Frédéric Y. Bois

Physiological Pharmacokinetic Analysis Using Population Modeling and Informative Prior Distributions

Abstract We describe a general approach using Bayesian analysis for the estimation of parameters in physiological pharmacokinetic models. The chief statistical difficulty in estimation with these models is that any physiological model that is even approximately realistic will have a large number of parameters, often comparable to the number of observations in a typical pharmacokinetic experiment (e.g., 28 measurements and 15 parameters for each subject). In addition, the parameters are generally poorly identified, akin to the well-known ill-conditioned problem of estimating a mixture of declining exponentials. Our modeling includes (a) hierarchical population modeling, which allows partial pooling of information among different experimental subjects; (b) a pharmacokinetic model including compartments for well-perfused tissues, poorly perfused tissues, fat, and the liver; and (c) informative prior distributions for population parameters, which is possible because the parameters represent real physiological...

PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals.

Generic PBPK models, applicable to a large number of substances, coupled to parameter databases and QSAR modules, are now available for predictive modelling of inter-individual variability in the absorption, distribution, metabolism and excretion of environmental chemicals. When needed, Markov chain Monte Carlo methods and multilevel population models can be jointly used for a Bayesian calibration of a PBPK model, to improve our understanding of the determinants of population heterogeneity and differential susceptibility. This article reviews those developments and illustrates them with recent applications to environmentally relevant questions.

Application of integrated transcriptomic, proteomic and metabolomic profiling for the delineation of mechanisms of drug induced cell stress.

High content omic techniques in combination with stable human in vitro cell culture systems have the potential to improve on current pre-clinical safety regimes by providing detailed mechanistic information of altered cellular processes. Here we investigated the added benefit of integrating transcriptomics, proteomics and metabolomics together with pharmacokinetics for drug testing regimes. Cultured human renal epithelial cells (RPTEC/TERT1) were exposed to the nephrotoxin Cyclosporine A (CsA) at therapeutic and supratherapeutic concentrations for 14days. CsA was quantified in supernatants and cellular lysates by LC-MS/MS for kinetic modeling. There was a rapid cellular uptake and accumulation of CsA, with a non-linear relationship between intracellular and applied concentrations. CsA at 15μM induced mitochondrial disturbances and activation of the Nrf2-oxidative-damage and the unfolded protein-response pathways. All three omic streams provided complementary information, especially pertaining to Nrf2 and ATF4 activation. No stress induction was detected with 5μM CsA; however, both concentrations resulted in a maximal secretion of cyclophilin B. The study demonstrates for the first time that CsA-induced stress is not directly linked to its primary pharmacology. In addition we demonstrate the power of integrated omics for the elucidation of signaling cascades brought about by compound induced cell stress.

Bioequivalence: Performance of Several Measures of Extent of Absorption

The highest point of the plasma concentration-time profile, Cmax , is currently used by regulatory agencies to assess the rate of drug absorption after single dose administration of oral products. It is, however, quite insensitive, and a number of new measures of rate have been proposed. Using simulations, several approaches toward measuring rate were tested. A set of model scenarios for drugs with typical mean characteristics and statistical distributions was investigated. Using different kinetic models of disposition, the time course of the concentration in plasma was simulated. Intraindividual and interindividual variability and assay error were modeled using Monte Carlo techniques. The accuracy, precision, and ease of use of the various measures of rate were evaluated by simulating crossover design clinical trials and then determining the probability of declaring bioequivalence as a function of differences in rates of absorption between test and reference formulations. All of the rate measures tested showed a degree of insensitivity to changes in rate and no universally superior measure was found. Indeed, the main conclusion is that the choice of a measure should be based on simulations of the particular situation in a bioequivalence trials.

Metabolomics in toxicology and preclinical research.

Metabolomics, the comprehensive analysis of metabolites in a biological system, provides detailed information about the biochemical/physiological status of a biological system, and about the changes caused by chemicals. Metabolomics analysis is used in many fields, ranging from the analysis of the physiological status of genetically modified organisms in safety science to the evaluation of human health conditions. In toxicology, metabolomics is the -omics discipline that is most closely related to classical knowledge of disturbed biochemical pathways. It allows rapid identification of the potential targets of a hazardous compound. It can give information on target organs and often can help to improve our understanding regarding the mode-of-action of a given compound. Such insights aid the discovery of biomarkers that either indicate pathophysiological conditions or help the monitoring of the efficacy of drug therapies. The first toxicological applications of metabolomics were for mechanistic research, but different ways to use the technology in a regulatory context are being explored. Ideally, further progress in that direction will position the metabolomics approach to address the challenges of toxicology of the 21st century. To address these issues, scientists from academia, industry, and regulatory bodies came together in a workshop to discuss the current status of applied metabolomics and its potential in the safety assessment of compounds. We report here on the conclusions of three working groups addressing questions regarding 1) metabolomics for in vitro studies 2) the appropriate use of metabolomics in systems toxicology, and 3) use of metabolomics in a regulatory context.

