### A

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### APPENDIX A: EXTERNAL REVIEW PANEL COMMENTS 1
2
3
4

5
6

7
8 

### To be added 9

### B

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### APPENDIX B: HUMAN PBTK DICHLOROMETHANE MODEL 10

11 

### B-1. Human Model Description 12 

### The basic model structure used by David et al. (2006) was that of Ande rsen et al. (1987), 13


### with the addition of the CO subm odel of Andersen et al. (1991) , refinements from the Marino et 14 

### al. (2006) mouse model, and an inclusion of CYP metabolism in rapidl y perfused tissue (see 15 

### Figure B-1). 16

17

18

19

20

21

22

23

24

25

26 

### Figure B-1. Schematic of the D avid et al. (2006) PBTK model 27 

### for dichloromethane in the human. 28 29 30 

### In order to incorporate k nown variability in human physiology and metabolism of 31 

### dichloromethane into internal dosimetry, Mo nte Carlo analysis of the human model was 32 

### performed to derive probability distributions of internal dose, as reporte d in David et al. (2006), 33 

### but with changes in some of the key distributions as described belo w. The shape of the resulting 34 

### dose distribution can be used to quantify the variabi lity in internal dose with respect to variability 35 

### in human physiology and dichloromethane metabo lism. The human model was run repeatedly, 36 

### using a random sample of each parameter from its respective parameter distribution in each 37 

### iteration. Internal doses predicted for all iterations coll ectively defined a dist ribution for internal 38 

### dose. The Monte Carlo analysis was run for 10,000 iterations. Repeated Monte Carlo analyses 39 

### (at 10,000 iterations each) yielded 99 th 

### percentile values of internal dose in the liver or lung that 40 

### differed by less than 2%. Truncated normal or lognormal distributions of physiological and 41 

### metabolic parameter and partition coefficient va lues were described by a mean, SD, and upper 42 

### and lower bounds. Physiological parameter and pa rtition coefficient values were initially taken 43 

### from the literature as described in David et al. (2006). 44 

### The first order oral absorption rate constant, k a 

### = 5.0/h, was used in conjunction with 45 

### human drinking water exposures si mulated as six discrete drinki ng water episodes for specified 46 

### times (25, 10, 25, 10, 25, and 5% of total daily intake at hours 0, 3, 5, 8, 11, and 15 of each day) 47 

### (Reitz et al., 1997). Metabolic parameter distri butions were derived from multiple human data 48 

### sets by using MCMC calibration, also de scribed in David et al. (2006). 49 Blood A lveola r

A i r

Endogenous Production Gas Exchange Lung

Fat

Richly Perfused

Slowly Perfused

Liver CYP GST

CYP GST CO Sub Model

CYP

### B

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### The variability of genotypic expression ofGST-T1 activity (the mechanism for GST50 

### mediated metabolism of dichloromethane) was simu lated as a uniform discrete distribution of the 51 

### three GST-T1 genotypes (+/+, +/–, –/–) with va rying activities in th e liver and lung. The 52 

### genotype frequency was based on data from Haber et al. (2002), with a frequency of genotypes 53 

### of 32, 48, and 20 in the +/+, +/–, and –/– groups , respectively. GST activities measured by 54 

### Warholm et al. (1994) for the th ree genotypes in a group of 208 hea lthy male and female subjects 55 

### from Sweden were scaled by David et al. (2006) to obtain distributions of k f 

### C for each genotype 56 

### that, when weighted by estimated frequencies of the genotypes in U.S. populations, would result 57


### in a distribution of k f 

### C activities with a mean equal to 0.852/hour-kg 0.3 

### . The resulting 58 

### distributions of internal lung and liver dose in human populations woul d have a theoretical 59 

### probability of 20% for zero exposure to GST- mediated metabolites. The final parameter 60 

### distributions used by David et al. (2006) are summarized in Table B1. 61

62



### Table B-1. 

### Parameter distributions used in human Monte Carlo analysis for dichloromethane by David et al. (2006)

Distribution

Parameter Mean (arithmetic) SD Source BW Body weight (kg) 70.0 21.0 Humans a QCC Cardiac output (L/hour-kg 0.74 ) 16.5 1.49 Humans a VPR Ventilation:perfusion ratio 1.45 0.203 Humans a QFC Fat 0.05 0.0150 Humans a QLC Liver 0.26 0.0910 Humans a QRC Rapidly perfused tissues 0.50 0.10 Humans a QSC Slow perfused tissues 0.19 0.0285 Humans a Tissue volumes (fraction BW)

VFC Fat 0.19 0.0570 Humans a

VLC Liver 0.026 0.00130 Humans a VLuC Lung 0.0115 0.00161 Humans a VRC Rapidly perfused tissues 0.064 0.00640 Humans a VSC Slowly perfused tissues (muscle) 0.63 0.189 Humans a
Partition coefficients

PB Blood:air 9.7 0.970 Humans b

PF Fat:blood 12.4 3.72 Rats b

PL Liver:blood 1.46 0.292 Rats b
PLu Lung:arterial blood 1.46 0.292 Rats b
PR Rapidly perfused tissue:blood 1.46 0.292 Rats b
PS Slowly perfused tissue (muscle:blood) 0.82 0.164 Rats b Metabolism parameters

V maxC Maximum metabolism rate (mg/hour-kg 0.7 ) 9.34 1.73 Calibration c

K

m

Affinity (mg/L) 0.433 0.146 Calibration c A1 Ratio of lung V
max to liver V
max 0.000993 0.000396 Calibration c A2 Ratio of lung KF to liver KF 0.0102 0.00739 Calibration c

### B

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### DRAFT- DO NOT CITE OR QUOTE Table B-1. 

### Parameter distributions used in human Monte Carlo analysis for dichloromethane by David et al. (2006)

Distribution

Parameter Mean (arithmetic) SD Source FracR Fractional CYP2E1 capacity in rapidly perfused tissue 0.0193 0.0152 Calibration c First order metabolism rate (/hour-kg 0.3 )

Homoz yg ous (

– /

– ) 0 0 Calibration c

Heteroz yg ous ( +/

– ) 0.676 0.123 Calibration c k f

C Homozygous (+/+) 1.31 0.167 Calibration c Oral absorption

k
a First order oral absorption rate (/hour) 5.0 Reitz et al. (1997) (point estimate)

a US EPA (2000d) Human PBTK model used for vinyl chloride.

b Andersen et al. (1987). Blood:air pa rtition measured by using human samples; other partition coefficients based on estimates from tissue measures in rats.

c Bayesian calibration based on five data sets (see text for de scription); posterior distributions presented in this table.

Source: David et al. (2006).

63


### B-2. Revisions to Parameter Dist ributions of Davi d et al. (2006) 64 

### An evaluation of the David et al. (2006) model and parameterization was undertaken, 65 

### focusing on the adequacy of the characterization of parameter distributions in the full human 66 

### population. EPA’s conclusion is that the reported distributions fo r physiological parameters in 67 

### particular, but also key metabolic parameters, on ly represented a narrow se t of adults (with the 68 

### exception of BW). Therefore supplemental da ta sources were chosen to define these 69 

### distributions in a way that should fully charac terize the variability in the human population for 70


### individuals between six months and eighty years of age. Sin ce many physiological parameters 71 

### vary with age and gender, a structured approach will be used, where an individual’s age and then 72 

### gender may be selected from the overall population distribution for these characteristics (the 73 

### male:female ratio in the population declines with age, for exampl e). Dosimetry simulations can 74 

### then be run for each such individual, to obtai n an overall population distribution of internal 75 

### doses. Thus each dosimetry distribution represents a “snapshot” of the dosimetry in a given 76 

### individual at a given age and age- and gende r-appropriate sampled body composition (fraction of 77 

### BW in each tissue group). 78 

### In estimating the human equivalent exposure levels for non-cancer endpoints, which are 79 

### presumed to be relatively short-term effects po ssibly occurring from expo sures of several weeks 80 

### or months, using the distribution of such dosim etry “snapshots” should provide precisely the 81 

### correct distribution to estimate overall populati on risk. For estimating cancer risk, however, 82


### where risk is due to the cumula tive exposure over months or years, the ideal approach would be 83


### to simulate the time-course of internal doses in a given individual, track ed over a lifetime or 84 

### significant portion thereof. But doing so w ould require estimating time-courses for each 85

### B

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### physiological and metabolic parameter in the in dividual over that time-period, a task which 86 

### would be far more complicated than the structured “snapshot” approach used here. For example, 87 

### while the CYP2E1 activity in an individual at age 12 is probably predictive of the activity in that 88 

### individual at age 70 (e.g., someone who has a bove-average CYP2E1 activity when younger may 89 

### well continue to be above average throughout his or her lifetime), we simply do not have the 90 

### information or model structure to predict the ti me-dependences. Further, we know for example 91 

### that some individuals who are lean in their yout h may become obese by middle-age, while others 92 

### (through lifestyle-changes) change in the opposite di rection; and these chan ges may be reversed 93 

### by the time the individual reache s 70 or 80 years of age. 94 

### Therefore a “life-course” dosimetry for specifi c individuals has not been calcuated. For 95 

### calculating human equivalent conc entrations for non-cancer effects, however, this means that the 96 

### exposure level is identified such that 99% of th e population at a given time is predicted to have 97 

### an internal dose at or below a poi nt-of-departure (POD, defined as an internal dose level). It then 98 

### seems highly likely that if one we re to track individual exposure over time, one would also find 99 

### that this equivalent exposure keeps 99% of the population from exceeding the POD. In short, if 100 

### 99% of individuals’ snapshot-inter nal doses (99% of the internal doses in a cross-section of 101 

### society on a given day) are belo w the POD, it can be anticipated that no more than 1% of 102 

### individuals will exceed that POD at any point in their life-times, even though the model 103 

### simulations did not specifically track the changes in internal dose with age. For cancer risk it can 104


### likewise be assumed that the av erage internal dose per unit expos ure in the population as a whole 105 

### (or the GST-T1 +/+ portion of the population) at a given point in time is a good estimate of the 106 

### average one would estimate if the internal dos e was tracked over the lifetime in the same 107 

### population – where the distribution of physiological and metabolic ch aracteristics at a given age 108 

### is the same as used to estimate the average in ternal dose distribution for a cross-section of 109 

### society. 110

111


### B-3. CY2E1 and GST-T1 112 

### This evaluation incorporated additional da ta concerning the variability in CYP2E1 113 

### activity among humans, based on Lipscomb et al. (2003). The Lipscomb et al. (2003) study was 114 

### based on in vitro analysis of liver sample s from 75 human tissue donors (activity towards 115 

### trichloroethylene and measurements of protein c ontent) to estimate a dist ribution of activity in 116 

### the population. The distribution used by David et al. (2006) for hepatic CYP2E1 activity for 117 

### dichloromethane (V max 

### c) was a truncated lognormal distri bution with geometric mean (GM) = 118 

### 9.34 mg/hr/kg 0.7 

### , geometric standard deviation (G SD) = 1.14, lower bound = 6.33 (68% of 119 

### mean), and upper bound = 13.8 (218% of mean). Ho wever, Lipscomb et al. (2003; Table IV), 120 

### analyzing data from a larger set of human ti ssue donors, derived an ultimate distribution for 121 

### CYP2E1 activity with TCE (in units of pm ol oxidized/min/g liver) with GSD = 1.7274, 5 th 122 

### percentile = 40.7% of the mean, and 95 th 

### percentile = 245.8% of the mean. These data support a 123

### B

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### wider distribution in CYP2E1 activity than had been used in the David et al. (2006) model, with 124 

### approximately a 6-fold range between the upper a nd lower bounds in Lipscomb et al. (2003) and 125 

### a 2-fold range in David et al. ( 2006). Thus the David et al. dist ribution parameters is replaced 126 

### by using the same GM = 9.34, but GSD = 1.73. Further, since even the data available to 127 

### Lipscomb et al. (2003) were limited, and the lo g-normal distribution is naturally bounded to be 128 

### greater than zero, a non-truncated distri bution was used for this parameter. 129 

### To assure that the age-dependence of CYP2 E1, particularly in children, is properly 130 

### characterized, data of Johsrud et al. (2003) 1 

### was analyzed. This study measures CYP2E1 activity 131 

### and other parameters from individuals up to 18 ye ars of age. For a signi ficant subset of the 132 

### individuals in that study, valu es were available for the liver CYP2E1 content (activity/mg 133 

### microsomal protein), liver weight (LW), and bod y weight (BW). For individuals 14-18 years old 134 

### there appeared no significant trend in the CY P2E1 activity and the average BW was 69.6 kg, 135 

### essentially identical to the value of 70 kg used as a representative adult. Therefore the data from 136 

### the 14-18 year-old individuals were assumed to represent adult values, and hence an evaluation 137 

### of the change versus adult could be made by nor malizing to these data. In particular, assuming 138 

### that the microsomal content per gram of liver is constant across the age-ra nge considered here (6 139 

### months-18 years), the total activity for an indivi dual relative to that in an adult could be 140


### estimated as: 141

142 

### Vmax(individual)/Vmax(14-18) = 143 

### CYP2E1(individual)·LW(individua l)/[CYP2E1(14-18)·LW(14-18)] 144

145 

### where Vmax(individual), CYP2E1(individual) a nd LW(individual) are the individual’s total 146 

### activity, activity (per mg microsomal protein) and liver weight, respectively, while Vmax(14- 147 

### 18), CYP2E1(14-18) and LW(14-18) are the resp ective average values for individuals 14-18 148 

### years old. (If not normalized, Vmax = CYP2E1·m sp·LW, where msp is the microsomal content 149 

### (mg/kg liver), but if msp is the same in all indi viduals, it drops from the equation when dividing 150 

### by the 14-18-year-old aver age.) These normalized activities ar e plotted agains t the relative body 151 

### weight, BW(individual)/B W(14-18) in Figure B-2. 152

153

1 

### individual data supplied by the co rresponding author D. Gail McCarver , to Paul Schlosser, U.S. EPA.

### B

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### DRAFT- DO NOT CITE OR QUOTE 0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6

0 0.2 0.4 0.6 0.8 1 1.2 1.4 

### BW/BW(14-18)Vmax/Vmax(14-18) Data

BW^0.70 scaling

BW^0.88 scaling

154 

### Figure B-2. Total CYP2E1 activity (Vmax) normalized to the average total 155 

### activity in 14-18 year-old indivi duals (Vmax(14-18)) plotted against 156 

### normalized body weight (BW) for indi viduals ranging from six months to 18 157 

### years of age. Data source: Johnsrud et al. (2003). 158
159 

### The data in Figure B-2 are compared to two model predictions: the allometric-based 160 

### prediction used by David et al. (2006) that Vmax will scale as BW 0.7 

### , and an alternate scaling 161 

### obtained by fitting to these data, BW 0.88 

### . Both alternatives do a fairly good job of representing 162 

### the average trend in the data, but the scaling by BW 0.7 

### tends to under-predict the data in the range 163 

### of 0.4-0.8 for BW/BW(14-18). Therefore this fitted coefficient will be used when estimating the 164 

### total activity for individuals less than 18 years of age. However, since these data indicate a 165 

### slower trend (rise in Vmax with BW) for norma lized BW ~ 0.7 and above, and there are no data 166 

### to indicate that total activity con tinues to increase so rapidly in adults over 70 kg, the coefficient 167 

### will be kept at 0.7 for individuals greater than 18 years of age. 168 

### Using the allometric coefficient of 0.88, the normalized V max 

### c values was computed in 169 

### the same group of individuals as: 170

171



### V max 

### c(individual)/ V max 

### c (14-18) = 172 

### [Vmax(individual)/BW(individual) 0.88 

### ]/[Vmax(14-18) /BW(14-18) 0.88 

### ]. 173

174


### To test the revised allometric function versus the data these values were then plotted against 175 

### individual age and a linear regres sion performed, as shown in Figur e B-3. While the slight trend 176 

### in the regression indicates that not all of the age-dependence may be captured by this function, 177


### the low R 2 

### and small value of the slope indicate that the observation is not statistically significant 178 

### and that further attempts to explicitly acc ount for age dependence would lead to minimal 179

### B

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### improvement. This representation of the data also clearly shows that the overall variability in the 180 

### scaled activity (V max 

### c) is fairly constant across ages: a pproximately six-fol d, ranging from ~ 0.3 181 

### to 1.8. This is the same range observed in adul ts by Lipscomb et al. ( 2003), as noted above. 182 

### Thus these data support the use of a consta nt variability (GSD) in simulating population 183 

### variability in CYP2E1 activity for children a bove six months of age, as well as adults. 184 

### Note that scaling CYP2E1 activity by BW 0.88 

### for children and by BW 0.7 

### for adults, rather 185 

### than per liver weight (which is expected to scale as body weight 1.0 

### with only a 5% coefficient of 186 

### variation in liver fraction) leads to a lower range in CYP2E1 than scaling by BW 1 

### would 187 

### indicate. In particular, the dist ribution predicted by the model for total CYP2E1 activity (mg/hr, 188 

### upper/lower bound, given body weight between 7 and 130 kg) will be about 2-fold less than if 189 

### one assumed the activity varied as liver weight and simply multiplied the two sets of upper/lower 190 

### bounds (body weight and activity/g li ver). However, given the data -base for this analysis, from 191 

### adults and children, the resulti ng distribution is expected to provide a good prediction of 192 

### variability in the overall population. 193

194

y = 0.0147x + 0.7907
R 2 = 0.0485

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

0 3 6 9 12 15 18 Age (y)VmaxC/VmaxC(14-18)

`

195 

### Figure B-3. Body-weight scaled CYP2E1 activity (V max 

### c) normalized to the 196 

### average scaled activity in 14-18 year-old individuals (V max 

### c (14-18)) plotted 197 

### against age individuals ranging from si x months to 18 years of age. Data 198 

### source: Johnsrud et al. (2003). 199
200 

### Unfortunately, there is not a rich data set fo r GST-T1 such as is available for CYP2E1. 201 

### Strange et al. (1989) examined the developmental patterns for two other GS T classes, mu and pi, 202


### and found that the child:adult activ ity for mu followed a similar pattern as CYP2E1, increasing 203 

### from a low level near birth over time, but generall y being higher than the ratio for CYP2E1 in a 204 

### given age-range. GST-pi, however , was expressed at 21 times adul t values in children up to 1 205

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### year of age, then declined with age. Thus it is difficult to draw specif ic conclusions regarding 206 

### age-dependent variation in GST-T1 from these da ta (i.e., a quantitative an alysis of the activity 207


### data for other GST classes will not be used to estimate the variation in GST-T1). 208 

### As is, the model uses a first-order rate constant for GST-T1 which is scaled as BW -0.3



### . 209 

### While the more recent “standard” for scaling of first-order constants is as BW -0.25 

### , the difference 210


### between these two is small and BW -0.3 

### was used in the Bayesian model calibration. Because 211 

### first-order constants are multiplied by tissue vo lumes in calculating total metabolism rates, and 212 

### tissue volume scales approximately as BW 1 

### , scaling the GST-T1 constant by BW -0.3 

### is equivalent 213 

### to scaling a Vmax by BW 0.7 

### . Even though the results cited a bove for GST-pi have the opposite 214 

### trend, data on CYP2E1 activity discussed above and the trend in GST-mu activity are both at 215 

### least qualitatively consistent with this scaling. Theref ore this scaling is as sumed to appropriately 216 

### accounts for age-dependent changes in GST-T1 activ ity for individuals over six months of age, 217 

### via the explicit dependence on body weight. 218 

### Finally, since normal distributions were used for GST-T1 activity, the application of a 219 

### lower bound at zero is necessary while being mi nimally restrictive. The upper bound for each 220 

### distribution (for the +/+ and +/- populations) was placed at 3 standard deviations above the mean 221 

### by David et al. (2006) and so w ould be expected to have minimal impact on the predicted 222 

### distribution. However, in keeping with the decision to use an unbounded (lognormal) 223 

### distribution for CYP2E1, to assu re characterization of the upper end of the human distribution, 224 

### the upper bound was set at 5 standa rd deviations above the mean. 225

226 

### B-4. Analysis of Human Physiologi cal Distributions for PBTK Modeling 227 

### While the body weight distribut ion in the David et al. (2006) PBTK model used ranges 228 

### from 7 to 130 kg, thus covering 6-month old child ren to obese adults, th ere are age-dependent 229 

### changes and gender-dependent differences in ventil ation rates and body-fat that are not explicitly 230


### included. To more accurately reflect the distribution of physiological parameters in the entire 231 

### population, the unstructured distributions of Davi d et al. (2006) for certain primary or key 232 

### parameters were replaced with distributions ba sed on available information that specifically 233 

### accounts for the population distributions of age and gender and the age- and gender- specific 234


### distributions or functions for BW, QCC, alveolar ventilati on, body fat (fraction), and liver 235 

### fraction. In the following, v ~ U[0,1] indicates that v is a ra ndom sample from the uniform 236


### distribution from 0 to 1. 237

238


### B-4.1. Age 239


### U.S. Census Bureau statistics for 6 mont hs to 80 years of age were normalized 240 

### (population for 6 months to 1 year assumed to be one-half of 0- to 1- year population), and the 241 

### resulting quantiles were plotted against the corresponding ages with a polynomial function fit, as 242

### B

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### shown in Figure B-4. A sample individual’s ag e can be determined by using the polynomial, 243 

### given v ~ U[0,1]. (Alternately, age can be specified.) 244



### y = 165.86x 4 

### - 253.19x 3 

### + 113.27x 2 

### + 53.356x + 0.5
R 2 

### = 0.9998

0 10 20 30 40 50 60 70 80

0 0.2 0.4 0.6 0.8 1 

### Empirical cumulative population fractionAge (years)

245 

### Figure B-4. U.S. age distribution, 6 months to 80 years (values 246 

### from U.S. Census Bureau). 247 248 

### B-4.2. Gender 249


### U.S. Census Bureau statistics for fraction of ma les versus age, in 5-year intervals, were 250 

### plotted and an empirical function was fit, as sh own in Figure B-5. Given the individual’s age, 251 

### the gender is randomly selected as male if v ~ U[ 0,1] is less than or equal to the polynomial and, 252 

### otherwise, female. (Alternately, it can be specified.) 253



### 0.25 0.3 0.35

0.4 0.45

0.5 0.55

0 20 40 60 80 100 

### Age (years)Male population fraction



### Fraction = 0.513·(125.3 - age) 4



### _ 33.7 4 

### + (125.36 - age) 4

254 

### Figure B-5. U.S. age-specific gende r distribution (values from U.S. 255 

### Census Bureau). 256
257

258

### B

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### B-4.3. Body weight 259 

### Portier et al. (2007) reported statistical mean and SD for BW as a function of age and 260 

### gender, based on the NHANES IV. Portier et al. ( 2007) also described empi rical functions fit to 261 

### these results; however, those functions were f ound to be exquisitely sensitive to the fitted 262 

### parameters, to the point that entering the func tions by using the four significant figures for 263 

### parameters given by Portier et al. (2007) gave results that signifi cantly deviated from the results 264 

### as shown by the authors in plots at higher ag e ranges. Therefore, a somewhat different 265 

### functional form was chosen. In particular for BW mean and SD, for each gender, a function of 266


### the form was fitted: 267

268



### value = ⎪ ⎩ ⎪ ⎨ ⎧

≥

− ≤

−

c c c c



### age age age age poly age age age age poly

)], 10 / ) (( exp[ )], 10 / ) (( exp[

2 1



### , 269

270


### where poly i 

### (x) = a · x + b i 

### · x 2 

### + c i 

### · x 3 

### , i = 1 and 2, age is in years, age c 

### is a “cut” age, dividing the 271 

### early-age function from th e later age function, and poly i 

### (i = 1 or 2). Beca use there is no explicit 272


### constant term in poly i 

### and the linear coefficient, a , is common to the two functions, the functions 273 

### will automatically satisfy the condition of being e qual and of having equal sl ope (first derivative) 274



### at age = age c 

### , so the overall function will be smooth and continuous. 275


### The functions were fitted to the summarized data based on minimizing a weighted sum of 276



### square errors, error = ∑ n age 

### × [data(age) – function(age)] 2 

### , where n age 

### was the number of 277 

### observations for the age. The resulting paramete r values are listed in Table B-2, and the curve 278 

### fits are shown in Figure B-6. Given the age and gender from sections B-4.1 and B-4.2, the BW 279 

### is then randomly selected from a normal distri bution, with the resulting mean and SD truncated 280


### at the 1 st 

### and 99 th 

### percentile. 281

282 283 

### Table B-2. Parameters for BW dist ributions as functions of age and gender

Male BW (kg) Female BW (kg)

Parameter Mean SD Mean SD

age c 21 16 16 13

a 4.406 2.87 4.146 2.574
b 1 –0.0285 0.06 –0.147 –0.358

c 1 –0.729 –2.56 –1.36 –2.55
b 2 0.115 0.96 0.44 1.16
b 2 0.0048 0.0448 –0.0278 –0.0861

284

### B

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### DRAFT- DO NOT CITE OR QUOTE 

### Female BW

0 20 40 60 80 100

0 1.5 3 4.5 6 7.5 9 

### Age (years/10)Mean BW (kg)

285



### Female SD

0 5 10 15 20 25 30

0 1.5 3 4.5 6 7.5 9 Age (years/10)SD(BW)

286



### Male BW

0 20 40 60 80 100

0 1.5 3 4.5 6 7.5 9 Age (years/10)Mean BW (kg)

287



### Male SD

0 5 10 15 20 25 30

0 1.5 3 4.5 6 7.5 9 

### Age (years/10)SD(BW)

288 

### Figure B-6. Function fits to age-dependent data for BW mean and 289 

### SDs for males and females in the U. S. (values from Portier et al. 290 

### [2007]). 291
292

### B

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### DRAFT- DO NOT CITE OR QUOTE 

### An example output BW distribution, from a Monte Carlo simulation for ages 0.5–80 293 

### years, both genders, is shown in Figure B-7. Th e range 6.6–131.4 kg is only s lightly larger than 294 

### that set by David et al. (2006) (i.e., 7–130 kg). But the bimodal form is an unexpected but 295 

### reproducible result, presumably occurring because the fraction of a given life span spent at 296 

### intermediate BW values is smaller, as evid enced by the most rapid growth rate occurring 297 

### between ~7 and 18 years of age (Figure B-6). 298

299

0 0.004 0.008 0.012 0.016

0.02

0 20 40 60 80 100 120 140 

### BW (kg)Frequnecy density

300 

### Figure B-7. Example BW histogram from Monte Carlo simulation 301 

### for 0.5- to 80-year-old males and females in the U.S. (simulated n = 302 

### 10,000). 303
304
305 

### B-4.4. Alveolar Ventilation 306 

### Clewell et al. (2004) tabulated values for th e alveolar ventilation constant QAlvC (L/h307 

### kg 0.75 

### ) for males and females at different ages (QAlvC is multiplied by BW 0.75 

### to obtain the total 308 

### rate). Smooth functions of age were fitted to those results to use as age- and gender-specific 309 

### mean values, shown in Figure B-8. Arcus-Arth and Blaisdell (2007) reported GSD values for 310 

### respiration rates for 0–18 years of age; a smooth f unction was fit to those results and the value at 311 

### 18 years was assumed to apply for all adults great er than age 18 (Figure B-9). An individual’s 312 

### QAlvC was then selected from a lognormal distri bution, with the resultin g mean and GSD (given 313 

### age and gender) truncated between the 5 th 

### and 95 th 

### percentile. 314

315

### B

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### DRAFT- DO NOT CITE OR QUOTE 

### Respiration constant

0 5 10 15 20 25 30 35

0 20 40 60 80 100 Age (years)QAlvC (L/h/kg0.75)

Males = 13.6 + 13.3*exp(-0.05*age)

Females = 10.7 + 22.1*exp(-0.08*age)

316 

### Figure B-8. Mean value respiration rates for males and females as 317 

### a function of age (values from Clewell et al. [2004]). 318 319 320



### Respiration variance

y = -0.1948x 3 + 0.6095x 2 - 0.3978x + 1.4261
R 2 = 0.8823

1.3 1.35

1.4 1.45

1.5 1.55

1.6 1.65

0 0.5 1 1.5 2 

### Age (years/10)GSD

321 

### Figure B-9. GSDs for respiration rates for males and females as a 322 

### function of age (values from A rcus-Arth and Blaisdell [2007]). 323 324
325


### B-4.5. Cardiac Output 326 

### Clewell at al. (2004) provide a function that will be taken to repres ent the mean for the 327 

### cardiac blood-flow coefficient (QCC), QCC mean 

### = 56.906 × (1.0 – e –0.681 × exp(0.0454 × QAlvC) 

### ) – 328 

### 29.747. However, using this function alone will tightly link cardia c flow and ventilation rates, 329 

### rather than using a distribution in the VP R (VPR = QAlv/QC = QAl vC/QCC) as was done by 330 

### David et al. (2006). Since a di stribution is already defined fo r QAlvC, above, a VPR subject to 331 

### variability will be estimated but renormalized to match the ratio of QAlvC and QCC mean 

### as 332 

### defined above. In effect, this means QCC sample 

### = QCC mean 

### × VPR mean 

### /VPR sample 

### will be chosen, 333

334


### where VPR mean 

### and VPR sample 

### are the mean and a random sample from the distribution defined 335 

### by David et al. (2006). Clewell et al. (2004) suggest scaling Q CC and alveolar ventilation as 336

### B

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### DRAFT- DO NOT CITE OR QUOTE 

