Quantitative to Extrapolation (QIVIVE) for Predicting Reduced Anogenital Distance Produced by Anti-Androgenic Pesticides in a Rodent Model for Male Reproductive Disorders.

Background: Many pesticides can antagonize the androgen receptor (AR) or inhibit androgen synthesis but their potential to cause reproductive toxicity related to disruption of androgen action during fetal life is difficult to predict. Currently no approaches for using data to anticipate such effects exist. Prioritization schemes that limit unnecessary testing are urgently needed.

Objectives: The aim was to develop a quantitative to extrapolation (QIVIVE) approach for predicting anti-androgenicity arising from gestational exposures and manifesting as a shortened anogenital distance (AGD) in male rats.

Methods: We built a physiologically based pharmacokinetic (PBK) model to simulate concentrations of chemicals in the fetus resulting from maternal dosing. The predicted fetal levels were compared with analytically determined concentrations, and these were judged against active concentrations for AR antagonism and androgen synthesis suppression.

Results: We first evaluated our model by using and anti-androgenic data for procymidone, vinclozolin, and linuron. Our PBK model described the measured fetal concentrations of parent compounds and metabolites quite accurately (within a factor of five). We applied the model to nine current-use pesticides, all with evidence for anti-androgenicity but missing data. Seven pesticides (fludioxonil, cyprodinil, dimethomorph, imazalil, quinoxyfen, fenhexamid, -phenylphenol) were predicted to produce a shortened AGD in male pups, whereas two (, pyrimethanil) were anticipated to be inactive. We tested these expectations for fludioxonil, cyprodinil, and dimethomorph and observed shortened AGD in male pups after gestational exposure. The measured fetal concentrations agreed well with PBK-modeled predictions.

Discussion: Our QIVIVE model newly identified fludioxonil, cyprodinil, and dimethomorph as anti-androgens. With the examples investigated, our approach shows great promise for predicting anti-androgenicity (i.e., AGD shortening) for chemicals with activity and for minimizing unnecessary testing. https://doi.org/10.1289/EHP6774.