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An evaluation of in-house and off-the-shelf in silico models: implications on guidance for mutagenicity assessment.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Jolly, Robert Ahmed, Kausar Begam Riaz Zwickl, Craig Watson, Ian Gombar, Vijay
Description	Author Affiliation: Jolly R ( Health/Safety/Environmental, Lilly Research Laboratories, Indianapolis, IN, United States. Electronic address: Jollyra@lilly.com.); Ahmed KB ( MD Anderson Cancer Center, Austin, TX, United States.); Zwickl C ( Non-Clinical Safety Assessment, Lilly Research Laboratories, Indianapolis, IN, United States.); Watson I ( Advanced Analytics, Lilly Research Laboratories, Indianapolis, IN, United States.); Gombar V ( Drug Disposition, Lilly Research Laboratories, Indianapolis, IN, United States.)
Abstract	The evaluation of impurities for genotoxicity using in silico models are commonplace and have become accepted by regulatory agencies. Recently, the ICH M7 Step 4 guidance was published and requires two complementary models for genotoxicity assessments. Over the last ten years, many companies have developed their own internal genotoxicity models built using both public and in-house chemical structures and bacterial mutagenicity data. However, the proprietary nature of internal structures prevents sharing of data and the full OECD compliance of such models. This analysis investigated whether using in-house internal compounds for training models is needed and substantially impacts the results of in silico genotoxicity assessments, or whether using commercial-off-the-shelf (COTS) packages such as Derek Nexus or Leadscope provide adequate performance. We demonstrated that supplementation of COTS packages with a Support Vector Machine (SVM) QSAR model trained on combined in-house and public data does, in fact, improve coverage and accuracy, and reduces the number of compounds needing experimental assessment, i.e., the liability load. This result indicates that there is added value in models trained on both internal and public structures and incorporating such models as part of a consensus approach improves the overall evaluation. Lastly, we optimized an in silico consensus decision-making approach utilizing two COTS models and an internal (SVM) model to minimize false negatives.
File Format	HTM / HTML
ISSN	02732300
Issue Number	3
Volume Number	71
e-ISSN	10960295
Journal	Regulatory Toxicology and Pharmacology
Language	English
Publisher	Elsevier
Publisher Date	2015-04-01
Publisher Place	Netherlands
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Pharmacology Computer Simulation Standards Drug Contamination Models, Biological Mutagenicity Tests Mutagens Toxicity Animals Databases, Factual Guidelines As Topic Humans Quantitative Structure-activity Relationship Reproducibility Of Results Risk Assessment Software Support Vector Machines Evaluation Studies Journal Article
Content Type	Text
Resource Type	Article
Subject	Medicine Toxicology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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