Literature DB >> 11568366

Prediction of organophosphorus acetylcholinesterase inhibition using three-dimensional quantitative structure-activity relationship (3D-QSAR) methods.

J El Yazal1, S N Rao, A Mehl, W Slikker.   

Abstract

Neurotoxic organophosphorous compounds are known to modulate their biological effects through the inhibition of a number of esterases including acetylcholinesterase (AChE), the enzyme responsible for the degradation of the neurotransmitter acetylcholine. In this light, molecular modeling studies were performed on a collection of organophosphorous acetylcholinesterase inhibitors by the combined use of conformational analysis and 3D-QSAR methods to rationalize their inhibitory potencies against the enzyme. The Catalyst program was used to identify the structural features in the group of 8 inhibitors whose IC(50) values ranged from 0.34 nM to 1.2 microM. The 3-D pharmacophore models are characterized by at least one hydrogen bond acceptor site and 2-3 hydrophobic sites and demonstrate very good correlation between the predicted and experimental IC(50) values. Our models can be useful in screening databases of organophosphorous compounds for their neurotoxicity potential via the inhibition of acetylcholinesterase. Also, the pharmacophores offer an additional means of designing AChE inhibitors as potential therapeutic agents for central nervous system diseases.

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Year:  2001        PMID: 11568366     DOI: 10.1093/toxsci/63.2.223

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  9 in total

1.  A mechanism-based 3D-QSAR approach for classification and prediction of acetylcholinesterase inhibitory potency of organophosphate and carbamate analogs.

Authors:  Sehan Lee; Mace G Barron
Journal:  J Comput Aided Mol Des       Date:  2016-04-07       Impact factor: 3.686

Review 2.  In Silico Models for Predicting Acute Systemic Toxicity.

Authors:  Ivanka Tsakovska; Antonia Diukendjieva; Andrew P Worth
Journal:  Methods Mol Biol       Date:  2022

3.  Predicting inhibitors of acetylcholinesterase by regression and classification machine learning approaches with combinations of molecular descriptors.

Authors:  Dmitriy Chekmarev; Vladyslav Kholodovych; Sandhya Kortagere; William J Welsh; Sean Ekins
Journal:  Pharm Res       Date:  2009-07-15       Impact factor: 4.200

4.  Interpretation of the mechanism of acetylcholinesterase inhibition ability by organophosphorus compounds through a new conformational descriptor. an experimental and theoretical study.

Authors:  Guido Mastrantonio; Hans-Georg Mack; Carlos Omar Della Védova
Journal:  J Mol Model       Date:  2008-06-26       Impact factor: 1.810

5.  Malathion, carbofuran and paraquat inhibit Bungarus sindanus (krait) venom acetylcholinesterase and human serum butyrylcholinesterase in vitro.

Authors:  Mushtaq Ahmed; João Batista T Rocha; Cinthia M Mazzanti; André L B Morsch; Denise Cargnelutti; Maísa Corrêa; Vânia Loro; Vera Maria Morsch; Maria R C Schetinger
Journal:  Ecotoxicology       Date:  2007-03-16       Impact factor: 2.823

6.  Identification of novel HIV 1--protease inhibitors: application of ligand and structure based pharmacophore mapping and virtual screening.

Authors:  Divya Yadav; Sarvesh Paliwal; Rakesh Yadav; Mahima Pal; Anubhuti Pandey
Journal:  PLoS One       Date:  2012-11-08       Impact factor: 3.240

7.  Surface display of recombinant Drosophila melanogaster acetylcholinesterase for detection of organic phosphorus and carbamate pesticides.

Authors:  Jingquan Li; Jun Yin; Songjie Wu; Fangfang Zhuan; Songci Xu; Junyang Li; Joelle K Salazar; Wei Zhang; Hui Wang
Journal:  PLoS One       Date:  2013-09-09       Impact factor: 3.240

8.  Strategies to improve the regulatory assessment of developmental neurotoxicity (DNT) using in vitro methods.

Authors:  Anna Bal-Price; Francesca Pistollato; Magdalini Sachana; Stephanie K Bopp; Sharon Munn; Andrew Worth
Journal:  Toxicol Appl Pharmacol       Date:  2018-02-22       Impact factor: 4.219

9.  Larvicidal Activities of 2-Aryl-2,3-Dihydroquinazolin -4-ones against Malaria Vector Anopheles arabiensis, In Silico ADMET Prediction and Molecular Target Investigation.

Authors:  Katharigatta N Venugopala; Pushpalatha Ramachandra; Christophe Tratrat; Raquel M Gleiser; Subhrajyoti Bhandary; Deepak Chopra; Mohamed A Morsy; Bandar E Aldhubiab; Mahesh Attimarad; Anroop B Nair; Nagaraja Sreeharsha; Rashmi Venugopala; Pran Kishore Deb; Sandeep Chandrashekharappa; Hany Ezzat Khalil; Osama I Alwassil; Sara Nidal Abed; Yazan A Bataineh; Ramachandra Palenge; Michelyne Haroun; Shinu Pottathil; Meravanige B Girish; Sabah H Akrawi; Viresh Mohanlall
Journal:  Molecules       Date:  2020-03-13       Impact factor: 4.411

  9 in total

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