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1 APPENDIX A: EXTERNAL REVIEW PANEL COMMENTS 

2 

3 

4 

5 

6 

7 

8 

9 To be added 
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10 APPENDIX B: HUMAN PBTK DICHLOROMETHANE MODEL 

11 

12 B-1. Human Model Description

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

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

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

16 Figure B-1). 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

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

28 for dichloromethane in the human. 

29 

30 

31 In order to incorporate known variability in human physiology and metabolism of 

32 dichloromethane into internal dosimetry, Monte Carlo analysis of the human model was 

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

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

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

36 in human physiology and dichloromethane metabolism. The human model was run repeatedly, 

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

38 iteration. Internal doses predicted for all iterations collectively defined a distribution for internal 

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

(at 10,000 iterations each) yielded 99th 40 percentile values of internal dose in the liver or lung that 

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

42 metabolic parameter and partition coefficient values were described by a mean, SD, and upper 

43 and lower bounds. Physiological parameter and partition coefficient values were initially taken 

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

45 The first order oral absorption rate constant, ka = 5.0/h, was used in conjunction with

46 human drinking water exposures simulated as six discrete drinking water episodes for specified 

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

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

49 sets by using MCMC calibration, also described in David et al. (2006). 

Blood

Alveolar

Air

Endogenous 

Production

Gas 

Exchange Lung

Fat

Richly 

Perfused

Slowly 

Perfused

Liver

GST CYP

GST CYP

CO Sub 

Model

CYP
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50 The variability of genotypic expression of GST-T1 activity (the mechanism for GST51 mediated metabolism of dichloromethane) was simulated as a uniform discrete distribution of the 

52 three GST-T1 genotypes (+/+, +/–, –/–) with varying activities in the liver and lung. The 

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

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

55 Warholm et al. (1994) for the three genotypes in a group of 208 healthy male and female subjects 

56 from Sweden were scaled by David et al. (2006) to obtain distributions of kfC for each genotype 

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

in a distribution of kfC activities with a mean equal to 0.852/hour-kg0.3 58 . The resulting 

59 distributions of internal lung and liver dose in human populations would have a theoretical 

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

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

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 Humansa

QCC Cardiac output (L/hour-kg0.74) 16.5 1.49 Humansa

VPR Ventilation:perfusion ratio 1.45 0.203 Humansa

QFC Fat 0.05 0.0150 Humansa

QLC Liver 0.26 0.0910 Humansa

QRC Rapidly perfused tissues 0.50 0.10 Humansa

QSC Slow perfused tissues 0.19 0.0285 Humansa

Tissue volumes (fraction BW) 

VFC Fat 0.19 0.0570 Humansa

VLC Liver 0.026 0.00130 Humansa

VLuC Lung 0.0115 0.00161 Humansa

VRC Rapidly perfused tissues 0.064 0.00640 Humansa

VSC Slowly perfused tissues (muscle) 0.63 0.189 Humansa

Partition coefficients 

PB Blood:air 9.7 0.970 Humansb

PF Fat:blood 12.4 3.72 Ratsb

PL Liver:blood 1.46 0.292 Ratsb

PLu Lung:arterial blood 1.46 0.292 Ratsb

PR Rapidly perfused tissue:blood 1.46 0.292 Ratsb

PS Slowly perfused tissue (muscle:blood) 0.82 0.164 Ratsb

Metabolism parameters 

VmaxC Maximum metabolism rate (mg/hour-kg0.7) 9.34 1.73 Calibrationc

Km Affinity (mg/L) 0.433 0.146 Calibrationc

A1 Ratio of lung Vmax to liver Vmax 0.000993 0.000396 Calibrationc

A2 Ratio of lung KF to liver KF 0.0102 0.00739 Calibrationc
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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 Calibrationc

First order metabolism rate (/hour-kg0.3) 

Homozygous (–/–) 0 0 Calibrationc

Heterozygous (+/–) 0.676 0.123 Calibrationc kfC 

Homozygous (+/+) 1.31 0.167 Calibrationc

Oral absorption 

ka 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 partition 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 description); posterior distributions presented in this table.

Source: David et al. (2006). 

63 

64 B-2. Revisions to Parameter Distributions of David et al. (2006) 

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

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

67 population. EPA’s conclusion is that the reported distributions for physiological parameters in 

68 particular, but also key metabolic parameters, only represented a narrow set of adults (with the 

69 exception of BW). Therefore supplemental data sources were chosen to define these 

70 distributions in a way that should fully characterize the variability in the human population for 

71 individuals between six months and eighty years of age. Since many physiological parameters 

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

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

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

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

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

77 individual at a given age and age- and gender-appropriate sampled body composition (fraction of 

78 BW in each tissue group). 

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

80 presumed to be relatively short-term effects possibly occurring from exposures of several weeks 

81 or months, using the distribution of such dosimetry “snapshots” should provide precisely the 

82 correct distribution to estimate overall population risk. For estimating cancer risk, however, 

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

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

85 significant portion thereof. But doing so would require estimating time-courses for each 
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86 physiological and metabolic parameter in the individual over that time-period, a task which 

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

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

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

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

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

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

93 (through lifestyle-changes) change in the opposite direction; and these changes may be reversed 

94 by the time the individual reaches 70 or 80 years of age. 

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

96 calculating human equivalent concentrations for non-cancer effects, however, this means that the 

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

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

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

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

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

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

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

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

105 likewise be assumed that the average internal dose per unit exposure in the population as a whole 

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

107 average one would estimate if the internal dose was tracked over the lifetime in the same 

108 population – where the distribution of physiological and metabolic characteristics at a given age 

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

110 society. 

111 

112 B-3. CY2E1 and GST-T1 

113 This evaluation incorporated additional data concerning the variability in CYP2E1 

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

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

116 trichloroethylene and measurements of protein content) to estimate a distribution of activity in 

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

118 dichloromethane (Vmaxc) was a truncated lognormal distribution with geometric mean (GM) = 

9.34 mg/hr/kg0.7 119 , geometric standard deviation (GSD) = 1.14, lower bound = 6.33 (68% of 

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

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

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

percentile = 40.7% of the mean, and 95th 123 percentile = 245.8% of the mean. These data support a 
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124 wider distribution in CYP2E1 activity than had been used in the David et al. (2006) model, with 

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

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

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

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

129 greater than zero, a non-truncated distribution was used for this parameter. 

130 To assure that the age-dependence of CYP2E1, particularly in children, is properly 

characterized, data of Johsrud et al. (2003)1 131 was analyzed. This study measures CYP2E1 activity 

132 and other parameters from individuals up to 18 years of age. For a significant subset of the 

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

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

135 there appeared no significant trend in the CYP2E1 activity and the average BW was 69.6 kg, 

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

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

138 of the change versus adult could be made by normalizing to these data. In particular, assuming 

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

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

141 estimated as: 

142 

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

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

145 

146 where Vmax(individual), CYP2E1(individual) and LW(individual) are the individual’s total 

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

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

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

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

151 by the 14-18-year-old average.) These normalized activities are plotted against the relative body 

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

153 

 

1 individual data supplied by the corresponding author D. Gail McCarver, to Paul Schlosser, U.S. 

EPA.
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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 

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

156 activity in 14-18 year-old individuals (Vmax(14-18)) plotted against 

157 normalized body weight (BW) for individuals ranging from six months to 18 

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

159 

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

prediction used by David et al. (2006) that Vmax will scale as BW0.7 161 , and an alternate scaling 

obtained by fitting to these data, BW0.88 162 . Both alternatives do a fairly good job of representing 

the average trend in the data, but the scaling by BW0.7 163 tends to under-predict the data in the range 

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

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

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

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

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

169 Using the allometric coefficient of 0.88, the normalized Vmaxc values was computed in 

170 the same group of individuals as: 

171 

172 Vmaxc(individual)/ Vmaxc (14-18) = 

[Vmax(individual)/BW(individual)0.88]/[Vmax(14-18) /BW(14-18)0.88 173 ]. 

174 

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

176 individual age and a linear regression performed, as shown in Figure B-3. While the slight trend 

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

the low R2 178 and small value of the slope indicate that the observation is not statistically significant 

179 and that further attempts to explicitly account for age dependence would lead to minimal 
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180 improvement. This representation of the data also clearly shows that the overall variability in the 

181 scaled activity (Vmaxc) is fairly constant across ages: approximately six-fold, ranging from ~ 0.3 

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

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

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

Note that scaling CYP2E1 activity by BW0.88 for children and by BW0.7 185 for adults, rather 

than per liver weight (which is expected to scale as body weight1.0 186 with only a 5% coefficient of 

variation in liver fraction) leads to a lower range in CYP2E1 than scaling by BW1 187 would 

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

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

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

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

192 adults and children, the resulting distribution is expected to provide a good prediction of 

193 variability in the overall population. 

194 

y = 0.0147x + 0.7907

R2

 = 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 

196 Figure B-3. Body-weight scaled CYP2E1 activity (Vmaxc) normalized to the 

197 average scaled activity in 14-18 year-old individuals (Vmaxc (14-18)) plotted 

198 against age individuals ranging from six months to 18 years of age. Data 

199 source: Johnsrud et al. (2003). 

200 

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

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

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

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

205 given age-range. GST-pi, however, was expressed at 21 times adult values in children up to 1 
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206 year of age, then declined with age. Thus it is difficult to draw specific conclusions regarding 

207 age-dependent variation in GST-T1 from these data (i.e., a quantitative analysis of the activity 

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

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-0.25 210 , the difference 

between these two is small and BW-0.3 211 was used in the Bayesian model calibration. Because 

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

tissue volume scales approximately as BW1, scaling the GST-T1 constant by BW-0.3 213 is equivalent 

to scaling a Vmax by BW0.7 214 . Even though the results cited above for GST-pi have the opposite 

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

216 least qualitatively consistent with this scaling. Therefore this scaling is assumed to appropriately 

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

218 via the explicit dependence on body weight. 

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

220 lower bound at zero is necessary while being minimally restrictive. The upper bound for each 

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

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

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

224 distribution for CYP2E1, to assure characterization of the upper end of the human distribution, 

225 the upper bound was set at 5 standard deviations above the mean. 

226 

227 B-4. Analysis of Human Physiological Distributions for PBTK Modeling

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

229 from 7 to 130 kg, thus covering 6-month old children to obese adults, there are age-dependent 

230 changes and gender-dependent differences in ventilation rates and body-fat that are not explicitly 

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

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

233 parameters were replaced with distributions based on available information that specifically 

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

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

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

237 distribution from 0 to 1. 

238 

239 B-4.1. Age

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

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

242 resulting quantiles were plotted against the corresponding ages with a polynomial function fit, as 
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243 shown in Figure B-4. A sample individual’s age can be determined by using the polynomial, 

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

y = 165.86x4 - 253.19x3 + 113.27x2 + 53.356x + 0.5

R2 = 0.9998

0

10

20

30

40

50

60

70

80

0 0.2 0.4 0.6 0.8 1

Empirical cumulative population fraction

Age (years)

245 

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

247 from U.S. Census Bureau). 

248 

249 B-4.2. Gender

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

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

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

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

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_

33.74

 + (125.36 - age)4

254 

255 Figure B-5. U.S. age-specific gender distribution (values from U.S. 

256 Census Bureau). 

257 

258 
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259 B-4.3. Body weight 

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

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

262 these results; however, those functions were found to be exquisitely sensitive to the fitted 

263 parameters, to the point that entering the functions by using the four significant figures for 

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

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

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

267 the form was fitted: 

268 

value = 

⎪

⎩

⎪

⎨

⎧

− ≥

− ≤

c c

c c

poly age age age age

poly age age age age

exp[ (( )/10)],

exp[ (( )/10)],

2

1

269 , 

270 

where polyi(x) = a·x + bi·x

2

 + ci·x

3 271 , i = 1 and 2, age is in years, agec is a “cut” age, dividing the 

272 early-age function from the later age function, and polyi (i = 1 or 2). Because there is no explicit 

273 constant term in polyi and the linear coefficient, a, is common to the two functions, the functions 

274 will automatically satisfy the condition of being equal and of having equal slope (first derivative) 

275 at age = agec, so the overall function will be smooth and continuous. 

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

square errors, error = ∑nage × [data(age) – function(age)]2 277 , where nage was the number of 

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

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

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

at the 1st and 99th 281 percentile. 

282 

283 

Table B-2. Parameters for BW distributions as functions of age and 

gender 

Male BW (kg) Female BW (kg) 

Parameter Mean SD Mean SD 

agec 21 16 16 13 

a 4.406 2.87 4.146 2.574 

b1 –0.0285 0.06 –0.147 –0.358 

c1 –0.729 –2.56 –1.36 –2.55 

b2 0.115 0.96 0.44 1.16 

b2 0.0048 0.0448 –0.0278 –0.0861 

284 
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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 

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

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

291 [2007]). 

292 
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293 An example output BW distribution, from a Monte Carlo simulation for ages 0.5–80 

294 years, both genders, is shown in Figure B-7. The range 6.6–131.4 kg is only slightly larger than 

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

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

297 intermediate BW values is smaller, as evidenced by the most rapid growth rate occurring 

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

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 

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

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

303 10,000). 

304 

305 

306 B-4.4. Alveolar Ventilation

307 Clewell et al. (2004) tabulated values for the alveolar ventilation constant QAlvC (L/hkg0.75) for males and females at different ages (QAlvC is multiplied by BW0.75 308 to obtain the total 

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

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

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

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

313 QAlvC was then selected from a lognormal distribution, with the resulting mean and GSD (given 

age and gender) truncated between the 5th and 95th 314 percentile. 

315 
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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 

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

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

319 

320 

Respiration variance

y = -0.1948x3 + 0.6095x2 - 0.3978x + 1.4261

R2 = 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 

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

323 function of age (values from Arcus-Arth and Blaisdell [2007]). 

324 

325 

326 B-4.5. Cardiac Output

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

cardiac blood-flow coefficient (QCC), QCCmean = 56.906 × (1.0 – e

–0.681 × exp(0.0454 × QAlvC) 328 ) – 

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

330 rather than using a distribution in the VPR (VPR = QAlv/QC = QAlvC/QCC) as was done by 

331 David et al. (2006). Since a distribution is already defined for QAlvC, above, a VPR subject to 

332 variability will be estimated but renormalized to match the ratio of QAlvC and QCCmean as 

333 defined above. In effect, this means QCCsample = QCCmean × VPRmean/VPRsample will be chosen, 

334 

335 where VPRmean and VPRsample are the mean and a random sample from the distribution defined 

336 by David et al. (2006). Clewell et al. (2004) suggest scaling QCC and alveolar ventilation as 
 B-14 DRAFT- DO NOT CITE OR QUOTE

BW0.75, while David et al. (2006) used BW0.74 337 . While virtually identical, it is noted that the 

implementation here uses BW0.75 338 . 

339 

340 B-4.6. Fat Fraction

341 Tabulated values from Clewell et al. (2004) show indistinguishable values for the fraction 

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

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

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

345 gender- and age-specific value to the VFCmean from David et al. (2006) was then used to scale the 

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

347 

348 B-4.7. Liver Fraction

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

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

351 higher than males between 18 and 40 years and the difference may be statistically significant, the 

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

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

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

355 from David et al. (2006) for the given age, as was done for body fat.
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356 

y = 0.1612x3 + 0.0846x2 - 0.3083x + 0.2709

R2

 = 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 = -0.0057x2 + 0.0293x + 0.1303

R2

 = 0.9941

y = -0.0458x2

 + 0.2082x + 0.0274

R2

 = 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 = -0.0015x2 + 0.0384x + 0.0908

R2

 = 0.8969

y = -0.0024x3

 + 0.0355x2 - 0.115x + 0.3678

R2

 = 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 

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

361 males and females (data from Clewell et al. [2004]). 

362 

363 

364 
 B-16 DRAFT- DO NOT CITE OR QUOTE

y = -0.0036x2 - 0.0051x + 0.0395

R2

 = 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 = -0.0004x2 + 0.0034x + 0.0169

R2

 = 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 

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

368 Clewell et al. [2004]). 

369 

370 

371 B-4.8. Tissue Volume Normalization

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

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

374 and other characteristics, it was assumed that any change in it would be modest and not 

375 significantly affect model predictions, given the fairly broad distribution implemented for total 

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

377 sample values of the tissue volume fractions from each of their respective distributions, the 

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

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

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

381 

382 B-5. Summary of Revised Human PBTK Model 

383 The resulting set of parameter distribution characteristics, including those used as defined 

384 by David et al. (2006), are described in Table B-3. The parameter statistics shown in Table B-3 

385 are summary statistics of the converged parameter chains obtained in that analysis, leaving out 

386 any evaluation of correlation or covariance among the updated parameters. As such, these 

387 statistics implicitly include both the inter-individual variability that would have been elucidated 
 B-17 DRAFT- DO NOT CITE OR QUOTE

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

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

390 population variability and parameter uncertainty, however, these statistics can be used primarily 

391 as-is (exceptions discussed previously) to define population distributions. Assuming that these 

392 parameters are distributed independently, ignoring the covariance that was likely represented in 

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

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

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

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

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

398 the approach used will not under-estimate risk or over-estimate lower bounds on human 

399 equivalent exposure levels. 

400 

401 

402 

403 
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404 

Table B-3. Parameter distributions for the human PBTK model for dichloromethane used by the EPA 

Distribution

Parameter 

Shape (Geometric) Mean a SD/GSD a Lower bound Upper bound Se

BW Body weight (kg) Normal f (age, gender) 1st %tile 99th %tile B-4

Flow rates 

QAlvC Alveolar ventilation (L/hr/kg0.75) Normal f (age, gender) f (age) 5th %tile 95th %tile B-4.4; mea

SD: Arcus-A

vprv Variability in ventilation/perfusion ratio Lognormal 1.00 0.203 0.69 1.42 VPR/VPRm

QCC Cardiac output (L/hr/kg0.75) QCCmean = f(QAlvC) QCC = QCCmean/vprv B-4.5; Clew

Fractional flow rates (fraction of cardiac output) 

QFC Fat Normal 0.05 0.0150 0.0050 0.0950 

QLC Liver Normal 0.26 0.0910 0.010 0.533 

QRC Rapidly perfused tissues Normal 0.50 0.10 0.20 0.80 

QSC Slow perfused tissues Normal 0.19 0.0285 0.105 0.276 

David et al. (

these dis

Tissue volumes (fraction body weight) 

VFC Fat Normal f (age, gender) 0.3·mean 0.1·mean 1.9·mean

VLC Liver Normal f (age) 0.05·mean 0.85·mea 1.15·mea

VLuC Lung Normal 0.0115 0.00161 0.00667 0.0163 

VRC Rapidly perfused tissues Normal 0.064 0.00640 0.0448 0.0832 

VSC Slowly perfused tissues Normal 0.63 0.189 0.431 0.829 

Fat mean: B-

Liver mea

2004); other

after samplin

=

Vi 

Partition coefficients 

PB Blood/air Lognormal 9.7 1.1 7.16 13.0 

PF Fat/blood Lognormal 11.9 1.34 4.92 28.7 

PL, PLu, 

& PR 

Liver/blood, lung/arterial blood, and 

rapidly perfused tissue/blood Lognormal 1.43 1.22 0.790 2.59 

PS Slowly perfused tissue (muscle)/blood Lognormal 0.80 1.22 0.444 1.46 

Geometric m

here, conver

and SD valu
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Table B-3. Parameter distributions for the human PBTK model for dichloromethane used by the EPA 

Distribution

Parameter 

Shape (Geometric) Mean a SD/GSD a Lower bound Upper bound Se

Metabolism parameters (based on Monte Carte calibration from 5 human data sets) 

Vmaxc Maximum metabolism rate (mg/hr/kgXvmax) Lognormal 9.34 1.73 (none) (none) 

B-3; mea

GSD & boun

Xvmax 

Xvmax 

Km Affinity (mg/L) Lognormal 0.41 1.39 0.154 1.10 

A1 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 0.0580 

FracR Fractional MFO capacity in rapidly perfused tissue Lognormal 0.0152 2.0 0.00190 0.122 

Geometric m

here, conver

and SD valu

First order metabolism rate (/hr/kg0.3) 

Homozygous (–/–) Normal 0 0 0 0

kfC Heterozygous (+/–) Normal 0.676 0.123 0 1.291

Homozygous (+/+) Normal 1.31 0.167 0 2.145 

Da

a Arithmetic mean and standard deviation (SD) listed for normal distributions; geometric mean and geometric standard deviat

normal distributions.
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405 APPENDIX C. RAT DICHLOROMETHANE PBTK MODELS

406 

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

408 based on nonneoplastic liver lesions in rats inhaling dichloromethane for 2 years (Serota et al., 

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

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

411 modeling and to extrapolate internal liver doses from rats to humans. Several deterministic 

412 PBTK rat models have been reported in the scientific literature (Sweeney et al., 2004; Andersen 

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

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

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

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

417 MCMC calibration of individual metabolic parameters for the CYP or GST pathways. For 

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

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

420 metabolism in isolation of CYP metabolism, thereby improving the estimate of metabolic flux 

421 through the competing pathways. Thus, the selected model includes parameter values estimated 

422 by deterministic methods only. In order to use the latest data for dichloromethane toxicokinetics 

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

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

425 

426 C-1. Methods of Analysis 

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

428 We reviewed published studies of dichloromethane metabolism and toxicokinetics in rats 

429 to identify data sets for use in model evaluation and possible calibration. Toxicokinetic data 

430 were available for: 

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

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

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

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

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

436 (constant concentration) inhalation exposure (Andersen et. al., 1991, 1987); 

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

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

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

440 500, 1000, or 2000 mg/kg in corn oil or water (Kirschman et al., 1986). 
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441 Four variations on the PBTK models of Anderson e al. (1987, 1991) (Figure 1) were 

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

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

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

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

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

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

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

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

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

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

452 sought to determine if use of these mouse-calibrated ratios would improve the model fit to the rat 

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

454 GST metabolism in the liver: Vmaxc (allometrically-scaled maximum rate of CYP metabolism), 

455 Km (CYP metabolic saturation constant), and kfc (allometrically-scaled first-order GST rate 

456 constant). 

