Literature DB >> 20017578

In silico toxicology in drug discovery - concepts based on three-dimensional models.

Angelo Vedani1, Martin Smiesko.   

Abstract

Animal testing is still compulsory worldwide, for the approval of drugs and chemicals produced in large quantities. Computer-assisted (in silico) technologies are considered to be efficient alternatives to in vivo experiments, and are therefore endorsed by many regulatory agencies, e.g. for use in the European REACH initiative. Advantages of in silico methods include: the possible study of hypothetical compounds; their low cost; and the fact that such virtual experiments are typically based on human data, thus making the question of interspecies transferability obsolete. Since the mid-1990s, computer-based technologies have become an indispensable tool in drug discovery - used primarily to identify small molecules displaying a stereospecific and selective binding to a regulatory macromolecule. Since toxic effects are still responsible for some 20% of the late-stage failures, there is a continuing need for in silico concepts which can be used to estimate a compound's ADMET (adsorption, distribution, metabolism, elimination, toxicity) properties - in particular, toxicity. The aim of this paper is to provide an insight into computational technologies that allow for the prediction of toxic effects triggered by pharmaceuticals. As most adverse and toxic effects are mediated by unwanted interactions with macromolecules involved in biological regulatory systems, we have focused on methodologies that are based on three-dimensional models of small molecules binding to such entities, and discuss the results at the molecular level. 2009 FRAME.

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Year:  2009        PMID: 20017578     DOI: 10.1177/026119290903700506

Source DB:  PubMed          Journal:  Altern Lab Anim        ISSN: 0261-1929            Impact factor:   1.303


  14 in total

1.  Prediction of drug-induced eosinophilia adverse effect by using SVM and naïve Bayesian approaches.

Authors:  Hui Zhang; Peng Yu; Ming-Li Xiang; Xi-Bo Li; Wei-Bao Kong; Jun-Yi Ma; Jun-Long Wang; Jin-Ping Zhang; Ji Zhang
Journal:  Med Biol Eng Comput       Date:  2015-06-05       Impact factor: 2.602

2.  In silico prediction of drug-induced myelotoxicity by using Naïve Bayes method.

Authors:  Hui Zhang; Peng Yu; Teng-Guo Zhang; Yan-Li Kang; Xiao Zhao; Yuan-Yuan Li; Jia-Hui He; Ji Zhang
Journal:  Mol Divers       Date:  2015-07-11       Impact factor: 2.943

3.  Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.

Authors:  Liying Zhang; Alexander Sedykh; Ashutosh Tripathi; Hao Zhu; Antreas Afantitis; Varnavas D Mouchlis; Georgia Melagraki; Ivan Rusyn; Alexander Tropsha
Journal:  Toxicol Appl Pharmacol       Date:  2013-05-23       Impact factor: 4.219

4.  A Workflow to Investigate Exposure and Pharmacokinetic Influences on High-Throughput in Vitro Chemical Screening Based on Adverse Outcome Pathways.

Authors:  Martin B Phillips; Jeremy A Leonard; Christopher M Grulke; Daniel T Chang; Stephen W Edwards; Raina Brooks; Michael-Rock Goldsmith; Hisham El-Masri; Yu-Mei Tan
Journal:  Environ Health Perspect       Date:  2015-05-15       Impact factor: 9.031

5.  CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.

Authors:  Kamel Mansouri; Ahmed Abdelaziz; Aleksandra Rybacka; Alessandra Roncaglioni; Alexander Tropsha; Alexandre Varnek; Alexey Zakharov; Andrew Worth; Ann M Richard; Christopher M Grulke; Daniela Trisciuzzi; Denis Fourches; Dragos Horvath; Emilio Benfenati; Eugene Muratov; Eva Bay Wedebye; Francesca Grisoni; Giuseppe F Mangiatordi; Giuseppina M Incisivo; Huixiao Hong; Hui W Ng; Igor V Tetko; Ilya Balabin; Jayaram Kancherla; Jie Shen; Julien Burton; Marc Nicklaus; Matteo Cassotti; Nikolai G Nikolov; Orazio Nicolotti; Patrik L Andersson; Qingda Zang; Regina Politi; Richard D Beger; Roberto Todeschini; Ruili Huang; Sherif Farag; Sine A Rosenberg; Svetoslav Slavov; Xin Hu; Richard S Judson
Journal:  Environ Health Perspect       Date:  2016-02-23       Impact factor: 9.031

6.  Computational methods for prediction of in vitro effects of new chemical structures.

Authors:  Priyanka Banerjee; Vishal B Siramshetty; Malgorzata N Drwal; Robert Preissner
Journal:  J Cheminform       Date:  2016-09-29       Impact factor: 5.514

7.  Mathematical modelling of vector-borne diseases and insecticide resistance evolution.

Authors:  Maria Laura Gabriel Kuniyoshi; Fernando Luiz Pio Dos Santos
Journal:  J Venom Anim Toxins Incl Trop Dis       Date:  2017-07-06

8.  OPERA models for predicting physicochemical properties and environmental fate endpoints.

Authors:  Kamel Mansouri; Chris M Grulke; Richard S Judson; Antony J Williams
Journal:  J Cheminform       Date:  2018-03-08       Impact factor: 5.514

9.  Endocrine Disruption at the Androgen Receptor: Employing Molecular Dynamics and Docking for Improved Virtual Screening and Toxicity Prediction.

Authors:  Joel Wahl; Martin Smieško
Journal:  Int J Mol Sci       Date:  2018-06-15       Impact factor: 5.923

10.  In silico mechanistic profiling to probe small molecule binding to sulfotransferases.

Authors:  Virginie Y Martiny; Pablo Carbonell; David Lagorce; Bruno O Villoutreix; Gautier Moroy; Maria A Miteva
Journal:  PLoS One       Date:  2013-09-06       Impact factor: 3.240

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