Metabolism, variability and risk assessment.

For non-genotoxic carcinogens, "thresholded toxicants", Acceptable/Tolerable Daily Intakes (ADI/TDI) represent a level of exposure "without appreciable health risk" when consumed everyday or weekly for a lifetime and are derived by applying an uncertainty factor of a 100-fold to a no-observed-adverse-effect-levels (NOAEL) or to a benchmark dose. This UF allows for interspecies differences and human variability and has been subdivided to take into account toxicokinetics and toxicodynamics with even values of 10(0.5) (3.16) for the human aspect. Ultimately, such refinements allow for chemical-specific adjustment factors and physiologically based models to replace such uncertainty factors. Intermediate to chemical-specific adjustment factors are pathway-related uncertainty factors which have been derived for phase I, phase II metabolism and renal excretion. Pathway-related uncertainty factors are presented here as derived from the result of meta-analyses of toxicokinetic variability data in humans using therapeutic drugs metabolised by a single pathway in subgroups of the population. Pathway-related lognormal variability was derived for each metabolic route. The resulting pathway-related uncertainty factors showed that the current uncertainty factor for toxicokinetics (3.16) would not cover human variability for genetic polymorphism and age differences (neonates, children, the elderly). Latin hypercube (Monte Carlo) models have also been developed using quantitative metabolism data and pathway-related lognormal variability to predict toxicokinetics variability and uncertainty factors for compounds handled by several metabolic routes. For each compound, model results gave accurate predictions compared to published data and observed differences arose from data limitations, inconsistencies between published studies and assumptions during model design and sampling. Finally, under the 6(th) framework EU project NOMIRACLE (http://viso.jrc.it/nomiracle/), novel methods to improve the risk assessment of chemical mixtures were explored (1) harmonization of the use of uncertainty factors for human and ecological risk assessment using mechanistic descriptors (2) use of toxicokinetics interaction data to derive UFs for chemical mixtures. The use of toxicokinetics data in risk assessment are discussed together with future approaches including sound statistical approaches to optimise predictability of models and recombinant technology/toxicokinetics assays to identify metabolic routes for chemicals and screen mixtures of environmental health importance.