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

458 Vmaxc, Km and GST, but lung metabolism is nullified. 

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

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

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

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

463 1989). Vmaxc, Km, and kfc were unchanged from Andersen et al. (1991). 

464 Model D is Model C with Vmaxc, Km, and kfc re-optimized against: the inhalation gas 

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

466 Andersen et al. (1987; blood dichloromethane levels); and the 24-hour cumulative exhalation 

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

468 latter data, which were insensitive to the oral absorption constant (because absorption and 

469 excretion are essentially complete by 24 hours) were used to inform the balance between GST470 mediated and CYP-mediated metabolism. 

471 
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472 

473 

474 

475 

476 

477 

478 

479 

480 

481 

482 

483 

484 

485 

486 

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

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

489 and D also included metabolism in the lung tissue compartment 

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

491 

492 C-1.2. Analysis

493 Models A-D were implemented using the acslXtreme simulation software (version 2.4, 

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

495 Nelder-Mead algorithm as implemented in the acslXtreme package. 

496 After completing the calibration for model D as described above, the oral absorption 

497 constant, ka, was then estimated by optimizing it, with all other parameters held constant, to the 

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

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

500 Only the 24-hour cumulative CO exhalation data were used to inform the estimate ka (earlier 

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

502 (Pankow et al., 1991; Kirschman et al., 1986) were also used in the evaluation of ka. 

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

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

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

506 low blood concentration data points disproportionately impacted the outcome, resulting in poor 

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

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

509 liver concentrations, and expiration of dichloromethane from Models A–D were assessed 

Blood 

Alveolar 

air 

Endogenous 

production 

Gas 

exchange 

Lung 

Fat 

Richly 

perfused 

Slowly 

perfused 

Liver 

GST CYP 

CO sub 

model 
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510 visually. 

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-kg0.74) 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 BW0.99) 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, Vmax (mg/hour-kg0.7) 6.21 4.0 4.0 3.93 

Affinity, Km (mg/L) 0.23 0.4 0.4 0.524 

Ratio of lung Vmax to liver Vmax, 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 

1st order metabolism rate (liver), kfC (1/hour-kg0.3 2.89 2.0 2.0 2.46 

Oral absorption constant, ka (1/hour) 5.0 5.0 4.0 1.80 

514 

515 

516 C-2. Results

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

518 goodness of fit to a common set of pharmacokinetic data. The oral absorption constant (ka) was 

519 then calibrated using oral PK data for the model that was deemed best. 
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520 

521 C-2.1. Evaluation of Model Structure for Description of Carboxyhemoglobin Levels 

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

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

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

525 exhaled dichloromethane and CO after oral administration, they found that blood 

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

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

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

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

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

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

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

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

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

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

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

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

538 simultaneously describe both the very short-time peak in blood dichloromethane with the 

539 concurrent rapid rise in exhaled dichloromethane levels (Figure C-2A) and the late peak blood 

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

541 to adequately predict dichloromethane exhalation data and dichloromethane blood concentration 

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

543 the results section). 

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

545 specifically be due to the assumption of instantaneous equilibrium between free CO and COHb 

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

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

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

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

550 rate of elimination of CO is determined by physiological parameters. Therefore, we believe that 

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

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

553 the dichloromethane data, or to alter physiological parameters which are well established (based 

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

555 error involved, then adjusting physiological parameters in an attempt to fit the CO exhalation 

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

557 dosimetry, particularly the pharmacokinetics of dichloromethane and its rates of metabolism, 
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558 which are critical to the assessment. In summary: it appears that given the model structure and 

559 assumptions, one cannot explain the difference between the exhalation time-courses for 

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

561 parameters; that difference might be explained if the release of CO from COHb is treated as rate562 limiting, in which case the two would not be in rapid equilibration. 

563 

564 

565 Figure C-2. A: Observations of exhaled 14C-labelled dichloromethane 

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

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

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

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

570 (Arrows indicate corresponding y-axis.) B: Blood carboxy-hemoglobin 

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

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

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

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

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

578 dichloromethane concentration data in various compartments as described below (primarily 

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

580 necessary to estimate model parameters for dichloromethane. Some comparisons of model 

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

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

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

584 reproduction of the COHb data in particular. 

585 

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

587 Dichloromethane

588 One data set used for model calibration is the closed chamber experiments of Gargas et 

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

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

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

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

593 the target concentrations of 100, 500, 1,000, and 3,000 ppm. Predictions from models B, C, and 

594 D all fit the observed chamber air concentrations reasonably well and noticeably better than 

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

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

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

598 saturated metabolism at 300 ppm, ostensibly because of the higher value for maximal rate of 

CYP metabolism, Vmaxc (6.21 mg/h/kg0.7 599 in model A), which prevented saturation of oxidative 

metabolism at these exposure levels. Use of Vmaxc ~ 4 mg/h/kg0.7 600 in models B, C, and D (Table 

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

602 Model simulations were next compared to measurements of blood dichloromethane 

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

604 are considered particularly useful for evaluating metabolic rate constants because the kinetics 

605 and complexity of uptake by other routes of exposure have been bypassed. All four models 

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

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

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

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

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

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

612 a log scale, the discrepancy between model simulations and the 50 mg/kg data appears similar to 
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613 the discrepancy vs. the 10 mg/kg data at later time points, but in fact is much larger in an 

614 absolute sense. 

615 

616 

617 

618 

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

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

621 dichloromethane in a 9-L closed chamber. 
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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 

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

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

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

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

629 at 5 and 25 mg/kg, are shown for comparison as thinner lines for model D. 

630 

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

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

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

634 significantly impact predictions for this particular route of administration). The discrepancy 

635 appears to exist primarily in the initial distribution phase, as the slope of the simulated clearance 

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

637 by running simulations with doses resulting in blood dichloromethane levels that more closely 

638 matched the data at the end of the initial distribution phase. These simulations can indicate 

639 whether the error is just in the initial distribution phase (primarily impacted by the tissue640 compartment volumes and partition coefficients) or is also in the rate of clearance by metabolism 

641 and other mechanisms (which determine the slope of the curve at later times. When the 

642 simulated administered doses were decreased 50% (i.e., to 5 and 25 mg/kg), the model 
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643 predictions under-predicts the early time-point data but closely matched the data from 10 

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

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

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

647 correctly predicting dosimetry from chronic exposures. 

648 Predictions of percent dichloromethane dose expired as dichloromethane were also 

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

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

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

652 exhaled from the 50 mg/kg dose. As with the simulations of blood concentrations (Figure C-4), 

653 the fit of model D to the 

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 

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

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

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

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

661 dichloromethane. Model predictions with doses at 5 and 25 mg/kg, are 

662 shown for comparison as thinner lines for model D. 
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663 % dichloromethane exhaled data is significantly improved if the simulated doses are reduced by 

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

665 describe the initial distribution, rather than longer-term clearance. 

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

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

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

669 models fit the post-exposure clearance phase of the inhalation-exposure blood dichloromethane 

670 data very well, especially the 1000 ppm data (Figure C-6). Model C describes the 200 ppm data 

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

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

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

674 As with the IV exposures where the initial distribution phase was over-predicted, we 

675 sought to determine if adjusting the uptake portion of the model simulation would also improve 

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

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

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

679 that such a large error in the exposure concentration would have occurred, but the simulation 

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

681 capable of reducing their respiration rate when exposed to irritant gases, thereby reducing the 

682 rate of inhalation. In the absence of plethysmography and aninmal handling data for the 

683 Andersen et al. (1987) experiments. we perfomed alternate simulations with the cardiac output 

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

685 provide a better fit to the data up to 4 h (during exposure), quite similar to the result shown in 

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

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

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

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

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

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

692 well as the 150 ppm exposure with default cardiac and respiration rates throughout (shown in 

693 Figure C-6). 

694 
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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 

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

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

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

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

701 line) also shown for comparison. 

702 

703 The results with the 150 ppm simulated exposure show that if the inhalation portion of 

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

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

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

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

708 it appears that the rate of metabolic (or other) clearance predicted by the model is slower than 

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

710 saturation (hence under-predicting metabolism) at that higher exposure level. 

711 

712 C-2.3. Evaluation of Relative Flux of CYP and GST Metabolism of Dichloromethane 

713 The relative flux of dichloromethane metabolism through the CYP or GST pathways 

714 during simulated chronic, medium-, and high-level inhalation exposures of rats to 
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715 dichloromethane is shown in Table C-2. Specifically, models B and D were considered as the 

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

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

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

719 

Table C-2. Effect of PBTK model configuration 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 

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

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

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

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

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

D has a higher GST-rate constant (kfC) than model B (2.46 versus 2.0/h/kg0.3 726 ; Table 1), as well 

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

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

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

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

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

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

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

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

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

736 up to ~ 100 ppm for both models. CYP metabolism becomes mostly saturated between 100 and 
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737 1000 ppm, but is not fully saturated even at 2000 ppm. CYP metabolism is not predicted to be 

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

739 quickly after each exposure. Since dichloromethane blood levels still increase with exposure 

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

741 period), there is also increased metabolite production with exposure level during these exposure742 off periods. However, the increase in metabolism per exposure-ppm is much less in this high743 concentration range than occurs in the lower-concentration range because blood concentrations 

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

745 predicted to become dominant above 1100-1300 ppm. 

746 

747 

748 

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

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

751 exposures. 

752 

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

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

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

756 mechanistic advantage of explicitly describing metabolism in the lung, model D is considered the 

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

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

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

760 
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761 C-2.4. Evaluation of Model Predictions of Oral Absorption of Dichloromethane 

762 For model D, the absorption constant, ka, was numerically fit to the data for blood and 

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

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

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

766 percentage declines with increased concentration, balancing the increased percentage exhaled as 

767 dichloromethane). The optimized value of ka = 1.8/h resulted primarily from fitting to 

768 dichloromethane data, as it was optimized against three dichloromethane data sets (blood, liver, 

769 and exhaled dichloromethane) and only one exhaled COHb data set. With ka = 1.8/h, model 

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

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

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

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

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

775 CO in the blood. 

776 

777 

778 

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

780 predictions for: (A) percent dose expired as dichloromethane (DCM); (B) 

781 blood dichloromethane; (C) percent expired as carbon monoxide; and (D) 

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

783 50 and 200 mg/kg, using a numerically fitted GI absorption rate (ka) of 1.8 h784 1. 

785 
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786 However, the predicted percent expired as CO following 50 mg/kg is considerably higher 

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

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

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

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

791 which might also be used to estimate ka. When ka was re-optimized against those data (other 

792 parameters as in model D), the resulting value was 0.47/h. Model simulations with both ka = 

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

794 al. (1991) in Figure C-9. While the lower value of ka (0.47/h) provides a better fit to the COHb 

795 data than simulations with ka = 1.8/h, the model still does not capture the more gradual rise to a 

796 peak and subsequent decline in the data. Moreover, with ka = 0.47/h the peak blood and liver 

797 concentrations of dichloromethane after oral gavage are predicted to occur at just beyond 1 h 

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

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

800 ka = 1.8/h) is ascribed primarily to the assumption of a rapid equilibrium between free and Hb801 bound CO in the blood: the more gradual rise and fall shown by the data in Figure C-9 could be 

802 better predicted if the binding and release steps were treated as rate-limiting. 

803 

804 

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

806 percent of total Hb) from a single gavage dose of 526 mg/kg dichloromethane 

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

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

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

810 

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

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

813 likely also explains part of the difference in kinetics between the respective experiments. The 
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814 kinetics of orally administered dichloromethane with the water/PEG vehicle are shown in Figure 

815 C-8, with the blood concentration already clearly falling from the peak at 30 min (panel B) and 

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

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

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

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

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

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

822 Pankow et al. But the failure to describe the COHb kinetics when the value of ka was reduced, as 

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

824 completely explain discrepancies between model predictions and the observed data. 

825 Model D simulations of total expired dichloromethane for oral doses with ka = 0.47 or 

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

827 for water vehicle at 250 and 500 mg/kg, for the model with ka = 1.8/h, is quite good, especially 

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

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

830 studies) could result from binding of dichloromethane metabolites to tissue macromolecules, a 

831 mechanism not included in the model, as well as incomplete quantitation. Using ka = 0.47/h 

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

833 trend of less dichloromethane exhaled than with the water vehicle after administration of a 250 

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

835 model assumes that 100% of an oral dose is absorbed, this discrepancy may result if a significant 

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

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

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

839 match these data provides reasonably good confirmation of the model predictions, given the 

840 shortcomings noted. While the two oral datasets used result in quite different estimates of ka, the 

841 difference appears to be at least partly due to the administration vehicle, with the lower ka = 

842 0.47/h obtained for an oil vehicle and a larger ka = 1.8/h for water as the vehicle. 
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 ka = 0.47 h-1 ka = 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 would result in unrealistic values; assuming this is 

supposed to be mg/kg, and assuming average value 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, where 55% was observed expired as dichloromethane 

from a dose of 500 mg/kg, with the % expired increasing with dose. 

844 

845 

846 C-3. Model Option Summary

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

848 of Andersen et al. (1991), with the inclusion of lung dichloromethane metabolism via CYP (4% 

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

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

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

852 prediction agreement of the various parameters with the available rat data sets. The resulting 

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

854 the metabolic parameters for the liver (Vmaxc, Km, and Kf) were re-optimized based on the 

855 presence of lung metabolism. 

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

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

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

859 CO from COHb would allow the model to describe a slower rise to peak COHb levels and 

860 slower rate of exhalation of CO, without compromising the model's ability to describe the 

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

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

863 dichloromethane data. 

 Reitz et al. (1997) employed a ka value of 5.0 h–1 864 for dichloromethane in deriving acute865 and intermediate-duration oral minimal risk levels. These investigators cited previous work in 

866 which a ka value of 5.0/h resulted in reasonable kinetic predictions for bolus or drinking water 
C-19 DRAFT- DO NOT CITE OR QUOTE

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

868 good agreement between toxicokinetic observations and predictions for bolus doses of 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

889 model D, which includes lung metabolism via the CYP and GST pathways, with primary 

890 metabolic parameters re-estimated for the liver (given defined lung:liver ratios), is the best of the 

891 four models. This evaluation indicates that the existing model structure for carbon monoxide 

892 and carboxy-hemoglobin does not adequately describe the corresponding data and that attempts 

893 to specifically use those data in setting key parameters would compromise the accuracy of those 

894 estimates. However, model D, the best of the models evaluated here, should provide adequately 

895 reliable PBTK-based risk extrapolation from rats to humans under chronic exposure conditions 

896 when using dichloromethane-specific dose metrics, such as dichloromethane blood concentration 

897 metrics or rates of dichloromethane metabolism. 

898 

899 

900 

901 
 D-1 DRAFT- DO NOT CITE OR QUOTE 

1 APPENDIX D. SUMMARY OF BENCHMARK DOSE (BMD) MODELING OF 

2 NONCANCER ENDPOINTS 

3 

4 

5 D-1. Oral Reference Dose: BMD Modeling of Nonneoplastic Liver Lesion Incidence Data 

6 for Rats Exposed to Dichloromethane in Drinking Water for 2 Years (Serota et al., 1986a) 

7 

8 BMD and BMDL refer to the model-predicted dose (and its lower 95% confidence limit) 

9 associated with 10% extra risk for the incidence of liver foci/areas of cellular alteration in male 

10 and female F344 rats given dichloromethane in drinking water for 2 years (Serota et al., 1986a) 

11 (Table D-1). 

12 

Table D-1. Incidence data for nonneoplastic liver lesions and internal liver 

doses, based on various metrics, in male and female F344 rats exposed to 

dichloromethane in drinking water for 2 years (Serota et al., 1986a)

Rat internal liver doseb

Sex 

Nominal 

(actual) 

daily intake 

(mg/kg-day) 

Rat liver 

lesion incidencea 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 

14 All available dichotomous models in the Benchmark Dose Software (version 2.0) were fit 

15 to male and female rat internal tissue doses of dichloromethane metabolized by the CYP pathway 

16 and incidences for animals with these liver lesions observed at the time of death (Table D-2). 

17 (The quantal model is identical to the one-stage multistage model, and so is not included in this 

18 set of models). The male rats exhibited a greater sensitivity compared to the female rats (based 

19 on lower BMDL10 values for all of the models) and thus the male data are used as the basis for 

20 the RfD derivation. The logistic model was the best fitting model for the male incidence data, 
 D-2 DRAFT- DO NOT CITE OR QUOTE 

1 based on AIC value among models with adequate fit (U.S. EPA,2000b). Results for this model 

2 are presented below. 

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 modela BMD10 BMDL10 

χ2 

goodness of fit 

p-value AIC 

Males 

Gammaa 151.73 48.93 0.62 185.33 

Logistic 85.17 61.78 0.75 183.61 

Log-logistica 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-probita 197.65 77.56 0.81 184.84 

Weibulla 117.29 48.39 0.57 185.49 

Females 

Gammaa 336.38 98.70 0.52 233.07 

Logistic 169.77 134.87 0.59 231.70 

Log-logistica 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-probita 400.95 173.57 0.60 232.80 

Weibulla 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 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 is reported (degree of polynomial 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 

1 Logistic Model , Male Rats (Serota et al., 1986a), CYP Metabolism Metric 

 0.5

 0.6

 0.7

 0.8

 0.9

 1

 0 500 1000 1500

Fraction Affected

dose

Logistic Model with 0.95 Confidence Level

11:43 02/25 2009

BMDL BMD

 

Logistic

2 

3 Figure D-1. Predicted (logistic model) and observed incidence of noncancer liver lesions in male 

4 F344 rats exposed to dichloromethane in drinking water for 2 years 

5 (Serota et al., 1986a) 

6 

7 ==================================================================== 

8 Logistic Model. (Version: 2.12; Date: 05/16/2008) 

9 Input Data File: 

10 C:\USEPA\IRIS\DCM\noncancer\dataset1_liverlesions\male\2LogSerlog.(d) 

11 Gnuplot Plotting File: 

12 C:\USEPA\IRIS\DCM\noncancer\dataset1_liverlesions\male\2LogSerlog.plt 

13 Wed Feb 25 11:43:00 2009 

14 ==================================================================== 

15 

16 BMDS Model Run 

17 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

18 The form of the probability function is: 

19 

20 P[response] = 1/[1+EXP(-intercept-slope*dose)] 

21 

22 

23 Dependent variable = incidence 

24 Independent variable = CYP 

25 Slope parameter is not restricted 

26 

27 Total number of observations = 5 

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-4 DRAFT- DO NOT CITE OR QUOTE 

1 

2 

3 Default Initial Parameter Values 

4 background = 0 Specified 

5 intercept = 0.683407 

6 slope = 0.00155073 

7 

8 

9 Asymptotic Correlation Matrix of Parameter Estimates 

10 

11 ( *** The model parameter(s) -background 

12 have been estimated at a boundary point, or have been specified by 

13 the user, and do not appear in the correlation matrix ) 

14 

15 intercept slope 

16 

17 intercept 1 -0.46 

18 

19 slope -0.46 1 

20 

21 

22 Parameter Estimates 

23 

24 95.0% Wald Confidence Interval 

25 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 

26 intercept 0.669705 0.208716 0.260629 1.07878 

27 slope 0.00182324 0.000412451 0.00101485 0.00263163 

28 

29 Analysis of Deviance Table 

30 

31 Model Log(likelihood) # Param's Deviance Test d.f. P-value 

32 Full model -89.2097 5 

33 Fitted model -89.8065 2 1.19363 3 0.7545 

34 Reduced model -106.616 1 34.8133 4 <.0001 

35 

36 AIC: 183.613 

37 

38 

39 Goodness of Fit 

40 Scaled 

41 Dose Est._Prob. Expected Observed Size Residual 

42 ------------------------------------------------------------------------ 

43 0.0000 0.6614 50.269 52.000 76 0.420 

44 133.9000 0.7138 24.269 22.000 34 -0.861 

45 872.7000 0.9056 34.412 35.000 38 0.326 

46 1433.1000 0.9638 33.734 34.000 35 0.241 

47 1868.6000 0.9833 40.316 40.000 41 -0.385 

48 

49 Chi^2 = 1.23 d.f. = 3 P-value = 0.7458 

50 

51 

52 Benchmark Dose Computation 

53 

54 Specified effect = 0.1 

55 

56 Risk Type = Extra risk 

57 

58 Confidence level = 0.95 

59 

60 BMD = 85.1668 

61 

62 BMDL = 61.7795 
 D-5 DRAFT- DO NOT CITE OR QUOTE 

1 D-2. Inhalation Reference Concentration: BMD modeling of Nonneoplastic Liver Lesion 

2 Incidence Data for Rats Exposed to Dichloromethane via Inhalation for 2 Years (Nitschke 

3 et al., 1988a)

4 

5 BMD and BMDL refer to the model-predicted dose (and its lower 95% confidence limit) 

6 associated with 10% extra risk for the incidence of hepatic vacuolation in female F344 rats 

7 exposed to dichloromethane via inhalation for 2 years (Nitscke et al., 1988a) (Table D-3). 

