Discovery of Potent and Selective Quinoxaline-Based Protease-Activated Receptor 4 (PAR4) Antagonists for the Prevention of Arterial Thrombosis.

PAR4 is a promising antithrombotic target with potential for separation of efficacy from bleeding risk relative to current antiplatelet therapies. In an effort to discover a novel PAR4 antagonist chemotype, a quinoxaline-based HTS hit 3 with low µM potency was identified. Optimization of the HTS hit through the use of positional SAR scanning and the design of conformationally constrained cores led to the discovery of a quinoxaline-benzothiazole series as potent and selective PAR4 antagonists. The lead compound 48, possessing a 2 nM IC50 against PAR4 activation by γ-thrombin in platelet-rich plasma (PRP) and greater than 2500-fold selectivity versus PAR1, demonstrated robust antithrombotic efficacy and minimal bleeding in the cynomolgus monkey models.

A simple strategy for mitigating the effect of data variability on the identification of active chemotypes from high-throughput screening data.

Among the several goals of a high-throughput screening campaign is the identification of as many active chemotypes as possible for further evaluation. Often, however, the number of concentration response curves (e.g., IC(50)s or K(i)s) that can be collected following a primary screen is limited by practical constraints such as protein supply, screening workload, and so forth. One possible approach to this dilemma is to cluster the hits from the primary screen and sample only a few compounds from each cluster. This introduces the question as to how many compounds must be selected from a cluster to ensure that an active compound is identified, if it exists at all. This article seeks to address this question using a Monte Carlo simulation in which the dependence of the success of sampling is directly linked to screening data variability. Furthermore, the authors demonstrate that the use of replicated compounds in the screening collection can easily assess this variability and provide a priori guidance to the screener and chemist as to the extent of sampling required to maximize chemotype identification during the triage process. The individual steps of the Monte Carlo simulation provide insight into the correspondence between the percentage inhibition and eventual IC(50) curves.