Abstract
Background
QT-interval prolonging drug-drug interactions (QT-DDIs) may increase the risk of life-threatening arrhythmias. 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, we sought to develop a data-driven pipeline for discovering QT-DDIs.
Methods
We mined 1.8 million adverse event reports for signals indicating a QT-DDI. Using 1.6 million electrocardiogram results from 380,000 patients in our institutional EHR, we either refuted or corroborated these putative interactions. In the laboratory, we used patch-clamp electrophysiology to measure the human Ether-à-go-go-Related Gene (hERG) block (the primary mechanism by which drugs prolong the QT interval) to evaluate our top candidate.
Results
We found both direct and indirect signals in the adverse event reports 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 on 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 these drugs block the hERG channel in combination at clinically-relevant concentrations. 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 suggest this interaction is 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
Keywords: data mining, data science, drug-drug interaction, prolonged QT interval
Torsades de pointes (TdP) is a ventricular tachycardia that can result in sudden death (1) and occurs as an adverse effect of over 40 medications that prolong the QT interval, referred to as acquired long QT syndrome (LQTS) (2). The U.S. Food and Drug Administration (FDA) has established strict guidelines for evaluating the risk of acquired LQTS for new compounds when administered individually. Nonantiarrhythmic compounds that increase the QT/QTc interval by 20 ms or more are unlikely to be approved, and a compound associated with an increase of 10 ms or more would face many challenges (3). Even a 5-ms increase would prompt an evaluation of the risks and benefits of the new compound (3). Studies of both cardiac and noncardiac compounds found that a QTc interval above 500 ms is associated with significant risk of TdP (4,5).
Acquired LQTS is of particular concern when it is not anticipated and occurs as the result of a QT interval-prolonging drug-drug interaction (QT-DDI) (2,6). QT-DDIs are not routinely evaluated pre-clinically and can go undiscovered for years. For example, quetiapine (an antipsychotic agent) was on the market for nearly 10 years before reports of a QT-DDI with methadone (an analgesic agent) prompted investigation into a possible mechanism (7). It took 3 more years before a label change was made to caution against the use of quetiapine in combination with other drugs known to prolong the QT interval.
Large clinical databases, such as electronic health records (EHR), represent an opportunity to rapidly detect QT-DDIs and save lives (8,9). Drug safety algorithms could be applied to health record data in near real-time, flagging potentially dangerous drug interactions before they become widespread. Furthermore, these analyses are in situ, and therefore focus on the most important drug combinations: those that are actually used in clinical practice. Unfortunately, analysis of medical records is complex, due to issues of missing data, noise, and bias (10). This leads to high false positive rates and algorithms that often will mislead health care providers. Laboratory experiments, especially if they are high-throughput, can be used to screen data-mined hypotheses for plausibility. Following observational analysis with confirmatory prospective experiments can remove the spurious signals, enabling clinically useful discoveries (11).
We developed a data science pipeline to mine potential QT-prolonging drug-drug interactions from clinical databases. In this pipeline, we combine evidence of QT-DDIs from the FDA Adverse Event Reporting System (FAERS) and the EHR at New York-Presbyterian/Columbia University Medical Center (CUMC-EHR). We identified a putative interaction between lansoprazole (a proton-pump inhibitor [PPI]) and ceftriaxone (a cephalosporin antibiotic). Importantly, this is an interaction that would not have been suspected using current surveillance methods. We used patch-clamp electrophysiology of cells stably expressing human Ether-à-go-go-Related Gene (hERG) channels to establish a physiological mechanism. We further confirmed the specificity of our pipeline by also investigating the combination of cefuroxime (another cephalosporin) and lansoprazole, a drug pair that did not have evidence in FAERS of an interaction. In the clinic, patients on the combination of ceftriaxone and lansoprazole had 12 ms (95% CI: 7 to 15 ms) longer QTc intervals than patients exposed to either drug alone and were 1.4 times as likely to have a QTc interval above 500 ms. The negative control showed no significant effect. A QT-DDI between ceftriaxone and lansoprazole has the potential for significant morbidity and mortality.
