Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions

Abstract

Drug-drug interactions (DDIs) are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 (CYP) metabolism enzymes identified from published in vitro pharmacology experiments. Using a clinical repository of over 800,000 patients, we narrowed this theoretical set of DDIs to 3670 drug pairs actually taken by patients. Finally, we sought to identify novel combinations that synergistically increased the risk of myopathy. Five pairs were identified with their p-values less than 1E-06: loratadine and simvastatin (relative risk or RR = 1.69); loratadine and alprazolam (RR = 1.86); loratadine and duloxetine (RR = 1.94); loratadine and ropinirole (RR = 3.21); and promethazine and tegaserod (RR = 3.00). When taken together, each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone. Based on additional literature data on in vitro drug metabolism and inhibition potency, loratadine and simvastatin and tegaserod and promethazine were predicted to have a strong DDI through the CYP3A4 and CYP2D6 enzymes, respectively. This new translational biomedical informatics approach supports not only detection of new clinically significant DDI signals, but also evaluation of their potential molecular mechanisms.

Figure 1. Drug names and drug interaction pairs filtering and mapping flow chart.

Figure 2. The van-diagram of predicted DDIs, DDIs with EMR data, and DDIs tested in vivo.

The predicted DDIs were from the literature mining. DDIs with EMR data mean DDIs with non-zero frequency among the co-medication data in the EMR. in vivo DDIs mean that DDIs were shown changing substrate concentration significantly (p<0.05 or fold-change>2); and in vivo non DDIs mean that DDIs were not shown changing substrate concentration significantly.

Figure 3. DDI enrichment plots among 9 CYP enzymes.

Both x- and y-axis represent different drug names from a DDI pair. A red-dot highlights a DDI pair showing a strong association with myopathy risk (p<0.0000136, odds ratio>1).

Figure 4. Metabolism enzymes and inhibition potencies of seven drugs.

The metabolism enzymes of a drug are characterized with major, partial, or not. The inhibition potencies of a drug are characterized with strong (Ki<10 uM), moderate (10100 uM).

Figure 5. in vitro PK study literature mining flow-chart for CYP substrates and inhibitors, and their DDI predictions.

Figure 6. Pharmaco-epidemiology design for myopathy cases and controls in the electronic medical records.

Figure 7. Drug interaction effect models on the myopathy risk.

(A) Additive DDI Model; and (B) Synergistic DDI Model.