A study using data analyzed by the YouScript Personalized Prescribing Software has quantified the role genotypes may play in causing adverse drug reactions.
The research, published in the journal Pharmacogenomics, showed that a combined 33.9 percent of all potential clinically significant drug interactions were due to drug-gene and drug-drug-gene interactions. These potential interactions are entirely missed by electronic health records and drug interaction software currently used by physicians, pharmacists, nurses and physician assistants.
Drug-drug-gene interactions (DDGIs) happen to drugs which have two or more metabolic pathways when one pathway is reduced by a genetic mutation and the other pathway is blocked by an inhibiting drug.
An individual’s genotype combined with multiple drugs can affect their ability to clear or metabolize a medication, in some cases causing adverse effects. Genetic testing, together with predictive computer algorithms, can now improve a medical professional’s ability to better manage drug regimens for at-risk individuals, beyond what is possible with drug-drug interaction data only.
The study, co-authored by Genelex Director of Pharmacy Tyler Mamiya, PharmD, details the results of a pilot study conducted in 2013 to understand the frequency of drug-drug (DDI), drug-gene (DGI) and drug-drug-gene (DDGI) interactions.
Of the 1053 cases of “substantial or major” clinical interactions identified, the study found that when compared with DDIs alone (which account for 66.1 percent of interactions), DGIs and DDGIs increased the total number by 51.3 percent.
The authors conclude from this initial study that drug-gene and drug-drug-gene interactions may comprise more than a third of all potential, clinically significant drug interactions, and, that the number of drug interactions due to DDGIs was greater than DGIs. This represents a large population that may be having adverse drug reactions due to DGIs and DDGIs. They recommend further studies to better determine the frequency of these previously undetected adverse drug reactions.
“Adverse drug reactions are a costly public health problem,” Mamiya said. “Given the proportion of patients that take more than one medication, measuring drug-drug-gene interactions is of particular importance. This research is the beginning of building a measured evidence base of possible interactions and takes us a step closer to truly personalized individual drug therapies.”
About the Study
The authors’ retrospective analysis included a population of 1,143 patients who underwent CYP polymorphism testing and submitted a list of their medications — including FDA-approved prescriptions, OTC drugs, and herbal products or supplements.
To identify all potential interactions, the authors used the YouScript software, a database and patented algorithm, to determine if the interaction was a DDI, DGI or DDGI.
The algorithm sums up all interactive effects, and generates a final rating of the clinical warning. Predictions made by the software were then independently confirmed by two clinical pharmacists.