Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment.

Top dose selection for repeated dose animal studies has generally focused on identification of apical endpoints, use of the limit dose, or determination of a maximum tolerated dose (MTD). The intent is to optimize the ability of toxicity tests performed in a small number of animals to detect effects for hazard identification. An alternative approach, the kinetically derived maximum dose (KMD), has been proposed as a mechanism to integrate toxicokinetic (TK) data into the dose selection process. The approach refers to the dose above which the systemic exposures depart from being proportional to external doses. This non-linear external-internal dose relationship arises from saturation or limitation of TK process(es), such as absorption or metabolism. The importance of TK information is widely acknowledged when assessing human health risks arising from exposures to environmental chemicals, as TK determines the amount of chemical at potential sites of toxicological responses. However, there have been differing opinions and interpretations within the scientific and regulatory communities related to the validity and application of the KMD concept. A multi-stakeholder working group, led by the Health and Environmental Sciences Institute (HESI), was formed to provide an opportunity for impacted stakeholders to address commonly raised scientific and technical issues related to this topic and, more specifically, a weight of evidence approach is recommended to inform design and dose selection for repeated dose animal studies. Commonly raised challenges related to the use of TK data for dose selection are discussed, recommendations are provided, and illustrative case examples are provided to address these challenges or refute misconceptions.

Moving beyond Binary Predictions of Human Drug-Induced Liver Injury (DILI) toward Contrasting Relative Risk Potential.

The hepatic risk matrix (HRM) was developed and used to differentiate lead clinical and back-up drug candidates against competitor/marketed drugs within the same pharmaceutical class for their potential to cause human drug-induced liver injury (DILI). The hybrid HRM scoring system blends physicochemical properties (Rule of Two Model: dose and lipophilicity or Partition Model: dose, ionization state, lipophilicity, and fractional carbon bond saturation) with common toxicity mechanisms (cytotoxicity, mitochondrial dysfunction, and bile salt export pump (BSEP) inhibition) that promote DILI. HRM scores are based on bracketed safety margins (<1, 1-10, 10-100, and >100× clinical Cmax,total). On the basis of well-established clinical safety experience of marketed/withdrawn drug candidates, the background analysis consists of 200 drugs from the Liver Toxicity Knowledge Base annotated as Most-DILI- (79), Less-DILI- (56), No-DILI- (47), and Ambiguous-DILI-concern (18) drugs. Scores were generated for over 21 internal and 7 external drug candidates discontinued for unacceptable incidence/magnitude of liver transaminase elevations during clinical trials or withdrawn for liver injury severity. Both hybrid scoring systems identified 70-80% Most-DILI-concern drugs, but more importantly, stratified successful/unsuccessful drug candidates for liver safety (incidence/severity of transaminase elevations and approved drug labels). Incorporating other mechanisms (reactive metabolite and cytotoxic metabolite generation and hepatic efflux transport inhibition, other than BSEP) to the HRM had minimal beneficial impact in DILI prediction/stratification. As is, the hybrid scoring system was positioned for portfolio assessments to contrast DILI risk potential of small molecule drug candidates in early clinical development. This stratified approach for DILI prediction aided decisions regarding drug candidate progression, follow-up mechanistic work, back-up selection, clinical dose selection, and due diligence assessments in favor of compounds with less implied clinical hepatotoxicity risk.

Explaining Ethnic Variability of Transporter Substrate Pharmacokinetics in Healthy Asian and Caucasian Subjects with Allele Frequencies of OATP1B1 and BCRP: A Mechanistic Modeling Analysis.

