Artificial Intelligence-Powered Discovery of Small Molecules Inhibiting CTLA-4 in Cancer

Abstract

Abstract Checkpoint inhibitors, which generate durable responses in many cancer patients, have revolutionized cancer immunotherapy. However, their therapeutic efficacy is limited, and immune-related adverse events are very sever, especially for monoclonal antibody treatment directed against cytotoxic T-lymphocyte–associated protein 4 (CTLA-4), which plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with the B7 proteins CD80 and CD86. Small molecules impairing the CTLA-4/CD80 interaction have been developed; however, they directly target CD80, not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to identify those targeting CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors (inhibitory concentration, 1 micromole) that disrupted the CTLA-4/CD80 interaction without degrading CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4–humanized mice. Our findings support using AI-based frameworks to design small molecules targeting immune checkpoints for cancer therapy.