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Updated: May 13, 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

Computational drug repositioning: from data to therapeutics.

M R Hurle¹, L Yang, Q Xie

¹Computational Biology, GlaxoSmithKline R&D, King of Prussia, Pennsylvania, USA.

Clinical Pharmacology and Therapeutics

|February 28, 2013

Summary

This review explores computational methods for drug repositioning, using transcriptomics, genetics, and side effect data to find new drug uses. These techniques offer promising avenues for discovering novel therapeutic indications beyond initial drug discovery.

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Last Updated: May 13, 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:

Computational biology
Pharmacology
Bioinformatics

Background:

Traditional drug discovery relies on phenotypic or target-based screening.
Drug indications are often expanded based on clinical observations.
Novel computational approaches can systematically identify new therapeutic uses for existing drugs.

Purpose of the Study:

To review computational techniques for drug repositioning.
To highlight the use of transcriptomics, genetics, and side effect data for hypothesis generation.
To discuss promising data domains for novel computational repositioning methods.

Main Methods:

Analysis of transcriptomics data (e.g., Connectivity Map, CMap).
Systematic analysis of drug side effect profiles.
Integration of genetics data, including genome-wide association studies (GWAS).
Exploration of electronic health records (EHRs) and phenotypic screening data.

Main Results:

Computational analysis generates novel hypotheses for drug repositioning.
Transcriptomics, genetics, and side effect data are valuable for identifying new indications.
Electronic health records and phenotypic screening show promise for future computational repositioning.

Conclusions:

Computational methods offer a systematic approach to drug repositioning.
Leveraging diverse data sources can accelerate the discovery of new therapeutic applications for drugs.
This review emphasizes the potential of computational strategies in modern drug development.