Optimization of Nanodrugs: A Novel Approach in Targeted Cancer Therapy

The increasing complexity of diseases, limitations of conventional treatments, and the emergence of multidrug resistance have further highlighted the need for innovative therapeutic paradigms. Nanomedicine, utilizing nanomaterials, holds transformative potential in providing precise, targeted, and multifunctional treatments. However, challenges such as systemic toxicity, immune system clearance, and clinical translation barriers have limited their widespread application. In this context, Artificial Intelligence (AI) is emerging as a powerful tool that is reshaping the landscape of nanomedicine. This review systematically examines the emerging strategies at the intersection of AI and nanomedicine. It focuses on the role of machine learning and deep learning algorithms in optimizing the design of nanomaterials, predicting nano-bio interactions, high- throughput screening of compounds, and accelerating the development of anti-cancer drugs. Furthermore, it explores the synergistic applications of these two technologies in advanced molecular imaging, targeted drug delivery to the tumor microenvironment (TME), overcoming drug resistance through targeting metabolic pathways, and developing personalized cancer vaccines based on mRNA. Finally, the article addresses key challenges such as data quality, interpretability of black-box models, and regulatory frameworks, while outlining the future outlook of this field, including integration with quantum computing and autonomous therapeutic systems. This synergy offers a robust framework for the development of next-generation smart nanodrugs aligned with the goals of precision oncology and personalized medicine.

کلیدواژه ها:

Microenvironment (TME) ، Lipid Nanoparticles (LNP) ، Explainable Artificial Intelligence (XAI)