Methods
Data Sources
We used 2 independent databases to investigate possible QT-DDIs. The first was a derivative of 1.8 million adverse event reports from FAERS mined for evidence of adverse drug-drug interactions that could not be explained by the individual effects of the drugs (TWOSIDES) (12). The second database was of 1.6 million electrocardiograms (ECGs) from 382,221 patients treated at New York-Presbyterian/CUMC between 1996 and 2014. To obtain the heart rate-corrected QT (QTc) intervals, we wrote a parser to automatically extract the patient identifier, laboratory date, and QTc value from the ECG reports. QTc values were calculated using Bazett’s formula. We manually checked 50 abnormal ECGs (defined as QTc >500 ms) to confirm we were extracting the correct values, and found that the parser obtained 100% precision and recall. We implemented the pipeline using Python 2.7.9 and R v3.2.2.
Identification of candidate QT-DDIs
We used the side effect reporting frequencies in TWOSIDES to find drug pairs significantly over-reported with the 6 adverse events in the standardized MedDRA query for “Torsade de Pointes/QT prolongation”; we call this the direct evidence model (12). However, most drug pairs are not directly reported with QT prolongation. In addition, we performed latent signal detection, a method we have previously validated (13,14), to identify candidate QT-DDIs that lacked prior direct evidence. To perform latent signal detection, we used machine learning to define and validate a side effect profile of 13 side effects associated with known QT-prolonging compounds. Some of these latently identified side effects (such as arrhythmia and rhabdomyolysis) are positively correlated with QT interval prolongation, whereas others (such as hemorrhage and myocardial infarction) are negatively correlated (Figure 1B). We previously validated the method using drug pairs containing a known QT-prolonging drug (2), and demonstrated high specificity and sensitivity (Online Figure 1). We then scanned for novel drug interactions in the TWOSIDES database that matched the side effect profile; we refer to this as indirect evidence. We scored each drug pair for the amount of both direct and indirect evidence.
Figure 1. Data Science and Experimental Pipeline for Identifying and Validating QT-DDIs.
(A) Chemical structures for ceftriaxone (cephalosporin) and lansoprazole (proton pump inhibitor, PPI), which we predicted would have a QT-DDI; we predicted cefuroxime (cephalosporin) and lansoprazole not to interact. (B) QT-DDI discovery in FAERS: data-driven side effect profile containing latent evidence of a QT-DDI (black boxes: positive correlation with QT prolongation; white boxes: negative correlation). Each bar represents the reporting frequency of a given side effect in FAERS for ceftriaxone (green), lansoprazole (blue), cefuroxime (orange), ceftriaxone + lansoprazole (red), and cefuroxime + lansoprazole (purple). (C) Retrospective corroboration in electronic health records (EHR). Left: Difference in QTc interval (mean ± 95% CI) between cases (patients prescribed the drug pair) and controls (patients on only 1 drug). We stratified the analysis by sex (men: gray; women: teal) and evaluated all races combined, as well as whites, blacks, and “other, including Hispanic” separately. Star indicates the change in QTc intervals is statistically significant (Mann-Whitney U test with Bonferroni correction). We obtained 95% CIs by bootstrapping case and control QTc distributions, and calculating the change in median QTc for each iteration. Right: Percentage of patients with a QTc interval ≥500 ms (mean ± 95% CI), stratified by sex and race. Star indicates the combination had a significantly greater proportion of patients with a QTc interval ≥500 ms than either drug alone (independent-samples Student t-test, with Bonferroni correction, comparing means of single drug and combination therapy percentage≥500 distributions generated using bootstrapping). (D) Experimental validation using patch-clamp electrophysiology. Left: Change in hERG current from control (mean and SD) for increasing concentrations of cephalosporin alone (dashed line), and increasing concentrations of cephalosporin in the presence of a single concentration of lansoprazole (solid lines). Right: Representative traces from each patch-clamp electrophysiology experiment. From top to bottom: hERG-channel current in the presence of vehicle only (control), and then cephalosporin at 3 concentrations (0.1 μM, 10 μM, and 100 μM); hERG-channel current in the presence of lansoprazole and then in combination with progressively increasing concentrations of cephalosporin. CI = confidence interval; FAERS = FDA Adverse Event Reporting System; hERG = human Ether-à-go-go Related Gene; QT-DDI = QT-prolonging drug-drug interaction.
