Glowienke, Susanne and Glogovac, Milica (2018) Making reliable negative predictions of human skin sensitisation using an in silico fragmentation approach. Regulatory toxicology and pharmacology.

Abstract

A previously published fragmentation method for making confident negative in silico predictions has been applied to the problem of predicting skin sensitisation in humans, making use of a dataset of over 2,750 chemicals with publicly available skin sensitisation data from 18 in vivo assays. An assay hierarchy was designed to enable the classification of chemicals within this dataset as either sensitisers and non-sensitisers where data from more than one in vivo test was available. The negative prediction approach was validated internally, using a 5-fold cross-validation, and externally, against a proprietary dataset of approximately 1,000 chemicals with in vivo reference data shared by members of the pharmaceutical, nutritional, and personal care industries. The negative predictivity for this proprietary dataset was high in all cases (>75%), and the model was also able to identify structural features that resulted in a lower accuracy or a higher uncertainty in the negative prediction, termed misclassified and unclassified features respectively. These features could serve as an aid for further expert assessment of the negative in silico prediction.

Item Type:	Article
Date Deposited:	19 Jun 2018 00:45
Last Modified:	19 Jun 2018 00:45
URI:	https://oak.novartis.com/id/eprint/35200