Computational Molecular Biology 2025, Vol.15, No.3, 131-140 http://bioscipublisher.com/index.php/cmb 133 clearer references and no longer rely solely on experience. Nowadays, many clinical guidelines include genetic testing, which can be regarded as an important step in individualized medication (Lee et al., 2022). Of course, reality is not so smooth either. The genetic differences among different groups of people, the unified standards for detection technologies, and the acceptance of genetic testing by hospitals are all still under exploration. Despite this, the emergence of pharmacogenomics has indeed transformed precision medicine from a concept into an operational reality. Figure 1 Allele and genotype frequencies. Data for allele and genotype frequencies of CYP2C19 are illustrated. (A) Allele frequency and (B) genotype frequency (Adopted from Angulo-Aguado et al., 2021) 3 Application of Omics Data in Drug Sensitivity Studies 3.1 Transcriptomics and analysis of drug response gene expression profiles When studying drug sensitivity, people often start with the transcriptome. Because changes in gene expression often reveal the attitude of cells towards drugs. The difference is that some cell lines "surrender obediently" upon encountering drugs, while others are almost unaffected. Researchers will compare the gene expression profiles of these two types of samples to see which ones are up-regulated and which ones are down-regulated. For instance, the drug-resistant group often activates the efflux pump or anti-apoptotic pathways, while the sensitive group instead activates the genes related to the drug target at a higher level. Such differentially expressed genes were later often used as potential markers to predict whether new samples would respond to drugs (Talwar and Carter, 2020). In addition to identifying differences, transcriptome data can also be used for model training - by combining the expression data of different cell lines with IC50 values and using regression or classification algorithms to calculate which genes best reflect drug efficacy (Mannheimer et al., 2016). Although it sounds complicated, the core idea is actually very simple: let the gene expression map tell us the story of the drug. These analysis results can not only help explain the mechanism but also provide clues for subsequent experiments and clinical decisions. 3.2 The role of proteomics and metabolomics in the study of drug action mechanisms When studying the mechanism of drugs, merely looking at genetic and transcriptional information is far from enough. What really works is often at the protein and metabolic levels. Proteomics can directly observe which proteins in cells are mobilized and which are modified, and the changes in pathways under different drug treatments are clear at a glance. Sometimes, drug-resistant cells quietly activate key proteins in backup signaling pathways or turn on the repair system more aggressively to evade drug attacks - all of which can be found as clues in the proteome (Figure 2) (Fortuin and Soares, 2022). In contrast, the transcriptome is merely a "plan", while the
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