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

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

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.

MeSH terms

  • Animals
  • Computer Simulation*
  • DNA, Bacterial / drug effects*
  • DNA, Bacterial / genetics
  • False Negative Reactions
  • Humans
  • Knowledge Bases
  • Models, Molecular*
  • Mutagenesis*
  • Mutagenicity Tests / methods*
  • Mutation*
  • Pattern Recognition, Automated
  • Quantitative Structure-Activity Relationship*
  • Risk Assessment

Substances

  • DNA, Bacterial