Identifying Drug–Drug Interactions by Data Mining

Polypharmacy is common in cardiovascular medicine, and drug–drug interactions may cause many unwarranted adverse effects. Today, most interactions are identified from premarket studies, based on knowledge of mechanisms of action, or from reports of potential adverse drug reactions. Presumably, many interactions are unknown because only a small fraction of adverse drug effects is reported. Important drug–drug interactions may, therefore, be unknown for a long period, and some may never be revealed. A way of discovering interactions without a prior hypothesis is therefore warranted to increase safety and efficacy of drug treatment. Data mining is a data-driven approach that operates without a hypothesis. The idea is to build a prediction model and subsequently identify important variables. To verify that the machine-learning model captures important and true trends, the ability to predict new and unseen data is tested. Often, data are divided into 2 parts, where one is used to construct the model (training set) and one is used to test the performance (test set). Random forest has been shown to be useful in agnostic gene association analyses, which shares many of the same methodological issues as studies aiming to search for drug–drug interactions. Because of the wealth of variables relative to the number of samples available in such analyses, a standard statistic approach like logistic regression would not be ideal, and multiple testing would require a high degree of correction (eg, Bonferroni). Random forest is more flexible (ie, handles interactions without interactions terms), is able to handle many variables compared with the size of the data set, have a built-in test set, and may be better at capturing weak signals compared with logistic regression. We hypothesized Background—Knowledge about drug–drug interactions commonly arises from preclinical trials, from adverse drug reports, or based on knowledge of mechanisms of action. Our aim was to investigate whether drug–drug interactions were discoverable without prior hypotheses using data mining. We focused on warfarin–drug interactions as the prototype. Methods and Results—We analyzed altered prothrombin time (measured as international normalized ratio [INR]) after initiation of a novel prescription in previously INR-stable warfarin-treated patients with nonvalvular atrial fibrillation. Data sets were retrieved from clinical work. Random forest (a machine-learning method) was set up to predict altered INR levels after novel prescriptions. The most important drug groups from the analysis were further investigated using logistic regression in a new data set. Two hundred and twenty drug groups were analyzed in 61 190 novel prescriptions. We rediscovered 2 drug groups having known interactions (β-lactamase-resistant penicillins [dicloxacillin] and carboxamide derivatives) and 3 antithrombotic/anticoagulant agents (platelet aggregation inhibitors excluding heparin, direct thrombin inhibitors [dabigatran etexilate], and heparins) causing decreasing INR. Six drug groups with known interactions were rediscovered causing increasing INR (antiarrhythmics class III [amiodarone], other opioids [tramadol], glucocorticoids, triazole derivatives, and combinations of penicillins, including β-lactamase inhibitors) and two had a known interaction in a closely related drug group (oripavine derivatives [buprenorphine] and natural opium alkaloids). Antipropulsives had an unknown signal of increasing INR. Conclusions—We were able to identify known warfarin–drug interactions without a prior hypothesis using clinical registries. Additionally, we discovered a few potentially novel interactions. This opens up for the use of data mining to discover unknown drug–drug interactions in cardiovascular medicine. (Circ Cardiovasc Qual Outcomes. 2016;9:621-628. DOI: 10.1161/CIRCOUTCOMES.116.003055.)