### BW 0.75 

### , while David et al. (2006) used BW 0.74 

### . While virtually identical, it is noted that the 337 

### implementation here uses BW 0.75



### . 338

339


### B-4.6. Fat Fraction 340 

### Tabulated values from Clewell et al. (2004) sh ow indistinguishable va lues for the fraction 341 

### of BW as fat (VFC) for males and females up to 7 years of age, after which they diverge. 342 

### Polynomial functions were fit separately for 0–7 years, 7–20 years, and 20–80 years, with the 343 

### latter two ranges being gender specific, as s hown in Figure B-10. The ratio of the resulting 344 

### gender- and age-specific value to the VFC mean 

### from David et al. (2006) was then used to scale the 345 

### bounded normal distribution as specified by David et al. (2006) for the sel ected age and gender. 346

347 

### B-4.7. Liver Fraction 348 

### Tabulated values from Clewell et al. (2004) showed an interesting age dependence for the 349 

### liver fraction of BW, VLC, as shown in Figure B-11. While female VLC values were somewhat 350 

### higher than males between 18 and 40 years and the di fference may be statistically significant, the 351 

### overall variation is not large, the two were indistinguishable du ring other age-ranges, and model 352 

### predictions are not overly sensit ive to VLC. Therefore, only the male data were used. Smooth 353 

### functions were fit separately for 0-18 years a nd 18-80 years and used to scale the distribution 354 

### from David et al. (2006) for the given age, as was done for body fat. 355

### B

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### DRAFT- DO NOT CITE OR QUOTE 356



### y = 0.1612x 3 

### + 0.0846x 2 

### - 0.3083x + 0.2709

R 2 

### = 0.9984

0 0.05

0.1 0.15

0.2 0.25

0.3

0 0.2 0.4 0.6 0.8 

### Age (years/10)Fat fraction Male

Female Poly. (Female) 

### 0 to 7 years

357

y = -0.0057x 2 + 0.0293x + 0.1303
R 2 = 0.9941 y = -0.0458x 2 + 0.2082x + 0.0274
R 2 = 0.9942

0 0.05

0.1 0.15

0.2 0.25

0.3

0.6 0.85 1.1 1.35 1.6 1.85 2.1 

### Age (years/10)Fat fraction Female

Male



### 7 to 20 years

358

y = -0.0015x 2 + 0.0384x + 0.0908
R 2 = 0.8969 y = -0.0024x 3 + 0.0355x 2 - 0.115x + 0.3678
R 2 = 0.9922

0 0.1 0.2 0.3 0.4 0.5 0.6

1.5 3.5 5.5 7.5 9.5 Age (years/10)Fat fraction

Male Female



### 20 to 90 years

359 

### Figure B-10. Fraction body fat (VFC) over various age ranges in 360 

### males and females (data from Clewell et al. [2004]). 361 362 363
364

### B

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### DRAFT- DO NOT CITE OR QUOTE y = -0.0036x 2 - 0.0051x + 0.0395
R 2 = 0.8995

0 0.01 0.02 0.03 0.04 0.05

0 0.3 0.6 0.9 1.2 1.5 1.8 Age (years/10)Liver fraction

Male liver
Female liver Poly. (Male liver) 

### 0 to 17 years

365

y = -0.0004x 2 + 0.0034x + 0.0169
R 2 = 0.8984

0.018

0.02 0.022 0.024 0.026 0.028

123456789 

### Age (years/10)Liver fraction

Male liver

Female liver Poly. (Male liver) 

### 17 to 83 years

366 

### Figure B-11. Fraction liver (VLC) as a function of age (data from 367 

### Clewell et al. [2004]). 368 369 370 

### B-4.8. Tissue Volume Normalization 371 

### While not explicitly stated by David et al . (2006), total tissue volume must remain 372 

### roughly the same as a fraction of BW. While this fraction could also change with age, gender, 373


### and other characteristic s, it was assumed that any change in it would be modest and not 374 

### significantly affect mode l predictions, given the fairly broa d distribution implemented for total 375 

### BW, so this normalization was applied irrespective of those factors. Therefore, after drawing 376 

### sample values of the tissue volume fractions fr om each of their respective distributions, the 377


### fractions were then normalized to a total fraction of 0.9215, which is the sum of the mean values 378


### for the fractions for the distributions as desc ribed by David et al. ( 2006). The remaining body 379 

### mass is taken to be bone, teeth, hair, nails, and any other minimally or non-perfused components. 380

381 

### B-5. Summary of Revised Human PBTK Model 382 

### The resulting set of parameter distribution char acteristics, including t hose used as defined 383 

### by David et al. (2006), are descri bed in Table B-3. The paramete r statistics shown in Table B-3 384 

### are summary statistics of the converged paramete r chains obtained in that analysis, leaving out 385 

### any evaluation of correlation or covariance am ong the updated parameters. As such, these 386 

### statistics implicitly include both the inter-individu al variability that would have been elucidated 387

### B

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### DRAFT- DO NOT CITE OR QUOTE 

### by the Bayesian analysis (variation between mean values for each individual for which data were 388 

### available) and uncertainty in those values. Since EPA’s objective is to account for both 389 

### population variability and paramete r uncertainty, however, these stat istics can be used primarily 390 

### as-is (exceptions discussed prev iously) to define population dist ributions. Assuming that these 391 

### parameters are distributed indepe ndently, ignoring the covariance that was likely represented in 392 

### the actual posterior chains, will tend to over-estimate the overall range of parameters and hence 393


### distribution of dose metrics in the population, compared to what one would obtain if the 394 

### covariance were explicitly include d. Thus if the covariance (i.e ., the variance-covariance matrix) 395 

### for the set of parameters had been reported by Davi d et al., it could have been used to narrow the 396 

### predicted distribution of internal doses, or equivalent applied dos es, but lacking such information 397 

### the approach used will not under-estimate ri sk or over-estimate lower bounds on human 398 

### equivalent exposure levels. 399

400

401

402

403B-18DRAFT- DO NOT CITE OR QUOTE404Table B-3. Parameter distributions for the humanPBTK model for dichloromethane used by the EPADistributionParameterShape(Geometric)MeanaSD/GSDaLowerboundUpperboundSection or sourceBW Body weight (kg) Normalf (age, gender)1st%tile99th%tileB-4.3; NHANES IVFlow ratesQAlvC Alveolar ventilation (L/hr/kg0.75) Normalf (age, gender)f (age)5th%tile95th%tileB-4.4; mean: Clewell et al. (2004);SD: Arcus-Arth and Blaisdell (2007)vprvVariability in ventilation/perfusionratioLognormal 1.00 0.203 0.69 1.42 VPR/VPRmeanof David et al. (2006)QCC Cardiac output (L/hr/kg0.75) QCCmean=f(QAlvC) QCC = QCCmean/vprv B-4.5; Clewell et al. (2004) (mean)Fractional flow rates (fraction of cardiac output)QFC Fat Normal 0.05 0.0150 0.0050 0.0950QLC Liver Normal 0.26 0.0910 0.010 0.533QRC Rapidly perfused tissues Normal 0.50 0.10 0.20 0.80QSC Slow perfused tissues Normal 0.19 0.0285 0.105 0.276David et al. (2006);after sampling fromthese distributions, normalize:∑⋅=QjCQiCQCQiTissue volumes (fraction body weight)VFC Fat Normalf (age, gender)0.3·mean 0.1·mean 1.9·meanVLC Liver Normalf (age) 0.05·mean0.85·mea 1.15·meaVLuC Lung Normal 0.0115 0.00161 0.00667 0.0163VRC Rapidly perfused tissues Normal 0.064 0.00640 0.0448 0.0832VSC Slowly perfused tissues Normal 0.63 0.189 0.431 0.829Fat mean: B-4.6; (Clewell et al., 2004);Liver mean: B-4.7; (Clewell et al.,2004); otherwise David et al. (2006);after sampling from these distributions,normalize:∑⋅⋅=VjCViCBWVi9215.0Partition coefficientsPB Blood/air Lognormal 9.7 1.1 7.16 13.0PF Fat/blood Lognormal 11.9 1.34 4.92 28.7PL, PLu,& PRLiver/blood, lung/arterial blood, andrapidly perfused tissue/bloodLognormal 1.43 1.22 0.790 2.59PSSlowly perfused tissue(muscle)/bloodLognormal 0.80 1.22 0.444 1.46Geometric mean & GSD values listedhere, converted from arithmetic meanand SD values of David et al. (2006)B-19DRAFT- DO NOT CITE OR QUOTETable B-3. Parameter distributions for the humanPBTK model for dichloromethane used by the EPADistributionParameterShape(Geometric)MeanaSD/GSDaLowerboundUpperboundSection or sourceMetabolism parameters (based on Monte Carte calibration from 5 human data sets)VmaxcMaximum metabolism rate(mg/hr/kgXvmax)Lognormal 9.34 1.73 (none) (none)B-3; mean: David et al. (2006);GSD & bounds: Lipscomb et al. (2003);Xvmax = 0.88 for age <18;.Xvmax = 0.70 for age≥18.KmAffinity (mg/L) Lognormal 0.41 1.39 0.154 1.10A1 Ratio of lung VMax to liver VMax Lognormal 0.00092 1.47 0.000291 0.00292A2 Ratio of lung KF to liver KF Lognormal 0.0083 1.92 0.00116 0.0580FracRFractional MFO capacity in rapidlyperfused tissueLognormal 0.0152 2.0 0.00190 0.122Geometric mean & GSD values listedhere, converted from arithmetic meanand SD values of David et al. (2006)First order metabolism rate (/hr/kg0.3)Homozygous(–/–)Normal 0 0 0 0Heterozygous(+/–)Normal 0.676 0.123 0 1.291kfCHomozygous (+/+) Normal 1.31 0.167 0 2.145David et al. (2006)aArithmetic mean and standard deviation (SD) listed for normal distributions; geometric mean and geometric standard deviation (GSD) listed for lognormal distributions.

### C

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### APPENDIX C. RAT DICHLO ROMETHANE PBTK MODELS 405

406


### The critical studies and data chosen for derivation of a recommended RfD and RfC were 407 

### based on nonneoplastic liver lesion s in rats inhaling dichlorometh ane for 2 years (Serota et al., 408 

### 1986a; Nitschke et al., 1988a). For this reason, a PBTK model for inhaled and orally absorbed 409 

### dichloromethane in rats was needed to provide estimates of internal dosimetry for dose-response 410 

### modeling and to extrapolate intern al liver doses from rats to humans. Several deterministic 411


### PBTK rat models have b een reported in the scientific litera ture (Sweeney et al., 2004; Andersen 412 

### et al., 1991; Reitz, 1991; Reitz et al., 1988a, b; U.S. EPA 1988b, 1987a , b; Andersen et al., 1987; 413 

### Gargas et al., 1986). Unlike in the mouse st udy (Marino et al., 2006), however, no probabilistic 414 

### models are available in which the uncertainty in model parameters was reduced by utilizing 415 

### multiple data sets for parameter estimation. Rat data were not available that would allow for 416 

### MCMC calibration of individual metabolic para meters for the CYP or GST pathways. For 417 

### example, the MCMC calibration of the m ouse model (Marino et al., 2006) relied on 418 

### dichloroethylene inhalation data (not available for the rat) to specifically estimate GST 419 

### metabolism in isolation of CYP metabolism, th ereby improving the estimate of metabolic flux 420 

### through the competing pathways. Thus, the selected model include s parameter values estimated 421 

### by deterministic methods only. In order to use th e latest data for dichlo romethane toxicokinetics 422 

### in rats, an assessment was conducted of multiple rat models (or modified versions of those 423 

### models) to select the most appropriate model fo r use in the derivation of RfDs and RfCs. 424

425 

### C-1. Methods of Analysis 426


### C-1.1. Selection of Evaluation Data Sets and PBTK Models 427 

### We reviewed published studies of dichlorome thane metabolism and toxicokinetics in rats 428 

### to identify data sets for use in model evalua tion and possible calibrati on. Toxicokinetic data 429


### were available for: 430



### • blood levels of dichloromethane, the percent saturation of hemoglobin as 431 

### carboxyhemoglobin (%COHb), and expired dichloromethane and CO following 432 

### intravenous injection (Angelo et al., 1986b); 433



### • dichloromethane air concentrations in closed chamber experiments (Gargas et al., 1986); 434



### • dichloromethane and %COHb blood levels during and after a 4-hour open chamber 435 

### (constant concentration) inhalation e xposure (Andersen et. al., 1991, 1987); 436



### • %COHb levels from 30 min to 12 hours post- exposure following a single oral dose of 437 

### 526 mg/kg (6.2 mmol/kg in Oleum pedum ta uri vehicle) (Pankow et al., 1991); 438



### • Cumulative dichloromethane (mg) expired up to 96 hours following gavage doses of 250, 439 

### 500, 1000, or 2000 mg/kg in corn oil or water (Kirschman et al., 1986). 440

### C

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### Four variations on the PBTK models of Anderson e al. (1987, 1991) (Figure 1) were 441 

### assessed for the ability to predict these data. In each model, metabolism involves two competing 442 

### pathways: the GST pathway, described with a li near first-order kinetic model, and the CYP 443 

### pathway, described with a saturable Michaelis-Menten kinetic model. 444


### Model A is a hybrid of Andersen et al. (1987) and Anderson et al. (1991), in that it 445 

### included both CO production resulting from CY P-mediated metabolism (Andersen et al., 1987) 446 

### and lung metabolism of the parent compound (And ersen et al., 1991). Anderson et al. (1987) 447 

### based the lung-to-liver metabolism ratios (A1 a nd A2 for CYP- and GST-mediated metabolism, 448 

### respectively) on the reaction rate s reported by Lorenz et al. (1984) . However, for model A we 449 

### used the Bayesian-calibrated va lues of A1 and A2 for mice (Marino et al., 2006), which were 450 

### identified using in-vivo data that distinguished CYP and GST metabolism. Specifically, we 451 

### sought to determine if use of th ese mouse-calibrated ra tios would improve the model fit to the rat 452 

### data. With A1 and A2 fixed, the model was fit to data by adjusting the parameters for CYP and 453


### GST metabolism in the liver: V max 

### c (allometrically-scaled maximum rate of CYP metabolism), 454



### K m 

### (CYP metabolic saturation constant), and k f 

### c (allometrically-scaled first-order GST rate 455 

### constant). 456



### Model B is the model of Andersen et al. ( 1991) using the same published values for 457



### V max 

### c, K m 

### and GST, but lung metabolism is nullified. 458


### Model C is the model of Andersen et al. (1991) with lung metabolism defined as 4% and 459


### 14% of CYP- and GST-mediated liver metabolit e production, respectively.. The lung-to-liver 460 

### metabolic ratios (A1 (CYP) and A2 (GST)) were estimated from in-vitro metabolic activity 461 

### measured in lung and liver cytosolic (GST) and mi crosomal (CYP) tissue fractions (Reitz et al., 462 

### 1989). V max 

### c, K m 

### , and k f 

### c were unchanged from Andersen et al. (1991). 463



### Model D is Model C with V max 

### c, K m 

### , and k f 

### c re-optimized against: the inhalation gas 464 

### uptake data of Gargas et al. ( 1986; closed-chamber gas-uptake data); the inhalation data of 465 

### Andersen et al. (1987; blood di chloromethane levels); and th e 24-hour cumulative exhalation 466 

### data of Angelo et al. (1986; e xhaled breath dichloromethane and CO levels, oral exposures). The 467 

### latter data, which were insensitive to the or al absorption constant (because absorption and 468 

### excretion are essentially comple te by 24 hours) were used to inform the balance between GST469


### mediated and CYP-mediated metabolism. 470

471

### C

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473

474

475

476

477

478

479

480

481

482

483

484
485
486 

### Figure C-1. Schematic of the And ersen et al. (1991) PBTK model 487 

### (model B) for dichloromethane (DCM ) in the rat. Models A, C, 488 

### and D also included metabolism in the lung tissue compartment 489 

### via glutathione transferase (GST) and cytochrome P450 (CYP). 490 491 

### C-1.2. Analysis 492 

### Models A-D were implemented using the acs lXtreme simulation software (version 2.4, 493 

### The Aegis Technologies Group, Huntsville, AL). Parameter estimation was performed using the 494 

### Nelder-Mead algorithm as implemente d in the acslXtreme package. 495 

### After completing the calibra tion for model D as described above, the oral absorption 496 

### constant, k a 

### , was then estimated by optimizing it, with al l other parameters held constant, to the 497 

### oral pharmacokinetic data of Angelo et al. (1986): blood and liver dichloromethane levels, 498 

### expired dichloromethane levels (all time-points) and expired CO levels (24-hour cumulative). 499 

### Only the 24-hour cumulative CO exhalation da ta were used to inform the estimate k a 

### (earlier 500 

### time-points not used) for reasons explained below. Two other da ta sets of gavage exposures 501 

### (Pankow et al., 1991; Kirschman et al., 1986) were also used in the evaluation of k a 

### . 502 

### In order to obtain reasonable fits, the assu med error in observations of blood and liver 503


### dichloromethane was modeled as absolute (e.g., unrelated to the magnitude of the response 504 

### variable). If the assumed error for these variable s was modeled as relative, then the fit to a few, 505 

### low blood concentration data points disproportiona tely impacted the outcome, resulting in poor 506 

### fits to the higher blood concentration data. The pa rameter values for each of the four models are 507 

### given in Table C-1. Differences between observe d values and predictions of uptake, blood and 508 

### liver concentrations, and expiration of dich loromethane from Models A–D were assessed 509 Blood Alveolar air

Endogenous production Gas exchange Lung

Fat

Richly perfused

Slowly perfused

Liver

CYP GST CO sub model

### C

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### DRAFT- DO NOT CITE OR QUOTE 

### visually. 510

511 512 513 

### Table C-1. Parameter values used in rat PBTK models

Parameter Model A Model B Model C Model D

Flow rates QCC (L/hour-kg 0.74 

### ) 15.9

VPR 0.94 Same as Model A

Fractional flow rates (percent of QCC)

Fat 9
Liver 20 Rapidly perfused tissues 56 Slowly perfused tissues 15 Same as Model A

Tissue volumes (percent BW)

Fat 7
Liver 4 Lung (scaled as BW 0.99 

### ) 1.15 Rapidly perfused tissues 5 Slowly perfused tissues 75 Same as Model A

Partition coefficients
Blood:air 19.4
Fat:blood 6.19

Liver/blood 0.732
Lung/arterial blood 0.46
Rapidly perfused tissue/blood 0.732
Slowly perfused tissue (muscle)/blood 0.408 Same as Model A

Metabolism & absorption parameters

Maximum metabolism rate, V
max 

### (mg/hour-kg 0.7 

### ) 6.21 4.0 4.0 3.93
Affinity, K
m



### (mg/L) 0.23 0.4 0.4 0.524
Ratio of lung V
max 

### to liver V
max 

### , A1 0.21 Not applicable 0.04 0.04
Ratio of lung KF to liver KF, A2 0.20 Not applicable 0.14 0.14

1 st 

### order metabolism rate (liver), 

### k f 

### C (1/hour-kg 0. 3 

### 2.89 2.0 2.0 2.46

Oral absorption constant, 

### k a 

### (1/hour) 5.0 5.0 4.0 1.80

514

515


### C-2. Results 516


### Four versions of the PBTK model for dichlo romethane (Models A-D) were evaluated for 517 

### goodness of fit to a common set of pharmacokinetic da ta. The oral absorpti on constant (ka) was 518 

### then calibrated using oral PK data for the model that was deemed best. 519

### C

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### C-2.1. Evaluation of Model Structure for De scription of Carboxyhemoglobin Levels 521 

### The model of Andersen et al. (1991) ca lculated the amount of COHb by assuming 522 

### instantaneous equilibration between the free CO and hemoglobin (Hb)-bound CO in the blood. 523 

### However, when Angelo et al. (1986b) observed the kinetics of dichloromethane in blood and 524


### exhaled dichloromethane and CO after or al administration, th ey found that blood 525 

### dichloromethane peaked rapidly, in less than 30 minutes. The rate of exhalation of 526 

### dichloromethane, shown by the rate of rise in the exhaled dichlo romethane data in Figure C-2A, 527 

### where the plateau in exhaled dichloromethan e levels from 6-24 h, indicates that blood 528 

### dichloromethane levels had dropped to nearly zero by 6 hours. Exhaled CO, however, shows a 529 

### more gradual rise than dichloromethane, c ontinuing between 6 and 24 hours. Pankow et al. 530 

### (1991) measured COHb in blood after dichlorome thane exposure, albeit at a higher dose (526 531 

### mg/kg versus 200 mg/kg by Angelo et al.), and f ound that blood COHb levels did not peak until 532 

### 6 h after dosing (Figure C-2B), while Angelo et al. found that blood dichloromethane had 533 

### declined to ~ 4% of the peak level (10-minute concentration) by 6 hours. Some delay between 534 

### peak dichloromethane and peak (cumulative) CO levels is predicted by the published (1991) and 535 

### modified (Models A and D) Andersen et al. models, because CO is produced from oxidative 536 

### dichloromethane metabolism. However, we found that none of the PBTK models could 537 

### simultaneously describe both the very short-tim e peak in blood dichloromethane with the 538 

### concurrent rapid rise in exhale d dichloromethane levels (Figur e C-2A) and the late peak blood 539 

### CO (Figure C-2B) and slower rate of CO exhalati on (Figure C-2A). The final models were able 540 

### to adequately predict dichloromethane exhalati on data and dichloromethane blood concentration 541 

### data, but over estimated CO exhalation data (the se findings are illustrated for Model D later in 542 

### the results section). 543 

### We hypothesized that the models’ inability to simultaneously fit these data might 544 

### specifically be due to the assumption of instan taneous equilibrium between free CO and COHb 545 

### in blood. The model structure (set of equations) characterizes the kinetics of free CO and COHb 546 

### as being at instantaneous equilibrium with one another, so that the kinetics of COHb are 547 

### completely determined by the predicted rate of appearance and elimination of CO. The rate of 548 

### production of CO is in turn determined by the rate of metabolism of dichloromethane and the 549 

### rate of elimination of CO is determined by physio logical parameters. Therefore, we believe that 550 

### the only way to alter the predicted time-course of COHb with this model structure and these 551 

### assumptions is to alter the rate of dichloromethane metabolism, which would degrade the fit to 552 

### the dichloromethane data, or to alter physiological para meters which are well established (based 553 

### on direct measurements). If this hypothesis is correct, or there is some other model-structure 554 

### error involved, then adjusting physiological parame ters in an attempt to fit the CO exhalation 555 

### would only cover up the error and reduce the model’ s reliability in predicting other aspects of 556 

### dosimetry, particularly the pharmacokinetics of dichloromethane and its rates of metabolism, 557

### C

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### DRAFT- DO NOT CITE OR QUOTE 

### which are critical to the assessment. In summar y: it appears that given the model structure and 558 

### assumptions, one cannot explain the differen ce between the exhalation time-courses for 559 

### dichloromethane and CO/COHb shown in Figure C-2 while retaining realistic physiological 560 

### parameters; that difference might be explained if th e release of CO from C OHb is treated as rate561 

### limiting, in which case the two woul d not be in rapid equilibration. 562

563

564 

### Figure C-2. A: Observations of ex haled 14C-labelled dichloromethane 565 

### (DCM, left y-axis) and carbon monoxide (CO, right y-axis) after a bolus 566 

### oral dose of 200 mg/kg 14C-DCM in ra ts (data of Angelo et al., 1986b). 567 

### Points are data collected on first, seventh, or fourteenth days of 568 

### exposure. Lines are plotted through average of days 1, 7, and 14 data. 569 

### (Arrows indicate corresponding y-axi s.) B: Blood carboxy-hemoglobin 570 

### (COHb, percent of total hemoglobin) from a single gavage dose of 526 571 

### mg/kg DCM in rats (Pankow et al., 1991). 572
573

574 

### 0 10 20 30 40 50 60 70 80

0 6 12 18 24% exhaled as DCM



### 0 2 4 6 8 10% exhaled as CO

DCM, day 1 DCM, day 7 DCM, day 14 CO, day 1 CO , day 7 CO, day 14 

# A

0 2 4 6 8 10

0 6 12 18 24
Time (hr)% COHb 

# B

### C

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### DRAFT- DO NOT CITE OR QUOTE 

### While the analysis of the CO and COHb data above led us to the conclusion that the 575 

### purpose of the model for chronic-exposure risk as sessment was best served by not using some of 576 

### those data (i.e., the short-term dichloromethan e and CO exhalation data), the combination of 577


### dichloromethane concentration da ta in various compartments as described below (primarily 578 

### blood and air) and the more limited long-term CO e xhalation data were considered adequate and 579 

### necessary to estimate model parameters for di chloromethane. Some comparisons of model 580 

### predictions to the CO/COHb data are shown, however , to both further illustrate the discrepancy 581 

### in kinetics and the agreement in mass balance. If the model was to be used for evaluation of the 582 

### risk of acute carbon-monoxide exposure, then it would be necessary to demonstrate adequate 583 

### reproduction of the COHb data in particular. 584

585


### C-2.2. Evaluation of Prediction of Uptake, Blood and Liver Concentrations, and Expiration of 586


### Dichloromethane 587


### One data set used for model calibration is th e closed chamber experiments of Gargas et 588 

### al. (1986) (100–3000 ppm dichloromethane). Because actual chamber concentrations can differ 589 

### from target concentrations, a simple exponential f unction was fit to the first four data points at 590 

### each concentration and used to extrapolate back to an actual initial conc entration. The initial 591


### concentrations so obtained and used in th e modeling were 107, 498, 1,028, and 3,206 ppm versus 592 

### the target concentratio ns of 100, 500, 1,000, and 3,000 ppm. Pred ictions from models B, C, and 593


### D all fit the observed chamber air concentratio ns reasonably well and not iceably better than 594 

### model A, but model D is clearly superior to mode l C and is slightly but noticeably better than 595 

### model B (Figure C-3; comparing model B and D fits to the 1000 and 3000 ppm data). Model A 596


### did not simulate the observed break between linear metabolism at 100 ppm and partially 597 

### saturated metabolism at 300 ppm, ostensibly beca use of the higher value for maximal rate of 598 

### CYP metabolism, V max 

### c (6.21 mg/h/kg 0.7 

### in model A), which preven ted saturation of oxidative 599 

### metabolism at these exposure levels. Use of V max 

### c ~ 4 mg/h/kg 0.7 

### in models B, C, and D (Table 600 

### 1) provides a much better fit to the metabolic saturation patter n across all exposure levels. 601 

### Model simulations were next compared to measurements of blood dichloromethane 602


### concentration after intrav enous (IV) injections of 10 or 50 mg/kg (Angelo et al. , 1986b). IV data 603 

### are considered particularly usef ul for evaluating metabolic rate constants because the kinetics 604 

### and complexity of uptake by othe r routes of exposure have been bypassed. All four models 605


### matched the observed blood dichloromethane levels fairly well following IV injections of 10 or 606 

### 50 mg/kg (Figure C-4): simulations are within a f actor of 3 of the data (often closer) and the 607 

### slope of the model curve after the initial distributi on phase (~ 5 min) appears close to that of the 608 

### data. However, each model over-predicted the obse rved data to some extent, particularly the 50 609 

### mg/kg dose, which over-predicts all of the observed data points, whereas the 10 mg/kg 610 

### simulation matches the first three data points well. Because the y axis in Figure C-4 is plotted on 611 

### a log scale, the discrepancy between model simula tions and the 50 mg/kg data appears similar to 612

### C

- 8 

### DRAFT- DO NOT CITE OR QUOTE 

### the discrepancy vs. the 10 mg/kg data at later ti me points, but in fact is much larger in an 613


### absolute sense. 614

615

616

617

618



### Figure C-3. Observations of Gargas et al. (1986; data) and predictions 619 

### (models A-D) for respiratory uptake by 3 rats of 100-3000 ppm 620 

### dichloromethane in a 9-L closed chamber. 621

### C

- 9 

### DRAFT- DO NOT CITE OR QUOTE 622



### Model A

0.1

1 10 100 1000

0 10 20 30 40 Time (min)Blood DCM (mg/L) 10 mg/kg data 50 mg/kg data 10 mg/kg sim 50 mg/kg sim



### Model B

0.1

1 10 100 1000

0 10 20 30 40 Time (min)Blood DCM (mg/L)

623



### Model C

0.1

1 10 100 1000

0 10 20 30 40 Time (min)Blood DCM (mg/L)



### Model D

0.1

1 10 100 1000

0 10 20 30 40 Time (min)Blood DCM (mg/L) 10 mg/kg data 50 mg/kg data 10 mg/kg sim 50 mg/kg sim 5 mg/kg sim 25 mg/kg sim

