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Sewer A, Talikka M, Calvino-Martin F, Luettich K, Iskandar A. Quantitative modeling of in vitro data using an adverse outcome pathway for the risk assessment of decreased lung function in humans. Toxicol Lett 2024; 393:107-113. [PMID: 38350531 DOI: 10.1016/j.toxlet.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/18/2024] [Accepted: 02/07/2024] [Indexed: 02/15/2024]
Abstract
In the absence of epidemiological data, there is a need to develop computational models that convert in vitro findings to human disease risk predictions following toxicant exposure. In such efforts, in vitro data can be evaluated in the context of adverse outcome pathways (AOPs) that organize mechanistic knowledge based on empirical evidence into a sequence of molecular-, cellular-, tissue-, and organ-level key events that precede an adverse outcome (AO). Here we combined data from advanced in vitro organotypic airway models exposed to combustible cigarette (CC) smoke or Tobacco Heating System (THS) aerosol with an AOP for increased oxidative stress leads to decreased lung function. The mathematical modeling predicted reduced risk of decreased ciliary beating frequency (CBF) based on oxidative stress measurements and reduced risk of decreased mucociliary clearance (MCC) based on CBF measurements in THS aerosol- compared with CC smoke-exposed cultures. To extend the predictions to the AO of decreased lung function, we leveraged human MCC data from current smokers, nonsmokers, former smokers, and users of heated tobacco products. This approach provided a plausible prediction of diminished reduction in lung function in response to THS use compared with continued smoking. The current approach may also present a basis for an integrated approach to testing and assessment of tobacco products for future regulatory decision-making.
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Affiliation(s)
- Alain Sewer
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland.
| | - Marja Talikka
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | | | - Karsta Luettich
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Anita Iskandar
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
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Ito S, Mukherjee S, Erami K, Muratani S, Mori A, Ichikawa S, White W, Yoshino K, Fallacara D. Proof of concept for quantitative adverse outcome pathway modeling of chronic toxicity in repeated exposure. Sci Rep 2024; 14:4741. [PMID: 38413641 PMCID: PMC10899215 DOI: 10.1038/s41598-024-55220-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 02/21/2024] [Indexed: 02/29/2024] Open
Abstract
Adverse Outcome Pathway (AOP) is a useful tool to glean mode of action (MOE) of a chemical. However, in order to use it for the purpose of risk assessment, an AOP needs to be quantified using in vitro or in vivo data. Majority of quantitative AOPs developed so far, were for single exposure to progressively higher doses. Limited attempts were made to include time in the modeling. Here as a proof-of concept, we developed a hypothetical AOP, and quantified it using a virtual dataset for six repeated exposures using a Bayesian Network Analysis (BN) framework. The virtual data was generated using realistic assumptions. Effects of each exposure were analyzed separately using a static BN model and analyzed in combination using a dynamic BN (DBN) model. Our work shows that the DBN model can be used to calculate the probability of adverse outcome when other upstream KEs were observed earlier. These probabilities can help in identification of early indicators of AO. In addition, we also developed a data driven AOP pruning technique using a lasso-based subset selection, and show that the causal structure of AOP is itself dynamic and changes over time. This proof-of-concept study revealed the possibility for expanding the applicability of the AOP framework to incorporate biological dynamism in toxicity appearance by repeated insults.
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Affiliation(s)
- Shigeaki Ito
- Scientific Product Assessment Center, Japan Tobacco Inc., 6-2, Umegaoka, Aoba-ku, Yokohama, Kanagawa, 227-8512, Japan.
| | | | - Kazuo Erami
- Scientific Product Assessment Center, Japan Tobacco Inc., 6-2, Umegaoka, Aoba-ku, Yokohama, Kanagawa, 227-8512, Japan
| | - Shugo Muratani
- Scientific Product Assessment Center, Japan Tobacco Inc., 6-2, Umegaoka, Aoba-ku, Yokohama, Kanagawa, 227-8512, Japan
| | - Akina Mori
- Scientific Product Assessment Center, Japan Tobacco Inc., 6-2, Umegaoka, Aoba-ku, Yokohama, Kanagawa, 227-8512, Japan
| | - Sakuya Ichikawa
- Scientific Product Assessment Center, Japan Tobacco Inc., 6-2, Umegaoka, Aoba-ku, Yokohama, Kanagawa, 227-8512, Japan
| | | | - Kei Yoshino
- Scientific Product Assessment Center, Japan Tobacco Inc., 6-2, Umegaoka, Aoba-ku, Yokohama, Kanagawa, 227-8512, Japan
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3
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Aleksic M, Rajagopal R, de-Ávila R, Spriggs S, Gilmour N. The skin sensitization adverse outcome pathway: exploring the role of mechanistic understanding for higher tier risk assessment. Crit Rev Toxicol 2024; 54:69-91. [PMID: 38385441 DOI: 10.1080/10408444.2024.2308816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
For over a decade, the skin sensitization Adverse Outcome Pathway (AOP) has served as a useful framework for development of novel in chemico and in vitro assays for use in skin sensitization hazard and risk assessment. Since its establishment, the AOP framework further fueled the existing efforts in new assay development and stimulated a plethora of activities with particular focus on validation, reproducibility and interpretation of individual assays and combination of assay outputs for use in hazard/risk assessment. In parallel, research efforts have also accelerated in pace, providing new molecular and dynamic insight into key events leading to sensitization. In light of novel hypotheses emerging from over a decade of focused research effort, mechanistic evidence relating to the key events in the skin sensitization AOP may complement the tools currently used in risk assessment. We reviewed the recent advances unraveling the complexity of molecular events in sensitization and signpost the most promising avenues for further exploration and development of useful assays.
