TY - JOUR

T1 - Identifying Drug-Drug Interactions by Data Mining

T2 - A Pilot Study of Warfarin-Associated Drug Interactions

AU - Hansen, Peter Waede

AU - Clemmensen, Line H.

AU - Sehested, Thomas S G

AU - Fosbøl, Emil Loldrup

AU - Torp-Pedersen, Christian

AU - Køber, Lars

AU - Gislason, Gunnar H

AU - Andersson, Charlotte

N1 - © 2016 American Heart Association, Inc.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Journal Article

U2 - 10.1161/CIRCOUTCOMES.116.003055

DO - 10.1161/CIRCOUTCOMES.116.003055

M3 - Journal article

C2 - 28263937

VL - 9

SP - 621

EP - 628

JO - Circulation: Cardiovascular Quality and Outcomes

JF - Circulation: Cardiovascular Quality and Outcomes

SN - 1941-7713

IS - 6

ER -