ehp.niehs.nih.gov fileehp.niehs.nih.gov

Development of a Physiologically Based Pharmacokinetic Model ofTrichloroethylene and Its Metabolites for Use in Risk AssessmentHarvey J. Clewell 111,1 P. Robinan Gentry,1 Tammie R. Covington,1 and Jeffery M. Gearhart2'The K.S. Crump Group, Inc., ICF Consulting, Ruston, Louisiana USA; 2Procter & Gamble Company, Cincinnati, Ohio USA

A physiologically based pharmacokinetic (PBPK) model was developed that provides acomprehensive description of the kinetics of trichloroethylene (TCE) and its metabolites,trichloroethanol (TCOH), trichloroacetic acid (TCA), and dichloroacetic acid (DCA), in the mouse, rat,and human for both oral and inhalation exposure. The model includes descriptions of the threeprincipal target tissues for cancer identified in animal bioassays: liver, lung, and kidney. Cancer dosemetrics provided in the model include the area under the concentration curve (AUC) for TCA andDCA in the plasma, the peak concentration and AUC for chloral in the tracheobronchial region of thelung, and the production of a thioacetylating intermediate from dichlorovinylcysteine in the kidney.Additional dose metrics provided for noncancer risk assessment include the peak concentrationsand AUCs for TCE and TCOH in the blood, as well as the total metabolism of TCE divided by thebody weight. Sensitivity and uncertainty analyses were performed on the model to evaluate itssuitability for use in a pharmacokinetic risk assessment for TCE. Model predictions of TCE, TCA,DCA, and TCOH concentrations in rodents and humans are in good agreement with a variety ofexperimental data, suggesting that the model should provide a useful basis for evaluating cross-species differences in pharmacokinetics for these chemicals. In the case of the lung and kidneytarget tissues, however, only limited data are available for establishing cross-speciespharmacokinetics. As a result, PBPK model calculations of target tissue dose for lung and kidneyshould be used with caution. Key words: dichloroacetic acid, dichlorovinylcysteine, metabolism,model, PBPK, pharmacokinetics, risk assessment, trichloroacetic acid, trichloroethanol,trichloroethylene. - Environ Health Perspect 1 08(suppl 2):283-305 (2000).http.//ehpnet 1. niehs. nih.gov/docs/2000/suppl-2/283-305clewell/abstract. html

IntroductionPhysiologically based pharmacokinetic(PBPK) modeling is widely held to be a usefulmethodology for improving the accuracy ofchemical risk assessment (1-6). The goal ofPBPK modeling is to simulate the uptake, dis-tribution, metabolism, and elimination of a

chemical in an organism, using as realistic a

description of the relevant physiology and bio-chemistry as is necessary and feasible (7-10).For its use in risk assessment, PBPK modelingattempts to describe the relationship betweenexternal measures of exposure (e.g., amount

administered or concentration in air) andinternal measures of biologically effective dose(e.g., amount metabolized or concentration ofan active metabolite in the tissue displayingthe toxic response) in both the experimentalanimal and the human (11,12).

Simple pharmacokinetic approaches haveoccasionally been used by regulatory agenciesin risk assessment; for example, the most

recent U.S. Environmental ProtectionAgency (U.S. EPA) cancer risk estimates fortrichloroethylene (TCE) were derived usingestimates of metabolized dose (13,14). Therecent U.S. EPA guidelines for the applica-tion of inhalation dosimetry in the derivationof inhalation reference concentrations (15)also make use of pharmacokinetic principles.However, the only case to date where a regu-latory agency has used a full PBPK approachin a published risk assessment was in the

U.S. EPA's latest revision of its inhalation riskassessment for methylene chloride (16). Thedecision to use the PBPK approach in thiscase was made only after a period of consider-able controversy, including a workshop spon-sored by the National Academy of Sciences atwhich the usefulness of PBPK modeling forchemical risk assessment was discussed. Thescientific consensus following the workshopwas that "relevant PBPK data can be used toreduce uncertainty in extrapolation and riskassessment" (1). In 1989, after a detailedmultiagency evaluation of the available PBPKinformation and a review by the U.S. EPAScientific Advisory Board, the U.S. EPArevised the inhalation unit risk and risk-specific air concentrations for methylenechloride in its Integrated Risk InformationSystem (IRIS) (17) database, citing thePBPK model of Andersen et al. (18). Theresulting risk estimates were lower than thoseobtained by the default approach by nearly afactor of 10. Application of the PBPK modelfor methylene chloride in a cancer risk assess-ment for occupational exposure has also beendescribed (19-21), and a modified version ofthe model was used by the OccupationalSafety and Health Administration (OSHA) intheir rulemaking for methylene chloride (22).

Risk assessments using PBPK models havealso been proposed for many other chemicals,including not only TCE (23-26), but alsoperchloroethylene (PERC) (24,27,28),

ethylene dichloride (29), vinyl chloride(26,30-32), dioxane (33,34), chloroform(35), benzene (36), and ethyl acrylate (37).However, apart from the case with methylenechloride described above, there still have beenno risk assessments published by a regulatoryagency in which a PBPK model was used forestimating target tissue dose. Part of the rea-son for the slow progress of incorporatingPBPK modeling in cancer risk assessment isthe concern of regulatory agency risk assessorsabout uncertainties in its implementation.The potential impact of uncertainty in phar-macokinetic risk assessment has been a sub-ject of some controversy (19-21,38-43). Thepurpose of the study reported here was todevelop a PBPK model for TCE thatincluded as complete a description as possibleof all of the metabolites and target tissues thatare relevant to the toxicity and carcinogenic-ity of TCE and to evaluate the suitability ofthe resulting model to provide dosimetry foreach of the target tissues in support of a com-prehensive pharmacokinetic risk assessmentfor TCE.

For completeness, aspects of both thecancer and noncancer risk assessment con-texts pertinent to the development and evalu-ation of the PBPK model are discussed in thisarticle. However, a companion article in thissame issue provides a detailed description ofthe application of the PBPK model in anoncancer risk assessment for TCE (44).Therefore, the discussions in this article willfocus primarily on the cancer end points.

Toxicity and Carcinogenicity ofTCETCE produces noncancer toxicity in a varietyof tissues; principal noncancer end pointsinclude neurological, hepatic, renal,

This article is part of the monograph on TrichloroethyleneToxicity.

Address correspondence to H.J. Clewell, 602 E.Georgia Ave., Ruston, LA 71270. Telephone: (318)255-4800. Fax: (318) 255-4960. E-mail: [email protected]

This model development effort was supported bythe U.S. Environmental Protection Agency Office ofHealth and Environmental Assessment and theOccupational Safety and Health AdministrationDepartment of Health Standards Policy. However, theviews presented in this paper are strictly those of theauthors and do not necessarily reflect the position ofeither of the agencies. The authors are greatlyindebted to the U.S. EPA and OSHA project officers, L.Rhomberg and C. Whittaker, for their guidance, sup-port, and thoughtful discussions.

Received 30 November 1998; accepted 9 July 1999.

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immunological, and developmental effects(45-47). The American Conference ofGovernment Industrial Hygienists (ACGIH)threshold limit value (TLV) for TCE is cur-rently 50 ppm as an 8-hr time-weightedaverage (TWA), based on central nervoussystem (CNS) effects (headaches, fatigue,irritability), with a short-term exposure limitof 200 ppm to protect against its anestheticeffects (48). In 1989, as part of a major rule-making that promulgated standards for morethan 200 chemicals, OSHA adopted a per-missible exposure level (PEL) for TCE of 50ppm as an 8-hr TWA based on the ACGIHTLV (49). However, since the entire 1989rulemaking has now been overturned by thecourts, the PEL for TCE has returned to itsprevious value of a 100-ppm 8-hr TWA, alsobased on CNS effects.TCE was widely used in industry for

many years because of its relatively low toxic-ity, its excellent solvent properties, and itsnonflammability. In recent years, however,use of TCE has been greatly reduced due toconcerns regarding its carcinogenicity(46,47). Nevertheless, the question of thehuman carcinogenicity ofTCE remains con-troversial. Although a large number ofstudies have demonstrated tumors in animalsfollowing exposure to TCE, the relevance ofthese animal results to the question of thehuman carcinogenicity of TCE has fre-quently been challenged (50-52). TheACGIH has classified TCE into Group A5,not suspected as a human carcinogen, basedon a well-conducted, negative epidemiologi-cal study performed in an aircraft mainte-nance facility at Hill Air Force Base (Ogden,UT) (53,54). The International Agency forResearch on Cancer (IARC), on the otherhand, classifies TCE into Group 2A, proba-bly carcinogenic to humans, based on theirassessment of sufficient data in animals andlimited data in humans (55). The humanevidence considered significant by IARC wasthe consistency of an association of TCEexposure with slightly increased incidences ofliver/biliary tract tumors and non-Hodgkinslymphoma in studies of three cohorts in theUnited States (53), Sweden (56), andFinland (57), despite the fact that all threestudies were characterized as negative by theoriginal investigators because the increaseswere not statistically significant.

Requirements for a PBPK Model toSupport TCE RiskAssessmentsQuantitative cancer risk estimates for TCEare currently based on animal bioassays,specifically liver and lung tumors in mice. In1983, the U.S. EPA calculated unit risks forTCE of 4.1 x 106 (pg/m3)-1 for inhalationand 0.54 x 10- (pg/L)-1 for drinking waterusing data on the incidence of liver tumors

in male B6C3F, mice given TCE in an oilvehicle by gavage (58,59); the linearizedmultistage model (60) was used with a calcu-lation of absorbed TCE dose scaled by bodysurface area (BSA) to obtain these estimates(61). In 1985, lower unit risks of 1.3 x 106(pglm3)-1 for inhalation and 0.32 x 10-6(jg/L)1' for drinking water were recalculatedon the basis of the same oral bioassays, usingthe results of pharmacokinetic studies(50,62,63) to calculate total metabolizeddose in both animals and humans, ratherthan absorbed dose. The BSA adjustmentwas still applied to obtain the human equiva-lent dose (13). In 1987, the U.S. EPA calcu-lated a new inhalation unit risk of 1.7 x 10-(ig/m3)-1 based on the incidence of mouseliver and lung tumors in inhalation bioassays(64-66), again using a calculation of metab-olized dose and the BSA adjustment (16).Statistically significant increases in the inci-dence of renal tubular cell adenoma and car-cinoma have also been observed in maleFischer 344 (F344) rats exposed to TCE bygavage (67). Although not yet used in aquantitative risk assessment for TCE, theincidences of these kidney tumors in ratshave raised concern, since they represent arare tumor that has also been associated withhuman occupational exposure to TCE (68).

For each of these three target tissues-liver, lung, and kidney-there is evidencethat the carcinogenicity of TCE may beassociated with one or more of its metabo-lites: trichloroacetic acid (TCA) anddichloroacetic acid (DCA) in the liver(69,70), chloral (CHL) in the lung (71),and 1,2-dichlorovinylcysteine (DCVC) inthe kidney (72). Thus, to be useful in acomprehensive cancer risk assessment forTCE, a PBPK model should include at leastthree target tissues-liver, lung, and kid-ney-along with a description of the kinet-ics of the metabolites imputed to play a rolein the carcinogenic activity.

Several target tissues have also been iden-tified for the noncancer toxicity of TCE,including the liver (73), kidney (65,66),CNS (74), immune system (75,76), anddeveloping fetus (77). As in the case of thecarcinogenicity of TCE, several of thesenoncancer end points appear to be associ-ated with exposure to the metabolites ofTCE rather than to the parent chemicalitself (73,78). In addition to the metabolitesmentioned above with regard to the carcino-genicity of TCE, it was felt (43) that aPBPK model developed to support a non-cancer risk assessment for TCE should alsoinclude a description of the kinetics oftrichloroethanol (TCOH), a major metabo-lite of TCE that has been suggested to beresponsible for the observed neurologicaleffects of chloral hydrate (79).

Previous PBPK Modeling ofTCEA number of PBPK models have beendeveloped for TCE. However, most are onlyparent chemical models; that is, they provide apharmacokinetic description ofTCE itself, butdo not include an explicit description of thepharmacokinetics of any of the metabolites(23,24,80-86). These models have been usedsuccessfully for predicting TCE concentrationsin the blood and tissues (86), for calculatingthe respiratory input from inhalation exposures(81,82,85), and for investigating the impact ofvariations in the physiological or biochemicalparameters on the kinetics ofTCE (80,83,84).Parent chemical models have also beenemployed to calculate total metabolized dosein support of a cancer risk assessment for TCE(23,24). However, these parent chemicalmodels cannot be used for predicting tissueexposure to specific metabolites.

Models of both TCE and its metaboliteshave also been developed. In a series of publi-cations, Sato and co-workers have describedthe use of a simple PBPK model to study thekinetics of TCE and its metabolites inhumans (87), to evaluate the impact ofchanges in physiological factors (88) andenvironmental factors (89) on the kinetics ofTCE in the human, and to predict the effectsof interactions with ethanol consumption onTCE kinetics (90). However, the structure ofthese models would not support the animal-to-human extrapolation of pharmacokineticdose metrics needed for risk assessment.Fisher and co-workers developed a PBPKmodel for TCE and its principal metabolite,TCA, in the pregnant (91) and lactating (92)rat, as well as in the mouse (93). Theserodent models, together with a similar modelofTCE and TCA in the human (94), servedas the basis for a PBPK-based risk assessmentfor TCE liver carcinogenicity (25) based oneither total metabolism or AUC for TCA.These models provided the first successfulcross-species pharmacokinetic description fora metabolite of TCE. The model develop-ment performed in the current study buildson the work of Fisher and Allen (25) byadding limited descriptions of additionalmetabolites (TCOH, DCA, CHL, DCVC)and target tissues (lung and kidney).

Metabolism ofTCEThe following discussion summarizes theexperimental evidence for the nature of thepharmacokinetics and metabolism of TCE,which formed the basis for the decisions thatwere made regarding the structure and para-meterization of the PBPK model. TCE is avolatile, lipophilic chemical that distributesreadily throughout all tissues, including thebrain, but partitions preferentially into fat tis-sue. In contrast, its major metabolite, TCA, isa water-soluble chemical that preferentially

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distributes into the plasma and richlyperfused organs and is found only in rela-tively lower concentrations in the musde andfat. The properties ofTCOH are somewhatintermediate between the other two com-pounds (95). Clearance ofTCE occurs bothby exhalation and by metabolism. Aschematic of the metabolic pathways for TCEis shown in Figure 1. The major oxidativepathway, which takes place primarily (but notexclusively) in the liver, is shown to the rightof the diagram; the minor glutathione-depen-dent pathway, which involves several loca-tions induding the liver and kidney, is shownto the left.