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Affiliation(s)
- Maja Aleksic
- Safety and Environmental Assurance Centre, Unilever, Sharnbrook, UK
| | - Ramya Rajagopal
- Safety and Environmental Assurance Centre, Unilever, Sharnbrook, UK
| | - Renato de-Ávila
- Safety and Environmental Assurance Centre, Unilever, Sharnbrook, UK
| | - Sandrine Spriggs
- Safety and Environmental Assurance Centre, Unilever, Sharnbrook, UK
| | - Nicola Gilmour
- Safety and Environmental Assurance Centre, Unilever, Sharnbrook, UK
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Gastellu T, Le Bizec B, Rivière G. Characterisation of the risk associated with chronic lifetime exposure to mixture of chemical hazards: case study of trace elements. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2023; 40:951-970. [PMID: 37428801 DOI: 10.1080/19440049.2023.2231086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 06/20/2023] [Accepted: 06/21/2023] [Indexed: 07/12/2023]
Abstract
Risk assessment methodology, mostly commonly used, faces the complexity of the environment. Populations are exposed to multiple sources of chemicals throughout life and the chemical mixtures they are exposed change during time (lifestyle factors, regulatory decisions, etc). The risk assessment needs to consider these dynamics and the evolution of the body with age, in order to refine the exposure assessment to chemicals and to predict the health impact of these exposures. This review highlights the latest methodologies developed to improve risk assessment, especially cor heavy metals. The methodologies aim to better describe the chemical toxicokinetic and toxicodynamic as well as the exposure assessment. Human Biomonitoring (HBM) data give great opportunities to link biomarkers of exposure with an adverse effect. Physiologically-Based Toxicokinetic (PBTK) models are more and more used to simulate the evolution of biomarkers in organisms, considering the external exposures and the physiological evolutions. PBTK models may also be used to determine the routes of exposure or to predict the impacts of schemes of exposure. The major limit is the integration of several chemicals in mixture with common adverse effects and the interactions between them.
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Affiliation(s)
- Thomas Gastellu
- Oniris, INRAE, LABERCA, Nantes, France
- Risk Assessment Department, French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Maisons-Alfort, France
| | | | - Gilles Rivière
- Risk Assessment Department, French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Maisons-Alfort, France
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5
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Deepika D, Kumar V. The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3473. [PMID: 36834167 PMCID: PMC9966583 DOI: 10.3390/ijerph20043473] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
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Affiliation(s)
- Deepika Deepika
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
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Bajard L, Adamovsky O, Audouze K, Baken K, Barouki R, Beltman JB, Beronius A, Bonefeld-Jørgensen EC, Cano-Sancho G, de Baat ML, Di Tillio F, Fernández MF, FitzGerald RE, Gundacker C, Hernández AF, Hilscherova K, Karakitsios S, Kuchovska E, Long M, Luijten M, Majid S, Marx-Stoelting P, Mustieles V, Negi CK, Sarigiannis D, Scholz S, Sovadinova I, Stierum R, Tanabe S, Tollefsen KE, van den Brand AD, Vogs C, Wielsøe M, Wittwehr C, Blaha L. Application of AOPs to assist regulatory assessment of chemical risks - Case studies, needs and recommendations. ENVIRONMENTAL RESEARCH 2023; 217:114650. [PMID: 36309218 PMCID: PMC9850416 DOI: 10.1016/j.envres.2022.114650] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/18/2022] [Accepted: 10/21/2022] [Indexed: 05/06/2023]
Abstract
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.
