Drug-Drug-Gene Interactions in Cardiovascular Medicine

Abstract

Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug-drug interactions, DDIs) and genomic factors (drug-gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug-drug-gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.

Keywords: drug–drug interactions; drug–drug–gene interactions; drug–gene interactions; drug–gene–gene interactions; pharmacogenomics.

Figure 1

Continued.

Figure 1

Drug-drug, drug-gene, and drug-drug-gene interactions., (A) Normal metabolism: expected drug exposure (black dotted line in plasma drug concentration–time curve, single exposure) for a drug/substrate that is metabolized by two cytochrome P450 (CYP) enzymes. (B) Drug–drug interaction (DDI): inhibition (eg null activity) or induction (eg increased enzyme copies) of CYP A by co-medications resulting in increased (red line in plasma drug concentration–time curve) or decreased (green line in plasma drug concentration–time curve) drug exposures, respectively. (C) Drug–gene interaction (DGI): genetic variation inactivates/reduces (loss-of-function/LoF variant) or increases (gain-of-function/GoF variant) CYP B activity resulting in increased or decreased drug exposures, respectively. (D) Drug-drug-gene interaction (DDGI): cumulative effects of comedications (DDIs) and genetic variations (DGIs). In a category 1 DDGI, a DDI and DGI on the same pathway (eg CYP A) and direction (eg inhibitor with a LOF variant) interact to significantly increase (or decrease) drug exposure while in a category 2 DDGI, the DDI and DGI act on different pathways but still in the same direction to also increase (or decrease) drug exposure. Lastly, category 3 comprises DDIs and DGIs with opposing effects (eg inhibitor with a GOF variant) that leads to increased (inhibitor effects greater than GOF variant effects), decreased (inhibitor effects lower than GOF variant effects) or unchanged (inhibitor effects similar to GOF variant effects) drug exposure. The above interactions also apply to bioactivation of prodrugs (in which decreased metabolism results in decreased systemic exposure) and other pathways (eg drug- and/or gene-mediated changes to drug transporters or drug targets). If a drug has a comparable clinical effect with its metabolites, the effects of metabolism-based DDIs, DGIs and DDGIs may not be apparent. Any compensation by CYP A for the loss (or increase) in CYP B’s activity, and vice-versa, is not depicted/accounted for in this over-simplified schematic.

Figure 2

Commonly used medications influenced by pharmacogenes. The bar chart shows the total number of prescriptions (top panel) or patients receiving the prescriptions (bottom panel) in 2019 in the United States. Corresponding ranks are shown in parentheses. Data from the ClinCalc DrugStats Database that used the Medical Expenditure Panel Survey 2013–2019 (Agency for Healthcare Research and Quality) as a prescription data source.