| New Generations of Anti-Cancer Drugs: Bridging Precision Medicine and Drug Discovery |
| Paper ID : 1188-IGA |
| Authors |
|
Yasaman Aliyan * Department of Biology, Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran |
| Abstract |
| Background and Aims Cancer treatment has evolved significantly, moving from broad-spectrum chemotherapies to targeted therapies designed to attack specific genetic mutations. Precision medicine has revolutionized oncology by offering treatments tailored to an individual’s genetic profile. However, despite these advancements, challenges such as drug resistance, tumor heterogeneity, and the complexity of cancer genetics continue to limit the effectiveness of current therapies. The next generation of anti-cancer drugs aims to bridge the gap between precision medicine and innovative drug discovery approaches. This review explores the latest advancements in anti-cancer drug development, focusing on next-generation targeted therapies, immunotherapies, and AI-driven drug discovery. It highlights how these innovations are shaping the future of precision oncology while addressing the persistent challenges in treatment efficacy and accessibility. Methods A systematic review of recent literature was conducted to assess emerging drug development strategies, including small molecule inhibitors, bispecific antibodies, immune checkpoint inhibitors, and RNA-based therapies. Additionally, advancements in computational drug discovery, such as AI-driven molecule design and virtual screening, were analyzed to evaluate their impact on accelerating drug development. Results Next-generation anti-cancer drugs are demonstrating improved specificity and reduced toxicity, with many novel therapies in clinical trials showing promising results. AI and machine learning are expediting the discovery of new drug candidates, optimizing drug-target interactions, and identifying resistance mechanisms more efficiently. However, challenges such as regulatory barriers, high development costs, and the need for comprehensive biomarker-driven patient selection remain significant. Conclusion The future of cancer treatment lies in the synergy between precision medicine and advanced drug discovery techniques. By integrating AI, biomarker-driven strategies, and novel therapeutic modalities, the next wave of anti-cancer drugs has the potential to significantly improve patient outcomes. However, continued collaboration among researchers, pharmaceutical companies, and regulatory agencies is crucial to translating these innovations into widely accessible treatments. |
| Keywords |
| Precision medicine, targeted therapy, immunotherapy, AI-driven drug discovery, cancer treatment, next-generation drugs, biomarkers, personalized oncology |
| Status: Accepted |