Evaluation of candidate QT-DDIs using EHR
We attempted to corroborate (or refute) each of the candidate QT-DDI hypotheses using the heart rate-corrected QTc values from ECGs stored in the CUMC-EHR. For each candidate drug-drug interaction (DDI) we defined an exposed cohort and 2 control cohorts. Those patients included in the exposed cohort were administered both drugs within a 7-day window. Those in the control cohorts had evidence of exposure to only 1 of the 2 drugs ever in their records. Only patients who had at least 1 ECG in the following 36 days after drug exposure (either combination or single) were included. Corroboration required that we found significantly longer heart rate-corrected QTc intervals in patients on combination treatment compared with patients on either drug alone. The CUMC-EHR uses Bazett’s formula by default; we also evaluated the change in QT interval using the Fridericia, Framingham, and Hodges correction formulae (15). Because the distributions of QTc intervals were non-normal, we assessed significance using a Mann-Whitney U test with a Bonferroni correction for multiple hypothesis testing. We further verified that this effect could not be explained by concomitant medications (analysis of covariance with concomitant medications modeled as categorical variables) (14). This analysis was stratified by sex because QT interval durations are known to differ between men and women (16). We evaluated the effects of each drug pair both on individual races and on all races combined (Mann-Whitney U test). We also performed a post hoc power analysis to estimate our ability to detect a change in QTc interval for the sample and effect sizes present in our EHR (17). Only those QT-DDIs corroborated by the EHR data (in either men, women, or both) were considered for laboratory analysis.
Patch-clamp electrophysiology
QT-prolonging drugs have in common the ability to block the hERG channel (which conducts IKr) in the heart. We evaluated the combination of ceftriaxone and lansoprazole by performing patch-clamp electrophysiology of cells stably expressing IKr. Using an automated patch-clamp system (PatchLiner, Nanion, Delaware) in voltage clamp mode, we examined the concentration-dependent block of the IKr current by each drug individually, as well as in combination, using dimethyl sulfoxide as vehicle control (Figure 1D). We applied a voltage protocol with a step to +40 mV, followed by a return to −40 mV, to elicit the inward-rectifying tail current. This protocol was repeated every 20s for the length of the experiment and, after 10 consecutive sweeps in each concentration, the concentration is increased. We then averaged the current at the end of each drug application and normalized it to the control to measure the block by each compound. We assessed significance using a test of repeated measures on the log-normalized block percentages.
We performed patch-clamp electrophysiology experiments as described for ceftriaxone alone, lansoprazole alone, and ceftriaxone and lansoprazole, and similarly for the negative controls of cefuroxime alone and cefuroxime and lansoprazole. We evaluated the ability of ceftriaxone or cefuroxime to block the hERG channel at 0.1 μM, 1 μM, 10 μM, 50 μM, and 100 μM. For lansoprazole, we evaluated at 0.1 μM, 1 μM, and 10 μM. We performed 3 combination experiments: for the combination of ceftriaxone and lansoprazole, we held lansoprazole constant at either 1 μM or 10 μM and increased the dose of ceftriaxone stepwise from 0.1 to 100 μM. To evaluate our negative control of cefuroxime and lansoprazole, we held lansoprazole constant at 1 μM and increased the dose of cefuroxime stepwise from 0.1 to 100 μM. The concentrations tested were chosen to include the range of plasma concentrations usually reached during routine clinical use of the drugs (1.9 to 3.9 μM for lansoprazole, 24 to 228 μM for ceftriaxone, and 35 to 428 μM for cefuroxime) (18–21).
Computational mechanistic model
We used a computational model of the human ventricular myocyte (22) to simulate the action potential for the hERG block we observed for ceftriaxone, lansoprazole, and the combination from our laboratory experiments. We ran the model for a ventricular action potential paced at 1 Hz with baseline conditions and 10% or 55% block of hERG current (chosen using the current block observed in the electrophysiology experiments). We evaluated the APD70, the action potential duration at 70% of repolarization.
Results
Candidate QT-DDI discovery via data science
We detected 889 putative signals in FAERS, of which 34 (1.42× more than expected by chance, p = 0.003) were corroborated by the CUMC-EHR, after multiplicity correction. Twenty-six were eliminated by confounder analysis for concomitant medications. The remaining 8 combinations could not be explained by concomitant medications and were not previously associated with acquired LQTS (14). We prioritized the combination of ceftriaxone and lansoprazole for experimental validation, as lansoprazole is available over the counter and is one of the top 200 most-prescribed drugs (totaling over 2.6 million prescriptions in 2010) (23). An interaction with a PPI could therefore have a profound impact on patient safety. As a negative control, we chose to evaluate the combination of cefuroxime and lansoprazole as, according to our algorithm, it did not match the side-effect profile for QT prolongation in FAERS (Figures 1A and 1B).
