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Regul Toxicol Pharmacol. 2016 Apr;76:79-86. doi: 10.1016/j.yrtph.2016.01.008. Epub 2016 Jan 16.

It's difficult, but important, to make negative predictions.

Author information

1
Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK. Electronic address: richard.williams@lhasalimited.org.
2
Sanofi-Aventis Deutschland GmbH, R&D DSAR/Preclinical Safety FF, Industriepark Hoechst, Bldg. H823, D-65926 Frankfurt am Main, Germany.
3
Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Grenzacherstrasse 124, CH-4070 Basel, Switzerland.
4
Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK.
5
GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire SG12 0DP, UK.
6
Novartis Pharma AG, Pre-clinical Safety, Werk Klybeck, CH-4057 Basel, Switzerland.
7
Compound Safety Prediction, Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT 06340, USA.
8
Health/Safety/Environmental, Lilly Research Laboratories, Indianapolis, IN, USA.
9
Vertex Pharmaceuticals Incorporated, 50 Northern Ave, Boston MA, USA.
10
Janssen Research & Development, 1400 McKean Road, Spring House, PA 19477, USA.
11
Bayer Pharma AG, Investigational Toxicology, Muellerstr. 178, S 116, D-13353, Berlin, Germany.

Abstract

At the confluence of predictive and regulatory toxicologies, negative predictions may be the thin green line that prevents populations from being exposed to harm. Here, two novel approaches to making confident and robust negative in silico predictions for mutagenicity (as defined by the Ames test) have been evaluated. Analyses of 12 data sets containing >13,000 compounds, showed that negative predictivity is high (∼90%) for the best approach and features that either reduce the accuracy or certainty of negative predictions are identified as misclassified or unclassified respectively. However, negative predictivity remains high (and in excess of the prevalence of non-mutagens) even in the presence of these features, indicating that they are not flags for mutagenicity.

KEYWORDS:

(Q)SAR; Expert assessment; Expert system; ICH M7; In silico toxicology; Negative predictions

PMID:
26785392
DOI:
10.1016/j.yrtph.2016.01.008
[Indexed for MEDLINE]

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