Drug‐Drug‐Gene Interactions: A Call for Clinical Consideration

It is widely accepted that both comedications and genetic factors may contribute to variation of drug response. Clinical decision support systems increasingly consider recommendations on drug–drug interactions (DDIs) during electronic prescribing, some guidelines drug–gene-interactions (DGI) have been implemented in labels. Potentially synergistically or antagonistically acting drug-drug-gene (DDGIs) hardly considered. This Perspective article highlights examples the complexity DDGI, aiming strengthen consideration into clinical support. The incidence complex diseases multimorbidity increasing with growing age population. As a consequence, rate polypharmacy increasing, bearing an elevated risk for potential particularly prevalent elderly patients. DDIs are challenge all prescribers since they major causes adverse events which can be harmful, sometimes causing treatment failure even being life-threatening. Routine developed introduced practice help avoid mitigate potentially harmful combinations. Moreover, pharmacogenetic information considered medicinal product introduced—at least cases—into hospital health records. Recommendations how deal respect interindividual differences response developed, e.g., by Pharmacogenetics Implementation Consortium (CPIC) more than 25 guidelines,1 covering hereditary variants metabolizing enzymes transporters contributing varying distribution. Furthermore, selected markers human leukocyte antigen (HLA) system included strongly associated hypersensitivity specific drugs. At DGIs prescribing obligation testing before prescription, HLA-B*5701 abacavir HLA-B*1502 carbamazepine, preferentially Asian populations. More recently, several countries mandatory dihydropyrimidine dehydrogenase (DPYD) gene 5-fluoruracil its prodrugs. So far it still combination putative problems, DDI DGI. In general, three scenarios should distinguished: category 1, DDGIs boost clinically relevant same pathway, while 2, cause different pathways. 3, lead contrary effects so diminished. individual pharmacokinetics as well DDGI illustrated Figure 1. A occur, when metabolism victim V (mediated normally active “wild-type” enzyme) inhibited induced perpetrator P, leading reduced increased V, respectively (Figure 1a). typical example inhibition induction cytochrome P450 (CYP) 2C9–mediated warfarin fluconazole rifampicin, respectively. DGI affect pathway case variant CYP2C9*3 genotype, conferring 1b). For DDGIs, various combinations need considered.2 An inhibitory takes place P inhibits respective enzyme has low activity due loss-of-function variants. combined effect could attenuated thereby interaction 1c). However, not always additive, maximum reduction might already reached variant, homozygous CYP2D6 poor metabolizers. this perpetrating inhibitor cannot further reduction. level will take place; hence if dosing any comediation made without affecting V. On other side, strong inhibitor, play only negligible role. Such scenario was demonstrated study warfarin, investigating consequences CYP2C9 inhibitors anticoagulatory properties depending VKORC1/CYP2C9 genotypes.3 One finding impact outcome parameters like international normalized ratio time reach therapeutic range no longer detectable presence inhibitor. Second, observed significant were wild-type carriers, but carriers variants, suggesting lack additive effects. If metabolized two enzymes, one these pathways minor effect. If, however, second genetically reduced, negatively affected 1d). direction voriconazole substrate CYP2C19 CYP3A4. bioavailability antimycotic markedly patients additionally treated atazanavir ritonavir, known CYP3A4.4 inactive prodrug, formation intermediates would clopidogrel, high affinity polymorphic another case, life-threatening opioid intoxication occurred after administration small doses codeine CYP3A4 inhibitors, patient exhibited ultrarapid genotype led bioactivation levels (due inhibition) morphine.5 Examples through inducing compounds gain-of-function theoretically possible yet described. undergo phenoconversion temporary phenotype shift. such phenomenon normalization nortriptyline metabolizers paroxetine Laine et al.6 1e). fact haplotypes transporter; likewise dependent constant concentration at site action. Extremes known, where 1 result 3 genotypes encoding intermediate phenotypes Consideration pharmacodynamic effects, receptor additional challenge. retrospective interactions, Verbeurgt al. identified prevalence 33.9% among total 1,143 individuals.7 pinpoints merge data receive better about DDGIs. number studies analyzing above-mentioned study3 quite low. attempt overcome gap prospective focusing usage modeling text mining tools.8 Recently, physiologically-based pharmacokinetic model applied estimate simvastatin considering SLCO1B1, ABCG2, CYP3A5 four drugs.9 simulations excellent tool give guidance fill knowledge there gain existing from records, monitoring, also identify quantify future. far, parallel confusion cases, regardless unnecessary prevention deleterious drugs disadvantage patient. long systematic evidence-based available, difficult develop solid top current alerts. Therefore, requires recording patients’ full history records newly algorithms information. Open Access funding enabled organized Projekt DEAL. authors declared competing interests work. Associate Editor Pharmacology Therapeutics, Ingolf Cascorbi involved review process paper.