A matter of trust: Learning lessons about causality will make qAOPs credible

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.

Keywords: Adverse Outcome Pathway; Causality; Model credibility; Next Generation Risk Assessment; qAOP.

Fig. 1

A. The AOP for Parkinsonian motor deficits is taken as an example to underline one of the characteristics of a qAOP model, mainly understanding the cause and effect in the context of predictive toxicology (https://aopwiki.org/aops/3). Numbers represent the indices of the events in the OECD AOP-Wiki Knowledge Base available at https://aopwiki.org/events/XXX, where xxx is the index in the node. B. A causal diagram representing the linkage between cigarette smoking and lung cancer. C. Scheme for the general process of qAOP model development and application. Depending on the available level of resources, an AOP can be used to generate data or model quantitatively to make predictions and test a hypothesis. D. The causal inference engine was proposed by Judea Pearl as described in the text and is taken from .