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Pharmacogenomic Approaches for Automated Medication Risk Assessment in People with Polypharmacy.

Jiazhen Liu1, Carol Friedman2, Joseph Finkelstein2

  • 1Stevens Institute of Technology, Hoboken, NJ, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|June 12, 2018
PubMed
Summary

This study developed a pharmacogenomic decision support tool to assess medication risks in polypharmacy patients. The tool accounts for multiple drug-gene interactions, improving risk assessment for adverse drug events.

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Area of Science:

  • Pharmacogenomics
  • Clinical Pharmacology
  • Computational Biology

Background:

  • Current pharmacogenomic tools often overlook complex drug-drug and drug-gene interactions in polypharmacy.
  • Polypharmacy increases the risk of adverse drug events and reduced drug efficacy.
  • Personalized medication management requires consideration of multiple interacting factors.

Purpose of the Study:

  • To develop a comprehensive pharmacogenomic decision support system for medication risk assessment in polypharmacy.
  • To integrate multiple drug-gene and drug-drug interactions into a unified risk scoring model.
  • To create a knowledge repository of actionable pharmacogenes and scoring approaches.

Main Methods:

  • Developed a comprehensive knowledge repository of actionable pharmacogenes.
  • Introduced scoring approaches for complex medication regimens, considering pharmacogenomic polymorphisms and multiple drug metabolizing pathways.
  • Pilot-tested a scoring algorithm using a limited dataset of older adults with polypharmacy.

Main Results:

  • A scoring algorithm was developed and pilot-tested.
  • Significantly different total risk scores were observed between frequently hospitalized and low-hospitalization older adults with polypharmacy (p<0.05).
  • The prototype demonstrated the feasibility of the approach and identified areas for algorithm improvement.

Conclusions:

  • The developed pharmacogenomic decision support tool shows feasibility for assessing medication risks in polypharmacy.
  • Accounting for multiple drug and gene effects can improve the accuracy of medication risk assessment.
  • Further refinement of risk scoring algorithms is warranted for clinical application.