Data-Driven Prediction of Drug Effects and Interactions

Abstract

Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.

Zoo or Savannah? Choice of Training Ground for Evidence-Based Pharmacovigilance

“…Now consider the challenge of evaluating signal detection performance against broad references of such positive and negative controls. Contemporary research has reported improved performance of multivariate analytics compared to disproportionality screening, for the analysis of individual case reports [6,8]. This seems plausible, since the new methods offer innovations such as adjustment for comedications and indications for treatment.…”

“…There are individual case reports [1], longitudinal health records [2], internet search patterns [3] and social media [4]. There is disproportionality analysis [1], regression [5,6], adjustment by propensity scores [7,8], self-controlled designs [2,9] and more. Expert judgment is important in choosing methods and datasets for pharmacovigilance, but ideally we would like to see objective evidence that a chosen approach can be expected to be effective.…”

Predicting Drug-Drug Interactions for Premarket Drug Development Process: A Network Based Approach

“…It also retrieves the information from the spontaneous reporting systems such as US Food and Drug Administration's Adverse Event Reporting System (AERS) [2][11] [13] by using the technologies like the semantic web [15] and linked data.…”

Automatic detection of drug interaction mismatches in package inserts

“…The paper also notes that there are inconsistencies in the severity grading system between the compendia. Tatonetti et al [7] present the Offsides database for drug effects and the Twosides database for drug-drug interaction side effects, and use these databases to attempt to identify drug targets, predict drug indications and discover drug class interactions. Shah et al [8] finds that the drug-drug interaction knowledge databases used for clinical decision support systems are not consistent with official package labels, causing spurious warnings and inaccurate information.…”