Literature DB >> 24998783

REACH and in silico methods: an attractive opportunity for medicinal chemists.

Orazio Nicolotti1, Emilio Benfenati2, Angelo Carotti3, Domenico Gadaleta3, Andrea Gissi3, Giuseppe Felice Mangiatordi3, Ettore Novellino4.   

Abstract

REACH, the most ambitious chemical legislation in the world, provides unprecedented opportunities for medicinal chemists. Companies must report (eco)toxicological information about their chemicals, disseminated to the public domain by the European Chemicals Agency after their registration. The availability of this wealth of new toxicological data, together with the promotion of REACH of in silico methods, appoints medicinal chemists to a leading role in the regulatory hazard assessment process. In fact, Quantitative Structure-Activity Relation ship (QSAR) models and predictive toxicology have been applied in drug design and development for decades. Here, we discuss toxicological endpoints and areas where further development is needed to provide an enlightened appraisal of this attractive new opportunity.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2014        PMID: 24998783     DOI: 10.1016/j.drudis.2014.06.027

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


  9 in total

1.  Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

Authors:  Vinicius M Alves; Eugene N Muratov; Alexey Zakharov; Nail N Muratov; Carolina H Andrade; Alexander Tropsha
Journal:  Food Chem Toxicol       Date:  2017-04-12       Impact factor: 6.023

Review 2.  Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment.

Authors:  Valérie Forest
Journal:  Nanomaterials (Basel)       Date:  2022-04-14       Impact factor: 5.719

3.  In silico prediction of chemical neurotoxicity using machine learning.

Authors:  Changsheng Jiang; Piaopiao Zhao; Weihua Li; Yun Tang; Guixia Liu
Journal:  Toxicol Res (Camb)       Date:  2020-04-29       Impact factor: 3.524

4.  ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling.

Authors:  Tailong Lei; Youyong Li; Yunlong Song; Dan Li; Huiyong Sun; Tingjun Hou
Journal:  J Cheminform       Date:  2016-02-01       Impact factor: 5.514

5.  A systems biology approach to predictive developmental neurotoxicity of a larvicide used in the prevention of Zika virus transmission.

Authors:  Karine Audouze; Olivier Taboureau; Philippe Grandjean
Journal:  Toxicol Appl Pharmacol       Date:  2018-02-21       Impact factor: 4.219

6.  Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data.

Authors:  Fabiola Pizzo; Domenico Gadaleta; Anna Lombardo; Orazio Nicolotti; Emilio Benfenati
Journal:  Chem Cent J       Date:  2015-11-05       Impact factor: 4.215

Review 7.  Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes.

Authors:  Hannu Raunio; Mira Kuusisto; Risto O Juvonen; Olli T Pentikäinen
Journal:  Front Pharmacol       Date:  2015-06-12       Impact factor: 5.810

8.  Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids.

Authors:  Chaitanya K Jaladanki; Yang He; Li Na Zhao; Sebastian Maurer-Stroh; Lit-Hsin Loo; Haiwei Song; Hao Fan
Journal:  Arch Toxicol       Date:  2020-09-09       Impact factor: 5.153

9.  Trimethoxylated Halogenated Chalcones as Dual Inhibitors of MAO-B and BACE-1 for the Treatment of Neurodegenerative Disorders.

Authors:  Payyalot Koyiparambath Vishal; Jong Min Oh; Ahmed Khames; Mohamed A Abdelgawad; Aathira Sujathan Nair; Lekshmi R Nath; Nicola Gambacorta; Fulvio Ciriaco; Orazio Nicolotti; Hoon Kim; Bijo Mathew
Journal:  Pharmaceutics       Date:  2021-06-08       Impact factor: 6.321

  9 in total

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