Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

• Published in: Genes Vol. 15; no. 4; p. 468
• Main Authors: Zhou, Yalan, Peng, Siqi, Wang, Huizhen, Cai, Xinyin, Wang, Qingzhong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.04.2024
• Subjects: Algorithms; anticancer; Antimitotic agents; Antineoplastic agents; Antineoplastic Agents - therapeutic use; Antineoplastic drugs; Artificial intelligence; Big Data; Biological Products - therapeutic use; Biomarkers; Cancer; Cancer therapies; Complications and side effects; Diagnostic tests; Disease; Drug approval; Drug development; Drug therapy; Drug therapy, Combination; Drugs; Gene mutations; Genes; Genomes; High-throughput screening; Humans; Kinases; Lung cancer; Machine learning; Mutation; Natural products; Neoplasms - drug therapy; Neoplasms - genetics; Oncology, Experimental; Patients; personalized medicine; personalized therapy; Pharmacogenetics; Pharmacogenetics - methods; Pharmacogenomics; Precision medicine; Precision Medicine - methods; Proteins; Proteomics; R&D; Research & development; Reviews; Technology; China; natural products; pharmacogenomics; anticancer; personalized medicine; personalized therapy
• ISSN: 2073-4425; 2073-4425
• DOI: 10.3390/genes15040468

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.