BMD BMDL Exponential

20000 25000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

14:44 04/13 2010 BMD BMDL Exponential

20000 25000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

14:44 04/13 2010 BMD BMDL Exponential

Page 11 of 4220000 25000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

14:44 04/13 2010 BMD BMDL Exponential

Adult Combined Sample Brain :

==================================================================== Exponential Model. (Version: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 16:47:34 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 16 5.044e+004 1324 0.03 16 4.785e+004 2252 0.05 16 4.658e+004 2099 0.065 16 4.586e+004 1705 0.08 16 4.258e+004 3242 0.15 16 3.653e+004 2490 0.3 16 2.713e+004 3281

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 12 of 42Var{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 -925.8769 8 1867.754 A2 -916.3648 14 1860.73 A3 -921.6568 9 1861.314 R -1060.358 2 2124.717 2 -927.494 4 1862.988 3 -926.6651 5 1863.33 4 -927.494 4 1862.988 5 -925.1675 6 1862.335

Additive constant for all log-likelihoods = -102.9. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.0502089 0.0469231 3 0.0558797 0.0481209 4 0.0502089 0.0458504 5 0.0607085 0.0517358

Page 13 of 4225000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

16:47 04/13 2010 BMD BMDL Exponential

25000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

16:47 04/13 2010 BMD BMDL Exponential

25000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

16:47 04/13 2010 BMD BMDL Exponential

Page 14 of 4225000 30000 35000 40000 45000 50000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

16:47 04/13 2010 BMD BMDL Exponential

Adult Male RBC:

!!! 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: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 15:10:52 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 2466 464 0.03 8 2175 788.7 0.05 8 993 422.9 0.065 8 786 431.8 0.08 7 303 221.9 0.15 8 24 30.6 0.3 8 83 89.5

Page 15 of 42Other 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 -356.9571 8 729.9143 A2 -324.2962 14 676.5923 A3 -326.2331 9 670.4662 R -407.4874 2 818.9749 2 -353.3988 4 714.7976 3 -8145.093 4 16298.19 4 -347.7356 5 705.4712 5 -333.7912 6 679.5824

Additive constant for all log-likelihoods = -50.54. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.00531446 0.00448441 3 0.00790546 Bad completion 4 0.00399133 0.0035305 5 0.0228211 0.0153299

Page 16 of 420 500 1000 1500 2000 2500 3000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

15:10 04/13 2010 BMD BMDL Exponential

0 500 1000 1500 2000 2500 3000

-1 -0.8 -0.6 -0.4 -0.2 0 0.2Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

15:10 04/13 2010 BMD BMDL Exponential

0 500 1000 1500 2000 2500 3000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

15:10 04/13 2010 BMD BMDL Exponential

Page 17 of 420 500 1000 1500 2000 2500 3000

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

15:10 04/13 2010 BMD BMDL Exponential

Adult Female RBC:

==================================================================== Exponential Model. (Version: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 15:17:07 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 2411 315.4 0.03 8 1726 753.1 0.05 8 1473 568.2 0.065 8 777 531.7 0.08 8 610 420.1 0.15 8 97 116.2 0.3 8 153 115.6

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 18 of 42Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -367.5027 8 751.0053 A2 -348.6014 14 725.2028 A3 -353.8902 9 725.7804 R -409.369 2 822.738 2 -364.3631 4 736.7263 3 -363.2777 5 736.5554 4 -362.0677 5 734.1353 5 -355.0277 6 722.0553

Additive constant for all log-likelihoods = -51.46. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.00674224 0.00558408 3 0.0141009 0.00619242 4 0.0057509 0.00465191 5 0.0242245 0.0144086

Page 19 of 420 500 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

15:17 04/13 2010 BMD BMDL Exponential

0 500 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

15:17 04/13 2010 BMD BMDL Exponential

0 500 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

15:17 04/13 2010 BMD BMDL Exponential

Page 20 of 420 500 1000 1500 2000 2500

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

15:17 04/13 2010 BMD BMDL Exponential

Adult Combined Sample RBC:

!!! 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: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 16:52:14 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 16 2439 384.3 0.03 16 1950 780.3 0.05 16 1233 543.6 0.065 16 781.1 467.9 0.08 15 466.9 366.7 0.15 16 60.13 90.28

Page 21 of 420.3 16 117.8 106.2

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 -730.4818 8 1476.964 A2 -688.0723 14 1404.145 A3 -694.7847 9 1407.569 R -817.1663 2 1638.333 2 -720.1858 4 1448.372 3 -5255.209 4 10518.42 4 -713.8838 5 1437.768 5 -696.4756 6 1404.951

