Preprint Review Version 1 This version is not peer-reviewed

Changing Trends in Computational Drug Repositioning

Version 1 : Received: 30 April 2018 / Approved: 1 May 2018 / Online: 1 May 2018 (12:27:22 CEST)

A peer-reviewed article of this Preprint also exists.

Yella, J.K.; Yaddanapudi, S.; Wang, Y.; Jegga, A.G. Changing Trends in Computational Drug Repositioning. Pharmaceuticals 2018, 11, 57. Yella, J.K.; Yaddanapudi, S.; Wang, Y.; Jegga, A.G. Changing Trends in Computational Drug Repositioning. Pharmaceuticals 2018, 11, 57.

Journal reference: Pharmaceuticals 2018, 11, 57
DOI: 10.3390/ph11020057

Abstract

Maximizing the indications potential and revenue from drugs that are already marketed offers a new take on the famous mantra of the Nobel Prize-winning pharmacologist, Sir James Black, “The most fruitful basis for the discovery of a new drug is to start with an old drug”. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Most of the repositioned drugs therefore are the result of “serendipity” - based on late phase clinical studies of unexpected findings. One of the reasons that the connection between drug candidates and their potential adverse drug reactions or new applications could not be identified earlier is that the underlying mechanism associating them is either very intricate and unknown or dispersed and buried in a sea of information. Discovery of such multi-domain pharmacomodules - pharmacologically relevant sub-networks of biomolecules and/or pathways - from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

Subject Areas

computational drug repositioning; drug repositioning; drug repurposing; machine learning; deep learning; crowdsourcing; open innovation; drug discovery

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