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.

Ligand placement based on prior structures: the guided ligand-replacement method

The process of iterative structure-based drug design involves the X-ray crystal structure determination of upwards of 100 ligands with the same general scaffold (i.e. chemotype) complexed with very similar, if not identical, protein targets. In conjunction with insights from computational models and assays, this collection of crystal structures is analyzed to improve potency, to achieve better selectivity and to reduce liabilities such as absorption, distribution, metabolism, excretion and toxicology. Current methods for modeling ligands into electron-density maps typically do not utilize information on how similar ligands bound in related structures. Even if the electron density is of sufficient quality and resolution to allow de novo placement, the process can take considerable time as the size, complexity and torsional degrees of freedom of the ligands increase. A new module, Guided Ligand Replacement (GLR), was developed in Phenix to increase the ease and success rate of ligand placement when prior protein-ligand complexes are available. At the heart of GLR is an algorithm based on graph theory that associates atoms in the target ligand with analogous atoms in the reference ligand. Based on this correspondence, a set of coordinates is generated for the target ligand. GLR is especially useful in two situations: (i) modeling a series of large, flexible, complicated or macrocyclic ligands in successive structures and (ii) modeling ligands as part of a refinement pipeline that can automatically select a reference structure. Even in those cases for which no reference structure is available, if there are multiple copies of the bound ligand per asymmetric unit GLR offers an efficient way to complete the model after the first ligand has been placed. In all of these applications, GLR leverages prior knowledge from earlier structures to facilitate ligand placement in the current structure.