ABSTRACT
IDH1 plays a critical role in a number of metabolic processes and serves as a key source of cytosolic NADPH under conditions of cellular stress. However, few inhibitors of wild-type IDH1 have been reported. Here we present the discovery and biochemical characterization of two novel inhibitors of wild-type IDH1. In addition, we present the first ligand-bound crystallographic characterization of these novel small molecule IDH1 binding pockets. Importantly, the NADPH competitive α,ß-unsaturated enone 1 makes a unique covalent linkage through active site H315. As few small molecules have been shown to covalently react with histidine residues, these data support the potential utility of an underutilized strategy for reversible covalent small molecule design.
Subject(s)
Drug Discovery , Enzyme Inhibitors/pharmacology , Histidine , Isocitrate Dehydrogenase/antagonists & inhibitors , Isocitrate Dehydrogenase/chemistry , Cell Line, Tumor , Enzyme Inhibitors/chemistry , Enzyme Inhibitors/metabolism , Humans , Isocitrate Dehydrogenase/genetics , Isocitrate Dehydrogenase/metabolism , Ligands , Molecular Docking Simulation , Mutation , Protein Conformation , Structure-Activity RelationshipABSTRACT
Efficient elucidation of the biological mechanism of action of novel compounds remains a major bottleneck in the drug discovery process. To address this need in the area of oncology, we report the development of a multiparametric high-content screening assay panel at the level of single cells to dramatically accelerate understanding the mechanism of action of cell growth-inhibiting compounds on a large scale. Our approach is based on measuring 10 established end points associated with mitochondrial apoptosis, cell cycle disruption, DNA damage, and cellular morphological changes in the same experiment, across three multiparametric assays. The data from all of the measurements taken together are expected to help increase our current understanding of target protein functions, constrain the list of possible targets for compounds identified using phenotypic screens, and identify off-target effects. We have also developed novel data visualization and phenotypic classification approaches for detailed interpretation of individual compound effects and navigation of large collections of multiparametric cellular responses. We expect this general approach to be valuable for drug discovery across multiple therapeutic areas.
