Association of Omeprazole‐Related Myopathy With Drug–Drug and Drug–Gene Interactions Involving CYP2C19 and CYP3A4: A Nested Case–Control Study

Background Omeprazole, a widely used proton pump inhibitor, has been associated with rare but serious adverse events such as myopathy. Previous research suggests that concurrent use of omeprazole with fluconazole, a potent cytochrome P450 (CYP) 2C19/3A4 inhibitor, may increase the risk of myopathy. However, the contribution of genetic polymorphisms in CYP enzymes remains unclear. Aims This study leveraged electronic health record (EHR) and biobank data to validate an interaction between omeprazole and fluconazole and to explore drug–gene interactions (DGIs) between omeprazole and polymorphisms in CYP enzymes. Materials and Methods A nested case–control design with incidence‐density matching was used. Cases were defined as patients who developed myopathy during ongoing omeprazole therapy. For each case, up to four controls were selected from patients who had not developed myopathy by the time the case was diagnosed. Conditional logistic regression models, adjusting for relevant covariates, evaluated (i) the association between concomitant fluconazole use and myopathy and (ii) genotype‐stratified myopathy risk. Results Among 902 cases and 3608 controls, the combined use of omeprazole and fluconazole was linked to an increased risk of myopathy (adjusted odds ratio [AOR] = 1.75, 95% confidence interval [CI]: 1.17–2.63, p = 0.007). In the DGI analysis, which included 862 cases and 3448 controls, individuals classified as CYP2C19 poor metabolizers paired with CYP3A4 extensive metabolizers showed a significantly higher myopathy risk (AOR = 1.62, 95% CI: 1.03–2.55, p = 0.036); those with CYP2C19 poor metabolizer/CYP3A4 intermediate metabolizer had an even greater risk (AOR = 4.77, 95% CI: 1.74–13.1, p = 0.002). Discussion These findings not only confirm previously reported drug–drug interactions (DDIs) between omeprazole and fluconazole but also reveal the emerging clinical implications of DGIs. Conclusion By integrating EHR and genetic data, the study showcases how informatics tools can translate DDI findings into DGI hypotheses, effectively bridging genetic insights and clinical outcomes.