Adverse Outcome Pathway (AOP) Development I: Strategies and Principles
Author(s) -
Daniel L. Villeneuve,
Doug Crump,
Natàlia GarcíaReyero,
Markus Hecker,
Thomas H. Hutchinson,
Carlie A. LaLone,
Brigitte Landesmann,
Teresa Lettieri,
Sharon Munn,
Nepelska Malgorzata,
Mary Ann Ottinger,
Lucia Vergauwen,
Maurice Whelan
Publication year - 2014
Publication title -
toxicological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.352
H-Index - 183
eISSN - 1096-6080
pISSN - 1096-0929
DOI - 10.1093/toxsci/kfu199
Subject(s) - adverse outcome pathway , computer science , modular design , set (abstract data type) , outcome (game theory) , relevance (law) , conceptual framework , process management , risk analysis (engineering) , data science , computational biology , biology , business , mathematics , political science , programming language , operating system , philosophy , mathematical economics , epistemology , law
An adverse outcome pathway (AOP) is a conceptual framework that organizes existing knowledge concerning biologically plausible, and empirically supported, links between molecular-level perturbation of a biological system and an adverse outcome at a level of biological organization of regulatory relevance. Systematic organization of information into AOP frameworks has potential to improve regulatory decision-making through greater integration and more meaningful use of mechanistic data. However, for the scientific community to collectively develop a useful AOP knowledgebase that encompasses toxicological contexts of concern to human health and ecological risk assessment, it is critical that AOPs be developed in accordance with a consistent set of core principles. Based on the experiences and scientific discourse among a group of AOP practitioners, we propose a set of five fundamental principles that guide AOP development: (1) AOPs are not chemical specific; (2) AOPs are modular and composed of reusable components-notably key events (KEs) and key event relationships (KERs); (3) an individual AOP, composed of a single sequence of KEs and KERs, is a pragmatic unit of AOP development and evaluation; (4) networks composed of multiple AOPs that share common KEs and KERs are likely to be the functional unit of prediction for most real-world scenarios; and (5) AOPs are living documents that will evolve over time as new knowledge is generated. The goal of the present article was to introduce some strategies for AOP development and detail the rationale behind these 5 key principles. Consideration of these principles addresses many of the current uncertainties regarding the AOP framework and its application and is intended to foster greater consistency in AOP development.
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