Increasing Accessibility of Bayesian Network-Based Defined Approaches for Skin Sensitisation Potency Assessment-Reference-Cited by-同舟云学术

Increasing Accessibility of Bayesian Network-Based Defined Approaches for Skin Sensitisation Potency Assessment

Published:2024-09-12 Issue:9 Volume:12 Page:666
ISSN:2305-6304
Container-title:Toxics
language:en
Short-container-title:Toxics

Author:

Mohoric Tomaz¹,Wilm Anke²,Onken Stefan²,Milovich Andrii¹,Logavoch Artem¹,Ankli Pascal¹,Tagorti Ghada¹,Kirchmair Johannes³^ORCID,Schepky Andreas²^ORCID,Kühnl Jochen²^ORCID,Najjar Abdulkarim²^ORCID,Hardy Barry¹,Ebmeyer Johanna²

Affiliation:

1. Edelweiss Connect GmbH, Hochbergerstrasse 60C, 4057 Basel, Switzerland

2. Beiersdorf AG, Beiersdorfstraße 1-9, 22529 Hamburg, Germany

3. Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria

Abstract

Skin sensitisation is a critical adverse effect assessed to ensure the safety of compounds and materials exposed to the skin. Alongside the development of new approach methodologies (NAMs), defined approaches (DAs) have been established to promote skin sensitisation potency assessment by adopting and integrating standardised in vitro, in chemico, and in silico methods with specified data analysis procedures to achieve reliable and reproducible predictions. The incorporation of additional NAMs could help increase accessibility and flexibility. Using superior algorithms may help improve the accuracy of hazard and potency assessment and build confidence in the results. Here, we introduce two new DA models, with the aim to build DAs on freely available software and the newly developed kDPRA for covalent binding of a chemical to skin peptides and proteins. The new DA models are built on an existing Bayesian network (BN) modelling approach and expand on it. The new DA models include kDPRA data as one of the in vitro parameters and utilise in silico inputs from open-source QSAR models. Both approaches perform at least on par with the existing BN DA and show 63% and 68% accuracy when predicting four LLNA potency classes, respectively. We demonstrate the value of the Bayesian network’s confidence indications for predictions, as they provide a measure for differentiating between highly accurate and reliable predictions (accuracies up to 87%) in contrast to low-reliability predictions associated with inaccurate predictions.

Publisher

MDPI AG

Link

https://www.mdpi.com/2305-6304/12/9/666/pdf

Reference45 articles.

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