Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Area of Science:

• Natural product chemistry
• Computational drug discovery
• Oncology

Background:

• Malignancies are a leading cause of global mortality, necessitating innovative therapeutic strategies.
• Despite advances in conventional and precision cancer treatments, challenges remain in drug development.
• Natural products have historically provided and continue to offer a rich source of anticancer compounds.

Purpose of the Study:

• To review the synergy between natural products and computational techniques in anticancer drug discovery.
• To highlight the progression of natural products from computational screening to clinical application.
• To discuss challenges and future directions in developing natural products as anti-cancer agents.

Main Methods:

• Literature review of natural products in cancer research.
• Analysis of computational approaches in identifying and optimizing natural product drug candidates.
• Examination of preclinical (in vitro, in vivo) and clinical study data for natural product-derived drugs.

Main Results:

• Natural products like Vincristine and Vinblastine are established anti-cancer drugs.
• Computational methods, including in silico screening, facilitate the identification of promising natural compounds.
• Examples like betulinic acid, withaferin A, silvestrol, and artemisinin demonstrate the successful transition from discovery to clinical trials.

Conclusions:

• The integration of computational approaches significantly enhances the discovery and development of natural product-based anti-cancer drugs.
• Further research and development, potentially incorporating artificial intelligence and deep learning, can unlock the full therapeutic potential of natural products.
• Overcoming challenges in natural product development is crucial for advancing novel cancer therapies.