Computational biology and chemistry · 2026
Integrated computational framework for drug repurposing in metastatic prostate cancer using transcriptomic meta-analysis, machine learning, and molecular modeling.
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Rafiee Javad, Arabi Baygi Seyedeh Tahereh, Aghaee-Bakhtiari Seyed Hamid
Аннотация
BACKGROUND: Metastatic prostate cancer (mPCa) continues to be the primary cause of mortality associated with prostate cancer, after considerable progress in targeted therapy. The rise of therapeutic resistance, coupled with the protracted and expensive nature of new drug development, has heightened interest in computational drug repurposing methodologies. This study sought to create a comprehensive computational framework that integrates transcriptome meta-analysis, network biology, machine learning, and molecular modelling to discover FDA-approved medications with potential therapeutic efficacy against metastatic prostate cancer.
METHODS: Three publicly available Gene Expression Omnibus datasets were integrated following quality control, normalization. Differentially expressed genes between primary and metastatic prostate cancer samples were discovered and assessed for functional pathway enrichment and protein-protein interaction networks to identify hub proteins. A machine learning-based quantitative structure-activity relationship model was created and later utilized on FDA-approved pharmaceuticals. Candidate compounds were prioritized using molecular docking, ADMET prediction, and molecular dynamics simulations.
RESULTS: A total of 236 DEGs were discovered, demonstrating considerable enrichment in pathways linked to cell cycle regulation and metastasis. CDK1 emerged as the hub gene. The optimized Light Gradient Boosting Machine model identified 67 FDA-approved compounds with predicted inhibitory activity against CDK1. Molecular docking and pharmacokinetic analyses prioritized Tucatinib and Sildenafil, and molecular dynamics simulations demonstrated superior structural stability and enhanced protein-ligand interactions for the CDK1-Tucatinib complex.
CONCLUSION: This comprehensive computational framework offers a robust approach for therapeutic repurposing in metastatic prostate cancer. CDK1 was identified as a promising therapeutic target, and Tucatinib emerged as the most promising repurposing candidate, warranting further clinical validation.
METHODS: Three publicly available Gene Expression Omnibus datasets were integrated following quality control, normalization. Differentially expressed genes between primary and metastatic prostate cancer samples were discovered and assessed for functional pathway enrichment and protein-protein interaction networks to identify hub proteins. A machine learning-based quantitative structure-activity relationship model was created and later utilized on FDA-approved pharmaceuticals. Candidate compounds were prioritized using molecular docking, ADMET prediction, and molecular dynamics simulations.
RESULTS: A total of 236 DEGs were discovered, demonstrating considerable enrichment in pathways linked to cell cycle regulation and metastasis. CDK1 emerged as the hub gene. The optimized Light Gradient Boosting Machine model identified 67 FDA-approved compounds with predicted inhibitory activity against CDK1. Molecular docking and pharmacokinetic analyses prioritized Tucatinib and Sildenafil, and molecular dynamics simulations demonstrated superior structural stability and enhanced protein-ligand interactions for the CDK1-Tucatinib complex.
CONCLUSION: This comprehensive computational framework offers a robust approach for therapeutic repurposing in metastatic prostate cancer. CDK1 was identified as a promising therapeutic target, and Tucatinib emerged as the most promising repurposing candidate, warranting further clinical validation.