624 

### Figure C-4. Observations (points) of Angelo et al. (1986b) and predictions 625 

### (curves, denoted “sim” in legend) for Models A-D. Legend for models B and 626 

### C is the same as for model A. Data are for dichloromethane (DCM) in blood 627 

### following 10 and 50 mg/kg iv injection in rats. Model predictions with doses 628 

### at 5 and 25 mg/kg, are shown for compar ison as thinner lines for model D. 629
630 

### The most likely explanation for the over-prediction of all four models vs. the 631 

### experimental observations in Figure C-4 is some shortcoming in model structure or parameter 632 

### specification (e.g., the model lacks an explicit blood-volume compartment, which could 633 

### significantly impact predictions for this particul ar route of administra tion). The discrepancy 634 

### appears to exist primarily in the initial distribu tion phase, as the slope of the simulated clearance 635 

### curve closely matches that of the data beyond 10 min (0.17 h). The error was further evaluated 636 

### by running simulations with doses resulting in bl ood dichloromethane levels that more closely 637


### matched the data at the end of the initial di stribution phase. These simulations can indicate 638 

### whether the error is just in the initial distri bution phase (primarily impacted by the tissue639 

### compartment volumes and partition co efficients) or is also in the rate of clearance by metabolism 640 

### and other mechanisms (which determine the sl ope of the curve at later times. When the 641


### simulated administered doses were decreased 50% (i.e., to 5 and 25 mg/kg), the model 642

### C

- 10 

### DRAFT- DO NOT CITE OR QUOTE 

### predictions under-predicts the ear ly time-point data but clos ely matched the data from 10 643 

### minutes on, as shown in the model D panel of Figu re C-4 (thin simulation curves). This result 644 

### gives further confidence in the model’s ability to predict dosimetry over longer periods of time, 645 

### if not immediate following IV exposures, which is appropriate since we are concerned with 646 

### correctly predicting dosimetry from chronic exposures. 647 

### Predictions of percent dichloromethane dos e expired as dichloromethane were also 648 

### similar between all models simulating IV injec tions of 10 or 50 mg/kg (Figure C-5), but with 649 

### notable differences. Model D predictions for the 10 and 50 mg/kg exposure were higher than 650 

### both the data and the predictions of the other models, but all models over-predict the amount 651 

### exhaled from the 50 mg/kg dose. As with the s imulations of blood concen trations (Figure C-4), 652


### the fit of model D to the 653

654



### Model A

0 15 30 45 60 75

01234 Time (h)Blood DCM (mg/L)

10 mg/kg data 50 mg/kg data 10 mg/kg sim 50 mg/kg sim 

### Model B

0 15 30 45 60 75

01234 Time (h)Blood DCM (mg/L)

655



### Model C

0 15 30 45 60 75

01234 Time (h)Blood DCM (mg/L)



### Model D

0 15 30 45 60 75

01234 Time (h)Blood DCM (mg/L)

10 mg/kg data 50 mg/kg data 10 mg/kg sim 50 mg/kg sim 5 mg/kg sim 25 mg/kg sim

656



### Figure C-5. Observations of Angelo et al. (1986b) and predictions (curves, 657 

### deonoted “sim” in legend) for models A-D, following 10 and 50 mg/kg iv 658 

### injection in rats. Legend for models B and C is the same as for model A. 659 

### Data are percent of dichloromethane (DCM) dose expired as 660 

### dichloromethane. Model prediction s with doses at 5 and 25 mg/kg, are 661 

### shown for comparison as thinner lines for model D. 662

### C

- 11 

### DRAFT- DO NOT CITE OR QUOTE 

### % dichloromethane exhaled data is significantly improved if the simulated doses are reduced by 663 

### 50% (model D panel of Figure C-5), suggesting that the primary error is in the model’s ability to 664 

### describe the initial distribution, ra ther than longer-term clearance. 665 

### Simulations were then run to match the open- chamber inhalation data of Andersen et al. 666 

### (1987), as shown in Figure C-6. Despite r easonably good fits for dichloromethane blood 667 

### concentrations during 4-hour inhalation phases of exposure to 200 or 1000 ppm, none of the 668 

### models fit the post-exposure cl earance phase of the inhalatio n-exposure blood dichloromethane 669 

### data very well, especially th e 1000 ppm data (Figure C-6). Mo del C describes the 200 ppm data 670 

### quite well, but the improvement versus models B a nd D is not sufficient to offset the lack-of-fit 671 

### by model C to the closed-chamber uptake data. Models B, C, and D all over-predict the blood 672 

### concentration for 200 ppm exposur e during the 4-h exposure period. 673 

### As with the IV exposures where the initial distribution phase wa s over-predicted, we 674 

### sought to determine if adjusting the uptake portio n of the model simulation would also improve 675 

### the model’s fit to the later data. Running a si mulation with the exposure concentration set to 150 676 

### ppm (Figure C-6, model D, thin dashed lines ) gives much better agreement with both the 677 

### inhalation phase (time ≤ 4 h) and clearance phase (time > 4 h) 200 ppm data. It seems unlikely 678 

### that such a large erro r in the exposure concentration woul d have occurred, but the simulation 679 

### result suggests that there was some alteration in uptake conditions. For example, rodents are 680 

### capable of reducing their respira tion rate when exposed to irrita nt gases, thereby reducing the 681 

### rate of inhalation. In the absence of plet hysmography and aninmal handling data for the 682 

### Andersen et al. (1987) experiment s. we perfomed alternate simu lations with the cardiac output 683


### and alveolar ventilation reduced by 38% (the two rates are held in constant proportion), which 684 

### provide a better fit to the data up to 4 h (duri ng exposure), quite similar to the resu lt shown in 685 

### Figure C-6 for model D with 150 ppm, while the clea rance after 4 h was slower than indicated by 686 

### the experimental observations (not shown). It is also possible th at the rats reduced respiration 687 

### and cardiac output due to the e xposure and resumed breathing at a normal rate once the exposure 688 

### ended. Experiments in which respiration rates are monitored could be conducted to test that 689 

### hypothetical explanation. However, none of our attempts to simulate reduced cardiac output and 690 

### respiration during exposure, with or without a return to normal ra tes at 4 h, matched the data as 691 

### well as the 150 ppm exposure with default card iac and respiration rate s throughout (shown in 692 

### Figure C-6). 693

694

### C

- 12 

### DRAFT- DO NOT CITE OR QUOTE 

### Model A

0.01

0.1

1 10 100

0123456 Time (h)Blood DCM (mg/L)

200 ppm data 1000 ppm data 200 ppm sim 1000 ppm sim 

### Model B

0.01

0.1

1 10 100

0123456 Time (h)Blood DCM (mg/L)

695



### Model C

0.01

0.1

1 10 100

0123456 Time (h)Blood DCM (mg/L) 

### Model D

0.01

0.1

1 10 100

0123456 Time (h)Blood DCM (mg/L)

200 ppm data 1000 ppm data 200 ppm sim 1000 ppm sim 150 ppm sim

696


### Figure C-6. Observations of Anders en et al. (1987; data points) and 697 

### simulations for models A-D (curve s, denoted “sim” in legend) for 698 

### dichloromethane (DCM) in rat blood from inhalation of 200 and 1000 ppm 699 

### DCM for 4 hours. Model prediction for a 150 ppm exposure (thin dashed 700 

### line) also shown for comparison. 701
702 

### The results with the 150 ppm simulated expos ure show that if the inhalation portion of 703


### the blood dichloromethane concentration curve for the 200 ppm exposure is correctly matched, 704 

### the clearance phase simulation (determined in large part by the metabolic parameters) then 705 

### matches those data well with cardiac output and resp iration at their default rates. However, since 706 

### the model simulation already matches the uptake portion of the data for the 1000 ppm exposure, 707 

### it appears that the rate of meta bolic (or other) clearance predic ted by the model is slower than 708 

### exhibited in that experiment. Thus the model appears to over-estimate the degree of metabolic 709 

### saturation (hence under-predic ting metabolism) at that higher exposure level. 710

711


### C-2.3 . Evaluation of Relative Flux of CYP an d GST Metabolism of Dichloromethane 712


### The relative flux of dichloromethane meta bolism through the CYP or GST pathways 713 

### during simulated chronic, medium-, and hi gh-level inhalation exposures of rats to 714

### C

- 13 

### DRAFT- DO NOT CITE OR QUOTE 

### dichloromethane is shown in Table C-2. Specifi cally, models B and D were considered as the 715


### best models with zero or non-zero lung metabolism, respectively, so the impact of this structural 716


### difference could be evaluated. Chronic e xposures of 200, 1000, 2000, and 4,000 ppm, reflecting 717 

### exposures used by Andersen et al. (1991) and NTP (1986), were simulated. 718

719 

### Table C-2. Effect of PBTK model configurat ion on predicted dichloromethane metabolite production in the liver of (male) rats from inhalation exposures *



### Model Configuration Exposure (ppm) CYP-mediated metabolite production (mg/L liver/d) GST-mediated metabolite production (mg/L liver/d) Total metabolite production (mg/L liver/d) CYP:GST metabolite production ratio

B 200 584 58 642 10 D 200 559 72 631 7.8

B 1000 813 549 1361 1.5 D 1000 785 646 1431 1.2

B 2000 830 1259 2089 0.66 D 2000 806 1502 2308 0.54

B 4000 892 2610 3502 0.34 D 4000 866 3111 3977 0.28

* 

### Inhalation exposures of 200 or 1000 ppm for 4 h/day (Andersen et al., 1991) or 2000 or 4000 ppm dichloromethane for 6 h/day, 5 days/week, for 2 years (National Toxicology Program, 1986)

720 

### Only a modest difference was seen in predicted dosimetry between models B and D. 721 

### Model B predicted 15-20% less GST metabolite production and 3-4% more CYP metabolite 722 

### production in the liver than model D, dependi ng on the exposure level (Table C-2). Total 723 

### metabolite production was predicted to be 2% hi gher by model B versus model D at 200 ppm, 724


### but 12% lower by model B versus model D at 4,000 ppm (T able 2). This occurs because model 725 

### D has a higher GST-rate constant (k f 

### C) than model B (2.46 versus 2.0/h/kg 0.3 

### ; Table 1), as well 726 

### as lung metabolism that is absent in m odel B, both of which reduce the amount of 727


### dichloromethane available for CYP metabolism. For both models, CYP-mediated metabolism 728 

### dominated during the 200 ppm exposur e, yielding 8- to 10-fold the metabolic production of GST. 729 

### However, GST-mediated metabolism domina ted at 2000 and 4,000 ppm , with CYP accounting 730 

### for approximately 1/3 of total metabo lite production during the 4,000 ppm exposure. 731 

### Finally, to elucidate the exposure ranges wher e internal doses are linear and where CYP 732 

### metabolism saturates, weekly average CYP and GST liver metabolic rates were simulated for 733 

### models B and D over a wide span of inhalation c oncentrations, given a regimen of 6 h/d, 5 d/wk, 734 

### with results shown in Figure C- 7. Metabolite production is predic ted to be approximately linear 735 

### up to ~ 100 ppm for both models. CYP metabolis m becomes mostly saturated between 100 and 736

### C

- 14 

### DRAFT- DO NOT CITE OR QUOTE 

### 1000 ppm, but is not fully saturated even at 2000 ppm. CYP metabolism is not predicted to be 737 

### fully saturated by 2000 ppm because exposures are only 6 h/d and blood concentrations fall 738 

### quickly after each exposure. Since dichlorome thane blood levels still increase with exposure 739 

### level during that exposure-off period (because th ey are higher at the end of the exposure-on 740 

### period), there is also increased metabolite produc tion with exposure level during these exposure741 

### off periods. However, the increase in metabolis m per exposure-ppm is much less in this high742 

### concentration range than occurs in the lower- concentration range because blood concentrations 743 

### are falling throughout the exposure-off period. Depending on the model, GST metabolism is 744 

### predicted to become domi nant above 1100-1300 ppm. 745

746

747 748 

### Figure C-7. Simulation results using models B and D for weekly average 749 

### metabolic rates by the GST and CYP pathways, for 6 h/d, 5 d/wk inhalation 750


### exposures. 751
752 

### Thus, models B and D have been shown to perform similarly in matching the inhalation 753 

### and IV data (see above), with model D fitting the closed chamber inhalation data slightly better 754 

### (Figure C-3), and in predicting liver metabo lism (Table C-2). Since model D has the 755 

### mechanistic advantage of explicitly describing me tabolism in the lung, model D is considered the 756


### better of the two models for the purpose of predicting dichloromethane dosimetry in rats. (As 757 

### discussed above, model D is also clearly superior to models A a nd C.) Therefore, only model D 758 

### was considered in the subsequent parameter estimation and evaluation of oral dosimetry. 759

760

### C

- 15 

### DRAFT- DO NOT CITE OR QUOTE 

### C-2.4

. Evaluation of Model Predictions of Or al Absorption of Dichloromethane 761 

### For model D, the absorption constant, k a 

### , was numerically fit to the data for blood and 762 

### liver dichloromethane levels, and total expired dichloromethane and CO levels, as measured by 763 

### Angelo et al. (1986b) for rats exposed to gava ge doses of 50 and 200 mg/kg dichloromethane. 764 

### Resulting fits to data are shown in Figure C-8 (n ote that for the % expired as CO, panel C, the 765


### percentage declines with increased concentra tion, balancing the increased percentage exhaled as 766 

### dichloromethane). The optimized value of k a 

### = 1.8/h resulted primarily from fitting to 767 

### dichloromethane data, as it was optimized agains t three dichloromethane data sets (blood, liver, 768 

### and exhaled dichloromethane) and only one exhaled COHb data set. With k a 

### = 1.8/h, model 769 

### simulations match fairly well the blood and liver dichloromethane levels (Figure C-8, panels B 770 

### and D), percent dose expired as dichloromethane (F igure C-8A), and percent expired as CO from 771 

### a 200 mg/kg dose (24-h cumulative value, Figure C-8C, lower curve and data). As discussed 772 

### previously, the model’s inability to describe the shorter-term CO ki netics (Figure C-8C, time ≤ 6 773 

### h) is believed an artifact of assuming rapid equilibrati on between free and hemoglobin-bound 774


### CO in the blood. 775

776

777

778


### Figure C-8. Observations of Angelo et al. (1986b; data) and model D 779 

### predictions for: (A) percent dose ex pired as dichloromethane (DCM); (B) 780 

### blood dichloromethane; (C) percent ex pired as carbon monoxide; and (D) 781 

### liver dichloromethane in rats given a single dichloromethane gavage doses of 782 

### 50 and 200 mg/kg, using a numerically fitted GI absorption rate (ka) of 1.8 h783 

### 1. 784
785

### C

- 16 

### DRAFT- DO NOT CITE OR QUOTE 

### However, the predicted percent expired as CO following 50 mg/kg is considerably higher 786 

### than the observed data even at 24 h (Figure C-8C , upper curve and data). The predicted levels in 787 

### liver are a bit higher than the observed liver concentrations (F igure C-8D), and the predicted 788 

### peak in blood concentration occurs somewh at later than observed (Figure C-8B). 789 

### Pankow et al. (1991) measured blood COHb following a gavage dose of 525 mg/kg, data 790 

### which might also be used to estimate k a 

### . When k a 

### was re-optimized against those data (other 791 

### parameters as in model D), the resulting va lue was 0.47/h. Model simulations with both k a 

### = 792 

### 0.47/h and the previously-estimated value of 1.8/h are compared to the COHb data of Pankow et 793 

### al. (1991) in Figure C-9. Wh ile the lower value of k a 

### (0.47/h) provides a be tter fit to the COHb 794


### data than simulations with k a 

### = 1.8/h, the model still does not captu re the more gradual rise to a 795 

### peak and subsequent decline in the data. Moreover, with k a 

### = 0.47/h the peak blood and liver 796


### concentrations of dichloromethan e after oral gavage are predicte d to occur at just beyond 1 h 797 

### after exposure (not shown), which is completely inconsistent with the data of Angelo et al. 798 

### (1986). Therefore, as discussed previously, the in ability to describe the kinetics of COHb (with 799


### k a 

### = 1.8/h) is ascribed primarily to the assumpti on of a rapid equilibrium between free and Hb800 

### bound CO in the blood: the more gradual rise and fall shown by the data in Figure C-9 could be 801 

### better predicted if the binding and rel ease steps were treated as rate-limiting. 802

803

804 

### Figure C-9. Model predictions with of blood carboxy-hemoglobin (COHb, 805 

### percent of total Hb) from a single g avage dose of 526 mg/kg dichloromethane 806 

### (DCM) in rats, compared to the data of Pankow et al. (1991). Model 807 

### simulations performed with model D (h eavy line, ka = 1.8/h) or with an 808 

### alternate value of ka = 0.47/h, fit to these data. 809
810 

### Despite the preceding conclusion regarding CO-Hb binding, the difference in vehicle 811 

### (Pankow et al., 1991, used Oleum pedum tauri while Angelo et al., 1986, used water with PEG) 812 

### likely also explains part of the differe nce in kinetics between the respective experiments. The 813

### C

- 17 

### DRAFT- DO NOT CITE OR QUOTE 

### kinetics of orally administered dichloromethan e with the water/PEG vehicle are shown in Figure 814 

### C-8, with the blood concentration al ready clearly falling from the p eak at 30 min (panel B) and 815


### most of the dichloromethane exhalation bei ng complete by 4 h (panel A). If such 816


### dichloromethane kinetics had occurre d in the experiments of Pankow et al., it seems unlikely that 817


### COHb levels would continue to rise between 4 and 6 h. In s hort, the late peak of COHb 818 

### observed by Pankow et al. strongly indicates that absorption was slower (a lower value of k a 819 

### applies) and hence the dichloromethane peak was also later in those experiments than observed 820 

### by Angelo et al.; such a shift could well result from the use of a much more lipophilic vehicle by 821


### Pankow et al. But the failure to describe the COHb kinetics when the value of k a 

### was reduced, as 822 

### discussed just above (Figure C-9) , indicate that changes in absorp tion rate due to vehicle do not 823 

### completely explain discrepancies between m odel predictions and th e observed data. 824 

### Model D simulations of total expired di chloromethane for oral doses with k a 

### = 0.47 or 825 

### 1.8/h are compared to the data of Kirschman et al. (1986) in Table 3. Agreement with the data 826


### for water vehicle at 250 and 500 mg/kg, for the model with k a 

### = 1.8/h, is quite good, especially 827 

### considering that in rats at 500 mg /kg, Kirschman et al. only acc ounted for 74% of the dose in all 828 

### excreta (breath, urine, and feces). The lack of mass balance by Kirschman et al. (normal for such 829 

### studies) could result from binding of dichlorome thane metabolites to tissue macromolecules, a 830 

### mechanism not included in the model, as we ll as incomplete quantitation. Using k a 

### = 0.47/h 831 

### obtained by fitting the oil-vehicle da ta of Pankow et al. (1991), the model predicts the qualitative 832


### trend of less dichloromethane e xhaled than with the water vehi cle after administration of a 250 833 

### mg/kg dose, but not the extent of reduction obser ved for the amount exhaled (Table 3). Since the 834


### model assumes that 100% of an or al dose is absorbed, this discre pancy may result if a significant 835


### fraction of the amount dosed in corn oil is ex creted in the feces. (Only 0.3 and 0.05 % was 836


### detected in the feces of mice after oral doses of 100 and 500 mg/kg dichloromethane in a water 837 

### vehicle, respectively, by Kirschman et al.) Thus the degree to which model simulations do 838 

### match these data provides reasonably good conf irmation of the model predictions, given the 839 

### shortcomings noted. While the two oral datasets used result in quite di fferent estimates of k a 

### , the 840 

### difference appears to be at least partly due to the administration vehi cle, with the lower k a 

### = 841 

### 0.47/h obtained for an oil vehicle and a larger k a 

### = 1.8/h for water as the vehicle. 842

### C

- 18 

### DRAFT- DO NOT CITE OR QUOTE 843 

### Table 3. Observations and predictions of total expired dichloromethane resulting from gavage doses in rats

Total dichloromethane exhaled (mg/kg)
Observations Predictions Dose (mg/kg) Corn oil vehicle Water vehicle k a 

### = 0.47 h -1 

### k a 

### = 1.8 h -1



### 250 91.7 166.3 171.4 193.4 500 not reported 391.4 376.9 403.6 1000 457.0 not reported 795.5 826.8 2000 1314.5 not reported 1640.2 1676.2 Source: Kirschman et al. (1986); The y-axis of Figure C-2 of Kirschman et al. appears to be mislabeled as mg instead of mg/kg; mg woul d result in unrealistic values; assuming this is supposed to be mg/kg, and assuming average valu e of 250 g for a F344 rat, an expiration of 1300 mg/kg given a dose of 2000 mg/kg corresponds to 65% of the administered dose. This value is consistent with their observation in mice, wh ere 55% was observed expired as dichloromethane from a dose of 500 mg/kg, with the % expired increasing with dose.

844
845 

### C-3. Model Option Summary 846


### Our evaluation indicates that a rat PBTK model represented by the basic model structure 847 

### of Andersen et al. (1991), with the inclusion of lu ng dichloromethane me tabolism via CYP (4% 848 

### of liver metabolite production) and GST (14% of liver metabolite production) pathways 849 

### (estimated from Reitz et al., 1989) , and with liver metabolic parame ters re-calibrated against data 850 

### of Gargas et al. (1986), provide s biological realism (including lung metabolism) and the best 851 

### prediction agreement of the various parameters w ith the available rat data sets. The resulting 852 

### model parameters (for model D) are clearly dependent on the incl usion of lung metabolism, since 853 

### the metabolic parameters for the liver (V max 

### c, K m 

### , and K f 

### ) were re-optimized based on the 854 

### presence of lung metabolism. 855


### Our evaluation also revealed some deficiencies of the available models. The on-off rate 856 

### of CO-Hb binding appears to be a significant, ra te-limiting step in determining the kinetics of 857 

### CO, which the existing model ignores . Explicitly including binding of CO to Hb and release of 858


### CO from COHb would allow the model to descri be a slower rise to peak COHb levels and 859


### slower rate of exhala tion of CO, without compromising the model's ability to describe the 860 

### observed more rapid dichloromethane kinetics. The existing model structure cannot accurately 861


### describe the COHb and most of the CO exhalati on data while also providing a good match to the 862


### dichloromethane data. 863 

### Reitz et al. (1997) employed a ka value of 5.0 h –1 

### for dichloromethane in deriving acute864 

### and intermediate-duration oral minimal risk levels. These investigators cited previous work in 865


### which a ka value of 5.0/h resulted in reasonable kinetic predictions for bolus or drinking water 866

### C

- 19 

### DRAFT- DO NOT CITE OR QUOTE 

### exposures of 1,1,1-trichloroethane in rats (Reitz et al., 1988), while a value of 5.4/h provided 867 

### good agreement between toxicokinetic observati ons and predictions for bolus doses of 868 

### trichloroethylene in rats (Fisher et al., 1989). Our analysis indicat es that ka depends on vehicle; 869 

### i.e., there is slower absorption fr om oil vehicles than water. Since a primary intention for the 870 

### model is to predict dosimetry during bioassays c onducted in drinking water, the ka estimate from 871 

### aqueous vehicle exposures (see Figure C-8) is co nsidered most relevant. The value obtained 872 

### here for aqueous vehicle, 1.8/h, is driven by th e observed rate of dichloromethane exhalation 873 

### (Figure C-8A) and the peak height for dichlo romethane concentration in blood (Figure C-8B) 874 

### and liver (Figure C-8D). It can be noted th at the timing of the blood dichloromethane peak 875 

### predicted in Figure C-8B is later than the actual peak; this timing would be better matched with a 876 

### larger value of ka. However incr easing ka would also increase the predicted peak height (that for 877 

### liver is already over-predicted, Figure C-8D) and resu lt in a more rapid rise in predicted exhaled 878 

### dichloromethane than measured (Figure C-8A). So different components or aspects of the data 879 

### set indicate different values for ka, and the va lue of ka obtained here can be considered an 880 

### average among those which would best represent each data set. 881 

### In summary, we have examined four PBTK model structures and parameter sets (models 882 

### A-D) to describe dichloromethane dosimetry in rats, as candidates for use in risk assessment, 883 

### where the purpose is to estimate internal doses of dichloromethane that occurred during various 884 

### bioassays. In comparing model pred ictions to a variety of data, one can say that while all of the 885 

### four models do a fairly good job of fitting some of th e data, none of the models fits all of the data 886 

### very well, and there are some data for which none of the models pr ovides a particularly good fit. 887


### Nevertheless there are some clear distinctions in model goodness of fit, and we conclude that 888 

### model D, which includes lung metabolism via the CYP and GST pathways, with primary 889 

### metabolic parameters re-estimated for the liver (giv en defined lung:liver ratios ), is the best of the 890


### four models. This evaluation indicates that the existing model structure for carbon monoxide 891 

### and carboxy-hemoglobin does not adequately descri be the corresponding data and that attempts 892 

### to specifically use those data in setting key parameters would compromise the accuracy of those 893 

### estimates. However, model D, the best of the mo dels evaluated here, should provide adequately 894 

### reliable PBTK-based risk extrapolation from rats to humans under chronic exposure conditions 895 

### when using dichloromethane-specific dose metric s, such as dichloromethane blood concentration 896


### metrics or rates of dichloromethane metabolism. 897

898

899

900

901

### D-1 DRAFT- DO NOT CITE OR QUOTE 

### APPENDIX D. SUMMARY OF BENCHM ARK DOSE (BMD) MODELING OF 1


### NONCANCER ENDPOINTS 2

3

4 

### D-1. Oral Reference Dose: BMD Modeling of No nneoplastic Liver Lesion Incidence Data 5 

### for Rats Exposed to Dichloromethane in Dr inking Water for 2 Years (Serota et al., 1986a) 6

7 

### BMD and BMDL refer to the model-predicte d dose (and its lower 95% confidence limit) 8


### associated with 10% extra risk for the incidence of liver foci/areas of cellular alteration in male 9 

### and female F344 rats given dichloromethane in drinking water for 2 years (Serota et al., 1986a) 10 

### (Table D-1). 11

12
Table D-1. Incidence data for nonneoplasti c liver lesions and internal liver doses, based on various metrics, in ma le and female F344 rats exposed to dichloromethane in drinking water for 2 years (Serota et al., 1986a)

Rat internal liver dose b

Sex Nominal (actual) daily intake (mg/kg-day) Rat liver lesion incidence a CYP GST GST and CYP Parent AUC 0 (0) 52/76 (68%) 0 0 0 0
5 ( 6 ) 22/34 ( 65% ) 133.9 2.1 136.1 0.47 50 ( 52 ) 35/38 ( 92% ) c 872.7 58.8 931.4 13.1 125 ( 125 ) 34/35 ( 97% ) c 1,433.1 236.0 1,669.1 52.6 Male (BW = 380 g)

250 (235) 40/41 (98%) c 1,868.6 561.5 2,430.0 125.0
0 ( 0 ) 34/67 ( 51% ) 0 0 0 0 5 ( 6 ) 12/29 ( 41% ) 134.5 2.1 136.6 0.4 50 ( 58 ) 30/41 ( 73% ) c 977.8 66.0 1,043.8 12.6 125 ( 136 ) 34/38 ( 89% ) c 1577.0 258.7 1,835.7 49.5 Female (BW = 229 g )

250 (263) 31/34 (91%) c 2070.0 642.4 2,712.3 122.9

a Liver foci/areas of cellular alteration; number affected divided by total sample size.

b Internal doses were estimated using a rat PBTK model from simulations of actual daily doses

reported by the study authors. CYP dose is in units of mg dichloromethane metabolized via CYP pathway/L tissue/day; GST dose is in units of mg dichloromethane metabolized via GST pathway/L tissue/day.; GST and CYP dose is in units of mg dichloromethane metabolized via CYP and GST pathways/L tissue/day; and Parent AUC dose is in units of mg dichloromethane*hrs)/L tissue.

c Significantly ( p < 0.05) different from control with Fisher’s exact test.

Source: Serota et al., 1986a.

13 

### All available dichotomous models in the Benc hmark Dose Software (version 2.0) were fit 14 

### to male and female rat internal tissue doses of dichloromethane metabolized by the CYP pathway 15


### and incidences for animals with these liver lesi ons observed at the time of death (Table D-2). 16 

### (The quantal model is identical to the one-stage multistage model, and so is not included in this 17 

### set of models). The male rats exhibited a greater sensitivity compared to the female rats (based 18


### on lower BMDL 10 

### values for all of the models) and thus the male data are used as the basis for 19 

### the RfD derivation. The logistic model was the be st fitting model for the male incidence data, 20

### D-2 DRAFT- DO NOT CITE OR QUOTE 

### based on AIC value among models with adequate fit (U.S. EPA,2000b). Results for this model 1 

### are presented below. 2

3


### Table D-2. BMD modeling results for incidence of noncancer liver lesions in male and female F344 rats exposed to dichloromethane in drinking water for 2 years, based liver-specific CYP metabolism dose metric (mg dichloromethane metabolism via CYP pathway per liter liver tissue per day)

Sex and model a BMD 10 BMDL 10 χ 2 goodness of fit p-value AIC 

### Males

Gamma a 151.73 48.93 0.62 185.33 

### Logistic 85.17 61.78 0.75 183.61 

### Log-logistic a 213.73 37.06 0.83 184.79 

### Multistage (1) a 68.62 47.58 0.71 183.74 

### Probit 98.87 75.49 0.69 183.81 

### Log-probit a 197.65 77.56 0.81 184.84 

### Weibull a 117.29 48.39 0.57 185.49 

### Females

Gamma a 336.38 98.70 0.52 233.07 

### Logistic 169.77 134.87 0.59 231.70 

### Log-logistic a 404.87 101.15 0.60 232.80 

### Multistage (1) a 123.59 91.46 0.47 232.32 

### Probit 179.59 146.27 0.59 231.70 

### Log-probit a 400.95 173.57 0.60 232.80 

### Weibull a 283.24 97.31 0.47 233.27

a 

### These models in EPA BMDS version 2.0 were fit to the rat dose-response data shown in

Table D-1 by using internal dose metrics calculated with the rat PBTK model. Details of the models are as follows: Gamma a nd Weibull models restrict power ≥ 1; Log-logistic and Log-probit models restrict to slope >1, multistage model restrict betas ≥ 0; lowest degree polynomial with an adequate fit is reported (degree of pol ynomial noted in parentheses). Bolded model is the best-fitting model in the most sensitive sex (males), which is used in the RfD derivation.

Source: Serota et al. (1986a).

