Peter Wæde Hansen1, Line Clemmensen2, Thomas S G Sehested2, Emil Loldrup Fosbøl2, Christian Torp-Pedersen2, Lars Køber2, Gunnar H Gislason2, Charlotte Andersson2. 1. From the Danish Heart Foundation, Copenhagen, Denmark (P.W.H., T.S.G.S., E.L.F., G.H.G.); DTU Compute, Technical University of Denmark, Lyngby (L.C.); The Heart Centre, Rigshospitalet (E.L.F., L.K.), and Department of Clinical Medicine (G.H.G.), University of Copenhagen, Denmark; Institute of Health, Science and Technology, Aalborg University, Denmark (C.T.-P.); The National Institute of Public Health, University of Southern Denmark, Copenhagen (G.H.G.); University of Copenhagen, Denmark; and Department of Medicine, Section of Cardiology, Glostrup Hospital, University of Copenhagen, Denmark (C.A.). Waede.sci@gmail.com. 2. From the Danish Heart Foundation, Copenhagen, Denmark (P.W.H., T.S.G.S., E.L.F., G.H.G.); DTU Compute, Technical University of Denmark, Lyngby (L.C.); The Heart Centre, Rigshospitalet (E.L.F., L.K.), and Department of Clinical Medicine (G.H.G.), University of Copenhagen, Denmark; Institute of Health, Science and Technology, Aalborg University, Denmark (C.T.-P.); The National Institute of Public Health, University of Southern Denmark, Copenhagen (G.H.G.); University of Copenhagen, Denmark; and Department of Medicine, Section of Cardiology, Glostrup Hospital, University of Copenhagen, Denmark (C.A.).
Abstract
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.
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.
Authors: Anna E Engell; Andreas L O Svendsen; Bent S Lind; Tore Bjerregaard Stage; Maja Hellfritzsch; Anton Pottegård Journal: Eur J Clin Pharmacol Date: 2021-04-24 Impact factor: 2.953
Authors: Ralf E Harskamp; Martina Teichert; Wim A M Lucassen; Henk C P M van Weert; Renato D Lopes Journal: Cardiovasc Drugs Ther Date: 2019-10 Impact factor: 3.727