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Affiliation(s)
- Lola Bajard
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Ondrej Adamovsky
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Karine Audouze
- Université Paris Cité, T3S, Inserm UMR S-1124, F-75006 Paris, France
| | - Kirsten Baken
- Unit Health, Flemish Institute for Technological Research (VITO NV), Boeretang 200, 2400 Mol, Belgium
| | - Robert Barouki
- Université Paris Cité, T3S, Inserm UMR S-1124, F-75006 Paris, France
| | - Joost B Beltman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
| | - Anna Beronius
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Solna, Sweden
| | - Eva Cecilie Bonefeld-Jørgensen
- Centre for Arctic Health & Molecular Epidemiology, Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark; Greenland Centre for Health Research, University of Greenland, Manutooq 1, 3905 Nuussuaq, Greenland
| | | | - Milo L de Baat
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Filippo Di Tillio
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
| | - Mariana F Fernández
- Center for Biomedical Research (CIBM) & School of Medicine, University of Granada, 18016 Granada, Spain; Instituto de Investigación Biosanitaria (ibs. GRANADA), 18012, Granada, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | - Rex E FitzGerald
- Swiss Centre for Applied Human Toxicology SCAHT, University of Basel, Missionsstrasse 64, CH-4055 Basel, Switzerland
| | - Claudia Gundacker
- Institute of Medical Genetics, Center for Pathobiochemistry and Genetics, Medical University of Vienna, 1090 Vienna, Austria
| | - Antonio F Hernández
- Instituto de Investigación Biosanitaria (ibs. GRANADA), 18012, Granada, Spain; Department of Legal Medicine and Toxicology, University of Granada School of Medicine, Avda. de la Investigación, 11, 18016, Granada, Spain; Consortium for Biomedical Research in Epidemiology & Public Health, CIBERESP, Madrid, Spain
| | - Klara Hilscherova
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Spyros Karakitsios
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece; HERACLES Research Centre on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Thessaloniki, Greece
| | - Eliska Kuchovska
- IUF-Leibniz Research Institute for Environmental Medicine, Auf'm Hennekamp 50, 40225, Duesseldorf, Germany
| | - Manhai Long
- Centre for Arctic Health & Molecular Epidemiology, Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark
| | - Mirjam Luijten
- National Institute for Public Health and the Environment (RIVM), Centre for Health Protection, Bilthoven, the Netherlands
| | - Sanah Majid
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Philip Marx-Stoelting
- German Federal Institute for Risk Assessment, Dept. Pesticides Safety, Berlin, Germany
| | - Vicente Mustieles
- Center for Biomedical Research (CIBM) & School of Medicine, University of Granada, 18016 Granada, Spain; Instituto de Investigación Biosanitaria (ibs. GRANADA), 18012, Granada, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | - Chander K Negi
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Dimosthenis Sarigiannis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece; HERACLES Research Centre on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Thessaloniki, Greece
| | - Stefan Scholz
- UFZ Helmholtz Center for Environmental Research, Dept Bioanalyt Ecotoxicol, D-04318 Leipzig, Germany
| | - Iva Sovadinova
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
| | - Rob Stierum
- Netherlands Organisation for Applied Scientific Research, Risk Analysis for Products in Development, Utrecht, the Netherlands
| | - Shihori Tanabe
- Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki, Japan
| | - Knut Erik Tollefsen
- Norwegian Institute for Water Research (NIVA), Section of Ecotoxicology and Risk Assessment, Gaustadalléen, Oslo, Norway; Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Norway
| | - Annick D van den Brand
- Institute for Public Health and the Environment (RIVM), Centre for Nutrition, Prevention and Health Services, 3720 BA Bilthoven, the Netherlands
| | - Carolina Vogs
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Solna, Sweden; Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, SE-75007 Uppsala, Sweden
| | - Maria Wielsøe
- Centre for Arctic Health & Molecular Epidemiology, Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark
| | | | - Ludek Blaha
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic.
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Enesca A, Cazan C. Polymer Composite-Based Materials with Photocatalytic Applications in Wastewater Organic Pollutant Removal: A Mini Review. Polymers (Basel) 2022; 14:polym14163291. [PMID: 36015545 PMCID: PMC9415733 DOI: 10.3390/polym14163291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/01/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
The development of new technologies using nanomaterials has allowed scientists to design advanced processes with many applications in environmental protection, energy production and storage, and medicinal bio-mediated processes. Due to their significant potential applications in different branches of science, the development of new polymer composites represents a priority, especially for nano-technological processes. Interest in polymeric composites was outlined by the synthesis of a large number of nano- or mezzo-scale materials with targeted functional properties for polymer matrix hybridization. The present mini review explores some of the most representative and recent papers reporting the photocatalytic activity of polymer composites toward different organic compounds (dyes, pharmaceutically active molecules, phenol, etc.). The polymer composites were divided based on their composition and photocatalytic activity. TiO2- and ZnO-based polymeric composites have been described here in light of their photocatalytic activity toward different pollutants, such as rhodamine B, phenol, or methyl orange. Polymeric composites based on WO3, Fe2O3, or Bi2MoO6 were also described. The influence of different polymeric composites and photocatalytic parameters (light spectra and intensity, pollutant molecule and concentration, irradiation time, and photocatalyst dosage) on the overall photocatalytic efficiency indicates that semiconductor (TiO2, ZnO, etc.) insertion in the polymeric matrix can tune the photocatalytic activity without compromising the structural integrity. Future perspectives and limitations are outlined considering the systematic and targeted description of the reported results. Adopting green route synthesis and application can add economic and scientific value to the knowledgebase by promoting technological development based on photocatalytic designs.
