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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
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DeSigN: connecting gene expression with therapeutics for drug repurposing and development
Bernard Kok Bang Lee1,2, Kai Hung Tiong1,2, Jit Kang Chang3,4
1Department of Oral & Maxillofacial Clinical Sciences, Faculty of Dentistry, University of Malaya, 50603, Kuala Lumpur, Malaysia.
BMC Genomics
|February 16, 2017
Summary
A new tool, DeSigN, predicts cancer drug efficacy using gene expression patterns. It successfully identified bosutinib as a potential treatment for oral squamous cell carcinoma, improving drug development efficiency.
Area of Science:
- Computational biology
- Genomics
- Drug discovery
Background:
- Drug development is lengthy, necessitating strategies for increased efficiency.
- Precision medicine leverages cancer cell genetics for drug response prediction.
- Simultaneous analysis of multiple genes can enhance gene-drug interaction predictions.
Purpose of the Study:
- To develop a computational tool for predicting drug efficacy in cancer.
- To identify novel drug candidates and repurpose existing drugs for cancer treatment.
Main Methods:
- Developed DeSigN, a web-based tool correlating gene signatures with drug response data (IC50).
- Utilized gene expression patterns from cancer cell lines and 140 drug response profiles.
- Validated predictions using published GEO studies and in vitro experiments.
Main Results:
- DeSigN accurately predicted drug sensitivity in four independent studies.
- Identified bosutinib as a promising inhibitor for oral squamous cell carcinoma (OSCC) cell lines.
- Experimental validation confirmed bosutinib's anti-proliferative activity in OSCC cell lines.
Conclusions:
- DeSigN is a robust method for identifying candidate drugs based on gene expression.
- The platform facilitates drug repurposing and enhances drug development efficiency.
- Enables identification of drugs with unexpected efficacy against specific cancer types.
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