Comedication of ceftriaxone and lansoprazole is associated with prolonged QT in the EHR
Overall, the QTc intervals (Bazett’s correction) for male patients taking this combination were 12 ms (95% CI: 7 to 15 ms; n = 934) longer than patients taking either drug alone (p < 0.001); for female patients, QTc intervals on the combination were 9 ms (95% CI: 5.2 to 11.3 ms; n = 1,414) longer than patients taking either drug alone (p < 0.001) (Figure 1C). We evaluated QT interval prolongation post hoc, using the Fridericia, Framingham, and Hodges correction formulae. In men, all 3 were significant, with p < 0.01 (Online Table 1), and, in women, Fridericia and Hodges were significant, with p < 0.01. When stratifying by race in addition to sex, the largest effect was observed in white men (12 ms increase; 95% CI: 6.5 to 17 ms; p < 0.001) and black women (12 ms increase; 95% CI: 3.7 to 18.5 ms; p < 0.001). We performed a regression analysis, which confirmed the increased sensitivity to the drug pair in white patients (p = 0.049, Online Table 2). In 19% of men taking the combination, the QTc was ≥500 ms, an accepted threshold for clinical concern (3), compared with 14% (p < 0.001) of patients taking only 1 drug (Table 1).
Table 1.
Demographic and Clinical Characteristics of Cohort
| Variable | Men | Women |
|---|---|---|
| Combination: ceftriaxone + lansoprazole | ||
| n | 934 | 1,414 |
| Demographic | ||
| Age, yrs (mean ± SD) | 61.3 ± 16.9 | 66.5 ± 18.5 |
| Race (% of group) | ||
| White | 57.3 | 52.9 |
| African American | 18.9 | 20.2 |
| Other/unknown | 23.8 | 26.9 |
| Median QTc (ms) (95% CI) | 458 (398–588) | 457 (401–571.7) |
| Patients with QTc ≥500 ms (%) | 19.27 | 16.34 |
| Combination: cefuroxime + lansoprazole | ||
| n | 107 | 228 |
| Demographic | ||
| Age, yrs (mean ± SD) | 66.1 ± 15.7 | 67.6 ± 17.9 |
| Race (% of group) | ||
| White | 56.1 | 60.1 |
| African American | 13.1 | 14.9 |
| Other/unknown | 30.8 | 25.0 |
| Median QTc, ms (95% CI) | 450 (393.6–579.4) | 443.5 (398.7–579.2) |
| Patients with QTc ≥500 ms (%) | 14.95 | 11.40 |
| Ceftriaxone only | ||
| N | 5,734 | 6,850 |
| Demographic | ||
| Age, yrs (mean ± SD) | 59.5 ± 17.9 | 63.7 ± 19.8 |
| Race (% of group) | ||
| White | 46.6 | 45.1 |
| African American | 19.0 | 18.4 |
| Other/unknown | 34.4 | 36.5 |
| Median QTc, ms (95% CI) | 446 (394–566) | 448 (398–560) |
| Patients with QTc ≥500 ms (%) | 14.21 | 11.43 |
| Cefuroxime only | ||
| N | 636 | 957 |
| Demographic | ||
| Age, yrs (mean ± SD) | 61.5 ± 17.6 | 66.0 ± 19.3 |
| Race (% of group) | ||
| White | 54.1 | 50.3 |
| African American | 20.6 | 19.3 |
| Other/unknown | 25.3 | 30.4 |
| Median QTc, ms (95% CI) | 435 (391.9–552.1) | 439 (397–551.1) |
| Patients with QTc ≥500 ms (%) | 11.16 | 9.09 |
| Lansoprazole only | ||
| n | 12,271 | 13,074 |
| Demographic | ||
| Age, yrs (mean ± SD) | 60.0 ± 15.8 | 63.1 ± 17.7 |
| Race (% of group) | ||
| White | 60.8 | 54.6 |
| African American | 13.9 | 16.7 |
| Other/unknown | 25.3 | 28.7 |
| Median QTc, ms (95% CI) | 443 (395–572) | 445 (399–569) |
| Patients with QTc ≥500 ms (%) | 12.84 | 12.07 |
Applying the same case-control analysis to cefuroxime and lansoprazole showed no significant difference in QTc interval for both men (7 ms increase; 95% CI: −4.5 to 17 ms, n = 107; p = 0.167) and women (1.5 ms decrease; 95% CI: −9.3 to 4.3 ms; n = 228; p = 0.155). We observed no significant changes in QTc interval when further stratifying by race. See Figure 1C for complete results.
