Computer-aided drug discovery of a dual-target inhibitor for ovarian cancer: therapeutic intervention targeting CDK1/TTK signaling pathway and structural insights in the NCI-60.
Journal:
Computers in biology and medicine
Published Date:
Jul 1, 2025
Abstract
Ovarian cancer remains the third most prevalent and deadliest gynecologic malignancy worldwide, with most patients eventually developing resistance to platinum-based chemotherapy. This highlights a critical unmet need for innovative multitargeted therapies to address current treatment challenges. In this study, we identified 35 differentially expressed genes (DEGs) through integrated analysis of four GEO ovarian cancer datasets, with validation using TCGA data. Gene Ontology (GO) and KEGG enrichment analyses highlighted key tumor-associated pathways, and protein-protein interaction (PPI) network modeling prioritized CDK1 and TTK as high-value therapeutic targets. We evaluated the association between molecular genomic features and drug responses across the NCI-60 ovarian cancer cell line panel (IGROV1, OVCAR-3, OVCAR-4, OVCAR-5, OVCAR-8, NCI/ADR-RES, and SK-OV-3), using a series of salicylanilide-derived compounds and four FDA-approved drugs (cabozantinib, paclitaxel, rapamycin, and niclosamide) from the NCI Developmental Therapeutics Program (DTP). Among these, NSC765690 (MCC22) emerged as the most promising candidate. It demonstrated potent antiproliferative activity, high target selectivity, and strong binding affinity to both CDK1 and TTK. Multi-omics integration, combined with AI-driven network modeling, further elucidated NSC765690's mechanism of action and its relevance to ovarian cancer pathogenesis. Additionally, ADMET and pharmacokinetic profiling confirmed its favorable drug-like properties and low predicted toxicity. Collectively, these findings establish NSC765690 as a potent dual-target inhibitor and exemplify a rational, data-driven drug discovery pipeline for overcoming chemotherapy resistance in ovarian cancer.
