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Transcriptomic Data Mining and Repurposing for Computational Drug Discovery

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Computational Methods for Drug Repurposing

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1903))

Abstract

Conventional drug discovery in general is costly and time-consuming with extremely low success and relatively high attrition rates. The disparity between high cost of drug discovery and vast unmet medical needs resulted in advent of an increasing number of computational approaches that can “connect” disease with a candidate therapeutic. This includes computational drug repurposing or repositioning wherein the goal is to discover a new indication for an approved drug. Computational drug discovery approaches that are commonly used are similarity-based wherein network analysis or machine learning-based methods are used. One such approach is matching gene expression signatures from disease to those from small molecules, commonly referred to as connectivity mapping. In this chapter, we will focus on how publicly available existing transcriptomic data from diseases can be reused to identify novel candidate therapeutics and drug repositioning candidates. To elucidate these, we will present two case studies: (1) using transcriptional signature similarity or positive correlation to identify novel small molecules that are similar to an approved drug and (2) identifying candidate therapeutics via reciprocal connectivity or negative correlation between transcriptional signatures from a disease and small molecule.

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Authors and Affiliations

  1. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Yunguan Wang, Jaswanth Yella & Anil G. Jegga

  2. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Anil G. Jegga

  3. Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH, USA

    Jaswanth Yella & Anil G. Jegga

Authors
  1. Yunguan Wang
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  2. Jaswanth Yella
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  3. Anil G. Jegga
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Correspondence to Anil G. Jegga .

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Editors and Affiliations

  1. Insilico Medicine, Inc., Rockville, MD, USA

    Quentin Vanhaelen

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Wang, Y., Yella, J., Jegga, A.G. (2019). Transcriptomic Data Mining and Repurposing for Computational Drug Discovery. In: Vanhaelen, Q. (eds) Computational Methods for Drug Repurposing. Methods in Molecular Biology, vol 1903. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8955-3_5

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  • DOI: https://doi.org/10.1007/978-1-4939-8955-3_5

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8954-6

  • Online ISBN: 978-1-4939-8955-3

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