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|Title:||Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity|
|Authors:||AHLBERG Ernst; AMBERG Alexander; BEILKE Lisa D.; BOWER David; CROSS Kevin P.; CUSTER Laura; FORD Kevin A.; VAN GOMPEL Jacky; HARVEY James; HONMA Masamistu; JOLLY Robert; JOOSSENS ELISABETH; KEMPER Ray; KENYON Michelle; KRUHLAK Naomi; KUHNKE Lara; LEAVITT Penny; NAVEN Russell; NEILAN Claire; QUIGLEY Donald P.; SHUEY Dana; SPIRKL Hans-Peter; STAVITSKAYA Lidiya; TEASDALE Andrew; WHITE Angela; WICHARD Joerg; ZWICKL Craig; MYATT Glenn|
|Citation:||REGULATORY TOXICOLOGY AND PHARMACOLOGY vol. 77 p. 1-12|
|Publisher:||ACADEMIC PRESS INC ELSEVIER SCIENCE|
|Type:||Articles in periodicals and books|
|Abstract:||Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated.|
|JRC Directorate:||Institute for Health and Consumer Protection Historical Collection|
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