We performed sample-size and effect-size analyses, which demonstrated that with 100 patients prescribed either combination, we would be able to detect a 10-ms QT interval prolongation with 80% power; with 1,000 patients, the same effect size could be detected with 100% power (Online Figure 2).
Six hundred and three patients on ceftriaxone and lansoprazole had ECGs both before and after they started combination treatment. To control for baseline confounders, we performed a paired analysis comparing each of these patient’s highest QTc interval from ECGs performed up to 36 days before and after exposure to ceftriaxone and lansoprazole. We stratified the analysis by both sex and race. We observed a statistically significant increase in QTc interval for both white men (14.0 ± 4.0 ms increase; p = 6.56 × 10−4) and white women (12.9 ± 3.3 ms increase; p = 1.03 × 10−4). We observed no significant change in QTc interval for patients prescribed our negative control. See Online Table 3 for complete results.
In combination, ceftriaxone and lansoprazole block the hERG channel
Using a test of repeated measures, we found no significant effect from ceftriaxone on the hERG channel (p = 0.096). We found a significant effect from lansoprazole alone (p = 1.63 × 10−4), causing a drop in current to 86.6 ± 16.7% at 10 μM (no effect at 1 μM or 0.1 μM). In the presence of 1 μM lansoprazole, ceftriaxone caused a dose-dependent drop in current (96.8 ± 13.2% of control at 0.1 μM, 89.3 ± 13.2% at 100 μM; p = 1.07 × 10−4). In the presence of 10 μM lansoprazole, ceftriaxone caused a dose-dependent drop in current (63.1 ± 10.9% of control at 0.1 μM, 42.4 ± 11.6% at 100 μM; p < 3.45 × 10−5) (Figure 1D, left). For our negative control, we saw a small block in cefuroxime alone (94.0 ± 14.8% of control at 100 μM cefuroxime; p = 5.62 × 10−5) but no dose-dependent response of cefuroxime combined with 1 μM lansoprazole (p = 0.083) (Figure 1D, right).
Computational model recapitulates clinical observations
Using the hERG current blocks observed in the electrophysiology experiments as input to the computational model, the APD prolongation (measured as APD70) was 9 ms for the combination of 1 μM lansoprazole and 100 μM ceftriaxone, and 50 ms for 10 μM lansoprazole and 100 μM ceftriaxone (Figure 2). For the combination of 1 μM lansoprazole and 100 μM cefuroxime, the APD70 was shortened by 2 ms.
Figure 2. Results of the Computational Model of Ventricular Epicardial Myocytes.
The APD prolongation (measured as APD70) for each case is 9 ms and 50 ms, simulating 1 μM lansoprazole + 100 μM ceftriaxone and 10 μM lansoprazole + 100 μM ceftriaxone, respectively. Briefly, the model was run for a ventricular action potential paced at 1 Hz with baseline conditions (black) and 10% or 55% block of peak hERG current (brown and red, respectively). APD70 = action potential duration at 70% of repolarization; hERG = human Ether-à-go-go Related Gene.
No evidence of class effects between cephalosporins and PPIs
Given our identification of a putative drug interaction between a cephalosporin antibiotic and a PPI, we systematically evaluated all combinations of cephalosporins and PPIs for evidence of a drug interaction in FAERS, EHR, or both (Figure 3). The combination of ceftriaxone and lansoprazole in men was the only drug pair that had evidence in both FAERS and the EHR that also passed our confounder analysis for concomitant medications.
Figure 3. Analysis of Class Effects Between Cephalosporins and PPIs.
We analyzed each cephalosporin and PPI pair for evidence of an interaction in FAERS (black box: drug pair matches side effect profile), EHR (red: patients on combination have significantly prolonged QT intervals compared with those on either drug alone; blue: no significant change between cases and controls; white: no patients on the drug pair in the EHR), and that the change seen in the EHR was not due to concomitant medications (red star). We stratified the analysis between men and women. Only ceftriaxone and lansoprazole in men passed each of these criteria. EHR = electronic health records; FAERS = FDA Adverse Event Reporting System; PPI = proton-pump inhibitor.
Discussion
New data sources present new avenues for discovery
Data science and large clinical databases present new opportunities to discover adverse drug effects and drug-drug interactions. This is especially true in situations where traditional methods are impractical or infeasible, as is often the case for DDIs. There are many advantages to taking a retrospective approach for detecting DDIs. The analyses are relatively rapid and inexpensive to perform, and, because they are in situ, they focus on drug combinations that are actually used together in clinical practice. In particular, our use of latent signal detection to mine for DDIs using side-effect profile models allows us to circumvent many of the limitations inherent to traditional data-mining approaches that rely solely on direct evidence between drug pairs and side effects (13,14). However, there are many disadvantages as well. Retrospective analysis, and data mining in particular, are notorious for their potential biases and high false discovery rates. There are simply too many potentially confounding variables to make strong statements about causal relationships.
