Abstract: Recent breakthroughs in precision medicine have significantly transformed the landscape of cancer treatment, propelling the development of individualized therapies characterized by enhanced therapeutic efficacy and reduced toxicity. This review examines the integration of high-throughput screening techniques with advanced computational methodologies, including artificial intelligence (AI) and machine learning, to expedite drug discovery and optimize treatment protocols in oncology. We explore the efficacy of targeted therapeutics, CAR T-cell therapies, and immune checkpoint inhibitors, alongside the role of combination therapies and biomarker identification in refining patient-specific treatment strategies. By aggregating scientific data from key databases, we evaluate the impact of in silico modeling on drug efficacy predictions, cost reduction, and time efficiency in the development process. This review highlights the collaborative potential of computational and synthetic approaches in redefining oncological pharmacotherapy and improving patient outcomes.
Thakur et al. (Thu,) studied this question.