Improvements to in silico skin sensitisation predictions through privacy-preserving data sharing.

Autor: Macmillan DS; Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK. Electronic address: dmacmillan@hsi.org., Chilton ML; Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK., Hillegass J; Bristol Myers Squibb, 1 Squibb Drive, New Brunswick, NJ, 08903, USA.
Jazyk: angličtina
Zdroj: Regulatory toxicology and pharmacology : RTP [Regul Toxicol Pharmacol] 2023 Jan; Vol. 137, pp. 105292. Date of Electronic Publication: 2022 Nov 15.
DOI: 10.1016/j.yrtph.2022.105292
Abstrakt: In silico models are often built solely on publicly available data which may mean that they are less predictive for proprietary chemical space. Data sharing initiatives can improve the performance of such models, but organisations are often unable to share their data due to the need to protect their business interests and maintain the confidentiality of the chemicals in their research and development programmes. In silico models like Derek Nexus, which use expert knowledge to develop structural alerts based on chemical toxicity, can use proprietary data to identify new areas of chemical space and/or refine existing alerts whilst still preserving the privacy of the confidential data. Five hundred and thirty seven proprietary chemicals with skin sensitisation data were shared which led to the implementation of 7 new alerts and 5 modified alerts, with a concomitant 19% increase in sensitivity and 3% increase in specificity of the model.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Martyn Chilton reports a relationship with Lhasa Limited that includes: employment. Corresponding author (Donna Macmillan) was previously employed by, and co-author Martyn Chilton is currently employed by, Lhasa Limited. Lhasa Limited produce Derek Nexus, the in silico tool used in the data sharing project and described within the manuscript.
(Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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