Towards a qAOP framework for predictive toxicology - Linking data to decisions

Paini, Alicia; Campia, Ivana; Cronin, Mark T.D.; Asturiol, David; Ceriani, Lidia; Exner, Thomas E.; Gao, Wang; Gomes, Caroline de Barros; Kruisselbrink, Johannes W.; Martens, Marvin; Meek, M.E.; Pamies, David; Pletz, Julia; Scholz, Stefan; Schüttler, Andreas; Spînu, Nicoleta; Villeneuve, Daniel L.; Wittwehr, Clemens; Worth, Andrew; Luijten, Mirjam

doi:10.1016/j.comtox.2021.100195

Cited by 22 publications

(12 citation statements)

References 59 publications

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“…In addition to their role in summarizing our understanding of the biological processes of particular pathways, a key feature of AOPs is that they provide a scaffold for the data to link these disparate endpoints that often occur in separate studies ( Maxwell et al, 2014 ; Ankley and Edwards, 2018 ). This enables a variety of quantitative modeling approaches that can integrate data from in silico, in vitro , and in vivo testing ( Jaworska et al, 2013 ; Foran et al, 2019 ; Perkins et al, 2019 ; Zgheib et al, 2019 ; Spinu et al, 2020 ; Paini et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements

et al. 2022

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Regulatory agencies around the world have committed to reducing or eliminating animal testing for establishing chemical safety. Adverse outcome pathways can facilitate replacement by providing a mechanistic framework for identifying the appropriate non-animal methods and connecting them to apical adverse outcomes. This study separated 11,992 chemicals with curated rat oral acute toxicity information into clusters of structurally similar compounds. Each cluster was then assigned one or more ToxCast/Tox21 assays by looking for the minimum number of assays required to record at least one positive hit call below cytotoxicity for all acutely toxic chemicals in the cluster. When structural information is used to select assays for testing, none of the chemicals required more than four assays and 98% required two assays or less. Both the structure-based clusters and activity from the associated assays were significantly associated with the GHS toxicity classification of the chemicals, which suggests that a combination of bioactivity and structural information could be as reproducible as traditional in vivo studies. Predictivity is improved when the in vitro assay directly corresponds to the mechanism of toxicity, but many indirect assays showed promise as well. Given the lower cost of in vitro testing, a small assay battery including both general cytotoxicity assays and two or more orthogonal assays targeting the toxicological mechanism could be used to improve performance further. This approach illustrates the promise of combining existing in silico approaches, such as the Collaborative Acute Toxicity Modeling Suite (CATMoS), with structure-based bioactivity information as part of an efficient tiered testing strategy that can reduce or eliminate animal testing for acute oral toxicity.

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Section: Introductionmentioning

confidence: 99%

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements

et al. 2022

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“…The development of a qAOP logically follows AOP development given its function as a mathematical representation of the key event relationships (KERs) in an AOP. Different approaches have been used including: 1) fitting functions to key event (KE) data bounding a KER(s) (response-response method) ( Doering et al, 2018 ; Doering et al, 2019 ; Zgheib et al, 2019 ; Song et al, 2020 ); 2) biologically based mathematical modeling using ordinary differential equations (aka systems biology modeling) ( Muller et al, 2015 ; Gillies et al, 2016 ; Conolly et al, 2017 ; Zgheib et al, 2019 ); and recently 3) a causal modeling approach using a Bayesian Network ( Jeong et al, 2018 ; Perkins et al, 2019a ; Zgheib et al, 2019 ; Burgoon et al, 2020 ; Moe et al, 2021 ; Paini et al, 2022 ). Bayesian Networks, in particular, are useful for describing complex AOPs involving multiple pathways leading to an AO as long as there are no feedback loops.…”

Section: Introductionmentioning

confidence: 99%

“…The KEs of the AOP can be taken as the nodes of the network and can even be used to model time dependencies in the form of Dynamic Bayesian Networks ( Zgheib et al, 2019 ). Note that in this article, response-response relationships are defined as mathematical functions determined by a regression analysis, whereas in other publications, e.g., Paini et al (2022) , the response-response relationship is defined more broadly to include biologically based models that quantitatively relate two KEs. The merits and pitfalls of the response-response approach and biologically based modeling have been discussed ( Schultz and Watanabe, 2018 ; Foran et al, 2019 ; Zgheib et al, 2019 ; Spinu et al, 2020 ), but a significant barrier to the development of qAOPs in any form is the availability of quantitative data amenable for mathematical model development.…”