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Chauhan V, Hamada N, Wilkins R, Garnier-Laplace J, Laurier D, Beaton D, Tollefsen KE. A high-level overview of the Organisation for Economic Co-operation and Development Adverse Outcome Pathway Programme. Int J Radiat Biol 2022; 98:1704-1713. [PMID: 35938955 DOI: 10.1080/09553002.2022.2110311] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Background The Organisation for Economic Co-operation and Development (OECD), through its Chemical Safety Programme, is delegated to ensure the safety of humans and wildlife from harmful toxicants. To support these needs, initiatives to increase the efficiency of hazard identification and risk management are under way. Amongst these, the adverse outcome pathway (AOP) approach integrates information on biological knowledge and test methodologies (both established and new) to support regulatory decision making. AOPs collate biological knowledge from different sources, assess lines of evidence through considerations of causality and undergo rigorous peer-review before being subsequently endorsed by the OECD. It is envisioned that the OECD AOP Development Programme will transform the toxicity testing paradigm by leveraging the strengths of mechanistic and modelling based approaches and enhance the utility of high throughput screening assays. Since its launch, in 2012, the AOP Development Programme has matured with a greater number of AOPs endorsed since inception, and the attraction of new scientific disciplines (e.g. the radiation field). Recently, a Radiation and Chemical (Rad/Chem) AOP Joint Topical Group has been formed by the OECD Nuclear Energy Agency High-Level Group on Low-Dose Research (HLG-LDR) under the auspices of the Committee on Radiological Protection and Public Health (CRPPH). The topical group will work to evolve the development and use of the AOP framework in radiation research and regulation. As part of these efforts, the group will bring awareness and understanding on the programme, as it has matured from the chemical perspective. In this context, this paper provides the radiation community with a high-level overview of the OECD AOP Development Programme, including examples of application using knowledge gleaned from the field of chemical toxicology, and their work towards regulatory implementation. Conclusion: Although the drivers for developing AOPs in chemical sector differ from that of the radiation field, the principles and transparency of the approach can benefit both scientific disciplines. By providing perspectives and an understanding of the evolution of the OECD AOP Development Programme including case examples and work towards quantitative AOP development, it may motivate the expansion and implementation of AOPs in the radiation field.
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Affiliation(s)
- Vinita Chauhan
- Environmental Health Science Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | - Nobuyuki Hamada
- Biology and Environmental Chemistry Division, Sustainable System Research Laboratory, Central Research Institute of Electric Power Industry (CRIEPI), Komae, Tokyo, Japan
| | - Ruth Wilkins
- Environmental Health Science Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | | | - Dominique Laurier
- Institute for Radiological Protection and Nuclear Safety (IRSN), Health and Environment Division, Fontenay-aux-Roses, F-92262, France
| | | | - Knut Erik Tollefsen
- Norwegian Institute for Water Research (NIVA), Oslo, Norway.,Norwegian University of Life Sciences (NMBU), Ås, Norway.,Centre for Environmental Radioactivity, Norwegian University of Life Sciences (NMBU), Ås, Norway
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9
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Abstract
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared to in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.
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Affiliation(s)
- Zhoumeng Lin
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA.,Center for Environmental and Human Toxicology, University of Florida, FL, 32608, USA
| | - Wei-Chun Chou
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA.,Center for Environmental and Human Toxicology, University of Florida, FL, 32608, USA
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10
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Liu A, Han N, Munoz-Muriedas J, Bender A. Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI). PLoS Comput Biol 2022; 18:e1010148. [PMID: 35687583 PMCID: PMC9292124 DOI: 10.1371/journal.pcbi.1010148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 07/18/2022] [Accepted: 04/26/2022] [Indexed: 01/10/2023] Open
Abstract
Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. In toxicology, Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance using the concept of “first activation” as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat-dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time-concordant gene expression-derived events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). We find known mechanisms in DILI to be time-concordant, and show further that significance, frequency and log fold change (logFC) of differential expression are metrics which can additionally prioritize events although not necessary to be mechanistically relevant. Moreover, we used the temporal order of transcription factor (TF) expression and regulon activity to identify transcriptionally regulated TFs and subsequently combined this with prior knowledge on functional interactions to derive detailed gene-regulatory mechanisms, such as reduced Hnf4a activity leading to decreased expression and activity of Cebpa. At the same time, also potentially novel events are identified such as Sox13 which is highly significantly time-concordant and shows sustained activation over time. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (https://anikaliu.shinyapps.io/dili_cascades), which allows users to query events of interest in more detail. Understanding mechanisms from systems-scale biological data is of great relevance in toxicology as well as drug discovery; however how to generate causal hypotheses instead of correlations is by no means clear. In this work, we study the conserved temporal order of events and present an automatable framework to quantify and characterize time concordance across a large set of time-series. We apply this concept to events derived from time-resolved gene expression and histopathology from the TG-GATEs in vivo liver data as a case study. We were able to recover known events involved in the pathogenesis of Drug-Induced Liver Injury (DILI), and identify potentially novel pathway and transcription factors (TFs) which precede adverse histopathology. As complementary sources of evidence for causality, we additionally show how time concordance and prior knowledge on plausible interactions between TFs can be combined to derive causal hypotheses on the TFs’ mode of regulation and interaction partners. Overall, the results derived in our case study can serve as valuable hypothesis-free starting points for the development of Adverse Outcome Pathways for DILI, and demonstrate that our approach provides a novel angle to prioritize mechanistically relevant events.