BACKGROUND: Ethnic variability in the pharmacokinetics of organic anion transporting polypeptide (OATP) 1B1 substrates has been observed, but its basis is unclear. A previous study hypothesizes that, without applying an intrinsic ethnic variability in transporter activity, allele frequencies of transporters cannot explain observed ethnic variability in pharmacokinetics. However, this hypothesis contradicts the data collected from compounds that are OATP1B1 substrates but not breast cancer resistance protein (BCRP) substrates. OBJECTIVE: The objective of this study is to evaluate a hypothesis that is physiologically reasonable and more consistent with clinical observations. METHODS: We evaluated if allele frequencies of two transporters (OATP1B1 and BCRP) are key contributors to ethnic variability. In this hypothesis, the same genotype leads to the same activity independent of ethnicity, in contrast to the previous hypothesis of intrinsic ethnic variability in OATP1B1 activity. As a validation, we perform mechanistic pharmacokinetic modeling for SLCO1B1 (encoding OATP1B1) and ABCG2 (encoding BCRP) genotyped pharmacokinetic data from 18 clinical studies with healthy Caucasian and/or Asian subjects. RESULTS: Simulations based on the current hypothesis reasonably describe SLCO1B1 and ABCG2 genotyped pharmacokinetic time course data for five transporter substrates (atorvastatin, pitavastatin, pravastatin, repaglinide, and rosuvastatin) in Caucasian and Asian populations. CONCLUSION: This hypothesis covers the observations that can (e.g., ethnic differences in rosuvastatin pharmacokinetics) or cannot (e.g., lack of differences for pitavastatin pharmacokinetics) be explained by the previous hypothesis. It helps to characterize sources of ethnic variability and provides a foundation for predicting ethnic variability in transporter substrate pharmacokinetics.

Physiologically based pharmacokinetic prediction of telmisartan in human.

A previously developed physiologically based pharmacokinetic model for hepatic transporter substrates was extended to an organic anion transporting polypeptide substrate, telmisartan. Predictions used in vitro data from sandwich culture human hepatocyte and human liver microsome assays. We have developed a novel method to calibrate partition coefficients (Kps) between nonliver tissues and plasma on the basis of published human positron emission tomography (PET) data to decrease the uncertainty in tissue distribution introduced by in silico-predicted Kps. With in vitro data-predicted hepatic clearances, published empirical scaling factors, and PET-calibrated Kps, the model could accurately recapitulate telmisartan pharmacokinetic (PK) behavior before 2.5 hours. Reasonable predictions also depend on having a model structure that can adequately describe the drug disposition pathways. We showed that the elimination phase (2.5-12 hours) of telmisartan PK could be more accurately recapitulated when enterohepatic recirculation of parent compound derived from intestinal deconjugation of glucuronide metabolite was incorporated into the model. This study demonstrated the usefulness of the previously proposed physiologically based modeling approach for purely predictive intravenous PK simulation and identified additional biologic processes that can be important in prediction.

A "middle-out" approach to human pharmacokinetic predictions for OATP substrates using physiologically-based pharmacokinetic modeling.

Physiologically based pharmacokinetic (PBPK) models provide a framework useful for generating credible human pharmacokinetic predictions from data available at the earliest, preclinical stages of pharmaceutical research. With this approach, the pharmacokinetic implications of in vitro data are contextualized via scaling according to independent physiological information. However, in many cases these models also require model-based estimation of additional empirical scaling factors (SFs) in order to accurately recapitulate known human pharmacokinetic behavior. While this practice clearly improves data characterization, the introduction of empirically derived SFs may belie the extrapolative power commonly attributed to PBPK. This is particularly true when such SFs are compound dependent and/or when there are issues with regard to identifiability. As such, when empirically-derived SFs are necessary, a critical evaluation of parameter estimation and model structure are prudent. In this study, we applied a global optimization method to support model-based estimation of a single set of empirical SFs from intravenous clinical data on seven OATP substrates within the context of a previously published PBPK model as well as a revised PBPK model. The revised model with experimentally measured unbound fraction in liver, permeability between liver compartments, and permeability limited distribution to selected tissues improved data characterization. We utilized large-sample approximation and resampling approaches to estimate confidence intervals for the revised model in support of forward predictions that reflect the derived uncertainty. This work illustrates an objective approach to estimating empirically-derived SFs, systematically refining PBPK model performance and conveying the associated confidence in subsequent forward predictions.

