Yuching Yang

In vitro to in vivo extrapolation and species response comparisons for drug-induced liver injury (DILI) using DILIsym™: a mechanistic, mathematical model of DILI

Drug-induced liver injury (DILI) is not only a major concern for all patients requiring drug therapy, but also for the pharmaceutical industry. Many new in vitro assays and pre-clinical animal models are being developed to help screen compounds for the potential to cause DILI. This study demonstrates that mechanistic, mathematical modeling offers a method for interpreting and extrapolating results. The DILIsym™ model (version 1A), a mathematical representation of DILI, was combined with in vitro data for the model hepatotoxicant methapyrilene (MP) to carry out an in vitro to in vivo extrapolation. In addition, simulations comparing DILI responses across species illustrated how modeling can aid in selecting the most appropriate pre-clinical species for safety testing results relevant to humans. The parameter inputs used to predict DILI for MP were restricted to in vitro inputs solely related to ADME (absorption, distribution, metabolism, elimination) processes. MP toxicity was correctly predicted to occur in rats, but was not apparent in the simulations for humans and mice (consistent with literature). When the hepatotoxicity of MP and acetaminophen (APAP) was compared across rats, mice, and humans at an equivalent dose, the species most susceptible to APAP was not susceptible to MP, and vice versa. Furthermore, consideration of variability in simulated population samples (SimPops™) provided confidence in the predictions and allowed examination of the biological parameters most predictive of outcome. Differences in model sensitivity to the parameters were related to species differences, but the severity of DILI for each drug/species combination was also an important factor.

Modeling Drug- and Chemical-Induced Hepatotoxicity with Systems Biology Approaches

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales.

A Mechanistic Model of Drug‐Induced Liver Injury Aids the Interpretation of Elevated Liver Transaminase Levels in a Phase I Clinical Trial

Entolimod (CBLB502) is a Toll‐like receptor 5 agonist in development as a single‐dose countermeasure against total body irradiation. Efficacy can be assessed from animal studies, but the “Animal Rule” does not apply to safety assessment. Marked elevations of serum aminotransferases (exceeding 1,000 IU/l) were observed in some human subjects receiving Entolimod in a safety study, threatening its continued development. The percentage of total hepatocytes undergoing necrosis in these subjects was estimated using a mechanistic, multiscale, mathematical model (DILIsym). The simulations suggested that no subject in the safety study experienced more than a modest loss of hepatocytes (<5%), which was comparable to estimates from a study of healthy volunteers receiving treatment with heparins. The predicted hepatocyte loss with Entolimod was lower than that required to cause liver dysfunction or that is routinely excised from volunteers donating for autologous liver transplantation and did not likely represent a serious health risk.

Cross-Species Transcriptomic Analysis of Mouse and Rat Lung Exposed to Chloroprene

β-Chloroprene (2-chloro-1,3-butadiene), a monomer used in the production of neoprene elastomers, is of regulatory interest due to the production of multiorgan tumors in mouse and rat cancer bioassays. A significant increase in female mouse lung tumors was observed at the lowest exposure concentration of 12.8 ppm, whereas a small, but not statistically significant increase was observed in female rats only at the highest exposure concentration of 80 ppm. The metabolism of chloroprene results in the generation of reactive epoxides, and the rate of overall chloroprene metabolism is highly species dependent. To identify potential key events in the mode of action of chloroprene lung tumorigenesis, dose-response and time-course gene expression microarray measurements were made in the lungs of female mice and female rats. The gene expression changes were analyzed using both a traditional ANOVA approach followed by pathway enrichment analysis and a pathway-based benchmark dose (BMD) analysis approach. Pathways related to glutathione biosynthesis and metabolism were the primary pathways consistent with cross-species differences in tumor incidence. Transcriptional BMD values for the pathway were more similar to differences in tumor response than were estimated target tissue dose surrogates based on the total amount of chloroprene metabolized per unit mass of lung tissue per day. The closer correspondence of the transcriptional changes with the tumor response is likely due to their reflection of the overall balance between metabolic activation and detoxication reactions, whereas the current tissue dose surrogate reflects only oxidative metabolism.

Development of an integrated multi-species and multi-dose route PBPK model for volatile methyl siloxanes - D4 and D5.

There are currently seven published physiologically based pharmacokinetic (PBPK) models describing aspects of the pharmacokinetics of octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5) for various exposure routes in rat and human. Each model addressed the biological and physico-chemical properties of D4 and D5 (highly lipophilic coupled with low blood: air partition coefficient and high liver clearance) that result in unique kinetic behaviors as well differences between D4 and D5. However, the proliferation of these models resulted in challenges for various risk assessment applications when needing to determine the optimum model for estimating dose metrics. To enhance the utility of these PBPK models for risk assessment, we integrated the suite of structures into one coherent model capable of simulating the entire set of existing data equally well as older more limited scope models. In this paper, we describe the steps required to develop this integrated model, the choice of physiological, partitioning and biochemical parameters for the model, and the concordance of the model behavior across key data sets. This integrated model is sufficiently robust to derive relevant dose metrics following individual or combined dermal and inhalation exposures of workers, consumer or the general population to D4 and D5 for route-to-route, interspecies and high to low dose extrapolations for risk assessment.