Oxidative metabolism. The primary routeof metabolism for TCE, shown on the rightside of the diagram in Figure 1, is oxidationvia the microsomal mixed-function oxidase(MFO) system, now referred to as cytochromeP450, or CYP (96-102). A minor pathwayfor TCE metabolism involving conjugationwith glutathione (GSH) by glutathione trans-ferase (GST) has also been observed (103).This pathway, which is shown on the left sideof the diagram, will be described in the sectionon conjunctive metabolism.

The principal oxidative metabolite formedin vitro is CHL (96,97,100), which is subse-quently reduced to TCOH in the cytosol oroxidized to TCA in either the cytosol or mito-chondria (96). CHL is not stable in vivo, andcirculating concentrations are relatively lowcompared to its breakdown products, TCAand TCOH (50). Within a few hours of theadministration of 50 mglkg chloral hydrate toa child, the rapid initial dearance ofCHL wasessentially complete. Subsequent blood con-centrations parallel the time course forTCOH but are approximately an order ofmagnitude lower, suggesting a continuingproduction ofCHL from TCOH (104).

The principal circulating metabolite ofTCE in the blood is TCA, which accumu-lates in the body due to protein binding(105) and slow excretion (106), whereasTCOH is readily excreted (107,108). TCAappears to be derived both directly fromCHL and indirectly from TCOH(107-109). TCE is much more extensivelymetabolized in the mouse than in the rat,whether TCE is administered orally (50) orby inhalation (91).

Based on both in vitro and in vivo studies,the metabolism ofTCE has been suggested toconsist of oxidation ofTCE to CHL by theMFO system, followed by either oxidation ofCHL to TCA by an aldehyde oxidase (alsoknown as chloral dehydrogenase) or reduc-tion to TCOH by alcohol dehydrogenase(ADH) with subsequent glucuronidation.Oxidation ofTCOH to TCA by the MFOsystem has also been proposed (110).Consistent with this proposed metabolic

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Figure 1. Metabolism of TCE. Abbreviations not given in text: (right pathway) CDH: chloral dehydrogenase (aldehydeoxidase); EHR: enterohepatic recirculation; FA: formic acid; GA: glyoxylic acid; OA: oxalic acid; TCE-0-P450: oxy-genated TCE-cytochrome P450 transition state complex; TCOG: TCOH glucuronide; UGT: UDP glucuronosyltransferase;(left pathway) BL: cysteine conjugate 1-lyase; CGDP: cysteinyl-glycine dipeptidase; DCVG: dichlorovinyl glutathione;DCVSH: dichlorovinyl mercaptan; GGTP: y-glutamyl transpeptidase; NADCVC: N-acetyl dichlorovinylcysteine; NAT:Nacetyltransferase.

description, oral co-administration of ethanolinhibited the metabolism of inhaled TCE toTCOH by more than 50% and the produc-tion of TCA was essentially abolished whileethanol was present (110). A similar study inrats demonstrated qualitatively similar, butquantitatively much less remarkable, effects ofethanol co-administration on the kinetics oforally administered TCE (111).

Inhalation exposures of human volunteersto TCE concentrations from 27 to 201 ppmshowed no evidence of metabolic saturationor of a change in the proportion of TCA toTCOH (112). Saturation of TCE metabo-lism has been observed in mice, rats, and dogs(66,113). The relative proportion of themajor metabolites does not appear to be astrong function of dose. However, repeateddosing does appear to increase the productionofTCA at the expense ofTCOH (114), andthe relative production of CO2 increases withincreasing dose in mice (115).

Human in vivo studies with TCE (116)have identified the major urinary metabolitesto be TCOH (50% of the administered TCEdose), primarily as the glucuronide, and TCA(19%). Monochloroacetic acid (MCA) wasalso identified as a minor metabolite (4%) inthese studies. Another minor metabolite,N-(hydroxyacetyl)-aminoethanol, has alsobeen identified in human (and rodent) urinefollowing TCE exposure, and TCE-derivedoxalic acid has been detected in the rodent

(117). A study of TCE metabolism innonhuman primates (118) found that TCAwas partially excreted as the glucuronide,particularly at longer times after dosing. Theauthors suggest that since the detection ofTCA glucuronide had not been reportedpreviously, TCA excretion may have beenunder-reported in earlier studies (such as thehuman study cited above). The glucuronida-tion ofTCA is supported by the observationthat TCA is excreted in the bile of rats andmice (114). Urinary excretion represents themajor route of elimination of the metabo-lites; fecal excretion, in the form of TCOHglucuronide, accounts for less than 5% ofthe total (118). The low fecal excretion isapparently associated to some extent withenterohepatic recirculation of TCOH (i.e.,biliary excretion of the glucuronide, fol-lowed by hydrolysis and reabsorption ofTCOH), which has also been suggested tooccur in rats (119).DCA has been identified as a minor

urinary metabolite of TCE (on the order of1%) in both rats and mice (114,115,117,120), whereas MCA appears to be pre-sent at less than 0.1% (114). A recent in vitrostudy with mouse and rat liver tissues con-cluded that unlike most other chlorinatedcompounds, which are metabolized primarilyby the microsomal enzymes of the MFO sys-tem, DCA degradation appears to occur pri-marily in the cytosol in a process that requires


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CLEWELL ET AL.

GSH (121). Although DCA has not beendetected as a urinary metabolite ofTCE in thehuman, its kinetics have been studied(122-124). The peak concentration andAUC of DCA in the plasma after intravenousadministration is linear up to approximately20 mg/kg, but above 20-30 mg/kg some indi-viduals display evidence of saturation ofmetabolism (121). The apparent volume ofdistribution and half-life for DCA are 0.3 L/kg(range: 0.09-0.60) and 1.05 hr (range:0.25-1.87), respectively (124). Significantly,the clearance in humans appears to be muchmore rapid than would be expected from allo-metric scaling of animal data. In a comparativestudy (122), the half-lives in rats, dogs, andhumans were 2.1-4.4 hr, 17.1-24.6 hr, and0.33-0.6 hr, respectively. The extremely highrate of metabolism ofDCA in humans is prob-ably responsible for the failure of investigatorsto detect it as a urinary metabolite ofTCE.

Studies have shown that DCA is producedfrom TCA (67). In addition, DCA is pro-duced in a roughly linear fashion from PERC(28,125) at levels consistent with productionfrom TCA, the principal metaboliteof PERC. An analysis of data on thedose-response and elimination kinetics ofDCA formed from TCE led to the conclu-sion that another source of DCA wasrequired in addition to TCA, but that thedata were inconsistent with the second sourcebeing the initial oxidation step (126).Instead, the production of DCA fromTCOH was hypothesized. A metabolic studyof TCA and DCA in rats and mice (67)found that DCA was more rapidly metabo-lized than TCA. More than 50% of theadministered TCA from an oral dose wasexcreted unchanged in the urine as comparedto only 2% of administered DCA. Plasmaconcentration-time curves for TCA weresimilar in mice and rats, whereas those forDCA were greater in rats than in mice, bothwhen DCA was administered and when itwas derived from TCA.

There is evidence that repeated exposureto high concentrations of DCA inhibits itsown metabolism. In studies with human vol-unteers (124,127), the excretion half-life forDCA increased 2- to 6-fold followingrepeated intravenous doses of DCA on theorder of 10-50 mg/kg. Inhibition was onlyslowly reversible, taking from 1 week togreater than 3 months. In the rat, but not themouse, exposure to 2 g/L DCA in drinkingwater for 14 days curtailed metabolic produc-tion of CO2 from DCA by more than anorder of magnitude and increased the excre-tion half-life by roughly 4-fold (128).

It has recently been reported that undersome conditions DCA can be artificiallyproduced from TCA in blood samples as anartifact of the analysis. If acidification of the

sample is performed rapidly after collection ofthe sample before the hemoglobin iron hasbeen oxidized by exposure to air, DCA can begenerated from TCA present in the sample(129). This artifact may have compromisedsome of the data reported on DCA plasmaconcentrations following administration ofTCA or TCE (66,67).

Metabolism in the lung. As with mostchemicals, the preponderance of the metabolicdearance ofTCE appears to take place in theliver. It has been demonstrated, however, instudies with an isolated, ventilated perfusedlung preparation (130), that the male F344rat lung also possesses a limited oxidativemetabolic capability for TCE. Although theaffinity (K,, for the lung metabolism observedin that study was similar to the affinityobserved in the liver, the capacity (Vmax) ofthe lung metabolism was less than 1% of thecapacity of the liver. These results suggest thatlung metabolism is not an important contrib-utor to total in vivo metabolism in the rat andthat the rat lung does not possess a significantfirst-pass (presystemic) clearance capability forinhaled TCE. However, these results do notrule out the possibility that metabolism in thelung could produce sufficient local exposureto metabolites to produce toxicity and/orcarcinogenicity.

Conjugative metabolism. A small propor-tion of TCE appears to be metabolized byenzymatic conjugation with GSH, principallyby GST in the liver, followed by furthermetabolism in the kidney to the cysteine con-jugate DCVC (131). The GST metabolicpathway is shown on the left side of Figure 1.Delivery ofDCVC to the kidney may also bemediated by enterohepatic recirculation, inwhich GSH conjugate excreted in the bile isconverted by gut bacteria to the cysteine con-jugate, which is then reabsorbed (132). TheGSH conjugate has been identified both invitro, with rat liver microsomes, and (at5 nmol/L) in the bile of rats given 2.2 g/kgTCE in corn oil (103). The cysteine conju-gate also has been identified in the urine ofanimals dosed with TCE (72).

The bioactivation of DCVC to a reactiveand mutagenic thioacylating intermediate isperformed by cysteine conjugate I-lyase inthe kidney (133). Although similar P-lyaseactivity has been measured in the kidney andliver, the two enzymes are distinct (134).Detoxification and dearance of DCVC takesplace by urinary excretion of the N-acetylderivative (103,135). In a study with PERC(136), it was determined that the excretion ofthe N-acetyl derivative was dose related (ahigher fraction of N-acetyl derivative wasexcreted at doses where the oxidative pathwaywas saturated) and was significantly greater inthe rat than in the mouse. However, measure-ments of acid-labile protein adducts associated

with DCVC suggest that the activation ofDCVC in the kidney may be as much as12-fold greater in mice than in rats and thatthe kidney tissue exposure to DCVC-derivedreactive species from oral dosing with TCEmay be twice as great in the mouse as in therat (137,138).

The activity of P-lyase has been measuredin the liver and kidney of both humans andrats. One research group has reported a spe-cific activity in human kidney on the order of2.0-3.6 nmol/min/mg cytosol (139,140),compared to 6.45-7.6 nmol/min/mg cytosolin the rat (134). Another research group,however, has reported a maximum velocity(Vmax) of only 0.8 nmol/min/mg cytosol inthe human, with an affinity (Ki) of 0.29mM, compared to Vmax = 7.5 nmol/min/mgcytosol and Km = 1.6 mM in rat kidneycytosol in the same study (141). Data forPERC on the relative activity of liver cytoso-lic GST and kidney cytosolic cysteine conju-gate ,B-lyase suggest that the human activityof both enzymes is roughly 10-fold lowerthan that in the rat (136). On the otherhand, the specific activity of N-acetyltrans-ferase in kidney cytosol appears to be verysimilar across species: 0.41 nmol/min/mgcytosol in the human, compared to0.35-0.61 in the rat and 0.94 in the mouse(142).

The fact that N-acetyl-DCVC has beenidentified in the urine of humans exposed toTCE both occupationally (142) and in con-trolled exposures (143) indicates that expo-sure of the kidney to DCVC does occur in thehuman. In the occupational study (142), theconcentrations of N-acetyl-DCVC in theworkers' urine was about one-third of thatmeasured in rats dosed orally with 50 mg/kgTCE. The ratio of N-acetyl-DCVC to TCAin the workers' urine ranged from 0.03 to 0.3,while in rats it ranged from 0.025 to 0.045,and in mice from 0.014 to 0.065. However,more recent data (143) obtained in controlledstudies with both rats and human subjectssuggest that relative urinary excretion of GST-pathway metabolites from TCE is actuallysomewhat lower in humans than in rats.

Description of the PBPK Modelfor TCEPBPK Model StructureA diagram of the PBPK model developed forTCE and its metabolites is shown in Figures2 and 3. The model was written in theAdvanced Continuous Simulation Language(ACSL, Mitchell and Gauthier, Concord,MA). The parent chemical portion of themodel (Figure 2) includes individual tissuecompartments for the liver, gut tissue, fat,and tracheobronchial region of the lungs. Allother tissues are lumped into rapidly perfused


PBPK MODEL OF TCE AND ITS METABOLITES

A

VMTB VMCTBKMTB | Chloral |KMCTB

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Figure 3. PBPK model for TCE: metabolism submodels for lung (A), kidney (B), and liver (C) target tissues.

Figure 2. PBPK model for TCE: parent chemical model.Abbreviations not listed in Table 1: CA, concentration inarterial blood; CF, concentration in the fat; CG, concen-tration in the gut; Cl, concentration in inhaled air; CL,concentration in the liver; CR, concentration in the richlyperfused tissues; CS, concentration in the slowly per-fused tissues; CTB, concentration in the tracheo-bronchial tissue; CV, concentration in venous blood; CVF,concentration in the venous blood leaving the fat com-partment; CVG, concentration in the venous blood leav-ing the gut compartment; CVL, concentration in thevenous blood leaving the liver; CVR, concentration in thevenous blood leaving the richly perfused tissues; CVS,concentration in the venous blood leaving the slowlyperfused tissues; CVTB, concentration in the venousblood leaving the tracheobronchial tissue; CX, concen-tration in exhaled air; KF, rate of production of DCVC inthe kidney; PDOSE, administered oral dose of TCE; QCLcardiac output; QF, blood flow to the fat; QG, blood flowto the gut; QL, blood flow to the liver; QP, alveolar venti-lation; QR, blood flow to the richly perfused tissues; QS,blood flow to the slowly perfused tissues; QTB, bloodflow to the liver; VR, volume of richly perfused tissue;VS, volume of slowly perfused tissue; VTB, volume ofthe tracheobronchial tissue.