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Affiliation(s)
- Anika Liu
- Milner Therapeutics Institute, University of Cambridge, Cambridge, United Kingdom
- Systems Modelling and Translational Biology, Data and Computational Sciences, GSK, London, United Kingdom
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
- * E-mail: (AL); (AB)
| | - Namshik Han
- Milner Therapeutics Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre for AI in Medicine, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Jordi Munoz-Muriedas
- Systems Modelling and Translational Biology, Data and Computational Sciences, GSK, London, United Kingdom
- Computer-Aided Drug Design, UCB, Slough, United Kingdom
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
- * E-mail: (AL); (AB)
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11
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Sinitsyn D, Garcia-Reyero N, Watanabe KH. From Qualitative to Quantitative AOP: A Case Study of Neurodegeneration. FRONTIERS IN TOXICOLOGY 2022; 4:838729. [PMID: 35434701 PMCID: PMC9006165 DOI: 10.3389/ftox.2022.838729] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 02/24/2022] [Indexed: 12/17/2022] Open
Abstract
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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Affiliation(s)
- Dennis Sinitsyn
- Arizona State University, School of Mathematical and Natural Sciences, Glendale, AZ, United States
- Oak Ridge Institute for Science and Education, Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, United States
| | - Natàlia Garcia-Reyero
- US Army Engineer Research and Development Center, Environmental Laboratory, Vicksburg, MS, United States
| | - Karen H. Watanabe
- Arizona State University, School of Mathematical and Natural Sciences, Glendale, AZ, United States
- *Correspondence: Karen H. Watanabe,
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Tebby C, Gao W, Delp J, Carta G, van der Stel W, Leist M, Jennings P, van de Water B, Bois FY. A quantitative AOP of mitochondrial toxicity based on data from three cell lines. Toxicol In Vitro 2022; 81:105345. [PMID: 35278637 DOI: 10.1016/j.tiv.2022.105345] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/22/2022] [Accepted: 03/07/2022] [Indexed: 11/30/2022]
Abstract
Adverse Outcome Pathways (AOPs) are increasingly used to support the integration of in vitro data in hazard assessment for chemicals. Quantitative AOPs (qAOPs) use mathematical models to describe the relationship between key events (KEs). In this paper, data obtained in three cell lines, LHUMES, HepG2 and RPTEC/TERT1, using similar experimental protocols, was used to calibrate a qAOP of mitochondrial toxicity for two chemicals, rotenone and deguelin. The objectives were to determine whether the same qAOP could be used for the three cell types, and to test chemical-independence by cross-validation with a dataset obtained on eight other chemicals in LHUMES cells. Repeating the calibration approach for both chemicals in three cell lines highlighted various practical difficulties. Even when the same readouts of KEs are measured, the mathematical functions used to describe the key event relationships may not be the same. Cross-validation in LHUMES cells was attempted by estimating chemical-specific potency at the molecular initiating events and using the rest of the calibrated qAOP to predict downstream KEs: toxicity of azoxystrobin, carboxine, mepronil and thifluzamide was underestimated. Selection of most relevant readouts and accurate characterization of the molecular initiating event for cross-validation are critical when designing in vitro experiments targeted at calibrating qAOPs.
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Affiliation(s)
- Cleo Tebby
- Experimental Toxicology and Modeling (TEAM) Unit, Ineris, rue Jacques Taffanel, 60550 Verneuil-en-Halatte, France.
