Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors
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ABSTRACT:
SUBMITTER: Martarelli N
PROVIDER: S-EPMC11242627 | biostudies-literature | 2024 Jun
REPOSITORIES: biostudies-literature
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Project description:Background/objectivesCheckpoint 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 severe, 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.Subjects/methodsIn this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to identify those targeting CTLA-4. We validated the hits molecules with biochemical, biophysical, immunological, and experimental animal assays.ResultsThe primary hits obtained from the virtual screening were successfully validated in vitro and in vivo. We then optimized lead compounds and obtained inhibitors (inhibitory concentration, 1 micromole) that disrupted the CTLA-4/CD80 interaction without degrading CTLA-4.ConclusionsSeveral 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.
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