(kidney, brain, alveolar region of lungs, etc.)and slowly perfused (muscle, skin, etc.) com-

partments. The model includes both inhala-tion and oral routes of exposure. Oral gavageis modeled using a two-compartment descrip-tion of the gastrointestinal tract (144), ratherthan the single-compartment description usedby Fisher and Allen (25), in order to bettersimulate the time course for the uptake ofTCE from corn oil gavage. Allometric scalingis used throughout the model (volumes scaledby body weight, flows and metabolic capaci-ties scaled by body weight to the three-quarters power, rate constants scaled by body

weight to the negative one-quarter power) to

simplify intraspecies and interspecies extrapo-lation. Parent chemical dose metrics providedin the model include the concentration ofTCE in blood and tissues, as well as the AUCfor TCE in the blood.

Lung submodel. The model includes threetarget tissue submodels in which metabolismtakes place: lung, kidney, and liver (Figure 3).Michaelis-Menten kinetics are assumed for allmetabolic processes. The tracheobronchialregion of the lungs, which receives its ownarterial blood supply, is described separately to

support the modeling of in situ metabolism inthis region by the Clara cells. This approachfor describing metabolism in the cells liningthe airways of the lung was felt to be more

biologically accurate than the sequential gas

exchange and lung tissue compartments usedin the methylene chloride model (18).However, as long as metabolism in the lung isunimportant for presystemic elimination, as is

the case for TCE and methylene chloride, thetwo descriptions should yield similar results.The dose metrics provided for the lung are theinstantaneous concentration and AUC forCHL in the tracheobronchial region, which isassumed to be produced by saturable produc-tion and clearance of CHL in Clara cells. Nosystemic circulation of CHL is considered inthe model.

Oxidative metabolism. Apart from thelimited metabolism occurring in the lung, themodel assumes that all oxidative metabolismtakes place in the liver. The dose metric pro-vided to describe metabolism is the totalamount of TCE metabolized divided by thebody weight. The model does not actuallycalculate the formation and metabolism of

CHL in the liver, but instead assumes thatTCA and TCOH are formed in a fixed yieldfrom the oxidative metabolism of TCE. Inthe model, TCOH can subsequently be oxi-dized to TCA, conjugated with glucuronicacid, or reduced to DCA. DCA can also beproduced from the reduction of TCA. Biliaryexcretion ofTCOH glucuronide and entero-hepatic recirculation of free TCOH isdescribed, with only the glucuronide beingexcreted in the urine. Dose metrics for use

with the liver target tissue include the con-

centrations and AUC for DCA and TCA inthe plasma, as well as a potency-weightedsum of the AUCs for DCA and TCA, whichwill be described in the discussion on dosemetric uncertainty. The concentration andAUC for TCOH in the blood are also pro-

vided as a noncancer dose metric.Conjugative metabolism. The model also

includes a linear metabolic pathway represent-ing conjugation of TCE by GST. The modelimplicitly assumes that all GSH conjugation ofTCE in the liver leads eventually to the appear-

ance of DCVC in the kidney. Clearance ofDCVC by N-acetyltransferase into the urine isalso modeled. The dose metric provided in themodel for the kidney (KTOX) is the total pro-

duction of a thioacetylating intermediate fromDCVC divided by the volume of the kidney.

PBPK Model ParametersThe parameters for the model are listed inTable 1, with the parameters for the parentchemical portion of the model listed first, fol-lowed by the parameters for each of themetabolites in turn.

Parametersfor the parent chemical. Thephysiological parameters, with two exceptions,


* urine

287

CLEWELL ET AL

Table 1. Parameter values used in the PBPK model for TCE.

Parameter Abbreviation Units Mouse Rat HumanBody weight BW

Alveolar ventilation QPCCardiac output QCC

Fractional blood flows to tissuesAll rapidly perfused QRC

Gut QGCLiver QLCTracheobronchial QTBC

All slowly perfused QSCFat QFC

Fractional volumes of tissuesAll rapidly perfused VRC

Gut VGCKidney VKCliver VLCTracheobronchial VTBC

All slowly perfused VSCFat VFC

Partition coefficientsBlood/air PBFat/blood PFGut/blood PGLiver/blood PLRich/blood PRSlow/blood PSTracheobronchial/blood PTB

Oral uptake of TCEStomach to liver KASDuodenum to liver KADStomach to duodenum KTSOFecal excretion KTD

TCE metabolismCapacity VMCAffinity KMFraction TCA PO

TCOH oxidation to TCACapacity VMOCAffinity KMO

TCOH reduction to DCACapacity VMRCAffinity KMR

TCOH glucuronidationCapacity VMGCAffinity KMG

Kinetics of glucuronideBiliary excretion KEHBCReabsorption KEHRCUrinary excretion KUGC

TCA reduction to DCACapacity VMTCAffinity KMTUrinary excretion KUTC

DCA reduction/eliminationCapacity VMDCAffinity KMDUrinary excretion KUDC

DCVC kinetics in kidneyProduction KFCActivation KBLCClearance KNATC

Chloral kinetics in lung Clara cellsProduction capacity VMTBCAffinity KMTBClearance capacity VMCTBCAffinity KMCTB

Volumes of distribution (fraction of body weight)TCA VDTCACDCA VDDCACTCOH VDBWC

Fraction of lung containing Clara cells

kgL/hraL/hra

0.035* (0.02-0.035)3018

0.5940.1410.020.0050.4060.07

0.1650.0420.0170.0570.00070.6380.072

14361.81.81.80.751.8

/hr/hr/hr/hr

mg/hramg/l

mg/hramg/L

mg/hramg/L

mg/hramg/L

/hrb/hrb/hrb

mg/hramg/L/hrb

mg/hramg/L/hrb

/hrb/hrb/hrb

mg/hramg/Lmg/hramg/L

01* (0.27-1.1)

100

39* (39-60.)0.250.035* (0.035-0.1)

1 * (0.5-1.5)0.25

10

10025

100.5

0* (0.-0.1)100.035* (0.035-0.1)

1001,000

0.035

0.0150.40.5

3.0.25

250250

0.2380.20.65


0.35* (0.19-0.35)2415

70.24* (18.)16.5* (13)

0.5940.1530.030.0210.4060.07

0.6990.1810.0460.0250.3010.052

0.1060.030.0070.0340.00070.7180.124

0.1010.0170.0040.0260.00070.6510.214

18.527.51.31.31.30.51.3

9.2736.86.86.82.36.8

00.6* (0.2-0.6)

100

12* (12.-20.)0.25* (0.25-18.)0.02* (0.02-0.06)

0.12* (0.08-0.25)0.25

0.110

100* (35.- 5O.)25.

0

10100

10* (6.-10.)1.5* (1.5-3.)0.08

25 (15.-25.)250

0.110

525

l03

0.10.0.023

17301000

0.023

0.0153719

0* (0.-0.3)0.5

0.1 * (0-0.1)100.05

501,000

0.05

0.015171.1

0.30.25

250250

0.250.20.65

0.00451.5

250250

0.10.10.65

FCLARA - 0.1 0.1 0.1

*Default value used for calculation of risk assessment dose metrics; different values (in parentheses) were used for comparison with pharmacokinetic studies (see text). "Scaled by body weight to the 314power. bScaled by body weight to the -1/4 power.

288


were based on the recommendations of theInternational Life Sciences Institute RiskScience Institute Working Group onPhysiological Parameters (145). The excep-tions were the cardiac output (QCC) in themouse, based on the recommendations ofArms and Travis (146), and the alveolar venti-lation (QPC) in the human, obtained fromAstrand and Rodahl (147). In the model, thetissue volumes and blood flows for the gut,liver, and tracheobronchial region are sub-tracted from the values shown for all rapidlyperfused tissues to obtain the parameters forthe rapidly perfused tissue compartmentshown in Figure 2; those for the fat are sub-tracted from the values shown for all slowlyperfused tissues to obtain the parameters forthe slowly perfused tissue compartment. Thekidney volume shown in the table is used onlyin calculations for the kidney dose surrogate; asshown in Figure 2, the kidney is not describedseparately in the parent chemical model.

The partition coefficients for TCE wereobtained from the work of Fisher and Allen(25,94) and Fisher et al. (93); the partitioncoefficients for the gut and tracheobronchialtissues were assumed to be the same as thosereported for the richly perfused tissues. Theoral uptake parameters were estimated fromdata on the appearance of TCE and itsmetabolites in the blood following corn oilgavage in mice and rats (50). For some para-meters, identified in Table 1, values chosenfor calculating risk assessment dose metricswere different from those chosen to repro-duce pharmacokinetic data. For example,human dose metrics were calculated using avalue for QPC of 24, which corresponds tothe U.S. EPA's standard assumption of a totalventilation rate of20 m3/day; the correspond-ing value for QCC of 16.5 was estimatedfrom Astrand and Rodahl (147). Similarly,animals used in pharmacokinetic studies tendto have lower average body weights than ani-mals used in cancer bioassays, so bodyweights appropriate to each case were used inthe model.

Parameters for oxidative metabolism.Initial values for the metabolic parameters forTCE were obtained from the work of Fisherand Allen (25,94) and Fisher et al. (93); how-ever, the metabolic and clearance parametersfor TCA, TCOH, and DCA were derived pri-marily on the basis of fitting the pharmacoki-netic data depicted in Figures 4-17. Whenpossible, parameters were also estimated fromindependent studies; for example, data fromrodents and humans dosed with TCA wereused to estimate the volumes of distributionand urinary excretion ofTCA (93,109). Sincethe model contains a large number of meta-bolic and clearance parameters, many ofwhich are highly correlated, the parameter val-ues estimated by this process (i.e., the kinetic

parameters for TCA, TCOH, and DCA)cannot be considered to be unequivocallyidentified. However, an additional biologicalconstraint was applied during the modeling bydemanding that all parameters be essentiallyconstant across exposure scenarios within agiven species and (to the extent justified bythe experimental data) across species. Thisconstraint greatly reduces the likelihood thatalternative parameterizations could demon-strate equivalent success in reproducing theentire body of data. Another constraint on theparameterization not obvious from the figuresis that of the total TCOH extractable fromthe blood, roughly 80% is present as freeTCOH in the human (110), whereas roughly70-85% is present as glucuronide in therodent (113,126). In the figures in this arti-cle, the model concentrations shown representeither free TCOH (in rodents) or total (inhumans), corresponding to the experimentaldata provided.

It is informative to note the departuresfrom simple allometric expectations that wererequired on the basis of the experimental dataacross species. As with most other xenobiotics,the mouse shows a relatively greater and morevariable capacity (VMC) for oxidative metab-olism ofTCE than the rat and human. Also inkeeping with evidence from other P450 sub-strates, the affinity for oxidative metabolism ofTCE in the human is roughly an order ofmagnitude less (i.e., the value ofKM is larger)than in the rodents. A striking differencebetween humans and rodents, which wasclearly demanded by the experimental data,was that the oxidation of TCOH to TCAappears to be a relatively high-affinity (smallvalue of KMO), low-capacity (small value ofVMOC) process in the rodent but low affin-ity, high capacity in the human. It may bethat this disparity reflects the involvement ofdifferent enzymes (e.g., MFO in the rodent vsADH in the human). The result of thisspecies difference is that although the modeluses a similar.value across species for PO(based on the initial split ofTCA and TCOHfrom CHL), the apparent ratio of TCA toTCOH predicted (and observed) over theentire time frame of an exposure to TCE ismuch higher in the human than in the rodent.The capacity (VMG) for glucuronidation ofTCOH in the human, on the other hand, ismuch lower than in the rodent, as reflected inthe greatly different ratios of free TCOH toglucuronide in the blood, mentioned above.The prolonged time courses ofTCOH in thehuman provide clear evidence of biliary excre-tion (KEHBC) and enterohepatic recircula-tion (KEHRC). Evidence for enterohepaticrecirculation was equivocal in the rodents,however, with recirculation being required toreproduce some data, but being contradictedby other data in the same species.

The least well-characterized portion of theoxidative metabolism pathway is the descrip-tion of the kinetics of DCA. The only speciesin which DCA has been reproduciblydetected in the blood following TCE expo-sure is the mouse, and these data were used toobtain values for the production (VMRC andKMR) and clearance (VMDC and KMDC)in the mouse. (The artifactual production ofDCA from TCA in blood samples, noted ear-lier in this report, may have compromisedsome of the data on DCA plasma concentra-tions used to parameterize the productionand clearance of DCA in the mouse.)Assuming that the affinities (KMR andKMD) are constant across species, the capaci-ties in the other species were estimated (forVMDC) from the reported half-lives of DCAacross species (122), or (for VMRC in thehuman) from data on peak DCA concentra-tions in human subjects exposed to TCE byinhalation (148). Since the clearance of DCAin the rat is actually slower than in the mouse(70), the capacity for production of DCA(VMRC) in the rat was set to the lowerhuman value rather than that of the mouse tobe consistent with the failure of investigatorsto observe DCA in the plasma of the rat fol-lowing administration of TCE (69). Therenal clearance of DCA (KUDC) wasassumed to be the same as that observed forTCA (KUTC) in the same species. As men-tioned earlier, the most striking departurefrom allometric expectations for the kineticsofDCA is the extremely high clearance in thehuman compared to the other species.

Parameters for lung metabolism. Theparameters in the PBPK model for predictingthe lung dose metric are the capacity andaffinity for the production ofCHL (VMTBCand KMTB) and the capacity and affinity forits clearance (VMCTBC and KMCTB). Inthe model, the production ofCHL in the tra-cheobronchial region was assumed to be asso-ciated with the P450 activity in that tissue.This is the assumption that was made in thepharmacokinetic risk assessment for methyl-ene chloride (18). The approach used in thatrisk assessment was also used to obtain theparameters in this case: the affinity in the lungwas assumed to be the same as in the liver forthe same species, and the relative capacity ofthe lung compared to the liver was deter-mined on the basis of P450 activity measuredwith standard substrates (18). Based on thesedata, P450 activity falls off much more rapidlywith body weight than would be expectedfrom allometric considerations. No data wereavailable on the clearance ofCHL in the lungacross species; therefore, it was assumed to bea low-affinity, high-capacity enzyme systemsuch as ADH. The parameters in the PBPKmodel were chosen such that concentrationsofCHL in the lung of the mouse predicted by


CLEWELL ET AL.

the model were consistent with those observedin recent studies (149). It was further assumedthat the clearance of CHL in the lung scalesacross species according to allometric expecta-tions (i.e., by body weight to the 3/4 power).This assumption leads to much lower CHLconcentrations in the lungs of rats andhumans compared to mice for the same TCEexposure conditions. An alternative assump-tion would have been that the activity of theenzyme responsible for the clearance of CHLscales in the same way as P450. This assump-tion would lead to similar concentrations ofCHL in the lungs of mice, rats, and humansfor the same TCE exposure conditions.

