Spinu, N, Cronin, MTD, Madden, JC and Worth, A (2021) A Matter of Trust: Learning Lessons About Causality Will Make qAOPs Credible. Computational Toxicology, 21. ISSN 2468-1113

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Abstract

Toxicology in the 21st Century has seen a shift from chemical risk assessment based on traditional animal tests, identifying apical endpoints and doses that are “safe”, to the prospect of Next Generation Risk Assessment based on non-animal methods. Increasingly, large and high throughput in vitro datasets are being generated and exploited to develop computational models. This is accompanied by an increased use of machine learning approaches in the model building process. A potential problem, however, is that such models, while robust and predictive, may still lack credibility from the perspective of the end-user. In this commentary, we argue that the science of causal inference and reasoning, as proposed by Judea Pearl, will facilitate the development, use and acceptance of quantitative AOP models. Our hope is that by importing established concepts of causality from outside the field of toxicology, we can be “constructively disruptive” to the current toxicological paradigm, using the “Causal Revolution” to bring about a “Toxicological Revolution” more rapidly.

Item Type:	Article
Subjects:	H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management R Medicine > RA Public aspects of medicine R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RM Therapeutics. Pharmacology
Divisions:	Pharmacy & Biomolecular Sciences
Publisher:	Elsevier
Date Deposited:	26 Nov 2021 12:22
Last Modified:	17 Dec 2021 12:45
DOI or ID number:	10.1016/j.comtox.2021.100205
URI:	https://researchonline.ljmu.ac.uk/id/eprint/15831

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