8 

Table D-3. Incidence data for nonneoplastic liver lesions (hepatic vacuolation) and 

internal liver doses, based on various metrics, in female Sprague-Dawley rats exposed to 

dichloromethane via inhalation for 2 years (Nitschke et al., 1988a)

Rat internal liver doseb

Sex 

Exposure 

(ppm) 

Liver lesion 

incidencea 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/m3, 200 ppm = 695 mg/m3, and 500 ppm = 1737 mg/m3) 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 

10 All available dichotomous models in the Benchmark Dose Software (version 2.0) were fit 

11 to male and female rat internal tissue doses of dichloromethane metabolized by the CYP pathway 

12 and incidences for animals with these liver lesions observed at the time of death (Table D-4). 

13 (The quantal model is identical to the one-stage multistage model, and so is not included in this 

14 set of models). The log-probit model was the best fitting model for the female incidence data, 

15 based on AIC value among models with adequate fit (U.S. EPA, 2000c). Results for this model 

16 are presented below. 
 D-6 DRAFT- DO NOT CITE OR QUOTE 

1 

Table D-4. BMD modeling results for incidence of noncancer liver 

lesions in female Sprague-Dawley rats 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) 

Modela

 BMD10 BMDL10 

χ2 

goodness of fit 

p-value AIC 

Gammaa 614.27 225.96 0.48 367.22 

Logistic 274.58 150.43 0.14 369.77 

Log-logistica 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-probita 728.96 523.94 0.98 365.82 

Weibulla 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 the 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 

1 Log Probit Model, Female Rats (Nitschke et al, 1988), CYP Metabolism Metric

 0.45

 0.5

 0.55

 0.6

 0.65

 0.7

 0.75

 0.8

 0.85

 0 100 200 300 400 500 600 700 800

Fraction Affected

dose

LogProbit Model with 0.95 Confidence Level

16:07 02/25 2009

BMDL BMD

 

LogProbit

2 

3 Figure D-2. Predicted (log-probit model) and observed incidence of noncancer liver lesions in 

4 female Sprague-Dawley rats inhaling dichloromethane for 2 years 

5 (Nitschke 1988a)

6 ==================================================================== 

7 Probit Model. (Version: 3.1; Date: 05/16/2008) 

8 Input Data File: 

9 C:\USEPA\BMDS21Beta\Data\dichloromethane\Dataset2\CYP\4LogCYPlog.(d) 

10 Gnuplot Plotting File: 

11 C:\USEPA\BMDS21Beta\Data\dichloromethane\Dataset2\CYP\4LogCYPlog.plt 

12 Wed Feb 25 16:07:52 2009 

13 ==================================================================== 

14 

15 BMDS Model Run 

16 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

17 The form of the probability function is: 

18 

19 P[response] = Background 

20 + (1-Background) * CumNorm(Intercept+Slope*Log(Dose)), 

21 

22 where CumNorm(.) is the cumulative normal distribution function 

23 

24 Dependent variable = Incidence 

25 Independent variable = Dose 

26 Slope parameter is restricted as slope >= 1 

27 

28 Total number of observations = 4 

29 Total number of records with missing values = 0 

30 Maximum number of iterations = 250 

31 Relative Function Convergence has been set to: 1e-008 

32 Parameter Convergence has been set to: 1e-008 
 D-8 DRAFT- DO NOT CITE OR QUOTE 

1 User has chosen the log transformed model 

2 

3 Default Initial (and Specified) Parameter Values 

4 background = 0.585714 

5 intercept = -7.69845 

6 slope = 1 

7 

8 Asymptotic Correlation Matrix of Parameter Estimates 

9 

10 ( *** The model parameter(s) -slope 

11 have been estimated at a boundary point, or have been specified by 

12 the user, 

13 and do not appear in the correlation matrix ) 

14 

15 background intercept 

16 

17 background 1 -0.37 

18 

19 intercept -0.37 1 

20 

21 Parameter Estimates 

22 95.0% Wald Confidence Interval 

23 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 

24 background 0.590379 0.0339871 0.523765 0.656992 

25 intercept -119.931 0.346784 -120.61 -119.251 

26 slope 18 NA 

27 

28 NA - Indicates that this parameter has hit a bound 

29 implied by some inequality constraint and thus 

30 has no standard error. 

31 

32 Analysis of Deviance Table 

33 

34 Model Log(likelihood) # Param's Deviance Test d.f. P-value 

35 Full model -180.889 4 

36 Fitted model -180.909 2 0.0403244 2 0.98 

37 Reduced model -184.186 1 6.5937 3 0.08604 

38 

39 AIC: 365.818 

40 Goodness of Fit 

41 Scaled 

42 Dose Est._Prob. Expected Observed Size Residual 

43 ------------------------------------------------------------------------ 

44 0.0000 0.5904 41.326 41.000 70 -0.079 

45 280.3400 0.5904 41.326 42.000 70 0.164 

46 656.4800 0.5907 41.349 41.000 70 -0.085 

47 772.5900 0.7571 52.998 53.000 70 0.001 

48 

49 Chi^2 = 0.04 d.f. = 2 P-value = 0.9801 

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 = 728.956 

60 

61 BMDL = 523.944 
 E-1 DRAFT- DO NOT CITE OR QUOTE 

1 APPENDIX E: SUMMARY OF BENCHMARK DOSE (BMD) MODELING OF 

2 CANCER ENDPOINTS 

3 

4 

5 E-1. Oral Cancer Slope Factors: BMD Modeling of Liver Tumor Incidence Data for Mice 

6 Exposed to Dichloromethane in Drinking Water for 2 Years (Serota et al., 1986b; Hazelton 

7 Laboratories, 1983) 

8 BMD10 and BMDL10 refer to the model-predicted dose (and its lower 95% confidence 

9 limit) associated with 10% extra risk for the incidence of hepatocellular adenoma and carcinoma 

10 in male mice given dichloromethane in drinking water for 2 years (Serota et al., 1986b; Hazelton 

11 Laboratories, 1983) (Table E-1). Multistage models were fit to male mouse internal tissue doses 

12 of dichloromethane metabolized by the GST pathway and incidences for animals with liver 

13 tumors observed at the time of death. The predicted BMD10 and BMDL10 for the incidence data 

14 are 73.0 and 39.6 mg dichloromethane metabolized via GST pathways per liter tissue per day, 

15 respectively for the internal liver metabolism metric, and 3.05 and 1.65 mg dichloromethane 

16 metabolized via GST pathway in lung and liver/kg-day), respectively, for the whole body 

17 metabolism metric (Table E-2).

18 

Table E-1. Incidence data for liver tumors and internal liver doses, 

based on GST metabolism dose metrics, in male B6C3F1 mice 

exposed to dichloromethane in drinking water for 2 years 

Sex 

Nominal (actual) 

daily intake 

(mg/kg-day) 

Mouse liver 

tumor incidencea

Mouse internal 

liver metabolism 

doseb 

Mouse whole body 

metabolism dosec 

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 B6C3F1 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 Factore

Internal 

dose metric 

BMDS 

modelb 

χ2

goodness of 

fit p- value

Mouse 

BMD10c 

Mouse 

BMDL10c 

Allometricscaled human 

BMDL10d 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

BMD10 and BMDL10 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 BMDL10 divided by (BWhuman/BWmouse)

0.25 = 7. e

Dichloromethane tumor risk factor (extra risk per unit internal dose) derived by dividing the BMR 

(0.1) by the mouse BMDL10 and by the allometric-scaled human BMDL10,, for the scaling = 1.0 and 

allometric-scaled risk factors, respectively. 

2 

3 Modeling results are presented in the subsequent sections for the tissue-specific liver4 metabolism metric (section E-1.1) and the whole-body metabolism metric (section E-1.2).
 E-3 DRAFT- DO NOT CITE OR QUOTE 

1 E-1.1. Modeling results for the internal liver metabolism metric 

2 

3 Serota et al. (1986b), Hazelton Laboratories, 1983: Internal liver dose-response, highest dose dropped 

4 1-degree polynomial 

 0.1

 0.15

 0.2

 0.25

 0.3

 0.35

 0.4

 0 20 40 60 80 100

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

13:46 02/19 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

5 

6 Figure E-1. Predicted and observed incidence of animals with hepatocellular carcinoma or 

7 adenoma in male B6C3F1 mice exposed to dichloromethane in drinking water 

8 for 2 years, using liver-specific metabolism dose metric (Serota et al., 1986b; Hazelton 

9 Laboratories, 1983). 

10 ==================================================================== 

11 Multistage Cancer Model. (Version: 1.7; Date: 05/16/2008) 

12 Input Data File: C:\USEPA\IRIS\DCM\Serota\highdosedropped\1MulSerMS_.(d) 

13 Gnuplot Plotting File: 

14 C:\USEPA\IRIS\DCM\Serota\highdosedropped\1MulSerMS_.plt 

15 Thu Feb 19 13:46:49 2009 

16 ==================================================================== 

17 BMDS Model Run 

18 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

19 The form of the probability function is: 

20 P[response] = background + (1-background)*[1-EXP( 

21 -beta1*dose^1)] 

22 

23 The parameter betas are restricted to be positive 

24 Dependent variable = incidence 

25 Independent variable = dose 

26 

27 Total number of observations = 4 

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 Degree of polynomial = 1 
 E-4 DRAFT- DO NOT CITE OR QUOTE 

1 

2 Maximum number of iterations = 250 

3 Relative Function Convergence has been set to: 2.22045e-016 

4 Parameter Convergence has been set to: 1.49012e-008 

5 

6 **** We are sorry but Relative Function and Parameter Convergence   

7 ** ** are currently unavailable in this model. Please keep checking   

8 ** ** the web sight for model updates which will eventually   

9 ** ** incorporate these convergence criterion. Default values used. **** 

10 

11 Default Initial Parameter Values 

12 Background = 0.218634 

13 Beta(1) = 0.00136788 

14 

15 Asymptotic Correlation Matrix of Parameter Estimates 

16 

17 Background Beta(1) 

18 

19 Background 1 -0.7 

20 

21 Beta(1) -0.7 1 

22 

23 Parameter Estimates 

24 95.0% Wald Confidence Interval 

25 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 

26 Background 0.218642 * * * 

27 Beta(1) 0.00144288 * * * 

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 -296.282 4 

35 Fitted model -296.87 2 1.1754 2 0.5556 

36 Reduced model -299.126 1 5.68747 3 0.1278 

37 

38 AIC: 597.74 

39 Goodness of Fit 

40 Scaled 

41 Dose Est._Prob. Expected Observed Size Residual 

42 ------------------------------------------------------------------------ 

43 0.0000 0.2186 27.330 24.000 125 -0.721 

44 17.5000 0.2381 47.387 51.000 199 0.601 

45 63.3000 0.2868 28.398 30.000 99 0.356 

46 112.0000 0.3352 32.853 31.000 98 -0.397 

47 

48 Chi^2 = 1.16 d.f. = 2 P-value = 0.5585 

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 = 73.0211 

58 

59 BMDL = 39.6034 

60 

61 BMDU = 335.18 

62 

63 Taken together, (39.6034, 335.18 ) is a 90 % two-sided confidence 

64 interval for the BMD 

65 

66 Multistage Cancer Slope Factor = 0.00252504 
 E-5 DRAFT- DO NOT CITE OR QUOTE 

1 E-1.2. Modeling results for the whole body metabolism metric

2 

3 Serota et al. (1986b), Hazelton Laboratories, 1983: Internal whole-body metabolism dose-response in mice, 

4 highest dose dropped 

5 1-degree polynomial 

6 

7 Figure 

 0.1

 0.15

 0.2

 0.25

 0.3

 0.35

 0.4

 0 1 2 3 4

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

17:33 02/21 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

8 

9 Figure E-2. Predicted and observed incidence of animals with hepatocellular carcinoma or 

10 adenoma in male B6C3F1 mice exposed to dichloromethane in drinking water 

11 for 2 years, using whole-body metabolism dose metric (Serota et al., 1986b; Hazelton 

12 Laboratories, 1983). 

13 ==================================================================== 

14 Multistage Cancer Model. (Version: 1.7; Date: 05/16/2008) 

15 Input Data File: C:\USEPA\IRIS\DCM\Serota\highdosedropped\1MulSerMS_.(d) 

16 Gnuplot Plotting File: 

17 C:\USEPA\IRIS\DCM\Serota\highdosedropped\1MulSerMS_.plt 

18 Sat Feb 21 17:33:49 2009 

19 ==================================================================== 

20 

21 BMDS Model Run 

22 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

23 The form of the probability function is: 

24 

25 P[response] = background + (1-background)*[1-EXP( 

26 -beta1*dose^1)] 

27 

28 The parameter betas are restricted to be positive 

29 Dependent variable = incidence 

30 Independent variable = dose 

31 
 E-6 DRAFT- DO NOT CITE OR QUOTE 

1 Total number of observations = 4 

2 Total number of records with missing values = 0 

3 Total number of parameters in model = 2 

4 Total number of specified parameters = 0 

5 Degree of polynomial = 1 

6 Maximum number of iterations = 250 

7 Relative Function Convergence has been set to: 2.22045e-016 

8 Parameter Convergence has been set to: 1.49012e-008 

9 

10 **** We are sorry but Relative Function and Parameter Convergence   

11 ** ** are currently unavailable in this model. Please keep checking   

12 ** ** the web sight for model updates which will eventually   

13 ** ** incorporate these convergence criterion. Default values used. **** 

14 

15 Default Initial Parameter Values 

16 Background = 0.218649 

17 Beta(1) = 0.032703 

18 

19 Asymptotic Correlation Matrix of Parameter Estimates 

20 

21 Background Beta(1) 

22 

23 Background 1 -0.7 

24 

25 Beta(1) -0.7 1 

26 Parameter Estimates 

27 

28 95.0% Wald Confidence Interval 

29 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 

30 Background 0.218662 * * * 

31 Beta(1) 0.0344939 * * * 

32 * - Indicates that this value is not calculated. 

33 

34 Analysis of Deviance Table 

35 

36 Model Log(likelihood) # Param's Deviance Test d.f. P-value 

37 Full model -296.282 4 

38 Fitted model -296.871 2 1.17643 2 0.5553 

39 Reduced model -299.126 1 5.68747 3 0.1278 

40 AIC: 597.741 

41 Goodness of Fit 

42 Scaled 

43 Dose Est._Prob. Expected Observed Size Residual 

44 ------------------------------------------------------------------------ 

45 0.0000 0.2187 27.333 24.000 125 -0.721 

46 0.7310 0.2381 47.385 51.000 199 0.602 

47 2.6470 0.2868 28.397 30.000 99 0.356 

48 4.6840 0.3352 32.853 31.000 98 -0.396 

49 

50 Chi^2 = 1.17 d.f. = 2 P-value = 0.5582 

51 

52 Benchmark Dose Computation 

53 Specified effect = 0.1 

54 Risk Type = Extra risk 

55 Confidence level = 0.95 

56 

57 BMD = 3.05447 

58 

59 BMDL = 1.65649 

60 

61 BMDU = 14.0263 

62 

63 Taken together, (1.65649, 14.0263) is a 90 % two-sided confidence 

64 interval for the BMD 

65 

66 Multistage Cancer Slope Factor = 0.0603686
 E-7 DRAFT- DO NOT CITE OR QUOTE 

1 E-2. Cancer Inhalation Unit Risk: BMD Modeling of Liver and Lung Tumor Incidence 

2 Data for Male Mice Exposed to Dichloromethane via Inhalation for 2 Years (Mennear et 

3 al., 1988; NTP, 1986). 

4 BMD10 and BMDL10 refer to the model-predicted dose (and its lower 95% confidence 

5 limit) associated with 10% extra risk for the combined incidence of adenoma and carcinoma of 

6 the liver or lung of male B6C3F1 mice inhaling dichloromethane for 2 years (Mennear et al., 

7 1988; NTP, 1986) (Table E-3). 

8 

Table E-3. Incidence data for liver and lung tumors and internal doses, based on GST 

metabolism dose metrics, in male B6C3F1 mice exposed to dichloromethane via inhalation 

for 2 years 

Sex, 

tumor type BW (g) 

External 

dichloromethane 

concentration 

(ppm) 

Mouse 

tumor incidence

Mouse internal 

tissue dosea 

Mouse whole body 

metabolism doseb

-- 0 22/50 (44%)e Male, liver 0 0 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, lungd

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 

11 Multistage models were fit to male mouse internal tissue doses of dichloromethane 

12 metabolized by the GST pathway and incidences for animals with liver tumors observed at the 

13 time of death. The predicted BMD10 and BMDL10 for the liver tumor incidence data are 913.9 

14 and 544.4 mg dichloromethane metabolized via GST pathways per liter liver per day, 

15 respectively for the internal liver metabolism metric, and 38.7 and 23.1 mg dichloromethane 

16 metabolized via GST pathway in lung and liver/kg-day), respectively, for the whole body 

17 metabolism metric (Table E-4). For lung tumors, the BMD10 and BMDL10 are 61.7 and 48.7, 

18 respectively for the lung-specific metric, and 13.1 and 10.3, respectively, for the whole body 

19 metabolism metric. 
 E-8 DRAFT- DO NOT CITE OR QUOTE 

Table E-4. BMD modeling results and tumor risk factors associated with 10% extra risk for liver and lung tumors in 

B6C3F1 mice exposed by inhalation to dichloromethane for 2 years, based on liver-specific GST metabolism and whol

body GST metabolism dose metrics

Tumor Risk 

Internal dose 

metrica

BMDS 

modelb 

χ2 

goodness of fit 

p-value

Mouse 

BMD10c 

Mouse 

BMDL10c 

Allometricscaled human 

BMDL10d Scaling = 1.0

Liver-specific Male, liver MS (0,1) 0.40 913.9 544.4 77.8 1.84 × 10–4  Male, lung MS (0,1) 0.64 61.7 48.6 7.0 2.06 × 10–3 

Whole body Male, liver MS (0,1) 0.40 38.7 23.1 3.3 -- 

 Male, lung MS (0,1) 0.66 13.1 10.3 1.5 -- 

aLiver 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/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 dos

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. 

cBMD10 and BMDL10 refer 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 BMDL10 divided 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 BMDL10 and by 

allometric-scaled human BMDL10,, for the scaling = 1.0 and allometric-scaled risk factors, respectively. 

1 2 Modeling results are presented in the subsequent sections for the tissue-specific liver-metabolism metric for l3 2.1), tissue-specific lung metabolism metric for lung tumors (section E-2.2), and the whole-body metabolism metric 4 2.3) and lung tumors (section E-2.4).
 E-9 DRAFT- DO NOT CITE OR QUOTE 

1 E-2.1. Modeling results for the internal liver metabolism metric, liver tumors 

2 Mennear et al. (1988); NTP (1986): Internal liver dose-response for liver tumors in male mice 

3 1-degree polynomial 

 0.3

 0.4

 0.5

 0.6

 0.7

 0.8

 0 1000 2000 3000 4000 5000

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

16:08 02/19 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

4 

5 Figure E-3. Predicted and observed incidence of animals with hepatocellular carcinoma or 

6 adenoma in male B6C3F1 mice exposed by inhalation to dichloromethane for 2 years, using liver7 specific 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 16:08:19 2009 

13 ==================================================================== 

14 

15 BMDS Model Run 

16 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

17 

18 The form of the probability function is: 

19 

20 P[response] = background + (1-background)*[1-EXP( 

21 -beta1*dose^1)] 

22 

23 The parameter betas are restricted to be positive 

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 Total number of specified parameters = 0 
 E-10 DRAFT- DO NOT CITE OR QUOTE 

1 Degree of polynomial = 1 

2 Maximum number of iterations = 250 

3 Relative Function Convergence has been set to: 2.22045e-016 

4 Parameter Convergence has been set to: 1.49012e-008 

5 

6 **** We are sorry but Relative Function and Parameter Convergence   

7 ** ** are currently unavailable in this model. Please keep checking   

8 ** ** the web sight for model updates which will eventually   

9 ** ** incorporate these convergence criterion. Default values used. **** 

10 

11 Default Initial Parameter Values 

12 Background = 0.406706 

13 Beta(1) = 0.00012805 

14 

15 Asymptotic Correlation Matrix of Parameter Estimates 

16 

17 Background Beta(1) 

18 

19 Background 1 -0.69 

20 

21 Beta(1) -0.69 1 

22 

23 Parameter Estimates 

24 

25 95.0% Wald Confidence Interval 

26 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 

27 Background 0.421771 * * * 

28 Beta(1) 0.000115283 * * * 

29 

30 * - Indicates that this value is not calculated. 

