Preprint Article Version 1 NOT YET PEER-REVIEWED

A Protocol for Identifying Potentially Repurposable Drugs Using Online Tools and Databases

  1. Grade 12, Upper St. Clair High School, Pittsburgh, PA 15241, USA
  2. Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, USA
  3. Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA
Version 1 : Received: 8 October 2016 / Approved: 9 October 2016 / Online: 9 October 2016 (08:42:23 CEST)

How to cite: Chattopadhyay, A.; Ganapathiraju, M. A Protocol for Identifying Potentially Repurposable Drugs Using Online Tools and Databases. Preprints 2016, 2016100025 (doi: 10.20944/preprints201610.0025.v1). Chattopadhyay, A.; Ganapathiraju, M. A Protocol for Identifying Potentially Repurposable Drugs Using Online Tools and Databases. Preprints 2016, 2016100025 (doi: 10.20944/preprints201610.0025.v1).

Abstract

Traditional methods for discovery and development of new drugs can be a very time-consuming and expensive process because it includes several stages such as compound identification, pre-clinical and clinical trials before the drug is approved by the US Food and Drug Administration (FDA). Therefore, drug repurposing, namely using currently FDA-approved drugs as therapeutics for other diseases than what they are originally prescribed for, is emerging to be a faster and more cost-effective alternative to current drug discovery methods. In this paper, we have described a three-step in silico protocol for analyzing transcriptomics data using online databases and bioinformatics tools for identifying potentially repurposable drugs. The efficacy of this protocol was evaluated by comparing its predictions with the findings of two case studies of recently reported repurposed drugs: HIV treating drug Zidovudine for the treatment of Dry Age-Related Macular Degeneration and the antidepressant Imipramine for Small-Cell Lung Carcinoma. The proposed protocol successfully identified the published findings, thus demonstrating the efficacy of this method. In addition, it also yielded several novel predictions that have not yet been published, including the finding that Imipramine could potentially treat Severe Acute Respiratory Syndrome (SARS), a disease that currently does not have any treatment or vaccine. Since this in-silico protocol is simple to use and does not require advanced computer skills, we believe any motivated participant with access to these databases and tools would be able to apply it to large datasets to identify other potentially repurposable drugs in the future.

Subject Areas

drug repurposing; translational bioinformatics; transcriptomics; transcriptome analysis; drug discovery; protocol; gene expression

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