Dataset Information

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.

ABSTRACT: Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations' data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).

SUBMITTER: Datta A

PROVIDER: S-EPMC8284671 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Introduction: The P2X3 receptor (P2X3R), an ATP-gated non-selective cation channel of the P2X receptor family, is expressed in sensory neurons and involved in nociception. P2X3R inhibition was shown to reduce chronic and neuropathic pain. In a previous screening of 2000 approved drugs, natural products, and bioactive substances, various non-steroidal anti-inflammatory drugs (NSAIDs) were found to inhibit P2X3R-mediated currents. Methods: To investigate whether the inhibition of P2X receptors contributes to the analgesic effect of NSAIDs, we characterized the potency and selectivity of various NSAIDs at P2X3R and other P2XR subtypes using two-electrode voltage clamp electrophysiology. Results: We identified diclofenac as a hP2X3R and hP2X2/3R antagonist with micromolar potency (with IC50 values of 138.2 and 76.7 µM, respectively). A weaker inhibition of hP2X1R, hP2X4R, and hP2X7R by diclofenac was determined. Flufenamic acid (FFA) inhibited hP2X3R, rP2X3R, and hP2X7R (IC50 values of 221 µM, 264.1 µM, and ∼900 µM, respectively), calling into question its use as a non-selective ion channel blocker, when P2XR-mediated currents are under study. Inhibition of hP2X3R or hP2X2/3R by diclofenac could be overcome by prolonged ATP application or increasing concentrations of the agonist α,β-meATP, respectively, indicating competition of diclofenac and the agonists. Molecular dynamics simulation showed that diclofenac largely overlaps with ATP bound to the open state of the hP2X3R. Our results suggest a competitive antagonism through which diclofenac, by interacting with residues of the ATP-binding site, left flipper, and dorsal fin domains, inhibits the gating of P2X3R by conformational fixation of the left flipper and dorsal fin domains. In summary, we demonstrate the inhibition of the human P2X3 receptor by various NSAIDs. Diclofenac proved to be the most effective antagonist with a strong inhibition of hP2X3R and hP2X2/3R and a weaker inhibition of hP2X1R, hP2X4R, and hP2X7R. Discussion: Considering their involvement in nociception, inhibition of hP2X3R and hP2X2/3R by micromolar concentrations of diclofenac, which are rarely reached in the therapeutic range, may play a minor role in analgesia compared to the high-potency cyclooxygenase inhibition but may explain the known side effect of taste disturbances caused by diclofenac.

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Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.

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