31 

32 Analysis of Deviance Table 

33 

34 Model Log(likelihood) # Param's Deviance Test d.f. P-value 

35 Full model -95.4892 3 

36 Fitted model -95.8368 2 0.695297 1 0.4044 

37 Reduced model -99.1316 1 7.28482 2 0.02619 

38 

39 AIC: 195.674 

40 

41 Goodness of Fit 

42 Scaled 

43 Dose Est._Prob. Expected Observed Size Residual 

44 ------------------------------------------------------------------------ 

45 0.0000 0.4218 21.089 22.000 50 0.261 

46 2363.7000 0.5597 26.305 24.000 47 -0.677 

47 4972.2000 0.6740 31.680 33.000 47 0.411 

48 

49 Chi^2 = 0.70 d.f. = 1 P-value = 0.4042 

50 

51 Benchmark Dose Computation 

52 Specified effect = 0.1 

53 

54 Risk Type = Extra risk 

55 

56 Confidence level = 0.95 

57 

58 BMD = 913.932 

59 

60 BMDL = 544.35 

61 

62 BMDU = 2569.01 

63 

64 Taken together, (544.35 , 2569.01) is a 90 % two-sided confidence 

65 interval for the BMD 

66 Multistage Cancer Slope Factor = 0.000183705 
 E-11 DRAFT- DO NOT CITE OR QUOTE 

1 E-2.2. Modeling results for the internal lung metabolism metric, lung tumors 

2 

3 Mennear et al. (1988); NTP (1986): Internal lung dose-response for lung tumors in male mice 

4 1-degree polynomial 

 0

 0.2

 0.4

 0.6

 0.8

 1

 0 200 400 600 800 1000

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

21:20 02/19 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

5 

6 Figure E-4. Predicted and observed incidence of animals with carcinoma or adenoma in the lung of 

7 male B6C3F1 mice exposed by inhalation to dichloromethane for 2 years, using liver-specific 

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 Thu Feb 19 21:20:36 2009 

14 ==================================================================== 

15 BMDS Model Run 

16 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

17 

18 The form of the probability function is: 

19 

20 P[response] = background + (1-background)*[1-EXP( 

21 -beta1*dose^1)] 

22 

23 The parameter betas are restricted to be positive 

24 

25 

26 Dependent variable = incidence 

27 Independent variable = dose 

28 

29 Total number of observations = 3 

30 Total number of records with missing values = 0 

31 Total number of parameters in model = 2 
 E-12 DRAFT- DO NOT CITE OR QUOTE 

1 Total number of specified parameters = 0 

2 Degree of polynomial = 1 

3 

4 Maximum number of iterations = 250 

5 Relative Function Convergence has been set to: 2.22045e-016 

6 Parameter Convergence has been set to: 1.49012e-008 

7 

8 **** We are sorry but Relative Function and Parameter Convergence   

9 ** ** are currently unavailable in this model. Please keep checking   

10 ** ** the web sight for model updates which will eventually   

11 ** ** incorporate these convergence criterion. Default values used. **** 

12 

13 Default Initial Parameter Values 

14 Background = 0.0642604 

15 Beta(1) = 0.00181622 

16 

17 Asymptotic Correlation Matrix of Parameter Estimates 

18 

19 Background Beta(1) 

20 

21 Background 1 -0.56 

22 

23 Beta(1) -0.56 1 

24 

25 Parameter Estimates 

26 95.0% Wald Confidence Interval 

27 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 

28 Background 0.0980033 * * * 

29 Beta(1) 0.00170868 * * * 

30 * - Indicates that this value is not calculated. 

31 

32 Analysis of Deviance Table 

33 

34 Model Log(likelihood) # Param's Deviance Test d.f. P-value 

35 Full model -68.0892 3 

36 Fitted model -68.199 2 0.219579 1 0.6394 

37 Reduced model -99.8132 1 63.4479 2 <.0001 

38 

39 AIC: 140.398 

40 Goodness of Fit 

41 Scaled 

42 Dose Est._Prob. Expected Observed Size Residual 

43 ------------------------------------------------------------------------ 

44 0.0000 0.0980 4.900 5.000 50 0.047 

45 475.0000 0.5994 28.171 27.000 47 -0.349 

46 992.2000 0.8345 39.219 40.000 47 0.306 

47 

48 Chi^2 = 0.22 d.f. = 1 P-value = 0.6408 

49 

50 Benchmark Dose Computation 

51 

52 Specified effect = 0.1 

53 Risk Type = Extra risk 

54 

55 Confidence level = 0.95 

56 

57 BMD = 61.6618 

58 

59 BMDL = 48.628 

60 

61 BMDU = 80.2137 

62 

63 Taken together, (48.628 , 80.2137) is a 90 % two-sided confidence 

64 interval for the BMD 

65 

66 Multistage Cancer Slope Factor = 0.00205643 
 E-13 DRAFT- DO NOT CITE OR QUOTE 

1 E-2.3. Modeling results for the whole body metabolism metric, liver tumors 

2 Mennear et al. (1988); NTP (1986): Internal whole-body metabolism dose-response for liver tumors in male 

3 mice 

4 1-degree polynomial 

 0.3

 0.4

 0.5

 0.6

 0.7

 0.8

 0 50 100 150 200

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

17:59 02/21 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

5 

6 FigureE-5. Predicted and observed incidence of animals with hepatocellular carcinoma or 

7 adenoma in male B6C3F1 mice exposed by inhalation to dichloromethane for 2 years, using whole8 body 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\liver\male\1MulNTPMS_.(d) 

12 Gnuplot Plotting File: C:\USEPA\IRIS\DCM\NTP\liver\male\1MulNTPMS_.plt 

13 Sat Feb 21 17:59:59 2009 

14 ==================================================================== 

15 BMDS Model Run 

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 Total number of specified parameters = 0 
 E-14 DRAFT- DO NOT CITE OR QUOTE 

1 Degree of polynomial = 1 

2 Maximum number of iterations = 250 

3 Relative Function Convergence has been set to: 2.22045e-016 

4 Parameter Convergence has been set to: 1.49012e-008 

5 

6 **** We are sorry but Relative Function and Parameter Convergence   

7 ** ** are currently unavailable in this model. Please keep checking   

8 ** ** the web sight for model updates which will eventually   

9 ** ** incorporate these convergence criterion. Default values used. **** 

10 

11 Default Initial Parameter Values 

12 Background = 0.406695 

13 Beta(1) = 0.00302163 

14 

15 Asymptotic Correlation Matrix of Parameter Estimates 

16 

17 Background Beta(1) 

18 

19 Background 1 -0.69 

20 

21 Beta(1) -0.69 1 

22 

23 Parameter Estimates 

24 95.0% Wald Confidence Interval 

25 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. Limit 

26 Background 0.421768 * * * 

27 Beta(1) 0.00272018 * * * 

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 -95.8372 2 0.696122 1 0.4041 

36 Reduced model -99.1316 1 7.28482 2 0.02619 

37 

38 AIC: 195.674 

39 Goodness of Fit 

40 Scaled 

41 Dose Est._Prob. Expected Observed Size Residual 

42 ------------------------------------------------------------------------ 

43 0.0000 0.4218 21.088 22.000 50 0.261 

44 100.2000 0.5597 26.307 24.000 47 -0.678 

45 210.7000 0.6740 31.679 33.000 47 0.411 

46 

47 Chi^2 = 0.70 d.f. = 1 P-value = 0.4039 

48 

49 Benchmark Dose Computation 

50 

51 Specified effect = 0.1 

52 

53 Risk Type = Extra risk 

54 

55 Confidence level = 0.95 

56 

57 BMD = 38.733 

58 

59 BMDL = 23.0698 

60 

61 BMDU = 108.885 

62 

63 Taken together, (23.0698, 108.885) is a 90 % two-sided confidence 

64 interval for the BMD 

65 

66 Multistage Cancer Slope Factor = 0.00433467 
 E-15 DRAFT- DO NOT CITE OR QUOTE 

1 E-2.4. Modeling results for the whole body metabolism metric, lung tumors 

2 Mennear et al. (1988); NTP (1986): Internal whole-body metabolism dose-response for lung tumors in male 

3 mice 

4 1-degree polynomial 

5 

 0

 0.2

 0.4

 0.6

 0.8

 1

 0 50 100 150 200

Fraction Affected

dose

Multistage Cancer Model with 0.95 Confidence Level

21:17 02/21 2009

BMDL BMD

 

Multistage Cancer

Linear extrapolation

6 

7 Figure E-4. Predicted and observed incidence of animals with carcinoma or adenoma in the lung of 

8 male B6C3F1 mice exposed by inhalation to dichloromethane for 2 years, using whole-body 

9 metabolism dose metric (Mennear et al., 1988; NTP, 1986). 

10 ==================================================================== 

11 Multistage Cancer Model. (Version: 1.7; Date: 05/16/2008) 

12 Input Data File: C:\USEPA\IRIS\DCM\NTP\lung\male\1MulNTPMS_.(d) 

13 Gnuplot Plotting File: C:\USEPA\IRIS\DCM\NTP\lung\male\1MulNTPMS_.plt 

14 Sat Feb 21 21:17:36 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 

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 Total number of specified parameters = 0 
 E-16 DRAFT- DO NOT CITE OR QUOTE 

1 Degree of polynomial = 1 

2 Maximum number of iterations = 250 

3 Relative Function Convergence has been set to: 2.22045e-016 

4 Parameter Convergence has been set to: 1.49012e-008 

5 

6 **** We are sorry but Relative Function and Parameter Convergence   

7 ** ** are currently unavailable in this model. Please keep checking   

8 ** ** the web sight for model updates which will eventually   

9 ** ** incorporate these convergence criterion. Default values used. **** 

10 

11 Default Initial Parameter Values 

12 Background = 0.0659119 

13 Beta(1) = 0.00855407 

14 

15 Asymptotic Correlation Matrix of Parameter Estimates 

16 

17 Background Beta(1) 

18 

19 Background 1 -0.56 

20 

21 Beta(1) -0.56 1 

22 

23 Parameter Estimates 

24 95.0% Wald Confidence Interval 

25 Variable Estimate Std. Err. Lower Conf. Limit Upper Conf. 

26 Limit 

27 Background 0.0980803 * * * 

28 Beta(1) 0.00807004 * * * 

29 

30 * - Indicates that this value is not calculated. 

31 

32 Analysis of Deviance Table 

33 

34 Model Log(likelihood) # Param's Deviance Test d.f. P-value 

35 Full model -68.0892 3 

36 Fitted model -68.1887 2 0.198975 1 0.6555 

37 Reduced model -99.8132 1 63.4479 2 <.0001 

38 

39 AIC: 140.377 

40 Goodness of Fit 

41 Scaled 

42 Dose Est._Prob. Expected Observed Size Residual 

43 ------------------------------------------------------------------------ 

44 0.0000 0.0981 4.904 5.000 50 0.046 

45 100.2000 0.5982 28.116 27.000 47 -0.332 

46 210.7000 0.8353 39.259 40.000 47 0.291 

47 

48 Chi^2 = 0.20 d.f. = 1 P-value = 0.6569 

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 = 13.0558 

58 

59 BMDL = 10.2947 

60 

61 BMDU = 16.9865 

62 

63 Taken together, (10.2947, 16.9865) is a 90 % two-sided confidence 

64 interval for the BMD 

65 

66 Multistage Cancer Slope Factor = 0.00971371
 F-1 DRAFT- DO NOT CITE OR QUOTE 

1 APPENDIX F. COMPARATIVE CANCER INHALATION UNIT RISK BASED ON 

2 FEMALE MICE DATA 

3 

4 Using the male B6C3F1 mouse data from a 2-year inhalation exposure study (Mennear et 

al., 1988; NTP, 1986), the recommended cancer IURs are 7 × 10–9(μg/m3)

–1 and 5 × 10–9 5 

(μg/m3)

–1 6 for the development of liver and lung cancer, respectively, based on the mean for the 

GST-T1+/+ 7 population. These values were derived using a tissue-specific GST metabolism dose 

8 metric, with allometric scaling. The combined the combined human equivalent IUR values for 

both tumor types is 1 × 10–8 (μg/m3)

 –1 9 . As described in detail below, the resulting combined 

10 human equivalent IUR values for both tumors did not differ appreciably by gender. 

11 BMD10 and BMDL10 refer to the model-predicted dose (and its lower 95% confidence 

12 limit) associated with 10% extra risk for the combined incidence of adenoma and carcinoma of 

13 the liver or lung of female B6C3F1 mice inhaling dichloromethane for 2 years (Mennear et al., 

14 1988; NTP, 1986) (Table F-1). 

15 

Table F-1. Incidence data for liver and lung tumors and internal doses, based on GST 

metabolism dose metrics, in female B6C3F1 mice exposed to dichloromethane via inhalation 

for 2 years 

Sex, 

tumor type BW (g) 

External 

dichloromethane 

concentration 

(ppm) 

Mouse 

tumor incidence

Mouse internal 

tissue dosea 

Mouse whole body 

metabolism doseb

-- 0 3/47 (6%)e Female, liver 0 0 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, lungd

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 

17 Multistage models were fit to the female mouse internal tissue doses of dichloromethane 

18 metabolized by the GST pathway and incidences for animals with liver tumors observed at the 

19 time of death. The predicted BMD10 and BMDL10 for the liver and lung tumor incidence data 

20 are shown in Table F-2. 
 F-2 DRAFT- DO NOT CITE OR QUOTE 

1 A probabilistic PBTK model for dichloromethane in humans adapted from David et al. 

2 (2006) (see Appendix B) was used with Monte Carlo sampling to calculate distributions of 

internal lung, liver, or blood doses associated with chronic unit inhalation (1 μg/m3 3 ) exposures. 

4 The model was then executed by using the external unit exposure as input, and the resulting 

5 human equivalent internal dose was recorded. This process was repeated for 10,000 iterations to 

6 generate a distribution of human internal doses. The resulting distribution of IURs shown in 

7 Table F-3 were derived by multiplying the human internal dose tumor risk factor (in units of 

8 reciprocal internal dose) by the respective distributions of human average daily internal dose 

resulting from a chronic unit inhalation exposure of 1 μg/m3 9 dichloromethane. Risk estimates 

10 were slightly higher for liver tumors and essentially equivalent for lung tumors in males 

11 compared to females. 

12 For the female mouse, the combined human equivalent IUR values for both tumor types 

is 1 × 10–8 (μg/m3)

 –1 in the most sensitive (GST-T1+/+ 13 ) population (Table F-4), which is the same 

14 value that was obtained using the male mouse data. 
 F-3 DRAFT- DO NOT CITE OR QUOTE 

1 

Table F-2. BMD modeling results and tumor risk factors associated with 10% extra risk for liver and lung tumors in 

female B6C3F1 mice exposed by inhalation to dichloromethane for 2 years, based on liver-specific GST metabolism an

whole body GST metabolism dose metrics

Tumor Risk 

Internal dose 

metrica

BMDS 

modelb 

χ2 

goodness of fit 

p-value

Mouse 

BMD10c 

Mouse 

BMDL10c 

Allometricscaled human 

BMDL10d Scaling = 1.0

Liver-specific Female, liver MS (0,2) 0.53 1224.1 659.7 94.2 1.52 × 10–4  Female, lung MS (0,1) 0.87 51.2 40.7 5.8 2.46 × 10–3 

Whole body Female, liver MS (0,2) 0.53 51.9 28.0 4.0 -- 

 Female, lung MS (0,1) 0.88 10.8 8.6 1.2 -- 

aLiver 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/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 dos

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. 

cBMD10 and BMDL10 refer 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 BMDL10 divided 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 BMDL10 and by 

allometric-scaled human BMDL10,, for the scaling = 1.0 and allometric-scaled risk factors, respectively. 

2 
 F-4 DRAFT- DO NOT CITE OR QUOTE 

1 

Table F-3. IURs for dichloromethane based on PBTK model-derived internal liver and lung doses in B6C3F1 female mice 

exposed via inhalation for 2 years, based on liver-specific GST metabolism and whole body metabolism dose metrics, by 

population genotype 

Distribution of human internal 

dichloromethane doses from 1 

μg/m3

exposured 

Resulting candidate 

IURe

(μg/m3

)

Internal dose metric 

and scaling factora

Population 

genotypeb Tumor type 

Human 

tumor risk 

factorc Mean 

95th

percentile

99th

percentile Mean 

95th

percentile 

GST-T1+/+ Liver 1.06 × 10–3 5.64 × 10–6 1.56 × 10–5 2.60 × 10–5 6.0 × 10–9 1.8 × 10–8 GST-T1+/+ Lung 1.72 × 10–2 3.31 × 10–7 8.55 × 10–7 1.34 × 10–6 5.7 × 10–9 1.5 × 10–8 Mixed Liver 1.06 × 10–3 2.62 × 10–6 8.65 × 10–6 1.45 × 10–5 2.8 × 10–9 9.2 × 10–9 

Tissue-specific, allometric-scaled 

Mixed Lung 1.72 × 10–2 1.81 × 10–7 5.67 × 10–7 9.84 × 10–7 3.1 × 10–9 9.8 × 10–9 

GST-T1+/+ Liver 1.52 × 10–4 5.64 × 10–6 1.56 × 10–5 2.60 × 10–5 8.6 × 10–10 2.5 × 10–9 GST-T1+/+ Lung 2.46 × 10–3 3.31 × 10–7 8.55 × 10–7 1.34 × 10–6 8.1 × 10–10 2.1 × 10–9 Mixed Liver 1.52 × 10–4 2.62 × 10–6 8.65 × 10–6 1.45 × 10–5 4.0 × 10–10 1.3 × 10–9 

Tissue-specific, scaling = 1.0 

Mixed Lung 2.46 × 10–3 1.81 × 10–7 5.67 × 10–7 9.84 × 10–7 3.5 × 10–10 1.4 × 10–9 

GST-T1+/+ Liver 2.50 × 10–2 1.53 × 10–7 4.87 × 10–7 9.20 × 10–7 3.8 × 10–9 1.2 × 10–8 GST-T1+/+ Lung 8.14 × 10–2 1.53 × 10–7 4.87 × 10–7 9.20 × 10–7 1.3 × 10–8 4.0 × 10–8 Mixed Liver 2.50 × 10–2 8.76 × 10–8 3.20 × 10–7 6.76 × 10–7 2.2 × 10–9 8.0 × 10–9 

Whole-body, allometric-scaled 

Mixed Lung 8.14 × 10–2 8.76 × 10–8 3.20 × 10–7 6.76 × 10–7 7.1 × 10–9 2.6 × 10–8 

aTissue specific dose units = mg dichloromethane metabolized via GST pathway per liter tissue (liver or lung, respectively, for liver and lun

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. populati

/–, 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 BM

mouse BMDL10 (from Table F-2) for the allometric-scaled and scaling = 1.0 risk factors, respectively. dMean, 95th, and 99th percentile of the human PBTK model-derived probability distribution of daily average internal dichloromethane dose r

chronic exposure to 1 

μg/m3 (0.00029 ppm). eDerived by multiplying the dichloromethane tumor risk factor by the PBTK model-derived probabilistic internal doses from daily exposure 

2 
 F-5 DRAFT- DO NOT CITE OR QUOTE 

1 

Table F-4. Upper bound estimates of combined human IURs for liver and lung tumors resulting from exposure to 1 

μg/m3 dichloromethane, based on liver-specific GST metabolism and whole body metabol

metrics, by population genotype, using female mouse data for derivation of risk factors 

 Internal dose 

metric and scaling 

factora

Population genotypeb Tumor site 

Upper 

bound IURc 

Central 

tendency 

IURd 

Variance of 

tissue-specific 

tumor riske 

Combined 

tumor risk 

SDf 

Liver 6.0 × 10–9 3.2 × 10–9 2.82 × 10–18 Lung 5.7 × 10–9 4.5 × 10–9 5.16 × 10–19 

Tissue-specific, allometric-scaled 

GST-T1+/+

Liver or lung 7.8 × 10–9 1.8 × 10–9 

Liver 2.8 × 10–9 1.5 × 10–9 6.08 × 10–19 Lung 3.1 × 10–9 2.5 × 10–9 1.54 × 10–19 

 Mixed 

Liver or lung 4.0 × 10–9 8.7 × 10–10 

Liver 8.6 × 10–10 4.6 × 10–10 5.74 × 10–20Lung 8.1 × 10–10 6.6 × 10–10 1.03 × 10–21

GST-T1+/+

Liver or lung 1.1 × 10–9 2.6 × 10–10 

Liver 4.0 × 10–10 2.1 × 10–10 1.24 × 10–20

Lung 4.6 × 10–10 3.5 × 10–10 3.07 × 10–21

Tissue-specific, scaling = 1.0 

Mixed 

Liver or lung 5.7 × 10–10 1.2 × 10–10 

Liver 3.8 × 10–9 2.1 × 10–9 1.15 × 10–18

Lung 1.3 × 10–8 9.9 × 10–9 2.97 × 10–18

GST-T1+/+

Liver or lung 1.2 × 10–8 2.0 × 10–9 

Liver 2.2 × 10–9 1.2 × 10–9 3.76 × 10–19

Lung 7.3 × 10–9 5.7 × 10–9 9.72 × 10–19

Whole-body, allometric-scaled 

Mixed 

Liver or lung 6.8 × 10–9 1.2 × 10–9 

aTissue specific dose units = mg dichloromethane metabolized via GST pathway per liter tissue (liver or lung, respectively, for liv

lung 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

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 the t statistic, 1.645. rCalculated as the square root of the sum of the variances for liver and lung tumors. gCalculated as the product of the cumulative tumor risk SD and the t statistic, 1.645, added to the sum of central tendency IURs. 

2 
 G-1 DRAFT- DO NOT CITE OR QUOTE 

3 APPENDIX G. COMPARATIVE CANCER INHALATION UNIT RISK BASED ON 

4 BENIGN MAMMARY GLAND TUMORS IN RATS 

5 

6 

7 Data for mammary gland tumors in male and female F344 rats following exposure to 

8 airborne dichloromethane were used to develop a comparative IUR for dichloromethane 

9 (Mennear et al., 1988; NTP, 1986) (Table G-1). Significantly increased incidences of mammary 

10 gland or subcutaneous tissue adenoma, fibroadenomas, or fibromas were observed in male rats at 

11 4,000 ppm, while mammary gland adenomas or fibroadenomas were increased in female rats 

12 exposed 6 hours/day, 5 days/week for 2 years at concentrations ≥1,000 ppm. Significant 

13 decreases in survival were observed in the treated groups of both sexes. The at-risk study 

14 populations (represented by the denominators in the incidence data) were determined by 

15 excluding all animals dying prior to 52 weeks.

