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Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

Archives of Toxicology 2020 104 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 50 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Nicoleta Sp̂înu, M Cronin, Steven J. Enoch, Judith C. Madden, Andrew Worth

Summary

This review examines the developing concept of quantitative adverse outcome pathways (qAOPs) — computational models linking molecular initiating events to population-level harm — and proposes a framework for their development, validation, and use in chemical risk assessment.

The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools. As a result, five probabilistic qAOPs and ten mechanistic qAOPs were evaluated against the common features. The review offers an overview of how the qAOP concept has advanced and how it can aid toxicity assessment in the future. Further efforts are required to achieve validation, harmonisation and regulatory acceptance of qAOP models.

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