Literature DB >> 21949217

The relationship between rational drug design and drug side effects.

Juan Wang1, Zhi-xin Li, Cheng-xiang Qiu, Dong Wang, Qing-hua Cui.   

Abstract

Previous analysis of systems pharmacology has revealed a tendency of rational drug design in the pharmaceutical industry. The targets of new drugs tend to be close with the corresponding disease genes in the biological networks. However, it remains unclear whether the rational drug design introduces disadvantages, i.e. side effects. Therefore, it is important to dissect the relationship between rational drug design and drug side effects. Based on a recently released drug side effect database, SIDER, here we analyzed the relationship between drug side effects and the rational drug design. We revealed that the incidence drug side effect is significantly associated with the network distance of drug targets and diseases genes. Drugs with the distances of three or four have the smallest incidence of side effects, whereas drugs with the distances of more than four or smaller than three show significantly greater incidence of side effects. Furthermore, protein drugs and small molecule drugs show significant differences. Drugs hitting membrane targets and drugs hitting cytoplasm targets also show differences. Failure drugs because of severe side effects show smaller network distances than approved drugs. These results suggest that researchers should be prudent on rationalizing the drug design. Too small distances between drug targets and diseases genes may not always be advantageous for rational design for drug discovery.

Mesh:

Substances:

Year:  2011        PMID: 21949217     DOI: 10.1093/bib/bbr061

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  10 in total

Review 1.  Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.

Authors:  Peter Csermely; Tamás Korcsmáros; Huba J M Kiss; Gábor London; Ruth Nussinov
Journal:  Pharmacol Ther       Date:  2013-02-04       Impact factor: 12.310

2.  RORγt-specific transcriptional interactomic inhibition suppresses autoimmunity associated with TH17 cells.

Authors:  Tae-Yoon Park; Sung-Dong Park; Jen-Young Cho; Jae-Seung Moon; Na-Yeon Kim; Kyungsoo Park; Rho Hyun Seong; Sang-Won Lee; Tomohiro Morio; Alfred L M Bothwell; Sang-Kyou Lee
Journal:  Proc Natl Acad Sci U S A       Date:  2014-12-19       Impact factor: 11.205

3.  Ellagic acid and human cancers: a systems pharmacology and docking study to identify principal hub genes and main mechanisms of action.

Authors:  Hamid Cheshomi; Ahmad Reza Bahrami; Maryam M Matin
Journal:  Mol Divers       Date:  2020-05-14       Impact factor: 2.943

4.  Entourage: visualizing relationships between biological pathways using contextual subsets.

Authors:  Alexander Lex; Christian Partl; Denis Kalkofen; Marc Streit; Samuel Gratzl; Anne Mai Wassermann; Dieter Schmalstieg; Hanspeter Pfister
Journal:  IEEE Trans Vis Comput Graph       Date:  2013-12       Impact factor: 4.579

5.  Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations.

Authors:  Áron R Perez-Lopez; Kristóf Z Szalay; Dénes Türei; Dezső Módos; Katalin Lenti; Tamás Korcsmáros; Peter Csermely
Journal:  Sci Rep       Date:  2015-05-11       Impact factor: 4.379

6.  ChemProt-3.0: a global chemical biology diseases mapping.

Authors:  Jens Kringelum; Sonny Kim Kjaerulff; Søren Brunak; Ole Lund; Tudor I Oprea; Olivier Taboureau
Journal:  Database (Oxford)       Date:  2016-02-13       Impact factor: 3.451

7.  Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine.

Authors:  Zhan Tong; Yuan Zhou; Juan Wang
Journal:  Sci Rep       Date:  2019-07-18       Impact factor: 4.379

8.  Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins.

Authors:  Sina Ghadermarzi; Xingyi Li; Min Li; Lukasz Kurgan
Journal:  Front Genet       Date:  2019-11-15       Impact factor: 4.599

9.  In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion.

Authors:  Xian Liu; Yuan Xu; Shanshan Li; Yulan Wang; Jianlong Peng; Cheng Luo; Xiaomin Luo; Mingyue Zheng; Kaixian Chen; Hualiang Jiang
Journal:  J Cheminform       Date:  2014-06-18       Impact factor: 5.514

10.  Large-Scale Analysis of Drug Side Effects via Complex Regulatory Modules Composed of microRNAs, Transcription Factors and Gene Sets.

Authors:  Xiaodong Jia; Qing Jin; Xiangqiong Liu; Xiusen Bian; Yunfeng Wang; Lei Liu; Hongzhe Ma; Fujian Tan; Mingliang Gu; Xiujie Chen
Journal:  Sci Rep       Date:  2017-07-20       Impact factor: 4.379

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.