Sandra Coecke, Post Doc, European Commision, Joint Research Center
Knowledge on absorption, distribution, metabolism and excretion of a substance is a prerequisite when assessing the safety of consumers, patients and the environment. A lot of the information can be generated at tissue, cell or sub-cellular level by using in vitro and in silico test methods and use the information gained as input parameters for physiologically-based toxicokinetic modelling (PBTK). Such computer modelling avoids unnecessary animal experimentation and improves the predictivity of in silico (QSAR) and in vitro tests by integrating the results of various in vitro and non-testing approaches. Essential input parameters for such models rely on the availability of reliable and relevant human metabolic competent sources, modelling the process of xenobiotic biotransformation, already at the level of the absorption barriers. The metabolic stability of a molecule and the rate of its biotransformation can affect the potential for its bioaccumulation, and whether it is likely to become more or less toxic, without it necessarily being converted into a more reactive metabolite. For the in vitro dynamics experiments, it is a prerequisite to know whether the cell or tissue is exposed to the parent compound and/or its metabolites. This information is required upfront and can be obtained from toxicokinetic alternative methods that identify the main metabolites and the clearance rates of the parent compound and/or its metabolites when human exposure conditions are applied. Although such simple in vitro screening tools for assessing metabolic stability and metabolic routes have been used for a long time (e.g. pharmaceutical sector), for the quantitative extrapolation of such in vitro observations to the in vivo consequences a substantial amount of work needs still to be done. A very concrete guidance to test developers, toxicologists, safety assessors and regulators on which priorities to focus will be needed in order to make further progress with kinetic and dynamic models based on in silico and in vitro input parameters.