Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

Arch Toxicol. 2020 May;94(5):1497-1510. doi: 10.1007/s00204-020-02774-7. Epub 2020 May 18.

Abstract

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.

Keywords: Bayesian network; Computational approach; Key event relationship; Predictive toxicology; Quantitative adverse outcome pathway (qAOP); Response-response relationship.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Adverse Outcome Pathways*
  • Animals
  • Forecasting
  • Humans
  • Risk Assessment
  • Software
  • Toxicity Tests*