Title: Review of QSAR Models and Software Tools for predicting Biokinetic Properties
Authors: MOSTRAG-SZLICHTYNG A.WORTH Andrew
Publisher: European Commission
Pubilcations Office of the European Union
Publication Year: 2010
JRC N°: JRC58570
ISBN: 978-92-79-15854-4
ISSN: 1018-5593
Other Identifiers: EUR 24377 EN
OPOCE LB-NA-24377-EN-C
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC58570
DOI: 10.2788/94537
Type: EUR - Scientific and Technical Research Reports
Abstract: In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.
JRC Institute:Institute for Health and Consumer Protection

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