Population toxicokinetics of tetrachloroethylene

Abstract In assessing the distribution and metabolism of toxic compounds in the body, measurements are not always feasible for ethical or technical reasons. Computer modeling offers a reasonable alternative, but the variability and complexity of biological systems pose unique challenges in model building and adjustment. Recent tools from population pharmacokinetics, Bayesian statistical inference, and physiological modeling can be brought together to solve these problems. As an example, we modeled the distribution and metabolism of tetrachloroethylene (PERC) in humans. We derive statistical distributions for the parameters of a physiological model of PERC, on the basis of data from Monster et al. (1979). The model adequately fits both prior physiological information and experimental data. An estimate of the relationship between PERC exposure and fraction metabolized is obtained. Our median population estimate for the fraction of inhaled tetrachloroethylene that is metabolized, at exposure levels exceeding current occupational standards, is 1.5% [95% confidence interval (0.52%, 4.1%)]. At levels approaching ambient inhalation exposure (0.001 ppm), the median estimate of the fraction metabolized is much higher, at 36% [95% confidence interval (15%, 58%)]. This disproportionality should be taken into account when deriving safe exposure limits for tetrachloroethylene and deserves to be verified by further experiments.

Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: The first steps

The increasing use of tissue dosimetry estimated using pharmacokinetic models in chemical risk assessments in various jurisdictions necessitates the development of internationally recognized good modelling practice (GMP). These practices would facilitate sharing of models and model evaluations and consistent applications in risk assessments. Clear descriptions of good practices for (1) model development i.e., research and analysis activities, (2) model characterization i.e., methods to describe how consistent the model is with biology and the strengths and limitations of available models and data, such as sensitivity analyses, (3) model documentation, and (4) model evaluation i.e., independent review that will assist risk assessors in their decisions of whether and how to use the models, and also model developers to understand expectations for various purposes e.g., research versus application in risk assessment. Next steps in the development of guidance for GMP and research to improve the scientific basis of the models are described based on a review of the current status of the application of physiologically based pharmacokinetic (PBPK) models in risk assessments in Europe, Canada, and the United States at the International Workshop on the Development of GMP for PBPK Models in Greece on April 27-29, 2007.

Addressing human variability in next-generation human health risk assessments of environmental chemicals.

Background: Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment. Objective: Our goal was to explore how next-generation human health risk assessments may better characterize variability in the context of the conceptual framework for the source-to-outcome continuum. Methods: This review was informed by a National Research Council workshop titled “Biological Factors that Underlie Individual Susceptibility to Environmental Stressors and Their Implications for Decision-Making.” We considered current experimental and in silico approaches, and emerging data streams (such as genetically defined human cells lines, genetically diverse rodent models, human omic profiling, and genome-wide association studies) that are providing new types of information and models relevant for assessing interindividual variability for application to human health risk assessments of environmental chemicals. Discussion: One challenge for characterizing variability is the wide range of sources of inherent biological variability (e.g., genetic and epigenetic variants) among individuals. A second challenge is that each particular pair of health outcomes and chemical exposures involves combinations of these sources, which may be further compounded by extrinsic factors (e.g., diet, psychosocial stressors, other exogenous chemical exposures). A third challenge is that different decision contexts present distinct needs regarding the identification—and extent of characterization—of interindividual variability in the human population. Conclusions: Despite these inherent challenges, opportunities exist to incorporate evidence from emerging data streams for addressing interindividual variability in a range of decision-making contexts.

Meeting Report: Moving Upstream—Evaluating Adverse Upstream End Points for Improved Risk Assessment and Decision-Making

Background Assessing adverse effects from environmental chemical exposure is integral to public health policies. Toxicology assays identifying early biological changes from chemical exposure are increasing our ability to evaluate links between early biological disturbances and subsequent overt downstream effects. A workshop was held to consider how the resulting data inform consideration of an “adverse effect” in the context of hazard identification and risk assessment. Objectives Our objective here is to review what is known about the relationships between chemical exposure, early biological effects (upstream events), and later overt effects (downstream events) through three case studies (thyroid hormone disruption, antiandrogen effects, immune system disruption) and to consider how to evaluate hazard and risk when early biological effect data are available. Discussion Each case study presents data on the toxicity pathways linking early biological perturbations with downstream overt effects. Case studies also emphasize several factors that can influence risk of overt disease as a result from early biological perturbations, including background chemical exposures, underlying individual biological processes, and disease susceptibility. Certain effects resulting from exposure during periods of sensitivity may be irreversible. A chemical can act through multiple modes of action, resulting in similar or different overt effects. Conclusions For certain classes of early perturbations, sufficient information on the disease process is known, so hazard and quantitative risk assessment can proceed using information on upstream biological perturbations. Upstream data will support improved approaches for considering developmental stage, background exposures, disease status, and other factors important to assessing hazard and risk for the whole population.

Comparison of three physiologically-based pharmacokinetic models of benzene disposition

We assess the goodness of fit of three physiologically based models of benzene pharmacokinetics to experimental data in Fischer-344 rats. These models were independently developed and published. Large differences in the quality of the fit are observed. In addition, the parameter values leading to acceptable fits are spread over the entire range of physiologically plausible values and can be quite different from average or standard values. On the other hand, choosing standard values for the parameters does not ensure good predictions of all tissue levels. These results emphasize the difficulty of a rigorous calibration of physiological models, and the need for further research in this area, including precise experimental determination of parameter values. Physiological models are powerful tools, but for risk assessment purposes simpler models, making equivalent use of the crucial data, are probably preferable.