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 

incidencea

Rat internal dose, AUC 

in bloodb 

-- 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 

19 The rat PBTK model of Andersen et al. (1991) (modified as described in Appendix D) 

20 was used to simulate inhalation exposures of 6 hours/day, 5 days/week and calculate long-term 

21 daily average internal doses for the 2-year bioassays (Mennear et al., 1988; NTP, 1986). Study-, 

22 group-, and sex-specific mean BWs for rats were used. The modified PBTK model did not 

23 include a compartment for the mammary gland nor did it account for metabolism of 

24 dichloromethane occurring in the mammary gland. The selected internal dose metric for 
 G-2 DRAFT- DO NOT CITE OR QUOTE 

25 mammary gland tumors was the average daily AUC for dichloromethane in slowly perfused 

26 tissue; the mammary gland and the regions around it consist primarily of fatty tissue, which is 

27 slowly perfused tissue. The role of CYP- or GST-mediated metabolism in the mammary gland is 

28 uncertain. GST-T1 (Lehmann and Wagner, 2008) and CYP2E1 (El-Rayes et al., 2003; Hellmold 

29 et al., 1998) expression has been detected in human mammary tissue, and it is also possible that 

30 some metabolites enter systemic circulation from the liver and lung, where they are formed. 

31 Figure G-1 shows the comparison between inhalation external and internal doses in the liver and 

32 lung, respectively, using this dose metric for the rat and the human. 

33 The multistage model was fit to the rat mammary gland tumor incidence and PBTK 

34 model-derived internal dose data to derive rat internal BMD10 and BMDL10 values associated 

35 with 10% extra risk (Table G-2). 

36 

37 

38 Figure G-1. PBTK model-derived internal doses (daily average AUC for 

39 dichloromethane in blood) in rats and humans, and their associated external 

40 exposures (ppm) used for the derivation of cancer IURs, based on mammary 

41 tumors in rats. Average daily doses were calculated from simulated rat exposures of 6 

42 hours/day, 5 days/week, while simulated human exposures were continuous. The GST 

43 metabolism rate in the human population for the mixed GSTT1 group (+/+, +/-, and -/-) in the 

44 current U.S. population was estimated as the mean of a simulated sample of 3000 individuals at 

45 each exposure concentration, based on GSTT1 polymorphism data from Haber et al. (2002). The 

46 results for the GSTT1 +/- and +/+ sub-populations were then calculated as the means of the 

47 subsets of the mixed population sample with the respective genotypes. 

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 dichloromethane for 2 years, based on AUC for 

dichloromethane in slowly perfused tissue 

Sex Tumor type 

BMDS 

modela 

χ2 

goodness of 

fit 

p-value

Rat 

BMD10c 

Rat 

BMDL10c 

Tumor Risk 

Factord

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 was fit to each of the two sets of rat dose-response 

data shown in Table G-1 using internal dose metrics 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

BMD10 and BMDL10 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 confidence limit, associated with a 10% extra risk for the 

incidence of tumors. 

d

Dichloromethane tumor risk factor (extra risk per average daily AUC for dichloromethane in slowly perfused tissue 

[mg × hour/L]) derived by dividing the BMR (0.1) by the rat BMDL10). The rat BMDL10 is assumed to be 

equivalent to human BMDL10; 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 

52 Rat mammary gland tumor risk factors (extra risk per unit internal dose) were calculated 

53 by dividing 0.1 by the rat internal BMDL10. Because this risk factor is based on the internal 

54 concentration of dichloromethane, rather than a rate of reaction, it is assumed that the human risk 

55 factor is equal to that of the rat (i.e., that humans exposed for a 70-year lifetime to the same 

56 weekly average AUC of dichloromethane will have the same risk as rats exposed for 2 years). 

57 The human PBTK model (David et al., 2006) was used to calculate a distribution of human 

58 average daily AUCs for dichloromethane in slowly perfused tissue resulting from chronic, 

inhalation exposure to a unit concentration of 1 μg/m3 59 dichloromethane (0.00029 ppm). The 

60 distribution of IURs for mammary gland tumors was generated by multiplying the human tumor 

61 risk factor for each sex by the distribution of internal doses from chronic human exposure to 1 

μg/m3 62 dichloromethane. Because this analysis is not based on the assumption that either 

63 metabolic pathway is or is not influencing the cancer risk, this distribution was derived by using 

64 weights reflecting the estimated frequency of GST-T1 genotypes in the current U.S. population 

(20% GST-T1–/–, 48% GST-T1+/–, and 32% GST-T1+/+ 65 ). As shown in Table G-3, the mean 

human IUR based on mammary gland tumors in rats is 4 × 10–8 and 1 × 10–7 (μg/m3)

–1 66 based on 

67 male and female rat-derived risk factors, respectively. Identical values were obtained using 

68 slowly perfused tissue as the internal dose metric.
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1 

Table G-3. IURs for dichloromethane, based on benign mammary tumors and PBTK 

model-derived internal doses in F344N rats exposed via inhalation for 2 years, based on 

AUC for dichloromethane in slowly perfused tissue dose metric 

 

Distribution of human internal 

dichloromethane doses from 1 μg/m3

exposureb

Resulting candidate human 

IURc

Sex, 

tumor type 

Human 

tumor risk 

factora Mean 

95th 

percentile 

99th 

percentile Mean 

95th 

percentile 

99th 

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 average daily AUC for dichloromethane in slowly perfused 

tissue [mg × hour/L]) derived by dividing the BMR (0.1) by the rat BMDL10 (from Table G-2). The rat 

BMDL10 is assumed to be equivalent to human BMDL10; 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, 95th, and 99th 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 inhalation exposure to a unit concentration of 1 μg/m3

 (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, 95th, and 99th percentile of a distribution of human IURs (extra risk per μg/m3) 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 

3 APPENDIX H: SOURCE CODE AND COMMAND FILES FOR 

4 DICHLOROMETHANE PBTK MODELS 

5 

6 The following is a copy of the primary acslXtreme code (.csl file for implementation 

7 under acslXtreme v. 2.4.2.1) used for the dichloromethane simulations. Portions of the code 

8 which had been commented out (i.e., unused code) were deleted for brevity. Note that most 

9 parameters are set in the subsequent script/m-files, used to specify simulations and call the .csl 

10 code. 

11 While the code indicates modifications by G Diamond and M Lumpkin (employees of the 

12 U.S. EPA contractor, Syracuse Research Center), these are changes from the version of the code 

13 as received by them to bring it into alignment with the version as described in the publications of 

14 Marino et al. (2006) and David et al. (2006) (changes noted by comments). Other changes allow 

15 for the calculation of various metrics to improve time-efficiency for computational convergence; 

16 for example, calculating an average AUC over only the last week of simulated exposure requires 

17 a much shorter overall simulated time for the calculation to give the steady-state or very-long18 term average, when the approach to steady-state or repeating periodic solution occurs over the 

19 first days or weeks of exposure. Thus the code exactly replicates the published models when the 

20 published parameter values are used.

21 

22 PROGRAM: DCM.csl 

23 

24 ! Code from Rietz et al. 1997, Addressing Data Needs for Methylene Chloride with 

25 !Physiologically Based Pharmacokinetic Modeling (Appendix I) 

26 ! Translated by GDiamond (05/2004); DCM.CSL.Reitz. 

27 ! Revision date 18-Dec-96 by RHR (Peak moved to dynamic) 

28 ! Modified by GDiamond (08/2004) based on Andersen et al. 1987 (TAP 87:185-205) 

29 ! and 1991 (TAP 108:14-27): 

30 

31 ! Deleted brain compartment 

32 ! Added lung compartment, with lung metabolism 

33 ! Adjusted metabolism parameter values to match Andersen et al. 1987 

34 ! Adjusted physiological parameters to match Andersen et al. 1987 

35 ! Modifed by M Lumpkin (11/2005) (MHL) to include extrahepatic MFO metabolism and 

36 ! CO kineticsin blood 

37 ! Additional comments and changes by Paul Schlosser (PS), U.S. EPA (7/2008 - 9/2009) 

38 ! Removed unused (legacy) code bits, added comments. PS 9/2009 

39 

40 INITIAL 

41 !Simulation T=hour 

42 NSTP=1000 ! Initial integration cycle length at CINT/1000 

43 !MERROR ALU=0.00001 ! Error tolerance for Gear 

44 CONSTANT POINTS=96.0 ! Number of points in plot 

45 CINTERVAL CINT=0.1 ! Communication interval 
 H-2 DRAFT- DO NOT CITE OR QUOTE 

46 CONSTANT TSTOP=24.0 ! Termination time (hr) 

47 

48 !Initial values for 250 g rat 

49 

50 !Body masses and fractional volumes 

51 CONSTANT BW=0.25 ! Body weight (kg) MHL 

52 CONSTANT VBL2C=0.059 ! Fractional volume of blood MHL 

53 CONSTANT VFC=0.07 ! Fractional volume fat 

54 CONSTANT VLC=0.04 ! Fractional volume of liver 

55 CONSTANT VRC=0.05 ! Fractional volume of rapidly-perfused tissues 

56 CONSTANT VSC=0.75 ! Fractional volume of slowly-perfused tissues ** 

57 CONSTANT VLUC=0.0115 ! Allometric scaling factor for lung volume ** 

58 

59 ! Tissue masses (kg) 

60 VTOT=VFC+VLC+(VLUC/(BW**0.01))+VRC+VSC 

61 !total volume fractions constrained to sum to 0.9215 (7.85% carcass) MHL 

62 VBL2=VBL2C*BW ! Blood 

63 VF=VFC*BW*0.9215/VTOT ! Fat 

64 VL=VLC*BW*0.9215/VTOT ! Liver 

65 VR=VRC*BW*0.9215/VTOT ! Rapidly-perfused tissue 

66 VS=VSC*BW*0.9215/VTOT ! Slowly-perfused tissue 

67 VLU=VLUC*(BW**0.99)*0.9215/VTOT ! Lung ** 

68 

69 ! Flow constants and fractions 

70 CONSTANT VPR=0.42 ! Ventilation/perfusion ratio (for QP calc) MHL 

71 CONSTANT DSPC=0.15 ! Fractional lung dead space MHL 

72 CONSTANT QCC=15.0 ! Allometric constant for cardiac output 

73 CONSTANT QCCR=10.0 ! For 'resting' period, PMS 8/4/09 

74 CONSTANT QCSW=0 ! Set to 1 to use QCCR (resting cardiac rate) up to TCHNG 

75 CONSTANT QLC=0.20 ! Fractional flow to liver 

76 CONSTANT QFC=0.09 ! Fractional flow to fat 

77 CONSTANT QSC=0.15 ! Fractional flow to slowly-perfused tissue 

78 CONSTANT QRC=0.56 ! Fractional flow to rapidly-perfused tissue ** MHL 

79 

80 ! Flow rates (L/hr) 

81 

82 IF (QCSW) THEN 

83 QC=QCCR*BW**0.74 ! Cardiac output if resting, PMS 8/4/09 

84 ELSE 

85 QC=QCC*BW**0.74 ! Cardiac output if not resting, PMS 8/4/09 

86 ENDIF 

87 QP=QC*VPR ! Alveolar ventilation rate MHL 

88 QPDP=QP/DL/PAIR ! MHL 

89 QTOT=QFC+QLC+QRC+QSC ! MHL 

90 QL=QLC*QC/QTOT ! Liver MHL 

91 QF=QFC*QC/QTOT ! Fat MHL 

92 QR=QRC*QC/QTOT ! Rapidly-perfused tissue MHL 

93 QS=QSC*QC/QTOT ! Slowly-perfused MHL 

94 

95 ! Partition coefficients 

96 CONSTANT PL=0.732 ! Liver:blood 
 H-3 DRAFT- DO NOT CITE OR QUOTE 

97 CONSTANT PF=6.19 ! Fat:blood 

98 CONSTANT PS=0.408 ! Muscle:blood 

99 CONSTANT PR=0.732 ! Liver:blood 

100 CONSTANT PLU=0.732 ! Lung:blood ** 

101 CONSTANT PB=19.4 ! Blood:air 

102 

103 ! Metabolism 

104 CONSTANT VMAXC=4.0 ! Allometric scaling constant for VMAX 

105 CONSTANT KM=0.4 ! Michaelis constant for MFO pathway (mg/L) 

106 CONSTANT FRACR=0.1 ! Oxidative metab in rapidly perfused MHL 

107 CONSTANT KFC=2.0 ! Allometric scaling constant for KF 

108 CONSTANT MOLWT=85.0 ! Molecular weight of DCM 

109 CONSTANT MWCO=28.0 ! Molecular weight of CO MHL 

110 CONSTANT A1=0.416 ! Ratio of specific activities of MFO, lung/liver MHL 

111 CONSTANT A2=0.137 ! Ratio of specific activities of GST, lung/liver MHL 

112 

113 VMAX=VMAXC*BW**0.7 ! Maximum rate of MFO pathway (mg/hr) 

114 VMAXR=VMAX*FRACR ! MHL 

115 KF=KFC/(BW**0.3) ! First-order rate constant for GSH pathway 

116 

117 ! CO Submodel params MHL 

118 CONSTANT DLC=0.060 ! MHL 

119 CONSTANT RENCOC=0.035 ! Rate of endogenous CO production MHL 

120 CONSTANT ABCOC=0.117 ! Conc of background CO (mg/kg) MHL 

121 CONSTANT HBTOT=10.0 ! Conc of hemoglobin (mmoles/L) MHL 

122 CONSTANT P1=0.80 ! CO yeild factor MHL 

123 CONSTANT F1=1.21 ! CO elimination factor MHL 

124 CONSTANT M=150.0 ! Haldane coefficient MHL 

125 CONSTANT COINH=2.2 ! Background CO inhalation concentration (ppm), MHL 

126 CONSTANT O2=0.13 ! 4/16/32 mmoles/L MHL 

127 CONSTANT PAIR=713.0 ! Pressure of air (mm Hg) MHL 

128 CONSTANT RHO=1102.0 ! Density of CO (mg/L) MHL 

129 CONSTANT SOL=0.03 ! mg/L/mmHG, MHL 

130 DL=DLC*BW**0.92 ! MHL 

131 RENCO=RENCOC*BW**0.7 ! MHL 

132 ABCO=ABCOC*BW ! MHL 

133 Mmm=1.1325*M ! MHL 

134 L12D=1.0/P1 ! MHL 

135 LCO=28.0/MOLWT ! MHL 

136 L12=LCO*P1 ! MHL 

137 

138 ! Exposure schedule 

139 CONSTANT CONC=0.0 ! Inhaled concentration (ppm) 

140 CONSTANT TCHNG=6.0 ! Exposure pulse 1 width (hr) 

141 CONSTANT TDUR=24.0 ! Exposure duration (hr) 

142 CONSTANT TCHNG2=120.0 ! Exposure pulse 2 width (hr) 

143 CONSTANT TDUR2=168.0 ! Exposure duration 2 (hr) 

144 CIZONE = 1.0 ! Start with inhalation on 

145 PEAK = 0.0 ! Zero peak concentration in brain 

146 CONSTANT IVDOSE=0.0 ! IV infusion (mg/kg) MHL 

147 CONSTANT TIV=0.0028 ! IV dosing time (hrs, or 10 seconds) MHL 
 H-4 DRAFT- DO NOT CITE OR QUOTE 

148 CONSTANT VCHC=10.0 ! Uncorrected rat chamber volume (L) MHL 

149 CONSTANT CC=0 ! Flag for closed chamber MHL 

150 CONSTANT NCH=5 ! Number of animals in chamber MHL 

151 CONSTANT KL=0.0 ! MHL 

152 VCH=VCHC-(NCH*BW) 

153 

154 ! Oral Gavage Dosing added by MHL 

155 CONSTANT BOLUS=0.0 

156 TOTALBOLUS=BOLUS*BW 

157 TOTALDOSE=(IVDOSE+BOLUS)*BW 

158 IVORBOL=(TOTALDOSE.GT.0.0) 

159 CONSTANT DRCONC=0.0 ! User-specified concentration in drinking water (mg/L) 

160 CONSTANT FIXDRDOSE=0.0 ! Set to constant DW dose in mg/kg-day 

161 ! Assume 70 kg human drinks 2 L/day, calculate rodent allometrically 

162 DRVOL=0.102*BW**0.7 ! Daily water intake, based on body weight 

163 DRDOSE=DRVOL*DRCONC + FIXDRDOSE*BW ! Total dose from water (mg (in a day)) 

164 DDOSE=DRDOSE/BW ! Daily dose (mg/kg/day) 

165 NEWDAY=0.0 ! To reset area-under-curve values each 24 hours 

166 PEAKPCTHBCO=0.0 

167 CONSTANT KA=5.0 ! Rate constant for absorption 

168 

169 ! Periodic drinking water intake schedule 

170 ! Assume T=0 is 7 AM (T+4=11 AM) 

171 INTEGER I 

172 I=2 

173 DIMENSION DRTIME(6) ! Store drinking times in array 

174 DIMENSION DRPCT(6) ! Store drinking percentages array 

175 CONSTANT DRTIME=6*0.0, DRPCT=6*0.0 

176 GASD=MOLWT/24450.0 

177 SCHEDULE drink .AT. DRTIME(2) ! Assume DRTIME(1)=0 

178 ! and initial drink applied here. 

179 CUMORALDOSE=DRPCT(1)*DRDOSE ! To calculate input to GI (mg) 

180 

181 ! Switching 'lastday' and 'lastwk' to be defined by discrete blocks, below, PS 3/2009 

182 lastday=0.0 

183 IF (TSTOP.GT.24) SCHEDULE lday .AT. TSTOP-24 

184 lastwk=0.0 

185 if (TSTOP.GT.168) SCHEDULE lwk .AT. TSTOP-168 

186 

187 CIDAY=1.0 

188 CIWK=1.0 

189 SCHEDULE idayoff .AT. TCHNG 

190 SCHEDULE idayon .AT. TDUR 

191 SCHEDULE iwkoff .AT. TCHNG2 

192 SCHEDULE iwkon .AT. TDUR2 

193 !zzstopflag = .FALSE. 

194 END ! End of INITIAL section of program 

195 

196 DYNAMIC 

197 DISCRETE idayoff 

198 CIDAY=0.0 
 H-5 DRAFT- DO NOT CITE OR QUOTE 

199 QC=QCC*BW**0.74 

200 SCHEDULE idayoff .AT. (T+TDUR) 

201 END 

202 DISCRETE idayon 

203 CIDAY=1.0 

204 IF (QCSW) THEN 

205 QC=QCCR*BW**0.74 

206 ENDIF 

207 SCHEDULE idayon .AT. (T+TDUR) 

208 END 

209 DISCRETE iwkoff 

210 CIWK=0.0 

211 SCHEDULE iwkoff .AT. (T+TDUR2) 

212 END 

213 DISCRETE iwkon 

214 CIWK=1.0 

215 SCHEDULE iwkon .AT. (T+TDUR2) 

216 END 

217 CIZONE = CIDAY*CIWK 

218 

219 DISCRETE lday ! Value -> 1 for last 24 h, PS 3/2009 

220 lastday=1.0 

221 end ! of lday 

222 DISCRETE lwk ! Value -> 1 for last 168 h, PS 3/2009 

223 lastwk=1.0 

224 end ! of lwk 

225 

226 DISCRETE drink ! Loop for drinking water intake schedule 

227 CUMORALDOSE = CUMORALDOSE + DRPCT(I)*DRDOSE 

228 I=I+1 

229 IF (I .EQ. 7) THEN 

230 I=1 

231 NEWDAY=NEWDAY + 24.0 

232 ENDIF 

233 SCHEDULE drink .AT. (NEWDAY+DRTIME(I)) 

234 End ! drink 

235 

236 DERIVATIVE 

237 ALGORITHM IALG=2 ! Gear for stiff systems 

238 

239 ! Following are daily averages over final week of simulations, PS 8/2008 

240 WAVGLIVCYPDOSE=integ(lastwk*RAM1L,0.0)/(7.0*VL) 

241 WAVGLIVGSTDOSE=integ(lastwk*RAM2L,0.0)/(7.0*VL) 

242 WAVGLUNGGSTDOSE=integ(lastwk*RAM2LU,0.0)/(7.0*VLU) 

243 WAVGWBDYGSTDOSE=integ(lastwk*(RAM2L+RAM2LU),0.0)/(7.0*BW) 

244 WAVGWBDYCYPDOSE=integ(lastwk*(RAM1L+RAM1LU),0.0)/(7.0*BW) 

245 WAVGAUCV=integ(lastwk*CV,0.0)/7.0 

246 WAVGAUCS=integ(lastwk*CS,0.0)/7.0 

247 WAVGAUCL=integ(lastwk*CL,0.0)/7.0 

248 

249 ! Following are daily averages calculated from final day's dose-rate, PS 8/2008 
 H-6 DRAFT- DO NOT CITE OR QUOTE 

250 LDAYLIVCYPDOSE=integ(lastday*RAM1L,0.0)/VL 

251 LDAYLIVGSTDOSE=integ(lastday*RAM2L,0.0)/VL 

252 LDAYLUNGGSTDOSE=integ(lastday*RAM2LU,0.0)/VLU 

253 LDAYWBDYGSTDOSE=integ(lastday*(RAM2L+RAM2LU),0.0)/BW 

254 LDAYWBDYCYPDOSE=integ(lastday*(RAM1L+RAM1LU),0.0)/BW 

255 LDAYLIVAUC=integ(lastday*CL,0.0) 

256 LDAYAUCV=integ(lastday*CV,0.0) 

257 LDAYAUCS=integ(lastday*CS,0.0) 

258 

259 ! GI compartment for drinking water inputs 

260 RSTOM=-KA*STOM 

261 STOM=INTEG(RSTOM,TOTALBOLUS)+CUMORALDOSE 

262 

263 RIV=IVDOSE*BW/TIV ! IV dose rate (mg/hr) MHL 

264 blip=STEP(tiv) 