Parameters for conjugative metabolism.The parameters in the PBPK model for pre-dicting the kidney dose metric are the pro-duction of DCVC by the GST pathway(KFC), its activation by 3-lyase (KBLC), andits clearance by N-acetyltransferase(KNATC). First-order rate constants areused because the production of metabolitesby the GST pathway is quite low, and satura-tion of enzyme capacity is unlikely. As dis-cussed earlier, the capacity and affinity ofi-lyase in the kidney have been measured inboth rats and humans (141). These datawere used to estimate the apparent first-orderrate constants (KBLC) used in the model.No data were available on the activity ofI-lyase in the mouse, so the relationshipsbetween P-lyase metabolic parameters inmice and rats reported for trichlorovinyl-cysteine derived from PERC (136) wereassumed to apply-for DCVC as well. ForN-acetyltransferase, only specific activity dataacross species are available (142). These datawere converted to the corresponding rateconstants (KNATC) by assuming the affinityof N-acetyltransferase for DCVC is the sameas that measured for ,B-lyase in the samespecies. This assumption is supported by thesimilarity of the affinities of N-acetyltrans-ferase and I-lyase for DCVC in the rat: 3.3mM and 1.6 mM, respectively (141,150).

Finally, measurements of oxidative andconjugative metabolites in the urine followingTCE exposure (143) were used to obtain

estimates of the GST pathway rate constant(KFC). The oxidative pathway was repre-sented by total excretion of TCA plusTCOH, while the conjugative pathway wasrepresented by excretion of 1,2-DCVC. Datafrom the same study on excretion of2,2-DCVC were not used. Unlike 1,2-DCVC, there was no evidence of a doseresponse for 2,2-DCVC as a function ofTCEexposure in humans or rodents; similaramounts of 2,2-DCVC were excreted forTCE exposures ranging from 40 to 160 ppm.Ignoring 2,2-DCVC is unlikely to signifi-cantly affect the risk assessment for TCE,since 1,2-DCVC is clearly the more toxic andmutagenic of the two isomers (151).

The results of this analysis are shown inTable 2. In performing this analysis, all of theparameters in the model were set at thedefault values except VMC, KM, and KFC(in particular, KBLC and KNATC were setto the values calculated as described above).The values of VMC, KM, and KFC in themodel were then varied to bring the modelinto agreement with the data for both theoxidative (MFO) and conjugative (GST)pathways. It can be seen that the model couldbe made to agree quite well with the urinarydata when allometric scaling was assumed forconjugative metabolism (i.e., using the samevalue of the scaled parameter KFC in rat andhuman). Although it was necessary to adjustKM to obtain the best agreement with theMFO pathway data, allometric scaling ofconjugative metabolism also gave the bestagreement with the GST pathway data whenthe default values for KM were used. Thisresult is consistent with the observed allo-metric scaling of the GST pathway formethylene chloride (152).

Additional data on urinary metabolite con-centrations following oral gavage of rats with50 mg/kg TCE (142), although not suitablefor comparing with the model, were consistentwith the inhalation data, suggesting that thereis not an effect due to route of exposure.Therefore, the value estimated from inhalationwas used to obtain the kidney dose metrics forthe rat for both inhalation and oral exposures.

Table 2. Estimation of glutathione pathway activity from urinary excretion data.

MFO pathwaya GST pathwayb ModelTotal urinary excretion (pmol TCA + TCOH) (pg NA-1,2-DCVC) parametersof metabolites Measured Predicted Measured Predicted VMC/KM/KFC

Rat, 6-hr inhalation40 ppm 6.9 8.9 0.001 0.001 12/18/0.01580 ppm 13.0 16.9 0.002 0.002 12/18/0.015160 ppm 33.3 30.6 0.006 0.005 12/18/0.015

Human, 6-hr inhalation40 ppm 823 943 0.074 0.074 10/3/0.01580 ppm 1,775 1,762 0.161 0.160 10/3/0.015160 ppm 3,080 3,029 0.223 0.379 10/3/0.015

Metabolites collected for 48 hr after exposure [(1431, Tables 1 and 21; reported as total pmoles TCA + TCOH excreted. bMetabolitescollected for 48 hr after exposure [(143), Figures 5 and 71; reported as total micrograms N-acetyl-1 ,2-DCVC excreted.

PBPK Model ValidationIn the strictest sense of the word, validationof a PBPK model would require testing thepredictions of the model against data notused in the development and parameteriza-tion of the model (153). Ideally, each of themodel parameters would have been esti-mated from separate experiments and theperformance of the model could then betested against pharmacokinetic data such asshown in Figures 4-17. In practice, there aresimply not enough experimental data to sep-arately identify all of the parameters in amodel as complex as the PBPK model forTCE described in this article. Moreover, asin this case, there are often no data availablewith which to validate important compo-nents of the model. Therefore, the validityof the model for its intended purpose mustbe evaluated on the basis of the comprehen-siveness of its predictive power and the rea-sonableness of the parameters used to fit thevarious data sets. The parameterization ofthe PBPK model for TCE has already beendiscussed. The ability of the model describedto reproduce data on TCE and TCA kinet-ics in the mouse, rat, and human for bothinhalation exposure and oral gavage isshown in Figures 4-17. In addition, thesefigures demonstrate the successful expansionof the Fisher and Allen model of TCE andTCA to describe TCOH kinetics in mice,rats, and humans, as well as DCA in themouse. No data suitable for plotting wereavailable for validation of the model predic-tions for CHL in the lung, DCVC in thekidney, or DCA in the human.

Figures 4-7 demonstrate the ability of thePBPK model to simulate the kinetics ofTCEand its metabolites in mice. Figure 4 comparesthe predictions of the model with experimentaldata on the concentrations of TCE in theblood and TCA in the plasma of male andfemale B6C3F1 mice exposed to 110 and 368ppm, respectively, of TCE by inhalation for4 hr (93). The model overpredicts the bloodconcentrations ofTCE during the exposure byabout 50%, but provides a good description ofthe time course for TCA in the plasma. Figure5 shows the ability of the model to simultane-ously reproduce experimental data on theblood concentrations of TCE, TCA, andTCOH in mice exposed to 1,000 mg/kg TCEby corn oil gavage (50). The model is also ableto simulate (Figure 6) the time course ofTCE,TCA, TCOH, and DCA in the B6C3F,mouse following an oral dose of 499 mg/kg inan aqueous vehicle (126). The DCA data at 6and 9 hr in Figure 6 are suspect due to prob-lems with the analytical method (129). Figure7 shows the predictions of the model for DCAdata collected under the same experimentalconditions but for oral administration of 99and 1,791 mg/kg TCE.



10 15Time (hours)

A m!E

20 25 c

0

0* TCE 0A TI%A 0

0 5 10 15 20 25 30 35Tirne (hours)

Figure 4. Comparison of predicted and experimental concentrations of TCE in bloodand TCA in plasma in B6C3F, mice exposed to TCE by inhalation. The figures showTCE-blood and TCA-plasma concentrations in (A) male mice exposed for 4 hr to 110ppm TCE vapors, and (B) female mice exposed for 4 hr to 368 ppm TCE vapors. Kineticdata are taken from Fisher et al. (93).

.An An

0 1 2 3 4 5 6 7 8 9 1

Time-(hours)

_, UV -

CDE 60.-c0

40.o 20.0on

2_)mE

0t-

0

00

2 3 4 5 6 7 8 9 10Time (hours)

T

- 20

F 15

10

0 5

0() 00 1 2 3 4 5 6 7 8 9 10

Time (hours)

0 10 20 30 40 50

Time (hours)- 35so 30._ 25.o 20.

15:02 5,000 ., . .-m r .... i . - 1 .... 1 . i ' . .

0 1 2 3 4 5 6 7 8 9 10

Time (hours)

Figure 5. Mean observed and predicted blood concentrations-of TCE (A), TCA (B), andfree TCOH (C) following corn oil gavage with 1,000 mg/kg TCE in mice. Kinetic data aretaken from Prout et al. (50).

... 1.s aww|a ||1.. Wr ss§ W ...wr0 5 10 15 20 25 30 35 40

Time (hours)10

8 * DCA

6

2D

0 .. . . ..-M I1

0 2 4 6 8 10 12 14

Time (hours)

Figure 6. Mean observed and predicted blood concentrations of TCE (A) and metabolites TCA (B), TCOH (C), and DCA(D) following an oral dose of 499 mg/kg TCE in B6C3F1 mice. Kinetic data are taken from Templin et al. (126).

Figures 8-13 show the results of exercisingthe model against similar data in the rat.

Figure 8 compares the predictions of themodel with experimental data on the concen-

trations of TCE in the blood and TCA in the

plasma of male and female F344 rats exposedto TCE by inhalation for 4 hr (93). Themodel overpredicts the blood concentrationsof TCE in the female rats during the exposureby about 50%, but provides a good description

6(Time (hours)

Figure 7. Mean observed and predicted plasma concen-trations of DCA following oral doses of 99 or 1,971mg/kg TCE in B6C3F1 mice. Kinetic data are taken fromTemplin et al. (126).

of the time course for TCA in the plasma ofthe female rats, and for both TCE and TCA inthe male rats. Figure 9 shows the ability of themodel to simultaneously reproduce experi-mental data on the blood concentrations ofTCE, TCA, and TCOH in rats exposed to1,000 mg/kg TCE by corn oil gavage (49). Asshown in Figures 10-13, the model is also ableto simuiate the time courses of TCE, TCA,and TCOH in the F344 rat following oraldoses of 100, 197, 591, and 3,000 mg/kg in anaqueous vehicle (69,113).


-_

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291

CLEWELL ET AL.

30.

.g.

1 15

1 10

5

n

0

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5 10 15 20Tien (hours)

25 30 3

0 5 10 15 20 25 30 35 40 45 50 55

Tins (hours)

Figure 8. Comparison of predicted and experimentalconcentrations of TCE in blood and TCA in plasma inF344 rats exposed to TCE by inhalation. The figuresshow (A) TCE blood concentrations in male rats exposedfor 4 hr to 529 ppm TCE vapors and TCA plasma concen-trations in male rats exposed for 4 hr to 505 ppm TCEvapors; (B) TCE blood and TCA plasma concentrations infemale rats exposed for 4 hr to 600 ppm TCE vapors.Kinetic data are taken from Fisher et al. (93).

Finally, Figures 14-17 demonstrate theability of the model to describe the humankinetics of TCE and its metabolites, TCAand TCOH. Figure 14 compares the modelpredictions with experimental data collectedon two different occasions for TCE in theblood, as well as for TCA and TCOH in theplasma and urine, following a 6-hr exposureof human subjects to 100 ppm TCE(109,110). The model provides a reasonablesimulation of the time course of TCE in theblood during the exposure and for severalhours afterward but underpredicts the long-term concentration of TCE in the blood.Model predictions for TCA in plasma andurine are close to the experimental datathroughout the experiment. The model over-predicts the early concentrations ofTCOH inthe plasma while underpredicting the laterconcentrations; however, the model predic-tions of TCOH glucuronide in the urine arevery close to the data throughout the experi-mental period. Figure 15 demonstrates theability of the model to reproduce data onmultiple exposures (in this case, 4-hr expo-sures of human subjects to 70 ppm TCErepeated daily for 5 days) (154). The modelunderpredicts the peak concentration ofTCE

in the blood in this experiment by a factor of2 but does reproduce the progressive failureof TCE concentrations to return to zero atthe end of the day after repeated exposure.The model also provides a reasonable simula-tion of the accumulation and excretion ofTCA in the plasma and urine, as well asTCOH glucuronide in the urine. Figures 16and 17 show similar results for TCE inexhaled air as well as TCA and TCOH in theurine of human subjects exposed 7 hr per dayfor 5 days to 200 ppm TCE (155), and forTCA and TCOH in the plasma and urine ofhuman subjects exposed 5 days for 6 hr to50 ppm TCE (110).

It can be readily seen from Figures 4-17that it was not possible to obtain completeagreement between the model and each of thestudies investigated using a single set of para-meters in each species. This failure undoubt-edly results from a combination of variationacross individuals and animal strains, experi-mental error, and model error. Nevertheless,given the general agreement of the modelwith a variety of data on TCE, TCA, andTCOH concentration time courses in bothrodents and humans, there can be relativelyhigh confidence in dose metrics based on the

-

m

Ec0

t00

12

1086

420

0 2 4 6 8 10 12 14 0

Time (hours)

-J

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Time (hours)

6~~~~~~~. TCOH

4

2

ow . * 1 *.. * I*.. .C

0 5 10 15 20

Time (hours)Figure 9. Mean observed and predicted blood concentrations of TCE (A), TCA (B), andfree TCOH (C) following corn oil gavage with 1,000 mg/kg TCE in rats. Kinetic data aretaken from Prout et al. (50).

-j

CD

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c

1 2 3 4

Time (hours)5

8 * TCA6

4

2 B

0 10 20 30 40 50 60

Time (hours)

0 1 2 3 4 5 6 7 8 9 10

Time (hours)Figure 10. Mean observed and predicted blood concentrations of TCE (A), TCA (B),and free TCOH (C) following an oral dose of 100 mg/kg TCE in F344 rats. Kinetic dataare taken from Templin et al. (113).


' * TCE - 529 ppm TCE/ TCA - 506 ppm TCE

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292


predictions of the PBPK model for thesechemicals. Similarly, model predictions forthe total amount of TCE metabolized perkilogram body weight were generally within afactor of 2 of data on inhalation and oralexposures of mice, rats, and humans (50,62,63,114,117). Unfortunately, as mentionedearlier, there is a lack of similar data to pro-vide confidence in the model predictions forDCVC in the kidney, CHL in the lung, andDCA in the human.

