The Utilization of Pharmacophore-based 3D QSAR Modeling and Virtual Screening in Safety Profiling: a Case Study to Identify Antagonistic Activities against Adenosince Receptor, A2aR, using 1,897 known drugs

Abstract

ABSTRACTSafety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand-and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based approaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2aR). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modelling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training compounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening.