Literature DB >> 16563668

In silico search of putative adverse drug reaction related proteins as a potential tool for facilitating drug adverse effect prediction.

Zhi Liang Ji1, Yi Wang, Lin Yu, Lian Yi Han, Chan Juan Zheng, Yu Zong Chen.   

Abstract

Adverse drug reaction (ADR) is a significant issue in drug development and post-market applications. Different experimental and computational approaches need to be explored for predicting ADRs due to the complexity of their molecular mechanisms. One approach for predicting ADRs of a drug is to search for its interaction with ADR-related proteins (ADRRPs). In this work, this approach is tested on 11 marketed anti-HIV drugs covering protease inhibitors (PIs), nucleoside reverse transcriptase inhibitors (NRTIs), and non-nucleoside reverse transcriptase inhibitors (NNRTIs). An in silico drug target search method, INVDOCK, is used for searching the ADRRPs of each of these drugs. The corresponding ADRs of the predicted ADRRPs of each of these drugs are compared to clinically observed ADRs reported in the literature. It is found that 86-89% of the INVDOCK predicted ADRs of these drugs are consistent with the literature reported ADRs, and about 67-100% of the literature-reported ADRs of these drugs to various degrees is agreed with INVDOCK predictions. These results suggest that it is feasible to explore in silico ADRRP search methods for facilitating drug toxicity prediction.

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Year:  2006        PMID: 16563668     DOI: 10.1016/j.toxlet.2005.11.017

Source DB:  PubMed          Journal:  Toxicol Lett        ISSN: 0378-4274            Impact factor:   4.372


  11 in total

1.  PDID: database of molecular-level putative protein-drug interactions in the structural human proteome.

Authors:  Chen Wang; Gang Hu; Kui Wang; Michal Brylinski; Lei Xie; Lukasz Kurgan
Journal:  Bioinformatics       Date:  2015-10-26       Impact factor: 6.937

2.  kNNsim: k-nearest neighbors similarity with genetic algorithm features optimization enhances the prediction of activity classes for small molecules.

Authors:  Dariusz Plewczynski
Journal:  J Mol Model       Date:  2008-07-29       Impact factor: 1.810

Review 3.  Structure-based systems biology for analyzing off-target binding.

Authors:  Lei Xie; Li Xie; Philip E Bourne
Journal:  Curr Opin Struct Biol       Date:  2011-02-01       Impact factor: 6.809

Review 4.  Docking-based inverse virtual screening: methods, applications, and challenges.

Authors:  Xianjin Xu; Marshal Huang; Xiaoqin Zou
Journal:  Biophys Rep       Date:  2018-02-01

5.  SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical-protein interactome.

Authors:  Lun Yang; Heng Luo; Jian Chen; Qinghe Xing; Lin He
Journal:  Nucleic Acids Res       Date:  2009-05-05       Impact factor: 16.971

Review 6.  In silico pharmacology for drug discovery: applications to targets and beyond.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

7.  In silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators.

Authors:  Lei Xie; Jian Wang; Philip E Bourne
Journal:  PLoS Comput Biol       Date:  2007-09-26       Impact factor: 4.475

8.  Computational molecular modeling for evaluating the toxicity of environmental chemicals: prioritizing bioassay requirements.

Authors:  James R Rabinowitz; Michael-Rock Goldsmith; Stephen B Little; Melissa A Pasquinelli
Journal:  Environ Health Perspect       Date:  2008-05       Impact factor: 9.031

9.  Target prediction utilising negative bioactivity data covering large chemical space.

Authors:  Lewis H Mervin; Avid M Afzal; Georgios Drakakis; Richard Lewis; Ola Engkvist; Andreas Bender
Journal:  J Cheminform       Date:  2015-10-24       Impact factor: 5.514

Review 10.  Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds.

Authors:  Hongbin Huang; Guigui Zhang; Yuquan Zhou; Chenru Lin; Suling Chen; Yutong Lin; Shangkang Mai; Zunnan Huang
Journal:  Front Chem       Date:  2018-05-09       Impact factor: 5.221

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