Discovery of Cobimetinib as a novel A-FABP inhibitor using machine learning and molecular docking-based virtual screening

Abstract

Adipocyte fatty acid-binding protein (A-FABP, also called FABP4, aP2) is an adipokine identified as a critical regulator of metabolic function due to its dual functions of fatty acid transport and pro-inflammation. Because of the high therapeutic potential of A-FABP inhibition for the treatment of metabolic diseases and related vascular complications, numerous inhibitors have been developed against A-FABP. However, none of these inhibitors have been approved for use in patients due to severe side effects. Here, we used a virtual screening (VS) strategy to identify potential inhibitors of A-FABP in the latest FDA-approved drug library (∼2600 compounds), aiming to explore the existing drugs with proven safety profiles. We firstly combined ligand-based machine learning and structure-based molecular docking to develop a screening pipeline for identifying A-FABP inhibitors. The screening of FDA-approved drugs identified four compounds (Cobimetinib, Larotrectinib, Pantoprazole, and Vildagliptin) with the highest scores, whose inhibitory effects on A-FABP were further assessed in cellular assays. Among the selected compounds, Cobimetinib significantly inhibited the activation of the JNK/c-Jun signaling pathway by A-FABP in mouse macrophages without causing obvious cytotoxicity. In summary, we present an integrated VS pipeline for A-FABP inhibitor screening, and identified Cobimetinib as a novel A-FABP inhibitor that may be repurposed for the treatment of metabolic diseases and associated vascular complications.