Artificial Intelligence-powered Drug Discovery against CTLA-4 in Cancer

Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy. Tumors from CTLA-4-humanized MC38 syngeneic mice (n = 3 - 5 mice) were extracted for single cell suspension using a gentleMACSTM Octo Dissociator. The cells were stained with anti-mouse CD45 antibody (PE-Cy7, Clone 30-F11, Cat. 103114, Lot: B354212) and sorted with a BD FACSAriaII Cell Sorting Flow Cytometer. Library preparation of CD45+ sorted cells for single-cell- RNA sequencing (scRNA-seq) was performed using the 10X Genomics Chromium Single Cell 5’ Library & Gel Bead reagent kit. For TCR sequencing, the Chromium Single Cell V(D)J Enrichment kit was used. The acquired scRNA-seq reads from the 10X Genomics platform were aligned using Cell Ranger (V.7.1, 10 X Genomics) to a reference genome (mm10) using default parameters. R studio Seurat package was used for analyses.