PHARMACOGENOMICS AND PERSONALIZED MEDICINE: OPTIMIZING DRUG THERAPY FOR BETTER CLINICAL OUTCOMES
Main Article Content
Keywords
Pharmacogenomics, Drug response variability, In vitro model, Personalized medicine
Abstract
The interindividual variability in drug response poses a major challenge to safe and effective pharmacotherapy. This variability is due to genetic polymorphism in major pharmacogenes, including CYP2C9, CYP2D6, and TPMT, but there is a major need to experimentally verify their functional implications in controlled laboratory environments. This paper set out to compare the effect of particular pharmacogenomic variations on the cellular response to three commonly prescribed drugs: warfarin, tamoxifen, and 6-mercaptopurine in in vitro human cell line models that were stratified by genotype. The selected drugs were administered on genetically characterized HepG2, MCF-7, and Caco-2 cells at different concentrations. The cytotoxicity evaluation was performed using the MTT assay, whereas the level of gene and protein expression was determined using qRT-PCR and Western blotting, respectively. The Annexin V/PI flow cytometry was used to analyze apoptosis, and the correlation of genotype and drug response was statistically measured using Pearson coefficients. Significant variations in the IC 50, gene/protein expression, and apoptotic reaction were found between wild-type and variant genotypes. Cells with CYP2C9 *3/*3, CYP2D6 *4/*4, and TPMT *3A/*3A showed very strong drug sensitivity and low expression of the enzyme in contrast to their normal counterparts. A heatmap visualization and correlation analysis supported robust genotype to phenotype associations of all tested parameters. The study provides mechanistic and functional confirmation of pharmacogenomic variability in response to drugs with genotype-stratified in vitro systems. These findings favor the introduction of pharmacogenomic screening into clinical practice and the necessity to develop more preclinical models in order to close the translational gap in personalized medicine.
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