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Regul Toxicol Pharmacol. 2015 Apr;71(3):388-97. doi: 10.1016/j.yrtph.2015.01.010. Epub 2015 Feb 3.

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

Author information

1
Health/Safety/Environmental, Lilly Research Laboratories, Indianapolis, IN, United States. Electronic address: Jollyra@lilly.com.
2
MD Anderson Cancer Center, Austin, TX, United States.
3
Non-Clinical Safety Assessment, Lilly Research Laboratories, Indianapolis, IN, United States.
4
Advanced Analytics, Lilly Research Laboratories, Indianapolis, IN, United States.
5
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.

KEYWORDS:

Ames test; Consensus analysis; Derek Nexus; Genotoxicity; In silico models; Leadscope; Mutagenicity prediction; Risk assessment; Support Vector Machine; Toxicity prediction

PMID:
25656493
DOI:
10.1016/j.yrtph.2015.01.010
[Indexed for MEDLINE]

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