265 IV=RIV*(1.0-blip) 

266 

267 ! Chamber calcs MHL 

268 RACH=CC*NCH*QP*((CA1/PB)-CCH)-(KL*ACH) ! MHL 

269 ACH=INTEG(RACH,CONC*GASD*VCH) !MHL added initial condition 

270 CCH=CC*ACH/VCH ! MHL 

271 CCHPPM=CCH/GASD ! MHL original 

272 

273 ! If/else statements removed by use of multipliers; PMS 7-23-08 

274 CI=(1.0-CC)*CONC*CIZONE*GASD !Convert to mg/L 

275 DIDOSE=24.0*QP*integ(CI,0.0)/(BW*(T+1.0E-8))!Daily inhaled dose (mg/kg-d) 

276 

277 ! CA1=Arterial blood concentration from gas exchange region 

278 ! to lung tissue compartment (mg DCM/L) 

279 CA1=(QP*CI+QP*CCH+QC*CV)/((QP/PB)+QC) !** 

280 

281 !AX=Amount eliminated by exhalation (mg DCM) 

282 CX=CA1/PB ! Concentration in air leaving gas exchange region 

283 RAX=QP*CX 

284 AX=INTEG(RAX, 0.0) 

285 

286 ! Concentration DCM in exhaled air 

287 CEX1=0.7*CX + 0.3*CI 

288 ! Assumes mixing with 30% of inhaled that only goes to deadspace 

289 RAEX1=CEX1*QP ! MHL 

290 AEX1=INTEG(RAEX1,0.0) ! MHL 

291 

292 PCTIVEXH=100.0*IVORBOL*AX/(TOTALDOSE + 1.0E-8) 

293 ! Percent IV/BOLUS dose exhaled as DCM. MHL 

294 

295 !Amount in lung tissue (mg DCM) !** 

296 RAM1LU=A1*VMAX*CA*(VLU/VL)/(KM+CA) 

297 AM1LU=INTEG(RAM1LU, 0.0) 

298 RAM2LU=A2*KF*CA*VLU 

299 RALU=QC*(CA1-CA)-RAM1LU-RAM2LU 

300 ALU=INTEG(RALU, 0.0) 
 H-7 DRAFT- DO NOT CITE OR QUOTE 

301 CLU=ALU/VLU 

302 AUCLU=INTEG(CLU,0.0) 

303 CA=CLU/PLU ! Amount in arterial blood to body 

304 

305 ! AF=Amount in fat (mg DCM) 

306 RAF=QF*(CA-CVF) 

307 AF=INTEG(RAF, 0.0) 

308 CF=AF/VF 

309 CVF=CF/PF 

310 

311 ! AL=Amount in liver (mg DCM) 

312 RAL=QL*(CA-CVL)-RAM1L-RAM2L+KA*STOM 

313 RAM1L=VMAX*CVL/(KM+CVL) !** 

314 RAM2L=KF*CVL*VL !** 

315 AL=INTEG(RAL, 0.0) 

316 CL=AL/VL 

317 CVL=CL/PL 

318 AUCL=INTEG(CL, 0.0) !** 

319 

320 ! AS= Amount in slowly-perfused tissues 

321 RAS=QS*(CA-CVS) !-RAMS ! VMAXS=0, so not needed, PS 7/2008 

322 AS=INTEG(RAS, 0.0) 

323 CS=AS/VS 

324 CVS=CS/PS 

325 

326 ! AR=Amount in rapidly-perfused tissues 

327 RAMR=VMAXR*CVR/(KM+CVR) ! MHL 

328 AMR=INTEG(RAMR,0.0) ! Total metabolized (MFO) in RP, MHL 

329 RAR=QR*(CA-CVR)-RAMR 

330 AR=INTEG(RAR, 0.0) 

331 CR=AR/VR 

332 CVR=CR/PR 

333 

334 ! AM1=Amount metabolized in MFO pathway (mg DCM) 

335 RAM1=RAM1LU+RAM1L+Ramr !+ams ! AMS = 0, term not needed, PS 7/2008 

336 

337 ! AM2=Amount metabolized in GSH pathway (mg DCM) 

338 RAM2=RAM2LU+RAM2L 

339 

340 ! Total rate/amount metabolized (MFO plus GSH pathways) 

341 RAMTOT=RAM1+RAM2 !** 

342 AMTOT=INTEG(RAMTOT, 0.0) 

343 

344 ! CV=Average venous blood concentration (mg DCM/L) 

345 CV=(QF*CVF+QL*CVL+QS*CVS+QR*CVR+RIV)/QC ! MHL added RIV 

346 CVP=0.23*CV ! Venous plasma conc 

347 ABL=(CA+CV)*VBL2 

348 AUCBL=INTEG(ABL, 0.0) 

349 AUCV=INTEG(CV, 0.0) !GLD 

350 

351 ! CO appearance in and elimination from blood, MHL 
 H-8 DRAFT- DO NOT CITE OR QUOTE 

352 ACO=ABL2/(VBL2*MWCO) !MHL 

353 BO2=O2/Mmm ! MHL 

354 CHBT=HBTOT ! MHL 

355 HBCO=((ACO+BO2+CHBT)-SQRT((ACO+BO2+CHBT)*(ACO+BO2+CHBT)-4.0*ACO*CHBT))/2.0 

356 ! Conc of HB-bound CO (mmol/L) MHL 

357 COFREE=ACO-HBCO ! Conc of free CO in blood (mmol/L) MHL 

358 PICO=COINH/1402.5 ! CO concentration in inhaled air (mg/L) MHL 

359 

360 PCTHBCO=100.0*HBCO/HBTOT ! Percent carboxyhemoglobin in blood 

361 PEAKPCTHBCO = MAX(PEAKPCTHBCO,PCTHBCO) ! Capture peak CO concentration 

362 

363 ! COE = Amount of expired CO, MHL 

364 PCCO=COFREE*MWCO/SOL 

365 PACO=(PCCO+PICO*QPDP)/(1.0+QPDP) 

366 RCOE=DL*RHO*(PCCO-PACO)*F1 

367 COE=INTEG(RCOE,0.0) !Expired amount of CO (mg) MHL 

368 COEC=0.7*COE/QP ! Conc expired CO (mg/L) 

369 ! Multiplier changed from 2/3 to 0.7 in above to be consistent with... 

370 ! other expired concentration calculations, PS 7/2008. 

371 COECPPM=COEC*24450.0/MWCO 

372 PCTIVCOEXH=100.0*IVORBOL*COE/(TOTALDOSE*MWCO/MOLWT + 1.0E-8) 

373 ! % dose exhaled as CO MHL 

374 

375 ! ABL2 = Amount of CO in blood (mg) MHL 

376 RFROMDCM=(RAM1L+RAM1LU+RAMR)*L12 ! RAMS left out since not used, =0 

377 ! Inputs calculated for mass balance 

378 AENCO = INTEG(RENCO, 0.0) ! Amount produced endogenously (mg) 

379 AFROMDCM = INTEG(RFROMDCM, 0.0) ! Amount produced from metabolism of DCM 

380 RABL2=RFROMDCM+RENCO-RCOE 

381 ABL2=INTEG(RABL2,0.0) ! MHL 

382 CABL2=ABL2/VBL2 ! MHL 

383 

384 ! AI=Total mass input (mg DCM) 

385 RAI=(QP*(CI+CCH))+RIV !MHL 

386 AI=INTEG(RAI, 0.0) 

387 

388 ! TMASS=Mass balance (mg DCM) 

389 MASSBAL=MASSIN-STOM-AF-AL-ALU-AS-AR-ABL-MASSOUT 

390 MASSIN=AI+CUMORALDOSE 

391 MASSOUT=AX+AMTOT 

392 

393 ! Mass Balance for CO 

394 MASSBALCO = 100*(-ABL2+AENCO-COE+AFROMDCM)/(AENCO+AFROMDCM+1e-10) 

395 

396 !if (T .GE. TSTOP) zzstopflag = .TRUE. ! Termination condition for model run 

397 END ! End of DERIVATIVE section of program 

398 TERMT(T.GE.TSTOP) 

399 END ! End of Dynamic section of program 

400 

401 TERMINAL 

402 avgt = 24.0/(T+1.0e-8) 
 H-9 DRAFT- DO NOT CITE OR QUOTE 

403 METDOS = avgt*RAMTOT/VL 

404 LDAYREFDOSE = LDAYLIVCYPDOSE + LDAYLIVGSTDOSE ! MHL 

405 WAVGREFDOSE = WAVGLIVCYPDOSE + WAVGLIVGSTDOSE ! PS, 8/2008 

406 LDAYWBDYCYPGSTDOSE = LDAYWBDYCYPDOSE + LDAYWBDYGSTDOSE 

407 METABRATIO = RAM1/(RAM2+1.0E-12) 

408 DDOSECHECK = CUMORALDOSE*avgt/BW ! Should equal DDOSE 

409 END ! End of TERMINAL section of program 

410 END ! End of PROGRAM 

411 Computational Files (.m files) 

412 The following are files (functions) created as .m files to support the Monte Carlo 

413 statistical sampling and subsequent calculations for the DCM analysis. 

414 

415 Utility files used for all human simulations

416 

417 function v=normbnd(mu, sigma, lo, up) 

418 % ---------------------------------------------------------------- 

419 % FILE: normbnd.m 

420 % 

421 % NORMBND - Returns a random sample from a truncated normal distribution with 

422 % mean = mu, standard deviation = sigma, lower bound = lo, & upper bound = up. 

423 % Modified by Paul Schlosser, U.S. EPA, 7/2008 

424 % ---------------------------------------------------------------- 

425 if (lo >= up) 

426 v = NaN; disp('Lower bound must be less than upper bound.') 

427 return; 

428 end 

429 FL = normcdf(lo, mu, sigma); % normal probability of lower bound 

430 FU = normcdf(up, mu, sigma); % normal probability of upper bound 

431 p = rand*(FU - FL) + FL; % rand = uniform random(0,1) 

432 v = norminv(p, mu, sigma); 

433 end 

434 

435 function v=lnormbnd(mu, sigma, lo, up) 

436 % ---------------------------------------------------------------- 

437 % FILE: lnormbnd.m 

438 % 

439 % LNORMBND - Returns a truncated LOGnormal distribution with distribution with 

440 % mean = mu, standard deviation = sigma, lower bound = lo, & upper bound = up. 

441 % Modified by Paul Schlosser, U.S. EPA, 7/2008 

442 % ---------------------------------------------------------------- 

443 v = exp(normbnd(log(mu), log(sigma), log(lo), log(up))); 

444 end 

445 
 H-10 DRAFT- DO NOT CITE OR QUOTE 

446 function v=prctile(y,x) 

447 % ---------------------------------------------------------------- 

448 % FILE: prctile.m 

449 % Computes percentiles of vector y at percentile values x 

450 % If x = [50 90 95], that's 50th, 90th, 95th 

451 % Created by Paul Schlosser, U.S. EPA, July, 2008 

452 % ---------------------------------------------------------------- 

453 if any((x<0) | (x>100)) 

454 disp('Error! All percentiles must be between 0 and 100!'); 

455 return 

456 end 

457 dy = sort(y); 

458 ps = 100*((1:length(dy))-0.5)/length(dy); 

459 v = x; 

460 for id=1:length(x) 

461 if x(id)<ps(1) 

462 v(id)=dy(1); 

463 elseif x(id)>max(ps) 

464 v(id)=max(dy); 

465 else 

466 v(id)=interp1(ps,dy,x(id)); 

467 end 

468 end 

469 % ---------------------------------------------------------------- 

470 % File clearT.m 

471 % Creates list of variable names for tracking, rnames, and 

472 % performs other initializations for human Monte Carlo chains. 

473 % Created by Paul Schlosser, U.S. EPA, 7/2008 

474 % ---------------------------------------------------------------- 

475 rnames=["age";"BW";"DRVOL";"DRCONC";"DDOSE";"CONC";"VMAXC";"KM";"FRACR";"KFC"; 

476 "A1";"A2";"BW";"VLC";"VFC";"VRC";"VLUC";"VSC";"QAlvC";"QCC";"QLC";"QFC";"QSC"; 

477 "QRC";"PL";"PLU";"PF";"PS";"PR";"PB";"GSTGT";"CV";"LDAYLIVGSTDOSE"; 

478 "LDAYLIVCYPDOSE";"LDAYLUNGGSTDOSE";"LDAYWBDYGSTDOSE";"LDAYREFDOSE"; 

479 "LDAYAUCV";"LDAYAUCS"]; 

480 sr='['; rns=length(rnames); 

481 for ir=1:(rns-1) 

482 sr=[sr,ctot(rnames(ir)),', ']; 

483 end 

484 sr=[sr,ctot(rnames(rns)),'];']; 

485 % save current model values before perturbations 

486 save @file='dcm_human.sav' 

487 runo=[]; 

488 % prepare time history values. 

489 prepare @clear T CV 

490 WESITG=0; WEDITG=0; CONC=0.0; IVDOSE=0.0; DRCONC=0.0; FIXDRDOSE=0.0; 

491 IVORBOL=0.0; 

492 DRPCT = [0.25, 0.1, 0.25, 0.1, 0.25, 0.05]; 

493 DRTIME= [0.0, 3.0, 5.0, 8.0, 11.0, 15.0]; 

494 TSTOP=600.; TCHNG=2000.0; TCHNG2=2000.0; TDUR=2000.0; TDUR2=2000.0; 

495 start @NoCallback 
 H-11 DRAFT- DO NOT CITE OR QUOTE 

496 

497 exist popn; % Test to see if 'popn' defined. 

498 if ~ans % If not... 

499 popn="mix"; % Mix of GST types. 

500 end 

501 exist agem; % Check if agem defined ... 

502 if ~ans % If not... 

503 agem=0; 

504 end 

505 exist gendm; % Check if gendm defined ... 

506 if ~ans % If not... 

507 gendm="both"; % males and females 

508 end 

509 function nn=findnames(nm,rnames) 

510 % ---------------------------------------------------------------- 

511 % File findnames.m 

512 % 

513 % Function to find indices of specific names in list nm 

514 % within the larger list (of all variables), rnames. 

515 % Created by Paul Schlosser, U.S. EPA, 7/2008 

516 % ---------------------------------------------------------------- 

517 nn=[]; 

518 for n=1:length(nm) 

519 n1=find(rnames==nm(n)); 

520 if isempty(n1) 

521 disp(['Error, ',ctot(nm(n)),' not in saved variable list.']); return 

522 end 

523 nn=[nn,n1]; 

524 end 

525 end 

526 

527 % File human_pars.m 

528 % 

529 % Code for selecting human model parameters from MC distribution for dichlormethane PBPK 

530 % model from probability density functions as described by David et al (2006), with revisions 

531 % as described in the U.S. EPA IRIS Toxicological Review for Dichloromethane, Appendix B. 

532 % Genotypic distribution of GSTT-1 activity taken from Tables 8 and 9 of Environ report for 

533 % Eastman Kodak. Arithmetic means and SDs for lognormal distributions converted by ML to 

534 % geometric mean/SD to match acsl m-file lnormbnd.m values 

535 % 

536 % Programmed by Michael Lumpkin (ML) 

537 % Syracuse Research Corporation, 11/2005 

538 % Modified by Paul Schlosser (PS), U.S. EPA 7/2008 

539 % 

540 % Gender parameter 'gendm' can be "both" (default), "male", or "female". 

541 % Age parameter 'agem' can be 0 (default) to simulate the full range 

542 % from 0.5-80 years, or any value in that range for a specific age. 

543 % Specific parameters, as noted below, set for those life-stages. 

544 % -------------------------------------------------------------------- 

545 
 H-12 DRAFT- DO NOT CITE OR QUOTE 

546 % Choose uniform discrete distribution for GSTT-1 genotypes. 

547 if popn=="++" 

548 GSTGT = 0; % for +/+ only 

549 elseif popn=="+-" 

550 GSTGT = 1; % for +/- only 

551 elseif popn=="--" 

552 GSTGT = 2; % for -/- only 

553 else 

554 r=rand(1)*gsmult; GSTGT=(r>0.32)+(r>0.8); %For general/mixed population 

555 popn = "mix of +/+, +/-, and -/-"; 

556 end 

557 

558 % selection of GSTT1 activity distribution based on ethnic distribution 

559 % upper bounds changed from mean + 3*SD to mean + 5*SD, P.S., 2/2009 

560 if (GSTGT == 0) 

561 KFC = normbnd(1.31, 0.167, 0.0, 2.145); 

562 elseif (GSTGT == 1) 

563 KFC = normbnd(0.676, 0.123, 0.0, 1.291); 

564 else 

565 KFC = 0.0; 

566 end 

567 

568 KA=5.0; %From Reitz et al (1997); this parameter has no variability data and is 

569 % described as point estimate 

570 VMAXC=lognorm(9.34, 1.73); %lnormbnd(9.34, 1.73, 3.8, 23.0); 

571 %geometric mean and GSD, converted from arith mean/SD of 9.42 and 1.23 

572 % VMAXC switched to unbounded distribution, PS, 2/2009 

573 KM=lnormbnd(0.410, 1.39, 0.154, 1.10); 

574 %geometric mean and SD, converted from arith mean/SD of 0.433 and 1.46 

575 FRACR=lnormbnd(0.0152, 2.0, 0.0019, 0.122); 

576 %geometric mean and SD, converted from arith mean/SD of 0.0193 and 0.0152 

577 A1=lnormbnd(0.00092, 1.47, 0.000291, 0.00292); 

578 %geometric mean & SD, converted from arith mean/SD of 0.000993 & 0.000396 

579 A2=lnormbnd(0.0083, 1.92, 0.00116, 0.0580); 

580 %geometric mean and SD, converted from arith mean/SD of 0.0102 and 0.00739 

581 

582 age=agem; 

583 if age==0 % agem=0 => age = random from population, otherwise leave as set 

584 p=rand; 

585 age = 165.86*p^4 - 253.19*p^3 + 113.27*p^2 + 53.356*p + 0.5; 

586 end 

587 VMP = 0.7 + 0.18*(age<18); 

588 % power for scaling Vmax is 0.88 if age < 18, otherwise 0.7 

589 % Change in VMP introduced 2/2009, PS 

590 

591 gend=gendm; 

592 if ~((gendm=="male")|(gendm=="female")) % if not defined as one 

593 gend="female"; 

594 if rand()<(0.513*((125.3 - age)^4)/(33.7^4 + (125.36 - age)^4)); 

595 gend="male"; 

596 end 
 H-13 DRAFT- DO NOT CITE OR QUOTE 

597 end 

598 

599 if mod(RUNN,50)==0 

600 disp(['GSTGT group: ',ctot(popn),'. GSTGT = ',num2str(GSTGT),'.']); 

601 disp(['Simulation for ',num2str(age),' year old ',ctot(gend),'; gender mix: ',ctot(gendm)]); 

602 disp(' '); 

603 end 

604 

605 % Human physiologic parameters 

606 

607 if gend=="female" 

608 aged=(16-age)/10; 

609 bwmean = 4.146 - 0.147*aged - 1.36*aged^2 + 0.44*aged^3; 

610 if aged<0 

611 bwmean = 4.146 - 0.147*aged - 0.0278*aged^2 - 0.00095*aged^3; 

612 end 

613 aged=(13-age)/10; 

614 bwSD = 2.574 - 0.358*aged - 2.55*aged^2 + 1.16*aged^3; 

615 if aged<0 

616 bwSD = 2.574 - 0.358*aged - 0.0861*aged^2 - 0.00469*aged^3; 

617 end 

618 else % males 

619 aged=(21-age)/10; 

620 bwmean = 4.406 - 0.0285*aged - 0.729*aged^2 + 0.115*aged^3; 

621 if aged<0 

622 bwmean = 4.406 - 0.0285*aged + 0.0048*aged^2 + 0.0018*aged^3; 

623 end 

624 aged=(16-age)/10; 

625 bwSD = 2.87 + 0.06*aged - 2.56*aged^2 + 0.96*aged^3; 

626 if aged<0 

627 bwSD = 2.87 + 0.06*aged + 0.0448*aged^2 + 0.0067*aged^3; 

628 end 

629 end 

630 BW=norminv((0.01+0.98*rand),exp(bwmean),exp(bwSD)); 

631 

632 QAlvmean = 13.6 + 13.3*exp(-0.05*age); 

633 if gend=="female" 

634 QAlvmean = 10.7 + 22.1*exp(-0.08*age); 

635 end 

636 Qgsd = -0.1948*(age/10)^3 + 0.6095*(age/10)^2 - 0.3978*(age/10) + 1.4261; 

637 if age>16.81 

638 Qgsd=1.554; 

639 end 

640 QAlvC = QAlvmean*exp(norminv((0.05+0.9*rand),0,log(Qgsd))); 

641 

642 QCCmean = 56.906*(1.0 - exp(-0.681*exp(0.0454*QAlvC))) - 29.747; 

643 QCC = QCCmean/lnormbnd(1.0, 0.203, 0.69, 1.42); 

644 VPR = QAlvC/QCC; 

645 

646 vlm=-0.0036*(age/10)^2 - 0.0051*(age/10) + 0.0395; 

647 if age>17 
 H-14 DRAFT- DO NOT CITE OR QUOTE 

648 vlm=-0.0004*(age/10)^2 + 0.0034*(age/10) + 0.0169; 

649 end 

650 VLC=vlm*normbnd(1.0, 0.05, 0.85, 1.15); 

651 

652 vfm=0.1612*(age/10)^3 + 0.0846*(age/10)^2 - 0.3083*(age/10) + 0.2709; 

653 if ((age>7)&(age<=20)) 

654 vfm=-0.0458*(age/10)^2 + 0.2082*(age/10) + 0.0274; 

655 if gend=="male" 

656 vfm= -0.0057*(age/10)^2 + 0.0293*(age/10) + 0.1303; 

657 end 

658 elseif age>20 

659 vfm=-0.0024*(age/10)^3 + 0.0355*(age/10)^2 - 0.115*(age/10) + 0.3678; 

660 if gend=="male" 

661 vfm= -0.0015*(age/10)^2 + 0.0384*(age/10) + 0.0908; 

662 end 

663 end 

664 VFC=vfm*normbnd(1.0, 0.3, 0.1, 1.9); 

665 

666 VRC=normbnd(0.064, 0.0064, 0.0448, 0.0832); 

667 VLUC=normbnd(0.0115, 0.00161, 0.00667, 0.0163); 

668 VSC=normbnd(0.63, 0.189, 0.431, 0.829); 

669 vtr=0.9215/(VFC+VLC+VLUC+VRC+VSC); 

670 VFC = VFC*vtr; VLC=VLC*vtr; VLUC=VLUC*vtr; VRC=VRC*vtr; VSC=VSC*vtr; 

671 VBL2C=0.059; 

672 

673 DSPC=0.15; 

674 QLC=normbnd(0.26, 0.0910, 0.010, 0.533); 

675 QFC=normbnd(0.05, 0.0150, 0.0050, 0.0950); 

676 QSC=normbnd(0.19, 0.0285, 0.105 ,0.276); 

677 QRC=normbnd(0.50 ,0.10, 0.20, 0.80); 

678 

679 %Human partition coefficients for DCM 

680 PL=lnormbnd(1.43, 1.22, 0.790, 2.59); 

681 %geometric mean and SD, converted from arith mean/SD of 1.46 and 0.292 

682 PLU=lnormbnd(1.43, 1.22, 0.790, 2.59); 

683 %geometric mean and SD, converted from arith mean/SD of 1.46 and 0.292 

684 PF=lnormbnd(11.9, 1.34, 4.92, 28.7); 

685 %geometric mean and SD, converted from arith mean/SD of 12.4 and 3.72 

686 PS=lnormbnd(0.80, 1.22, 0.444, 1.46); 

687 %geometric mean and SD, converted from arith mean/SD of 0.82 and 1.64 

688 PR=lnormbnd(1.43, 1.22, 0.790, 2.59); 

689 %geometric mean and SD, converted from arith mean/SD of 1.46 and 0.292 

690 PB=lnormbnd(9.7, 1.10, 7.16, 13.0); 

691 %geometric mean and SD, converted from arith mean/SD of 9.7 and 0.97 
 H-15 DRAFT- DO NOT CITE OR QUOTE 

692 % -------------------------------------------------------------------- 

693 % File: finish.m 

694 % Programmed by Paul Schlosser, U.S. EPA, 8/2008 

695 % Performs final analysis on saved results in runo from simulations. 

