Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity
| Autor: | Lisa Beilke, Lidiya Stavitskaya, Penny Leavitt, Alexander Amberg, Donald P. Quigley, Angela White, James Harvey, Naomi L. Kruhlak, Raymond Kemper, Craig Zwickl, Claire L. Neilan, Ernst Ahlberg, Kevin P. Cross, Joerg Wichard, Glenn J. Myatt, Dana Shuey, Hans-Peter Spirkl, Michelle O. Kenyon, Masamitsu Honma, Robert A. Jolly, David Bower, Elisabeth Joossens, Jacky Van Gompel, Lara Kuhnke, Laura Custer, Kevin A. Ford, Russell T. Naven, Andrew Teasdale |
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| Rok vydání: | 2016 |
| Předmět: |
Models
Molecular 0301 basic medicine endocrine system Databases Factual Knowledge Bases Quantitative Structure-Activity Relationship 010501 environmental sciences Toxicology computer.software_genre Risk Assessment 01 natural sciences Pattern Recognition Automated 03 medical and health sciences Animals Data Mining Humans Organic chemistry Computer Simulation Amines 0105 earth and related environmental sciences chemistry.chemical_classification Molecular Structure Mutagenicity Tests Chemistry fungi food and beverages Aromatic amine General Medicine 030104 developmental biology Mutagenesis Data mining computer Mutagens Applicability domain |
| Zdroj: | Regulatory Toxicology and Pharmacology. 77:1-12 |
| ISSN: | 0273-2300 |
| DOI: | 10.1016/j.yrtph.2016.02.003 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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