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Coupling Data Mining and Laboratory Experiments to Discover Drug Interactions Causing QT Prolongation.

ABSTRACT: BACKGROUND:QT interval-prolonging drug-drug interactions (QT-DDIs) may increase the risk of life-threatening arrhythmia. Despite guidelines for testing from regulatory agencies, these interactions are usually discovered after drugs are marketed and may go undiscovered for years. OBJECTIVES:Using a combination of adverse event reports, electronic health records (EHR), and laboratory experiments, the goal of this study was to develop a data-driven pipeline for discovering QT-DDIs. METHODS:1.8 million adverse event reports were mined for signals indicating a QT-DDI. Using 1.6 million electrocardiogram results from 380,000 patients in our institutional EHR, these putative interactions were either refuted or corroborated. In the laboratory, we used patch-clamp electrophysiology to measure the human ether-à-go-go-related gene (hERG) channel block (the primary mechanism by which drugs prolong the QT interval) to evaluate our top candidate. RESULTS:Both direct and indirect signals in the adverse event reports provided evidence that the combination of ceftriaxone (a cephalosporin antibiotic) and lansoprazole (a proton-pump inhibitor) will prolong the QT interval. In the EHR, we found that patients taking both ceftriaxone and lansoprazole had significantly longer QTc intervals (up to 12 ms in white men) and were 1.4 times more likely to have a QTc interval above 500 ms. In the laboratory, we found that, in combination and at clinically relevant concentrations, these drugs blocked the hERG channel. As a negative control, we evaluated the combination of lansoprazole and cefuroxime (another cephalosporin), which lacked evidence of an interaction in the adverse event reports. We found no significant effect of this pair in either the EHR or in the electrophysiology experiments. Class effect analyses suggested this interaction was specific to lansoprazole combined with ceftriaxone but not with other cephalosporins. CONCLUSIONS:Coupling data mining and laboratory experiments is an efficient method for identifying QT-DDIs. Combination therapy of ceftriaxone and lansoprazole is associated with increased risk of acquired long QT syndrome.

SUBMITTER: Lorberbaum T

PROVIDER: S-EPMC5082283 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Coupling Data Mining and Laboratory Experiments to Discover Drug Interactions Causing QT Prolongation.

Lorberbaum Tal T Sampson Kevin J KJ Chang Jeremy B JB Iyer Vivek V Woosley Raymond L RL Kass Robert S RS Tatonetti Nicholas P NP

Journal of the American College of Cardiology 20161001 16

<h4>Background</h4>QT interval-prolonging drug-drug interactions (QT-DDIs) may increase the risk of life-threatening arrhythmia. Despite guidelines for testing from regulatory agencies, these interactions are usually discovered after drugs are marketed and may go undiscovered for years.<h4>Objectives</h4>Using a combination of adverse event reports, electronic health records (EHR), and laboratory experiments, the goal of this study was to develop a data-driven pipeline for discovering QT-DDIs.<h ...[more]

PMID: 27737742

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Project description:BackgroundDrug-induced long-QT syndrome (diLQTS) is a major concern among patients who are hospitalized, for whom prediction models capable of identifying individualized risk could be useful to guide monitoring. We have previously demonstrated the feasibility of machine learning to predict the risk of diLQTS, in which deep learning models provided superior accuracy for risk prediction, although these models were limited by a lack of interpretability.ObjectiveIn this investigation, we sought to examine the potential trade-off between interpretability and predictive accuracy with the use of more complex models to identify patients at risk for diLQTS. We planned to compare a deep learning algorithm to predict diLQTS with a more interpretable algorithm based on cluster analysis that would allow medication- and subpopulation-specific evaluation of risk.MethodsWe examined the risk of diLQTS among 35,639 inpatients treated between 2003 and 2018 with at least 1 of 39 medications associated with risk of diLQTS and who had an electrocardiogram in the system performed within 24 hours of medication administration. Predictors included over 22,000 diagnoses and medications at the time of medication administration, with cases of diLQTS defined as a corrected QT interval over 500 milliseconds after treatment with a culprit medication. The interpretable model was developed using cluster analysis (K=4 clusters), and risk was assessed for specific medications and classes of medications. The deep learning model was created using all predictors within a 6-layer neural network, based on previously identified hyperparameters.ResultsAmong the medications, we found that class III antiarrhythmic medications were associated with increased risk across all clusters, and that in patients who are noncritically ill without cardiovascular disease, propofol was associated with increased risk, whereas ondansetron was associated with decreased risk. Compared with deep learning, the interpretable approach was less accurate (area under the receiver operating characteristic curve: 0.65 vs 0.78), with comparable calibration.ConclusionsIn summary, we found that an interpretable modeling approach was less accurate, but more clinically applicable, than deep learning for the prediction of diLQTS. Future investigations should consider this trade-off in the development of methods for clinical prediction.