On translation of antibody drug conjugates efficacy from mouse experimental tumors to the clinic: a PK/PD approach.

Objectives of the present investigation were: (1) to compare three literature reported tumor growth inhibition (TGI) pharmacodynamic (PD) models and propose an optimal new model that best describes the xenograft TGI data for antibody drug conjugates (ADC), (2) to translate efficacy of the ADC Trastuzumab-emtansine (T-DM1) from mice to patients using the optimized PD model, and (3) to apply the translational strategy to predict clinically efficacious concentrations of a novel in-house anti-5T4 ADC, A1mcMMAF. First, the performance of all four of the PD models (i.e. 3 literature reported + 1 proposed) was evaluated using TGI data of T-DM1 obtained from four different xenografts. Based on the estimates of the pharmacodynamic/pharmacokinetic (PK/PD) modeling, a secondary parameter representing the efficacy index of the drug was calculated, which is termed as the tumor static concentration (TSC). TSC values derived from all four of the models were compared with each other, and with literature reported values, to assess the performance of these models. Subsequently, using the optimized PK/PD model, PD parameters obtained from different cell lines, human PK, and the proposed translational strategy, clinically efficacious doses of T-DM1 were projected. The accuracy of projected efficacious dose range for T-DM1 was verified by comparison with the clinical doses. Aforementioned strategy was then applied to A1mcMMAF for projecting its efficacious concentrations in clinic. TSC values for A1mcMMAF, obtained by fitting TGI data from 4 different xenografts with the proposed PK/PD model, were estimated to range from 0.6 to 11.5 µg mL⁻¹. Accordingly, the clinically efficacious doses for A1mcMMAF were projected retrospectively. All in all, the improved PD model and proposed translational strategy presented here suggest that appropriate correction for the clinical exposure and employing the TSC criterion can help translate mouse TGI data to predict first in human doses of ADCs.

Model-based approaches to predict drug-drug interactions associated with hepatic uptake transporters: preclinical, clinical and beyond.

INTRODUCTION: Membrane transporters have been recognized to play a key role in determining the absorption, distribution and elimination processes of drugs. The organic anion-transporting polypeptide (OATP)1B1 and OATP1B3 isoforms are selectively expressed in the human liver and are known to cause significant drug-drug interactions (DDIs), as observed with an increasing number of drugs. It is evident that DDIs involving hepatic transporters are capable of altering systemic, as well as tissue-specific, exposure of drug substrates resulting in marked differences in drug safety and/or efficacy. It is therefore essential to quantitatively predict such interactions early in the drug development to mitigate clinical risks. AREAS COVERED: The role of hepatic uptake transporters in drug disposition and clinical DDIs has been reviewed with an emphasis on the current state of the models applicable for quantitative predictions. The readers will also gain insight into the in vitro experimental tools available to characterize transport kinetics, while appreciating the knowledge gaps in the in vitro-in vivo extrapolation (IVIVE), which warrant further investigation. EXPERT OPINION: Static and dynamic models can be convincingly applied to quantitatively predict drug interactions, early in drug discovery, to mitigate clinical risks as well as to avoid unnecessary clinical studies. Compared to basic models, which focus on individual processes, mechanistic models provide the ability to assess DDI potential for compounds with systemic disposition determined by both transporters and metabolic enzymes. However, complexities in the experimental tools and an apparent disconnect in the IVIVE of transport kinetics have limited the physiologically based pharmacokinetic modeling strategies. Emerging data on the expression of transporter proteins and tissue drug concentrations are expected to help bridge these gaps. In addition, detailed characterization of substrate kinetics can facilitate building comprehensive mechanistic models.

A multiscale, mechanism-driven, dynamic model for the effects of 5α-reductase inhibition on prostate maintenance.

