Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals

We
present a practical and easy-to-run in silico workflow
exploiting a structure-based strategy making use of docking
simulations to derive highly predictive classification models of the
androgenic potential of chemicals. Models were trained on a high-quality
chemical collection comprising 1689 curated compounds made available
within the CoMPARA consortium from the US Environmental Protection
Agency and were integrated with a two-step applicability domain whose
implementation had the effect of improving both the confidence in
prediction and statistics by reducing the number of false negatives.
Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from
the Protein Data Bank) was associated with the best performing structure-based
classification model. Three validation sets comprising each 2590 compounds
extracted by the DUD-E collection were used to challenge model performance
and the effectiveness of Applicability Domain implementation. Next,
the 2PNU model
was applied to screen and prioritize two collections of chemicals.
The first is a small pool of 12 representative androgenic compounds
that were accurately classified based on outstanding rationale at
the molecular level. The second is a large external blind set of 55450
chemicals with potential for human exposure. We show how the use of
molecular docking provides highly interpretable models and can represent
a real-life option as an alternative nontesting method for predictive
toxicology.