Section: Introductionmentioning

confidence: 99%

“…The goal of this article is to improve the efficiency of converting a qualitative AOP into a qAOP. A workflow for qAOP development, electronic resources, and three case studies are described in Paini et al (2022) based on a recent Lorentz workshop. In the following, challenges to qAOP development were identified by reviewing AOPs with WPHA/WNT 1 endorsement by the Organisation for Economic Co-operation and Development ( OECD, 2021 ) in the AOPWiki 2 , and through a case study on developing a qAOP for acetylcholinesterase (AChE) inhibition leading to neurodegeneration ( Conrow et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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From Qualitative to Quantitative AOP: A Case Study of Neurodegeneration

2022

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Adverse outcome pathways (AOPs) include a sequence of events that connect a molecular-level initiating event with an adverse outcome at the cellular level for human health endpoints, or at the population level for ecological endpoints. When there is enough quantitative understanding of the relationships between key events in an AOP, a mathematical model may be developed to connect key events in a quantitative AOP (qAOP). Ideally, a qAOP will reduce the time and resources spent for chemical toxicity testing and risk assessment and enable the extrapolation of data collected at the molecular-level by in vitro assays, for example, to predict whether an adverse outcome may occur. Here, we review AOPs in the AOPWiki, an AOP repository, to determine best practices that would facilitate conversion from AOP to qAOP. Then, focusing on a particular case study, acetylcholinesterase inhibition leading to neurodegeneration, we describe specific methods and challenges. Examples of challenges include the availability and collection of quantitative data amenable to model development, the lack of studies that measure multiple key events, and model accessibility or transferability across platforms. We conclude with recommendations for improving key event and key event relationship descriptions in the AOPWiki that facilitate the transition of qualitative AOPs to qAOPs.

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“…This challenge is becoming even more pressing with modern toxicology moving towards higher dependence on alternative test methods rather than more traditional large-scale animal toxicity testing for screening and prioritizing chemical substances of concern ( Pistollato et al, 2021 ). This is not least important in hypothesis-driven testing, or predictive toxicology, where knowledge about toxicological mechanisms and measurable key events increasingly feed into causal adverse outcome pathways (AOPs) ( Ankley & Edwards, 2018 ; Audouze et al, 2021 ; Paini et al, 2022 ; Svingen et al, 2022 ). Once robust AOPs have been developed, chemicals can increasingly be assessed using alternative methods; in essence reducing the reliance on more traditional in vivo (animal) protocols.…”

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confidence: 99%

Editorial: Methods and Protocols in Developmental and Reproductive Toxicology

Hougaard

Svingen²

2022

Front. Toxicol.

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Editorial on the Research Topic Methods and Protocols in Developmental and Reproductive ToxicologyUndisturbed reproductive development is a prerequisite for life-long reproductive health. Many stages of perinatal life are vulnerable to disturbances such as exposure to environmental chemicals and drugs that can interfere with molecular and cellular processes. Of concern is the enormous number of chemicals and pharmaceuticals in existence and for which humans and wildlife can be exposed through their life cycles (Rockström et al., 2009;Svingen and Vinggaard, 2016;Hougaard, 2021).Although there are international regulations and guidelines in place for testing and assessing chemical substances for their potential toxicity, they are in many ways inadequate in dealing with the vast number of chemicals in use coupled with the myriad of potential effect they can have on complex living organisms. This challenge is becoming even more pressing with modern toxicology moving towards higher dependence on alternative test methods rather than more traditional large-scale animal toxicity testing for screening and prioritizing chemical substances of concern (Pistollato et al., 2021). This is not least important in hypothesisdriven testing, or predictive toxicology, where knowledge about toxicological mechanisms and measurable key events increasingly feed into causal adverse outcome pathways (AOPs)

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Towards a qAOP framework for predictive toxicology - Linking data to decisions

Cited by 22 publications

References 59 publications

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements

From Qualitative to Quantitative AOP: A Case Study of Neurodegeneration

Editorial: Methods and Protocols in Developmental and Reproductive Toxicology

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