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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Related Experiment Video

Updated: Jun 24, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Generating genome-scale candidate gene lists for pharmacogenomics.

N T Hansen¹, S Brunak, R B Altman

¹Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.

Clinical Pharmacology and Therapeutics

|April 17, 2009

Summary

We developed a new computational method to identify potential drug-response genes. This approach effectively ranks genes by integrating drug-target interactions and drug similarity, aiding pharmacogenomic research.

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Published on: October 3, 2025

Area of Science:

Pharmacogenomics
Computational Biology
Drug Discovery

Background:

Identifying genes influencing drug response is crucial in pharmacogenomics.
Existing methods like high-throughput screening have limitations (false positives, incomplete knowledge).
Manual candidate gene list generation is costly and may lack comprehensive coverage.

Purpose of the Study:

To develop and validate a computational method for ranking genes based on their relevance to specific drugs and indications.
To improve the efficiency and accuracy of identifying candidate genes for drug response prediction.

Main Methods:

Developed a gene ranking method considering 12,460 human genes.
Integrated known gene-drug interactions and gene-gene interaction networks.
Assessed drug similarity using structural and indication-based measures within a local interaction network.

Main Results:

The method achieved a high performance with an overall area under the curve of 0.82 in benchmark tests.
Successfully identified novel gene candidates for drugs including warfarin, gefitinib, carboplatin, and gemcitabine.
Provided molecular hypotheses supporting the predicted gene-drug associations.

Conclusions:

The developed method offers a powerful, knowledge-driven approach to identify potential pharmacogenomic targets.
This computational strategy enhances the discovery of novel gene candidates for drug response.
The findings support the utility of integrating interaction networks and drug similarity for predictive pharmacogenomics.