Sensitivity and UncertaintyAnalysisIn order to evaluate the level of confidencethat could be given to the calculations per-formed with the PBPK model, a series ofquantitative and qualitative analyses were per-formed to characterize the uncertainty in themodel structure, parameterization, and dosemetric selection. Since the intended use of thePBPK model is to calculate target tissue dosemetrics, any evaluation of the model shouldfocus on this aspect of the model capabilities.Therefore, both the sensitivity analysis andthe uncertainty analysis were conducted withrespect to model predictions of the target

2 4 6 8

Time (hours)

tissue dose metrics. These dose metrics arecalculated as a lifetime average daily dose(LADD). The most obvious (and most time-consuming) way to calculate a LADD withthe PBPK model is to run the model for theentire lifetime of the animal or human, repro-ducing the entire exposure scenario. Forexample, the dose metrics for the inhalationbioassay performed by Maltoni et al. (65,66)could be obtained by simulating repeatedexposures at each concentration for 7 hr perday, 5 days per week, until 78 weeks, atwhich point the exposures would be termi-nated and the simulation continued until 104weeks. Dividing the resulting dose metrics by728 days would produce the LADDs. Inpractice it is faster and sufficiently accurate torun the exposure scenario only until theweekly increase in the dose metric is constant,multiply the weekly result by the fraction oflifetime over which the exposures were con-ducted, and divide by 7 to obtain the LADD.Except for the AUC for TCA in the human,which can sometimes take more than 7 weeksto reach steady state, simulations usually needto be run for only 2 or 3 weeks to obtain dosemetrics in this fashion.

160

8120

9 40

0.

* TCE

A

0 10 20 30 40 50 60

Time (hours)

^; 70mo 60 * * TCA_E 50.o 40

, 20Y 10. Bo 0 ...i I

0 10 20 30 40 50 80 70 80

Time (hours)

E 50

030

t- 20

0~

* TCOH

C

0 10 20 30 4.0 50 60

Tune (hours)

Figure 13. Mean observed and predicted blood concen-trations of TCE (A), TCA (B), and free TCOH (C) followingan oral dose of 3,000 mg/kg TCE in F344 rats. Kineticdata are taken from Larson and Bull (69).

- 40*TCE

1-1130c

0- 20

0 10A c

10 12 14 0 5 10 15

Time (hours)

0 5 10 15 20 25 30 35 40 45 50 55

Time (hours)

- 30.

- 20I

o

0o 0

c 10 20 30

Time (hours)

20 25 30

40 50 60

0 5 10 15

Time (hours)

20 25 30

Figure 11. Mean observed and predicted blood concentrations of TCE (A), TCA (B),and free TCOH (C) following an oral dose of 197 mg/kg TCE in F344 rats. Kinetic dataare taken from Larson and Bull (69).

Figure 12. Mean observed and predicted blood concentrations of TCE (A), TCA (B),and free TCOH (C) following an oral dose of 591 mg/kg TCE in F344 rats. Kinetic dataare taken from Larson and Bull (69).


0E

c

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0)1i0)

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Bif11a

-.im

E

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0)

00c00

50

40

30

20

10

0

1-

0)Ec

0

0!

30

Time (hours)

|~~~~~~ . . 1 . . . 1

r-

F-

293

CLEWELL ET AL

10,0001,000 * TCA

10

0 c1

0 10 20 30 40 W0 60 70 80

Time (hours)

10_J

E101

<|> 0.110000.01

0 50 100

Time (hours)

2

0xwJc

02

table are defined in Table 1. Sensitivity coeffi-cients of less than 0.1 in absolute value wereomitted from the table for clarity, andcoefficients above 0.5 are oudined.

It can be seen that of the 29 parameters inthe TCE/TCA portion of the model, 12 haveessentially no impact on risk predictionsbased on the two dose metrics, and only 5have a significant impact: the alveolar ventila-tion (QPC), the capacity for metabolism ofTCE (VM), the fraction of TCA producedfrom the metabolism of TCE (PO), thevolume of distribution (VDTCAC) and rateof excretion (KUTC) of TCA. None of the

Figure 14. Mean observed and predicted kinetics of TCEand its metabolites during and after a single 6-hr expo-sure of human subjects to 100 ppm TCE. Kinetic data aretaken from Muller et al. (109) and Muller et al. (110): (A)TCE blood concentrations (mg/L); (B) TCA plasma concen-trations (mg/L); (C) cumulative urinary TCA excretion(mg); (D) total TCOH plasma concentrations (mg/L); (E)cumulative urinary TCOH excretion (mg).

_100E-J

100c

A 0B

150 200 0 50 100 150 200 250 300 350

Time (hours)

'-1 1,000..o

E 100.0

0

o° 1

0 50 100 150 200 250 300 350 400

Time (hours)0 50 100 150 200 250 300 350 400

Time (hours)

Figure 15. Mean observed and predicted kinetics of TCE and its metabolites during and after 4-hr exposures ofhuman subjects to 70 ppm TCE for 5 days. Kinetic data are taken from Monster et al. (154): (A) TCE venous bloodconcentrations (mg/L); (B) TCA plasma concentrations (mg/L); (C) cumulative urinary TCA excretion (mg); (D) cumula-tive urinary TCOH excretion (mg).

parameters are associated with sensitivitiesgreater than 1.0, indicating that there is noamplification of error from the inputs of themodel to the outputs. This is, of course, adesirable trait in a model to be used for riskassessment.

Sensitivities for the other metabolites inthe model are not shown, but the results weresimilar to those for TCA; that is, none of theparameters were associated with sensitivitiesgreater than 1 in absolute value, and (exceptfor QPC) only the parameters directly relatedto the production and dearance of a metabo-lite were associated with significant sensi-tivities (close to 1) for dose metrics based onthat metabolite.

PBPK Model Parameter UncertaintyThere are a number of ways of characterizingthe uncertainty associated with use of a PBPKmodel. The best approach depends on the levelof uncertainty. In the case of the TCE model,the level of uncertainty varies considerablyfrom one portion of the model to another.Some parameters in the model, such as thosefor TCE, TCA, and TCOH, are relatively wellestablished by data, and the uncertainties canbe addressed fairly quantitatively. Under theseconditions the preferred method for character-izing the overall model uncertainty is to per-form Monte Carlo analysis, as discussed below.On the other hand, other parameters in themodel, such as those associated with the pro-duction and clearance ofDCVC in the kidneyand CHL in the lung, are based on inadequate

E A

0.~~~~~~~~~~~~ C

0 50 100

Time (hours)150 200

10,000 Bi 1.0001. -

-9 100

t 101 C

.' I * TC

0 50 100 150 200 250 300Time (hours)

-

0 50 100 150 200 260 300 350 400 450

Time (hours)

Figure 16. Mean observed and predicted kinetics of TCEand its metabolites during and following interrupted,7-hr exposures of human subjects to 200 ppm TCE (3-hrexposure, a half-hour break, then 4-hr exposure) for 5days. Kinetic data are taken from Stewart et al. (155):(A) TCE concentration in exhaled breath (ppm); (B)cumulative urinary TCA excretion (mg); (C) cumulativeurinary TCOH excretion (mg).


PBPK Model Parameter SensitivityTable 3 shows the normalized analyticalsensitivities for the TCE and TCA parametersin the PBPK model described above. The nor-malized analytical sensitivity coefficient repre-sents the fractional change in outputassociated with a fractional change in theinput parameter. For example, if a 1% changein the input parameter results in a 2% changein the output, the sensitivity coefficient wouldbe 2.0. In Table 3, the outputs evaluated arethe dose metrics for the total amount ofTCEmetabolized per kilogram body weight andthe AUC for TCA. The parameters in the

10 10

cTCE CD 1Aol TCOHh0 .0

A * D

0.0_1

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

Time (hours) Time(hours)

1000 '~10,000*TCA E *00TCOH

c 10*vv:~

'2 10 10

00 E.. 10510 1520 2530 3540 45 50 556080 0 10 20 30 40 50 60 70 60

Time (hours) Tinle (hours)

2

I

* TCOH

e Fa D

| ~~~~D

-I , v v v -

,um m.., m.1

in nnn

294


and often conflicting data, and the uncertain-ties cannot be adequately quantified to support

such a rigorous analysis. For these aspects ofthe model, an appropriate method for charac-terizing uncertainty is to simply calculate therange of dose metrics that could reasonably beexpected given the existing data.

Liver dose metric: Monte Carlo analysis.The sensitivity analysis described above doesnot consider the potential interactionsbetween parameters; the parameters are

tested individually. Also, sensitivity analysisdoes not reflect the uncertainty associatedwith each parameter. For example, the factthat the output is highly sensitive to a partic-ular parameter is not important if the para-

meter is known exactly. To estimate thecombined impact of uncertainty regardingthe values of the various parameters, a MonteCarlo analysis was performed on an early ver-

sion of the PBPK model for a characteristicdose metric, the average daily AUC for TCAin the plasma. The version of the modeltested in this analysis was essentially identicalto that of Fisher and Allen (25) and did not

include the description of TCOH and DCAkinetics provided in the current version ofthe model. In support of the Monte Carloanalysis, the distributions of possible valuesfor each of the input parameters were esti-mated, as shown in Table 4. The MonteCarlo software (PBPK_SIM, K.S. CrumpGroup, Ruston, LA) randomly selects a set ofparameter values from the distributions forthe bioassay animal and runs the PBPKmodel to obtain dose metric values for eachof the bioassay dose groups. It then selects a

set of parameter values from the distributionsfor the human and runs the PBPK model to

obtain a dose metric value for a specifiedhuman exposure scenario. This process isrepeated the specified number of times (400in this case) until the distributions of dosemetrics have been obtained.

Table 4 lists the means and coefficients ofvariation (CV) used in a Monte Carlo uncer-

tainty analysis of the AUC-TCA dose metric.Truncated (greater than zero) normal distribu-tions were used for all parameters except thekinetic parameters, which were assumed to belog-normally distributed. The CVs for thephysiological parameters were estimated fromdata on the variability of published values(146,156). In order to maintain mass balancein the PBPK model, after sampled parametervalues for cardiac output and fractional tissueblood flows were used to calculate bloodflows to each of the tissues, total cardiac out-

put was recalculated as the sum of the indi-vidual tissue blood flows. The CVs for thepartition coefficients were based on repeateddeterminations for two other chemicals,PERC (28) and chloropentafluorobenzene(6). The CVs for the metabolic and kinetic

constants were estimated from a comparisonof reported values in the literature and byexercising the model against various data sets

to determine the identifiability of the para-

meters estimated from pharmacokinetic data.It should be understood, however, that the

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0)

.o

oc

0

0

0 50 100 150 200 250 300 350 400 450

Time (hours)

_- 10

1

0

° 0.01C.

0

0.001

most uncertain part of uncertainty analysis isquantifying uncertainty.

The results of the Monte Carlo analysisare shown in Table 5, which lists the meansand 5th and 95th percentiles for the animaland human dose metrics. For dose metrics

_ 1000

ETC100

1010

E B< 1

0 50 100 150 200 250 300 350 400 450

Time (hours)

_ 10,000 10)

E0 1,0001

100Xu 1

10.

m 10

E<

0 50 100 150 200 250 300 350 400 450 0 50 100 150 200 250 300 350 400 450

Time (hours) Time (hours)

Figure 17. Mean observed and predicted kinetics of TCE and its metabolites during and after 6-hr exposures ofhuman subjects to 50 ppm TCE for 5 days. Kinetic data are taken from Muller et al. (110): (A) TCA plasma concentra-tions (mg/L); (B) cumulative urinary TCA excretion (mg); (C) total TCOH plasma concentrations (mg/L); (D) cumulativeurinary TCOH excretion (mg).

Table 3. Normalized analytical sensitivity coefficients for PBPK model predictions of amount TCE metabolized per kgbody weight (AMET) and area under the curve for trichloroacetic acid (AUC-TCA).a

Parameter

BWQCC

QPCQFCQGCQLCQRCQSC

QTBCVFCVGCVKCVLCVRCvsc

VTBCVDTCACPBPFPGPLPRPSPTBKASVMC

KMPOKUTC

Mouse gavage Mouse inhalation Human inhalation Human ingestion(1,000 mg/kg) (1,000 ppm) (1 ppm) (1 mg/kg/day)

AMET AUC-TCA AMET AUC-TCA AMET AUC-TCA AMET AUC-TCA

-0.1

-0.3

0.3

0.30.3

-0.40.6

1.1

-0.3

0.3

0.30.3

-0.40.7

1.0-1.0

-0.20.1

0.1

0.1

0.30.1

0.7

"Units: AMET (mg/kg body weight); AUC-TCA (mg-hr/L).

1.00.1

0.1

0.1

0.30.1

0.7

1.0-0.9

-0.30.30.7

-0.10.30.1

0.3

0.1-0.2

-0.20.30.7

-0.10.30.1

-0.1

-0.90.3

-0.1

0.2-0.21.0

-0.1

-0.1

-0.1

0.1

0.2

-0.2

0.1-0.1-0.1

-0.9

0.1

0.2-0.21.0

-0.1

-, Less than 0.1 in absolute value.


* TCOH_vw ,

| ~~~~D.. .. ......... . .. ....ill ......

............................................

