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      Quantifying intrinsic chemical reactivity of molecular structural features for protein binding and reactive toxicity, using the MOSES chemoinformatics system

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      Journal of Cheminformatics
      BioMed Central
      7th German Conference on Chemoinformatics: 25 CIC-Workshop
      6-8 November 2011

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          Abstract

          Covalent binding of xenobiotic compounds to endogenous biomolecular sites, e.g. protein residues, leads to potentially irreversible toxic effects such as enhanced acute toxicity or skin sensitization [1]. This mechanistic knowledge provides the basis for the in silico prediction of these toxicities, as required by the EU REACH legislation and the EU Cosmetics Directive. A general toxicity prediction can be based on three consecutive steps [2]: (1.) Identification of a potential reactive protein binding mechanism via a set of molecular structural features. Those structural features can be encoded by the Chemical Subgraph Representation Markup Language (CSRML), that supports a flexible annotation of meta information, including physicochemical properties as annotations. (2.) Confirmation and quantification of (bio)chemical reactivity. The potential for a chemical to be reactive can be captured by mechanistically based QSAR models. This intrinsic reactivity is calculated rapidly by descriptors of the chemoinformatics platform Molecular Structure Encoding System (MOSES) [3]. It represents electronic, steric and resonance effects in chemical structures. The performances obtained by these reactivity models are close to time-consuming quantum chemical reactivity calculations, e.g., se = 0.44 versus 0.40 for glutathione adduct formation via Michael addition, comparing predicted values to an experimental reactivity dataset [1]. (3.) Establishing a relationship between calculated reactivity and toxicity. The predicted intrinsic reactivity values were linked to the computational prediction for different modes of toxic action, with good correlations between predicted and in vitro toxicity (up to r 2=0.86). The combined use of structural information and computed reactivity could assist in the non-animal based risk assessment of chemicals for regulatory purposes and in the application of integrated testing strategies (ITS). The research has received funding from the EU FP7 COSMOS Project (grant agreement n° 266835) and financing from COLIPA.

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          Measurement and estimation of electrophilic reactivity for predictive toxicology.

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            Application of a computational model for Michael addition reactivity in the prediction of toxicity to Tetrahymena pyriformis.

            A computational model to predict acute aquatic toxicity to the ciliate Tetrahymena pyriformis has been developed. A general prediction of toxicity can be based on three consecutive steps: 1. Identification of a potential reactive mechanism via structural alerts; 2. Confirmation and quantification of (bio)chemical reactivity; 3. Establishing a relationship between calculated reactivity and toxicity. The method described herein uses a combination of a reactive toxicity (RT) model, including computed kinetic rate constants for adduct formation (log k) via a Michael acceptor mechanism of action, and baseline toxicity (BT), modelled by hydrophobicity (octanol-water partition coefficient). The maximum of the RT and BT values defines acute toxicity for a particular compound. The reactive toxicity model is based on site-specific steric and quantum chemical ground state electronic properties. The performance of the model was examined in terms of predicting the toxicity of 106 potential Michael acceptor compounds covering several classes of compounds (aldehydes, ketones, esters, heterocycles). The advantages of the computational method are described. The method allows for a closer and more transparent mechanistic insight into the molecular initiating events of toxicological endpoints. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              Author and article information

              Conference
              J Cheminform
              J Cheminform
              Journal of Cheminformatics
              BioMed Central
              1758-2946
              2012
              1 May 2012
              : 4
              : Suppl 1
              : O8
              Affiliations
              [1 ]Molecular Networks GmbH, Henkestraße 91, D-91052 Erlangen, Germany
              [2 ]School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
              Article
              1758-2946-4-S1-O8
              10.1186/1758-2946-4-S1-O8
              3341280
              1683b7ba-e70b-4f83-ab03-fe765d73d6d7
              Copyright ©2012 Schwöbel et al; licensee BioMed Central Ltd.

              This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

              7th German Conference on Chemoinformatics: 25 CIC-Workshop
              Goslar, Germany
              6-8 November 2011
              History
              Categories
              Oral Presentation

              Chemoinformatics
              Chemoinformatics

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