Here, we present a novel strategy that couples observational data mining with laboratory experiments to identify QT-DDIs (Central Illustration). Our observational analysis establishes the presence of a clinically-significant association between comedication and a prolonged QT interval. There are many hypotheses that may explain such an association. For example, a patient prescribed the putative interacting drugs may also be prescribed a known QT-prolonging agent. In fact, this is what we observed. Of 34 drug combinations that were associated with increased QT intervals, 26 could be dismissed as likely confounded by a known agent. Alternatively, it may be that there is a real drug interaction, in the pharmacological sense. The most common physiological explanation would be hERG block; therefore, we tested this hypothesis for our top prediction (ceftriaxone/lansoprazole) using patch-clamp electrophysiology. This atypical path, going from the clinic into the lab, has great potential to increase the efficiency of DDI discovery.
Central Illustration. Ceftriaxone and Lansoprazole are Associated With Acquired LQTS.
We combined mining of adverse event reports, corroboration in electronic health records, and experimental validation using single cell patch clamp to discover and validate a QT-DDI between ceftriaxone and lansoprazole. We used a data-driven profile of side effects that are predictive of LQTS to prioritize drug pairs pairs in FAERS. We corroborated these findings in the electronic health records by comparing the QTc intervals of patients administered the prioritized drug pair to patients exposed to either drug alone. We then validated our top prediction (ceftriaxone/lansoprazole) by measuring the dose-dependent changes in hERG channel current using patch-clamp electrophysiology.
AFib = atrial fibrillation; FAERS = FDA Adverse Event Reporting System; hERG = human Ether-à-go-go-Related Gene; LQTS = long QT syndrome; QT-DDI = QT-prolonging drug-drug interaction; VTach = ventricular tachycardia.
Critical evaluation of data mining using laboratory experiments
We combined data from FAERS with our local EHR to find evidence of QT-prolonging drug interactions. Either data source alone provides only weak evidence of a potential DDI producing thousands of equivalent hypotheses. By integrating these data, we increase power and focus the analysis on only the strongest candidates. Most importantly, we follow up on these DDI hypotheses using laboratory experiments to identify a possible mechanism.
An interaction between ceftriaxone and lansoprazole is unexpected
Our top candidate, ceftriaxone and lansoprazole, would not have been suspected using current surveillance methods. In the clinical records we found that comedication of these 2 common drugs is associated with significantly prolonged QTc intervals. This increase was highest for white men and black women, where we observed an average increase of 12 ms. It is important to note that if this effect size was observed for a single drug, it would be well above the threshold for regulatory concern during the approval stage (3). In the laboratory, we found that, in combination, lansoprazole and ceftriaxone block the hERG channel up to 57.6%, corresponding with an APD70 increase of 50 ms. At these higher lansoprazole concentrations, it is likely that, if treated as a single entity, the combination would not have received regulatory approval.
Molecular mechanism of the interaction between ceftriaxone and lansoprazole
We discovered that ceftriaxone and lansoprazole were significantly associated with prolonged QT intervals using clinical data. Our laboratory analysis suggests that this effect may be mediated through the hERG potassium channel, the most common mechanism by which drugs prolong the QT interval. However, the molecular explanation is not clear. Possibilities include a chemical interaction between the 2 compounds, cooperative binding to the channel, or an indirect mechanism through proteins that function with hERG. Furthermore, we found significantly different effects when our analysis was stratified by race and ethnicity. White men and women appear to be sensitive to the interaction, whereas black men experience only an intermediate change, and women identifying as “other, including Hispanic” experience no detectable effect. This is consistent with the large amount of ethnic heterogeneity in cardiac potassium channels (24,25), and may guide a structural analysis of the interaction.
Prior evidence of related adverse events
Lansoprazole is a commonly used PPI that is available over-the-counter. In retrospective analyses, PPIs were associated with a slight increased risk of myocardial infarction (26). Additionally, there have been a large number of deaths reported to the FDA for patients on this class of drugs, although this association is not statistically significant. Our discovery of a drug interaction with a PPI may explain these observations, although this requires follow-up study. Notably, evaluation of cefuroxime and lansoprazole, a pair predicted not to interact from the FAERS reporting frequencies, suggests that our pipeline is capable of distinguishing between safe and unsafe pairs, even within the same drug class.
