Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products.
Yalan ZhouSiqi PengHuizhen WangXinyin CaiQingzhong WangPublished in: Genes (2024)
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.
Keyphrases
- artificial intelligence
- machine learning
- deep learning
- genome wide
- small cell lung cancer
- big data
- stem cells
- copy number
- gene expression
- mass spectrometry
- young adults
- dna methylation
- advanced non small cell lung cancer
- clinical decision support
- multidrug resistant
- transcription factor
- epidermal growth factor receptor
- papillary thyroid
- smoking cessation
- gram negative
- childhood cancer
- label free
- drug administration