Literature DB >> 22063083

The value of in silico chemistry in the safety assessment of chemicals in the consumer goods and pharmaceutical industries.

Sandeep Modi1, Michael Hughes, Andrew Garrow, Andrew White.   

Abstract

In silico toxicology prediction is an extremely challenging area because many toxicological effects are a result of changes in multiple physiological processes. In this article we discuss limitations and strengths of these in silico tools. Additionally, we look at different parameters that are necessary to make the best use of these tools, and also how to gain acceptance outside the modelling community and into the regulatory arena. As a solution, we propose an integrated workflow for combined use of data extraction, quantitative structure activity relationships and read-across methods. We also discuss how the recent advances in this field can enable transition to a new paradigm of the discovery process, as exemplified by the Toxicity Testing in the 21st Century initiative.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 22063083     DOI: 10.1016/j.drudis.2011.10.022

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  11 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.  Modelling compound cytotoxicity using conformal prediction and PubChem HTS data.

Authors:  Fredrik Svensson; Ulf Norinder; Andreas Bender
Journal:  Toxicol Res (Camb)       Date:  2016-10-31       Impact factor: 3.524

4.  Integrated in silico approaches for the prediction of Ames test mutagenicity.

Authors:  Sandeep Modi; Jin Li; Sophie Malcomber; Claire Moore; Andrew Scott; Andrew White; Paul Carmichael
Journal:  J Comput Aided Mol Des       Date:  2012-08-24       Impact factor: 3.686

5.  Computational Modeling of Mixture Toxicity.

Authors:  Mainak Chatterjee; Kunal Roy
Journal:  Methods Mol Biol       Date:  2022

Review 6.  Alternatives to In Vivo Draize Rabbit Eye and Skin Irritation Tests with a Focus on 3D Reconstructed Human Cornea-Like Epithelium and Epidermis Models.

Authors:  Miri Lee; Jee-Hyun Hwang; Kyung-Min Lim
Journal:  Toxicol Res       Date:  2017-07-15

Review 7.  Microfluidic-Based Multi-Organ Platforms for Drug Discovery.

Authors:  Ahmad Rezaei Kolahchi; Nima Khadem Mohtaram; Hassan Pezeshgi Modarres; Mohammad Hossein Mohammadi; Armin Geraili; Parya Jafari; Mohsen Akbari; Amir Sanati-Nezhad
Journal:  Micromachines (Basel)       Date:  2016-09-08       Impact factor: 2.891

8.  COVER: conformational oversampling as data augmentation for molecules.

Authors:  Jennifer Hemmerich; Ece Asilar; Gerhard F Ecker
Journal:  J Cheminform       Date:  2020-03-18       Impact factor: 5.514

9.  Forced Degradation Studies of Ivabradine and In Silico Toxicology Predictions for Its New Designated Impurities.

Authors:  Piotr Pikul; Marzena Jamrógiewicz; Joanna Nowakowska; Weronika Hewelt-Belka; Krzesimir Ciura
Journal:  Front Pharmacol       Date:  2016-05-04       Impact factor: 5.810

Review 10.  In silico toxicology: computational methods for the prediction of chemical toxicity.

Authors:  Arwa B Raies; Vladimir B Bajic
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2016-01-06
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