Using a physiologically based pharmacokinetic model to link urinary biomarker concentrations to dietary exposure of perchlorate

Exposure to perchlorate is widespread in the United States and many studies have attempted to character the perchlorate exposure by estimating the average daily intakes of perchlorate. These approaches provided population-based estimates, but did not provide individual-level exposure estimates. Until recently, exposure activity database such as CSFII, TDS and NHANES become available and provide opportunities to evaluate the individual-level exposure to chemical using exposure surveillance dataset. In this study, we use perchlorate as an example to investigate the usefulness of urinary biomarker data for predicting exposures at the individual level. Specifically, two analyses were conducted: (1) using data from a controlled human study to examine the ability of a physiologically based pharmacokinetic (PBPK) model to predict perchlorate concentrations in single-spot and cumulative urine samples; and (2) using biomarker data from a population-based study and a PBPK model to demonstrate the challenges in linking urinary biomarker concentrations to intake doses for individuals. Results showed that the modeling approach was able to characterize the distribution of biomarker concentrations at the population level, but predicting the exposure-biomarker relationship for individuals was much more difficult. The type of information needed to reduce the uncertainty in estimating intake doses, for individuals, based on biomarker measurements is discussed.

Kinetic modeling of β-chloroprene metabolism: Probabilistic in vitro–in vivo extrapolation of metabolism in the lung, liver and kidneys of mice, rats and humans

β-Chloroprene (chloroprene) is carcinogenic in inhalation bioassays with B6C3F1 mice and Fischer rats, but the potential effects in humans have not been adequately characterized. In order to provide a better basis for evaluating chloroprene exposures and potential effects in humans, we have explored species and tissue differences in chloroprene metabolism. This study implemented an in vitro-in vivo extrapolation (IVIVE) approach to parameterize a physiologically based pharmacokinetic (PBPK) model for chloroprene and evaluate the influence of species and gender differences in metabolism on target tissue dosimetry. Chloroprene metabolism was determined in vitro using liver, lung and kidney microsomes from male or female mice, rats, and humans. A two compartment PK model was used to estimate metabolism parameters for chloroprene in an in vitro closed vial system, which were then extrapolated to the whole body PBPK model. Two different strategies were used to estimate parameters for the oxidative metabolism of chloroprene: a deterministic point-estimation using the Nelder-Mead nonlinear optimization algorithm and probabilistic Bayesian analysis using the Markov Chain Monte Carlo technique. Target tissue dosimetry (average amount of chloroprene metabolized in lung per day) was simulated with the PBPK model using the in vitro-based metabolism parameters. The model-predicted target tissue dosimetry, as a surrogate for a risk estimate, was similar between the two approaches; however, the latter approach provided a measure of uncertainty in the metabolism parameters and the opportunity to evaluate the impact of that uncertainty on predicted risk estimates.

Exposure Science: Pharmacokinetic Modeling

In this chapter, the interpretation of biomonitoring data is used as an example to demonstrate how quantitative tools are used to evaluate the likelihood of adverse effects occurring due to exposures to a substance. These tools include exposure models that aggregate all major contributing exposure factors to estimate intake, and physiologically based pharmacokinetic (PBPK) models that can estimate internal dose resulting from specified exposure intakes. Reverse dosimetry is a complementary approach that provides a useful perspective on the implications of biomonitoring data from a human health risk perspective. In either approach, exposure and PBPK models are used to integrate information on the pharmacokinetics of the compound and information on the nature of exposures to the compound. In the case of non-persistent compounds, quantitative interpretation of biomonitoring data can best be accomplished by linking PBPK models with exposure pathway models within a probabilistic framework in order to consider exposure uncertainty and human inter-individual variability. The steps in both forward and reverse dosimetry are described in this chapter, and their value is discussed in relation to the nature of the biomonitoring data that is available and the persistence characteristics of different compounds.

A constrained maximum likelihood approach to evaluate the impact of dose metric on cancer risk assessment: Application to β-chloroprene

β-Chloroprene (2-chloro-1,3-butadiene, CD) is used in the manufacture of polychloroprene rubber. Chronic inhalation studies have demonstrated that CD is carcinogenic in B6C3F1 mice and Fischer 344 rats. However, epidemiological studies do not provide compelling evidence for an increased risk of mortality from total cancers of the lung. Differences between the responses observed in animals and humans may be related to differences in toxicokinetics, the metabolism and detoxification of potentially active metabolites, as well as species differences in sensitivity. The purpose of this study was to develop and apply a novel method that combines the results from available physiologically based kinetic (PBK) models for chloroprene with a statistical maximum likelihood approach to test commonality of low-dose risk across species. This method allows for the combined evaluation of human and animal cancer study results to evaluate the difference between predicted risks using both external and internal dose metrics. The method applied to mouse and human CD data supports the hypothesis that a PBK-based metric reconciles the differences in mouse and human low-dose risk estimates and further suggests that, after PBK metric exposure adjustment, humans are equally or less sensitive than mice to low levels of CD exposure.