A systems-level mathematical model is presented that describes the effects of inhibiting the enzyme 5α-reductase (5aR) on the ventral prostate of the adult male rat under chronic administration of the 5aR inhibitor, finasteride. 5aR is essential for androgen regulation in males, both in normal conditions and disease states. The hormone kinetics and downstream effects on reproductive organs associated with perturbing androgen regulation are complex and not necessarily intuitive. Inhibition of 5aR decreases the metabolism of testosterone (T) to the potent androgen 5α-dihydrotestosterone (DHT). This results in decreased cell proliferation, fluid production and 5aR expression as well as increased apoptosis in the ventral prostate. These regulatory changes collectively result in decreased prostate size and function, which can be beneficial to men suffering from benign prostatic hyperplasia (BPH) and could play a role in prostate cancer. There are two distinct isoforms of 5aR in male humans and rats, and thus developing a 5aR inhibitor is a challenging pursuit. Several inhibitors are on the market for treatment of BPH, including finasteride and dutasteride. In this effort, comparisons of simulated vs. experimental T and DHT levels and prostate size are depicted, demonstrating the model accurately described an approximate 77% decrease in prostate size and nearly complete depletion of prostatic DHT following 21 days of daily finasteride dosing in rats. This implies T alone is not capable of maintaining a normal prostate size. Further model analysis suggests the possibility of alternative dosing strategies resulting in similar or greater effects on prostate size, due to complex kinetics between T, DHT and gene occupancy. With appropriate scaling and parameterization for humans, this model provides a multiscale modeling platform for drug discovery teams to test and generate hypotheses about drugging strategies for indications like BPH and prostate cancer, such as compound binding properties, dosing regimens, and target validation.

In vitro evaluation of hepatic transporter-mediated clinical drug-drug interactions: hepatocyte model optimization and retrospective investigation.

To assess the feasibility of using sandwich-cultured human hepatocytes (SCHHs) as a model to characterize transport kinetics for in vivo pharmacokinetic prediction, the expression of organic anion-transporting polypeptide (OATP) proteins in SCHHs, along with biliary efflux transporters, was confirmed quantitatively by liquid chromatography-tandem mass spectrometry. Rifamycin SV (Rif SV), which was shown to completely block the function of OATP transporters, was selected as an inhibitor to assess the initial rates of active uptake. The optimized SCHH model was applied in a retrospective investigation of compounds with known clinically significant OATP-mediated uptake and was applied further to explore drug-drug interactions (DDIs). Greater than 50% inhibition of active uptake by Rif SV was found to be associated with clinically significant OATP-mediated DDIs. We propose that the in vitro active uptake value therefore could serve as a cutoff for class 3 and 4 compounds of the Biopharmaceutics Drug Disposition Classification System, which could be integrated into the International Transporter Consortium decision tree recommendations to trigger clinical evaluations for potential DDI risks. Furthermore, the kinetics of in vitro hepatobiliary transport obtained from SCHHs, along with protein expression scaling factors, offer an opportunity to predict complex in vivo processes using mathematical models, such as physiologically based pharmacokinetics models.

Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data.

With efforts to reduce cytochrome P450-mediated clearance (CL) during the early stages of drug discovery, transporter-mediated CL mechanisms are becoming more prevalent. However, the prediction of plasma concentration-time profiles for such compounds using physiologically based pharmacokinetic (PBPK) modeling is far less established in comparison with that for compounds with passively mediated pharmacokinetics (PK). In this study, we have assessed the predictability of human PK for seven organic anion-transporting polypeptide (OATP) substrates (pravastatin, cerivastatin, bosentan, fluvastatin, rosuvastatin, valsartan, and repaglinide) for which clinical intravenous data were available. In vitro data generated from the sandwich culture human hepatocyte system were simultaneously fit to estimate parameters describing both uptake and biliary efflux. Use of scaled active uptake, passive distribution, and biliary efflux parameters as inputs into a PBPK model resulted in the overprediction of exposure for all seven drugs investigated, with the exception of pravastatin. Therefore, fitting of in vivo data for each individual drug in the dataset was performed to establish empirical scaling factors to accurately capture their plasma concentration-time profiles. Overall, active uptake and biliary efflux were under- and overpredicted, leading to average empirical scaling factors of 58 and 0.061, respectively; passive diffusion required no scaling factor. This study illustrates the mechanistic and model-driven application of in vitro uptake and efflux data for human PK prediction for OATP substrates. A particular advantage is the ability to capture the multiphasic plasma concentration-time profiles for such compounds using only preclinical data. A prediction strategy for novel OATP substrates is discussed.