Additive constant for all log-likelihoods = -102. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.00598819 0.00526723 3 0.00916555 Bad completion 4 0.00494336 0.00449284 5 0.0223073 0.0161069

Page 22 of 420 500 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

16:52 04/13 2010 BMD BMDL Exponential

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

16:52 04/13 2010 BMD BMDL Exponential

0 500 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

16:52 04/13 2010 BMD BMDL Exponential

Page 23 of 420 500 1000 1500 2000 2500

0 0.05 0.1 0.15 0.2 0.25 0.3Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

16:52 04/13 2010 BMD BMDL Exponential

Pups Male Brain:

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

==================================================================== Exponential Model. (Version: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 15:27:24 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 2.561e+004 846.3 0.005 8 2.56e+004 1026 0.01 8 2.368e+004 2084 0.02 8 2.146e+004 1427 0.04 8 1.437e+004 2275 0.08 8 1.036e+004 1177

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 24 of 42Model 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 -373.8812 7 761.7625 A2 -367.8322 12 759.6645 A3 -373.5702 8 763.1404 R -441.5602 2 887.1204 2 -381.9826 4 771.9652 3 -441.5602 5 893.1204 4 -381.8149 5 773.6297 5 -374.8147 6 761.6295

Additive constant for all log-likelihoods = -44.11. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.00833219 0.0075546 3 0.790289 Bad completion 4 0.00786261 0.00687095 5 0.0143096 0.01124

Page 25 of 4210000 15000 20000 25000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

09:56 06/29 2010 BMD BMDL Exponential

10000 15000 20000 25000

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

09:56 06/29 2010 BMD BMDL Exponential

10000 15000 20000 25000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

09:56 06/29 2010 BMD BMDL Exponential

Page 26 of 4210000 15000 20000 25000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

09:56 06/29 2010 BMD BMDL Exponential

Pups Female Brain :

==================================================================== Exponential Model. (Version: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 15:50:21 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 2.608e+004 1017 0.005 8 2.563e+004 1107 0.01 8 2.394e+004 830 0.02 8 2.207e+004 2190 0.04 8 1.614e+004 2206 0.08 8 1.068e+004 1745

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 27 of 42Model 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 -375.3848 7 764.7695 A2 -368.8802 12 761.7605 A3 -373.7771 8 763.5541 R -439.7824 2 883.5649 2 -377.2345 4 762.4689 3 -376.2967 5 762.5935 4 -377.2345 4 762.4689 5 -374.5785 6 761.157

Additive constant for all log-likelihoods = -44.11. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.00925925 0.00839859 3 0.0112715 0.00876684 4 0.00925925 0.00807613 5 0.0136115 0.0103155

Page 28 of 4210000 15000 20000 25000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

15:50 04/13 2010 BMD BMDL Exponential

10000 15000 20000 25000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

15:50 04/13 2010 BMD BMDL Exponential

10000 15000 20000 25000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

15:50 04/13 2010 BMD BMDL Exponential

Page 29 of 4210000 15000 20000 25000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

15:50 04/13 2010 BMD BMDL Exponential

Pups Combined Brain :

==================================================================== Exponential Model. (Version: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 16:58:08 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 16 2.585e+004 935.6 0.005 16 2.562e+004 1032 0.01 16 2.381e+004 1538 0.02 16 2.176e+004 1814 0.04 16 1.525e+004 2351 0.08 16 1.052e+004 1447

Other models for which likelihoods are calculated:

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

Page 30 of 42Var{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 -752.7054 7 1519.411 A2 -743.4776 12 1510.955 A3 -750.5554 8 1517.111 R -881.489 2 1766.978 2 -761.2836 4 1530.567 3 -759.9404 5 1529.881 4 -761.2836 4 1530.567 5 -752.6409 6 1517.282

Additive constant for all log-likelihoods = -88.22. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.00876019 0.00816909 3 0.0105247 0.00864872 4 0.00876019 0.00771366 5 0.0139776 0.0116531

Page 31 of 4210000 12000 14000 16000 18000 20000 22000 24000 26000 28000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

16:58 04/13 2010 BMD BMDL Exponential

10000 12000 14000 16000 18000 20000 22000 24000 26000 28000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

16:58 04/13 2010 BMD BMDL Exponential

10000 12000 14000 16000 18000 20000 22000 24000 26000 28000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

16:58 04/13 2010 BMD BMDL Exponential

Page 32 of 4210000 12000 14000 16000 18000 20000 22000 24000 26000 28000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

