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Multilevel Modelling for Predictive Toxicology

Final Report Summary - COMPTOX (Multilevel Modelling for Predictive Toxicology)

Our modern industrial society makes extensive use of chemical substances; from plant protection products used in the production of food to the chemicals resulting from the pharmaceutical industry. Because living systems are potentially exposed to these compounds, the risk of adverse effects must be determined. These issues are of European concern and initiatives at the European level have been taken with the adoption of a new regulation on chemicals (REACH) that deals with the Registration, Evaluation, Authorisation and Restriction of chemical substances. In the near future the risk associated with tens of thousands of chemicals has to be evaluated. This policy has environmental and economical implications. It also represents a scientific challenge since it is impossible to evaluate the risk of every chemical with standard toxicity testing. Moreover, reducing the use of animal tests requires the development of alternative methods. The study of the adverse effects of drugs or chemicals on living systems is part of toxicology and thus further research in toxicology is needed to define a new paradigm in toxicity assessment. It is an important challenge facing scientists, public health decision-makers and regulatory authorities whose aim is to protect humans and the environment from exposures to chemicals and environmental stressors.

It is now accepted that toxicology has entered a new era. Previously mainly based on animal testing, toxicology is now turning to in vitro and in silico experiments. To gather and to interpret the massive amounts of experimental data generated by modern toxicology, the development of mathematical and computational tools are essential. The application of the tools of computational biology to assess the risks that chemicals pose to human health and the environment is termed Computational Toxicology and is the main topic of this project. Computational tools are necessary to produce a more-detailed understanding of the hazards and risks of a large number of chemicals, to prioritize testing and to understand a chemical's fate from environment to the organism.

Among all the tools use in computational toxicology, physiologically based pharmacokinetics (PBPK) models are expected to have an increasing importance in the future, especially due to their role central to a complete understanding of the mode of action of toxic substances. PBPK models describe the kinetic processes that monitor the fate of compounds and because of their physiologically based structure, they allow for extrapolations. In particular, prediction of the kinetics of a substance in humans can be predicted based on data obtained from animals or on numerical data coming from other well established in -silico tools (as Quantitative Structure Activity predictors). In this project, two main goals were pursued; a fundamental study that aimed at elucidating the dynamics of PBPK models and an important application of these models to the screening for human bioaccumulation potential.

PBPK models are dynamical systems that belong to the well-known class of compartmental systems. Despite the fact that PBPK are widely used in the pharmaceutical industry or for risk assessment, a precise characterization of their dynamical behaviour is still lacking. Dynamical system theory gives a sound insight into the dynamics of PBPK models and provides an identification of the parameters that monitor the temporal evolution, the steady states and the transitory regimes of PBPK models. We have shown that PBPK models follow specific dynamics where the distribution and the elimination of a drug defined a hysteretic curve. The long-term behaviour of the system is mainly determined by the elimination processes and in particular is characterized by the parameters of the Michaelis Menten equation. Consequences for the controllability and for reverse-engineering applications of PBPK models have been stressed. Moreover other properties can be derived from the mathematical structure of the model like for instance a possible reduction of the model (lumping) and an optimization of the numerical simulation based on the linearity of most of the equations.

In parallel, we developed a predictive tool that based on PBPK modelling was able to assess the human bioaccumulation potential. Human bioaccumulation is an important element in the risk assessment of chemicals. We used a generic PBPK model which, based on in vitro human liver metabolism, minimal excretion and a constant exposure, was able to assess the bioaccumulative potential of a chemical. The approach has been analysed using literature data on well-known bioaccumulative compounds and liver metabolism data from ECVAM (European Centre for the Validation of Alternative Methods) database and a subset of ToxCast (Computational Toxicology Research Program, US EPA) phase I chemical library. Our results provide further evidence that partitioning properties do not allow for a reliable screening criteria for human chemical hazard. Our model, based on a 100% intestinal absorption assumption, suggests that metabolic clearance, plasma protein-binding properties and renal excretion are the main factors in determining if bioaccumulation will occur and its amount. It is therefore essential that in vitro metabolic clearance tests with metabolically competent cell lines as well as plasma protein binding assays be performed for suspected bioaccumulative compounds. Also more research and development has to be done to obtain the first generation of reliable in vitro excretion models.