| | - Wang Gao
- Experimental Toxicology and Modeling (TEAM) Unit, Ineris, rue Jacques Taffanel, 60550 Verneuil-en-Halatte, France; Applied Mathematics Laboratory (LMAC EA 2222), Université Technologique de Compiègne (UTC), 57 avenue de Landshut, 60203 Compiègne Cedex, France
| | - Johannes Delp
- In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Universitaetsstr. 10, 78464, Konstanz, Germany; Cooperative Doctorate College InViTe, University of Konstanz, Konstanz, Germany
| | - Giada Carta
- Division of Molecular and Computational Toxicology, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Wanda van der Stel
- Division of Drug Discovery and Safety, Leiden Academic Centre of Drug Research, Leiden University, Leiden, the Netherlands
| | - Marcel Leist
- In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Universitaetsstr. 10, 78464, Konstanz, Germany
| | - Paul Jennings
- Division of Molecular and Computational Toxicology, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Bob van de Water
- Division of Drug Discovery and Safety, Leiden Academic Centre of Drug Research, Leiden University, Leiden, the Netherlands
| | - Frederic Y Bois
- Experimental Toxicology and Modeling (TEAM) Unit, Ineris, rue Jacques Taffanel, 60550 Verneuil-en-Halatte, France; Certara, Simcyp, Sheffield, United Kingdom
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13
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Edwards SW, Nelms M, Hench VK, Ponder J, Sullivan K. Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements. FRONTIERS IN TOXICOLOGY 2022; 4:824094. [PMID: 35295211 PMCID: PMC8915918 DOI: 10.3389/ftox.2022.824094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 01/31/2022] [Indexed: 12/16/2022] Open
Abstract
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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Affiliation(s)
- Stephen W. Edwards
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, Research Triangle Park, Durham, NC, United States
- *Correspondence: Stephen W. Edwards, ; Kristie Sullivan,
| | - Mark Nelms
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, Research Triangle Park, Durham, NC, United States
| | - Virginia K. Hench
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, Research Triangle Park, Durham, NC, United States
| | - Jessica Ponder
- Physicians Committee for Responsible Medicine, Washington, DC, United States
| | - Kristie Sullivan
- Physicians Committee for Responsible Medicine, Washington, DC, United States
- *Correspondence: Stephen W. Edwards, ; Kristie Sullivan,
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Paini A, Campia I, Cronin MTD, Asturiol D, Ceriani L, Exner TE, Gao W, Gomes C, Kruisselbrink J, Martens M, Meek MEB, Pamies D, Pletz J, Scholz S, Schüttler A, Spînu N, Villeneuve DL, Wittwehr C, Worth A, Luijten M. Towards a qAOP framework for predictive toxicology - Linking data to decisions. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 21:100195. [PMID: 35211660 PMCID: PMC8850654 DOI: 10.1016/j.comtox.2021.100195] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/23/2021] [Accepted: 10/09/2021] [Indexed: 12/22/2022]
Abstract
Chemical toxicity assessment depends on the quantification of kinetics and dynamics. Quantitative AOPs (qAOPs) are toxicodynamic models based on Adverse Outcome Pathways. Existing e-resources could form the basis of an e-infrastructure for qAOP modelling. Best practices for qAOP development, assessment and application are needed. Three qAOP case studies are presented to illustrate a modelling workflow.
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.
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Affiliation(s)
- Alicia Paini
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Ivana Campia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - David Asturiol
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - Thomas E Exner
- Edelweiss Connect GmbH, Technology Park Basel, Basel, Switzerland
| | - Wang Gao
- Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France
| | | | | | | | | | - David Pamies
- Department of Physiology, Lausanne and Swiss Centre for Applied Human Toxicology (SCAHT), University of Lausanne, Lausanne, Switzerland
| | - Julia Pletz
- Liverpool John Moores University, Liverpool, United Kingdom
| | - Stefan Scholz
- Helmholtz Centre for Environmental Research GmbH - UFZ, Leipzig, Germany
| | - Andreas Schüttler
- Helmholtz Centre for Environmental Research GmbH - UFZ, Leipzig, Germany
| | - Nicoleta Spînu
- Liverpool John Moores University, Liverpool, United Kingdom
| | - Daniel L Villeneuve
- US Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Duluth, MN, USA
| | | | - Andrew Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Mirjam Luijten
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
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15
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Spînu N, Cronin MT, Lao J, Bal-Price A, Campia I, Enoch SJ, Madden JC, Mora Lagares L, Novič M, Pamies D, Scholz S, Villeneuve DL, Worth AP. Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network. COMPUTATIONAL TOXICOLOGY 2022; 21:100206. [PMID: 35211661 PMCID: PMC8857173 DOI: 10.1016/j.comtox.2021.100206] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/08/2021] [Accepted: 11/25/2021] [Indexed: 12/14/2022]
Abstract
A developmental neurotoxicity Adverse Outcome Pathway network was simplified. Common key events were chosen based on topology analysis and expert judgement. Quantification of causal relationships was informed by key event relationships. Various types of information were integrated for probability prediction. Bayesian hierarchical modelling was applied for hazard identification.
In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as in silico and in vitro information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.