Project description:Importance:Women are at higher risk of drug-induced torsade de pointes (TdP) than men. Androgens are protective. Influence of oral contraception on drug-induced TdP and QT prolongation is controversial. Objective:To determine if the extent of sotalol-induced corrected QT (QTc) prolongation and specific T-wave morphological changes, which are biomarkers for the risk of drug-induced TdP, differ in patients according to the androgenic activity of the type of oral contraceptive (OCs) they take compared with patients who took no pills. Design, Setting, and Participants:A cohort of 498 healthy, nonmenopausal women received 80 mg of oral sotalol, a drug with known risk of drug-induced TdP, during this study in a clinical investigation center. The participants also took either no oral contraception or received OCs with different types of progestin: levonorgestrel (which has high androgenic potency), desogestrel or gestodene (which has intermediate androgenic potency), or drospirenone (which has antiandrogenic properties). Women were enrolled from February 2008 to February 2012, and data analysis took place from September 2014 to May 2018. Main Outcomes and Measures:Electrocardiographic changes 3 hours after sotalol administration. Results:A total of 137 women received levonorgestrel, 41 received desogestrel, 51 received gestodene, and 62 received drospirenone; another 207 received no OCs. Baseline QTc duration, plasma sotalol levels, and potassium levels did not significantly differ among groups. However, 3 hours after sotalol exposure, QTc prolongation was greater in women taking drospirenone (mean [SD] increase, 31.2 [12.6] milliseconds from baseline) than in women taking no OCs (mean [SD] increase, 24.6 [12.5] milliseconds; P?=?.005) or those taking levonorgestrel (mean [SD] increase, 24.2 [13.7] milliseconds; P?=?.005). The frequency of sotalol-induced T-wave alteration was higher in women taking drospirenone (n?=?13 of 61 [21.0%]) than those taking levonorgestrel (n?=?20 of 137 [14.6%]) or women taking no OCs (n?=?24 of 207 [11.6%]; P?=?.01). Disproportionality analysis using the European pharmacovigilance database showed a higher reporting rate of OC-induced prolonged QT and ventricular arrhythmias in women taking drospirenone than levonorgestrel (drug-induced long QT syndrome: reporting odds ratio [ROR], 6.2 [95% CI, 1.3-30.8]; P?=?.01; ventricular arrhythmia: ROR, 3.3 [95% CI, 1.7-6.3]; P?<?.001). Conclusions and Relevance:Contraceptive pills are associated with variable drug-induced alterations of ventricular repolarization in healthy nonmenopausal women. Drospirenone, an antiandrogenic pill, was associated with increased sotalol-induced QTc prolongation, although absolute QTc prolongation was modest. This finding was supported by the European pharmacovigilance database, which showed a higher reporting rate of suspected OC-induced ventricular arrhythmias on drospirenone compared with levonorgestrel. More data are required on whether antiandrogenic OCs lead to clinically significant adverse events in patients taking QTc-prolonging drugs.

Project description:BACKGROUND: Several antipsychotic agents are known to prolong the QT interval in a dose dependent manner. Corrected QT interval (QTc) exceeding a threshold value of 450 ms may be associated with an increased risk of life threatening arrhythmias. Antipsychotic agents are often given in combination with other psychotropic drugs, such as antidepressants, that may also contribute to QT prolongation. This observational study compares the effects observed on QT interval between antipsychotic monotherapy and psychoactive polytherapy, which included an additional antidepressant or lithium treatment. METHOD: We examined two groups of hospitalized women with Schizophrenia, Bipolar Disorder and Schizoaffective Disorder in a naturalistic setting. Group 1 was composed of nineteen hospitalized women treated with antipsychotic monotherapy (either haloperidol, olanzapine, risperidone or clozapine) and Group 2 was composed of nineteen hospitalized women treated with an antipsychotic (either haloperidol, olanzapine, risperidone or quetiapine) with an additional antidepressant (citalopram, escitalopram, sertraline, paroxetine, fluvoxamine, mirtazapine, venlafaxine or clomipramine) or lithium. An Electrocardiogram (ECG) was carried out before the beginning of the treatment for both groups and at a second time after four days of therapy at full dosage, when blood was also drawn for determination of serum levels of the antipsychotic.Statistical analysis included repeated measures ANOVA, Fisher Exact Test and Indipendent T Test. RESULTS: Mean QTc intervals significantly increased in Group 2 (24 +/- 21 ms) however this was not the case in Group 1 (-1 +/- 30 ms) (Repeated measures ANOVA p < 0,01). Furthermore we found a significant difference in the number of patients who exceeded the threshold of borderline QTc interval value (450 ms) between the two groups, with seven patients in Group 2 (38%) compared to one patient in Group 1 (7%) (Fisher Exact Text, p < 0,05). CONCLUSIONS: No significant prolongation of the QT interval was found following monotherapy with an antipsychotic agent, while combination of these drugs with antidepressants caused a significant QT prolongation. Careful monitoring of the QT interval is suggested in patients taking a combined treatment of antipsychotic and antidepressant agents.

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Coupling Data Mining and Laboratory Experiments to Discover Drug Interactions Causing QT Prolongation.

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Coupling Data Mining and Laboratory Experiments to Discover Drug Interactions Causing QT Prolongation.

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