Assessment factors in human health risk assessment and their associated level of safety

Human health risk assessment requires solid information on adverse effects after long-term exposure. Because of ethical considerations, human data or long-term studies with animals are in general scarce. Consequently, reliable assessment factors (AF) are needed in risk assessment to overcome differences between short-term animal studies and the human situation. In human health risk assessment, traditional AF are established, e.g. a factor of 100 (10 x 10) for interspecies/intraspecies extrapolation. Some of these AF were substantiated by either deterministic or probabilistic approaches, whereas other factors lack a scientific rationale. Furthermore, the level of safety achieved with the application of more than one AF has remained an open question so far. Our analyses aimed therefore at the derivation of AF and their corresponding level of safety when applied solely or in combination in human health risk assessment. The toxicological database RepDose(TM) (containing appr. 2500 repeated dose studies on appr. 700 chemicals) served as the basis for the derivation of AF. Pairs of NOEL ratios - e.g. obtained from two studies with the same chemical and species, but different application durations if time AF were analyzed - were plotted in a distribution curve. Distribution curves for interspecies and time extrapolation were evaluated. All distribution functions were best described by lognormal curves. Furthermore, the combinations of these distributions were analyzed in probabilistic approaches by means of Monte Carlo simulations. On the basis of the resulting distribution curves, overall AF were determined, for which the corresponding percentile indicates the associated level of safety. Our results demonstrate that the probabilistic approach is well suited to derive AF, provided that the database is comprehensive and contains studies of good quality. RepDose: Trademark of the Fraunhofer Gesellschaft zur Foerderung der angewandten Forschung e.V., 80686 München, D