The evolution of the OATP hepatic uptake transport protein family in DMPK sciences: from obscure liver transporters to key determinants of hepatobiliary clearance.

Over the last two decades the impact on drug pharmacokinetics of the organic anion transporting polypeptides (OATPs: OATP-1B1, 1B3 and 2B1), expressed on the sinusoidal membrane of the hepatocyte, has been increasingly recognized. OATP-mediated uptake into the hepatocyte coupled with subsequent excretion into bile via efflux proteins, such as MRP2, is often referred to as hepatobiliary excretion. OATP transporter proteins can impact some drugs in several ways including pharmacokinetic variability, pharmacodynamic response and drug-drug interactions (DDIs). The impact of transporter mediated hepatic clearance is illustrated with case examples, from the literature and also from the Pfizer portfolio. The currently available in vitro techniques to study the hepatic transporter proteins involved in the hepatobiliary clearance of drugs are reviewed herein along with recent advances in using these in vitro data to predict the human clearance of compounds recognized by hepatic uptake transporters.

Approaches for applications of physiologically based pharmacokinetic models in risk assessment.

Physiologically based pharmacokinetic (PBPK) models are particularly useful for simulating exposures to environmental toxicants for which, unlike pharmaceuticals, there is often little or no human data available to estimate the internal dose of a putative toxic moiety in a target tissue or an appropriate surrogate. This article reviews the current state of knowledge and approaches for application of PBPK models in the process of deriving reference dose, reference concentration, and cancer risk estimates. Examples drawn from previous U.S. Environmental Protection Agency (EPA) risk assessments and human health risk assessments in peer-reviewed literature illustrate the ways and means of using PBPK models to quantify the pharmacokinetic component of the interspecies and intraspecies uncertainty factors as well as to conduct route to route, high dose to low dose and duration extrapolations. The choice of the appropriate dose metric is key to the use of the PBPK models for the various applications in risk assessment. Issues related to whether uncertainty factors are most appropriately applied before or after derivation of human equivalent dose (or concentration) continue to be explored. Scientific progress in the understanding of life stage and genetic differences in dosimetry and their impacts on variability in susceptibility, as well as ongoing development of analytical methods to characterize uncertainty in PBPK models, will make their use in risk assessment increasingly likely. As such, it is anticipated that when PBPK models are used to express adverse tissue responses in terms of the internal target tissue dose of the toxic moiety rather than the external concentration, the scientific basis of, and confidence in, risk assessments will be enhanced.

Application of PBPK modeling in support of the derivation of toxicity reference values for 1,1,1-trichloroethane.

PBPK modeling has been increasingly applied in chemical risk assessment for dose, route, and species extrapolation. The use of PBPK modeling was explored in deriving toxicity reference values for 1,1,1-trichloroethane (1,1,1-TCE). This effort involved a 5-step process: (i) reconstruction of several published PBPK models for 1,1,1-TCE in the rat and human; (ii) selection of appropriate pharmacokinetic datasets for model comparison; (iii) determination of the most suitable PBPK model for supporting reference value derivation; (iv) PBPK model simulation of two critical studies to estimate internal dose metrics; and (v) calculation of internal dose metrics for human exposure scenarios for reference value derivation. The published model by Reitz et al. [Reitz, R.H., McDougal, J.N., Himmelstein, M.W., Nolan, R.J., Schumann, A.M., 1988. Physiologically based pharmacokinetic modeling with methylchloroform: implications for interspecies, high dose/low dose, and dose route extrapolations. Toxicol. Appl. Pharmacol. 95, 185-199] was judged the most suitable. This model has liver, fat, and rapidly and slowly perfused compartments, contains a saturable process for 1,1,1-TCE hepatic metabolism, and accommodates multiple exposure pathways in three species. Data from a human volunteer study involving acute inhalation exposure [Mackay, C.J., Campbell, L., Samuel, A.M., Alderman, K.J., Idzikowski, C., Wilson, H.K., Gompertz, D., 1987. Behavioral changes during exposure to 1,1,1-trichloroethane: time-course and relationship to blood solvent levels. Am. J. Ind. Med. 11, 223-239] and a chronic rat inhalation study [Quast, J.F., Calhoun, L.L., Frauson, L.E., 1988. 1,1,1-Trichloroethane formulation: a chronic inhalation toxicity and oncogenicity study in Fischer 344 rats and B6C3F1 mice. Fundam. Appl. Toxicol. 11, 611-625] were selected to simulate appropriate internal dosimetry data from which to derive reference value points of departure. Duration, route, and species extrapolations were performed based on internal dose metrics.

