Search

While human regulatory risk assessment (RA) still largely relies on animal studies, new approach methodologies (NAMs) based on in vitro, in silico or non-mammalian alternative models are increasingly used to evaluate chemical hazards. Moreover, human epidemiological studies with biomarkers of effect (BoE) also play an invaluable role in identifying health effects associated with chemical exposures. To move towards the next generation risk assessment (NGRA), it is therefore crucial to establish bridges between NAMs and standard approaches, and to establish processes for increasing mechanistically-based biological plausibility in human studies. The Adverse Outcome Pathway (AOP) framework constitutes an important tool to address these needs but, despite a significant increase in knowledge and awareness, the use of AOPs in chemical RA remains limited. The objective of this paper is to address issues related to using AOPs in a regulatory context from various perspectives as it was discussed in a workshop organized within the European Union partnerships HBM4EU and PARC in spring 2022. The paper presents examples where the AOP framework has been proven useful for the human RA process, particularly in hazard prioritization and characterization, in integrated approaches to testing and assessment (IATA), and in the identification and validation of BoE in epidemiological studies. Nevertheless, several limitations were identified that hinder the optimal usability and acceptance of AOPs by the regulatory community including the lack of quantitative information on response-response relationships and of efficient ways to map chemical data (exposure and toxicity) onto AOPs. The paper summarizes suggestions, ongoing initiatives and third-party tools that may help to overcome these obstacles and thus assure better implementation of AOPs in the NGRA., European Commission 733032 857560 101057014, Ministry of Education, Youth and Sports by the RECETOX Research Infrastructure LM2018121, OP RDE project CETOCOEN Excellence CZ.02.1.01/0.0/0.0/17_043/0009632, Japan Agency for Medical Research and Development (AMED) JP21mk0101216 JP22mk0101216, Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT), Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (KAKENHI) 21K12133

International audience; The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.

Adverse Outcome Pathways (AOP) provide structured frameworks for the systematic organization of research data and knowledge. The AOP framework follows a set of key principles that allow for broad application across diverse disciplines related to human health, including toxicology, pharmacology, virology and medical research. The COVID-19 pandemic engages a great number of scientists world-wide and data is increasing with exponential speed. Diligent data management strategies are employed but approaches for systematically organizing the data-derived information and knowledge are lacking. We believe AOPs can play an important role in improving interpretation and efficient application of scientific understanding of COVID-19. Here, we outline a newly initiated effort, the CIAO project (https://www.ciao-covid.net/), to streamline collaboration between scientists across the world toward development of AOPs for COVID-19, and describe the overarching aims of the effort, as well as the expected outcomes and research support that they will provide.

The CIAO project (Modelling the Pathogenesis of COVID-19 using the Adverse Outcome Pathway framework) aims at a holistic assembly of knowledge to deliver a truly transdisciplinary description of the entire COVID-19 physiopathology starting with the initial contact with the SARS-CoV-2 virus and ending with one or several adverse outcomes, e.g., respiratory failure. On 27-28 January 2021, a group of 50+ scientists from numerous organizations around the world met in the 2nd CIAO AOP Design Workshop to discuss the depiction of the COVID-19 disease process as a series of key events (KEs) in a network of AOPs. During the workshop, 74 such KEs forming 13 AOPs were identified, covering COVID-19 manifestations that affect the respiratory, neurological, liver, cardiovascular, kidney and gastrointestinal systems. Modulating factors influencing the course and severity of the disease were also addressed, as was a possible extension of the investigations beyond purely biological phenomena. The workshop ended with the creation of seven working groups, which will further elaborate on the AOPs to be presented and discussed in the 3rd CIAO workshop on 28-29 April 2021.

The Adverse Outcome Pathway (AOP) framework is increasingly used to integrate data based on traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in computable terms. Herein, we present a comprehensive annotation of 172 AOPs housed in the AOP-Wiki as of December 4, 2016, using terms from existing biological ontologies. AOP Key Events (KEs) were assigned ontology terms using a concept called the Event Component, which consists of a Process, an Object, and an Action term, with each term originating from ontologies and other controlled vocabularies. Annotation of KEs with ontology classes from 14 ontologies and controlled vocabularies resulted in a total of 685 KEs being annotated with a total of 809 Event Components. A set of seven conventions resulted, defining the annotation of KEs via Event Components. This expanded annotation of AOPs allows computational reasoners to aid in both AOP development and applications. I...

