Precision medicine as a promising tool to empower research in healthcare industry
Department of Pharmacy, School of Medical and Allied Sciences, GD Goenka University, Sohna Road, Gurugram-122103, Haryana, India.
Research Article
GSC Biological and Pharmaceutical Sciences, 2018, 04(03), 074-079
Article DOI: 10.30574/gscbps.2018.4.3.0068
Publication history:
Received on 18 July 2018; revised on 29 August 2018; accepted on 01 September 2018
Abstract:
Ever since the first human genome was sequenced in 2001, there has been growing investment and research interest in precision medicine aimed at targeting medicines for precise therapy. This concept uses genomic sequencing to diagnose rare disease conditions where conventional therapies have failed. Precision medicine is a treatment regime specifically tailored to have the highest chance of success and lowest risk of adverse effects. It promises three benefits first, to patients by improving efficacy of drugs and reducing adverse effects. Second, it saves the healthcare systems from the cost burden of treating adverse effects by reducing expenditure on ineffective drugs. Third, to pharmaceutical industry by enabling drug makers to select and evaluate drug candidates much more cheaply and quickly than is possible currently. It can be emphasized that, the “precision” of precision medicine relies on the availability of large amounts of high quality, diverse data (both clinical and genomic) to accurately and equitably inform diagnosis and treatment approaches. So, the quality of the outcomes facilitated will be determined and limited by the quality of the data used to inform them. This can be complemented and validated with an experienced physician, scientist and pathologist to realize its full potential and success. In this review, the methodologies adopted and recent advances in the precision medicine are summarized.
Keywords:
Precision medicine; In silico; USFDA; Data mining; Algorithm; Artificial intelligence
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Copyright © 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0.