Assessing susceptibility from early-life exposure to carcinogens.

Cancer risk assessment methods currently assume that children and adults are equally susceptible to exposure to chemicals. We reviewed available scientific literature to determine whether this was scientifically supported. We identified more than 50 chemicals causing cancer after perinatal exposure. Human data are extremely limited, with radiation exposures showing increased early susceptibility at some tumor sites. Twenty-seven rodent studies for 18 chemicals had sufficient data after postnatal and adult exposures to quantitatively estimate potential increased susceptibility from early-life exposure, calculated as the ratio of juvenile to adult cancer potencies for three study types: acute dosing, repeated dosing, and lifetime dosing. Twelve of the chemicals act through a mutagenic mode of action. For these, the geometric mean ratio was 11 for lifetime exposures and 8.7 for repeat exposures, with a ratio of 10 for these studies combined. The geometric mean ratio for acute studies is 1.5, which was influenced by tissue-specific results [geometric mean ratios for kidney, leukemia, liver, lymph, mammary, nerve, reticular tissue, thymic lymphoma, and uterus/vagina > 1 (range, 1.6-8.1); forestomach, harderian gland, ovaries, and thyroid < 1 (range, 0.033-0.45)]. Chemicals causing cancer through other modes of action indicate some increased susceptibility from postnatal exposure (geometric mean ratio is 3.4 for lifetime exposure, 2.2 for repeat exposure). Early exposures to compounds with endocrine activity sometimes produce different tumors after exposures at different ages. These analyses suggest increased susceptibility to cancer from early-life exposure, particularly for chemicals acting through a mutagenic mode of action.

Evaluation of oral and intravenous route pharmacokinetics, plasma protein binding, and uterine tissue dose metrics of bisphenol A: a physiologically based pharmacokinetic approach.

Bisphenol A (BPA) is a weakly estrogenic monomer used in the production of polycarbonate plastic and epoxy resins, both of which are used in food contact and other applications. A physiologically based pharmacokinetic (PBPK) model of BPA pharmacokinetics in rats and humans was developed to provide a physiological context in which the processes controlling BPA pharmacokinetics (e.g., plasma protein binding, enterohepatic recirculation of the glucuronide [BPAG]) could be incorporated. A uterine tissue compartment was included to allow the correlation of simulated estrogen receptor (ER) binding of BPA with increases in uterine wet weight (UWW) in rats. Intravenous- and oral-route blood kinetics of BPA in rats and oral-route plasma and urinary elimination kinetics in humans were well described by the model. Simulations of rat oral-route BPAG pharmacokinetics were less exact, most likely the result of oversimplification of the GI tract compartment. Comparison of metabolic clearance rates derived from fitting rat i.v. and oral-route data implied that intestinal glucuronidation of BPA is significant. In rats, but not humans, terminal elimination rates were strongly influenced by enterohepatic recirculation. In the absence of BPA binding to plasma proteins, simulations showed high ER occupancy at doses without uterine effects. Restricting free BPA to the measured unbound amount demonstrated the importance of including plasma binding in BPA kinetic models: the modeled relationship between ER occupancy and UWW increases was consistent with expectations for a receptor-mediated response with low ER occupancy at doses with no response and increasing occupancy with larger increases in UWW.

