Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology

Rovida, Costanza; Barton-Maclaren, Tara S.; Benfenati, Emilio; Caloni, F.; Chandrasekera, P. Charukeshi; Chesné, Christophe; Cronin, Mark T.D.; Knecht, Joop de; Dietrich, Daniel R.; Escher, Sylvia; Fitzpatrick, Suzanne; Flannery, Brenna M.; Herzler, Matthias; Bennekou, Susanne Hougaard; Hubesch, Bruno; Kamp, Hennicke; Kisitu, Jaffar; Kleinstreuer, Nicole; Kovarich, Simona; Leist, Marcel; Maertens, Alexandra; Nugent, K. W.; Pallocca, Giorgia; Pastor, Manuel; Patlewicz, Grace; Pavan, Manuela; Presgrave, Octávio Augusto França; Smirnova, Lena; Schwarz, Michael; Yamada, Takashi; Hartung, Thomas

doi:10.14573/altex.1912181

Cited by 56 publications

(39 citation statements)

References 110 publications

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“…For this reason, EU-ToxRisk provided many discussion platforms in the form of stakeholder meetings, conference sessions, and closed workshops with regulators and parallel projects. Recent examples of such activities are the workshops on microphysiological systems and their regulatory application (Marx et al 2020 ) and on international acceptance of read-across approaches (Rovida et al 2020a , b ) (Box 5 ).…”

Section: Gaps and Future Requirementsmentioning

confidence: 99%

“…This entails the implementation of NAM-based safety science into regulatory toxicology (Krebs et al 2020b ). The project initially focused on read-across (RAx) (Escher et al 2019 ; Rovida et al 2020a ) and here we outline key outcomes and general learnings on the use of NAMs for RAx. This collection of outputs will facilitate new efforts in next-generation risk assessment (NGRA) (Dent et al 2018 ; Dearfield et al 2017 ; Cote et al 2016 ; Krewski et al 2014 ) and thus advance the field of safety assessment by more mechanistically driven and animal-free approaches (Suppl.…”

Section: Introductionmentioning

confidence: 99%

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Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

et al. 2020

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In 2016, the European Commission launched the EU-ToxRisk research project to develop and promote animal-free approaches in toxicology. The 36 partners of this consortium used in vitro and in silico methods in the context of case studies (CSs). These CSs included both compounds with a highly defined target (e.g. mitochondrial respiratory chain inhibitors) as well as compounds with poorly defined molecular initiation events (e.g. short-chain branched carboxylic acids). The initial project focus was on developing a science-based strategy for read-across (RAx) as an animal-free approach in chemical risk assessment. Moreover, seamless incorporation of new approach method (NAM) data into this process (= NAM-enhanced RAx) was explored. Here, the EU-ToxRisk consortium has collated its scientific and regulatory learnings from this particular project objective. For all CSs, a mechanistic hypothesis (in the form of an adverse outcome pathway) guided the safety evaluation. ADME data were generated from NAMs and used for comprehensive physiological-based kinetic modelling. Quality assurance and data management were optimized in parallel. Scientific and Regulatory Advisory Boards played a vital role in assessing the practical applicability of the new approaches. In a next step, external stakeholders evaluated the usefulness of NAMs in the context of RAx CSs for regulatory acceptance. For instance, the CSs were included in the OECD CS portfolio for the Integrated Approach to Testing and Assessment project. Feedback from regulators and other stakeholders was collected at several stages. Future chemical safety science projects can draw from this experience to implement systems toxicology-guided, animal-free next-generation risk assessment.

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Section: Gaps and Future Requirementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

et al. 2020

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“…Read‐across (RAx) is being explored as a method for translating available information from well‐characterized to untested chemicals (Rovida et al., 2020). RAx is used to predict endpoint information for one chemical by using data from the same endpoint from other, similar chemicals based on a combination of in vivo , in vitro , and in silico data.…”

Section: Novel Risk Assessment Methods and Toolsmentioning

confidence: 99%

Evolving Science and Practice of Risk Assessment

Stackelberg

Williams²

2020

Risk Analysis

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Managing public health risks from environmental contaminants has historically relied on a risk assessment process defined by the regulatory context in which these risks are assessed. Risk assessment guidance follows a straightforward, chemical‐by‐chemical approach to inform regulatory decisions around the question “what is the risk‐based concentration protective of human and ecological health outcomes?” Here we briefly summarize regulatory risk assessment in the context of innovative risk assessment approaches based on an evolving understanding of the underlying scientific disciplines that support risk analysis more broadly. We discuss scientific versus regulatory tensions in the application of these approaches for future risk assessments, and challenges in translating our improved understanding of the underlying scientific complexity to the regulatory landscape to better inform decision making that extends beyond conventional regulatory mandates.

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“…As a minimum, every model must be accompanied by documents allowing to reproduce the algorithm in full, and to understand and interpret the prediction results. Other purposes, like demonstrating to regulatory bodies the quality of the prediction for replacing experimental tests [18], are only seldom required. Therefore, we recommend a layered documentation structure, including basic mandatory information and more detailed optional layers.…”

Section: Model Managementmentioning

confidence: 99%

Flame: An Open Source Framework for Model Development, Hosting, and Usage in Production Environments

Pastor

Gómez-Tamayo

Sanz

2020

Preprint

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This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application, with a Python backend and a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures, since the software supports structural normalization, molecular descriptor generation and machine learning building, using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code adding unlimited model customization. Models generated with Flame can be easily exported facilitating collaborative model development. All models are stored in a persistent model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly three years of development in collaboration with users from pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high quality predictive models based on shared legacy data for assessing the safety of drug candidates.

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Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology

Cited by 56 publications

References 110 publications

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

Evolving Science and Practice of Risk Assessment

Flame: An Open Source Framework for Model Development, Hosting, and Usage in Production Environments

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