ReviewToxicokinetic models and related tools in environmental risk assessment of chemicals
Graphical abstract
Introduction
Environmental risk assessment (ERA) of chemicals is a well-established regulatory and scientific research field which is undergoing constant development including harmonisation with the human risk assessment (HRA) area (Pery et al., 2013, EFSA, 2015). In this context, mechanistic approaches for risk assessment purposes offer the opportunity to bring together ecological and human dimensions particularly while taking into account taxa-specific traits resulting from ecological, physiological and biochemical features for a quantitative mechanistic understanding of toxicity (EFSA, 2014, EFSA, 2016).
Over the last decade, these approaches have been applied to ERA through a number of in vivo and in vitro models and methodologies, as well as in silico models to investigate the mode of action and adverse outcome pathways of chemicals at different levels of biological organisation (van de Waterbeemd and Gifford, 2003, Raunio, 2011, EFSA, 2014). In the same fashion as for the HRA field, the integration of mechanistic approaches in ERA can describe the processes linking exposure or environmental concentrations (external dose) to internal dose, up to toxicity in a given taxa or the whole ecosystem. Due to the complexity of those processes, the relationship between effective dose and external exposure is not necessarily linear. Two phases can be distinguished in the dose-response relationship of a chemical in an organism: the toxicokinetics (TK), or pharmacokinetics (PK) for pharmaceuticals, that is the fate of the compound in the organism including absorption, distribution, metabolism and excretion (ADME), and the toxicodynamics (TD), or pharmacodynamics (PD) for pharmaceuticals, that is the expression of the toxicity/effect of the substance. Schematically, TK can be viewed as the action of the body on the compound “what the body does to the chemical”, and TD as the action of the compound on the body “what the chemical does to the body” (Spurgeon et al., 2010). Depending on the chemical and its mode of action (TK and TD), the appropriate internal dosimetry can be related to the levels of the active substance/toxic moiety (parent or metabolite) that may reach the target tissues, i.e. the biologically effective dose (Escher and Hermens, 2004), or the quantity of metabolites produced by xenobiotic-metabolising enzymes through bioactivation (e.g. cytochrome P-450), detoxification reactions (e.g. glucuronidation) or renal excretion (Brochot et al., 2007, Dorne, 2010). TK models can be used in ERA and HRA to perform various extrapolations of external dose to internal (effective) dose: extrapolations between exposure levels, exposure routes, individuals or between species (IPCS, 2010). TK models can be used to estimate external exposures or internal dose from indirect biomarker (e.g., metabolites) measurements (EPA, 2006).
The simplest approaches to take into account TK are based on steady-state assumptions which reflect the equilibrium between accumulation of a compound and its elimination from all exposure routes i.e., both the exposure and the environmental/physiological factors affecting the uptake and the elimination of the chemical remain constant. The bioconcentration factor (BCF) is based on this assumption and is defined as the ratio of the chemical concentration in the organism over the chemical concentration in water or any other biological media at steady-state (Landrum et al., 1992). Other steady-state approaches, such as simple empirical statistical relationships (reviewed in Landrum et al. (1992)), or quantitative structure-activity relationships (QSAR) models have also been proposed and applied to the prediction of internal concentrations or TK descriptors (e.g. BCF, reviewed in Pavan et al. (2006)).
