Advancements in anticancer Drug development: Challenges and solutions
Keywords:
Anticancer drug development, Machine learning (ML), Natural product (NP), Drug discovery and delivery, Cancer treatmentAbstract
A local disease that may spread and affect other organs or vital functions is what cancer is, an occurrence of uncontrolled cell growth that results in the formation of an abnormally developing tumour. Cancer is one of the most deadly illnesses in modern times, taking countless lives every year. Variations in the illness throughout the world, the influence of accessible medical facilities, and other socioeconomic concerns have all affected the successful care of this disorder. In this article, review the various literature’s study on the challenges and solution in anticancer drug development. From the review it concluded that development of anticancer drugs remains challenging due to high costs, long timelines, and frequent failures. While progress has been made, obstacles like toxicity and poor efficacy persist. Machine learning (ML) is transforming drug discovery, especially for natural product (NP)-derived therapies, yet more ML tools are needed. Nanocarrier advancements, combined with ML, enhance drug delivery and reduce side effects. Novel heterometallic complexes and heterocycles further improve treatment. Integrating NP-based chemotherapies with deep learning offers promising results, emphasizing the need for innovation, collaboration, and technology-driven approaches to develop more effective cancer therapies.
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