Computational modeling of serum-binding proteins and clearance in extrapolations across life stages and species for endocrine active compounds.

One measure of the potency of compounds that lead to the effects through ligand-dependent gene transcription is the relative affinity for the critical receptor. Endocrine active compounds that are presumed to act principally through binding to the estrogen receptor (e.g., estradiol, genistein, bisphenol A, and octylphenol) comprise one class of such compounds. For making simple comparisons, receptor-binding affinity has been equated to in vivo potency, which consequently defines the dose-response characteristics for the compound. Direct extrapolation of in vitro estimated affinities to the corresponding in vivo system and to specific species or life stages (e.g., neonatal, pregnancy) can be misleading. Accurate comparison of the potency of endocrine active compounds requires characterization of biochemical and pharmacokinetic factors that affect their free concentration. Quantitative in vitro and in vivo models were developed for integrating pharmacokinetics factors (e.g., serum protein and receptor-binding affinities, clearance) that affect potency. Data for parameterizing these models for several estrogenic compounds were evaluated and the models exercised. While simulations of adult human or rat sera were generally successful, difficulties in describing early life stages were identified. Exogenous compounds were predicted to be largely ineffective at competing estradiol off serum-binding proteins, suggesting this was unlikely to be physiologically significant. Discrepancies were identified between relative potencies based upon modeling in vitro receptor-binding activity versus in vivo activity in the presence of clearance and serum-binding proteins. The examples illustrate the utility of this approach for integrating available experimental data from in vitro and in vivo studies to estimate the relative potency of these compounds.

Interspecies dose extrapolation for inhaled dimethyl sulfate: a PBPK model-based analysis using nasal cavity N7-methylguanine adducts.

Dimethyl sulfate (DMS) is a volatile sulfuric acid ester used principally as a methylating agent in a wide variety of industrial applications. DMS reacts with organic macromolecules by a SN2 mechanism. The weight of experimental evidence suggests that DMS possesses genotoxic and carcinogenic potential. Inhalation studies have shown that repeated exposure to DMS leads to tumors in the nasal cavity and lower respiratory tract in both rats and mice. Here we present a quantitative assessment for cross-species dose extrapolation for inhaled DMS using a physiologically based pharmacokinetic (PBPK) model. The model is designed to simulate N7-methylguanine (N7 mG) DNA adduct levels in the nasal mucosa following DMS exposure in rats and humans. This model was parameterized and predictions were tested by comparison against experimentally measured N7 mG DNA adduct levels in rat nasal mucosa following inhalation exposure to DMS. The model-based interspecies dose comparison, using N7 mG adduct levels in the nasal respiratory tissue as the appropriate dose metrics, predicts a dose rate seven times higher in rats compared to humans.

A consistent approach for the application of pharmacokinetic modeling in cancer and noncancer risk assessment.

Physiologically based pharmacokinetic modeling provides important capabilities for improving the reliability of the extrapolations across dose, species, and exposure route that are generally required in chemical risk assessment regardless of the toxic end point being considered. Recently, there has been an increasing focus on harmonization of the cancer and noncancer risk assessment approaches used by regulatory agencies. Although the specific details of applying pharmacokinetic modeling within these two paradigms may differ, it is possible to identify important elements common to both. These elements expand on a four-part framework for describing the development of toxicity: a) exposure, b) tissue dosimetry/pharmacokinetics, c) toxicity process/pharmacodynamics, and d) response. The middle two components constitute the mode of action. In particular, the approach described in this paper provides a common template for incorporating pharmacokinetic modeling to estimate tissue dosimetry into chemical risk assessment, whether for cancer or noncancer end points. Chemical risk assessments typically depend upon comparisons across species that often simplify to ratios reflecting the differences. In this paper we describe the uses of this ratio concept and discuss the advantages of a pharmacokinetic-based approach as compared to the use of default dosimetry.