Language
English
Publication Date
1-1-2024
Journal
BJC Reports
DOI
10.1038/s44276-023-00035-5
PMID
38312352
PMCID
PMC10838660
PubMedCentral® Posted Date
1-23-2024
PubMedCentral® Full Text Version
Post-Print
Abstract
BACKGROUND/OBJECTIVES: 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 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/METHODS: In 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.
RESULTS: The 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.
CONCLUSIONS: 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.
Published Open-Access
yes
Recommended Citation
Sobhani, Navid; Tardiel-Cyril, Dana Rae; Chai, Dafei; et al., "Artificial Intelligence-Powered Discovery of Small Molecules Inhibiting CTLA-4 in Cancer" (2024). Faculty and Staff Publications. 1197.
https://digitalcommons.library.tmc.edu/baylor_docs/1197
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