295

CLEWELL ET AL

Table 4. Parameter values and coefficients of variation(CV) for Monte Carlo analysis of the TCE/TCA portion ofthe PBPK model.a

Preferred value CV (%)CParameterb Mouse (M/F) Human Mouse Human

BW 0.031/0.027 70.0 11.0QPC 29.0 24.0 58.0QCC 16.5 16.5 8.5Tissue blood flows (fraction of cardiac output)

QGC 0.165 0.195 25.0QlC 0.035 0.07 96.0QFC 0.03 0.05 60.0QSC 0.25 0.24 40.0QRC 0.47 0.395 50.0QTBC 0.05 0.05 50.0

Tissue volumes (fraction of body weight)VGC 0.031 0.045 30.0VLC 0.046 0.023 6.0VFC 0.04/0.1 0.16 30.0VSC 0.553/0.513 0.48 30.0VRC 0.049 0.039 30.0VTBC 0.0007 0.0007 30.0

Partition coefficientsPB 13.2/14.3PG 2.0/1.6Pl 2.0/1.6PF 41.3/31.4PS 1.0/0.5PR 2.0/1.6PTB 2.0/1.6

9.26.86.8

73.02.36.86.8

Kinetic parametersVMC 39.0/27.6 12.0KM 0.25 1.5VDTCAC 0.238/0.176 0.1KAS 1.2/1.0 -P0 0.06/ 0.33

0.07-0.1 8dKUTC 0.035/0.125 0.023

'Based on an earlier version of the model befcTCOH and DCA were added. bDefinitions of p,same as those given in Table 1. cCoefficient100 x standard deviation/mean. dDose-depeused in this earlier version of model.

based on the average daily AUCthe plasma, the 5th and 95th pothe dose metric distributions awithin a factor of 2 to 3 of the nof the 90% confidence intervalsmetrics range over somewhat ]order of magnitude. These resultto those reported for a PBPK moylene chloride (21) and are probaltative of the uncertainty associatedose metrics in the validated poimodel such as AUC-TCOH, AUthe mouse), and total metabolism

Lung dose metric. The great4uncertainty regarding the calcullung dose metric is the lack ofmetabolic clearance of CHL iTable 6 shows the predicted lungfor the principal inhalation bioasing a dose response for lung tum4for the highest oral exposure of ththe highest rat exposures, andhuman exposure scenarios. TIexposures have been ordered accc

30.015.89.1

10.035.030.015.020.020.0

10.05.0

30.030.010.010.0

Table 5. Estimated variationa in mouse and human dose metrics for area under the curve for trichloroacetic acid(mg-hr/L).Mouse inhalationbMean5th percentile95th percentile

Mouse gavagecMean5th percentile95th percentile

Human inhalationMean5th percentile95th percentile

Human drinking waterMean5th percentile95th percentile

i100 pm495148

1,1331.169 mg/kg1,197384

2,5761 ppb0.2930.0870.630

0.0120.0030.026

30p.pDm532167

1,1792.339 mg/kg1,519481

3,381

00ppRm811271

2,033

'Based on 400-iteration Monte Carlo analysis. bConcentrations used in inhalation bioassay 165,66). 0Doses employed in oralbioassay (59).

predicted value of the LADD based on theA T Tr' C-. CrTT T- *Lr- -- ^C to ^^Ann

15.0 10.0 nut- orI[ rL. ill neI casC or neI rat anaU30.0 3010 human, two dose metric values are shown.20.0 20.0 The first number represents the dose metric30.0 30.0 calculated based on the assumption that the20.0 20.0 dearance ofCHL scales across species accord-30.0 3010 ing to allometric expectations (proportional to

body weight to the 3/4 power).20.0 30.0 The much lower dose metric values in the30.0 500 human compared to the rodent result primar-30.0 3010 ily from the assumption that the clearance of50.0 - CHL in the lung scales according to allometric30.0 30.0 expectations, whereas measured P450 activity

in the lung across species is used directly. The30.0 30.0 measured lung P450 activity falls off much

ore descriptions of more rapidly than allometric expectations, per-oarameters are the haps due to the much greater number of meta-indent values (25) bolically active Clara cells in the mouse than in

the human (71). Although no data could befound on the cross-species activity ofADH in

for TCA in the lung, ADH activity in the rat airwayercentiles of appears to be restricted to the Clara cell (157),Ire generally suggesting that the enzymes responsible for thenean, and all clearance of CHL may also fall off morefor the dose rapidly than allometric expectations.less than an The alternative dose metric calculations,ts are similar shown in parentheses in Table 6, weredel of meth- obtained under the assumption that the dear-bly represen- ance of CHL scales in the same way as lungd with other P450. Using this assumption yields dose met-rtions of the rics roughly 10-fold higher in the rat andJC-DCA (in 700-fold higher in the human. This signifi-ofTCE. cant uncertainty regarding the relative expo-est source of sure to CHL across species could be resolvedlation of the by the collection of data on CHL dearance indata on the the lung across species, similar to the datain the lung. that have been reported on P450. Of course,dose metrics as discussed in the section on the lung para-ssays provid- meters, there are also other sources of para-ors (64-66), meter uncertainty associated with the lungbe mouse, for dose metric. Additional experimental data arefor several needed before a quantitative estimate of the

he different overall uncertainty associated with this metric)rding to the could be confidently attempted.

Kidney dose metric. The overriding sourceof uncertainty regarding calculation of thekidney dose metric is the inadequate and oftenconflicting data in the literature for the con-jugative pathway. Specific data gaps includethe affinity of kidney N-acetyltransferase forDCVC in the human and the activities of theGST pathway in the rat and human. Table 7shows the predicted kidney dose metrics forthe principal bioassays providing a doseresponse for kidney tumors (65-67), for thehighest oral exposure of the mouse, and forseveral human exposure scenarios. The differ-ent exposures have been ordered according tothe predicted value of the LADD based on theproduction of the toxic thiol per gram of kid-ney tissue (KTOX). The human dose metricsshown in Table 7 are those calculated assum-ing allometric scaling of the GSH pathwayrate constant. There is, of course, additionalparameter uncertainty associated with thelimited data on the other enzymes involvedin the production, intoxication, and clear-ance of DCVC. As with the lung target tis-sue, it will not be possible to provide anaccurate assessment of the overall uncertaintyin the kidney dose metric until reproducibledata are collected.

Dose Metric Sdection UncertaintyThe pharmacokinetic dose metrics mostcommonly applied to characterize the expo-sure of a tissue to a chemical are the peakconcentration and the AUC, and these arethe principal types of dose metrics providedin the PBPK model. However, there areother possible forms for dose metrics thatmight be useful for describing nonlinearprocesses. For example, time above a criticalconcentration (TACC) has been suggested asan appropriate dose metric for the effects ofmethotrexate, whose toxicity demonstrates astrong dependence on dose rate (158). Thefollowing discussion provides a rationale for



Table 6. Lung tumor dose metrics.

Chloral in LungAUC-LADDa CMAX'

Mouse***, 600 ppmc 9.4 2.6Mouse*, 450 ppmd 7.9 1.6Mouse 1000 mg/kge 5.9 3.4Mouse**, 300 ppmc 3.7 1.0Rat, 600 ppmc 2.8 (28)i 0.3 (3.4)iMouse*, 100 ppmd 1.6 0.3Mouse, 100 ppmc 1.2 0.3Mouse, 50 ppmd 0.8 0.2Human, 100 ppmf 0.016 (10.5) 0.003 (2.2)Human, 50 ppmg 0.01 (7.0Y 0.002 (1.6)Human, 1 ppmh 0.002 (1.3) -

Human, 1 mg/Li 2 x 10-5 (0.01 y`*Significantly increased lung tumors in more than one study.`Significantly increased tumors in at least one study.*Increased tumors in at least one study (not statistically signifi-cant). 'Lifetime average daily area under the chloral concentra-tion curve in the tracheebronchial region (mg/L-hr). bMaximumconcentration achieved in the tracheobronchial region (mg/L).clnhalation, 7 hr/day, 5 days/week, 78/104 weeks (65,66).dinhalation, 7 hr/day, 5 days/week, 104 weeks (64). 'Oil gavage,5 days/week, 103 weeks (65). fOccupational exposure (8 hr/day,5 days/week, 45 years)-current PEL. Occupational exposure-current TLV-TWA. hContinuous inhalation over a lifetime.iDrinking water-lifetime continuous. iAlternate (worst-case)calculation-see text.

the pharmacokinetic dose metrics providedin the PBPK model and considers otherpossible dose metrics that could be selected.

Liver dose metric. If, as was once thought,reactive species produced during the metabo-lism ofTCE were responsible for its liver car-cinogenicity, an appropriate dose metricwould be total daily metabolism divided bythe volume of the liver (13.18). However,current information suggests that two stablemetabolites, TCA and DCA, are primarilyresponsible for the liver tumor incidenceobserved in mice dosed with TCE (69,70).The commonly accepted form of the dosemetric for the chronic interaction of a stablemetabolite with a tissue is the AUC in the tis-sue. This mathematical form implicitlyassumes that the cumulative effect of themetabolite on the tissue is linear over bothconcentration and time. In this case, the mostappropriate dose metric would reflect liver tis-sue exposure (AUC) to both TCA and DCA(69,70). If it is assumed that both DCA andTCA contribute to the carcinogenicity ofTCE in the liver, the proportion of theobserved tumor risk to assign to each metabo-lite could be based on their relative potencieswhen dosed directly. However, as mentionedearlier, data on the AUCs for DCA resultingfrom exposures to TCA [e.g., (70)] may havebeen compromised by a sampling artifact thatcould lead to overestimates of DCA concen-trations in the presence ofTCA (129), mak-ing it impossible to estimate the individualpotencies ofTCA and DCA. As a simplifyingassumption, all of the tumorigenicity ofTCEcan simply be ascribed to TCA, as wasassumed by Fisher and Allen (25). Since

Table 7. Kidney tumor dose metrics.

Reactive thiol in kidneyLADD(KTOX)a

Rat**, 1,000 mg/kgb 73.6Rat, 500 mg/kgb 32.0Rat*, 600 ppmc 19.6Mouse, 1,000 mg/kgb 13.5Rat, 300 ppmc 6.3Rat, 100 ppmc 0.23Human, 100 ppmd 0.23Human, 50 ppme 0.09Human, 1 ppmf 0.008Human, 1 mg/Lg 0.0004

`Significantly increased tumors in at least one study.*Increased tumors in at least one study (not statistically signifi-cant). 'Lifetime average daily amount (mg) reactive metabolitegenerated per gram of kidney. bOil gavage, 5 days/week, 103weeks (67). Inhalation, 7 hr/day, 5 days/week, 78/104 weeks(65,66). dOccupational inhalation (8 hr/day, 5 days/week, 45years)-current PEL. 'Occupational inhalation-currentTLV-TWA. fContinuous inhalation over a lifetime. 'Drinkingwater-lifetime continuous.

DCA has been detected in the mouse to amuch greater extent than in the human fol-lowing TCE exposure, the use of the AUCfor TCA alone as the dose metric is almostcertainly safe-sided (in the direction of overes-timating the human risk estimate) comparedto including potency-weighted AUCs forboth TCA and DCA.

Strictly speaking, the AUC for TCAshould actually be calculated for the concen-tration in the liver. However, the use of theAUC in the plasma provides a surrogate forthe liver AUC that can be validated morereadily against experimental data. Since riskestimates are based on the ratio of animal andhuman dose metrics, this effectively amountsto an assumption that the ratios of the plasmaconcentrations of the acids to their concentra-tions in the liver are constant across species.In fact, data on the binding of TCA in theplasma of rats and humans (113) suggest thatTCA in plasma is bound to a much greaterextent in the human (- 80%) than in therodent (- 50%). Based on these data, it canbe estimated that the liver-to-plasma TCAconcentration ratio in the human is about40% of the ratio in the rodent. This estimateis also consistent with the ratio of reportedrelative volumes of distribution of TCA inthe two species, which are on the order of 10and 25% of body weight in the human androdent, respectively. Thus, using the AUC ofTCA in the plasma as the dose metric pro-vides a conservative estimate of the cross-species relationship for the AUCs in the liver,tending to overestimate liver exposure toTCA in the human by about 2-fold.

Table 8 shows the predicted liver dosemetrics for the principal animal bioassaysproviding a dose response for liver tumors(59,65-67,159), for the highest rat exposuresin these same studies, and for several humanexposure scenarios. The different exposures

have been ordered according to the predictedvalue of the LADD based on the AUC forTCA. Bioassay exposures associated withLADDs for AUC-TCA of greater than1,150 were uniformly positive, whereasbioassay exposures with LADDs less than700 were uniformly negative. The highestexposures of rats produced AUC-LADDsconsiderably less than those producingtumors in the mouse, consistent with thenegative results in the rat bioassays.

The most striking feature of the results forthis target tissue compared to the lung andkidney is that two of the three highest dosemetrics were obtained for the human occupa-tional exposure scenarios. The relatively highdose metrics for AUC-TCA in the humanreflect the much slower clearance of TCAcompared to the rodent. It is interesting tonote that the rank ordering in Table 8 wouldbe different if it were based on AUC-DCA.In that case, all of the human dose metricswould be uniformly below the positive ani-mal bioassay dose metrics, reflecting the rapidclearance ofDCA in the human.

Although AUC is a standard metric fortissue exposure, other forms of the dose met-rics for DCA and TCA might be more appro-priate for their modes of action. If it ispossible that the tumorigenic effects of thesechemicals are related to some aspect of theirinteraction with a receptor, peak concentra-tions (CMAX), or TACC, might actually bemore appropriate than AUCs. Another non-linear dose metric recently discussed forreceptor-mediated effects is based on averagereceptor occupancy (160). Unfortunately, themore an attempt is made to include pharmaco-dynamic events in a dose metric, the moredifficult it becomes to collect the data neces-sary for its use. In the case of TCE, there arecurrently no experimental data available toevaluate the use of such alternative pharmaco-dynamic dose metric approaches. Of the pos-sible dose metrics, only AUC, CMAX, andTACC can be estimated from the data cur-rently available. All three of these metrics areavailable for TCA in the PBPK model.

Lung dose metric. As described earlier,tumors have been observed in the lungs ofmice exposed to TCE by inhalation. Themechanism in this case appears to be entirelydifferent from that in the liver. In a well-designed experimental effort (71), investiga-tors at ICI combined in vivo and in vitroexperiments to elucidate the mechanism ofTCE carcinogenicity in the mouse lung.

In the in vivo studies, female mice andrats were exposed to TCE at a range ofinhaled concentrations at and below theconcentrations at which lung tumors havebeen observed in mice, and the effects ofTCEin the lung were determined. A specific lesion,characterized by vacuolization of lung Clara


CLEWELL ET AL

CMAPTCA DCA

Human, 100 ppmc 5,490 0.26 413 0.03Human, 50 ppmd 2,854 0.17 215 0.02Mouse***, 600 ppme 1,748 24.0 157 5.1Mouse***, 600 ppmf 1,488 20.8 175 5.1Mouse**, 300 ppme 1,322 17.3 123 4.0Mouse**, 2,339 mg/kg9 1,270 17.0 134 5.1Mouse**, 1,739 mg/kg9 1,184 15.6 126 5.0Mouse**, 1,000 mg/kgh 1,184 15.3 111 5.0Mouse, 300 ppmf 1,135 15.1 138 4.0Mouse', 1,169 mg/kg9 1,069 14.0 115 5.0Mouse, 869 mg/kg9 986 12.6 107 4.9Mouse**, 100 ppme 798 6.7 76 1.6Mouse, 100 ppmf 687 5.8 86 1.6Rat, 1,000 mg/kgh 331 3.5 23 0.4Rat, 600 ppmf 249 2.6 23 0.4Human, 1 ppmi 303 0.025 13 0.001Human, 1 mg/Li 14 0.0011 0.6 5 xl-5`*Significantly increased lung tumors in more than one study. `Significantly increased tumors in at least one study. alifetime aver-

age daily area under the plasma concentration curve (mg-hr/L). bMaximum concentration achieved during exposure (mg/L).cOccupational inhalation (8 hr/day, 5 days/week, 45 years-current PEL. dOccupational inhalation-current TLV-TWA. Inhalation, 6hr/day, 5 days/week, 104 weeks (159). Inhalation, 7 hr/day, 5 days/week, 78/104 weeks (65,66). 'Oil gavage, 5 days/week, 78/90weeks (59). hOil gavage, 5 days/week, 103 weeks (67). ilnhalation-lifetime continuous. 'Drinking water-lifetime continuous.

cells, was observed in mice, but not rats.

