Boolean network-based identification of optimal drug combinations for prostate cancer.

Prostate cancer is one of the most common cancers among men in the United States and is a leading cause of cancer-related deaths and the second most common cancer in men worldwide. In this study, we used a Boolean network model to analyze prostate cancer signaling pathways and to identify optimal drug combinations for precision therapy. By integrating publicly available biological signaling pathway data with recent research findings, we developed a comprehensive model that represents protein-protein interactions, gene mutations, and pathway dysregulation. Faults induced by mutations were modeled using the "stuck at 0" or "stuck at 1" fault paradigms, capturing the impact of genetic alterations on pathway behavior. The model was simulated across various drug combinations to determine which therapies could most effectively alleviate the aberrant signaling caused by specific mutations. To quantify therapeutic efficacy, we calculated a Size Difference (SD) score, a metric analogous to Hamming distance, measuring the deviation from normal, for each drug combination and fault scenario. The results revealed that drug combinations involving Berberine, Docetaxel, Olaparib, and Enzalutamide showed promising prediction efficacy (more than 90 %), indicating higher therapeutic potential. A distinguishing feature of this work is that, in addition to the standard prostate cancer drugs, we have included Berberine, a non-toxic natural compound with beneficial effects. These computational findings provide a framework for future experimental and clinical validation, which is necessary to confirm the therapeutic relevance of the predicted drug combinations.