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Affiliation(s)
- Nicoleta Spînu
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
| | - Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
| | - Junpeng Lao
- Department of Psychology, University of Fribourg, Fribourg CH-1700, Switzerland
| | - Anna Bal-Price
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Ivana Campia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Steven J. Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
| | - Judith C. Madden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
| | - Liadys Mora Lagares
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, 1000 Ljubljana, Slovenia
| | - Marjana Novič
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, 1000 Ljubljana, Slovenia
| | - David Pamies
- Department of Biomedical Science, University of Lausanne, Lausanne, Vaud, Switzerland
- Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Stefan Scholz
- Helmholtz-Centre for Environmental Research − UFZ, Department of Bioanalytical Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany
| | - Daniel L. Villeneuve
- US Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804, MN, USA
| | - Andrew P. Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
- Corresponding author.
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16
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Spînu N, Cronin MT, Madden JC, Worth AP. A matter of trust: Learning lessons about causality will make qAOPs credible. COMPUTATIONAL TOXICOLOGY 2022; 21:100205. [PMID: 35224319 PMCID: PMC8855346 DOI: 10.1016/j.comtox.2021.100205] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/01/2021] [Accepted: 11/25/2021] [Indexed: 12/03/2022]
Abstract
Quantitative AOPs are toxicodynamic models based on Adverse Outcome Pathways. Mathematical models, e.g. Bayesian networks, can inform the quantification of AOPs. Model credibility is enhanced by applying the principles of causality theory. A causal diagram is illustrated for an AOP for Parkinsonian motor deficits. Computational resources to help assess causality are listed.
Toxicology in the 21st Century has seen a shift from chemical risk assessment based on traditional animal tests, identifying apical endpoints and doses that are “safe”, to the prospect of Next Generation Risk Assessment based on non-animal methods. Increasingly, large and high throughput in vitro datasets are being generated and exploited to develop computational models. This is accompanied by an increased use of machine learning approaches in the model building process. A potential problem, however, is that such models, while robust and predictive, may still lack credibility from the perspective of the end-user. In this commentary, we argue that the science of causal inference and reasoning, as proposed by Judea Pearl, will facilitate the development, use and acceptance of quantitative AOP models. Our hope is that by importing established concepts of causality from outside the field of toxicology, we can be “constructively disruptive” to the current toxicological paradigm, using the “Causal Revolution” to bring about a “Toxicological Revolution” more rapidly.
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Chhipi-Shrestha G, Rodriguez M, Behmel S, Pulicharla R, Proulx F, Hewage K, Sadiq R. Probabilistic framework for assessing ecological risk of Contaminants of Emerging Concern: Application to a Canadian lake system. CHEMOSPHERE 2022; 287:131910. [PMID: 34454220 DOI: 10.1016/j.chemosphere.2021.131910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 07/30/2021] [Accepted: 08/14/2021] [Indexed: 06/13/2023]
Abstract
Contaminants of Emerging Concern (CECs) in natural water pose risks to ecosystems. The concentration of CECs varies spatially and temporally, and their estimated ecotoxicities differ widely by toxicological studies. This study extensively reviewed literature on ecological risk assessment and proposed a probabilistic framework for assessing ecological risk and its uncertainties (aleatory and epistemic). The framework integrated Adverse Outcome Pathway in risk assessment and was applied to a Canadian lake system for seven CECs: salicylic acid, acetaminophen, caffeine, carbamazepine, ibuprofen, drospirenone, and sulfamethoxazole. Altogether 264 water samples were collected and analyzed from 15 sites May 2016 to September 2017. Phytoplankton, zooplankton, and fish were also sampled and analyzed. The results show ecological risk estimates (Risk Quotient, RQ) varied considerably indicating a range of uncertainty. Based on the conservative estimate, the central tendency estimate of the ecological risk of mixture compounds was medium (RQ = 0.6) including drospirenone. However, the reasonably maximum estimate of the risk was high (RQ = 1.4) for mixture compounds including drospirenone. The high risk is primarily due to drospirenone as its individual risk was high (RQ = 1.1) to fish. The specific site and time of high drospirenone exposure was identified for implementing control measures. Classification of ecotoxicity values based on environmental parameters such as climate and water quality, can reduce uncertainty in the risk estimate.
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Affiliation(s)
- Gyan Chhipi-Shrestha
- School of Engineering, University of British Columbia, Okanagan Campus, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada; École Supérieure d'Aménagement du Territoire, Université Laval, 1628 Pavillon Savard, Université Laval, Québec City, QC, G1K 7P4, Canada.
| | - Manuel Rodriguez
- École Supérieure d'Aménagement du Territoire, Université Laval, 1628 Pavillon Savard, Université Laval, Québec City, QC, G1K 7P4, Canada.
| | - Sonja Behmel
- École Supérieure d'Aménagement du Territoire, Université Laval, 1628 Pavillon Savard, Université Laval, Québec City, QC, G1K 7P4, Canada.
| | - Rama Pulicharla
- École Supérieure d'Aménagement du Territoire, Université Laval, 1628 Pavillon Savard, Université Laval, Québec City, QC, G1K 7P4, Canada.
| | - Francois Proulx
- École Supérieure d'Aménagement du Territoire, Université Laval, 1628 Pavillon Savard, Université Laval, Québec City, QC, G1K 7P4, Canada.
| | - Kasun Hewage
- School of Engineering, University of British Columbia, Okanagan Campus, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada.
| | - Rehan Sadiq
- School of Engineering, University of British Columbia, Okanagan Campus, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada.