In some cases, the steady-state assumption may not hold particularly in environmental conditions under which chemical occurrence and exposure may vary considerably. For example, Ashauer et al. (2006a) review models used to predict effects of pesticides on aquatic organisms from pulses or fluctuating exposure. Historically, one of the first approach to consider time as an explicit variable in risk assessment is through the Haber's rule (for reviews see Bunce and Remillard (2003), Miller et al. (2000) and Rozman and Doull (2000)). Currently, the most common approach to describe the TK of a compound is to represent the organism as a system of compartments. A TK model aims to describe the time course of blood or tissue concentrations resulting from the ADME processes of the chemical. TK models fall generally into two classes: empirical models and models based on the physiology of the organism considered. The first TK/PK modelling was based on physiological descriptions of the human body in Teorell (1937). However, at the time, such mathematical models were too complex to be solved. Later on, research and applications then focused on simpler one-, two- or three-compartment models that were not necessarily based on physiology, but which proved to be adequate for describing and interpolating concentration-time profiles of many compounds in the blood or in other biological matrices (Tarr et al., 1990, Ritterhoff and Zauke, 1997). However, simple models such as one compartmental TK models are insufficient for substances with complex kinetics, or when inter-species extrapolations are required and research is pursued to further develop physiological models (Nichols et al., 1990, Pery et al., 2014, Brinkmann et al., 2016). These models are now recognised and recommended by national and international scientific advisory bodies particularly for evaluation of toxicological properties and chemical risk assessment in general (EPA, 2006, IPCS, 2010). Moreover, approaches based on in vitro testing and PBTK models to evaluate the toxicity of compounds were developed. Such combinatorial approaches are very promising to investigate interspecies differences in exposure patterns and toxicological sensitivity to chemicals in relation to taxa specific traits influencing physiology, level of expression of xenobiotic metabolising enzymes, and life cycle (EFSA, 2016).
This review provides a state-of-the-art review of mechanistic TK models applied to ERA, i.e. risk identification and dose-response analysis or exposure quantification, for relevant terrestrial and aquatic species using the results of extensive literature searches (ELS). Both empirical (data-based) and physiologically based TK (PBTK) models are presented with particular attention to the tools available for their parameterisation, and an overview of applications. Data gaps and further research needs are highlighted.
Section snippets
Empirical compartment TK models
Compartmental models describe toxicant movements between compartments which may or may not have a physiologic or anatomic meaning. The toxicant enters into a compartment according to an uptake rate coefficient and exits according to an elimination rate coefficient (Gibaldi and Perrier, 1982). Two sub-groups of TK compartmental models can be distinguished: (i) models based on a one-compartment assumption, according to which the chemical concentration is the same throughout the organism; (ii)
Description of models
A physiologically based toxicokinetic (PBTK) model is defined as “a model that estimates the dose to target tissue by taking into account the rate of absorption into the body, distribution and storage in tissues, metabolism and excretion on the basis of interplay among critical physiological, physicochemical and biochemical determinants” (U.S. EPA, 2006). A PBTK (or PBPK for physiologically based pharmacokinetic) model subdivides the body in compartments representing real tissues or organs
Tools for the calibration of PBTK models
A PBTK model requires a large amount of information for its parameterisation. Two subsets of parameters can be identified: (i) substance-independent parameters that represent anatomical (e.g., organ volume), physiological (e.g., cardiac output) and biochemical entities (e.g., enzyme concentrations); and (ii) substance-specific parameters which reflect the specific interactions between the body and the substance of interest. Substance-independent parameters can be, in theory, directly measured
Available software to build PBTK models
Many software can actually be used to build a PBTK model and run simulations (Schmitt and Willmann, 2005, Bouzom et al., 2012). Software can be subdivided into two types: (i) general simulation platforms which are not designed for PBTK modelling and require the user to write and code the model equations, e.g. R CRAN, GNU MCSim, Matlab® and Mathematica®; and (ii) “ready to use” tools which have been developed expressly for PBTK modelling, e.g. Simcyp® and MERLIN-Expo for humans. However, in that
Conclusions, data gaps and perspectives
This review has highlighted that a great diversity of TK tools and models have been developed to take into account TK processes for ERA applications e.g., empirical one- and multi-compartment models and mechanistic physiologically based toxicokinetic models. These models have been mostly developed for aquatic species and in particular for fish species, and have been applied to a diversity of compound classes (e.g., metals, persistent organic compounds, biocides, nanoparticles…). Whatever the
Funding information
This work was supported by the European Food Safety Authority (EFSA) and the French Ministry of Ecology and Sustainable Development [Contract number: EFSA/SCER/2014/06 and Program 190, respectively].
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