There was evidence of a threshold for theClara cell effects at about 20 ppm. The major-ity of Clara cells were unaffected at 20 ppmand all enzyme markers were normal, whereasat 200 ppm most of the Clara cells showedmarked vacuolization accompanied by markedloss of CYP450 activity. Mice exposed to 100ppm CHL by inhalation displayed Clara celllesions similar to those observed with 1,000ppm TCE. In contrast to these results, onlymild effects were observed with TCOHinhaled at 100 ppm, and none were observedwith 500 mg/kg TCA given intraperitoneally.(The effects had been observed withintraperitoneally administered TCE at 2,000mg/kg.) These results suggested that CHLwas responsible for the toxicity.

In the in vitro studies conducted byOdum et al. (71), mouse lung Clara cellsmetabolized TCE to CHL, TCOH, andTCA, with CHL being the major metabolite.Significantly, no TCOH glucuronide was

detected. In comparison with mouse Claracells, mouse hepatocytes produced primarilyTCOH and its glucuronide. In both cellpreparations, a steady-state concentration ofCHL was achieved. Separate in vitro studiesdemonstrated that mouse Clara cells possess a

relatively low activity for the glucuronidationof TCOH compared either to the glu-curonidation of other substrates in the lungor to the glucuronidation ofTCOH in theliver. It has also been determined that ADH,the enzyme that converts CHL to TCOH,has low activity in the mouse lung (161),consistent with the relatively low productionobserved in the Clara cells. On the basis ofthis evidence, the investigators concluded thatthe observed acute toxicity in the lung was a

result of accumulation ofCHL in Clara cells,resulting from a limitation in the formationof TCOH and its glucuronide. The speci-ficity of this lesion for the Clara cells can berationalized in terms of their relatively highCYP450 activity, coupled with a limitedADH and glucuronosyltransferase activities.

The implications of these results for thelung tumorigenicity of TCE are 2-fold. First,the accumulation of CHL, if it does occur invivo, has clear carcinogenic implications,since CHL was genotoxic in a number ofstudies (71). Second, the recurrent toxicityobserved with intermittent exposure couldproduce increased cell proliferation, exacer-

bating the genotoxic effect. In terms of therequirements for a lung dose metric in thePBPK model of TCE, it would appear thatthe model should include, at minimum, a

description of the in situ metabolism of TCEin the Clara cell, to the extent of providingdose measures based on achieved CHL con-

centrations. Although a number of significantqualitative and quantitative uncertainties

remain concerning the carcinogenicityobserved in the lung, the use of the PBPKmodel could provide insights on the quantita-tive consistency of various mechanistichypotheses with experimental data. There donot appear to be sufficient data at this pointto support a quantitative description of thespecies-dependent pharmacokinetic doseresponse for the lung carcinogenicity.

The lung dose metric calculations shownin Table 6 can be used to evaluate the consis-tency of the CHL dose metrics with thebioassay results. As mentioned previously, theentries in the table are arranged in decreasingorder of LADD for AUC-CHL. Bioassayexposures associated with LADDs for

AUC-CHL greater than 1.5 and CMAX forCHL greater than 0.3 tended to be positive,whereas bioassay exposures with AUC-CHLLADDs less than 1.5 and CAMX for CHL lessthan 0.3 were uniformly negative. The dailypeak concentration dose metric (CMAX forCHL) appears to be more consistent with thenegative response of the rats. Neither metricexplains the fact that the oral mouse bioassayswere negative for lung tumors, suggesting thepossibility of a portal-of-entry effect.

Kidney dose metric. A variety of mecha-nisms have been identified for the kidneyeffects of halogenated hydrocarbons (162).The fact that tumors are observed only in therat might suggest that they are associated withthe male rat nephropathy described for manyhydrocarbons, in which the accumulation ofa male-rat-specific a20-globulin in proximaltubular cells leads to hyaline droplet accumu-lation, necrosis, increased cell proliferation,and cancer (163). However, a study designedspecifically to evaluate this possibility showedevidence of the hyaline droplet accumulationand increased cell replication with PERC butnot with TCE (163). It was also felt that theoxidative metabolism ofTCE was unlikely toexplain the kidney carcinogenicity in rats,since the rate of metabolism in the livergreatly exceeds that in the kidney, and noliver tumors are seen in the rat (72). An alter-native mechanism was proposed, in whichdirect conjugation of TCE with GSH in theliver was followed by further metabolism inthe kidney to a cysteine conjugate that couldthen be cleaved to a reactive intermediate inthe kidney tubular cells (131). The cysteineconjugate formed from TCE, DCVC, hasbeen shown to be highly nephrotoxic (78)and mutagenic in the Ames test (115).

As with the lung carcinogenicity of TCE,more than one mechanism may play a role inthe kidney tumors. The tumors produced inthe kidney by TCE are very rare tumors incontrol animals and do not appear to be asso-ciated with the exacerbation of spontaneousprocesses, suggesting that a genotoxic mecha-nism may be responsible. On the other hand,in the only bioassay that reported a significantincrease in kidney tumors from TCE (67),cytotoxicity was observed in the kidney atboth the low and high doses, whereas tumorswere observed only at the high dose. Kidneycytotoxicity was also reported in associationwith a nonstatistically significant incidence ofkidney tumors in another study (65,66).However, dosing of mice with DCVC indrinking water for 46 weeks produced clearevidence of toxicity at 87 weeks but no evi-dence of tumors (164). The significance ofthis result is enhanced by the observation thatactivation ofDCVC in the kidney appears tobe much greater in the mouse than in the rat,and the mouse also appears to be more


Table 8. Liver tumor dose metrics.

LADD (AUC)aTCA DCA

298


responsive to the induction of cell prolifera-tion by DCVC than the rat (138). Moreover,measurements of acid-labile protein adductsin the kidney associated with DCVC suggestthat the production of DCVC-derived reac-tive species in the kidney resulting from anoral dose of 1,000 mg/kg TCE may actuallybe greater in mice than in rats (137,138), andmice but not rats showed increased cell prolif-eration in the kidney in response to treatmentwith TCE at 1,000 mg/kg. Other studies inthe rat also fail to support the suggestion thatsignificant hyperplasia is produced in the kid-ney from exposure of rats to TCE (163).Thus, whether a genotoxic or cytotoxicmechanism involving DCVC is proposed, itis difficult to explain either the negativeresults of the DCVC bioassay in the mouse orthe greater sensitivity of the rat compared tothe mouse with regard to kidney tumors fromTCE. Nevertheless, a mode of action forTCE in the kidney involving mutagenicityand cytotoxicity from DCVC is the mostsupportable choice at present, especially sinceno suggestion for an alternative source of theobserved tumorigenicity has been provided inany of the studies just described.

The kidney dose metric calculationsshown in Table 7 can be used to evaluate theconsistency of the KTOX dose metric withthe bioassay results. Bioassay exposures associ-ated with KTOX LADDs greater than 15tended to be positive, whereas bioassay expo-sures with KTOX LADDs less than 15 werenegative. The mouse dose metric is wellbelow that of the rat, consistent with bioassayresults. The lower dose metric values in themouse result from the higher ratio of clear-ance (KNATC) to intoxification (KBLC) inthe mouse as compared to the rat.

Noncancer dose metrics. The issues associ-ated with the selection of dose metrics for thenoncancer toxicity of TCE are discussed in acompanion article in this same issue (44).Therefore, only a short summary of therationale for the selection of noncancer dosemetrics is included here. The relationship ofvarious noncancer dose metrics across speciesis shown in Table 9. In this table, the valuesof the dose metrics are shown for equaladministered dose. Clearly, the human equiv-alent dose or concentration for a given animalstudy depends on both the route of exposureand the dose metric chosen, which in turndepend on the mode of action assumed forthe specific end point being considered.

Neurological effects. The neurologicaleffects of short-term exposure to solvents suchas TCE are rapidly reversible, suggesting thatthey result from a physicochemical effect ofthe parent chemical on the proper function oflipophilic cellular membranes. Appropriatedose metrics for these effects would be thepeak concentration or AUC for the parent

chemical in the brain. Since tissue-bloodpartition coefficients are relatively uniformacross species, the peak concentrationin the blood can be used as a surrogate.Alternatively, in the case of TCE, an appro-priate dose metric might be the peak concen-tration for TCOH, which has been suggestedto be responsible for the observed neurologicaleffects of chloral hydrate (79).

Hepatotoxicity. Pharmacokinetic studies(73) have demonstrated that the relationshipbetween the acute hepatotoxicity of TCE andthe total production of urinary metabolites islinear, and it has been suggested that thisresult is consistent with the hypothesis thatthe toxicity is produced by reactive interme-diates (13). Based on this assumption, themost reasonable dose metric for the hepatictoxicity ofTCE would be the total amount ofmetabolism divided by the volume of theliver (18). On the other hand, a comparisonof the toxic potency for TCE and PERC (73)suggests that TCA, rather than total metabo-lism, is responsible for the liver effects ofthese chemicals (44).

Nephrotoxicity. As already discussed, thetoxicity observed in the kidney appears to bedue to metabolism of DCVC. Therefore, thecancer dose metric (KTOX) provides a usefulmetric for the kidney toxicity as well.

Immunological and developmentaleffects. Significant uncertainty exists regard-ing the appropriate dose metric for immuno-logical and developmental effects. Possiblemetrics include the peak concentrations andAUCs for TCE and TCA. In the case of fetaleffects, the dose metric would most properlybe calculated using a PBPK model with adescription of the fetus. However, dose met-rics based on maternal blood should provide areasonable surrogate for the effects of TCE,since TCE and its metabolites appear tomove readily across the placenta (91).

Table 9. Noncancer daily dose metrics for continuous expos

Pharmacodynamic Dose MetricsThe discussion in this article has beenrestricted primarily to pharmacokinetic issues.However, the line between pharmacokineticsand pharmacodynamics is ill defined, and apharmacokinetic model can often beextended somewhat into the pharmacody-namic realm. The following discussiontouches on some of the areas where there ispotential to develop improved dose metricsfor the PBPK model and that include somelevel of pharmacodynamics.

Liver. Evidence regarding the mode ofaction ofTCA and DCA could potentially beused to develop a dose metric more closelyassociated with tumorigenicity. In principle,if information on the differential responseacross species to mitogenic effects from TCAand DCA were obtained, it could be incorpo-rated into a pharmacodynamic tissue dosemetric. Possible pharmacodynamic metrics inthe case of the liver carcinogenicity of TCEmight be the expression of TGF-3 or a meas-urable suppression of cell proliferation inhepatocytes as a marker of an early responseto a presumed mitogenic signal. The use of asimilar approach, based on the observed dosedependence of hormonal response, has beenproposed for an analogous carcinogenicmechanism associated with follicular cellcarcinoma in the thyroid (165).

Lung and kidney. If the kidney and lungcarcinogenicity ofTCE is considered to resultprimarily from enhanced cell proliferationsecondary to recurrent toxicity, possible dosemetrics would include measures of cytotoxic-ity, cell death, or cell division, as has beenproposed for the liver carcinogenicity of chlo-roform (35,166). In the case of chloroform,fairly complicated metrics involving theinstantaneous rates of metabolism and distri-butions of cellular sensitivity have been sug-gested (5,35). To apply these approaches to

Mouse Rat Human

1 ppm inhalationTCE, peak concentrationa 0.028 0.028 0.020

AUCb 0.77 0.80 0.56Total metabolismc 89 79 16TCA, peak concentration 6.9 1.0 9.7AUC 164 24 230

TCOH, peak concentration 0.013 0.022 0.13AUC 0.31 0.52 3.2

KTOX, production of reactive mercaptand 0.0012 0.006 0.0061 mg/kg/day drinking water

TCE, peak concentration 0.00007 0.00048 0.009AUC 0.0016 0.011 0.19

Total metabolism (per gram liver) 17.4 28.8 33TCA, peak concentration 1.36 0.38 20.13AUC 12.6 9.15 483

TCOH, peak concentration 0.0024 0.008 0.275AUC 0.057 0.19 6.6

KTOX, production of reactive mercaptand 0.0002 0.002 0.013

"Concentration units: mg/L. bAUC unit: mg-hr/L. cTotal metabolism units: mg/kg liver. dKTOX units: mg/kg kidney.


CLEWELL ET AL.

the kidney and lung carcinogenicity of TCEwould require extensive studies similar tothose that have recently been conducted withchloroform at the Chemical IndustryInstitute of Toxicology (CIIT) (167-170).Of particular note, the dose response for cyto-toxicity in these subchronic studies withchloroform is markedly different from thatobserved in acute and in vitro studies (35). Itappears that caution must be used when thedose response for a surrogate measure of tissueresponse is derived from in vitro or short-termin vivo experiments. If exposure to a genotoxicmetabolite is also considered to be quantita-tively important in the lung and kidney targettissues, an even more complicated dose metricwould be required. The use of a dose metricbased on the product of DNA-protein cross-links and cell-labeling index has been sug-gested for evaluating the incidence of nasaltumors from formaldehyde exposure, assum-ing a carcinogenic mode of action involvingboth genotoxicity and cytotoxicity (171).Again, extensive studies paralleling CIIT'sefforts with formaldehyde would be requiredto apply this approach to TCE.

Target rIsue CorrespondenceAnother aspect of uncertainty relevant to theincorporation of pharmacokinetic modeling inrisk assessment is the question of how to dealwith a lack of correspondence of target tissuesacross species. For example, none of thetumors observed in TCE bioassays were repro-duced in both mice and rats. This behaviorcan be contrasted with that of a trans-speciescarcinogen such as vinyl chloride, which pro-duces tumors in the same target tissue (theliver) in all species tested, as well as inhumans, with similar potency (26). The lackof site correspondence for TCE in differentanimal species clearly has important implica-tions for the expectation of site correspon-dence between animals and humans (which isimplicit in the pharmacokinetic approach forrisk assessment). Nevertheless, the assumptionnecessarily underlying the application of phar-macokinetic modeling in a risk assessment forTCE would be that the human target tissuesof concern would be the same tissues identi-fied in the animal studies. As pointed out ear-lier, there is at least some suggestion fromepidemiological studies that another target tis-sue for TCE in the human could be the lym-phatic system. If sufficient evidence of a linkbetween TCE exposure and lymphoma wereobtained from epidemiological studies, butwithout sufficient dose-response informationto support a potency estimate, it is unclearhow pharmacokinetic modeling could be usedto provide an animal-based estimate unless astatistically significant dose response for lym-phoma could be demonstrated in the rodent.Even then, data would also be required on the

metabolism and mode of action of TCE inthis target tissue.