. 4

### D-3 DRAFT- DO NOT CITE OR QUOTE Logistic Model , Male Rats (Serota et al., 1986a), CYP Metabolism Metric 1

0.5 0.6 0.7 0.8 0.9

1

0 500 1000 1500Fraction Affected

dose Logistic Model with 0.95 Confidence Level

11:43 02/25 2009 BMDL BMD Logistic

2 **Figure D-1.** Predicted (logistic model) and observed inci dence of noncancer liver lesions in male 3 F344 rats exposed to dichloromethane in drinking water for 2 years 4 (Serota et al., 1986a) 5 6

==================================================================== 7 Logistic Model. (Version: 2.12; Date: 05/16/2008) 8 Input Data File: 9 C:\USEPA\IRIS\DCM\noncancer\dataset1_liverlesions\male\2LogSerlog.(d) 10 Gnuplot Plotting File: 11 C:\USEPA\IRIS\DCM\noncancer\dataset1_liverlesions\male\2LogSerlog.plt 12
Wed Feb 25 11:43:00 2009 13

==================================================================== 14 15
BMDS Model Run 16

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 17 The form of the probability function is: 18 19 P[response] = 1/[1+EXP(-intercept-slope*dose)] 20 21 22 Dependent variable = incidence 23 Independent variable = CYP 24 Slope parameter is not restricted 25 26
Total number of observations = 5 27 Total number of records with missing values = 0 28
Maximum number of iterations = 250 29 Relative Function Convergence has been set to: 1e-008 30 Parameter Convergence has been set to: 1e-008 31 32

### D-4 DRAFT- DO NOT CITE OR QUOTE 1 2
Default Initial Parameter Values 3 background = 0 Specified 4 intercept = 0.683407 5 slope = 0.00155073 6 7 8 Asymptotic Correlation Matrix of Parameter Estimates 9 10 ( *** The model parameter(s) -background 11 have been estimated at a boundary point, or have been specified by 12 the user, and do not appear in the correlation matrix ) 13 14 intercept slope 15 16 intercept 1 -0.46 17 18 slope -0.46 1 19 20 21
Parameter Estimates 22 23
95.0% Wald Confidence Interval 24 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 25 intercept 0.669705 0.208716 0.260629 1.07878 26 slope 0.00182324 0.000412451 0.00101485 0.00263163 27 28 Analysis of Deviance Table 29 30 Model Log(likelihood) # Param's Deviance Test d.f. P-value 31
Full model -89.2097 5 32
Fitted model -89.8065 2 1.19363 3 0.7545 33
Reduced model -106.616 1 34.8133 4 <.0001 34 35
**AIC: 183.613** 36 37 38
Goodness of Fit 39
Scaled 40 Dose Est._Prob. Expected Observed Size Residual 41

------------------------------------------------------------------------ 42
0.0000 0.6614 50.269 52.000 76 0.420 43
133.9000 0.7138 24.269 22.000 34 -0.861 44
872.7000 0.9056 34.412 35.000 38 0.326 45
1433.1000 0.9638 33.734 34.000 35 0.241 46
1868.6000 0.9833 40.316 40.000 41 -0.385 47 48
**Chi^2 = 1.23 d.f. = 3 P-value = 0.7458** 49 50 51 Benchmark Dose Computation 52 53 Specified effect = 0.1 54 55 Risk Type = Extra risk 56 57
Confidence level = 0.95 58 59
**BMD = 85.1668** 60 61
**BMDL = 61.7795** 62

### D-5 DRAFT- DO NOT CITE OR QUOTE 

### D-2. Inhalation Reference Concentration: BM D modeling of Nonneoplastic Liver Lesion 1 

### Incidence Data for Rats Exposed to Dichloro methane via Inhalation for 2 Years (Nitschke 2 

### et al., 1988a) 3

4 

### BMD and BMDL refer to the model-predicte d dose (and its lower 95% confidence limit) 5


### associated with 10% extra risk for the inciden ce of hepatic vacuolation in female F344 rats 6 

### exposed to dichloromethane via inhalation for 2 years (Nitscke et al., 1988a) (Table D-3). 7

8 Table D-3. Incidence data for nonneoplastic liv er lesions (hepatic vacuolation) and internal liver doses, based on various metrics, in female Spr ague-Dawley rats exposed to dichloromethane via inhalation for 2 years (Nitschke et al., 1988a)

Rat internal liver dose b

Sex Exposure (ppm) Liver lesion incidence a CYP GST GST and CYP Parent AUC Male 0 22/70 (31) 50 Not reported 200 Not reported Not modeled because results for middle two doses were not reported

500 28/70 (40)

0 41/70 (59%) 0 0 0 0 50 42/70 (60%) 280.3 6.3 286.6 1.2 200 41/70 ( 58% ) 656.5 93.2 749.7 17.8 Female (BW = 229 g ) 500 53/70 (76%) c 772.6 359.0 1,131.6 68.7

a Number affected divided by total sample size.

b Internal doses were estimated using a rat PBTK model using exposures reported by study authors (50 ppm =

174 mg/m 3 , 200 ppm = 695 mg/m 3 , and 500 ppm = 1737 mg/m 3 ) and are weighted-average daily values for 1 week of exposure @ 6 h/day, 5 day/week. CYP dose is in units of mg dichloromethane metabolized via CYP pathway/L tissue/day; GST dose is in units of mg dichloromethane metabolized via GST pathway/L tissue/day.; GST and CYP dose is in units of mg dichloromethane metabolized via CYP and GST pathways/L tissue/day; and Parent AUC dose is in units of mg dichloromethane*hrs)/L tissue.

c Significantly ( p < 0.05) different from control with Fisher’s exact test.

Source: Nitschke et al., 1988a.
9


### All available dichotomous models in the Benc hmark Dose Software (version 2.0) were fit 10 

### to male and female rat internal tissue doses of dichloromethane metabolized by the CYP pathway 11


### and incidences for animals with these liver lesi ons observed at the time of death (Table D-4). 12 

### (The quantal model is identical to the one-stage multistage model, and so is not included in this 13 

### set of models). The log-probit model was the be st fitting model for the female incidence data, 14 

### based on AIC value among models with adequate fit (U.S. EPA, 2000c). Results for this model 15 

### are presented below. 16

### D-6 DRAFT- DO NOT CITE OR QUOTE 1 

### Table D-4. BMD modeling result s for incidence of noncancer liver lesions in female Sprague-Dawley ra ts exposed to dichloromethane by inhalation for 2 years, based on liver specific CYP metabolism metric (mg dichloromethane metabolized via CYP pathway per liter liver tissue per day)

Model a 

### BMD 10 

### BMDL 10 

### χ 2 

### goodness of fit p-value AIC Gamma a 

### 614.27 225.96 0.48 367.22 Logistic 274.58 150.43 0.14 369.77 Log-logistic a 

### 697.90 499.42 0.94 365.90 Multistage (3) a 

### 506.94 153.13 0.25 368.53 Probit 275.49 152.52 0.14 369.75 Log-probit a 

### 728.96 523.94 0.98 365.82 Weibull a 

### 706.45 487.45 0.95 365.87

a 

### These models in EPA BMDS version 2.0 were fit to the rat dose-response data shown in Table D-3 by using internal dose metrics calculated with the rat PBTK model. Gamma and Weibull models restrict power ≥ 1; Log-logistic and Log-probit models restrict to slope >1, multistage model restrict betas ≥ 0; lowest degree polynomial with an adequate fit reported (degree of polynomial in parentheses). Bolded model is the best-fitting model in th e most sensitive sex (males), which is used in the RfC derivation.

Source: Nitschke et al., (1988a).

2

### D-7 DRAFT- DO NOT CITE OR QUOTE Log Probit Model, Female Rats (Nitsc hke et al, 1988), CYP Metabolism Metric 1

0.45 0.5 0.55

0.6 0.65

0.7 0.75

0.8 0.85

0 100 200 300 400 500 600 700 800Fraction Affected

dose LogProbit Model with 0.95 Confidence Level

16:07 02/25 2009 BMDL BMD LogProbit

2 Figure D-2. Predicted (log-probit model) and ob served incidence of noncancer liver lesions in 3 female Sprague-Dawley rats inhalin g dichloromethane for 2 years 4 (Nitschke 1988a) 5

==================================================================== 6 Probit Model. (Version: 3.1; Date: 05/16/2008) 7 Input Data File: 8 C:\USEPA\BMDS21Beta\Data\dichloromethane\Dataset2\CYP\4LogCYPlog.(d) 9 Gnuplot Plotting File: 10 C:\USEPA\BMDS21Beta\Data\dichloromethane\Dataset2\CYP\4LogCYPlog.plt 11
Wed Feb 25 16:07:52 2009 12

==================================================================== 13 14
BMDS Model Run 15

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 16 The form of the probability function is: 17 18 P[response] = Background 19 + (1-Background) * CumNorm(Intercept+Slope*Log(Dose)), 20 21 where CumNorm(.) is the cumulative normal distribution function 22 23 Dependent variable = Incidence 24 Independent variable = Dose 25 Slope parameter is restricted as slope >= 1 26 27
Total number of observations = 4 28 Total number of records with missing values = 0 29
Maximum number of iterations = 250 30 Relative Function Convergence has been set to: 1e-008 31 Parameter Convergence has been set to: 1e-008 32

### D-8 DRAFT- DO NOT CITE OR QUOTE User has chosen the log transformed model 1 2 Default Initial (and Specified) Parameter Values 3 background = 0.585714 4 intercept = -7.69845 5 slope = 1 6 7 Asymptotic Correlation Matrix of Parameter Estimates 8 9 ( *** The model parameter(s) -slope 10 have been estimated at a boundary point, or have been specified by 11 the user, 12 and do not appear in the correlation matrix ) 13 14 background intercept 15 16 background 1 -0.37 17 18 intercept -0.37 1 19 20
Parameter Estimates 21
95.0% Wald Confidence Interval 22 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 23 background 0.590379 0.0339871 0.523765 0.656992 24 intercept -119.931 0.346784 -120.61 -119.251 25 slope 18 NA 26 27 NA - Indicates that this parameter has hit a bound 28 implied by some inequality constraint and thus 29
has no standard error. 30 31 Analysis of Deviance Table 32 33 Model Log(likelihood) # Param's Deviance Test d.f. P-value 34
Full model -180.889 4 35
Fitted model -180.909 2 0.0403244 2 0.98 36
Reduced model -184.186 1 6.5937 3 0.08604 37 38
**AIC: 365.818** 39
Goodness of Fit 40
Scaled 41 Dose Est._Prob. Expected Observed Size Residual 42

------------------------------------------------------------------------ 43
0.0000 0.5904 41.326 41.000 70 -0.079 44
280.3400 0.5904 41.326 42.000 70 0.164 45
656.4800 0.5907 41.349 41.000 70 -0.085 46
772.5900 0.7571 52.998 53.000 70 0.001 47 48
**Chi^2 = 0.04 d.f. = 2 P-value = 0.9801** 49 50 Benchmark Dose Computation 51 52 Specified effect = 0.1 53 54 Risk Type = Extra risk 55 56
Confidence level = 0.95 57 58
**BMD = 728.956** 59 60
**BMDL = 523.944** 61

### E-1 DRAFT- DO NOT CITE OR QUOTE 

### APPENDIX E: SUMMARY OF BENCHM ARK DOSE (BMD) MODELING OF 1


### CANCER ENDPOINTS 2

3

4 

### E-1. Oral Cancer Slope Factors: BMD Modelin g of Liver Tumor Incidence Data for Mice 5 

### Exposed to Dichloromethane in Drinking Wat er for 2 Years (Serota et al., 1986b; Hazelton 6 

### Laboratories, 1983) 7



### BMD 10 

### and BMDL 10 

### refer to the model-predicted dos e (and its lower 95% confidence 8 

### limit) associated with 10% extra risk for the in cidence of hepatocellular adenoma and carcinoma 9 

### in male mice given dichloromethane in drinking wa ter for 2 years (Serota et al., 1986b; Hazelton 10 

### Laboratories, 1983) (Table E-1). Multistage models were fit to male mous e internal tissue doses 11


### of dichloromethane metabolized by the GST pathway and incidences for animals with liver 12 

### tumors observed at the time of death. The predicted BMD 10 

### and BMDL 10 

### for the incidence data 13 

### are 73.0 and 39.6 mg dichloromethane metabolized via GST pathways per liter tissue per day, 14 

### respectively for the internal liver metabolis m metric, and 3.05 and 1.65 mg dichloromethane 15 

### metabolized via GST pathway in lung and liv er/kg-day), respectiv ely, for the whole body 16 

### metabolism metric (Table E-2)

. 17

18 

### Table E-1. Incidence data for liver tumors and internal liver doses, based on GST metabolism dose metrics, in male B6C3F 1 

### mice exposed to dichloromethane in drinking water for 2 years

Sex Nominal (actual) daily intake (mg/kg-day) Mouse liver tumor incidence a Mouse internal liver metabolism dose b Mouse whole body metabolism dose c 0 (0) 24/125 (19%) 0 0 60 ( 61 ) 51/199 ( 26% ) 17.5 0.73 125 ( 124 ) 30/99 ( 30% ) d 63.3 2.65 185 ( 177 ) 31/98 ( 32% ) d 112.0 4.68 Male (BW = 37.3 g)

250 (234) 35/123 (28%) d 169.5 7.1

a Hepatocellular carcinoma or adenoma, combined. Mice dying prior to 52 weeks were excluded from the denominators. Cochran-Armitage trend p-value = 0.058.

b mg dichloromethane metabolized via GST pathway/ L liver/day. Internal doses were estimated from simulations of actual daily doses reported by the study authors.

c Based on the sum of dichloromethane metabolized via the GST pathway in the lung plus the liver, normalized to total BW (i.e., [lung GST metabolism (mg/day) + liver GST metabolism (mg/day)]/kg BW. Units = mg dichloromethane metabolized via GST pathway in lung and liver/kg-day.

d Significantly ( p ≤ 0.05) different from control incidence by Fisher’s exact test performed by Syracuse Research Corporation.

Sources: Serota et al., 1986b ; Hazelton Laboratories, 1983
19

### E-2 DRAFT- DO NOT CITE OR QUOTE 1
Table E-2. BMD modeling results and tumor risk factors for internal dose metric associated with 10% extra risk for liver tumors in male B6C3F 1 mice exposed to dichloromethane in drinking water for 2 years, based on liver-specific GST metabolism and whole body GST metabolism dose metrics

Tumor Risk Factor e

Internal dose metric BMDS model b χ 2 goodness of fit p- value Mouse BMD 10 c Mouse BMDL 10 c Allometricscaled human

BMDL 10 d Scaling = 1.0 Allometricscaled
Liverspecific MS (1,1) 0.56 73.0 39.6 5.66 2.53 × 10 –3 1.77 × 10 –2

Whole-body MS (1,1) 0.56 3.05 1.65 0.24 -- 4.24 × 10 –1

a Liver specific dose units = mg dichloromethane metabolized via GST pathway per liter tissue per day;Whole-body dose units = mg dichloromethane metabolized via GST pathway in lung and liver/kgday

b The multistage (MS) model in EPA BMDS version 2.0 was fit to the mouse dose-response data

shown in Table E-1 using internal dose metrics calculated with the mouse PBTK model. Numbers in parentheses indicate (1) the number of dose groups dropped in order to obtain an adequate fit and (2) the degree polynomial of the model.

c BMD 10 and BMDL 10 refer to the BMD-model-predicted mouse internal and its 95% lower confidence

limit, associated with a 10% extra risk for the incidence of tumors.

d Mouse BMDL 10 divided by (BW human /BW
mouse ) 0.25 = 7.

e Dichloromethane tumor risk factor (extra risk per unit internal dose) derived by dividing the BMR

(0.1) by the mouse BMDL 10 and by the allometric-scaled human BMDL 10 ,, for the scaling = 1.0 and allometric-scaled risk factors, respectively.
2 

### Modeling results are presented in the subse quent sections for th e tissue-specific liver3 

### metabolism metric (section E-1.1) and the w hole-body metabolism metr ic (section E-1.2). 4

### E-3 DRAFT- DO NOT CITE OR QUOTE 

### E-1.1. Modeling results for the int ernal liver metabolism metric 1

2 Serota et al. (1986b), Hazelton Laboratories, 1983: Internal liver dose-response, highest dose dropped 3 1-degree polynomial 4

0.1 0.15

0.2 0.25

0.3 0.35

0.4

0 20 40 60 80 100Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

13:46 02/19 2009 BMD BMDL Multistage Cancer Linear extrapolation

5 Figure E-1. Predicted and observed incidence of animals with hepatocellular carcinoma or 6 adenoma in male B6C3F 1 mice exposed to dichloromet hane in drinking water 7 for 2 years, using liver-specific metabolism dose metric (Serota et al., 1986b; Hazelton 8 Laboratories, 1983). 9

==================================================================== 10 Multistage Cancer Model. (Version: 1.7; Date: 05/16/2008) 11 Input Data File: C:\USEPA\IRIS\DCM\Serota\highdosedropped\1MulSerMS_.(d) 12 Gnuplot Plotting File: 13 C:\USEPA\IRIS\DCM\Serota\highdosedropped\1MulSerMS_.plt 14
Thu Feb 19 13:46:49 2009 15

==================================================================== 16
BMDS Model Run 17

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 18 The form of the probability function is: 19 P[response] = background + (1-background)*[1-EXP( 20 -beta1*dose^1)] 21 22 The parameter betas are restricted to be positive 23 Dependent variable = incidence 24 Independent variable = dose 25 26
Total number of observations = 4 27 Total number of records with missing values = 0 28 Total number of parameters in model = 2 29 Total number of specified parameters = 0 30 Degree of polynomial = 1 31

### E-4 DRAFT- DO NOT CITE OR QUOTE 1
Maximum number of iterations = 250 2 Relative Function Convergence has been set to: 2.22045e-016 3 Parameter Convergence has been set to: 1.49012e-008 4 5 **** We are sorry but Relative Function and Parameter Convergence   6 ** ** are currently unavailable in this model. Please keep checking   7 ** ** the web sight for model updates which will eventually   8 ** ** incorporate these convergence criterion. Default values used. **** 9 10
Default Initial Parameter Values 11 Background = 0.218634 12 Beta(1) = 0.00136788 13 14 Asymptotic Correlation Matrix of Parameter Estimates 15 16 Background Beta(1) 17 18 Background 1 -0.7 19 20 Beta(1) -0.7 1 21 22
Parameter Estimates 23
95.0% Wald Confidence Interval 24 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 25 Background 0.218642 * * * 26 Beta(1) 0.00144288 * * * 27 28
* - Indicates that this value is not calculated. 29 30 Analysis of Deviance Table 31 32 Model Log(likelihood) # Param's Deviance Test d.f. P-value 33
Full model -296.282 4 34
Fitted model -296.87 2 1.1754 2 0.5556 35
Reduced model -299.126 1 5.68747 3 0.1278 36 37
**AIC: 597.74** 38
Goodness of Fit 39
Scaled 40 Dose Est._Prob. Expected Observed Size Residual 41

------------------------------------------------------------------------ 42
0.0000 0.2186 27.330 24.000 125 -0.721 43
17.5000 0.2381 47.387 51.000 199 0.601 44
63.3000 0.2868 28.398 30.000 99 0.356 45
112.0000 0.3352 32.853 31.000 98 -0.397 46 47
**Chi^2 = 1.16 d.f. = 2 P-value = 0.5585** 48 49 Benchmark Dose Computation 50 Specified effect = 0.1 51 52 Risk Type = Extra risk 53 54
Confidence level = 0.95 55 56
**BMD = 73.0211** 57 58
**BMDL = 39.6034** 59 60
BMDU = 335.18 61 62 Taken together, (39.6034, 335.18 ) is a 90 % two-sided confidence 63
interval for the BMD 64 65 **Multistage Cancer Slope Factor = 0.00252504** 66

### E-5 DRAFT- DO NOT CITE OR QUOTE 

### E-1.2. Modeling results for the whole body metabolism metric 1
2 Serota et al. (1986b), Hazelton Laboratories, 1983: In ternal whole-body metabolis m dose-response in mice, 3 highest dose dropped 4 1-degree polynomial 5 6 Figure 7

0.1 0.15

0.2 0.25

0.3 0.35

0.4

0 1 2 3 4Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

17:33 02/21 2009 BMD BMDL Multistage Cancer Linear extrapolation

8 Figure E-2. Predicted and observed incidence of animals with hepatocellular carcinoma or 9 adenoma in male B6C3F 1 mice exposed to dichloromet hane in drinking water 10 for 2 years, using whole-body metabolism do se metric (Serota et al., 1986b; Hazelton 11 Laboratories, 1983). 12

==================================================================== 13 Multistage Cancer Model. (Version: 1.7; Date: 05/16/2008) 14 Input Data File: C:\USEPA\IRIS\DCM\Serota\highdosedropped\1MulSerMS_.(d) 15 Gnuplot Plotting File: 16 C:\USEPA\IRIS\DCM\Serota\highdosedropped\1MulSerMS_.plt 17
Sat Feb 21 17:33:49 2009 18

==================================================================== 19 20
BMDS Model Run 21

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 22 The form of the probability function is: 23 24 P[response] = background + (1-background)*[1-EXP( 25 -beta1*dose^1)] 26 27 The parameter betas are restricted to be positive 28 Dependent variable = incidence 29 Independent variable = dose 30 31

### E-6 DRAFT- DO NOT CITE OR QUOTE Total number of observations = 4 1 Total number of records with missing values = 0 2 Total number of parameters in model = 2 3 Total number of specified parameters = 0 4 Degree of polynomial = 1 5
Maximum number of iterations = 250 6 Relative Function Convergence has been set to: 2.22045e-016 7 Parameter Convergence has been set to: 1.49012e-008 8 9 **** We are sorry but Relative Function and Parameter Convergence   10 ** ** are currently unavailable in this model. Please keep checking   11 ** ** the web sight for model updates which will eventually   12 ** ** incorporate these convergence criterion. Default values used. **** 13 14
Default Initial Parameter Values 15 Background = 0.218649 16 Beta(1) = 0.032703 17 18 Asymptotic Correlation Matrix of Parameter Estimates 19 20 Background Beta(1) 21 22 Background 1 -0.7 23 24 Beta(1) -0.7 1 25
Parameter Estimates 26 27
95.0% Wald Confidence Interval 28 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 29 Background 0.218662 * * * 30 Beta(1) 0.0344939 * * * 31
* - Indicates that this value is not calculated. 32 33 Analysis of Deviance Table 34 35 Model Log(likelihood) # Param's Deviance Test d.f. P-value 36
Full model -296.282 4 37
Fitted model -296.871 2 1.17643 2 0.5553 38
Reduced model -299.126 1 5.68747 3 0.1278 39
**AIC: 597.741** 40
Goodness of Fit 41
Scaled 42 Dose Est._Prob. Expected Observed Size Residual 43

------------------------------------------------------------------------ 44
0.0000 0.2187 27.333 24.000 125 -0.721 45
0.7310 0.2381 47.385 51.000 199 0.602 46
2.6470 0.2868 28.397 30.000 99 0.356 47
4.6840 0.3352 32.853 31.000 98 -0.396 48 49
**Chi^2 = 1.17 d.f. = 2 P-value = 0.5582** 50 51 Benchmark Dose Computation 52 Specified effect = 0.1 53 Risk Type = Extra risk 54
Confidence level = 0.95 55 56
**BMD = 3.05447** 57 58
**BMDL = 1.65649** 59 60
BMDU = 14.0263 61 62 Taken together, (1.65649, 14.0263) is a 90 % two-sided confidence 63
interval for the BMD 64 65 **Multistage Cancer Slope Factor = 0.0603686** 66

### E-7 DRAFT- DO NOT CITE OR QUOTE 

### E-2. Cancer Inhalation Unit Risk: BMD Mode ling of Liver and Lung Tumor Incidence 1 

### Data for Male Mice Exposed to Dichloromethan e via Inhalation for 2 Years (Mennear et 2 

### al., 1988; NTP, 1986). 3



### BMD 10 

### and BMDL 10 

### refer to the model-predicted dos e (and its lower 95% confidence 4 

### limit) associated with 10% extra risk for the co mbined incidence of adenoma and carcinoma of 5 

### the liver or lung of male B6C3F 1 

### mice inhaling dichloromethane for 2 years (Mennear et al., 6 

### 1988; NTP, 1986) (Table E-3). 7

8

Table E-3. Incidence data for liver an d lung tumors and internal doses, based on GST metabolism dose metrics, in male B6C3F 1 mice exposed to dichloro methane via inhalation for 2 years

Sex, tumor type BW (g) External dichloromethane

concentration (ppm) Mouse tumor incidence Mouse internal tissue dose a Mouse whole body metabolism dose b
-- 0 22/50 (44%) e 0 0 Male, liver c
34.0 2,000 24/47 (51%) 2,363.7 100.2
32.0 4,000 33/47 (70%) 4,972.2 210.7
-- 0 5/50 (10%) e 0 0 34.0 2,000 27/47 (55%) 475.0 100.2 Male, lung d

32.0 4,000 40/47 (85%) 992.2 210.7

a For liver tumors: mg dichloromethane metabolized via GST pathway/L liver tissue/day from 6 hours per day, 5

days per week exposure; for lung tumors: mg dichloromethane metabolized via GST pathway/L lung tissue/day from 6 hours per day, 5 days per week exposure.

b Based on the sum of dichloromethane metabolized via the GST pathway in the lung plus the liver, normalized to

total BW (i.e., [lung GST metabolism (mg/day) + liver GST metabolism (mg/day)]/kg BW). Units = mg dichloromethane metabolized via GST pathway in lung and liver/kg-day.

c Hepatocellular carcinoma or adenoma. Mice dying prior to 52 weeks were excluded from the denominators.

d Bronchoalveolar carcinoma or adenoma. Mice dying prior to 52 weeks were excluded from the denominators.

Sources: Mennear et al., 1988; NTP, 1986
9
10 

### Multistage models were fit to male mouse internal tissue doses of dichloromethane 11 

### metabolized by the GST pathway and incidences fo r animals with liver tumors observed at the 12 

### time of death. The predicted BMD 10 

### and BMDL 10 

### for the liver tumor incidence data are 913.9 13 

### and 544.4 mg dichloromethane metabolized vi a GST pathways per liter liver per day, 14 

### respectively for the internal liver metabolis m metric, and 38.7 and 23.1 mg dichloromethane 15 

### metabolized via GST pathway in lung and liv er/kg-day), respectiv ely, for the whole body 16 

### metabolism metric (Table E-4). For lung tumors, the BMD 10 

### and BMDL 10 

### are 61.7 and 48.7, 17 

### respectively for the lung-specific metric, a nd 13.1 and 10.3, respectively, for the whole body 18


### metabolism metric. 19E-8 DRAFT- DO NOT CITE OR QUOTETable E-4.BMD modeling results and tumor risk factors associated with 10% extra riskfor liver and lung tumors in maleB6C3F1mice exposed by inhalation to dichloromethane for 2 years, based onliver-specific GST metabolism and wholebody GST metabolism dose metricsTumor Risk FactoreInternal dosemetricaBMDSmodelbχ2goodness of fitp-valueMouseBMD10cMouseBMDL10cAllometricscaled humanBMDL10dScaling = 1.0AllometricscaledLiver-specific Male, liver MS (0,1) 0.40 913.9 544.4 77.8 1.84 × 10–41.29 × 10–3Male, lung MS (0,1) 0.64 61.7 48.6 7.0 2.06 × 10–31.44 × 10–2Whole body Male, liverMS (0,1)0.40 38.7 23.1 3.3--3.03 × 10–2Male, lungMS (0,1)0.66 13.1 10.3 1.5--6.80 × 10–2aLiver specific dose units = mg dichloromethane metabolized via GST pathway per liter tissue per day; Whole-body dose units = mgdichloromethane metabolized via GST pathway in lung and liver/kg-day)bThe multistage (MS) model in EPA BMDS version 2.0 was fit to the mouse dose-response data shown in Table E-3 using internal dosemetrics calculated with the mouse PBTK model. Numbers in parentheses indicate (1) the number of dose groups dropped in order toobtain an adequate fit and (2) the degree polynomial of the model.cBMD10and BMDL10refer to the BMD-model-predicted mouse internal dose and its 95% lower confidence limit, associated with a 10%extra risk for the incidence of tumors.dMouse BMDL10divided by (BWhuman/BWmouse)0.25= 7.eDichloromethane tumor risk factor (extra risk per unit internal dose) derived by dividing the BMR (0.1) by the mouse BMDL10and by theallometric-scaled human BMDL10,, for the scaling = 1.0 and allometric-scaled risk factors, respectively.1Modeling results are presented in the subsequent sections for the tissue-specific liver-metabolism metric for liver tumors (section E22.1), tissue-specific lung metabolism metric for lung tumors (section E-2.2), and the whole-body metabolism metric liver tumors(section E32.3) and lung tumors (section E-2.4).4

### E-9 DRAFT- DO NOT CITE OR QUOTE 

### E-2.1. Modeling results for th e internal liver metaboli sm metric, liver tumors 1
Mennear et al. (1988); NTP (1986): Internal liv er dose-response for liver tumors in male mice 2 1-degree polynomial 3

0.3 0.4 0.5 0.6 0.7 0.8

0 1000 2000 3000 4000 5000Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

16:08 02/19 2009 BMD BMDL Multistage Cancer Linear extrapolation

4 Figure E-3. Predicted and observed incidence of animals with hepatocellular carcinoma or 5 adenoma in male B6C3F 1 mice exposed by inhalation to dich loromethane for 2 years, using liver6 specific metabolism dose metric (M ennear et al., 1988; NTP, 1986 7

==================================================================== 8 Multistage Cancer Model. (Version: 1.7; Date: 05/16/2008) 9 Input Data File: C:\USEPA\IRIS\DCM\NTP\lung\male\1MulNTPMS_.(d) 10 Gnuplot Plotting File: C:\USEPA\IRIS\DCM\NTP\lung\male\1MulNTPMS_.plt 11
Thu Feb 19 16:08:19 2009 12

==================================================================== 13 14
BMDS Model Run 15

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 16 17 The form of the probability function is: 18 19 P[response] = background + (1-background)*[1-EXP( 20 -beta1*dose^1)] 21 22 The parameter betas are restricted to be positive 23 24 Dependent variable = incidence 25 Independent variable = dose 26 27
Total number of observations = 3 28 Total number of records with missing values = 0 29 Total number of parameters in model = 2 30 Total number of specified parameters = 0 31

