A defined approach for predicting skin sensitisation hazard and potency based on the guided integration of in silico, in chemico and in vitro data using exclusion criteria.

Autor: Macmillan DS; Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK. Electronic address: donna.macmillan@lhasalimited.org., Chilton ML; Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK.
Jazyk: angličtina
Zdroj: Regulatory toxicology and pharmacology : RTP [Regul Toxicol Pharmacol] 2019 Feb; Vol. 101, pp. 35-47. Date of Electronic Publication: 2018 Nov 12.
DOI: 10.1016/j.yrtph.2018.11.001
Abstrakt: A decision tree-based defined approach (DA) has been designed using exclusion criteria based on applicability domain knowledge of in chemico/in vitro information sources covering key events 1-3 in the skin sensitisation adverse outcome pathway and an in silico tool predicting the adverse outcome (Derek Nexus). The hypothesis is that using exclusion criteria to de-prioritise less applicable assays and/or in silico outcomes produces a rational, transparent, and reliable DA for the prediction of skin sensitisation potential. Five exclusion criteria have been established: Derek Nexus reasoning level, Derek Nexus negative prediction, metabolism, lipophilicity, and lysine-reactivity. These are used to prioritise the most suitable information sources for a given chemical and results from which are used in a '2 out of 3' approach to provide a prediction of hazard. A potency category (and corresponding GHS classification) is then assigned using a k-Nearest Neighbours model containing human and LLNA data. The DA correctly identified the hazard (sensitiser/non-sensitiser) for 85% and 86% of a dataset with reference LLNA and human data. The correct potency category was identified for 59% and 68% of chemicals, and the GHS classification accurately predicted for 73% and 76% with reference LLNA and human data, respectively.
(Copyright © 2018 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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