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Predictive structure-based toxicology approaches to assess the androgenic potential of chemicals
Citation:
Trisciuzzi, D., D. Alberga, K. Mansouri, R. Judson, E. Novellino, G. Mangiatordi, AND O. Nicolotti. Predictive structure-based toxicology approaches to assess the androgenic potential of chemicals. Journal of Chemical Information and Modeling. American Chemical Society, Washington, DC, 57(11):2874-2884, (2017). https://doi.org/10.1021/acs.jcim.7b00420
Impact/Purpose:
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. The Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within 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.
Description:
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 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 AD 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 nicely classified based on outstanding rationale at 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 alternative non-testing method for predictive toxicology.
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DOI: Predictive structure-based toxicology approaches to assess the androgenic potential of chemicals