### E-10 DRAFT- DO NOT CITE OR QUOTE Degree of polynomial = 1 1
Maximum number of iterations = 250 2 Relative Function Convergence has been set to: 2.22045e-016 3 Parameter Convergence has been set to: 1.49012e-008 4 5 **** We are sorry but Relative Function and Parameter Convergence   6 ** ** are currently unavailable in this model. Please keep checking   7 ** ** the web sight for model updates which will eventually   8 ** ** incorporate these convergence criterion. Default values used. **** 9 10
Default Initial Parameter Values 11 Background = 0.406706 12 Beta(1) = 0.00012805 13 14 Asymptotic Correlation Matrix of Parameter Estimates 15 16 Background Beta(1) 17 18 Background 1 -0.69 19 20 Beta(1) -0.69 1 21 22
Parameter Estimates 23 24
95.0% Wald Confidence Interval 25 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 26 Background 0.421771 * * * 27 Beta(1) 0.000115283 * * * 28 29
* - Indicates that this value is not calculated. 30 31 Analysis of Deviance Table 32 33 Model Log(likelihood) # Param's Deviance Test d.f. P-value 34
Full model -95.4892 3 35
Fitted model -95.8368 2 0.695297 1 0.4044 36
Reduced model -99.1316 1 7.28482 2 0.02619 37 38
**AIC: 195.674** 39 40
Goodness of Fit 41
Scaled 42 Dose Est._Prob. Expected Observed Size Residual 43

------------------------------------------------------------------------ 44
0.0000 0.4218 21.089 22.000 50 0.261 45
2363.7000 0.5597 26.305 24.000 47 -0.677 46
4972.2000 0.6740 31.680 33.000 47 0.411 47 48
**Chi^2 = 0.70 d.f. = 1 P-value = 0.4042** 49 50 Benchmark Dose Computation 51 Specified effect = 0.1 52 53 Risk Type = Extra risk 54 55 **Confidence level = 0.95** 56 57
**BMD = 913.932** 58 59
**BMDL = 544.35** 60 61
BMDU = 2569.01 62 63 Taken together, (544.35 , 2569.01) is a 90 % two-sided confidence 64
interval for the BMD 65 **Multistage Cancer Slope Factor = 0.000183705** 66

### E-11 DRAFT- DO NOT CITE OR QUOTE 

### E-2.2. Modeling results for th e internal lung metaboli sm metric, lung tumors 1
2 Mennear et al. (1988); NTP (1986): Internal lung dose-response for lung tumors in male mice 3 1-degree polynomial 4

0 0.2 0.4 0.6 0.8

1

0 200 400 600 800 1000Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

21:20 02/19 2009 BMD BMDL Multistage Cancer Linear extrapolation

5 Figure E-4. Predicted and observed incidence of an imals with carcinoma or adenoma in the lung of 6 male B6C3F 1 mice exposed by inhalation to dichloro methane for 2 years, using liver-specific 7 metabolism dose metric (Mennear et al., 1988; NTP, 1986). 8

==================================================================== 9 Multistage Cancer Model. (Version: 1.7; Date: 05/16/2008) 10 Input Data File: C:\USEPA\IRIS\DCM\NTP\lung\male\1MulNTPMS_.(d) 11 Gnuplot Plotting File: C:\USEPA\IRIS\DCM\NTP\lung\male\1MulNTPMS_.plt 12
Thu Feb 19 21:20:36 2009 13

==================================================================== 14
BMDS Model Run 15

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 16 17 The form of the probability function is: 18 19 P[response] = background + (1-background)*[1-EXP( 20 -beta1*dose^1)] 21 22 The parameter betas are restricted to be positive 23 24 25 Dependent variable = incidence 26 Independent variable = dose 27 28
Total number of observations = 3 29 Total number of records with missing values = 0 30 Total number of parameters in model = 2 31

### E-12 DRAFT- DO NOT CITE OR QUOTE Total number of specified parameters = 0 1 Degree of polynomial = 1 2 3
Maximum number of iterations = 250 4 Relative Function Convergence has been set to: 2.22045e-016 5 Parameter Convergence has been set to: 1.49012e-008 6 7 **** We are sorry but Relative Function and Parameter Convergence   8 ** ** are currently unavailable in this model. Please keep checking   9 ** ** the web sight for model updates which will eventually   10 ** ** incorporate these convergence criterion. Default values used. **** 11 12
Default Initial Parameter Values 13 Background = 0.0642604 14 Beta(1) = 0.00181622 15 16 Asymptotic Correlation Matrix of Parameter Estimates 17 18 Background Beta(1) 19 20 Background 1 -0.56 21 22 Beta(1) -0.56 1 23 24
Parameter Estimates 25
95.0% Wald Confidence Interval 26 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 27 Background 0.0980033 * * * 28 Beta(1) 0.00170868 * * * 29
* - Indicates that this value is not calculated. 30 31 Analysis of Deviance Table 32 33 Model Log(likelihood) # Param's Deviance Test d.f. P-value 34
Full model -68.0892 3 35
Fitted model -68.199 2 0.219579 1 0.6394 36
Reduced model -99.8132 1 63.4479 2 <.0001 37 38
**AIC: 140.398** 39
Goodness of Fit 40
Scaled 41 Dose Est._Prob. Expected Observed Size Residual 42

------------------------------------------------------------------------ 43
0.0000 0.0980 4.900 5.000 50 0.047 44
475.0000 0.5994 28.171 27.000 47 -0.349 45
992.2000 0.8345 39.219 40.000 47 0.306 46 47
**Chi^2 = 0.22 d.f. = 1 P-value = 0.6408** 48 49 Benchmark Dose Computation 50 51 Specified effect = 0.1 52 Risk Type = Extra risk 53 54
Confidence level = 0.95 55 56
**BMD = 61.6618** 57 58
**BMDL = 48.628** 59 60
BMDU = 80.2137 61 62 Taken together, (48.628 , 80.2137) is a 90 % two-sided confidence 63
interval for the BMD 64 65 **Multistage Cancer Slope Factor = 0.00205643** 66

### E-13 DRAFT- DO NOT CITE OR QUOTE 

### E-2.3. Modeling results for the whole body metabolism metric, liver tumors 1 Mennear et al. (1988); NTP (1986): Internal whole-body metabolism dose-response for liver tumors in male 2 mice 3 1-degree polynomial 4

0.3 0.4 0.5 0.6 0.7 0.8

0 50 100 150 200Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

17:59 02/21 2009 BMD BMDL Multistage Cancer Linear extrapolation

5 FigureE-5. Predicted and observed incidence of animals with hepatocellular carcinoma or 6 adenoma in male B6C3F 1 mice exposed by inhalation to dich loromethane for 2 years, using whole7 body metabolism dose metric (Mennear et al., 1988; NTP, 1986 8

==================================================================== 9 Multistage Cancer Model. (Version: 1.7; Date: 05/16/2008) 10 Input Data File: C:\USEPA\IRIS\DCM\NTP\liver\male\1MulNTPMS_.(d) 11 Gnuplot Plotting File: C:\USEPA\IRIS\DCM\NTP\liver\male\1MulNTPMS_.plt 12
Sat Feb 21 17:59:59 2009 13

==================================================================== 14
BMDS Model Run 15

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 16 The form of the probability function is: 17 18 P[response] = background + (1-background)*[1-EXP( 19 -beta1*dose^1)] 20 21 The parameter betas are restricted to be positive 22 23 24 Dependent variable = incidence 25 Independent variable = dose 26 27
Total number of observations = 3 28 Total number of records with missing values = 0 29 Total number of parameters in model = 2 30 Total number of specified parameters = 0 31

### E-14 DRAFT- DO NOT CITE OR QUOTE Degree of polynomial = 1 1
Maximum number of iterations = 250 2 Relative Function Convergence has been set to: 2.22045e-016 3 Parameter Convergence has been set to: 1.49012e-008 4 5 **** We are sorry but Relative Function and Parameter Convergence   6 ** ** are currently unavailable in this model. Please keep checking   7 ** ** the web sight for model updates which will eventually   8 ** ** incorporate these convergence criterion. Default values used. **** 9 10
Default Initial Parameter Values 11 Background = 0.406695 12 Beta(1) = 0.00302163 13 14 Asymptotic Correlation Matrix of Parameter Estimates 15 16 Background Beta(1) 17 18 Background 1 -0.69 19 20 Beta(1) -0.69 1 21 22
Parameter Estimates 23
95.0% Wald Confidence Interval 24 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 25 Background 0.421768 * * * 26 Beta(1) 0.00272018 * * * 27 28
* - Indicates that this value is not calculated. 29 30 Analysis of Deviance Table 31 32 Model Log(likelihood) # Param's Deviance Test d.f. P-value 33
Full model -95.4892 3 34
Fitted model -95.8372 2 0.696122 1 0.4041 35
Reduced model -99.1316 1 7.28482 2 0.02619 36 37
**AIC: 195.674** 38
Goodness of Fit 39
Scaled 40 Dose Est._Prob. Expected Observed Size Residual 41

------------------------------------------------------------------------ 42
0.0000 0.4218 21.088 22.000 50 0.261 43
100.2000 0.5597 26.307 24.000 47 -0.678 44
210.7000 0.6740 31.679 33.000 47 0.411 45 46
**Chi^2 = 0.70 d.f. = 1 P-value = 0.4039** 47 48 Benchmark Dose Computation 49 50 Specified effect = 0.1 51 52 Risk Type = Extra risk 53 54
Confidence level = 0.95 55 56
**BMD = 38.733** 57 58
**BMDL = 23.0698** 59 60
BMDU = 108.885 61 62 Taken together, (23.0698, 108.885) is a 90 % two-sided confidence 63
interval for the BMD 64 65 **Multistage Cancer Slope Factor = 0.00433467** 66

### E-15 DRAFT- DO NOT CITE OR QUOTE 

### E-2.4. Modeling results for the whole bo dy metabolism metric, lung tumors 1 Mennear et al. (1988); NTP (1986): Internal whole-body metabolism dose-response for lung tumors in male 2 mice 3 1-degree polynomial 4 5

0 0.2 0.4 0.6 0.8

1

0 50 100 150 200Fraction Affected

dose Multistage Cancer Model with 0.95 Confidence Level

21:17 02/21 2009 BMD BMDL Multistage Cancer Linear extrapolation

6 Figure E-4. Predicted and observed incidence of an imals with carcinoma or adenoma in the lung of 7 male B6C3F 1 mice exposed by inhalation to dichloro methane for 2 years, using whole-body 8 metabolism dose metric (Mennear et al., 1988; NTP, 1986). 9

==================================================================== 10 Multistage Cancer Model. (Version: 1.7; Date: 05/16/2008) 11 Input Data File: C:\USEPA\IRIS\DCM\NTP\lung\male\1MulNTPMS_.(d) 12 Gnuplot Plotting File: C:\USEPA\IRIS\DCM\NTP\lung\male\1MulNTPMS_.plt 13
Sat Feb 21 21:17:36 2009 14

==================================================================== 15
BMDS Model Run 16

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 17 The form of the probability function is: 18 P[response] = background + (1-background)*[1-EXP( 19 -beta1*dose^1)] 20 21 The parameter betas are restricted to be positive 22 23 24 Dependent variable = incidence 25 Independent variable = dose 26 27
Total number of observations = 3 28 Total number of records with missing values = 0 29 Total number of parameters in model = 2 30 Total number of specified parameters = 0 31

### E-16 DRAFT- DO NOT CITE OR QUOTE Degree of polynomial = 1 1
Maximum number of iterations = 250 2 Relative Function Convergence has been set to: 2.22045e-016 3 Parameter Convergence has been set to: 1.49012e-008 4 5 **** We are sorry but Relative Function and Parameter Convergence   6 ** ** are currently unavailable in this model. Please keep checking   7 ** ** the web sight for model updates which will eventually   8 ** ** incorporate these convergence criterion. Default values used. **** 9 10
Default Initial Parameter Values 11 Background = 0.0659119 12 Beta(1) = 0.00855407 13 14 Asymptotic Correlation Matrix of Parameter Estimates 15 16 Background Beta(1) 17 18 Background 1 -0.56 19 20 Beta(1) -0.56 1 21 22
Parameter Estimates 23
95.0% Wald Confidence Interval 24 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. 25
Limit 26 Background 0.0980803 * * * 27 Beta(1) 0.00807004 * * * 28 29
* - Indicates that this value is not calculated. 30 31 Analysis of Deviance Table 32 33 Model Log(likelihood) # Param's Deviance Test d.f. P-value 34
Full model -68.0892 3 35
Fitted model -68.1887 2 0.198975 1 0.6555 36
Reduced model -99.8132 1 63.4479 2 <.0001 37 38
**AIC: 140.377** 39
Goodness of Fit 40
Scaled 41 Dose Est._Prob. Expected Observed Size Residual 42

------------------------------------------------------------------------ 43
0.0000 0.0981 4.904 5.000 50 0.046 44
100.2000 0.5982 28.116 27.000 47 -0.332 45
210.7000 0.8353 39.259 40.000 47 0.291 46 47
**Chi^2 = 0.20 d.f. = 1 P-value = 0.6569** 48 49 Benchmark Dose Computation 50 Specified effect = 0.1 51 52 Risk Type = Extra risk 53 54
Confidence level = 0.95 55 56
**BMD = 13.0558** 57 58
**BMDL = 10.2947** 59 60
BMDU = 16.9865 61 62 Taken together, (10.2947, 16.9865) is a 90 % two-sided confidence 63
interval for the BMD 64 65 **Multistage Cancer Slope Factor = 0.00971371** 66

### F-1 DRAFT- DO NOT CITE OR QUOTE 

### APPENDIX F. COMPARATIVE CANCER IN HALATION UNIT RISK BASED ON 1 

### FEMALE MICE DATA 2
3 

### Using the male B6C3F 1 

### mouse data from a 2-year inha lation exposure study (Mennear et 4 

### al., 1988; NTP, 1986), the recommended cancer IURs are 7 × 10 –9 

### ( μ g/m 3 

### ) –1 

### and 5 × 10 –9 5 

### ( μ g/m 3 

### ) –1 

### for the development of liver and lung can cer, respectively, based on the mean for the 6


### GST-T1 +/+ 

### population. These values were derived using a tissue-specific GST metabolism dose 7 

### metric, with allometric scaling. The combined the combined human equivalent IUR values for 8 

### both tumor types is 1 × 10 –8 

### ( μ g/m 3 

### ) –1

. 

### As described in detail below, the resulting combined 9 

### human equivalent IUR values for both tumo rs did not differ appreciably by gender. 10



### BMD 10 

### and BMDL 10 

### refer to the model-predicted dos e (and its lower 95% confidence 11 

### limit) associated with 10% extra risk for the co mbined incidence of adenoma and carcinoma of 12 

### the liver or lung of female B6C3F 1 

### mice inhaling dichloromethane for 2 years (Mennear et al., 13 

### 1988; NTP, 1986) (Table F-1). 14

15
Table F-1. Incidence data for liver and lung tumors and internal doses, based on GST metabolism dose metrics, in female B6C3F 1 mice exposed to dichloromethane via inhalation for 2 years

Sex, tumor type BW (g) External dichloromethane

concentration (ppm) Mouse tumor incidence Mouse internal tissue dose a Mouse whole body metabolism dose b
-- 0 3/47 (6%) e 0 0 Female, liver c 30.0 2,000 16/46 (35%) 2,453.2 104.0 29.0 4,000 40/46 (87%) 5,120.0 217.0 -- 0 3/45 (6%) e 0 0 30.0 2,000 30/46 (65%) 493.0 104.0 Female, lung d

29.0 4,000 41/46 (89%) 1,021.8 217.0

a For liver tumors: mg dichloromethane metabolized via GST pathway/L liver tissue/day from 6 hours per day, 5

days per week exposure; for lung tumors: mg dichloromethane metabolized via GST pathway/L lung tissue/day from 6 hours per day, 5 days per week exposure.

b Based on the sum of dichloromethane metabolized via the GST pathway in the lung plus the liver, normalized to

total BW (i.e., [lung GST metabolism (mg/day) + liver GST metabolism (mg/day)]/kg BW). Units = mg dichloromethane metabolized via GST pathway in lung and liver/kg-day.

c Hepatocellular carcinoma or adenoma. Mice dying prior to 52 weeks were excluded from the denominators.

d Bronchoalveolar carcinoma or adenoma. Mice dying prior to 52 weeks were excluded from the denominators.

e Statistically significant increasing trend (by incidental and life-table tests; p ≤ 0.01).

Sources: Mennear et al., 1988; NTP, 1986

16 

### Multistage models were fit to the female m ouse internal tissue doses of dichloromethane 17 

### metabolized by the GST pathway and incidences fo r animals with liver tumors observed at the 18 

### time of death. The predicted BMD 10 

### and BMDL 10 

### for the liver and lung tumor incidence data 19


### are shown in Table F-2. 20

### F-2 DRAFT- DO NOT CITE OR QUOTE 

### A probabilistic PBTK model fo r dichloromethane in humans adapted from David et al. 1 

### (2006) (see Appendix B) was used with Monte Carlo sampling to calculate distributions of 2 

### internal lung, liver, or blood doses asso ciated with chronic unit inhalation (1 μ g/m 3 

### ) exposures. 3 

### The model was then executed by using the external unit exposure as input, and the resulting 4 

### human equivalent internal dose was recorded. This process wa s repeated for 10,000 iterations to 5 

### generate a distribution of human internal doses. The resulting distribut ion of IURs shown in 6 

### Table F-3 were derived by multiplying the human in ternal dose tumor risk factor (in units of 7 

### reciprocal internal dose) by th e respective distributions of hum an average daily internal dose 8 

### resulting from a chronic un it inhalation exposure of 1 μ g/m 3 

### dichloromethane. Risk estimates 9 

### were slightly higher for liver tumors and esse ntially equivalent for lung tumors in males 10 

### compared to females. 11 

### For the female mouse, the combined human equivalent IUR values for both tumor types 12


### is 1 × 10 –8 

### ( μ g/m 3 

### ) –1 

### in the most sensitive (GST-T1 +/+ 

### ) population (Table F-4), which is the same 13 

### value that was obtained using the male mouse data. 14F-3 DRAFT- DO NOT CITE OR QUOTE1Table F-2.BMD modeling results and tumor risk factors associated with 10% extra risk for liver and lung tumors infemale B6C3F1mice exposed by inhalation to dichloromethane for 2 years, based onliver-specific GST metabolism andwhole body GST metabolism dose metricsTumor Risk FactoreInternal dosemetricaBMDSmodelbχ2goodness of fitp-valueMouseBMD10cMouseBMDL10cAllometricscaled humanBMDL10dScaling = 1.0AllometricscaledLiver-specific Female, liver MS (0,2) 0.53 1224.1 659.7 94.2 1.52 × 10–41.06 × 10–3Female, lung MS (0,1) 0.87 51.2 40.7 5.8 2.46 × 10–31.72 × 10–2Whole body Female, liverMS (0,2)0.53 51.9 28.0 4.0--2.50 × 10–2Female, lungMS (0,1)0.88 10.8 8.6 1.2--8.14 × 10–2aLiver specific dose units = mg dichloromethane metabolized via GST pathway per liter tissue per day; Whole-body dose units = mgdichloromethane metabolized via GST pathway in lung and liver/kg-day)bThe multistage (MS) model in EPA BMDS version 2.0 was fit to the mouse dose-response data shown in Table F-1 using internal dosemetrics calculated with the mouse PBTK model. Numbers in parentheses indicate (1) the number of dose groups dropped in order toobtain an adequate fit and (2) the degree polynomial of the model.cBMD10and BMDL10refer to the BMD-model-predicted mouse internal dose and its 95% lower confidence limit, associated with a 10%extra risk for the incidence of tumors.dMouse BMDL10divided by (BWhuman/BWmouse)0.25= 7.eDichloromethane tumor risk factor (extra risk per unit internal dose) derived by dividing the BMR (0.1) by the mouse BMDL10and by theallometric-scaled human BMDL10,, for the scaling = 1.0 and allometric-scaled risk factors, respectively.2F-4 DRAFT- DO NOT CITE OR QUOTE1Table F-3.IURs for dichloromethane based on PBTK model-derived internal liver and lung doses in B6C3F1femalemiceexposed via inhalation for 2 years, based on liver-specific GST metabolism and whole body metabolism dose metrics, bypopulation genotypeDistribution of human internaldichloromethane doses from 1μg/m3exposuredResulting candidate humanIURe(μg/m3)–1Internal dose metricand scaling factoraPopulationgenotypebTumortypeHumantumor riskfactorcMean95thpercentile99thpercentileMean95thpercentile99thpercentileGST-T1+/+Liver 1.06 × 10–35.64 × 10–61.56 × 10–52.60 × 10–56.0 × 10–91.8 × 10–82.8 × 10–8GST-T1+/+Lung 1.72 × 10–23.31 × 10–78.55 × 10–71.34 × 10–65.7 × 10–91.5 × 10–82.3 × 10–8Mixed Liver 1.06 × 10–32.62 × 10–68.65 × 10–61.45 × 10–52.8 × 10–99.2 × 10–91.5 × 10–8Tissue-specific,allometric-scaledMixed Lung 1.72 × 10–21.81 × 10–75.67 × 10–79.84 × 10–73.1 × 10–99.8 × 10–91.7 × 10–8GST-T1+/+Liver 1.52 × 10–45.64 × 10–61.56 × 10–52.60 × 10–58.6 × 10–102.5 × 10–93.9 × 10–9GST-T1+/+Lung 2.46 × 10–33.31 × 10–78.55 × 10–71.34 × 10–68.1 × 10–102.1 × 10–93.3 × 10–9Mixed Liver 1.52 × 10–42.62 × 10–68.65 × 10–61.45 × 10–54.0 × 10–101.3 × 10–92.2 × 10–9Tissue-specific,scaling = 1.0Mixed Lung 2.46 × 10–31.81 × 10–75.67 × 10–79.84 × 10–73.5 × 10–101.4 × 10–92.4 × 10–9GST-T1+/+Liver 2.50 × 10–21.53 × 10–74.87 × 10–79.20 × 10–73.8 × 10–91.2 × 10–82.3 × 10–8GST-T1+/+Lung 8.14 × 10–21.53 × 10–74.87 × 10–79.20 × 10–71.3 × 10–84.0 × 10–87.5 × 10–8Mixed Liver 2.50 × 10–28.76 × 10–83.20 × 10–76.76 × 10–72.2 × 10–98.0 × 10–91.7 × 10–8Whole-body,allometric-scaledMixed Lung 8.14 × 10–28.76 × 10–83.20 × 10–76.76 × 10–77.1 × 10–92.6 × 10–85.5 × 10–8aTissue specific dose units = mg dichloromethane metabolized viaGST pathway per liter tissue (liveror lung, respectively, forliver and lungtumors) per day; Whole-body dose units = mg dichloromethane metabolized via GST pathway in lung and liver/kg-day.bGST-T1+/+= homozygous, full enzyme activity;); mixed = population reflecting estimated frequency of genotypes in current U.S. population: 20% GST-T–/–, 48% GST-T1+/–, and 32% GST-T1+/+(Haber et al., 2002).cDichloromethane tumor risk factor (extra risk per unit internal dose) derived by dividing the BMR (0.1) by the allometric-scaled human BMDL10or by themouse BMDL10(from Table F-2) for the allometric-scaled andscaling = 1.0 risk factors, respectively.dMean, 95th, and 99thpercentile of the human PBTK model-derived probability distribution of daily average internal dichloromethane dose resulting fromchronic exposure to 1μg/m3(0.00029 ppm).eDerived by multiplying the dichloromethane tumor risk factor bythe PBTK model-derived probabilistic internal doses from dailyexposure to 1μg/m32F-5 DRAFT- DO NOT CITE OR QUOTE1Table F-4. Upper bound estimates of combined humanIURs for liver and lung tumors resulting from lifetimeexposure to 1μg/m3dichloromethane, based on liver-specific GST metabolism and wholebody metabolism dosemetrics, by population genotype, using femalemouse data for derivation of risk factorsInternal dosemetric and scalingfactoraPopulationgenotypebTumor siteUpperbound IURcCentraltendencyIURdVariance oftissue-specifictumor riskeCombinedtumor riskSDfUpper bound oncombined tumorriskg(μg/m3)–1Liver 6.0 × 10–93.2 × 10–92.82 × 10–18Lung 5.7 × 10–94.5 × 10–95.16 × 10–19Tissue-specific,allometric-scaledGST-T1+/+Liver or lung 7.8 × 10–91.8 × 10–91.1 × 10–8Liver 2.8 × 10–91.5 × 10–96.08 × 10–19Lung 3.1 × 10–92.5 × 10–91.54 × 10–19MixedLiver or lung 4.0 × 10–98.7 × 10–105.4 × 10–9Liver 8.6 × 10–104.6 × 10–105.74 × 10–20Lung 8.1 × 10–106.6 × 10–101.03 × 10–21GST-T1+/+Liver or lung 1.1 × 10–92.6 × 10–101.5 × 10–9Liver 4.0 × 10–102.1 × 10–101.24 × 10–20Lung 4.6 × 10–103.5 × 10–103.07 × 10–21Tissue-specific,scaling = 1.0MixedLiver or lung 5.7 × 10–101.2 × 10–107.7 × 10–10Liver 3.8 × 10–92.1 × 10–91.15 × 10–18Lung 1.3 × 10–89.9 × 10–92.97 × 10–18GST-T1+/+Liver or lung 1.2 × 10–82.0 × 10–91.5 × 10–8Liver 2.2 × 10–91.2 × 10–93.76 × 10–19Lung 7.3 × 10–95.7 × 10–99.72 × 10–19Whole-body,allometric-scaledMixedLiver or lung 6.8 × 10–91.2 × 10–98.8 × 10–9aTissue specific dose units = mg dichloromethane metabolized viaGST pathway per liter tissue (liveror lung, respectively, forliver andlung tumors) per day; Whole-body dose units = mg dichloromethane metabolized via GST pathway in lung and liver/kg-day.bGST-T1+/+= homozygous, full enzyme activity;); mixed = population reflecting estimated frequency of genotypes in current U.S. population: 20%GST-T–/–, 48% GST-T1+/–, and 32% GST-T1+/+(Haber et al., 2002).cEstimated at the human equivalent BMDL10(0.1/BMDL10) (see Table F-2).dEstimated at the human equivalent BMD10(0.1/BMD) (see Table F-2).eCalculated as the square of the difference of the upper bound and central tendency IURs divided by thetstatistic, 1.645.rCalculated as the square root of the sum ofthe variances for liver and lung tumors.gCalculated as the product of thecumulative tumor risk SD and thetstatistic, 1.645, added to the sum of central tendency IURs.2

### G-1 DRAFT- DO NOT CITE OR QUOTE 

### APPENDIX G. COMPARATIVE CANCER IN HALATION UNIT RISK BASED ON 3 

### BENIGN MAMMARY GLAND TUMORS IN RATS 4

5
6 

### Data for mammary gland tumors in male and female F344 rats following exposure to 7


### airborne dichloromethane were used to de velop a comparative IUR for dichloromethane 8 

### (Mennear et al., 1988; NTP, 1986) (Table G-1). Significantly incr eased incidences of mammary 9 

### gland or subcutaneous tissue adenom a, fibroadenomas, or fibromas were observed in male rats at 10 

### 4,000 ppm, while mammary gland adenomas or fibr oadenomas were increas ed in female rats 11 

### exposed 6 hours/day, 5 days/week for 2 years at concentrations ≥ 1,000 ppm. Significant 12


### decreases in survival were observed in the tr eated groups of both sexes. The at-risk study 13 

### populations (represented by the de nominators in the incidence data) were determined by 14 

### excluding all animals dyi ng prior to 52 weeks. 15

16

Table G-1. Incidence data for mammary gland tumors and internal doses, based on different dose metrics, in male and female F344 rats exposed to dichloromethane via inhalation for 2 years

Sex BW (g) External dichloromethane concentration (ppm) Rat tumor incidence a Rat internal dose, AUC in blood b

-- 0 1/50 ( 2% ) c 0
390.5 1

, 000 1/50 ( 2% ) 93.3 385.2 2

, 000 4/50 ( 8% ) 196.4 Male

384.8 4,000 9/50 (18%) 403.5
-- 0 6/49 ( 12% ) c 0 245.5 1

, 000 13/50 ( 26% ) 93.3 244.3 2

, 000 14/50 ( 28% ) 196.2 Female

242.2 4,000 23/50 (46%) 403.0

a Male tumors include mammary gland or subcutaneous tissue adenoma, fibroadenomas, or

fibroma. Female tumors include mammary gland adenoma or fibroadenomas. Rats dying prior to 52 weeks were excluded from the denominators.

b Average daily AUC for dichloromethane in slowly perfused tissue (mg × hour/L) (See text for

rationale for using this dose metric.)

c Statistically significant increasing trend (p ≤ 0.01).

