Literature DB >> 19937847

An expert system approach to the assessment of hepatotoxic potential.

Carol A Marchant1, Lilia Fisk, Reine R Note, Mukesh L Patel, Diana Suárez.   

Abstract

Hepatotoxicity is a major cause of pharmaceutical drug attrition and is also a concern within other chemical industries. In silico approaches to the prediction of hepatotoxicity are an important tool in the early identification of adverse effects in the liver associated with exposure to a chemical. Here, we describe work in progress to develop an expert system approach to the prediction of hepatotoxicity, focussing particularly on the identification of structural alerts associated with its occurrence. The development of 74 such structural alerts based on public-domain literature and proprietary data sets is described. Evaluation results indicate that, whilst these structural alerts are effective in identifying the hepatotoxicity of many chemicals, further research is needed to develop additional structural alerts to account for the hepatotoxicity of a number of chemicals which is not currently predicted. Preliminary results also suggest that the specificity of the structural alerts may be improved by the combined use of applicability domains based on physicochemical properties such as log P and molecular weight. In the longer term, the performance of predictive models is likely to benefit from the further integration of diverse data and prediction model types.

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Year:  2009        PMID: 19937847     DOI: 10.1002/cbdv.200900133

Source DB:  PubMed          Journal:  Chem Biodivers        ISSN: 1612-1872            Impact factor:   2.408


  6 in total

1.  Mixed learning algorithms and features ensemble in hepatotoxicity prediction.

Authors:  Chin Yee Liew; Yen Ching Lim; Chun Wei Yap
Journal:  J Comput Aided Mol Des       Date:  2011-09-06       Impact factor: 3.686

Review 2.  Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction.

Authors:  Antonio Segovia-Zafra; Daniel E Di Zeo-Sánchez; Carlos López-Gómez; Zeus Pérez-Valdés; Eduardo García-Fuentes; Raúl J Andrade; M Isabel Lucena; Marina Villanueva-Paz
Journal:  Acta Pharm Sin B       Date:  2021-11-18       Impact factor: 11.413

Review 3.  In Silico Models for Hepatotoxicity.

Authors:  Claire Ellison; Mark Hewitt; Katarzyna Przybylak
Journal:  Methods Mol Biol       Date:  2022

4.  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

5.  A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts.

Authors:  Fabiola Pizzo; Anna Lombardo; Alberto Manganaro; Emilio Benfenati
Journal:  Front Pharmacol       Date:  2016-11-22       Impact factor: 5.810

Review 6.  The use of integrated and intelligent testing strategies in the prediction of toxic hazard and in risk assessment.

Authors:  Michael Balls; Robert D Combes; Nirmala Bhogal
Journal:  Adv Exp Med Biol       Date:  2012       Impact factor: 2.622

  6 in total

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