Courage for simplification and imperfection in the 21st century assessment of "Endocrine disruption"

"Endocrine disruption" is a public and political buzzword that has and is still receiving high media attention. Based on the latter, numerous tiered testing strategies have evolved that should ensure that humans will not run a health risk due to the voluntary or involuntary exposure to endocrine active compounds (EAS). An analysis of the currently available knowledge on EAS mediated endocrine disruption in humans demonstrates that there are very few EAS that causally induce endocrine disruptive effects. Conversely, the association EAS exposure with increased risk or incidences of endocrine disruptive effects in humans are difficult to reconcile with the results from animal studies. Consequently, the analysis of the traditional and historically grown tiered approach in EAS testing, often at very high doses or concentrations, demonstrates that the likelihood of detecting EAS with true potential for endocrine disruption in humans is very low, primarily due to inherent differences between the surrogate species and the human, and will provide for a high number of false-positives commensurate with low efficiency, high cost, and often violently disputed interpretations of what the data would mean for human risk assessment. It is thus proposed that EAS testing for putative endocrine disruption in humans and qualitative and quantitative evaluation for risk assessment purposes should be entirely focused on human data, and derived from a combination of in silico and in vitro systems, PBPK modeling, metabonomic or genomic profiling of human tissue, realistic human EAS exposure, dose-effect principles and adverse effect scenarios, human patient or exposure cohort datasets, etc. Animals models should be used only where specific pathways in endocrine physiology and thus development and reproduction is nearly identical to the situation in the human, thereby guaranteeing that causal exposure and effect relationships in the animals can be extrapolated to the human.