Sources: Mennear et al., 1988; NTP, 1986. 17 18 

### The rat PBTK model of Anders en et al. (1991) (modified as described in Appendix D) 19 

### was used to simulate inhalation exposures of 6 hours/day, 5 days/week a nd calculate long-term 20 

### daily average internal do ses for the 2-year bioassays (Menne ar et al., 1988; NTP, 1986). Study-, 21 

### group-, and sex-specific mean BW s for rats were used. The modified PBTK model did not 22 

### include a compartment for the mammary gla nd nor did it account for metabolism of 23 

### dichloromethane occurring in the mammary gla nd. The selected internal dose metric for 24

### G-2 DRAFT- DO NOT CITE OR QUOTE 

### mammary gland tumors was the average daily AUC for dichloromethane in slowly perfused 25 

### tissue; the mammary gland and the regions around it consist primarily of fatty tissue, which is 26 

### slowly perfused tissue. The role of CYP- or GST-mediated metabolism in the mammary gland is 27 

### uncertain. GST-T1 (Lehmann a nd Wagner, 2008) and CYP2E1 (El-Rayes et al., 2003; Hellmold 28 

### et al., 1998) expression has been detected in human mammary tissue, and it is also possible that 29 

### some metabolites enter systemic circulation from the liver and lung, where they are formed. 30 

### Figure G-1 shows the comparison be tween inhalation external and in ternal doses in the liver and 31 

### lung, respectively, using this dose me tric for the rat and the human. 32 

### The multistage model was fit to the rat mammary gland tumor incidence and PBTK 33


### model-derived internal dose data to derive rat internal BMD 10 

### and BMDL 10 

### values associated 34 

### with 10% extra risk (Table G-2)

. 35

36

37 

### Figure G-1. PBTK model-derived in ternal doses (daily average AUC for 38 

### dichloromethane in blood) in rats and humans, and their associated external 39 

### exposures (ppm) used for the derivation of cancer IURs, based on mammary 40 

### tumors in rats. Average daily doses were calculated from simulated rat exposures of 6 41 hours/day, 5 days/week, while simulated human exposures were continuous. The GST 42 metabolism rate in the human population for the mixed GSTT1 group (+/+, +/-, and -/-) in the 43 current U.S. population was estimated as the mean of a simulated sample of 3000 individuals at 44 each exposure concentration, based on GSTT1 poly morphism data from Haber et al. (2002). The 45 results for the GSTT1 +/- and +/+ sub-populations were then calculated as the means of the 46 subsets of the mixed population sample with the respective genotypes. 47
48

### G-3 DRAFT- DO NOT CITE OR QUOTE 49

Table G-2. BMD modeling results associated with 10% extra risk for mammary gland tumors in F344 rats exposed by inhalation to di chloromethane for 2 years, based on AUC for dichloromethane in slowly perfused tissue

Sex Tumor type BMDS model a 

### χ 2 

### goodness of fit p-value Rat
BMD 10 c Rat
BMDL 10 c Tumor Risk Factor d

Male Mammary gland or subcutaneous tissue adenoma, fibroadenoma, or fibroma MS (0,1) 0.53 275.3 172.3 5.80 × 10 –4

Female Mammary gland adenoma or fibroadenoma MS (0,1) 0.71 91.0 61.5 1.63 × 10 –3

a The multistage (MS) model in EPA BMDS version 2.0 wa s fit to each of the two sets of rat dose-response

data shown in Table G-1 using internal dose metric s calculated with the rat PBTK model. Numbers in parentheses indicate (1) the number of dose groups dropped in order to obtain an adequate and (2) the degree polynomial of the model.

c BMD 10 and BMDL 10 refer to the BMD-model-predicted rat internal dose (average daily AUC for dichloromethane in

slowly perfused tissue [mg × hour/L]), and its 95% lower co nfidence limit, associated with a 10% extra risk for the incidence of tumors.

d Dichloromethane tumor risk factor (extra risk per averag e daily AUC for dichloromethane in slowly perfused tissue

[mg × hour/L]) derived by dividing the BMR (0.1) by the rat BMDL 10 ). The rat BMDL 10 is assumed to be equivalent to human BMDL 10 ; humans exposed to the same average daily AUC for dichloromethane in slowly perfused tissue as rats will have the same risks for mammary tumors.

50 51 

### Rat mammary gland tumor risk factors (extra risk per unit internal dose) were calculated 52 

### by dividing 0.1 by the rat internal BMDL 10 

### . Because this risk factor is based on the internal 53 

### concentration of dichloromethane, rather than a rate of reaction, it is assumed that the human risk 54 

### factor is equal to that of th e rat (i.e., that humans exposed for a 70-year lifetime to the same 55 

### weekly average AUC of dichlorome thane will have the same risk as rats exposed for 2 years). 56 

### The human PBTK model (David et al., 2006) was used to calc ulate a distribution of human 57 

### average daily AUCs for dichloromethane in slow ly perfused tissue resulting from chronic, 58 

### inhalation exposure to a unit concentration of 1 μ g/m 3 

### dichloromethane (0.00029 ppm). The 59 

### distribution of IURs for mammary gland tumors was generated by multiplying the human tumor 60 

### risk factor for each sex by the distribution of in ternal doses from chronic human exposure to 1 61



### μ g/m 3 

### dichloromethane. Because this analysis is not based on the assumption that either 62 

### metabolic pathway is or is not influencing the ca ncer risk, this distribution was derived by using 63 

### weights reflecting the estimated frequency of GS T-T1 genotypes in the current U.S. population 64 

### (20% GST-T1 –/– 

### , 48% GST-T1 +/– 

### , and 32% GST-T1 +/+ 

### ). As shown in Table G-3, the mean 65 

### human IUR based on mammary gland tumors in rats is 4 × 10 –8 

### and 1 × 10 –7 

### ( μ g/m 3 

### ) –1 

### based on 66 

### male and female rat-derived risk factors, resp ectively. Identical valu es were obtained using 67 

### slowly perfused tissue as the internal dose metric. 68

### G-4 DRAFT- DO NOT CITE OR QUOTE 1

Table G-3. IURs for dichloromethane, ba sed on benign mammary tumors and PBTK model-derived internal doses in F344N rats ex posed via inhalation for 2 years, based on AUC for dichloromethane in slowly perfused tissue dose metric

Distribution of human internal dichloromethane doses from 1 μ g/m 3

exposure b Resulting candidate human IUR c

Sex, tumor type Human tumor risk

factor a Mean 95 th percentile 99 th percentile Mean 95 th percentile 99 th percentile

Male 5.80 × 10 –4 6.39 × 10 –5 1.02 × 10 –4 1.29 × 10 –4 3.7 × 10 –8 5.9 × 10 –8 7.5 × 10 –8

Female 1.63 × 10 –3 6.39 × 10 –5 1.02 × 10 –4 1.29 × 10 –4 1.0 × 10 –7 1.7 × 10 –7 2.1 × 10 –7

a Dichloromethane tumor risk factor (extra risk per aver age daily AUC for dichloromethane in slowly perfused

tissue [mg × hour/L]) derived by dividing the BMR (0.1) by the rat BMDL 10 (from Table G-2). The rat
BMDL 10 is assumed to be equivalent to human BMDL 10 ; humans exposed to the same average daily AUC for dichloromethane in slowly perfused tissue as rats will have the same risks for mammary tumors.

b Mean, 95 th , and 99 th percentile of the human PBTK model-derived probability distribution of daily average

internal dichloromethane dose (average daily AUC for dichloromethane in slowly perfused tissue [mg × hour/L]), resulting from chronic inhalatio n exposure to a unit concentration of 1 μ g/m 3 (0.00029 ppm), based on a distribution of GST-T1 genotypes that reflects the frequency distribution in the current U.S. population (Haber et al., 2002).

c Mean, 95 th , and 99 th percentile of a distribution of human IURs (extra risk per μ g/m 3 ) derived by multiplying the

dichloromethane tumor risk factor by the PBTK model-derived probabilistic distribution of human internal dichloromethane doses from unit dichloromethane inhalation exposure.