16:58 04/13 2010 BMD BMDL Exponential

Pups Male RBC :

!!! 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: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 16:18:14 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 5104 795.4 0.005 8 4569 929.1 0.01 8 3654 1104 0.02 7 2153 517.5

Page 33 of 420.04 8 931 169.6 0.08 7 654 253.8

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 -322.9408 7 659.8815 A2 -307.5104 12 639.0208 A3 -310.4851 8 636.9702 R -369.01 2 742.02 2 -322.6171 4 653.2342 3 -3086.727 4 6181.454 4 -314.4776 5 638.9552 5 -310.5536 6 633.1071

Additive constant for all log-likelihoods = -42.27. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.00284479 0.00235863 3 0.00381536 Bad completion 4 0.00196772 0.00174498 5 0.00477107 0.00293821

Page 34 of 420 1000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

16:18 04/13 2010 BMD BMDL Exponential

0 1000 2000 3000 4000 5000 6000

-1 -0.8 -0.6 -0.4 -0.2 0Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

16:18 04/13 2010 BMD BMDL Exponential

0 1000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

16:18 04/13 2010 BMD BMDL Exponential

Page 35 of 420 1000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

16:18 04/13 2010 BMD BMDL Exponential

Pups RBC Female:

==================================================================== Exponential Model. (Version: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 16:23:08 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 8 5279 973.8 0.005 7 4823 681.3 0.01 8 3493 771.1 0.02 7 2881 1017 0.04 8 1009 340.1 0.08 8 816 145.1

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 36 of 42Model R: Yij = Mu + e(i) Var{e(ij)} = Sigma^2

Likelihoods of Interest

Model Log(likelihood) DF AIC

------- ----------------- ---- ------------ A1 -322.4457 7 658.8913 A2 -308.6109 12 641.2219 A3 -312.6577 8 641.3154 R -369.06 2 742.1201 2 -324.7433 4 657.4866 3 -324.7433 4 657.4866 4 -319.3706 5 648.7411 5 -316.0431 6 644.0862

Additive constant for all log-likelihoods = -42.27. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.00336759 0.00274242 3 0.00336759 0.00274242 4 0.00243774 0.00202279 5 0.00731427 0.00387471

Page 37 of 421000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

16:23 04/13 2010 BMD BMDL Exponential

1000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

16:23 04/13 2010 BMD BMDL Exponential

1000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

16:23 04/13 2010 BMD BMDL Exponential

Page 38 of 421000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

16:23 04/13 2010 BMD BMDL Exponential

Pups Combined sample RBC:

==================================================================== Exponential Model. (Version: 1.61; Date: 7/24/2009) Input Data File: C:\USEPA\BMDS21\expDax_Setting.(d) Gnuplot Plotting File: Tue Apr 13 17:01:52 2010

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

BMDS Model Run

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

Table of Stats From Input Data

Dose N Obs Mean Obs Std Dev

----- --- ---------- ------------- 0 16 5191 863.7 0.005 15 4687 804.8 0.01 16 3573 923.8 0.02 14 2517 862.6 0.04 16 970 262.7 0.08 15 740.7 212.4

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 39 of 42Model 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 -647.9977 7 1309.995 A2 -624.8456 12 1273.691 A3 -628.4926 8 1272.985 R -738.1013 2 1480.203 2 -648.744 4 1305.488 3 -648.744 4 1305.488 4 -636.6791 5 1283.358 5 -630.2878 6 1272.576

Additive constant for all log-likelihoods = -84.54. This constant added to the above values gives the log-likelihood including the term that does not depend on the model parameters.

Benchmark Dose Computations:

Specified Effect = 0.100000

Risk Type = Relative deviation

Confidence Level = 0.950000

BMD and BMDL by Model

Model BMD BMDL

------- ------------ ---------- 2 0.00308837 0.00268371 3 0.00308837 0.00268371 4 0.00222711 0.00195621 5 0.00572972 0.00386834

Page 40 of 421000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 2 with 0.95 Confidence Level

17:01 04/13 2010 BMD BMDL Exponential

1000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 3 with 0.95 Confidence Level

17:01 04/13 2010 BMD BMDL Exponential

1000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 4 with 0.95 Confidence Level

17:01 04/13 2010 BMD BMDL Exponential

Page 41 of 421000 2000 3000 4000 5000 6000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08Mean Response

dose Exponential Model 5 with 0.95 Confidence Level

17:01 04/13 2010 BMD BMDL Exponential

Page 42 of 42