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Updated: May 30, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Systems genetics for drug target discovery.

Nadia M Penrod¹, Richard Cowper-Sal-lari, Jason H Moore

¹Computational Genetics Laboratory Dartmouth Medical School, One Medical Center Drive, Lebanon, NH 03756, USA.

Trends in Pharmacological Sciences

|August 25, 2011

Summary

This summary is machine-generated.

Genomic data analysis can identify disease targets, but traditional methods miss key genetic information. Network analysis offers a way to better translate genomic discoveries into effective drug targets.

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Last Updated: May 30, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

Genomics
Pharmacology
Bioinformatics

Background:

Genomic data analysis holds promise for discovering novel druggable targets.
Despite advances in genomic data collection, the approval of drugs targeting new molecular entities has stagnated.
Conventional single-gene analysis methods fail to capture the complexity of genetic regulatory systems, limiting translation.

Purpose of the Study:

To discuss the utilization of genomic data for identifying disease-associated genes and regions.
To explain the validation process for disease-associated regions as functional drug targets.
To highlight the role of network analysis in improving drug target identification from genomic data.

Main Methods:

Review of current methodologies for genomic data analysis in disease research.
Discussion of target validation strategies for disease-associated genomic regions.
Exploration of network analysis approaches to integrate genetic information.

Main Results:

Genomic data can pinpoint disease-associated genes and regions.
Validation confirms the functional relevance of identified genomic regions as potential targets.
Network analysis provides a systems-level view, enhancing the identification of translatable drug targets.

Conclusions:

Current genomic analysis methods are insufficient for effective drug target discovery.
Network analysis is crucial for bridging the gap between genomic data and clinical applications.
Integrating network analysis with genomic data promises to accelerate the development of novel therapeutics.