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.

Seven undescribed phenylpropanoid constituents, including three new bibenzyl derivatives (1–3) along with four new benzofuran stilbene derivatives (4–7), were isolated from the aerial parts of Dioscorea polystachya. The structures of these compounds were elucidated using a combination of spectroscopic analyses, including UV, IR, HRESIMS, 1D, and 2D NMR. Further, all the compounds were evaluated on the anti-inflammatory activity for their inhibition of nitric oxide (NO) production by RAW 264.7 macrophages cells, and some of them (1–3 and 6) displayed inhibitory activity with IC₅₀ values in the range of 9.3–32.3 μM. Moreover, compound 3 decreased the expression of iNOS in Western blot analysis, suggesting compound 3 is mediated via the suppression of an LPS-induced NF-κB inflammasome pathway.

Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects millions worldwide. This review aims to investigate the role of pharmacogenomics in the treatment of AD. Pharmacogenomic strategies aim to improve the efficacy and safety of treatments by identifying genetic factors that influence drug metabolism. These approaches include genetic testing to identify individuals at higher risk for AD and discovering new drug targets based on the genetic causes of the disease. For example, genetic variations in the CYP2D6 gene can significantly affect the metabolism of donepezil, a commonly used cholinesterase inhibitor in AD treatment. Recognizing these genetic differences could lead to personalized drug dosing or the selection of alternative medications. In addition, genetic testing of the APOE gene can identify individuals at higher risk of developing AD, allowing for earlier interventions that may delay or prevent disease onset. Furthermore, research into the genetic basis of AD is driving the development of drugs targeting beta-amyloid, a protein that accumulates in the brains of AD patients. In sum, pharmacogenomic approaches have the potential to revolutionize AD treatment by tailoring treatments to the unique genetic profiles of patients.

In recent years, the FDA has approved numerous antitumor 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 elaborated 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 summarized 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.