Harnessing Artificial Intelligence to Predict and Overcome Drug Resistance in Breast Cancer: A Translational Approach.
TL;DR
This review discusses how AI can accelerate computational drug discovery, virtual screening (VS), and the repurposing of already-approved medications, and it can also optimize combination therapies through computational simulation.
Abstract
Drug resistance is a major obstacle to the efficient treatment of breast cancer, which is still one of the most common cancers in the world. This study emphasizes the potential of artificial intelligence (AI) in addressing these issues, while highlighting the biological mechanisms underlying resistance and the related therapeutic implications. Advanced applications in genomics, transcriptomics, and proteomics, as well as predictive modeling of treatment outcomes, have been made possible by AI-driven techniques such as machine learning (ML), deep learning, and data mining. This review discusses how AI can accelerate computational drug discovery, virtual screening (VS), and the repurposing of already-approved medications. It can also optimize combination therapies through computational simulation. From a translational standpoint, rigorous assessment of data quality, bias, ethical issues, and cost-effectiveness is necessary for the successful integration of AI into clinical operations. AI is useful in the real world, especially in adaptive clinical trial design, as demonstrated by case studies of HER2/HER3-positive breast cancer. In the future, lifestyle-based preventative interventions combined with cutting-edge methods like digital twins and federated learning could improve patient outcomes. Creating interdisciplinary teams will be essential to the effective application of AI in oncology. The review's overall findings highlight the translational significance of AI in combating drug resistance in breast cancer and offer a path forward for its future integration into clinical practice and policy.