Conclusion
We present evidence of a novel QT-DDI between lansoprazole and ceftriaxone. This interaction was discovered using a combination of data mining and laboratory experiments. Our clinical data suggest that patients on this pair of interacting drugs are more likely to have acquired LQTS, and the experimental study suggests that this effect may be mediated by blocking the hERG channel, the most common mechanism of acquired LQTS. This interaction appears to be specific to ceftriaxone, and does not extend to other cephalosporin antibiotics in combination with lansoprazole. Follow-up studies are required to confirm our findings and should include evaluating the mechanism of the interaction at the hERG channel, the effect of ceftriaxone and lansoprazole on other ion channels, and investigation of these drugs in combination with other hERG-blockers.
Supplementary Material
Clinical Perspectives.
Competency in Systems-Based Practice
Data science methodologies may accelerate evaluation of the safety of drug combinations. Data from large numbers of patients and ECGs in EHR can corroborate clinical event reports of adverse drug interactions and, for those involving QT-interval prolongation, laboratory methods can then be used to elucidate the electrophysiological mechanisms involved.
Translational Outlook
Further work is needed to systematically extend this paradigm to evaluate the safety of commonly used drug combinations in other cardiovascular domains.
Acknowledgments
The authors thank Sam Roe for his insight and perspective throughout the design and execution of this study.
Abbreviations and Acronyms
- APD70
action potential duration at 70% of repolarization
- DDI
drug-drug interaction
- ECG
electrocardiogram
- EHR
electronic health records
- FAERS
Food and Drug Administration Adverse Event Reporting System
- hERG
human Ether-à-go-go-Related Gene
- LQTS
long QT syndrome
- PPI
proton-pump inhibitor
- QT-DDI
QT interval-prolonging drug-drug interaction
- TdP
torsades de pointes
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Disclosures: Mr. Lorberbaum is supported by National Institute of General Medical Sciences (NIGMS) training grant (T32GM082797). Mr. Lorberbaum and Dr. Tatonetti are supported by the NIGMS (R01GM107145). Drs. Sampson and Kass are supported by the National Institute of Health (NIH; 5R01GM109762-02). Dr. Iyer is supported by the NIH (K08HL116790). Dr. Tatonetti is a paid advisor to Advera Health, Inc.; he declares no conflict of interest. Dr. Woolsey is an uncompensated officer of the nonprofit organization AZCERT.org, which is supported by a contract from the Food and Drug Administration’s Safe Use Initiative (HHSF223201400189C) and sponsors the website www.CredibleMeds.org utilized in this study; he declares no conflict of interest. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
References
- 1.Roden DM. Repolarization reserve: a moving target. Circulation. 2008;118:981–2. doi: 10.1161/CIRCULATIONAHA.108.798918. [DOI] [PubMed] [Google Scholar]
- 2.Woosley RL, Romero K. Assessing cardiovascular drug safety for clinical decision-making. Nat Rev Cardiol. 2013;10:330–7. doi: 10.1038/nrcardio.2013.57. [DOI] [PubMed] [Google Scholar]
- 3.Fermini B, Fossa AA, Fermini B, et al. The impact of drug-induced QT interval prolongation on drug discovery and development. Nat Rev Drug Discov. 2003;2:439–47. doi: 10.1038/nrd1108. [DOI] [PubMed] [Google Scholar]
- 4.Woosley RL, Chen Y, Freiman JP, et al. Mechanism of the cardiotoxic actions of terfenadine. JAMA. 1993;269:1532–6. [PubMed] [Google Scholar]
- 5.Shah RR. Drug-induced prolongation of the QT interval: regulatory dilemmas and implications for approval and labelling of a new chemical entity. Fundam Clin Pharmacol. 2002;16:147–56. doi: 10.1046/j.1472-8206.2002.00083.x. [DOI] [PubMed] [Google Scholar]
- 6.Itzhaki I, Maizels L, Huber I, et al. Modelling the long QT syndrome with induced pluripotent stem cells. Nature. 2011;471:225–9. doi: 10.1038/nature09747. [DOI] [PubMed] [Google Scholar]