Human VariabilityStandard cancer risk assessment practiceestimates the risk for an average individual,whereas noncancer risk assessment typicallyapplies an uncertainty factor of 10 to accountfor human variability and the possibility ofsensitive subpopulations. Human variabilityplays an important role in determining theactual risk to an individual compared to theaverage risk to a population. Part of this vari-ability is pharmacokinetic and is subject toquantification. For example, the variation inthe human dose metrics for TCA presented inTable 5 primarily reflects the impact of vari-ability, as opposed to uncertainty, in humanpharmacokinetics on target tissue dose (in thiscase, for tissue exposure to TCA resultingfrom environmental exposure to TCE).

Pharmacokinetic factors affecting theresponse of an individual to the toxicity andcarcinogenicity of a chemical such as TCEinclude size, weight, condition, fat content,and level of physical activity. These factorsmodify the uptake, distribution, and elimina-tion ofTCE associated with a given exposure(172). For example, an individual with alarge proportion of fat will absorb more of achemical such as TCE and retain it longerthan a lean individual. This longer storageincreases the opportunity for metabolism tothe active species. Studies on normal humanvolunteers have shown significant variation inindividual pharmacokinetic behavior, and it isclear that this variability in pharmacokineticfactors is an important component of theoverall interindividual variability of suscepti-bility to the toxic effects of chemicals (173).

By far the most important variabilityimpacting target tissue dose is in metabolism.Four different isozymes of CYP450 have beenfound to play a role in the oxidative metabo-lism of TCE in rodents: 1A1/2, 2B1/2,2C1116, and 2E1. Of these, only 2C11/6 isnot found in humans. CYP 2E1 appears tohave the highest affinity for TCE, although theother isozymes can become important athigher concentrations (100). Sex, pregnancy,and age-related differences in metabolism canresult from normal variations in CYP 2E1 con-tent (101); increased metabolism can resultfrom the inducibility of CYP 1A1/2 (e.g., byaromatics), 2B1 (e.g., by phenobarbital), or2E1 (e.g., by ethanol) (102,115). Studies ofhuman populations have shown that the activ-ity of the CYP enzymes can vary by more thana factor of 10 between individuals (152,174,175), and that there is a genetic difference(polymorphism) between individuals withhigh activity and low activity that is associatedwith a different susceptibility to cancer (176).Genetic polymorphisms of the CYP enzymes

across racial and ethnic groups have beenobserved (177), as have quantitative differ-ences in metabolic capacity (178).

Sex differences in the excretion of TCEmetabolites have also been noted in thehuman (179), with females excreting a largerproportion of TCA and a smaller proportionof TCOH than males. The differencebetween females and males in the ratio ofTCA to TCOH excreted is greatest initially(as much as a factor of 5.5 during the first24 hr after exposure), suggesting that the dif-ference derives from a relatively greater rate ofthe production of TCA from CHL ratherthan from TCOH. The production of TCAin humans appears to be highly variable andgenerally somewhat higher than in other ani-mals. For example, in one study the produc-tion ofTCA from chloral hydrate in differentindividuals varied from 5 to 47% (107).

There are still other factors such as diseaseand hormonal status that could also affect theindividual risk from exposure to a TCE,either because of an impact on pharmacoki-netics or metabolism, or due to other interac-tions. Estrogens, for example, have beenassociated with both increased risk (for breastcancer) and decreased risk (for colon cancer)and are also metabolized by the CYP system(175). Therefore, the possibility of interac-tion with TCE exposure includes metabolicinhibition or induction as well as tumorpromotion or repression.

Pharmacokinetic and metabolic differencesalone cannot explain the overall interindividualvariation in susceptibility observed in exposedpopulations (173,180). Clearly there are other,less well understood interindividual differences,both acquired (due to environmental exposuresor disease states) and inherited (due to geneticdifferences) that are also important determi-nants of the individual risk for development oftoxicity from exposure to a chemical. However,to the extent that we can quantitatively describeand evaluate pharmacokinetic and metabolicvariation, it will become increasingly possible toestimate the range of risks in an exposed popu-lation and to identify the factors that put indi-viduals at the greatest risk.

ConclusionsThe PBPK model described in this articleprovides reasonably accurate and precise esti-mates of dose metrics based on TCE and itsmajor metabolites, TCA, TCOH, and DGA,in both experimental animals and humans.Tissue dose metrics calculated with themodel should therefore be useful in riskassessments for end points where the modeof action involves tissue exposure to thesechemicals. Other target tissue dose metricsthat can be calculated with the model,including CHL in the lung and DCVC inthe kidney, are highly uncertain due to a lack



of adequate pharmacokinetic data acrossspecies. Additional studies could greatlyreduce the uncertainty associated with thesedose metrics and make their use in risk assess-ments more viable. However, it must beunderstood that pharmacokinetics is only onedimension of the process of estimatinghuman risk from animal studies; the otherpotentially more important dimension ispharmacodynamics. Species differences inpharmacodynamics may lead to wide differ-ences in susceptibility to tumors or to othertoxic outcomes at the same target tissue doses.

AppendixEquations forTCE PBPKModd

Equations for Main (Parent Chemical)ModelConcentration ofTCE in inhaled air (m/L):

Clnh = MWE_ x CIh(ppm)24450.0

Rate of exhalation ofTCE (mg/hr):

= QPX CABdt PB

Concentration ofTCE in exhaled air (ppm):

CAB X24450.0C - PB

WTCE

Rate of change in amount of TCE in duo-denum (mglhr):

IADU = ktst x Ast - kaD. xADU-ktD. X ADU

Rate of change in amount of TCE in gut(mglhr):

dt QG X (CAB -CVG) + kpZm + kaStxAs, + kaD x AD.

Rate of change in amount of TCE in liver(mg/hr):

dALQ (C )Qdt = % X (CAB - CVI ) + Q-G

X(CVG -CVL)-RAML-KF x CVLx VL

Rate of metabolism in liver (mglhr):

RAML = -= VMaxL XCVLdt I@IL + CVL

Rate of change in amount ofTCE in stomach(mglhr):

'= TOTAL - kast x Ast - ktst x AstdtSt S St S

Rate of change in amount of TCE in othertissues (mg/hr):

dt=Q"tiQue X(CAB -CVdS,U)

Concentration ofTCE in tissue venous blood(mg/L):

cvt.-

Rate of excretion ofTCE (mg/hr):

dExTCE = ktDu x ADudt

Concentration of TCE in arterial blood(mg/L):

C _ QC XCVB+ QPX CIhAB QPQC+-

PB

Concentration of TCE in mixed venousblood (mg/L):

QF XCVF +(QL +QG)XCVL+QSP X CV5P + QRP X CVRP

C - +QTB X CVTBQC

Liver Metabolism SubmodelRate of change in amount ofTCA (mg/hr):

dATCA -POXM XJ?RAMLdt MWTCE

+ MWTC x RAOXTCOHMWTCOH TO

-RAMTCA -kuTC x ATCA

Rate of reduction ofTCA to DCA (mg/hr):

PR/MTCA = MTCA VMaxTCA x CTCAdt KMTCA + CTCA

Concentration ofTCA (mg/L):CATCACT -A

VDTCA

Rate of excretion ofTCA (mg/hr):dAExcTA = kuTC XATC

dt 4 T-

Rate of change in amount ofTCOH (mg/hr):dATO MWTHTCOH = (1.0-PO) X TCOH XRAML

dt MWTCE+kehrTCOH x AG! - RAOXTCOH-RAReTCOH - RAGITCOH

Rate of oxidation ofTCOH to TCA (mg/hr):

RAOxTCOH = dAOXTCOHTCH dtVMafxOTCOH X CTCOHKAOTCOH + CTCOH

Rate of reduction ofTCOH to DCA (mg/hr):

RAReTO = VMaxRTCOH X CTCOHARTCOH KMRTCOH + CTCOH

Rate of glucuronidation ofTCOH to TCOG(mg/hr):

RAGITCOH =VMaxGTCOH X CTCOHIKAIGTCOH + CTCOH

Concentration ofTCOH (mg/L):

C -ATCOHCTCOH =ATH

VDTCOHURate of excretion ofTCOH (mg/L):dAExcTCOH - MWTCOH xkuTCOG XATCOG

dt MWTCOG

Rate of change in amount ofTCOH (mg/hr):

dATCOG = MWTCOG x RAGITCOHdt MWTCOH

-kehbTCOGx ATCOG- kUTCOGXATCOG

Concentration ofTCOH (mg/L):

CTCOG = ATCOGG

TCOH

Rate of enterohepatic recirculation ofglucuronide (mg/hr):

t = kehbTCOG x ATCOG X MWTCOHdt MWTCOG

-kehrTcOH X AGL

Rate of change in amount ofDCA (mg/hr):dADCA - MWDCA x RAReTCOH +dt MWTCOH MWTCA

XRAMTCA-RAMDCA-kuDcA X ADCA


CLEWELL ET AL.

Rate ofdearance ofDCA (mg/hr):

RAMDCA VAIaxDCA X CDCAM~DCA + CDCA

Concentration ofDCA (mg/L):.A

CDCA DAD

VDDCWDCA

Rate of excretion ofDCA (mglhr):

AExcDCA = kuDCA x ADCAdt

LungMetabolism SubmodelConcentration of TCE in tracheobronchialvenous blood (mg/L):

RAMTCVTB = CAB

QTB

Rate of TCE metabolism in tracheobronchi(mg/hr):

RAMT=-dAMTB VMaxTBX CVTBdt 'o"TB + CVTB

Concentration of chloral in Clara cells(mg/L):

KMCTB X(~ TB IXW-VMaXCTB MWTCE

Kidney Metabolism SubmodelRate of change in amount ofDCVC (mglhr):

d4DCVC = MWDCVC X AT X CVL VLdt MWTCE

-(knat + kbl) x ADcVC

Concentration ofDCVC (mg/L):

CDO_.c ADCVCCDCVC-=VK

Rate ofdearance ofDCVC (mg/hr):

dA=NADC MWNADC x knat x ADCVC

dt MWDCVCDV

Kidney toxicity from DCVC (mglhr):

Irox =t kbl x ADcVC0 Vk

VariabisAmountsAtissue = amount of TCE in compart-

ment (mg)Achemical = amount of chemical (mg)AE.c<chemical = amount of chemical excreted

in urine (mg)Amtissue = amount ofTCE metabolized

in compartment (mg)AMchemical = amount of chemical metabo-

lized (mg)AOXTCOH = amount ofTCOH oxidized

(mg)AReTCOH = amount of TCOH reduced

(mg)AglTCOH = amount of TCOH glucuro-

nidated (mg)Rates

RAMtissue = rate of metabolism ofTCE in

compartment (mg)RAMchemical = rate of metabolism of chemi-

cal (mg)RAOXTCOH = rate of oxidation of TCOH

(mg)RAReTCOH = rate of reduction of TCOH

(mg)RAGITCOH = amount of glucuronidation of

TCOH (mg)ConcentrationsCtissue = concentration of TCE in

compartment (mg/L)Cchemica = concentration of chemical

(mg/L)CVtissue = venous concentration ofTCE

in compartment (mg/L)ParametersFlow ratesQCQ£issue

QPVolumesVtissueVDchemical

PartitionsPB

Ptissue

Rate constan

= cardiac output (L/hr)= blood flow to compartment

(L/hr)= pulmonary ventilation (LIhr)

= volume of compartment (kg)= volume of distribution for

chemical (kg)

= blood to air partition coeffi-cient

= tissue to blood partition coef-ficient

Its

katiue = oral uptake rate from com-

partment (/hr)kehbTcOG = biliary excretion rate of

TCOG (Ihr)kehrTcoH = enterohepatic recirculation

rate for TCOH (/hr)kpZero = input rate for TCE in drink-

ing water (mg/hr)kttissue = transfer rate from compart-

ment (/hr)kuchemica = urinary excretion rate of

chemical (/hr)

Parameters (cont'd)Metabolism parameters

PO = percent oxidation of chloralKF = rate of production of DCVC

from TCE (/hr)kbl = rate of metabolism of CDVC

by P-lyase (/hr)knat = clearance rate of DCVC by

NAT (/hr)Vmaxtissue = capacity for metabolism of

TCE in compartment (mg/hr)VMaxCtissue = capacity for chloral clearance

in compartment (mg/hr)VMaxchemical = capacity for metabolism of

chemical (mg/hr)VMarGrCOH= capacity for glucuronidation

ofTCOH (mg/hr)VMaxTCOH= capacity for oxidation of

TCOH (mg/hr)VMaxRTCOH = capacity for reduction of

TCOH (mg/hr)Kmtissue = affinity for metabolism of

TCE in compartment (mg/L)KMCudssue = affinity for chloral clearance in

compartment (mg/L)KMchemica1 = affinity for metabolism of

chemical (mg/L)KMGTCOH = affinity for glucuronidation of

TCOH (mg/L)KMOTCOH = affinity for oxidation of

TCOH (mg/L)KMRTCOH = affinity for reduction of

TCOH (mg/L)OtherKTox = metric for kidney cytotoxicity

from DCVCMWchemical = molecular weight of chemical

(mg/mole)TOTAL = total oral dose ofTCE (mg)Compartments (denoted by "tissue'"AB = arterial bloodDu = duodenumExh = exhaled air

F = fatG = gut tissueGL = gut lumenInh = inhaled airL = liverRP = rapidly perfused tissuesSP = slowly perfused tissuesSt = stomachTB = tracheo-bronchiVB = venous bloodChemicals (denoted by "chemical')ChlDCADCVCNADCTCATCETCOGTCOH

= chloral= dicloroacetic acid= dichlorovinylcysteine= N-acetyl-DCVC= trichloroacetic acid= trichlorethylene= TCOH glucuronide= trichloroethanol


Abbreviations Used in Equations

302


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