Machine learning- and structure-based discovery of a novel chemotype as FXR agonists for potential treatment of nonalcoholic fatty liver disease

Abstract

Farnesoid X receptor (FXR) is a promising target for drug discovery against nonalcoholic fatty liver disease (NAFLD). However, no FXR agonist has been approved for NAFLD so far. The R & D of FXR agonists are somewhat hindered by the lack of effective and safe chemotypes. To this end, we developed a multi-stage computational workflow to screen the Specs and ChemDiv chemical library for FXR agonists, which consisted of machine learning (ML)-based classifiers, shape-based and electrostatic-based models, a FRED-based molecular docking protocol, an ADMET prediction protocol and substructure search. As a result, we identified a novel chemotype that has never been reported before, with compound XJ02862 (ChemDiv ID: Y020-6413) as the representative. By designing an asymmetric synthesis strategy, we were able to prepare four isomers of compound XJ02862. Interestingly, one of the isomers, 2-((S)-1-((2S,4R)-2-methyl-4-(phenylamino)-3,4-dihydroquinolin-1(2H)-yl)-1-oxopropan-2-yl)hexahydro-1H-isoindole-1,3(2H)-dione (XJ02862-S2), showed potent FXR agonistic activity in HEK293T cells. The molecular docking, molecular dynamics simulations and site-directed mutagenesis suggested the hydrogen bond between compound XJ02862-S2 and HIS294 of FXR is essential for ligand binding. We further demonstrated that compound XJ02862-S2 had no agonistic effect on TGR5. Further biological experiments have shown that compound XJ02862-S2 could ameliorate hypercholesterolemia, hepatic steatosis, hyperglycemia, insulin resistance (IR) in high-fat-diet induced obese (DIO) mice. In term of molecular mechanism, compound XJ02862-S2 regulates the expression of FXR downstream genes involved in lipogenesis, cholesterol transport and bile acid biosynthesis and transport. Taken together, we have discovered a novel chemotype as potent FXR agonists for NAFLD by computational modeling, chemical synthesis and biological evaluation.

Keywords: Asymmetric synthesis; FXR agonists; Machine learning; Nonalcoholic fatty liver disease; Structure-based drug discovery.