- 7.Uehlinger C, Crettol S, Chassot P, et al. Increased (R)-methadone plasma concentrations by quetiapine in cytochrome P450s and ABCB1 genotyped patients. J Clin Psychopharmacol. 2007;27:273–8. doi: 10.1097/JCP.0b013e3180592ad2. [DOI] [PubMed] [Google Scholar]
- 8.Hripcsak G, Duke JD, Shah NH, et al. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform. 2015;216:574–8. [PMC free article] [PubMed] [Google Scholar]
- 9.Psaty BM, Breckenridge AM. Mini-Sentinel and regulatory science--big data rendered fit and functional. N Engl J Med. 2014;370:2165–7. doi: 10.1056/NEJMp1401664. [DOI] [PubMed] [Google Scholar]
- 10.Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2012;20:117–21. doi: 10.1136/amiajnl-2012-001145. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Tatonetti NP, Denny JC, Murphy SN, et al. Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels. Clin Pharmacol Ther. 2011;90:133–42. doi: 10.1038/clpt.2011.83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Tatonetti NP, Ye PP, Daneshjou R, et al. Data-driven prediction of drug effects and interactions. Sci Transl Med. 2012;4:125ra31. doi: 10.1126/scitranslmed.3003377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Tatonetti NP, Fernald GH, Altman RB. A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. J Am Med Inform Assoc. 2012;19:79–85. doi: 10.1136/amiajnl-2011-000214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lorberbaum T, Sampson KJ, Woosley RL, et al. An integrative data science pipeline to identify novel drug interactions that prolong the QT interval. Drug Saf. 2016;39:433–41. doi: 10.1007/s40264-016-0393-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Indik J, Pearson EC, Fried K, et al. Bazett and Fridericia QT correction formulas interfere with measurement of drug-induced changes in QT interval. Heart Rhythm. 2006;3:1003–7. doi: 10.1016/j.hrthm.2006.05.023. [DOI] [PubMed] [Google Scholar]
- 16.Rautaharju PM, Zhou SH, Wong S, et al. Sex differences in the evolution of the electrocardiographic QT interval with age. Can J Cardiol. 1992;8:690–5. [PubMed] [Google Scholar]
- 17.Collings BJ, Hamilton MA. Estimating the power of the two-sample Wilcoxon test for location shift. Biometrics. 1988;44:847–60. [PubMed] [Google Scholar]
- 18.Sakurai Y, Hirayama M, Hashimoto M, et al. Population pharmacokinetics and proton pump inhibitory effects of intravenous lansoprazole in healthy Japanese males. Biol Pharm Bull. 2007;30:2238–43. doi: 10.1248/bpb.30.2238. [DOI] [PubMed] [Google Scholar]
- 19.Tolman KG, Sanders SW, Buchi KN, et al. The effects of oral doses of lansoprazole and omeprazole on gastric pH. J Clin Gastroenterol. 1997;24:65–70. doi: 10.1097/00004836-199703000-00003. [DOI] [PubMed] [Google Scholar]
- 20.Rocephin (ceftriaxone sodium) for injection [prescribing information] South San Francisco, CA: Genentech USA, Inc; 2015. [Google Scholar]
- 21.Foord RD. Cefuroxime: human pharmacokinetics. Antimicrob Agents Chemother. 1976;9:741–7. doi: 10.1128/aac.9.5.741. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Iyer V, Mazhari R, Winslow RL. A computational model of the human left-ventricular epicardial myocyte. Biophys J. 2004;87:1507–25. doi: 10.1529/biophysj.104.043299. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Pharmaceutical Sales 2010. Verispan, VONA; Available at: http://www.drugs.com/top200_units.html. Accessed August 9, 2016. [Google Scholar]
- 24.Modell SM, Lehmann MH. The long QT syndrome family of cardiac ion channelopathies: a HuGE review. Genet Med. 2006;8:143–55. doi: 10.1097/01.gim.0000204468.85308.86. [DOI] [PubMed] [Google Scholar]
- 25.Ackerman MJ, Tester DJ, Jones GS, et al. Ethnic differences in cardiac potassium channel variants: implications for genetic susceptibility to sudden cardiac death and genetic testing for congenital long QT syndrome. Mayo Clin Proc. 2003;78:1479–87. doi: 10.4065/78.12.1479. [DOI] [PubMed] [Google Scholar]
- 26.Shah NH, LePendu P, Bauer-Mehren A, et al. Proton pump inhibitor usage and the risk of myocardial infarction in the general population. PLoS ONE. 2015;10:e0124653. doi: 10.1371/journal.pone.0124653. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.