696 % -------------------------------------------------------------------- 

697 disp([num2str(size(runo,1)),' simulations completed.']); 

698 contsim=0; p=ctot(popn); p=p(1:min([3,length(p)])); 

699 eval(['save runo @file=',astp,num2str(agem),p,'_',ctot(gendm), ... 

700 '.csv @format=ascii @separator=comma']); 

701 eval(['save @file=',astp,num2str(agem),p,'_',ctot(gendm),'.mat']); 

702 disp(['GSTT-1 group: ',ctot(popn)]); 

703 aget=['a ',num2str(agem),' year-old; ']; 

704 if agem==0 

705 aget='0.5-80 years of age; '; 

706 end 

707 gendt=[ctot(gendm),' population']; 

708 if gendm=="both" 

709 gendt='males and females'; 

710 end 

711 disp(['Simulation for ',aget,gendt,'.']); 

712 disp(['Metric =',dtxt,'.']); 

713 res=[]; 

714 pcs=num2str(percs(1)); 

715 for n=2:length(percs) 

716 pcs=[pcs,', ',num2str(percs(n))]; 

717 end 

718 for n=1:length(nm) 

719 disp([ctot(nm(n)),' mean, median, percentiles = ',pcs]); 

720 r=[mean(runo(:,nn(n))),median(runo(:,nn(n))),prctile(runo(:,nn(n)),percs/gsmult)]*mult; 

721 disp(r); disp(' '); res=[res;r]; 

722 end 

723 

724 

725 Files used specifically for cancer analysis

726 % -------------------------------------------------------------------- 

727 % File: straightsims.m 

728 % Created by Paul Schlosser, U.S. EPA, 8/2008 

729 % Runs Monte Carlo (MC) simulations withOUT search (for human equivalent exposures) 

730 % Requires set exposure, NRUNS and rnames (list of variables to save) 

731 % PBPK parameters set using MC selection by file human_pars.m. 

732 % -------------------------------------------------------------------- 

733 exist contsim; % Test to see if contsim defined. 

734 if ~ans % If not... 

735 contsim=0; % Not a continuation. 

736 end 

737 if contsim==0 % When starting a new set of simulation; 

738 % set contsim=1 if continuing an interupted chain. 

739 runo=[]; rns=length(rnames); ns=1; 
 H-16 DRAFT- DO NOT CITE OR QUOTE 

740 else 

741 ns=RUNN; 

742 end 

743 for RUNN = ns:NRUNS 

744 if mod(RUNN,50)==0 

745 disp(['Remaining runs = ',num2str(NRUNS-RUNN)]) 

746 end 

747 human_pars; start @NoCallback 

748 runo=[runo;eval(sr)]; 

749 end 

750 % -------------------------------------------------------------------- 

751 % File: Human drinking water MCA OSF.m 

752 % 

753 % Programmed by Michael Lumpkin 

754 % Syracuse Research Corporation, 10/2007 

755 % Modified by Paul Schlosser, U.S. EPA 8/2008 

756 % 

757 % This script file sets up the control parameters to simulate human unit 

758 % drinking water exposure (1 mg/kg/day) and calls straightsim which 

759 % generates a Monte-Carlo chain for the internal doses (identified 

760 % in text array nn) to be used in calculating oral slope factors (OSFs). 

761 % -------------------------------------------------------------------- 

762 % Test to see if contsim defined 

763 exist contsim 

764 if ~ans % If not... 

765 contsim=0; % Not a continuation. 

766 end 

767 if contsim==0 % When starting a new set of simulation; 

768 % set contsim=1 if continuing an interrupted chain. 

769 clearT 

770 nm=["LDAYLIVGSTDOSE";"LDAYWBDYGSTDOSE"]; nn=findnames(nm,rnames); 

771 if (length(nn) ~= length(nm)) 

772 disp("Not all names in list nm.") 

773 return 

774 end 

775 TSTOP=95.0; NRUNS=10000; CINT=0.1; % Total iterations for Monte Carlo analysis 

776 FIXDRDOSE=1.0; % Drinking fixed mg/kg-day 

777 gendm="female"; % Gender mix; choose "male, "female", or "both" 

778 agem=70; % Age "mix"; if 0 draws from distribution (0.5-80 years) 

779 % otherwise agem value is used exclusively 

780 popn="++"; % GSTGT "++", "+-", or "mix" of +/+, +/-, and -/- 

781 astp='OSF_age'; percs=[95 99]; dtxt='n/a'; gsmult=1.0; 

782 end 

783 straightsims; mult=1.0/DDOSE; finish 
 H-17 DRAFT- DO NOT CITE OR QUOTE 

784 % -------------------------------------------------------------------- 

785 % File: Human inhalation MCA IUR.m 

786 % 

787 % Programmed by Michael Lumpkin 

788 % Syracuse Research Corporation, 11/2007 

789 % Modified by Paul Schlosser, 7/2008 

790 % 

791 % This script file sets up the control parameters to simulate human unit inhalation exposures 

792 % (1 mcg/m3) and calls straightsim which generates a Monte-Carlo chain for the internal 

793 % doses (identified in text array nn) to be used in calculating inhalation unit risks (IURs) 

794 % ----------------------------------------------------------------------- 

795 exist contsim; % Test to see if contsim defined. 

796 if ~ans % If not... 

797 contsim=0; % Not a continuation. 

798 end 

799 if contsim==0 % When starting a new set of simulation; 

800 % set contsim=1 if continuing an interupted chain. 

801 clearT; CINT=0.1; 

802 TSTOP=24*4; CONC=1.0e-6/GASD; CC=0; % Daily dose-rates now calculated from 

803 % final day of simulations, & only need to go to 60 hr to reach SS 

804 % CONC in ppm; 0.00029 ppm = 1 ug/m3 DCM = 1e-3 ug/L = 1e-6 mg/L 

805 nm=["LDAYLIVGSTDOSE";"LDAYLUNGGSTDOSE";"LDAYWBDYGSTDOSE"; 

806 "CV";"LDAYAUCS"]; 

807 nn=findnames(nm,rnames); 

808 if (length(nn) ~= length(nm)) 

809 disp("Not all names in list nm.") 

810 return 

811 end 

812 NRUNS=20000; %Total iterations for Monte Carlo analysis 

813 gendm="both"; % Gender mix; choose "male, "female", or "both" 

814 agem=0; % Age "mix"; if 0 draws from distribution (0.5-80 years) 

815 % otherwise agem value is used exclusively 

816 popn="mix"; % GSTGT "++", "+-", or "mix" of +/+, +/-, and -/- 

817 gsmult = 1.0; 

818 % Multiplies draw for GST individual selection, divides the percentiles at end. 

819 astp='IUR_age'; mult= 1.0e-6/(CONC*GASD); percs=[95 99]; dtxt=' n/a'; gsmult=1.0; 

820 end 

821 straightsims; finish 

822 

823 
 H-18 DRAFT- DO NOT CITE OR QUOTE 

824 Files used specifically for non-cancer analysis

825 % -------------------------------------------------------------------- 

826 % File: searchsim_refdose.m 

827 % Created by Paul Schlosser, U.S. EPA, 8/2008 

828 % Runs Monte Carlo (MC) simulations WITH search for human equivalent exposures, using variable 

829 % LDAYREFDOSE ('reference dose based on last day of simulated exposure -- presumed "periodicity") 

830 % Requires exposure variable named = expnm = "DRCONC" or "CONC", 

831 % target value = heqt, relative tolerance = hetol, initial set 

832 % exposure (CONC or DRCONC value >0), NRUNS, and rnames (list of 

833 % variables to save) 

834 % PBPK parameters set using MC selection by file human_pars.m. 

835 % -------------------------------------------------------------------- 

836 % set contsim=1 if continuing an interupted chain. 

837 if contsim==0 % When starting a new set of simulations 

838 if ~(expnm=="CONC" | expnm=="DRCONC") 

839 disp('Variable expnm must be "CONC" or "DRCONC", in double quotes.'); 

840 return 

841 end 

842 if expnm=="CONC" % atxt used as variable command below 

843 DRCONC=0.0; atxt='CONC = c1'; c2=CONC; 

844 else 

845 CONC=0.0; atxt='DRCONC = c1'; c2=DRCONC; 

846 end 

847 runo=[]; rns=length(rnames); ns=1; c1s=['c1 = c2*heqt/',dtxt]; 

848 v1s=['v1 = ',dtxt]; v2s=['v2 = ',dtxt]; 

849 else % Continuing a set of simulations 

850 ns=RUNN; 

851 end 

852 for RUNN = ns:NRUNS 

853 disp(['Remaining runs = ',num2str(NRUNS-RUNN)]) 

854 human_pars 

855 % Following block calculates daily dose resulting in specified internal dose 

856 start @NoCallback 

857 nstep=1; eval(v2s); eval(c1s); eval(atxt); 

858 start @NoCallback 

859 eval(v1s); het1=hetol; 

860 while (abs((v1/heqt)-1)>hetol); % Specify corresponding dose metric 

861 cn = abs((heqt-v2)*(c1-c2)/(v1-v2) + c2); % Linear interpolation to heqt 

862 c2=c1; v2=v1; c1=cn; eval(atxt); % Assign values 

863 start @NoCallback 

864 eval(v1s); 

865 nstep=nstep+1; 

866 if nstep==20 

867 hetol=hetol*10; nstep=1; 

868 disp('WARNING! hetol increased to allow convergence!') 

869 end 

870 end 

871 hetol=het1; runo=[runo;eval(sr)]; 

872 end 
 H-19 DRAFT- DO NOT CITE OR QUOTE 

873 % -------------------------------------------------------------------- 

874 % File: human drinking water MCA RfD.m 

875 % 

876 % Programmed by Michael Lumpkin 

877 % Syracuse Research Corporation, 11/2005 

878 % Modified by Paul Schlosser, U.S. EPA 8/2008 

879 % 

880 % This script file sets up the control parameters to simulate human 

881 % drinking water exposure for a given internal dose (heqt) and calls 

882 % searchsim_refdose which generates a Monte-Carlo chain for the human 

883 % equivalent administered daily dose (HEAD; mg/kg/d). 

884 % -------------------------------------------------------------------- 

885 exist contsim; 

886 if ~ans 

887 contsim=0; 

888 end 

889 if contsim==0 % When starting a new set of simulation; 

890 % set contsim=1 if continuing an interupted chain. 

891 clearT 

892 nm=["DDOSE"]; nn=findnames(nm,rnames); 

893 if (length(nn) ~= length(nm)) 

894 disp("Not all names in list nm.") 

895 return 

896 end 

897 hetol=1.0e-4; %relative tolerance 

898 expnm="DRCONC"; DRCONC=9.0; % 35 mg/L is 1 mg/kg/day for 70 kg human drinking 2L 

899 TSTOP=95.0; NRUNS=10000; %Total iterations for Monte Carlo analysis 

900 gendm="female"; % Gender mix; choose "male, "female", or "both" 

901 agem=70; % Age "mix"; if 0 draws from distribution (0.5-80 years) 

902 % otherwise agem value is used exclusively 

903 popn="--"; % GSTGT "++", "+-", "--", or "mix" of +/+, +/-, and -/- 

904 % dtxt = dose metric to use 

905 % heqt = target for HEQ search; set value for specific dose metric 

906 dtxt='LDAYREFDOSE'; dt1='W'; 

907 dtxt='LDAYLIVGSTDOSE'; dt1='G'; 

908 dtxt='LDAYLIVAUC'; dt1='A'; heqt=0.0562; 

909 dtxt='LDAYLIVCYPDOSE'; dt1='Y'; heqt=15.1; 

910 astp=['RfD_',dt1,'_',num2str(heqt),'_age']; percs=[5 1]; mult=1; gsmult=1; 

911 end 

912 searchsim_refdose; finish 

913 
 H-20 DRAFT- DO NOT CITE OR QUOTE 

914 % -------------------------------------------------------------------- 

915 % File: Human inhalation MCA RfC.m 

916 % 

917 % Programmed by Michael Lumpkin 

918 % Syracuse Research Corporation, 11/2005 

919 % Modified by Paul Schlosser, U.S. EPA, 7/2008 

920 % 

921 % This script file sets up the control parameters to simulate human 

922 % inhalation exposure for a given internal dose (heqt) and calls 

923 % searchsim_refdose which generates a Monte-Carlo chain for the human 

924 % Equivalent concentration (HEC; mg/m^3) 

925 % -------------------------------------------------------------------- 

926 exist contsim; 

927 if ~ans 

928 contsim=0; 

929 end 

930 if contsim==0 % When starting a new set of simulation; 

931 % set contsim=1 if continuing an interupted chain. 

932 use clearT 

933 nm=["CONC"]; nn=findnames(nm,rnames); 

934 if (length(nn) ~= length(nm)) 

935 disp("Not all names in list nm.") 

936 return 

937 end 

938 TSTOP=96; NRUNS=10000; %Total iterations for Monte Carlo analysis 

939 expnm="CONC"; CONC=67; % CONC in ppm; 0.29 ppm = 1 mg/m3 DCM 

940 hetol=1.0e-5; %relative tolerance 

941 gendm="female"; % Gender mix; choose "male, "female", or "both" 

942 agem=70; % Age "mix"; if 0 draws from distribution (0.5-80 years) 

943 % otherwise agem value is used exclusively 

944 popn="--"; % GSTGT "++", "+-", or "mix" of +/+, +/-, and -/- 

945 gsmult = 1.0; % Multiplies draw for GST individual selection, divides the percentiles at end. 

946 % dtxt = dose metric to use 

947 % heqt = target for HEQ search; set value for specific dose metric 

948 % Last statement below is the metric and target value used 

949 % Move desired one to end and save this file before running. 

950 dtxt='LDAYLIVAUC'; dt1='A'; heqt=0.0562; 

951 dtxt='LDAYREFDOSE'; dt1='W'; heqt=76.71; 

952 dtxt='LDAYLIVGSTDOSE'; dt1='G'; heqt=10.97; 

953 dtxt='LDAYLIVCYPDOSE'; dt1='Y'; heqt=128.1; 

954 astp=['RfC_',num2str(heqt),'_age']; mult=GASD*1000; percs=[5 1]; 

955 end 

956 searchsim_refdose; finish 

957 
 H-21 DRAFT- DO NOT CITE OR QUOTE 

958 Files used for mouse dose analyses

959 % -------------------------------------------------------------------- 

960 % File: mouse_set.m 

961 % Programmed by Paul Schlosser, U.S. EPA, 7/2008 

962 % Clears previous variables and sets parameters for mouse simulations. 

963 % -------------------------------------------------------------------- 

964 % prepare time history values. 

965 prepare @clear @all 

966 CINT=0.1; CONC=0.0; IVDOSE=0.0; BOLUS=0.0; DRCONC=0.0; FIXDRDOSE=0.0; 

967 WESITG=0; WEDITG=0; CC=0; 

968 % from Reitz et al. 1997, Table 1 

969 DRPCT = [0.233, 0.1, 0.1, 0.1, 0.233, 0.234]; 

970 DRTIME= [0.0, 4.0, 8.0, 12.0, 16.0, 20.0]; 

971 TSTOP=336; % 2 weeks 

972 TCHNG=6; % daily exposure duration = 6 hours/day 

973 TCHNG2=120; % weekly dose width = 5 days/week = 120 hours 

974 TDUR=24; % daily dose period = 24 hours 

975 TDUR2=168; % weekly exposure period = 7 days = 168 hours 

976 

977 % EPA (1988) reference value for B6C3F1 mice: males=0.0373, females=0.0353 

978 BW=0.0373; 

979 % Mouse uptake & metabolism parameters (defined by MCMC calibration) 

980 KA=5.0; VMAXC=9.27; KM=0.574; FRACR=0.0; KFC=1.41; A1=0.207; A2=0.196; 

981 % Mouse physiologic parameters (from prior distributions or MCMC 

982 % calibration) 

983 VLC=0.04; VFC=0.04; VRC=0.05; VLUC=0.0115; VBL2C=0.059; VSC=0.78; 

984 QCC=24.2; VPR=1.45; DSPC=0.15; QLC=0.24; QFC=0.05; QSC=0.19; QRC=0.52; 

985 %Mouse partition coefficients for DCM 

986 PL=1.6; PLU=0.46; PF=5.1; PS=0.44; PR=0.52; PB=23.0; 

987 start @NoCallBack 

988 % -------------------------------------------------------------------- 

989 % File: Mouse drinking water National Coffee 1983.m 

990 % 

991 % Programmed by Michael Lumpkin 

992 % Syracuse Research Corporation, 01/2007 

993 % Modified by Paul Schlosser, U.S. EPA, 8/2008 

994 % 

995 % This run time file sets species-specific constants and exposure 

996 % values for National Coffee Association study of DCM in drinking water 

997 % in the mouse; parameterized to run with DCM.07.rev3.csl. 

998 % -------------------------------------------------------------------- 

999 use mouse_set 

1000 BW=0.0373; TSTOP=4*7*24; runo=[]; 

1001 

1002 % Mouse exposure parameters: DDOSE males: 61, 124, 177, or 244 mg/kg-day 

1003 for FIXDRDOSE=[61, 124, 177, 244] 

1004 start @NoCallback; 

1005 runo=[runo;[BW,DDOSE,WAVGLIVGSTDOSE,WAVGLUNGGSTDOSE, 
 H-22 DRAFT- DO NOT CITE OR QUOTE 

1006 WAVGWBDYGSTDOSE]]; 

1007 end 

1008 runo 

1009 save runo @file='mouseOSFdrink.csv' @format=ascii @separator=comma 

1010 

1011 % -------------------------------------------------------------------- 

1012 % File: Mouse Inhalation NTP 1986.m 

1013 % 

1014 % Programmed by Michael Lumpkin 

1015 % Syracuse Research Corporation, 11/2007 

1016 % Modified by Paul Schlosser, U.S. EPA, 7/2008 

1017 % 

1018 %This run time file sets species-specific constants and exposure 

1019 %values for NTP (1986) inhalation exposures of DCM in the mouse. 

1020 % -------------------------------------------------------------------- 

1021 use mouse_set 

1022 CINT=0.01; 

1023 % Kodak value; study average male: 2k=0.034, 4k=0.032; female 2k=0.030, 4k=0.029 

1024 TSTOP=5*7*24; Bws=[0.034, 0.032; 0.030,0.029]; runo=[]; Cs=[2000, 4000]; 

1025 for s=[1 2] 

1026 for c=[1,2]; 

1027 BW=Bws(s,c); CONC=Cs(c); 

1028 start @NoCallback 

1029 runo=[runo;[BW,CONC,WAVGLIVGSTDOSE,WAVGLUNGGSTDOSE,WAVGWBDYGSTDOSE]]; 

1030 end 

1031 end 

1032 runo 

1033 %%% Saving last simulation results to file 

1034 run=[_t _cv _cvl _cvf _cvs _cvr _cabl2 _wavglivgstdose _wavglunggstdose _ddose]; 

1035 save run @file='RUNOUT_NTP_inhal.csv' @format=ascii @separator=comma 

1036 

1037 

1038 Files used for rat dose analyses

1039 % -------------------------------------------------------------------- 

1040 % File: rat_set_D.m 

1041 % Programmed by Paul Schlosser, U.S. EPA, 9/2009 

1042 % Clears previous variables and sets parameters for rat simulations. 

