Pharmacogenomics of Anti-Obesity Drugs: A Bioinformatics Approach

Introduction: Despite the increasing utilization of anti-obesity medications, the individual variability in treatment response remains poorly understood. This study aims to address this gap by integrating pharmacogenomics and bioinformatics to identify predictive biomarkers. Objective : To investigate how genetic variants influence the efficacy and adverse effects of anti-obesity drugs, employing bioinformatics to integrate genomic, pharmacological, and clinical data. Methods: This study utilized publicly available data (PharmGKB) to analyze genetic variants and gene expression associated with anti-obesity drugs. Specific drugs (liraglutide, semaglutide, tirzepatide) and target genes (Molecular Targets: GLP1R , GIPR ; Metabolism and Elimination: DPP4, CYP3A4, CYP2C8, ALB ) were selected, and variants were annotated (PharmGKB). Machine learning models were employed to predict therapeutic response, while biological networks ( KEGG ) mapped affected pathways. This approach integrated pharmacogenomics and bioinformatics to identify drug response biomarkers. Results : This integrated pharmacogenomic analysis identified key variants impacting GLP-1RA efficacy: GLP1R (rs6923761, Gly168Ser) reducing receptor binding affinity (↓30%) and adipose tissue expression (p=3.2×10⁻⁵). GIPR (rs10423928, Ser37Gly) modulates the incretin effect of tirzapatide through cAMP signaling. CYP3A422 (rs35599367) delays drug metabolism. GTEx reveals tissue-specific target expression ( GLP1R -Subcutaneous Adipose Tissue: TPM 1.2; DPP4: TPM 15.3). Machine learning predicted genotype-dependent body mass index (BMI) reduction: liraglutide (8.5%), semaglutide (14.2%), tirzapatide (16.8%). Protein-protein interaction networks highlight the GLP1R-GNAS-IRS1 axis (combined score >0.9) and adipocyte PPARG crosstalk. Functional annotations classified 38% of variants as clinically actionable (PharmGKB Level 1/2). Conclusion : This study demonstrated that variants in GLP1R, GIPR , and metabolic genes significantly influence the response to anti-obesity drugs. The integration of genomic data and predictive models identified promising biomarkers for personalized therapy, optimizing efficacy and safety in obesity treatment.