IN-SILICO PHARMACOGENOMIC ANALYSIS OF SLC5A2 AND UGT1A9 VARIANTS RELEVANT TO SGLT2 INHIBITOR RESPONSE IN THE INDIAN DIABETIC POPULATION

Background: Interindividual variability in response to sodium-glucose cotransporter-2 (SGLT2) inhibitors may be influenced by genetic polymorphisms affecting drug transport and metabolism. Variants in the SLC5A2 and UGT1A9 genes may influence therapeutic response, pharmacokinetics, and susceptibility to adverse effects associated with SGLT2 inhibitors. However, population-specific pharmacogenomic data for the Indian population remain limited. Objective: To compare allele frequencies of selected SLC5A2 and UGT1A9 variants and predict their potential pharmacogenetic implications for SGLT2 inhibitor response in the Indian population using in-silico analysis. Methods: This in-silico pharmacogenomic study analyzed selected SLC5A2 variants (rs9934336, rs3813008) and UGT1A9 variants (rs2741049, rs72551330) using publicly available genomic databases including IndiGenomes and the 1000 Genomes Project. Functional annotation and pathogenicity prediction were performed using dbSNP, Ensembl, SIFT, PolyPhen-2, Mutation Assessor, and MutationTaster tools. Hardy–Weinberg equilibrium-based genotype simulations were used to estimate potential pharmacodynamic and pharmacokinetic variability associated with SGLT2 inhibitor therapy. All findings represent computational predictions derived from publicly available genomic datasets. Results: The Indian population demonstrated distinct allele frequencies for rs9934336 (0.1753), rs3813008 (0.2889), rs2741049 (0.6791), and rs72551330 (0.0019). Compared with East Asian and European populations, Indian individuals showed intermediate allele distribution patterns. HWE-based genotype simulation in an Indian population (n = 1000) suggested variability in predicted transporter expression and metabolic activity associated with SGLT2 inhibitor response. Integrated genotype-based analysis indicated potential variability in predicted pharmacodynamic and pharmacokinetic response patterns among individuals in the Indian population. Conclusion: Selected SLC5A2 and UGT1A9 polymorphisms may contribute to variability in predicted pharmacodynamic and pharmacokinetic responses to SGLT2 inhibitors. These findings support the potential relevance of population-specific pharmacogenomic profiling for future precision-medicine research in diabetes management. However, clinical validation studies are required to confirm these in-silico observations.