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Primary aromatic amines (pAAs) are attractive building blocks in medicinal chemistry programmes yet their potential for mutagenic activity causes real concern owing to the risk of genotoxicity-related drug attrition. In addition, despite the existence of a substantial body of experimental data, the prediction of aromatic amine mutagenicity still poses a significant challenge for in silico tools. Major contributors to this dilemma are the stability and physicochemical properties of a subset of aromatic amines that affords them capricious mutagenic properties in the Ames test. Such inconsistent mutagenic potential is also compounded by the inherent variability with the assay itself and underscores the need for a rigorous approach in executing the experimental protocol. In order to understand the utility of the in silico approach towards the prediction of pAAs mutagenicity and to widen the availability of mutagenicity data, a group of pharmaceutical companies has formed a consortium with the aim of exchanging their in-house data and making them publicly available for the first time. Summary data compiled during the first phase of this effort is disclosed here and its utility in conjunction with in silico prediction is discussed. Conclusions from this analysis highlight the critical role of expert judgement in rationalizing the experimental activity seen in the Ames test with predictions from in silico models. This collaboration demonstrates the value of sharing such data pre-competitively to aid in both the selection of Ames negative building blocks for drug development while simultaneously helping to develop better in silico tools.

Endocrine disrupting chemicals raise a lot of interest and concern regarding their risk for human health and the environment. They represent a broad variety of natural and synthetic chemicals with different levels of endocrine activity evaluation. In particular, for high production volume chemicals, new methods are required to enable the evaluation of the vast number of chemicals for their potential to alter the endocrine system and prioritize them for deeper characterization. The ToxCast program from the US EPA provides data from high throughput screening assays to develop computational tools aimed at rapid in vitro bioactivity screening and prioritization. Using publicly available data (ToxCast and ToxRef databases), we evaluate whether in vitro assay evaluations could predict in vivo outcomes observed in rat long-term studies for more than 400 chemicals. We focus on effects observed in three endocrine and two sex accessory organs and 42 in vitro assays related to pathways associated with endocrine related toxicity. First, using simple statistical correlation we demonstrate that there is no mutual linear correlation between the selected in vitro assays and any in vivo outcome, with balanced accuracies around 50% for each assay-outcome pair. Then, by applying machine learning to investigate potential non-linear correlations, we show that the combination of different in vitro assays is not correlated with the long-term in vivo effects and cannot help to predict them since balanced accuracies are also around 50%. Moreover, the prediction based on in vitro assays is not better than the one based on classical QSAR methods. This study highlights that the selected in vitro assays do not provide information about in vivo outcomes observed in endocrine and associated organs in long-term rat in vivo studies and stresses the need for the development of in vitro assays that reflect the compounds’ pharmacokinetic properties.