2

### H-1 DRAFT- DO NOT CITE OR QUOTE APPENDIX H: SOURCE CODE AND COMMAND FILES FOR 3


### DICHLOROMETHANE PBTK MODELS 4

5 

### The following is a copy of the primary acslXtreme code (.csl file for implementation 6 

### under acslXtreme v. 2.4.2.1) used for the dichlorome thane simulations. Portions of the code 7 

### which had been commented out (i .e., unused code) were deleted fo r brevity. Note that most 8 

### parameters are set in the subsequent script/m-file s, used to specify simulations and call the .csl 9


### code. 10 

### While the code indicates modifications by G Diamond and M Lumpkin (employees of the 11 

### U.S. EPA contractor, Syracuse Re search Center), these are change s from the version of the code 12 

### as received by them to bring it into alignment with the version as described in the publications of 13 

### Marino et al. (2006) and David et al. (2006) (changes noted by co mments). Other changes allow 14 

### for the calculation of various metrics to improve time-efficiency for computational convergence; 15 

### for example, calculating an averag e AUC over only the last week of simulated exposure requires 16


### a much shorter overall simulated time for the ca lculation to give the st eady-state or very-long17 

### term average, when the approach to steady-stat e or repeating periodic solution occurs over the 18 

### first days or weeks of exposure. Thus the code exactly replicates the published models when the 19 

### published parameter values are used. 20

21



### PROGRAM: DCM.csl 22
23 

### ! Code from Rietz et al. 1997, Addressing Data Needs for Methylene Chloride with 24 

### !Physiologically Based Pharmacokinetic Modeling (Appendix I) 25 

### ! Translated by GDiamond (05/2004); DCM.CSL.Reitz. 26 

### ! Revision date 18-Dec-96 by RHR (Peak moved to dynamic) 27 

### ! Modified by GDiamond (08/2004) based on Andersen et al. 1987 (TAP 87:185-205) 28 

### ! and 1991 (TAP 108:14-27): 29
30 

### ! Deleted brain compartment 31 

### ! Added lung compartment, with lung metabolism 32 

### ! Adjusted metabolism parameter valu es to match Andersen et al. 1987 33 

### ! Adjusted physiological parameters to match Andersen et al. 1987 34 

### ! Modifed by M Lumpkin (11/2005) (MHL) to include extrahepatic MFO metabolism and 35 

### ! CO kineticsin blood 36 

### ! Additional comments and changes by Paul Schlosser (PS), U.S. EPA (7/2008 - 9/2009) 37 

### ! Removed unused (legacy) code bits, added comments. PS 9/2009 38
39 

### INITIAL 40 

### !Simulation T=hour 41 

### NSTP=1000 ! Initial integration cycle length at CINT/1000 42 

### !MERROR ALU=0.00001 ! Error tolerance for Gear 43 

### CONSTANT POINTS=96.0 ! Number of points in plot 44 

### CINTERVAL CINT=0.1 ! Communication interval 45

### H-2 DRAFT- DO NOT CITE OR QUOTE CONSTANT TSTOP=24.0 ! Termination time (hr) 46
47 

### !Initial values for 250 g rat 48
49 

### !Body masses and fractional volumes 50 

### CONSTANT BW=0.25 ! Body weight (kg) MHL 51 

### CONSTANT VBL2C=0.059 ! Fractional volume of blood MHL 52 

### CONSTANT VFC=0.07 ! Fractional volume fat 53 

### CONSTANT VLC=0.04 ! Fractional volume of liver 54 

### CONSTANT VRC=0.05 ! Fractional volume of rapidly-perfused tissues 55 

### CONSTANT VSC=0.75 ! Fractional volume of slowly-perfused tissues ** 56 

### CONSTANT VLUC=0.0115 ! Allometric scaling factor for lung volume ** 57
58 

### ! Tissue masses (kg) 59 

### VTOT=VFC+VLC+(VLUC/(BW**0.01))+VRC+VSC 60 

### !total volume fractions constrained to sum to 0.9215 (7.85% carcass) MHL 61 

### VBL2=VBL2C*BW ! Blood 62 

### VF=VFC*BW*0.9215/VTOT ! Fat 63 

### VL=VLC*BW*0.9215/VTOT ! Liver 64 

### VR=VRC*BW*0.9215/VTOT ! Rapidly-perfused tissue 65 

### VS=VSC*BW*0.9215/VTOT ! Slowly-perfused tissue 66 

### VLU=VLUC*(BW**0.99)*0.9215/VTOT ! Lung ** 67
68 

### ! Flow constants and fractions 69 

### CONSTANT VPR=0.42 ! Ventilation/perfusion ratio (for QP calc) MHL 70 

### CONSTANT DSPC=0.15 ! Fractional lung dead space MHL 71 

### CONSTANT QCC=15.0 ! Allometric constant for cardiac output 72 

### CONSTANT QCCR=10.0 ! For 'resting' period, PMS 8/4/09 73 

### CONSTANT QCSW=0 ! Set to 1 to use QCCR (resting cardiac rate) up to TCHNG 74 

### CONSTANT QLC=0.20 ! Fractional flow to liver 75 

### CONSTANT QFC=0.09 ! Fractional flow to fat 76 

### CONSTANT QSC=0.15 ! Fractional flow to slowly-perfused tissue 77 

### CONSTANT QRC=0.56 ! Fractional flow to rapidly-perfused tissue ** MHL 78
79 

### ! Flow rates (L/hr) 80
81 

### IF (QCSW) THEN 82 

### QC=QCCR*BW**0.74 ! Cardiac output if resting, PMS 8/4/09 83 

### ELSE 84 

### QC=QCC*BW**0.74 ! Cardiac output if not resting, PMS 8/4/09 85 

### ENDIF 86 

### QP=QC*VPR ! Alveolar ventilation rate MHL 87 

### QPDP=QP/DL/PAIR ! MHL 88 

### QTOT=QFC+QLC+QRC+QSC ! MHL 89 

### QL=QLC*QC/QTOT ! Liver MHL 90 

### QF=QFC*QC/QTOT ! Fat MHL 91 

### QR=QRC*QC/QTOT ! Rapidly-perfused tissue MHL 92 

### QS=QSC*QC/QTOT ! Slowly-perfused MHL 93
94 

### ! Partition coefficients 95 

### CONSTANT PL=0.732 ! Liver:blood 96

### H-3 DRAFT- DO NOT CITE OR QUOTE CONSTANT PF=6.19 ! Fat:blood 97 

### CONSTANT PS=0.408 ! Muscle:blood 98 

### CONSTANT PR=0.732 ! Liver:blood 99 

### CONSTANT PLU=0.732 ! Lung:blood ** 100 

### CONSTANT PB=19.4 ! Blood:air 101
102 

### ! Metabolism 103 

### CONSTANT VMAXC=4.0 ! Allometric scaling constant for VMAX 104 

### CONSTANT KM=0.4 ! Michaelis constant for MFO pathway (mg/L) 105 

### CONSTANT FRACR=0.1 ! Oxidative metab in rapidly perfused MHL 106 

### CONSTANT KFC=2.0 ! Allometric scaling constant for KF 107 

### CONSTANT MOLWT=85.0 ! Molecular weight of DCM 108 

### CONSTANT MWCO=28.0 ! Molecular weight of CO MHL 109 

### CONSTANT A1=0.416 ! Ratio of speci fic activities of MFO, lung/liver MHL 110 

### CONSTANT A2=0.137 ! Ratio of speci fic activities of GST, lung/liver MHL 111
112 

### VMAX=VMAXC*BW**0.7 ! Maximum rate of MFO pathway (mg/hr) 113 

### VMAXR=VMAX*FRACR ! MHL 114 

### KF=KFC/(BW**0.3) ! First-order ra te constant for GSH pathway 115
116 

### ! CO Submodel params MHL 117 

### CONSTANT DLC=0.060 ! MHL 118 

### CONSTANT RENCOC=0.035 ! Rate of endogenous CO production MHL 119 

### CONSTANT ABCOC=0.117 ! Conc of background CO (mg/kg) MHL 120 

### CONSTANT HBTOT=10.0 ! Conc of hemoglobin (mmoles/L) MHL 121 

### CONSTANT P1=0.80 ! CO yeild factor MHL 122 

### CONSTANT F1=1.21 ! CO elimination factor MHL 123 

### CONSTANT M=150.0 ! Haldane coefficient MHL 124 

### CONSTANT COINH=2.2 ! Background CO inhalation concentration (ppm), MHL 125 

### CONSTANT O2=0.13 ! 4/16/32 mmoles/L MHL 126 

### CONSTANT PAIR=713.0 ! Pressure of air (mm Hg) MHL 127 

### CONSTANT RHO=1102.0 ! Density of CO (mg/L) MHL 128 

### CONSTANT SOL=0.03 ! mg/L/mmHG, MHL 129 

### DL=DLC*BW**0.92 ! MHL 130 

### RENCO=RENCOC*BW**0.7 ! MHL 131 

### ABCO=ABCOC*BW ! MHL 132 

### Mmm=1.1325*M ! MHL 133 

### L12D=1.0/P1 ! MHL 134 

### LCO=28.0/MOLWT ! MHL 135 

### L12=LCO*P1 ! MHL 136
137 

### ! Exposure schedule 138 

### CONSTANT CONC=0.0 ! Inha led concentration (ppm) 139 

### CONSTANT TCHNG=6.0 ! Exposure pulse 1 width (hr) 140 

### CONSTANT TDUR=24.0 ! Exposure duration (hr) 141 

### CONSTANT TCHNG2=120.0 ! Exposure pulse 2 width (hr) 142 

### CONSTANT TDUR2=168.0 ! Exposure duration 2 (hr) 143 

### CIZONE = 1.0 ! Start with inhalation on 144 

### PEAK = 0.0 ! Zero peak concentration in brain 145 

### CONSTANT IVDOSE=0.0 ! IV infusion (mg/kg) MHL 146 

### CONSTANT TIV=0.0028 ! IV dosi ng time (hrs, or 10 seconds) MHL 147

### H-4 DRAFT- DO NOT CITE OR QUOTE CONSTANT VCHC=10.0 ! Uncorrected rat chamber volume (L) MHL 148 

### CONSTANT CC=0 ! Flag for closed chamber MHL 149 

### CONSTANT NCH=5 ! Number of animals in chamber MHL 150 

### CONSTANT KL=0.0 ! MHL 151 

### VCH=VCHC-(NCH*BW) 152
153 

### ! Oral Gavage Dosing added by MHL 154 

### CONSTANT BOLUS=0.0 155 

### TOTALBOLUS=BOLUS*BW 156 

### TOTALDOSE=(IVDOSE+BOLUS)*BW 157 

### IVORBOL=(TOTALDOSE.GT.0.0) 158 

### CONSTANT DRCONC=0.0 ! User-specified c oncentration in drinking water (mg/L) 159 

### CONSTANT FIXDRDOSE=0.0 ! Set to constant DW dose in mg/kg-day 160 

### ! Assume 70 kg human drinks 2 L/day, calculate rodent allometrically 161 

### DRVOL=0.102*BW**0.7 ! Daily water intake, based on body weight 162 

### DRDOSE=DRVOL*DRCONC + FIXDRDOSE*BW ! Total dose from water (mg (in a day)) 163 

### DDOSE=DRDOSE/BW ! Daily dose (mg/kg/day) 164 

### NEWDAY=0.0 ! To reset area-under-curve values each 24 hours 165 

### PEAKPCTHBCO=0.0 166 

### CONSTANT KA=5.0 ! Rate constant for absorption 167
168 

### ! Periodic drinking water intake schedule 169 

### ! Assume T=0 is 7 AM (T+4=11 AM) 170 

### INTEGER I 171 

### I=2 172 

### DIMENSION DRTIME(6) ! Store drinking times in array 173 

### DIMENSION DRPCT(6) ! Store drinking percentages array 174 

### CONSTANT DRTIME=6*0.0, DRPCT=6*0.0 175 

### GASD=MOLWT/24450.0 176 

### SCHEDULE drink .AT. DRTIME(2) ! Assume DRTIME(1)=0 177 

### ! and initial drink applied here. 178 

### CUMORALDOSE=DRPCT(1)*DRDOSE ! To calculate input to GI (mg) 179
180 

### ! Switching 'lastday' and 'lastwk' to be de fined by discrete blocks, below, PS 3/2009 181 

### lastday=0.0 182 

### IF (TSTOP.GT.24) SCHEDULE lday .AT. TSTOP-24 183 

### lastwk=0.0 184 

### if (TSTOP.GT.168) SCHEDULE lwk .AT. TSTOP-168 185
186 

### CIDAY=1.0 187 

### CIWK=1.0 188 

### SCHEDULE idayoff .AT. TCHNG 189 

### SCHEDULE idayon .AT. TDUR 190 

### SCHEDULE iwkoff .AT. TCHNG2 191 

### SCHEDULE iwkon .AT. TDUR2 192 

### !zzstopflag = .FALSE. 193 

### END ! End of INITIAL section of program 194
195 

### DYNAMIC 196 

### DISCRETE idayoff 197 

### CIDAY=0.0 198

### H-5 DRAFT- DO NOT CITE OR QUOTE QC=QCC*BW**0.74 199 

### SCHEDULE idayoff .AT. (T+TDUR) 200 

### END 201 

### DISCRETE idayon 202 

### CIDAY=1.0 203 

### IF (QCSW) THEN 204 

### QC=QCCR*BW**0.74 205 

### ENDIF 206 

### SCHEDULE idayon .AT. (T+TDUR) 207 

### END 208 

### DISCRETE iwkoff 209 

### CIWK=0.0 210 

### SCHEDULE iwkoff .AT. (T+TDUR2) 211 

### END 212 

### DISCRETE iwkon 213 

### CIWK=1.0 214 

### SCHEDULE iwkon .AT. (T+TDUR2) 215 

### END 216 

### CIZONE = CIDAY*CIWK 217
218 

### DISCRETE lday ! Value -> 1 for last 24 h, PS 3/2009 219 

### lastday=1.0 220 

### end ! of lday 221 

### DISCRETE lwk ! Value -> 1 for last 168 h, PS 3/2009 222 

### lastwk=1.0 223 

### end ! of lwk 224
225 

### DISCRETE drink ! Loop for drinking water intake schedule 226 

### CUMORALDOSE = CUMORALDOSE + DRPCT(I)*DRDOSE 227 

### I=I+1 228 

### IF (I .EQ. 7) THEN 229 

### I=1 230 

### NEWDAY=NEWDAY + 24.0 231 

### ENDIF 232 

### SCHEDULE drink .AT. (NEWDAY+DRTIME(I)) 233 

### End ! drink 234
235 

### DERIVATIVE 236 

### ALGORITHM IALG=2 ! Gear for stiff systems 237
238 

### ! Following are daily averages over final week of simulations, PS 8/2008 239 

### WAVGLIVCYPDOSE=integ(lastwk*RAM1L,0.0)/(7.0*VL) 240 

### WAVGLIVGSTDOSE=integ(lastwk*RAM2L,0.0)/(7.0*VL) 241 

### WAVGLUNGGSTDOSE=integ(lastwk*RAM2LU,0.0)/(7.0*VLU) 242 

### WAVGWBDYGSTDOSE=integ(lastwk*(RAM2L+RAM2LU),0.0)/(7.0*BW) 243 

### WAVGWBDYCYPDOSE=integ(lastwk*(RAM1L+RAM1LU),0.0)/(7.0*BW) 244 

### WAVGAUCV=integ(lastwk*CV,0.0)/7.0 245 

### WAVGAUCS=integ(lastwk*CS,0.0)/7.0 246 

### WAVGAUCL=integ(lastwk*CL,0.0)/7.0 247
248 

### ! Following are daily averages calculated from final day's dose-rate, PS 8/2008 249

### H-6 DRAFT- DO NOT CITE OR QUOTE LDAYLIVCYPDOSE=integ(lastday*RAM1L,0.0)/VL 250 

### LDAYLIVGSTDOSE=integ(lastday*RAM2L,0.0)/VL 251 

### LDAYLUNGGSTDOSE=integ(lastday*RAM2LU,0.0)/VLU 252 

### LDAYWBDYGSTDOSE=integ(lastday*(RAM2L+RAM2LU),0.0)/BW 253 

### LDAYWBDYCYPDOSE=integ(lastday*(RAM1L+RAM1LU),0.0)/BW 254 

### LDAYLIVAUC=integ(lastday*CL,0.0) 255 

### LDAYAUCV=integ(lastday*CV,0.0) 256 

### LDAYAUCS=integ(lastday*CS,0.0) 257
258 

### ! GI compartment for drinking water inputs 259 

### RSTOM=-KA*STOM 260 

### STOM=INTEG(RSTOM,TOTALBOLUS)+CUMORALDOSE 261
262 

### RIV=IVDOSE*BW/TIV ! IV dose rate (mg/hr) MHL 263 

### blip=STEP(tiv) 264 

### IV=RIV*(1.0-blip) 265
266 

### ! Chamber calcs MHL 267 

### RACH=CC*NCH*QP*((CA1/PB)-CCH)-(KL*ACH) ! MHL 268 

### ACH=INTEG(RACH,CONC*GASD*VCH) !MHL added initial condition 269 

### CCH=CC*ACH/VCH ! MHL 270 

### CCHPPM=CCH/GASD ! MHL original 271
272 

### ! If/else statements removed by use of multipliers; PMS 7-23-08 273 

### CI=(1.0-CC)*CONC*CIZONE* GASD !Convert to mg/L 274 

### DIDOSE=24.0*QP*integ(CI,0.0)/(BW*(T+1.0E-8))!Daily inhaled dose (mg/kg-d) 275
276 

### ! CA1=Arterial blood concentration from gas exchange region 277 

### ! to lung tissue compartment (mg DCM/L) 278 

### CA1=(QP*CI+QP*CCH+QC*CV)/((QP/PB)+QC) !** 279
280 

### !AX=Amount eliminated by exhalation (mg DCM) 281 

### CX=CA1/PB ! Concentration in air leaving gas exchange region 282 

### RAX=QP*CX 283 

### AX=INTEG(RAX, 0.0) 284
285 

### ! Concentration DCM in exhaled air 286 

### CEX1=0.7*CX + 0.3*CI 287 

### ! Assumes mixing with 30% of inhaled that only goes to deadspace 288 

### RAEX1=CEX1*QP ! MHL 289 

### AEX1=INTEG(RAEX1,0.0) ! MHL 290
291 

### PCTIVEXH=100.0*IVORBOL*AX/(TOTALDOSE + 1.0E-8) 292 

### ! Percent IV/BOLUS dose exhaled as DCM. MHL 293
294 

### !Amount in lung tissue (mg DCM) !** 295 

### RAM1LU=A1*VMAX*CA*(VLU/VL)/(KM+CA) 296 

### AM1LU=INTEG(RAM1LU, 0.0) 297 

### RAM2LU=A2*KF*CA*VLU 298 

### RALU=QC*(CA1-CA)-RAM1LU-RAM2LU 299 

### ALU=INTEG(RALU, 0.0) 300

### H-7 DRAFT- DO NOT CITE OR QUOTE CLU=ALU/VLU 301 

### AUCLU=INTEG(CLU,0.0) 302 

### CA=CLU/PLU ! Amount in arterial blood to body 303
304 

### ! AF=Amount in fat (mg DCM) 305 

### RAF=QF*(CA-CVF) 306 

### AF=INTEG(RAF, 0.0) 307 

### CF=AF/VF 308 

### CVF=CF/PF 309
310 

### ! AL=Amount in liver (mg DCM) 311 

### RAL=QL*(CA-CVL)-RAM1L-RAM2L+KA*STOM 312 

### RAM1L=VMAX*CVL/(KM+CVL) !** 313 

### RAM2L=KF*CVL*VL !** 314 

### AL=INTEG(RAL, 0.0) 315 

### CL=AL/VL 316 

### CVL=CL/PL 317 

### AUCL=INTEG(CL, 0.0) !** 318
319 

### ! AS= Amount in slowly-perfused tissues 320 

### RAS=QS*(CA-CVS) !-RAMS ! VMAXS=0, so not needed, PS 7/2008 321 

### AS=INTEG(RAS, 0.0) 322 

### CS=AS/VS 323 

### CVS=CS/PS 324
325 

### ! AR=Amount in rapidly-perfused tissues 326 

### RAMR=VMAXR*CVR/(KM+CVR) ! MHL 327 

### AMR=INTEG(RAMR,0.0) ! Total metabolized (MFO) in RP, MHL 328 

### RAR=QR*(CA-CVR)-RAMR 329 

### AR=INTEG(RAR, 0.0) 330 

### CR=AR/VR 331 

### CVR=CR/PR 332
333 

### ! AM1=Amount metabolized in MFO pathway (mg DCM) 334 

### RAM1=RAM1LU+RAM1L+Ramr !+ams ! AM S = 0, term not needed, PS 7/2008 335
336 

### ! AM2=Amount metabolized in GSH pathway (mg DCM) 337 

### RAM2=RAM2LU+RAM2L 338
339 

### ! Total rate/amount metabolized (MFO plus GSH pathways) 340 

### RAMTOT=RAM1+RAM2 !** 341 

### AMTOT=INTEG(RAMTOT, 0.0) 342
343 

### ! CV=Average venous blood concentration (mg DCM/L) 344 

### CV=(QF*CVF+QL*CVL+QS*CVS+QR*CVR+RIV)/QC ! MHL added RIV 345 

### CVP=0.23*CV ! Venous plasma conc 346 

### ABL=(CA+CV)*VBL2 347 

### AUCBL=INTEG(ABL, 0.0) 348 

### AUCV=INTEG(CV, 0.0) !GLD 349
350 

### ! CO appearance in and elimination from blood, MHL 351

### H-8 DRAFT- DO NOT CITE OR QUOTE ACO=ABL2/(VBL2*MWCO) !MHL 352 

### BO2=O2/Mmm ! MHL 353 

### CHBT=HBTOT ! MHL 354 

### HBCO=((ACO+BO2+CHBT)-SQRT((ACO+BO2+CHBT)*(ACO+BO2+CHBT)-4.0*ACO*CHBT))/2.0 355 

### ! Conc of HB-bound CO (mmol/L) MHL 356 

### COFREE=ACO-HBCO ! Conc of free CO in blood (mmol/L) MHL 357 

### PICO=COINH/1402.5 ! CO concentra tion in inhaled air (mg/L) MHL 358
359 

### PCTHBCO=100.0*HBCO/HBTOT ! Percent carboxyhemoglobin in blood 360 

### PEAKPCTHBCO = MAX(PEAKPCTHBCO,PCTHBCO) ! Capture peak CO concentration 361
362 

### ! COE = Amount of expired CO, MHL 363 

### PCCO=COFREE*MWCO/SOL 364 

### PACO=(PCCO+PICO*QPDP)/(1.0+QPDP) 365 

### RCOE=DL*RHO*(PCCO-PACO)*F1 366 

### COE=INTEG(RCOE,0.0) !Expired amount of CO (mg) MHL 367 

### COEC=0.7*COE/QP ! Conc expired CO (mg/L) 368 

### ! Multiplier changed from 2/3 to 0.7 in above to be consistent with... 369 

### ! other expired concentration calculations, PS 7/2008. 370 

### COECPPM=COEC*24450.0/MWCO 371 

### PCTIVCOEXH=100.0*IVORBOL*COE/(TOTAL DOSE*MWCO/MOLWT + 1.0E-8) 372 

### ! % dose exhaled as CO MHL 373
374 

### ! ABL2 = Amount of CO in blood (mg) MHL 375 

### RFROMDCM=(RAM1L+RAM1LU+RAMR)*L12 ! RAMS left out since not used, =0 376 

### ! Inputs calculated for mass balance 377 

### AENCO = INTEG(RENCO, 0.0) ! Amount produced endogenously (mg) 378 

### AFROMDCM = INTEG(RFROMDCM, 0.0) ! Amount produced from metabolism of DCM 379 

### RABL2=RFROMDCM+RENCO-RCOE 380 

### ABL2=INTEG(RABL2,0.0) ! MHL 381 

### CABL2=ABL2/VBL2 ! MHL 382
383 

### ! AI=Total mass input (mg DCM) 384 

### RAI=(QP*(CI+CCH))+RIV !MHL 385 

### AI=INTEG(RAI, 0.0) 386
387 

### ! TMASS=Mass balance (mg DCM) 388 

### MASSBAL=MASSIN-STOM-AF-AL-ALU-AS-AR-ABL-MASSOUT 389 

### MASSIN=AI+CUMORALDOSE 390 

### MASSOUT=AX+AMTOT 391
392 

### ! Mass Balance for CO 393 

### MASSBALCO = 100*(-ABL2+AENCO-COE+AF ROMDCM)/(AENCO+AFROMDCM+1e-10) 394
395 

### !if (T .GE. TSTOP) zzstopflag = .TRUE. ! Termination condition for model run 396 

### END ! End of DERIVATIVE section of program 397 

### TERMT(T.GE.TSTOP) 398 

### END ! End of Dynamic section of program 399
400 

### TERMINAL 401 

### avgt = 24.0/(T+1.0e-8) 402

### H-9 DRAFT- DO NOT CITE OR QUOTE METDOS = avgt*RAMTOT/VL 403 

### LDAYREFDOSE = LDAYLIVCYPDOSE + LDAYLIVGSTDOSE ! MHL 404 

### WAVGREFDOSE = WAVGLIVCYPDOSE + WAVGLIVGSTDOSE ! PS, 8/2008 405 

### LDAYWBDYCYPGSTDOSE = LDAYWBDYCYPDOSE + LDAYWBDYGSTDOSE 406 

### METABRATIO = RAM1/(RAM2+1.0E-12) 407 

### DDOSECHECK = CUMORALDOSE*avgt/BW ! Should equal DDOSE 408 

### END ! End of TERMINAL section of program 409 

### END ! End of PROGRAM 410



### Computational Files (.m files) 411 

### The following are files (functions) created as .m files to support the Monte Carlo 412 

### statistical sampling and subsequent calculations for the DCM analysis. 413

414 

### Utility files used for all human simulations 415

416 

### function v=normbnd(mu, sigma, lo, up) 417 

### % ---------------------------------------------------------------- 418 

### % FILE: normbnd.m 419 

### % 420 

### % NORMBND - Returns a random sample from a truncated normal distribution with 421 

### % mean = mu, standard deviation = sigma, lower bound = lo, & upper bound = up. 422 

### % Modified by Paul Schlosser, U.S. EPA, 7/2008 423 

### % ---------------------------------------------------------------- 424 

### if (lo >= up) 425 

### v = NaN; disp('Lower bound must be less than upper bound.') 426 

### return; 427 

### end 428 

### FL = normcdf(lo, mu, sigma); % normal probability of lower bound 429 

### FU = normcdf(up, mu, sigma); % normal probability of upper bound 430 

### p = rand*(FU - FL) + FL; % rand = uniform random(0,1) 431 

### v = norminv(p, mu, sigma); 432 

### end 433
434 

### function v=lnormbnd(mu, sigma, lo, up) 435 

### % ---------------------------------------------------------------- 436 

### % FILE: lnormbnd.m 437 

### % 438 

### % LNORMBND - Returns a truncated LOGnormal distribution with distribution with 439 

### % mean = mu, standard deviation = sigma, lower bound = lo, & upper bound = up. 440 

### % Modified by Paul Schlosser, U.S. EPA, 7/2008 441 

### % ---------------------------------------------------------------- 442 

### v = exp(normbnd(log(mu), log(sigma), log(lo), log(up))); 443 

### end 444
445

### H-10 DRAFT- DO NOT CITE OR QUOTE function v=prctile(y,x) 446 

### % ---------------------------------------------------------------- 447 

### % FILE: prctile.m 448 

### % Computes percentiles of vector y at percentile values x 449 

### % If x = [50 90 95], that's 50th, 90th, 95th 450 

### % Created by Paul Schlosser, U.S. EPA, July, 2008 451 

### % ---------------------------------------------------------------- 452 

### if any((x<0) | (x>100)) 453 

### disp('Error! All percentiles must be between 0 and 100!'); 454


### return 455 

### end 456 

### dy = sort(y); 457 

### ps = 100*((1:length(dy))-0.5)/length(dy); 458



### v = x; 459 

### for id=1:length(x) 460 

### if x(id)<ps(1) 461 

### v(id)=dy(1); 462 

### elseif x(id)>max(ps) 463 

### v(id)=max(dy); 464 

### else 465 

### v(id)=interp1(ps,dy,x(id)); 466 

### end 467 

### end 468



### % ---------------------------------------------------------------- 469 

### % File clearT.m 470 

### % Creates list of variable names for tracking, rnames, and 471 

### % performs other initializations for human Monte Carlo chains. 472 

### % Created by Paul Schlosser, U.S. EPA, 7/2008 473 

### % ---------------------------------------------------------------- 474 

### rnames=["age";"BW";"DRVOL";"DRCONC";"DDO SE";"CONC";"VMAXC";"KM";"FRACR";"KFC"; 475 

### "A1";"A2";"BW";"VLC";"VFC";"VRC";"VLUC"; "VSC";"QAlvC";"QCC";"QLC";"QFC";"QSC"; 476 

### "QRC";"PL";"PLU";"PF";"PS";"PR";"PB";"GSTGT";"CV";"LDAYLIVGSTDOSE"; 477 

### "LDAYLIVCYPDOSE";"LDAYLUNGGSTDOSE";"LDAYWBDYGSTDOSE";"LDAYREFDOSE"; 478 

### "LDAYAUCV";"LDAYAUCS"]; 479 

### sr='['; rns=length(rnames); 480 

### for ir=1:(rns-1) 481 

### sr=[sr,ctot(rnames(ir)),', ']; 482 

### end 483 

### sr=[sr,ctot(rnames(rns)),'];']; 484 

### % save current model values before perturbations 485 

### save @file='dcm_human.sav' 486 

### runo=[]; 487 

### % prepare time history values. 488 

### prepare @clear T CV 489 

### WESITG=0; WEDITG=0; CONC=0.0; IVDOSE=0.0; DRCONC=0.0; FIXDRDOSE=0.0; 490 

### IVORBOL=0.0; 491 

### DRPCT = [0.25, 0.1, 0.25, 0.1, 0.25, 0.05]; 492 

### DRTIME= [0.0, 3.0, 5.0, 8.0, 11.0, 15.0]; 493 

### TSTOP=600.; TCHNG=2000.0; TCHNG2=2000. 0; TDUR=2000.0; TDUR2=2000.0; 494 

### start @NoCallback 495

### H-11 DRAFT- DO NOT CITE OR QUOTE 496 

### exist popn; % Test to see if 'popn' defined. 497 

### if ~ans % If not... 498 

### popn="mix"; % Mix of GST types. 499 

### end 500 

### exist agem; % Check if agem defined ... 501 

### if ~ans % If not... 502 

### agem=0; 503 

### end 504 

### exist gendm; % Check if gendm defined ... 505 

### if ~ans % If not... 506 

### gendm="both"; % males and females 507 

### end 508



### function nn=findnames(nm,rnames) 509 

### % ---------------------------------------------------------------- 510 

### % File findnames.m 511 

### % 512 

### % Function to find indices of specific names in list nm 513 

### % within the larger list (of all variables), rnames. 514 

### % Created by Paul Schlosser, U.S. EPA, 7/2008 515 

### % ---------------------------------------------------------------- 516 

### nn=[]; 517 

### for n=1:length(nm) 518 

### n1=find(rnames==nm(n)); 519 

### if isempty(n1) 520 

### disp(['Error, ',ctot(nm(n)),' not in saved variable list.']); return 521 

### end 522 

### nn=[nn,n1]; 523 

### end 524 

### end 525
526 

### % File human_pars.m 527 

### % 528 

### % Code for selecting human model parameters fro m MC distribution for dichlormethane PBPK 529 

### % model from probability density functions as descr ibed by David et al (2006), with revisions 530 

### % as described in the U.S. EPA IRIS Toxicological Review for Dichloromethane, Appendix B. 531 

### % Genotypic distribution of GSTT-1 activity take n from Tables 8 and 9 of Environ report for 532 

### % Eastman Kodak. Arithmetic means and SDs for lognormal distributions converted by ML to 533 

### % geometric mean/SD to match acsl m-file lnormbnd.m values 534 

### % 535 

### % Programmed by Michael Lumpkin (ML) 536 

### % Syracuse Research Corporation, 11/2005 537 

### % Modified by Paul Schlosser (PS), U.S. EPA 7/2008 538 

### % 539 

### % Gender parameter 'gendm' can be "both" (default), "male", or "female". 540 

### % Age parameter 'agem' can be 0 (def ault) to simulate the full range 541 

### % from 0.5-80 years, or any value in that range for a specific age. 542 

### % Specific parameters, as noted belo w, set for those life-stages. 543 

### % -------------------------------------------------------------------- 544
545

### H-12 DRAFT- DO NOT CITE OR QUOTE % Choose uniform discrete distribution for GSTT-1 genotypes. 546 

### if popn=="++" 547 

### GSTGT = 0; % for +/+ only 548 

### elseif popn=="+-" 549 

### GSTGT = 1; % for +/- only 550 

### elseif popn=="--" 551 

### GSTGT = 2; % for -/- only 552 

### else 553 

### r=rand(1)*gsmult; GSTGT=(r>0.32)+(r>0. 8); %For general/mixed population 554 

### popn = "mix of +/+, +/-, and -/-"; 555 

### end 556
557 

### % selection of GSTT1 activity distribution based on ethnic distribution 558 

### % upper bounds changed from mean + 3*SD to mean + 5*SD, P.S., 2/2009 559 

### if (GSTGT == 0) 560 

### KFC = normbnd(1.31, 0.167, 0.0, 2.145); 561 

### elseif (GSTGT == 1) 562 

### KFC = normbnd(0.676, 0.123, 0.0, 1.291); 563 

### else 564 

### KFC = 0.0; 565 

### end 566
567 

### KA=5.0; %From Reitz et al (1997); this parameter has no vari ability data and is 568 

### % described as point estimate 569 

### VMAXC=lognorm(9.34, 1.73); %lnormbnd(9.34, 1.73, 3.8, 23.0); 570 

### %geometric mean and GSD, converted from arith mean/SD of 9.42 and 1.23 571 

### % VMAXC switched to unbounded distribution, PS, 2/2009 572 

### KM=lnormbnd(0.410, 1.39, 0.154, 1.10); 573 

### %geometric mean and SD, converted fr om arith mean/SD of 0.433 and 1.46 574 

### FRACR=lnormbnd(0.0152, 2.0, 0.0019, 0.122); 575 

### %geometric mean and SD, converted from arith mean/SD of 0.0193 and 0.0152 576 

### A1=lnormbnd(0.00092, 1.47, 0.000291, 0.00292); 577 

### %geometric mean & SD, converted from arith mean/SD of 0.000993 & 0.000396 578 

### A2=lnormbnd(0.0083, 1.92, 0.00116, 0.0580); 579 

### %geometric mean and SD, converted from arith mean/SD of 0.0102 and 0.00739 580
581


### age=agem; 582 

### if age==0 % agem=0 => age = random from population, otherwise leave as set 583 

### p=rand; 584 

### age = 165.86*p^4 - 253.19*p^3 + 113.27*p^2 + 53.356*p + 0.5; 585 

### end 586 

### VMP = 0.7 + 0.18*(age<18); 587 

### % power for scaling Vmax is 0.88 if age < 18, otherwise 0.7 588 

### % Change in VMP introduced 2/2009, PS 589
590 

### gend=gendm; 591 

### if ~((gendm=="male")|(gendm=="f emale")) % if not defined as one 592 

### gend="female"; 593 

### if rand()<(0.513*((125.3 - age)^4)/(33.7^4 + (125.36 - age)^4)); 594 

### gend="male"; 595 

### end 596

### H-13 DRAFT- DO NOT CITE OR QUOTE end 597
598 

### if mod(RUNN,50)==0 599 

### disp(['GSTGT group: ',ctot(popn),'. GSTGT = ',num2str(GSTGT),'.']); 600 

### disp(['Simulation for ',num2str(age),' year old ',ctot(gend),'; gender mix: ',ctot(gendm)]); 601 

### disp(' '); 602 

### end 603
604 

### % Human physiologic parameters 605
606 

### if gend=="female" 607 

### aged=(16-age)/10; 608 

### bwmean = 4.146 - 0.147*aged - 1.36*aged^2 + 0.44*aged^3; 609 

### if aged<0 610 

### bwmean = 4.146 - 0.147*aged - 0.0278*aged^2 - 0.00095*aged^3; 611 

### end 612 

### aged=(13-age)/10; 613 

### bwSD = 2.574 - 0.358*aged - 2.55*aged^2 + 1.16*aged^3; 614 

### if aged<0 615 

### bwSD = 2.574 - 0.358*aged - 0.0861*aged^2 - 0.00469*aged^3; 616 

### end 617 

### else % males 618 

### aged=(21-age)/10; 619 

### bwmean = 4.406 - 0.0285*aged - 0.729*aged^2 + 0.115*aged^3; 620 

### if aged<0 621 

### bwmean = 4.406 - 0.0285*aged + 0.0048*aged^2 + 0.0018*aged^3; 622 

### end 623 

### aged=(16-age)/10; 624 

### bwSD = 2.87 + 0.06*aged - 2.56*aged^2 + 0.96*aged^3; 625 

### if aged<0 626 

### bwSD = 2.87 + 0.06*aged + 0.0448*aged^2 + 0.0067*aged^3; 627 

### end 628 

### end 629 

### BW=norminv((0.01+0.98*rand) ,exp(bwmean),exp(bwSD)); 630
631 

### QAlvmean = 13.6 + 13.3*exp(-0.05*age); 632 

### if gend=="female" 633 

### QAlvmean = 10.7 + 22.1*exp(-0.08*age); 634 

### end 635 

### Qgsd = -0.1948*(age/10)^3 + 0.6095*(age/10)^2 - 0.3978*(age/10) + 1.4261; 636 

### if age>16.81 637 

### Qgsd=1.554; 638 

### end 639 

### QAlvC = QAlvmean*exp(norminv((0.05+0.9*rand),0,log(Qgsd))); 640
641 

### QCCmean = 56.906*(1.0 - exp(-0 .681*exp(0.0454*QAlvC))) - 29.747; 642 

### QCC = QCCmean/lnormbnd(1.0, 0.203, 0.69, 1.42); 643 

### VPR = QAlvC/QCC; 644
645 

### vlm=-0.0036*(age/10)^2 - 0.0051*(age/10) + 0.0395; 646 

### if age>17 647

### H-14 DRAFT- DO NOT CITE OR QUOTE vlm=-0.0004*(age/10)^2 + 0.0034*(age/10) + 0.0169; 648 

### end 649 

### VLC=vlm*normbnd(1.0, 0.05, 0.85, 1.15); 650
651 

### vfm=0.1612*(age/10)^3 + 0.0846*(age/10)^2 - 0.3083*(age/10) + 0.2709; 652 

### if ((age>7)&(age<=20)) 653 

### vfm=-0.0458*(age/10)^2 + 0.2082*(age/10) + 0.0274; 654 

### if gend=="male" 655 

### vfm= -0.0057*(age/10)^2 + 0.0293*(age/10) + 0.1303; 656 

### end 657 

### elseif age>20 658 

### vfm=-0.0024*(age/10)^3 + 0.0355*(age/10)^2 - 0.115*(age/10) + 0.3678; 659 

### if gend=="male" 660 

### vfm= -0.0015*(age/10)^2 + 0.0384*(age/10) + 0.0908; 661 

### end 662 

### end 663 

### VFC=vfm*normbnd(1.0, 0.3, 0.1, 1.9); 664
665 

### VRC=normbnd(0.064, 0.0064, 0.0448, 0.0832); 666 

### VLUC=normbnd(0.0115, 0.00161, 0.00667, 0.0163); 667 

### VSC=normbnd(0.63, 0.189, 0.431, 0.829); 668 

### vtr=0.9215/(VFC+VLC+VLUC+VRC+VSC); 669 

### VFC = VFC*vtr; VLC=VLC*vtr; VLUC=VLUC*vtr; VRC=VRC*vtr; VSC=VSC*vtr; 670 

### VBL2C=0.059; 671
672 

### DSPC=0.15; 673 

### QLC=normbnd(0.26, 0.0910, 0.010, 0.533); 674 

### QFC=normbnd(0.05, 0.0150, 0.0050, 0.0950); 675 

### QSC=normbnd(0.19, 0.0285, 0.105 ,0.276); 676 

### QRC=normbnd(0.50 ,0.10, 0.20, 0.80); 677
678 

### %Human partition coefficients for DCM 679 

### PL=lnormbnd(1.43, 1.22, 0.790, 2.59); 680 

### %geometric mean and SD, converted fr om arith mean/SD of 1.46 and 0.292 681 

### PLU=lnormbnd(1.43, 1.22, 0.790, 2.59); 682 

### %geometric mean and SD, converted fr om arith mean/SD of 1.46 and 0.292 683 

### PF=lnormbnd(11.9, 1.34, 4.92, 28.7); 684 

### %geometric mean and SD, converted fr om arith mean/SD of 12.4 and 3.72 685 

### PS=lnormbnd(0.80, 1.22, 0.444, 1.46); 686 

### %geometric mean and SD, converted fr om arith mean/SD of 0.82 and 1.64 687 

### PR=lnormbnd(1.43, 1.22, 0.790, 2.59); 688 

### %geometric mean and SD, converted fr om arith mean/SD of 1.46 and 0.292 689 

### PB=lnormbnd(9.7, 1.10, 7.16, 13.0); 690 

### %geometric mean and SD, converted from arith mean/SD of 9.7 and 0.97 691

### H-15 DRAFT- DO NOT CITE OR QUOTE % -------------------------------------------------------------------- 692 

### % File: finish.m 693 

### % Programmed by Paul Schlosser, U.S. EPA, 8/2008 694 

### % Performs final analysis on saved results in runo from simulations. 695 

### % -------------------------------------------------------------------- 696 

### disp([num2str(size(runo,1)),' simulations completed.']); 697 

### contsim=0; p=ctot(popn); p= p(1:min([3,length(p)])); 698 

### eval(['save runo @file=',astp,num2str(agem),p,'_',ctot(gendm), ... 699 

### '.csv @format=ascii @separator=comma']); 700 

### eval(['save @file=',astp,num2str(age m),p,'_',ctot(gendm),'.mat']); 701 

### disp(['GSTT-1 group: ',ctot(popn)]); 702 

### aget=['a ',num2str(agem),' year-old; ']; 703 

### if agem==0 704 

### aget='0.5-80 years of age; '; 705 

### end 706 

### gendt=[ctot(gendm),' population']; 707 

### if gendm=="both" 708 

### gendt='males and females'; 709 

### end 710 

### disp(['Simulation for ',aget,gendt,'.']); 711 

### disp(['Metric =',dtxt,'.']); 712 

### res=[]; 713 

### pcs=num2str(percs(1)); 714 

### for n=2:length(percs) 715 

### pcs=[pcs,', ',num2str(percs(n))]; 716 

### end 717 

### for n=1:length(nm) 718 

### disp([ctot(nm(n)),' mean, me dian, percentiles = ',pcs]); 719 

### r=[mean(runo(:,nn(n))),median(runo(:,nn(n ))),prctile(runo(:,nn(n)),percs/gsmult)]*mult; 720 

### disp(r); disp(' '); res=[res;r]; 721 

### end 722
723
724 

