Data-driven prediction of adverse drug reactions induced by drug-drug interactions

Abstract

The expanded use of multiple drugs has increased the occurrence of adverse drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions are typically not observed in clinical drug development studies because most of them focus on single-drug therapies. ADR reporting systems collect information on adverse health effects caused by both single drugs and DDIs. A major challenge is to unambiguously identify the effects caused by DDIs and to attribute them to specific drug interactions. A computational method that provides prospective predictions of potential DDI-induced ADRs will help to identify and mitigate these adverse health effects.

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Document Details

Document Type
Technical Report
Publication Date
Jun 08, 2017
Accession Number
AD1039209

Entities

People

  • Anders Wallqvist
  • Jaques Reifman
  • Kamal Kumar
  • Mohamed Diwan M AbdulHameed
  • Ruifeng Liu
  • Xueping Yu

Organizations

  • United States Department of Defense

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Anticonvulsants
  • Chemotherapy
  • Databases
  • Diseases And Disorders
  • Drug Abuse
  • Drug Addiction
  • Enzyme Inhibitors
  • Graphical User Interface
  • Heart Diseases
  • Medical Personnel
  • Network Science
  • Pharmacology
  • Predictive Modeling
  • Pregnancy Complications
  • Protein-Protein Interactions
  • Side Effects
  • Web Browsers

Fields of Study

  • Biology

Readers

  • Distributed Systems and Data Platform Development
  • Oncology (Cancer Research).
  • Psychological Intervention/Treatment for Stress, Anxiety, PTSD, and Related Emotional and Cognitive Health Symptoms.