• Daniel Dowd, Gabriela Williams, Daniel VanDorn, Stephen Clarke, David S Krause, Jeffrey Botbyl, and Stephen R Saklad.
• Genomind, Inc, 2200 Renaissance Blvd, Ste 100, King of Prussia, PA 19406-2755. Email: ddowd@genomind.com.
• Am J Manag Care. 2022 Nov 1; 28 (11): 566571566-571.

ObjectivesDrug-drug interactions (DDIs) are among the most common causes of adverse drug reactions and are further complicated by genetic variants of drug-metabolizing enzymes. The aim of this study is to quantify and describe potential DDIs, drug-gene interactions (DGIs), and drug-drug-gene interactions (DDGIs) in a community-based population.Study DesignThis was an analysis of deidentified retail pharmacy prescription data for 4761 individuals.MethodsData were first assessed for DDIs, and individuals were stratified to a risk category using the logic of a commercially available digital DDGI tool. To calculate the frequency of potential DGIs and DDGIs, genotypes were imputed and randomly allocated to the cohort 100 times via Monte Carlo simulation according to each variant's frequency in the general population.ResultsThe probability of a DDI of any impact was 26.0% and increased to 49.6% (95% CI, 48.4%-50.7%) when drug-metabolizing phenotypes were ascribed according to the distribution of variants of 11 genes as found in a Caucasian population. There was a 7.8% probability of major DDIs, which increased to a 10.1% (95% CI, 9.5%-10.8%) probability with the addition of genetic contributions. The probability of DDGIs of any impact was correlated with the number of medications. Antidepressants, antiemetics, blood products and modifiers, analgesics, and antipsychotics had the highest probability of DDGIs.ConclusionsThe probability of drug interaction risk increased when phenotypes associated with genetic polymorphisms were attributed to the population. These data suggest that pharmacogenomic assessment may be useful in predicting drug interactions and severity when evaluating patient medication profiles.

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