1043 % Rat model "D". 

1044 % -------------------------------------------------------------------- 

1045 % prepare time history values. 

1046 prepare @clear @all 

1047 WESITG=0; WEDITG=0; 

1048 % Rat exposure controls 

1049 CINT=0.01; CONC=0.0; IVDOSE=0.0; DRCONC=0.0; FIXDRDOSE=0.0; BOLUS=0; CC=0; 

1050 DRPCT = [0.233, 0.1, 0.1, 0.1, 0.233, 0.234]; 

1051 DRTIME = [0.0, 4.0, 8.0, 12.0, 16.0, 20.0]; 

1052 TSTOP=6; % 7 days 

1053 TCHNG=6; % daily exposure = 6 hours 

1054 TCHNG2=120; % weekly dose width = 5 days/week = 120 hours 
 H-23 DRAFT- DO NOT CITE OR QUOTE 

1055 TDUR=24; % daily dose period = 24 hours 

1056 TDUR2=168; % weekly exposure period = 7 days = 168 hours 

1057 

1058 % Rat UPTAKE & metabolism parameters 

1059 KA=5.0; FRACR=0.0; 

1060 VMAXC=3.66; KM=0.59; KFC=2.97; % MLE fitted 

1061 A1=0.04; A2=0.14; % from Reitz et al. 1989 

1062 % Rat physiologic parameters (from Andersen et al 1991) 

1063 BW=0.380; % males:0.380 kg, females:0.229 kg, from EPA (1988) 

1064 VLC=0.04; VFC=0.07; VRC=0.05; VLUC=0.0115; VBL2C=0.059; VSC=0.75; 

1065 QCC=15.9; VPR=0.94; DSPC=0.15; QLC=0.20; QFC=0.09; QSC=0.15; QRC=0.56; 

1066 %Rat partition coefficients for DCM (Andersen et al 1991) 

1067 PL=0.732; PLU=0.46; PF=6.19; PS=0.408; PR=0.732; PB=19.4; 

1068 %CO submodel parameters 

1069 DLC=0.060; RENCOC=0.035; ABCOC=0.117; HBTOT=10.0; P1=0.80; F1=1.21; 

1070 M=197.0; COINH=2.2; O2=0.13; PAIR=713.0; RHO=1102.0; SOL=0.03; 

1071 start @NoCallBack 

1072 %RENCOC=0.0; 

1073 VMAXC = 3.923; KM = 0.524; KFC = 2.46; KA=1.8; 

1074 use allfit_rat-params 

1075 use KAfit-params 

1076 RENCOC=0.0; ABCO=0.0; COINH=0.0; 

1077 

1078 % -------------------------------------------------------------------- 

1079 % File: rat_set_A.m 

1080 % Programmed by Paul Schlosser, U.S. EPA, 9/2009 

1081 % Clears previous variables and sets parameters for rat simulations. 

1082 % Rat model "A". 

1083 % -------------------------------------------------------------------- 

1084 use rat_set_D 

1085 VMAXC = 6.21; KM = 0.23; 

1086 A1 = 0.21; A2 = 0.20; 

1087 KFC = 2.89; KA = 5.0; 

1088 

1089 % -------------------------------------------------------------------- 

1090 % File: rat_set_B.m 

1091 % Programmed by Paul Schlosser, U.S. EPA, 9/2009 

1092 % Clears previous variables and sets parameters for rat simulations. 

1093 % Rat model "B". 

1094 % -------------------------------------------------------------------- 

1095 use rat_set_D 

1096 VMAXC = 4.0; KM = 0.4; 

1097 A1 = 0.0; A2 = 0.0; 

1098 KFC = 2.0; KA = 5.0; 

1099 

1100 % -------------------------------------------------------------------- 

1101 % File: rat_set_C.m 

1102 % Programmed by Paul Schlosser, U.S. EPA, 9/2009 

1103 % Clears previous variables and sets parameters for rat simulations. 

1104 % Rat model "C". 
 H-24 DRAFT- DO NOT CITE OR QUOTE 

1105 % -------------------------------------------------------------------- 

1106 use rat_set_D 

1107 VMAXC = 4.0; KM = 0.4; 

1108 A1 = 0.4; A2 = 0.14; 

1109 KFC = 2.0; KA = 4.0; 

1110 

1111 % -------------------------------------------------------------------- 

1112 % File: Rat inhalation Nitschke 1988.M 

1113 % 

1114 % Programmed by Michael Lumpkin 

1115 % Syracuse Research Corporation, 10/2007 

1116 % Modified by Paul Schlosser, U.S. EPA, 7/2008 

1117 % 

1118 % This run time file sets species-specific constants values for modified 

1119 % Andersen et al (1987, 1991) simulating Nitschke et al. (1988) exposures 

1120 % of DCM in the male and female rats. 

1121 % -------------------------------------------------------------------- 

1122 use rat_set_D 

1123 TSTOP=5*7*24; runo=[]; BW=0.229; TCHNG=6; TCHNG2=120; 

1124 % males:0.380 kg, females:0.229 kg, from EPA (1988) 

1125 for CONC=[50, 200, 500] 

1126 start @NoCallback 

1127 runo=[runo;[BW,CONC,DIDOSE,WAVGREFDOSE,WAVGLIVGSTDOSE,WAVGLIVCYPD

1128 OSE,WAVGDAILYAUCL]]; 

1129 end 

1130 runo 

1131 save runo @file='RUNOUT_rat_inhal_Nitschke.csv' @format=ascii @separator=comma 

1132 % -------------------------------------------------------------------- 

1133 % File: Rat inhalation Burek 1984.m 

1134 % 

1135 % Programmed by Michael Lumpkin 

1136 % Syracuse Research Corporation, 10/2007 

1137 % Modified by Paul Schlosser, U.S. EPA, 7/2008 

1138 % 

1139 % This run time file sets species-specific constants values for modified Andersen et al 

1140 % (1987, 1991) simulating Burek et al. (1984) exposures of DCM in male & female rats 

1141 % -------------------------------------------------------------------- 

1142 use rat_set_D 

1143 TSTOP=5*7*24; TCHNG=6; TCHNG2=120; runo=[]; 

1144 for BW=[0.523, 0.338] 

1145 for CONC=[500, 1500, 3500] 

1146 start @NoCallback 

1147 

1148 runo=[runo;[BW,DIDOSE,CONC,WAVGREFDOSE,WAVGLIVGSTDOSE,WAVGLIVCYPD

1149 OSE,WAVGDAILYAUCL]]; 

1150 end 

1151 end 

1152 runo 

1153 save runo @file='RUNOUT_rat_inhal_Burek.csv' @format=ascii @separator=comma 

1154 
 H-25 DRAFT- DO NOT CITE OR QUOTE 

1155 % -------------------------------------------------------------------- 

1156 % File: Rat drinking water Serota 1986a.m 

1157 % 

1158 % Programmed by Michael Lumpkin 

1159 % Syracuse Research Corporation, 1/2007 

1160 % Modified by Paul Schlosser, U.S. EPA, 7/2008 

1161 % 

1162 % This run time file sets species-specific constants values for modified Andersen et al 

1163 % (1987, 1991) simulating Serota et al. (1986a) exposures of DCM to male & female rats 

1164 % -------------------------------------------------------------------- 

1165 use rat_set_D 

1166 %DRCONC values from Serota et al. (1986a) 

1167 % males:0, 44.00, 381.36, 916.74, 1723.47 

1168 % females: 0, 37.80, 365.41, 856.82, 1656.94 

1169 TSTOP=7*24; runo=[]; BW=0.380; % Males 

1170 for FIXDRDOSE=[6, 52, 125, 235] 

1171 start @NoCallBack 

1172 runo=[runo;[BW,DDOSE,LDAYREFDOSE,LDAYLIVGSTDOSE,LDAYLIVCYPDOSE,LDA

1173 YLIVAUC]]; 

1174 end 

1175 BW=0.229; % Females 

1176 for FIXDRDOSE=[6, 58, 136, 263] 

1177 start @NoCallBack 

1178 runo=[runo;[BW,DDOSE,LDAYREFDOSE,LDAYLIVGSTDOSE,LDAYLIVCYPDOSE,LDA

1179 YLIVAUC]]; 

1180 end 

1181 runo 

1182 save runo @File="Serota_rat_DW.csv" @Format=ascii @Separator=comma 

1183 

1184 % -------------------------------------------------------------------- 

1185 % File: Rat inhalation NTP 1986.M 

1186 % 

1187 % Programmed by Michael Lumpkin 

1188 % Syracuse Research Corporation, 11/2007 

1189 % Modified by Paul Schlosser, U.S. EPA, 7/2008 

1190 % 

1191 % This run time file sets species-specific constants values for modified Andersen et al 

1192 % (1987, 1991) simulating NTP (1986) exposures of DCM to male & female rats 

1193 % -------------------------------------------------------------------- 

1194 use rat_set_D 

1195 CINT=0.1; 

1196 bws=[0.3905, 0.3852, 0.3848, 0.2455, 0.2443, 0.2422]; 

1197 TSTOP=6*24*7; concs=[1 2 4 1 2 4]*1000; runo=[]; 

1198 for n=1:length(bws) 

1199 BW=bws(n); CONC=concs(n); start @NoCallback 

1200 runo=[runo;[BW,CONC,DIDOSE,WAVGLIVGSTDOSE,WAVGAUCV,WAVGAUCS]]; 

1201 end 

1202 runo 

1203 save runo @File="RUNOUT_rat_inhal_NTP.csv" @Format=ascii @Separator=comma 

1204 
 H-26 DRAFT- DO NOT CITE OR QUOTE 

1205 % -------------------------------------------------------------------- 

1206 % File: rat inhal Gargas 86.m 

1207 % Figure C-3 (creates 4 sub-plots) 

1208 % 

1209 % Programmed by Michael Lumpkin 

1210 % Syracuse Research Corporation, 10/2007 

1211 % Modified by Paul Schlosser, U.S. EPA, 9/2009 

1212 % 

1213 % This run time file sets species-specific constants values for modified 

1214 % Andersen et al (1987, 1991) simulating Burek et al. (1984) exposures 

1215 % of DCM in the male and female rats. 

1216 % -------------------------------------------------------------------- 

1217 dch1=[0.103, 93.73; 0.2178, 81.12; 0.3707, 66.26; 0.5543, 52.58; 0.7227, 41.73; 

1218 0.8834, 34.085; 1.044, 27.84; 1.22, 22.095; 1.3807, 18.312; 1.55, 14.532; 

1219 1.717, 12.58; 1.893, 10.28; 2.061, 8.3948]; 

1220 

1221 dch5=[0.1015, 448.9; 0.216, 400.0; 0.3843, 326.7; 0.5522, 282.85; 0.7128, 234.4; 

1222 0.8806, 208.9; 1.056, 183.5; 1.231, 163.5; 1.384, 145.7; 1.5514, 133.7; 

1223 1.719, 119.14; 1.887, 106.2; 2.0395, 95.99; 2.192, 85.5385; 2.360, 75.13; 

1224 2.5276, 65.99; 2.688, 57.96; 2.8484, 49.45; 3.024, 40.99; 3.1996, 36.00; 

1225 3.360, 31.165; 3.5356, 25.46; 3.719, 21.10; 3.864, 18.00; 4.025, 15.585; 

1226 4.193, 13.297; 4.353, 11.51]; 

1227 

1228 dch10=[0.0973, 927.1; 0.2195, 826.0; 0.3957, 627.6; 0.5556, 575.7; 0.7235, 513.1; 

1229 0.8836, 457.2; 1.059, 419.5; 1.226, 401.9; 1.3936, 379.6; 1.561, 348.2; 

1230 1.721, 333.66; 1.873, 329.1; 2.0556, 301.9; 2.200, 285.1; 2.3754, 265.4; 

1231 2.5426, 258.0; 2.695, 236.7; 2.87, 226.8; 3.030, 211.1; 3.1975, 193.66; 

1232 3.365, 182.9; 3.532, 175.24; 3.7076, 156.175; 3.875, 145.4; 4.035, 135.3; 

1233 4.195, 124.1; 4.340, 112.2; 4.515, 101.5; 4.683, 89.12]; 

1234 

1235 dch30=[0.1058, 2958.6; 0.2288, 2280; 0.3819, 1862.5; 0.5419, 1684; 0.7095, 1545; 

1236 0.8848, 1397; 1.067, 1338.6; 1.227, 1264; 1.387, 1229; 1.5615, 1195; 

1237 1.7136, 1178.5; 1.881, 1129; 2.056, 1051; 2.208, 1052; 2.383, 1008; 

1238 2.5424, 994.2; 2.6945, 966.46; 2.869, 939.6; 3.0366, 913.45; 3.196, 914.2; 

1239 3.356, 863.3; 3.531, 851.6; 3.713, 816.1; 3.880, 805.0; 4.032, 794.0; 

1240 4.192, 760.75; 4.352, 750.4; 4.5114, 708.6; 4.686, 709.2; 4.853, 699.6; 

1241 5.0055, 670.3; 5.173, 642.26; 5.34, 633.53]; 

1242 

1243 for model=['A' 'B' 'C' 'D'] 
 H-27 DRAFT- DO NOT CITE OR QUOTE 

1244 eval(['use rat_set_',model]); 

1245 BW=0.225; TSTOP=6; TCHNG=7; CC=1; VCHC=9; NCH=3; CINT=0.05; 

1246 prepare @clear T CCHPPM 

1247 res=[]; 

1248 for CONC=[107, 498, 1028, 3206] 

1249 start @nocallback 

1250 res=[res,_t,_cchppm]; 

1251 end 

1252 eval(['save res @file=gargas-inh-sim-',model,'.csv @format=ascii @separator=comma']) 

1253 plot(dch1(:,1),dch1(:,2),dch5(:,1),dch5(:,2),dch10(:,1),dch10(:,2), ... 

1254 dch30(:,1),dch30(:,2),res(:,1),res(:,2),res(:,3),res(:,4), ... 

1255 res(:,5),res(:,6),res(:,7),res(:,8),['gargas86',model,'.aps']) 

1256 end 

1257 

1258 dch1=[dch1;dch5;dch10;dch30]; 

1259 save dch1 @file=gargas-inh-dat.csv @format=ascii @separator=comma 

1260 

1261 % -------------------------------------------------------------------- 

1262 % File: Angelo_IV_comp.m 

1263 % Figures C-4 and C-6 (creates 4 sub-plots for each) 

1264 % 

1265 % Programmed by Michael Lumpkin 

1266 % Syracuse Research Corporation, 10/2007 

1267 % Modified by Paul Schlosser, U.S. EPA, 9/2009 

1268 % 

1269 % This run time file sets species-specific constants values for the 

1270 % modified Andersen et al (1987, 1991) PBPK model for dichloromethane, 

1271 % simulating Anelo et al. (1986) IV exposures of DCM in rats. 

1272 % -------------------------------------------------------------------- 

1273 d10bl=[2, 17.624; 5, 8.6635; 10, 3.533; 15, 2.858; 20, 1.7125; 

1274 30, 0.826; 40, 0.6192]; 

1275 d50bl=[2, 62.686; 5, 34.105; 10, 16.56; 15, 12.33; 20, 12.80; 

1276 30, 10.25; 40, 5.561]; 

1277 d10ex=[0.3333, 32.245; 0.6667, 38.56; 1, 41.396; 4, 44.42]; 

1278 d50ex=[0.3333, 35.38; 0.6667, 45.95; 1, 51.38; 4, 57.59]; 

1279 

1280 for model=['A' 'B' 'C' 'D'] 
 H-28 DRAFT- DO NOT CITE OR QUOTE 

1281 eval(['use rat_set_',model]); 

1282 prepare @clear T CV PCTIVEXH 

1283 TSTOP=10; CINT=0.01; 

1284 IVDOSE=10 

1285 start @nocallback 

1286 res=[_t,_cv,_pctivexh]; r2=[_t*60,_cv,_t,_pctivexh]; 

1287 IVDOSE=5; start @nocallback 

1288 res=[res,_t,_cv,_pctivexh]; r2=[r2,_t*60,_cv,_t,_pctivexh]; 

1289 IVDOSE=25; start @nocallback 

1290 res=[res,_t,_cv,_pctivexh]; r2=[r2,_t*60,_cv,_t,_pctivexh]; 

1291 IVDOSE=50; start @nocallback 

1292 res=[res,_t,_cv,_pctivexh];r2=[r2,_t*60,_cv,_t,_pctivexh]; 

1293 plot(d10bl(:,1),d10bl(:,2),d50bl(:,1),d50bl(:,2), ... 

1294 res(:,1)*60,res(:,2),_t*60,_cv,res(:,4)*60,res(:,5), ... 

1295 res(:,7)*60,res(:,8),['angivbl',model,'.aps']) 

1296 plot(d10ex(:,1),d10ex(:,2),d50ex(:,1),d50ex(:,2), ... 

1297 res(:,1),res(:,3),_t,_pctivexh,res(:,4),res(:,6), ... 

1298 res(:,7),res(:,9),['angivex',model,'.aps']) 

1299 eval(['save r2 @file=Angelo_IV_sims_',model,'.csv @format=ascii @separator=comma']) 

1300 end 

1301 

1302 % -------------------------------------------------------------------- 

1303 % File: rat_inhal_Andersen.m 

1304 % Figures C-5 (creates 4 sub-plots) 

1305 % 

1306 % Programmed by Michael Lumpkin 

1307 % Syracuse Research Corporation, 10/2007 

1308 % Modified by Paul Schlosser, U.S. EPA, 9/2009 

1309 % 

1310 % This run time file sets species-specific constants values for the 

1311 % modified Andersen et al (1987, 1991) PBPK model for dichloromethane, 

1312 % simulating Andersen et al. (1987) inhalation exposures of DCM in rats. 

1313 % -------------------------------------------------------------------- 

1314 dan2=[1, 2.6634; 2, 4.290; 3, 4.9915; 4, 4.464; 4.25, 1.3334; 4.5, 0.5513; 

1315 4.75, 0.4234; 5, 0.2618; 5.25, 0.1206; 5.5, 0.1049]; 

1316 dan10=[1, 29.34; 2, 49.50; 3, 47.10; 4, 50.72; 4.25, 14.92; 4.5, 9.225; 
 H-29 DRAFT- DO NOT CITE OR QUOTE 

1317 4.75, 6.260; 5, 3.811; 5.25, 1.3085; 5.5, 0.6515; 5.75, 0.6025]; 

1318 can2=[0.9942, 3.967; 1.027, 4.121; 1.039, 3.798; 2.045, 7.151; 

1319 2.0565, 6.598; 2.057, 5.7836; 3.053, 8.4295; 3.076, 7.493; 

1320 3.086, 8.7215; 4.073, 9.094; 4.074, 8.003; 4.084, 8.940; 4.5545, 5.302; 

1321 4.597, 6.853; 4.608, 7.1145; 5.078, 3.5525; 5.079, 2.723; 5.0795, 2.124; 

1322 5.302, 1.464; 5.313, 1.971; 5.325, 1.142]; 

1323 can10=[1.006, 4.004; 1.018, 4.305; 1.021, 5.114; 2.0494, 9.257; 

1324 2.061, 9.488; 2.068, 7.9395; 3.047, 10.42; 3.0535, 8.777; 3.055, 9.170; 

1325 4.030, 6.449; 4.030, 10.31; 4.036, 12.11; 5.0275, 7.448; 5.061, 7.656; 

1326 5.067, 9.598; 5.5285, 4.978; 5.540, 5.001; 5.552, 9.115; 6.028, 1.999; 

1327 6.030, 6.460; 6.049, 1.653; 6.510, 0.4305; 6.525, 2.072; 6.539, 2.973]; 

1328 for model=['A' 'B' 'C' 'D'] 

1329 eval(['use rat_set_',model]); 

1330 QCSW=0; 

1331 prepare @clear T CV PCTHBCO 

1332 TSTOP=6; CINT=0.01; TCHNG=4.0; 

1333 CONC=200; start @nocallback 

1334 res=[_t,_cv,_pcthbco]; 

1335 if model=='D' 

1336 CONC=150; start @nocallback 

1337 res=[res,_cv]; 

1338 %use rat_set_D 

1339 %QCSW=0; 

1340 end 

1341 

1342 CONC=750; CINT=0.01; TCHNG=4.0; start @nocallback 

1343 res2=[_t,_cv]; res=[res,res2]; 

1344 TSTOP=7; CONC=1014; start @nocallback 

1345 plot(dan2(:,1),dan2(:,2),dan10(:,1),dan10(:,2), ... 

1346 res(:,1),res(:,2),res2(:,1),res2(:,2),res(:,1),res(:,4),['andinhbl',model,'.aps']) 

1347 %plot(can2(:,1),can2(:,2),can10(:,1),can10(:,2), ... 

1348 % res(:,1),res(:,3),_t,_pcthbco,['andinhhb',model,'.aps']) 

1349 eval(['save res @file=Andersen87_inh_sims_',model,'.csv @format=ascii @separator=comma']) 

1350 end 

1351 

1352