Highlights • Artificial Intelligence (AI) has potential to improve chemical risk assessment (CRA) and associated regulatory decisions. • AI could influence the scientific-technical evaluation process and the social aspects of the CRA decision making process. • Systematic Reviews are among the CRA processes that will profit most from the application of AI techniques. • Using AI as external objective monitor could make Weight of Evidence considerations less subjective and more trustable. • AI could identify pitfalls not observable by individuals participating in the CRA process., As the basis for managing the risks of chemical exposure, the Chemical Risk Assessment (CRA) process can impact a substantial part of the economy, the health of hundreds of millions of people, and the condition of the environment. However, the number of properly assessed chemicals falls short of societal needs due to a lack of experts for evaluation, interference of third party interests, and the sheer volume of potentially relevant information on the chemicals from disparate sources. In order to explore ways in which computational methods may help overcome this discrepancy between the number of chemical risk assessments required on the one hand and the number and adequateness of assessments actually being conducted on the other, the European Commission's Joint Research Centre organised a workshop on Artificial Intelligence for Chemical Risk Assessment (AI4CRA). The workshop identified a number of areas where Artificial Intelligence could potentially increase the number and quality of regulatory risk management decisions based on CRA, involving process simulation, supporting evaluation, identifying problems, facilitating collaboration, finding experts, evidence gathering, systematic review, knowledge discovery, and building cognitive models. Although these are interconnected, they are organised and discussed under two main themes: scientific-technical process and social aspects and the decision making process.

Toxicology data are generated on large scales by toxicogenomic studies and high-throughput screening (HTS) programmes, and on smaller scales by traditional methods. Both big and small data have value for elucidating toxicological mechanisms and pathways that are perturbed by chemical stressors. In addition, years of investigations comprise a wealth of knowledge as reported in the literature that is also used to interpret new data, though knowledge is not often captured in traditional databases. With the big data era, computer automation to analyse and interpret datasets is needed, which requires aggregation of data and knowledge from all available sources. This chapter reviews ongoing efforts to aggregate toxicological knowledge in a knowledge base, based on the Adverse Outcome Pathways framework, and provides examples of data integration and inferential analysis for use in (predictive) toxicology.

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24–25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.

While progress in chemical safety research in recent years has been impressive, especially in the area of in-vitro and 3R approaches, an obvious gap between scientific ambition on the one hand and ...

Adverse Outcome Pathways (AOPs) play an increasingly important role in risk and hazard assessment, giving scientists across many disciplines and organisations the opportunity to jointly collect and discuss their knowledge about the biological and toxicological processes leading to an Adverse Outcome. While this knowledge becomes more distributed and accepted by the scientific mainstream, its acceptance in a regulatory context is still not well developed. A promising avenue for increasing visibility and applicability of AOPs in the regulatory context is the role they play in the development of Integrated Approaches to Testing and Assessment (IATA), where the mechanistic knowledge AOPs provide will underpin testing strategies accepted by regulators and relying less and less on animal testing. The development of a standardised exchange format for AOPs (AOP-XML) and of an OECD-endorsed regulatory reporting format for results of non-classical test methods (OHT 201) are further enabling factors for any upcoming adoption of AOPs in the regulatory world.

ChemAgora, a web application designed and developed in the context of the “Data Infrastructure for Chemical Safety Assessment” (diXa) project, provides search capabilities to chemical data from resources available online, enabling users to cross-reference their search results with both regulatory chemical information and public chemical databases. ChemAgora, through an on-the-fly search, informs whether a chemical is known or not in each of the external data sources and provides clikable links leading to the third-party web site pages containing the information. The original purpose of the ChemAgora application was to correlate studies stored in the diXa data warehouse with available chemical data. Since the end of the diXa project, ChemAgora has evolved into an independent portal, currently accessible directly through the ChemAgora home page, with improved search capabilities of online data sources.

eChemPortal erleichtert den Zugang zu Informationen uber Chemikalien. Daten, die von Industrie und Wissenschaft in unterschiedlichste nationale und internationale Datenbanken eingespeist wurden, stehen nun in einem einheitlichen Abfrageformat zur Verfugung: Informationen uber physikalisch-chemische Eigenschaften sowie uber Gefahren fur Umwelt und Gesundheit, die von Chemikalien ausgehen, werden ubersichtlich dargestellt, und Links zu den teilnehmenden Datenbanken helfen bei der eingehenderen Erorterung relevanter Daten. eChemPortal simplifies the access to information about chemical substances. Data submitted by industry and science to diverse national and international data bases are now available in a uniform query format: Information about physico-chemical properties hazard for environment and human health posed by chemicals is clearly arranged, and links to the participating data bases help when investigating underlying data.

Motivation: The field of toxicogenomics (the application of ‘-omics’ technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available. Results: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal. Availability and implementation: http://www.dixa-fp7.eu Contact: d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.eu Supplementary information: Supplementary data are available at Bioinformatics online.