Literature DB >> 32415493

Data science-driven analyses of drugs inducing hypertension as an adverse effect.

Reetu Sharma1.   

Abstract

The utilization of approved medication is a requisite to combat certain diseases for health; however, the undesirable adverse effects (AEs) due to medication are generally unavoidable. Hypertension is one of such AEs resulting from approved medication in which blood pressure in the arteries gets elevated and is a risk factor for several diseases including heart and kidney failure. HTs are the approved drugs that can cause hypertension as an AE. Here, the goal of the study is to investigate the structural and functional diversities of HTs. In our quest to unravel the structural parameters of the HTs, a systematic analysis of the HTs having a different number and type of ring systems was conducted. The cellular component, molecular function and biological processes adopted by the gene products were analyzed. Moreover, our systematically done analysis suggests that all the target families are active in a common pathway, that is, nerve transmission. A comparison of the selected structural and functional aspect of HTs with anti-hypertensives suggests that HTs follow certain structural and functional features in spite of many possibilities. Our study provides a promising methodology that considers the influence of structural diversity of AE causing agents on a functional perspective for precursory clinical decision making. This could be extended to explore the structural and functional trends that are adopted by agents causing certain diseases or AEs.

Entities:  

Keywords:  Adverse effect; Approved drugs; Data science; Hypertension

Year:  2020        PMID: 32415493     DOI: 10.1007/s11030-020-10059-5

Source DB:  PubMed          Journal:  Mol Divers        ISSN: 1381-1991            Impact factor:   2.943


  42 in total

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Authors:  José L Medina-Franco; Marc A Giulianotti; Gregory S Welmaker; Richard A Houghten
Journal:  Drug Discov Today       Date:  2013-01-20       Impact factor: 7.851

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Authors:  Matthew E Welsch; Scott A Snyder; Brent R Stockwell
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9.  Comparative analyses of structural features and scaffold diversity for purchasable compound libraries.

Authors:  Jun Shang; Huiyong Sun; Hui Liu; Fu Chen; Sheng Tian; Peichen Pan; Dan Li; Dexin Kong; Tingjun Hou
Journal:  J Cheminform       Date:  2017-04-21       Impact factor: 5.514

10.  Network-based prediction of drug combinations.

Authors:  Feixiong Cheng; István A Kovács; Albert-László Barabási
Journal:  Nat Commun       Date:  2019-03-13       Impact factor: 14.919

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  1 in total

1.  Network-based approach highlighting interplay among anti-hypertensives: target coding-genes: diseases.

Authors:  Reetu Sharma
Journal:  Sci Rep       Date:  2020-11-19       Impact factor: 4.379

  1 in total

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