### Files used specifically for cancer analysis 725



### % -------------------------------------------------------------------- 726 

### % File: straightsims.m 727 

### % Created by Paul Schlosser, U.S. EPA, 8/2008 728 

### % Runs Monte Carlo (MC) simulations withOUT search (for human equivalent exposures) 729 

### % Requires set exposure, NRUNS and rnames (list of variables to save) 730 

### % PBPK parameters set using MC selection by file human_pars.m. 731 

### % -------------------------------------------------------------------- 732 

### exist contsim; % Test to see if contsim defined. 733 

### if ~ans % If not... 734 

### contsim=0; % Not a continuation. 735 

### end 736 

### if contsim==0 % When starting a new set of simulation; 737 

### % set contsim=1 if continuing an interupted chain. 738 

### runo=[]; rns=length(rnames); ns=1; 739

### H-16 DRAFT- DO NOT CITE OR QUOTE else 740 

### ns=RUNN; 741 

### end 742 

### for RUNN = ns:NRUNS 743 

### if mod(RUNN,50)==0 744 

### disp(['Remaining runs = ',num2str(NRUNS-RUNN)]) 745 

### end 746 

### human_pars; start @NoCallback 747 

### runo=[runo;eval(sr)]; 748 

### end 749



### % -------------------------------------------------------------------- 750 

### % File: Human drinking water MCA OSF.m 751 

### % 752 

### % Programmed by Michael Lumpkin 753 

### % Syracuse Research Corporation, 10/2007 754 

### % Modified by Paul Schlosser, U.S. EPA 8/2008 755 

### % 756 

### % This script file sets up the control parameters to simulate human unit 757 

### % drinking water exposure (1 mg/kg/day) and calls straightsim which 758 

### % generates a Monte-Carlo chain for the internal doses (identified 759 

### % in text array nn) to be used in calculating oral slope factors (OSFs). 760 

### % -------------------------------------------------------------------- 761 

### % Test to see if contsim defined 762 

### exist contsim 763 

### if ~ans % If not... 764 

### contsim=0; % Not a continuation. 765 

### end 766 

### if contsim==0 % When starting a new set of simulation; 767 

### % set contsim=1 if continuing an interrupted chain. 768 

### clearT 769 

### nm=["LDAYLIVGSTDOSE";"LDAYWBDYGSTDOSE"]; nn=findnames(nm,rnames); 770 

### if (length(nn) ~= length(nm)) 771 

### disp("Not all names in list nm.") 772


### return 773 

### end 774 

### TSTOP=95.0; NRUNS=10000; CINT=0.1; % Total iterations for Monte Carlo analysis 775 

### FIXDRDOSE=1.0; % Drinking fixed mg/kg-day 776 

### gendm="female"; % Gender mix; c hoose "male, "female", or "both" 777 

### agem=70; % Age "mix"; if 0 draws from distribution (0.5-80 years) 778 

### % otherwise agem value is used exclusively 779 

### popn="++"; % GSTGT "++", "+-", or "mix" of +/+, +/-, and -/- 780 

### astp='OSF_age'; percs=[95 99] ; dtxt='n/a'; gsmult=1.0; 781 

### end 782 

### straightsims; mult=1.0/DDOSE; finish 783

### H-17 DRAFT- DO NOT CITE OR QUOTE % -------------------------------------------------------------------- 784 

### % File: Human inhalation MCA IUR.m 785 

### % 786 

### % Programmed by Michael Lumpkin 787 

### % Syracuse Research Corporation, 11/2007 788 

### % Modified by Paul Schlosser, 7/2008 789 

### % 790 

### % This script file sets up the control paramete rs to simulate human unit inhalation exposures 791 

### % (1 mcg/m3) and calls straightsim which generates a Monte-Carlo chain for the internal 792 

### % doses (identified in text array nn) to be used in calculating inhalation unit risks (IURs) 793 

### % ----------------------------------------------------------------------- 794 

### exist contsim; % Test to see if contsim defined. 795 

### if ~ans % If not... 796 

### contsim=0; % Not a continuation. 797 

### end 798 

### if contsim==0 % When starting a new set of simulation; 799 

### % set contsim=1 if continuing an interupted chain. 800 

### clearT; CINT=0.1; 801 

### TSTOP=24*4; CONC=1.0e-6/GASD; CC=0; % Daily dose-rates now calculated from 802 

### % final day of simulations, & only need to go to 60 hr to reach SS 803 

### % CONC in ppm; 0.00029 ppm = 1 ug/m3 DCM = 1e-3 ug/L = 1e-6 mg/L 804 

### nm=["LDAYLIVGSTDOSE";"LDAYLUNGGSTDOSE";"LDAYWBDYGSTDOSE"; 805 

### "CV";"LDAYAUCS"]; 806 

### nn=findnames(nm,rnames); 807 

### if (length(nn) ~= length(nm)) 808 

### disp("Not all names in list nm.") 809


### return 810 

### end 811 

### NRUNS=20000; %Total iterations for Monte Carlo analysis 812 

### gendm="both"; % Gender mix; choose "male, "female", or "both" 813 

### agem=0; % Age "mix"; if 0 draws from distribution (0.5-80 years) 814 

### % otherwise agem value is used exclusively 815 

### popn="mix"; % GSTGT "++", "+-", or "mix" of +/+, +/-, and -/- 816 

### gsmult = 1.0; 817 

### % Multiplies draw for GST individual selection, divides the percentiles at end. 818 

### astp='IUR_age'; mult= 1.0e-6/(CONC*GASD); pe rcs=[95 99]; dtxt=' n/a'; gsmult=1.0; 819 

### end 820 

### straightsims; finish 821
822
823

### H-18 DRAFT- DO NOT CITE OR QUOTE Files used specifically for non-cancer analysis 824



### % -------------------------------------------------------------------- 825 

### % File: searchsim_refdose.m 826 

### % Created by Paul Schlosser, U.S. EPA, 8/2008 827 

### % Runs Monte Carlo (MC) simulations WITH search fo r human equivalent exposures, using variable 828 

### % LDAYREFDOSE ('reference dose based on last day of simulated exposure -- presumed "periodicity") 829 

### % Requires exposure variable name d = expnm = "DRCONC" or "CONC", 830 

### % target value = heqt, relative tolerance = hetol, initial set 831 

### % exposure (CONC or DRCONC value >0), NRUNS, and rnames (list of 832 

### % variables to save) 833 

### % PBPK parameters set using MC selection by file human_pars.m. 834 

### % -------------------------------------------------------------------- 835 

### % set contsim=1 if continuing an interupted chain. 836 

### if contsim==0 % When starting a new set of simulations 837 

### if ~(expnm=="CONC" | expnm=="DRCONC") 838 

### disp('Variable expnm must be "C ONC" or "DRCONC", in double quotes.'); 839


### return 840 

### end 841 

### if expnm=="CONC" % atxt used as variable command below 842 

### DRCONC=0.0; atxt='CONC = c1'; c2=CONC; 843 

### else 844 

### CONC=0.0; atxt='DRCONC = c1'; c2=DRCONC; 845 

### end 846 

### runo=[]; rns=length(rnames); ns=1; c1s=['c1 = c2*heqt/',dtxt]; 847 

### v1s=['v1 = ',dtxt]; v2s=['v2 = ',dtxt]; 848 

### else % Continuing a set of simulations 849 

### ns=RUNN; 850 

### end 851 

### for RUNN = ns:NRUNS 852 

### disp(['Remaining runs = ',num2str(NRUNS-RUNN)]) 853 

### human_pars 854 

### % Following block calculates daily dose resulting in specified internal dose 855 

### start @NoCallback 856 

### nstep=1; eval(v2s); eval(c1s); eval(atxt); 857 

### start @NoCallback 858 

### eval(v1s); het1=hetol; 859 

### while (abs((v1/heqt)-1)>hetol); % Specify corresponding dose metric 860 

### cn = abs((heqt-v2)*(c1-c2)/(v1-v2) + c2); % Linear interpolation to heqt 861 

### c2=c1; v2=v1; c1=cn; eval(atxt); % Assign values 862 

### start @NoCallback 863 

### eval(v1s); 864 

### nstep=nstep+1; 865 

### if nstep==20 866 

### hetol=hetol*10; nstep=1; 867 

### disp('WARNING! hetol increased to allow convergence!') 868 

### end 869 

### end 870 

### hetol=het1; runo=[runo;eval(sr)]; 871 

### end 872

### H-19 DRAFT- DO NOT CITE OR QUOTE % -------------------------------------------------------------------- 873 

### % File: human drinking water MCA RfD.m 874 

### % 875 

### % Programmed by Michael Lumpkin 876 

### % Syracuse Research Corporation, 11/2005 877 

### % Modified by Paul Schlosser, U.S. EPA 8/2008 878 

### % 879 

### % This script file sets up the control parameters to simulate human 880 

### % drinking water exposure for a given internal dose (heqt) and calls 881 

### % searchsim_refdose which generates a Monte-Carlo chain for the human 882 

### % equivalent administered daily dose (HEAD; mg/kg/d). 883 

### % -------------------------------------------------------------------- 884 

### exist contsim; 885 

### if ~ans 886 

### contsim=0; 887 

### end 888 

### if contsim==0 % When starting a new set of simulation; 889 

### % set contsim=1 if continuing an interupted chain. 890 

### clearT 891 

### nm=["DDOSE"]; nn=fi ndnames(nm,rnames); 892 

### if (length(nn) ~= length(nm)) 893 

### disp("Not all names in list nm.") 894


### return 895 

### end 896 

### hetol=1.0e-4; %relative tolerance 897 

### expnm="DRCONC"; DRCONC=9.0; % 35 mg/L is 1 mg/kg/day for 70 kg human drinking 2L 898 

### TSTOP=95.0; NRUNS=10000; %Total itera tions for Monte Carlo analysis 899 

### gendm="female"; % Gender mix; c hoose "male, "female", or "both" 900 

### agem=70; % Age "mix"; if 0 draws from distribution (0.5-80 years) 901 

### % otherwise agem value is used exclusively 902 

### popn="--"; % GSTGT "++", "+-", "--", or "mix" of +/+, +/-, and -/- 903 

### % dtxt = dose metric to use 904 

### % heqt = target for HEQ search; set value for specific dose metric 905 

### dtxt='LDAYREFDOSE'; dt1='W'; 906 

### dtxt='LDAYLIVGSTDOSE'; dt1='G'; 907 

### dtxt='LDAYLIVAUC'; dt1='A'; heqt=0.0562; 908 

### dtxt='LDAYLIVCYPDOSE'; dt1='Y'; heqt=15.1; 909 

### astp=['RfD_',dt1,'_',num2str(heqt),'_a ge']; percs=[5 1]; mult=1; gsmult=1; 910 

### end 911 

### searchsim_refdose; finish 912
913

### H-20 DRAFT- DO NOT CITE OR QUOTE % -------------------------------------------------------------------- 914 

### % File: Human inhalation MCA RfC.m 915 

### % 916 

### % Programmed by Michael Lumpkin 917 

### % Syracuse Research Corporation, 11/2005 918 

### % Modified by Paul Schlosser, U.S. EPA, 7/2008 919 

### % 920 

### % This script file sets up the control parameters to simulate human 921 

### % inhalation exposure for a given internal dose (heqt) and calls 922 

### % searchsim_refdose which generates a Monte-Carlo chain for the human 923 

### % Equivalent concentration (HEC; mg/m^3) 924 

### % -------------------------------------------------------------------- 925 

### exist contsim; 926 

### if ~ans 927 

### contsim=0; 928 

### end 929 

### if contsim==0 % When starting a new set of simulation; 930 

### % set contsim=1 if continuing an interupted chain. 931 

### use clearT 932 

### nm=["CONC"]; nn=findnames(nm,rnames); 933 

### if (length(nn) ~= length(nm)) 934 

### disp("Not all names in list nm.") 935


### return 936 

### end 937 

### TSTOP=96; NRUNS=10000; %Total iterations for Monte Carlo analysis 938 

### expnm="CONC"; CONC=67; % CONC in ppm; 0.29 ppm = 1 mg/m3 DCM 939 

### hetol=1.0e-5; %relative tolerance 940 

### gendm="female"; % Gender mix; c hoose "male, "female", or "both" 941 

### agem=70; % Age "mix"; if 0 draws from distribution (0.5-80 years) 942 

### % otherwise agem value is used exclusively 943 

### popn="--"; % GSTGT "++", "+-", or "mix" of +/+, +/-, and -/- 944 

### gsmult = 1.0; % Multiplies draw for GST individual selection, divides the percentiles at end. 945 

### % dtxt = dose metric to use 946 

### % heqt = target for HEQ search; set value for specific dose metric 947 

### % Last statement below is the metric and target value used 948 

### % Move desired one to end and save this file before running. 949 

### dtxt='LDAYLIVAUC'; dt1='A'; heqt=0.0562; 950 

### dtxt='LDAYREFDOSE'; dt1='W'; heqt=76.71; 951 

### dtxt='LDAYLIVGSTDOSE'; dt1='G'; heqt=10.97; 952 

### dtxt='LDAYLIVCYPDOSE'; dt1='Y'; heqt=128.1; 953 

### astp=['RfC_',num2str(heqt),'_age'] ; mult=GASD*1000; percs=[5 1]; 954 

### end 955 

### searchsim_refdose; finish 956
957

### H-21 DRAFT- DO NOT CITE OR QUOTE Files used for mouse dose analyses 958



### % -------------------------------------------------------------------- 959 

### % File: mouse_set.m 960 

### % Programmed by Paul Schlosser, U.S. EPA, 7/2008 961 

### % Clears previous variables and set s parameters for mouse simulations. 962 

### % -------------------------------------------------------------------- 963 

### % prepare time history values. 964 

### prepare @clear @all 965 

### CINT=0.1; CONC=0.0; IVDOSE=0.0; BOLUS=0.0; DRCONC=0.0; FIXDRDOSE=0.0; 966 

### WESITG=0; WEDITG=0; CC=0; 967 

### % from Reitz et al. 1997, Table 1 968 

### DRPCT = [0.233, 0.1, 0.1, 0.1, 0.233, 0.234]; 969 

### DRTIME= [0.0, 4.0, 8.0, 12.0, 16.0, 20.0]; 970 

### TSTOP=336; % 2 weeks 971 

### TCHNG=6; % daily exposure duration = 6 hours/day 972 

### TCHNG2=120; % weekly dose width = 5 days/week = 120 hours 973 

### TDUR=24; % daily dose period = 24 hours 974 

### TDUR2=168; % weekly exposure period = 7 days = 168 hours 975
976 

### % EPA (1988) reference value for B6C3 F1 mice: males=0.0373, females=0.0353 977 

### BW=0.0373; 978 

### % Mouse uptake & metabolism parameters (defined by MCMC calibration) 979 

### KA=5.0; VMAXC=9.27; KM=0.574; FRACR=0.0; KFC=1.41; A1=0.207; A2=0.196; 980 

### % Mouse physiologic parameters (from prior distributions or MCMC 981 

### % calibration) 982 

### VLC=0.04; VFC=0.04; VRC=0.05; VLUC=0.0115; VBL2C=0.059; VSC=0.78; 983 

### QCC=24.2; VPR=1.45; DSPC=0.15; QLC= 0.24; QFC=0.05; QSC=0.19; QRC=0.52; 984 

### %Mouse partition coefficients for DCM 985 

### PL=1.6; PLU=0.46; PF=5.1; PS=0.44; PR=0.52; PB=23.0; 986 

### start @NoCallBack 987



### % -------------------------------------------------------------------- 988 

### % File: Mouse drinking water National Coffee 1983.m 989 

### % 990 

### % Programmed by Michael Lumpkin 991 

### % Syracuse Research Corporation, 01/2007 992 

### % Modified by Paul Schlosser, U.S. EPA, 8/2008 993 

### % 994 

### % This run time file sets speci es-specific constants and exposure 995 

### % values for National Coffee Association study of DCM in drinking water 996 

### % in the mouse; parameterized to run with DCM.07.rev3.csl. 997 

### % -------------------------------------------------------------------- 998 

### use mouse_set 999 

### BW=0.0373; TSTOP=4*7*24; runo=[]; 1000
1001 

### % Mouse exposure parameters: DDOSE male s: 61, 124, 177, or 244 mg/kg-day 1002 

### for FIXDRDOSE=[61, 124, 177, 244] 1003 

### start @NoCallback; 1004 

### runo=[runo;[BW,DDOSE,WAVGLIVGSTDOSE,WAVGLUNGGSTDOSE, 1005

### H-22 DRAFT- DO NOT CITE OR QUOTE WAVGWBDYGSTDOSE]]; 1006 

### end 1007



### runo 1008 

### save runo @file='mouseOSFdrink.csv' @format=ascii @separator=comma 1009
1010


### % -------------------------------------------------------------------- 1011 

### % File: Mouse Inhalation NTP 1986.m 1012 

### % 1013 

### % Programmed by Michael Lumpkin 1014 

### % Syracuse Research Corporation, 11/2007 1015 

### % Modified by Paul Schlosser, U.S. EPA, 7/2008 1016 

### % 1017 

### %This run time file sets species- specific constants and exposure 1018 

### %values for NTP (1986) inhalation exposures of DCM in the mouse. 1019 

### % -------------------------------------------------------------------- 1020 

### use mouse_set 1021 

### CINT=0.01; 1022 

### % Kodak value; study average male: 2k= 0.034, 4k=0.032; female 2k=0.030, 4k=0.029 1023 

### TSTOP=5*7*24; Bws=[0.034, 0.032; 0.030, 0.029]; runo=[]; Cs=[2000, 4000]; 1024 

### for s=[1 2] 1025 

### for c=[1,2]; 1026 

### BW=Bws(s,c); CONC=Cs(c); 1027 

### start @NoCallback 1028 

### runo=[runo;[BW,CONC,WAVGLIVGSTDOSE,WAVGLUNGGSTDOSE,WAVGWBDYGSTDOSE]]; 1029 

### end 1030 

### end 1031



### runo 1032 

### %%% Saving last simulation results to file 1033 

### run=[_t _cv _cvl _cvf _cvs _cvr _cabl2 _wavglivgstdose _wavglunggstdose _ddose]; 1034 

### save run @file='RUNOUT_NTP_inhal.csv' @format=ascii @separator=comma 1035
1036
1037 

### Files used for rat dose analyses 1038



### % -------------------------------------------------------------------- 1039 

### % File: rat_set_D.m 1040 

### % Programmed by Paul Schlosser, U.S. EPA, 9/2009 1041 

### % Clears previous variables and sets parameters for rat simulations. 1042 

### % Rat model "D". 1043 

### % -------------------------------------------------------------------- 1044 

### % prepare time history values. 1045 

### prepare @clear @all 1046 

### WESITG=0; WEDITG=0; 1047 

### % Rat exposure controls 1048 

### CINT=0.01; CONC=0.0; IVDOSE=0.0; DRCONC=0.0; FIXDRDOSE=0.0; BOLUS=0; CC=0; 1049 

### DRPCT = [0.233, 0.1, 0.1, 0.1, 0.233, 0.234]; 1050 

### DRTIME = [0.0, 4.0, 8.0, 12.0, 16.0, 20.0]; 1051 

### TSTOP=6; % 7 days 1052 

### TCHNG=6; % daily exposure = 6 hours 1053 

### TCHNG2=120; % weekly dose width = 5 days/week = 120 hours 1054

### H-23 DRAFT- DO NOT CITE OR QUOTE TDUR=24; % daily dose period = 24 hours 1055 

### TDUR2=168; % weekly exposure period = 7 days = 168 hours 1056
1057 

### % Rat UPTAKE & metabolism parameters 1058 

### KA=5.0; FRACR=0.0; 1059 

### VMAXC=3.66; KM=0.59; KFC=2.97; % MLE fitted 1060 

### A1=0.04; A2=0.14; % from Reitz et al. 1989 1061 

### % Rat physiologic parameters (from Andersen et al 1991) 1062 

### BW=0.380; % males:0.380 kg, females:0.229 kg, from EPA (1988) 1063 

### VLC=0.04; VFC=0.07; VRC=0.05; VLUC=0.0115; VBL2C=0.059; VSC=0.75; 1064 

### QCC=15.9; VPR=0.94; DSPC=0.15; QLC= 0.20; QFC=0.09; QSC=0.15; QRC=0.56; 1065 

### %Rat partition coefficients for DCM (Andersen et al 1991) 1066 

### PL=0.732; PLU=0.46; PF=6.19; PS=0.408; PR=0.732; PB=19.4; 1067 

### %CO submodel parameters 1068 

### DLC=0.060; RENCOC=0.035; ABCOC=0.117; HBTOT=10.0; P1=0.80; F1=1.21; 1069 

### M=197.0; COINH=2.2; O2=0.13; PA IR=713.0; RHO=1102.0; SOL=0.03; 1070 

### start @NoCallBack 1071 

### %RENCOC=0.0; 1072 

### VMAXC = 3.923; KM = 0.524; KFC = 2.46; KA=1.8; 1073 

### use allfit_rat-params 1074 

### use KAfit-params 1075 

### RENCOC=0.0; ABCO=0.0; COINH=0.0; 1076
1077 

### % -------------------------------------------------------------------- 1078 

### % File: rat_set_A.m 1079 

### % Programmed by Paul Schlosser, U.S. EPA, 9/2009 1080 

### % Clears previous variables and sets parameters for rat simulations. 1081 

### % Rat model "A". 1082 

### % -------------------------------------------------------------------- 1083 

### use rat_set_D 1084 

### VMAXC = 6.21; KM = 0.23; 1085 

### A1 = 0.21; A2 = 0.20; 1086 

### KFC = 2.89; KA = 5.0; 1087
1088 

### % -------------------------------------------------------------------- 1089 

### % File: rat_set_B.m 1090 

### % Programmed by Paul Schlosser, U.S. EPA, 9/2009 1091 

### % Clears previous variables and sets parameters for rat simulations. 1092 

### % Rat model "B". 1093 

### % -------------------------------------------------------------------- 1094 

### use rat_set_D 1095 

### VMAXC = 4.0; KM = 0.4; 1096 

### A1 = 0.0; A2 = 0.0; 1097 

### KFC = 2.0; KA = 5.0; 1098
1099 

### % -------------------------------------------------------------------- 1100 

### % File: rat_set_C.m 1101 

### % Programmed by Paul Schlosser, U.S. EPA, 9/2009 1102 

### % Clears previous variables and sets parameters for rat simulations. 1103 

### % Rat model "C". 1104

### H-24 DRAFT- DO NOT CITE OR QUOTE % -------------------------------------------------------------------- 1105 

### use rat_set_D 1106 

### VMAXC = 4.0; KM = 0.4; 1107 

### A1 = 0.4; A2 = 0.14; 1108 

### KFC = 2.0; KA = 4.0; 1109
1110


### % -------------------------------------------------------------------- 1111 

### % File: Rat inhalation Nitschke 1988.M 1112 

### % 1113 

### % Programmed by Michael Lumpkin 1114 

### % Syracuse Research Corporation, 10/2007 1115 

### % Modified by Paul Schlosser, U.S. EPA, 7/2008 1116 

### % 1117 

### % This run time file sets species-specific constants values for modified 1118 

### % Andersen et al (1987, 1991) simula ting Nitschke et al. (1988) exposures 1119 

### % of DCM in the male and female rats. 1120 

### % -------------------------------------------------------------------- 1121 

### use rat_set_D 1122 

### TSTOP=5*7*24; runo=[]; BW=0.229; TCHNG=6; TCHNG2=120; 1123 

### % males:0.380 kg, females:0.229 kg, from EPA (1988) 1124 

### for CONC=[50, 200, 500] 1125 

### start @NoCallback 1126 

### runo=[runo;[BW,CONC,DIDOSE,WAVGREFDOSE,WAVGLIVGSTDOSE,WAVGLIVCYPD 1127 

### OSE,WAVGDAILYAUCL]]; 1128 

### end 1129



### runo 1130 

### save runo @file='RUNOUT_rat_inhal_Nitschke .csv' @format=ascii @separator=comma 1131



### % -------------------------------------------------------------------- 1132 

### % File: Rat inhalation Burek 1984.m 1133 

### % 1134 

### % Programmed by Michael Lumpkin 1135 

### % Syracuse Research Corporation, 10/2007 1136 

### % Modified by Paul Schlosser, U.S. EPA, 7/2008 1137 

### % 1138 

### % This run time file sets species-specific c onstants values for modified Andersen et al 1139 

### % (1987, 1991) simulating Burek et al. (1984) exposures of DCM in male & female rats 1140 

### % -------------------------------------------------------------------- 1141 

### use rat_set_D 1142 

### TSTOP=5*7*24; TCHNG=6; TCHNG2=120; runo=[]; 1143 

### for BW=[0.523, 0.338] 1144 

### for CONC=[500, 1500, 3500] 1145 

### start @NoCallback 1146
1147 

### runo=[runo;[BW,DIDOSE,CONC,WAVGR EFDOSE,WAVGLIVGSTDOSE,WAVGLIVCYPD 1148 

### OSE,WAVGDAILYAUCL]]; 1149 

### end 1150 

### end 1151



### runo 1152 

### save runo @file='RUNOUT_rat_inhal_Burek. csv' @format=ascii @separator=comma 1153
1154

### H-25 DRAFT- DO NOT CITE OR QUOTE % -------------------------------------------------------------------- 1155 

### % File: Rat drinking water Serota 1986a.m 1156 

### % 1157 

### % Programmed by Michael Lumpkin 1158 

### % Syracuse Research Corporation, 1/2007 1159 

### % Modified by Paul Schlosser, U.S. EPA, 7/2008 1160 

### % 1161 

### % This run time file sets species-specific c onstants values for modified Andersen et al 1162 

### % (1987, 1991) simulating Serota et al. (1986a) exposures of DCM to male & female rats 1163 

### % -------------------------------------------------------------------- 1164 

### use rat_set_D 1165 

### %DRCONC values from Serota et al. (1986a) 1166 

### % males:0, 44.00, 381.36, 916.74, 1723.47 1167 

### % females: 0, 37.80, 365.41, 856.82, 1656.94 1168 

### TSTOP=7*24; runo=[]; BW=0.380; % Males 1169 

### for FIXDRDOSE=[6, 52, 125, 235] 1170 

### start @NoCallBack 1171 

### runo=[runo;[BW,DDOSE,LDAYREFDOSE,LDAYLIVGSTDOSE,LDAYLIVCYPDOSE,LDA 1172 

### YLIVAUC]]; 1173 

### end 1174 

### BW=0.229; % Females 1175 

### for FIXDRDOSE=[6, 58, 136, 263] 1176 

### start @NoCallBack 1177 

### runo=[runo;[BW,DDOSE,LDAYREFDOSE,LDAYLIVGSTDOSE,LDAYLIVCYPDOSE,LDA 1178 

### YLIVAUC]]; 1179 

### end 1180



### runo 1181 

### save runo @File="Serota_rat_DW.csv" @Format=ascii @Separator=comma 1182
1183


### % -------------------------------------------------------------------- 1184 

### % File: Rat inhalation NTP 1986.M 1185 

### % 1186 

### % Programmed by Michael Lumpkin 1187 

### % Syracuse Research Corporation, 11/2007 1188 

### % Modified by Paul Schlosser, U.S. EPA, 7/2008 1189 

### % 1190 

### % This run time file sets species-specific c onstants values for modified Andersen et al 1191 

### % (1987, 1991) simulating NTP (1986) expos ures of DCM to male & female rats 1192 

### % -------------------------------------------------------------------- 1193 

### use rat_set_D 1194 

### CINT=0.1; 1195 

### bws=[0.3905, 0.3852, 0.3848, 0.2455, 0.2443, 0.2422]; 1196 

### TSTOP=6*24*7; concs=[1 2 4 1 2 4]*1000; runo=[]; 1197 

### for n=1:length(bws) 1198 

### BW=bws(n); CONC=concs(n); start @NoCallback 1199 

### runo=[runo;[BW,CONC,DIDOSE,WAVGL IVGSTDOSE,WAVGAUC V,WAVGAUCS]]; 1200 

### end 1201



### runo 1202 

### save runo @File="RUNOUT_rat_inhal_NTP.csv " @Format=ascii @Separator=comma 1203 1204

### H-26 DRAFT- DO NOT CITE OR QUOTE % -------------------------------------------------------------------- 1205 

### % File: rat inhal Gargas 86.m 1206 

### % Figure C-3 (creates 4 sub-plots) 1207



### % 1208 

### % Programmed by Michael Lumpkin 1209 

### % Syracuse Research Corporation, 10/2007 1210 

### % Modified by Paul Schlosser, U.S. EPA, 9/2009 1211



### % 1212 

### % This run time file sets species-specific constants values for modified 1213 

### % Andersen et al (1987, 1991) simulating Burek et al. (1984) exposures 1214


### % of DCM in the male and female rats. 1215



### % -------------------------------------------------------------------- 1216 

### dch1=[0.103, 93.73; 0.2178, 81.12; 0.3707, 66.26; 0.5543, 52.58; 0.7227, 41.73; 1217 

### 0.8834, 34.085; 1.044, 27.84; 1.22, 22.095; 1.3807, 18.312; 1.55, 14.532; 1218 

### 1.717, 12.58; 1.893, 10.28; 2.061, 8.3948]; 1219

1220 

### dch5=[0.1015, 448.9; 0.216, 400.0; 0.3843, 326.7; 0.5522, 282.85; 0.7128, 234.4; 1221 

### 0.8806, 208.9; 1.056, 183.5; 1.231, 163.5; 1.384, 145.7; 1.5514, 133.7; 1222 

### 1.719, 119.14; 1.887, 106.2; 2.0395, 95.99; 2.192, 85.5385; 2.360, 75.13; 1223 

### 2.5276, 65.99; 2.688, 57.96; 2.8484, 49.45; 3.024, 40.99; 3.1996, 36.00; 1224 

### 3.360, 31.165; 3.5356, 25.46; 3.719, 21.10; 3.864, 18.00; 4.025, 15.585; 1225 

### 4.193, 13.297; 4.353, 11.51]; 1226

1227 

### dch10=[0.0973, 927.1; 0.2195, 826.0; 0.3957, 627.6; 0.5556, 575.7; 0.7235, 513.1; 1228 

### 0.8836, 457.2; 1.059, 419.5; 1.226, 401.9; 1.3936, 379.6; 1.561, 348.2; 1229 

### 1.721, 333.66; 1.873, 329.1; 2.0556, 301.9; 2.200, 285.1; 2.3754, 265.4; 1230 

### 2.5426, 258.0; 2.695, 236.7; 2.87, 226.8; 3.030, 211.1; 3.1975, 193.66; 1231 

### 3.365, 182.9; 3.532, 175.24; 3.7076, 156.175; 3.875, 145.4; 4.035, 135.3; 1232 

### 4.195, 124.1; 4.340, 112.2; 4.515, 101.5; 4.683, 89.12]; 1233

1234 

### dch30=[0.1058, 2958.6; 0.2288, 2280; 0.3819, 1862.5; 0.5419, 1684; 0.7095, 1545; 1235 

### 0.8848, 1397; 1.067, 1338.6; 1.227, 1264; 1.387, 1229; 1.5615, 1195; 1236 

### 1.7136, 1178.5; 1.881, 1129; 2.056, 1051; 2.208, 1052; 2.383, 1008; 1237 

### 2.5424, 994.2; 2.6945, 966.46; 2.869, 939.6; 3.0366, 913.45; 3.196, 914.2; 1238 

### 3.356, 863.3; 3.531, 851.6; 3.713, 816.1; 3.880, 805.0; 4.032, 794.0; 1239 

### 4.192, 760.75; 4.352, 750.4; 4.5114, 708.6; 4.686, 709.2; 4.853, 699.6; 1240 

### 5.0055, 670.3; 5.173, 642.26; 5.34, 633.53]; 1241

1242 

### for model=['A' 'B' 'C' 'D'] 1243

### H-27 DRAFT- DO NOT CITE OR QUOTE eval(['use rat_set_',model]); 1244 

### BW=0.225; TSTOP=6; TCHNG=7; CC =1; VCHC=9; NCH=3; CINT=0.05; 1245 

### prepare @clear T CCHPPM 1246 

### res=[]; 1247 

### for CONC=[107, 498, 1028, 3206] 1248 

### start @nocallback 1249 

### res=[res,_t,_cchppm]; 1250


### end 1251 

### eval(['save res @file=gargas-inh-sim-',mode l,'.csv @format=ascii @separator=comma']) 1252 

### plot(dch1(:,1),dch1(:,2),dch5(:,1) ,dch5(:,2),dch10(:,1),dch10(:,2), ... 1253 

### dch30(:,1),dch30(:,2),res(:,1),res(:,2),res(:,3),res(:,4), ... 1254 

### res(:,5),res(:,6),res(:,7),res(:,8),['gargas86',model,'.aps']) 1255


### end 1256

1257 

### dch1=[dch1;dch5;dch10;dch30]; 1258 

### save dch1 @file=gargas-inh-dat.csv @format=ascii @separator=comma 1259

1260



### % ---------------

------------------ ----------------- ---------- -------- 1261 

### % File: Angelo_IV_comp.m 1262 

### % Figures C-4 and C-6 (creates 4 sub-plots for each) 1263


### % 1264 

### % Programmed by Michael Lumpkin 1265 

### % Syracuse Research Corporation, 10/2007 1266 

### % Modified by Paul Schlosser, U.S. EPA, 9/2009 1267


### % 1268 

### % This run time file sets species-s pecific constants values for the 1269 

### % modified Andersen et al (1987, 1991) PBPK model for dichloromethane, 1270 

### % simulating Anelo et al. (1986) IV exposures of DCM in rats. 1271


### % ---------------

------------------ ----------------- ---------- -------- 1272 

### d10bl=[2, 17.624; 5, 8.6635; 10, 3.533; 15, 2.858; 20, 1.7125; 1273 

### 30, 0.826; 40, 0.6192]; 1274 

### d50bl=[2, 62.686; 5, 34.105; 10, 16.56; 15, 12.33; 20, 12.80; 1275 

### 30, 10.25; 40, 5.561]; 1276 

### d10ex=[0.3333, 32.245; 0.6667, 38.56; 1, 41.396; 4, 44.42]; 1277 

### d50ex=[0.3333, 35.38; 0.6667, 45.95; 1, 51.38; 4, 57.59]; 1278

1279 

### for model=['A' 'B' 'C' 'D'] 1280

### H-28 DRAFT- DO NOT CITE OR QUOTE eval(['use rat_set_',model]); 1281 

### prepare @clear T CV PCTIVEXH 1282 

### TSTOP=10; CINT=0.01; 1283


### IVDOSE=10 1284 

### start @nocallback 1285 

### res=[_t,_cv,_pctivexh]; r2=[_t*60,_cv,_t,_pctivexh]; 1286 

### IVDOSE=5; start @nocallback 1287 

### res=[res,_t,_cv,_pctivexh]; r2=[r2,_t*60,_cv,_t,_pctivexh]; 1288 

### IVDOSE=25; start @nocallback 1289 

### res=[res,_t,_cv,_pctivexh]; r2=[r2,_t*60,_cv,_t,_pctivexh]; 1290 

### IVDOSE=50; start @nocallback 1291 

### res=[res,_t,_cv,_pctivexh] ;r2=[r2,_t*60,_cv,_t,_pctivexh]; 1292 

### plot(d10bl(:,1),d10bl(:,2),d50bl (:,1),d50bl(:,2), ... 1293 

### res(:,1)*60,res(:,2),_t *60,_cv,res(:,4)*60,res(:,5), ... 1294 

### res(:,7)*60,res(:,8),['angivbl',model,'.aps']) 1295 

### plot(d10ex(:,1),d10ex(:, 2),d50ex(:,1),d50ex(:,2), ... 1296 

### res(:,1),res(:,3),_t,_pc tivexh,res(:,4),res(:,6), ... 1297 

### res(:,7),res(:,9),['angivex',model,'.aps']) 1298 

### eval(['save r2 @file=Angelo_IV_sims_',model, '.csv @format=ascii @separator=comma']) 1299


### end 1300

1301



### % ---------------

------------------ ----------------- ---------- -------- 1302 

### % File: rat_inhal_Andersen.m 1303 

### % Figures C-5 (creates 4 sub-plots) 1304


### % 1305 

### % Programmed by Michael Lumpkin 1306 

### % Syracuse Research Corporation, 10/2007 1307 

### % Modified by Paul Schlosser, U.S. EPA, 9/2009 1308


### % 1309 

### % This run time file sets species-s pecific constants values for the 1310 

### % modified Andersen et al (1987, 1991) PBPK model for dichloromethane, 1311 

### % simulating Andersen et al. (1987) i nhalation exposures of DCM in rats. 1312


### % ---------------

------------------ ----------------- ---------- -------- 1313 

### dan2=[1, 2.6634; 2, 4.290; 3, 4.9915; 4, 4.464; 4.25, 1.3334; 4.5, 0.5513; 1314 

### 4.75, 0.4234; 5, 0.2618; 5.25, 0.1206; 5.5, 0.1049]; 1315 

### dan10=[1, 29.34; 2, 49.50; 3, 47.10; 4, 50.72; 4.25, 14.92; 4.5, 9.225; 1316

### H-29 DRAFT- DO NOT CITE OR QUOTE 4.75, 6.260; 5, 3.811; 5.25, 1.3085; 5.5, 0.6515; 5.75, 0.6025]; 1317 

### can2=[0.9942, 3.967; 1.027, 4.121; 1.039, 3.798; 2.045, 7.151; 1318 

### 2.0565, 6.598; 2.057, 5.7836; 3.053, 8.4295; 3.076, 7.493; 1319 

### 3.086, 8.7215; 4.073, 9.094; 4.074, 8.003; 4.084, 8.940; 4.5545, 5.302; 1320 

### 4.597, 6.853; 4.608, 7.1145; 5.078, 3.5525; 5.079, 2.723; 5.0795, 2.124; 1321 

### 5.302, 1.464; 5.313, 1.971; 5.325, 1.142]; 1322 

### can10=[1.006, 4.004; 1.018, 4.305; 1.021, 5.114; 2.0494, 9.257; 1323 

### 2.061, 9.488; 2.068, 7.9395; 3.047, 10.42; 3.0535, 8.777; 3.055, 9.170; 1324 

### 4.030, 6.449; 4.030, 10.31; 4.036, 12.11; 5.0275, 7.448; 5.061, 7.656; 1325 

### 5.067, 9.598; 5.5285, 4.978; 5.540, 5.001; 5.552, 9.115; 6.028, 1.999; 1326 

### 6.030, 6.460; 6.049, 1.653; 6.510, 0.4305; 6.525, 2.072; 6.539, 2.973]; 1327 

### for model=['A' 'B' 'C' 'D'] 1328 

### eval(['use rat_set_',model]); 1329 

### QCSW=0; 1330 

### prepare @clear T CV PCTHBCO 1331 

### TSTOP=6; CINT=0.01; TCHNG=4.0; 1332 

### CONC=200; start @nocallback 1333 

### res=[_t,_cv,_pcthbco]; 1334


### if model=='D' 1335 

### CONC=150; start @nocallback 1336 

### res=[res,_cv]; 1337 

### %use rat_set_D 1338 

### %QCSW=0; 1339


### end 1340

1341 

### CONC=750; CINT=0.01; TCHNG=4.0; start @nocallback 1342 

### res2=[_t,_cv]; res=[res,res2]; 1343 

### TSTOP=7; CONC=1014; start @nocallback 1344 

### plot(dan2(:,1),dan2(:,2),d an10(:,1),dan10(:,2), ... 1345 

### res(:,1),res(:,2),res2(: ,1),res2(:,2),res(:,1),res(:,4 ),['andinhbl',model,'.aps']) 1346 

### %plot(can2(:,1),can2(:,2) ,can10(:,1),can10(:,2), ... 1347 

### % res(:,1),res(:,3),_t,_pcthbc o,['andinhhb',model,'.aps']) 1348 

### eval(['save res @file=Andersen87_inh_sims_',mode l,'.csv @format=ascii @separator=comma']) 1349


### end 1350

1351

1352