Reprint of PopGen: A virtual human population generator

Abstract

The risk assessment of environmental chemicals and drugs is moving towards a paradigm shift in approach which seeks the full replacement animal testing with high throughput, mechanistic, in vitro systems. This new vision will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated using in vitro, in silico and in chemico systems, can be integrated and utilised for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited for this and are obligatory in order to translate in vitro concentration–response relationships to an exposure or dose, route and duration regime in people. In this report we describe PopGen a virtual human population generator which is a user friendly, open access web-based application for the prediction of realistic anatomical, physiological and phase 1 metabolic variation in a wide range of healthy human populations. We demonstrate how PopGen can be used for QIVIVE by providing input to a PBPK model, at an appropriate level of detail, to reconstruct exposure from human biomonitoring data. We discuss how the process of exposure reconstruction from blood biomarkers, in general, is analogous to exposure or dose reconstruction from concentration–response measurements made in proposed in vitro cell based systems which are assumed to be surrogates for target organs.

Introduction

In a recent review of the current status and future prospects for the full replacement of animal testing, pharmacokinetics has been recognised as an “absolutely crucial and indispensable first step in translating the observations in vitro to the human in vivo situation” (Adler et al., 2011). This is certainly the case with the proposed new risk assessment paradigm based on the perturbation of intracellular ‘toxicity’ pathways (NRC, 2007, Judson et al., 2009, Kavlock, 2009, Bhattacharya et al., 2011, Krewski et al., 2011, Basketter et al., 2012). Toxicity pathways are innate sub-cellular biochemical pathways that may be disturbed by environmental stressors (NRC, 2007, Bhattacharya et al., 2011, Krewski et al., 2011). This new vision will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of toxicity pathways are likely to lead to adverse health effects in an intact organism (NRC, 2007, Judson et al., 2009, Kavlock, 2009, Bhattacharya et al., 2011, Krewski et al., 2011, Basketter et al., 2012). Computational systems biology pathway (CSBP) models provide the quantitative description of multiple cellular response (toxicity) pathways perturbed following exposure to environmental stressors. This approach, therefore, requires a framework, into which disparate data generated using in vitro, in silico and in chemico systems, can be integrated and utilised for quantitative in vitro-to-in vivo extrapolation (QIVIVE). Physiologically based pharmacokinetic (PBPK) models are ideally suited for this and are obligatory in order to link CSBP models, with exposure or dose (Blaauboer et al., 1996, Blaauboer et al., 1999, DeJongh et al., 1999, Blaauboer, 2001, Blaauboer, 2002, Blaauboer, 2003b, Blaauboer, 2003a, Blaauboer, 2010, Bouvier d'Yvoire et al., 2007, NRC, 2007, Jamei et al., 2009b, Bhattacharya et al., 2011, Krewski et al., 2011, Basketter et al., 2012). PBPK models are independent, structural models, comprising compartments that correspond directly and realistically to the organs and tissues of the body connected by the cardiovascular system (Rowland et al., 2004, NRC, 2007, Zhao et al., 2011).

The successful application of these approaches requires the ability to extrapolate from a perturbed toxicity pathway observed at the cellular level to an exposure or dose regime, describing route (inhalation, oral, dermal) and duration (single dose, repeated dose etc.) that could cause that effect at a population level. This is often described as ‘reverse dosimetry’ and is a similar class of inverse problem to that required to reconstruct exposure from human biological monitoring (BM) data such as hair, blood, breath and urinary biomarkers (Georgopoulos et al., 1994, Roy and Georgopoulos, 1998, Clewell et al., 2000, Lyons et al., 2008, Mosquin et al., 2009, McNally et al., 2012). Both require an adequate description of the underlying biology for a variety of exposure routes and both require the quantification of uncertainty due to population variability. In mathematical terms this is a class of ‘ill-posed problem’, where for a given set of outputs we seek the ‘initial conditions’ (Lyons et al., 2008, McNally et al., 2012). In principle PBPK and CSBP models offer a framework for solving this type of inverse problem, however there are significant barriers to overcome. A model for the ‘mean individual’ requires a parameterised mathematical model that provides an adequate description of detailed biology. A population-based model that accounts for inter-individual differences in anatomy, physiology and biochemistry at both organ and cellular levels requires that population differences be modelled; this translates to a problem of using probability distributions to quantify the uncertainty (due to population variability) in model parameters.

In the past decade there has been substantial progress in the quantification of variability in (human) anatomical, physiological and biochemical parameters (Price et al., 2003, Willmann et al., 2007, Jamei et al., 2009a). Freely available software, linked to population databases, such as physiological parameters for PBPK modelling (P³M) (P3M™ Database, 2003) and commercially available software such as PK-Sim^®1 and Simcyp² can generate realistic anatomically correct human populations. However, in the area of environmental toxicology and chemical risk assessment there is a dearth and an expectation for free to use tools (Yoon et al., 2012). The latter may be due to the significant contribution of publically funded strategic research to the development of better risk assessment methodologies. Regulatory agencies certainly advocate transparency and the ability to scrutinize and audit any new tools proposed for use in the development of a more data-informed, biologically based chemical risk assessment (Clark et al., 2004, Bouvier d'Yvoire et al., 2007, Adler et al., 2011, Basketter et al., 2012, McLanahan et al., 2012). Several international initiatives have proposed such tools as essential to effecting change in the current risk assessment paradigm (Loizou et al., 2008, WHO, 2010, Loizou and Hogg, 2011, McLanahan et al., 2012).

In this report we describe PopGen a virtual human population generator which is a free to use web-based application for the prediction of realistic anatomical, physiological and phase 1 metabolic variation in healthy human populations. Various cohorts will be generated and compared with experimental data to assess the performance of PopGen. The process of population-based exposure reconstruction will be illustrated using human BM data which, in this instance, serve as a surrogate for in vitro concentration response measurements although is analogous to QIVIVE (Fig. 1). By using data generated from controlled human volunteer studies where both the BM outputs and the exposure are known, exposure can be treated as an unknown variable to be estimated from the data, which allows the PBPK model and the QIVIVE process to be evaluated and any inadequacies addressed.