Identification and Mechanistic Investigation of Drug–Drug Interactions Associated With Myopathy: A Translational Approach

Abstract

Myopathy is a group of muscle diseases that can be induced or exacerbated by drug–drug interactions (DDIs). We sought to identify clinically important myopathic DDIs and elucidate their underlying mechanisms. Five DDIs were found to increase the risk of myopathy based on analysis of observational data from the Indiana Network of Patient Care. Loratadine interacted with simvastatin (relative risk 95% confidence interval [CI] = [1.39, 2.06]), alprazolam (1.50, 2.31), ropinirole (2.06, 5.00), and omeprazole (1.15, 1.38). Promethazine interacted with tegaserod (1.94, 4.64). In vitro investigation showed that these DDIs were unlikely to result from inhibition of drug metabolism by CYP450 enzymes or from inhibition of hepatic uptake via the membrane transporter OATP1B1/1B3. However, we did observe in vitro synergistic myotoxicity of simvastatin and desloratadine, suggesting a role in loratadine–simvastatin interaction. This interaction was epidemiologically confirmed (odds ratio 95% CI = [2.02, 3.65]) using the data from the US Food and Drug Administration Adverse Event Reporting System.

Biomedical Informatics Approaches to Identifying Drug–Drug Interactions

“…Compared to the previous studies that identified interaction signals from spontaneous adverse drug event reports, our study avoids the biases stemming from the use of anecdotal case reports. 14,15 The pharmacoepidemiologic screening of our study has a few advantages over the drug–drug interaction screening performed by Han et al 17 that used a cohort design and an electronic healthcare records (EHR) database. First, our screening was more computationally efficient as the self-controlled case series design includes only cases.…”

“…16 Screening studies using longitudinal databases may offer more promise in identifying interactions, but can face the challenge of simultaneous confounding adjustment across a large number of drug pairs. 17 The self-controlled case series design has been shown to have reasonable performance in screening for drug–adverse event associations using longitudinal databases. 18 This design appears to be promising in alleviating the issue of confounding for time-invariant factors when screening for drug–drug interactions because each subject implicitly serves as her/his own control.…”

Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research

“…There are a number of drug‐related databases that integrate bioinformatics, cheminformatics, and/or DDI knowledge, which have been widely used for the drug interaction alerting in a large range of clinical decision support and electronic prescribing systems. Meanwhile, clinical signal‐based databases can be helpful for understanding the mechanism of action for drugs . In addition, part of premarket drug development relies on the drug information and DDI knowledge to predict interactions between a new drug candidate and drugs currently on the market.…”

A super‐combo‐drug test to detect adverse drug events and drug interactions from electronic health records in the era of polypharmacy

“…The severe myopathy, also called rhabdomyolysis, leads to renal failure, and sometimes can be fatal. The EHR data were obtained from a deidentified subset of the Indiana Network for Patient Care (INPC), which contains lab tests, diagnoses, and medications for almost five million of patients from 2004 to 2009 . In a nested case‐control design, controls were selected as patients who did not have myopathy at the same time (ie, index time) when cases experienced myopathy.…”