Integrated Bioinformatics and Pharmacogenomic Profiling of a Gene Panel in Diabetes Mellitus Treatment Response

Background: Pharmacogenomic variability significantly influences diabetes mellitus (DM) treatment outcomes, yet systematic integration of multi-gene panels combining bioinformatics-driven discovery with cross-database validation remains limited across diverse populations. Objective: To develop and validate a comprehensive 20-gene pharmacogenomic panel for predicting drug metabolism variability and treatment response in DM through integrated bioinformatics approaches. Methods: Systematic literature mining identified candidate genes through PubMed searches (2015-2025). Multi-criteria decision analysis prioritized genes across insulin secretion, insulin sensitivity, glucose metabolism, and drug metabolism pathways. Analyses included Gene Ontology enrichment, KEGG pathway mapping, STRING protein-protein interaction networks, variant annotation (dbSNP/ClinVar/PharmGKB), pathogenicity prediction (CADD/PolyPhen-2/SIFT), GTEx tissue-specific expression profiling, and DrugBank drug-gene interaction mapping. Cross-database validation assessed concordance across PharmGKB, DrugBank, GWAS Catalog, and PhKB. Results: The panel encompassed 20 genes distributed across 14 chromosomes. Network analysis revealed 87 edges with clustering coefficient 0.653, identifying 5 hub genes. Variant annotation catalogued 3,847 polymorphisms, including 247 pathogenic/likely pathogenic variants. Population analyses demonstrated 3.8-fold inter-ethnic allele frequency variations. PharmGKB integration identified 127 gene-drug pairs (23 Level 1A associations). Cross-database concordance achieved 87.3% (PharmGKB-DrugBank), 82.6% (GWAS Catalog), and 79.4% (PhKB). DrugBank identified 89 antidiabetic drug-gene interactions. Novel associations from recent publications demonstrated statistical significance in cohorts exceeding 2,000 patients. Conclusions: This integrated framework provides validated foundations for precision diabetes therapeutics. Prospective clinical validation remains essential to translate computational discoveries into actionable decision-support tools optimizing therapeutic outcomes. Keywords: Pharmacogenomics, Diabetes Mellitus, Bioinformatics, Drug-Gene Interactions, Precision Medicine, Genetic Polymorphisms.