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Kasteel EEJ, Westerink RHS. Refining in vitro and in silico neurotoxicity approaches by accounting for interspecies and interindividual differences in toxicodynamics. Expert Opin Drug Metab Toxicol 2021; 17:1007-1017. [PMID: 33586568 DOI: 10.1080/17425255.2021.1885647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
INTRODUCTION The process of chemical risk assessment traditionally relies on animal experiments and associated default uncertainty factors to account for interspecies and interindividual differences. To work toward a more precise and personalized risk assessment, these uncertainty factors should be refined and replaced by chemical-specific adjustment factors (CSAFs). AREAS COVERED This concise review discusses alternative (in vitro/in silico) approaches that can be used to assess interspecies and interindividual differences in toxicodynamics, ranging from targeted to more integrated approaches. Although data are available on interspecies differences, the increasing use of human-induced pluripotent stem cell (hiPSC)-derived neurons may provide opportunities to also assess interindividual variability in neurotoxicity. More integrated approaches, like adverse outcome pathways (AOPs) can provide a more quantitative understanding of the toxicodynamics of a chemical. EXPERT OPINION To improve chemical risk assessment, refinement of uncertainty factors is crucial. In vitro and in silico models can facilitate the development of CSAFs, but still these models cannot always capture the complexity of the in vivo situation, thereby potentially hampering regulatory acceptance. The combined use of more integrated approaches, like AOPs and physiologically based kinetic models, can aid in structuring data and increasing suitability of alternative approaches for regulatory purposes.
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Affiliation(s)
- Emma E J Kasteel
- Toxicology Division, Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Remco H S Westerink
- Toxicology Division, Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
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Chauhan V, Villeneuve D, Cool D. Collaborative efforts are needed among the scientific community to advance the adverse outcome pathway concept in areas of radiation risk assessment. Int J Radiat Biol 2021; 97:815-823. [PMID: 33253609 PMCID: PMC8312481 DOI: 10.1080/09553002.2020.1857456] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/05/2020] [Accepted: 11/23/2020] [Indexed: 12/21/2022]
Abstract
Disease prevention and prediction have led to the generation of phenotypically based methods for deriving the limits of safety across toxicological disciplines. In the ionizing radiation field, human data has formed the basis of the linear-no-threshold (LNT) model for risk estimates. However, uncertainties around its accuracy at low doses and low dose-rates have led to passionate debates on its effectiveness to derive radiation risk estimates under these conditions. Concerns arise from the linear extrapolation of data from high doses to low doses, below 0.1 Gy where there is considerable variability in the scientific literature. Efforts to address these controversies have led to a mountain of mechanistic data to improve the understanding of molecular and cellular effects related to phenotypic changes. These data provide fragments of information that have yet to be combined and used effectively to improve modeling, reduce uncertainties, and update radiation protection approaches. This paper suggests a better consolidation of mechanistic research may serve to guide priority research and facilitate translation to risk assessment. An effective approach that may be implemented is the organization of data using the adverse outcome pathway (AOP) framework, a programme that has been launched by the Organization for Economic Cooperation and Development in the chemical toxicology field. The AOP concept has proved beneficial to human health and ecological toxicological fields, demonstrating possibilities for better linkages of mechanistic data to phenotypic effects. A similar approach may be beneficial to the field of radiation research. However, for this to work effectively, collaborative efforts are needed among the scientific communities in the area of AOP development and documentation. Studies will need to be evaluated, re-organized and integrated into AOPs. Here, details of the AOP approach and areas it could support in the radiation field are discussed. In addition, challenges are highlighted and steps to integration are outlined. Organizing studies in this manner will facilitate a better understanding of our current knowledge in the radiation field and help identify areas where more focused work can be undertaken. This will, in turn, allow for improved linkage of mechanistic data to human relevance and better support radiation risk assessments.
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Affiliation(s)
- Vinita Chauhan
- Environmental Health Science Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Daniel Villeneuve
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804, USA
| | - Donald Cool
- Electric Power Research Institute, Charlotte, NC, US
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Moe SJ, Wolf R, Xie L, Landis WG, Kotamäki N, Tollefsen KE. Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2021; 17:147-164. [PMID: 32965776 PMCID: PMC7820971 DOI: 10.1002/ieam.4348] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 06/30/2020] [Accepted: 09/22/2020] [Indexed: 05/04/2023]
Abstract
The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data-demanding systems biology models. Here, we propose a less data-demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, "Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition," using a small experimental data set from exposure of Lemna minor to the pesticide 3,5-dichlorophenol. The AOP-BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose-response and response-response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose-response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP-BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP-BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP-BN is limited by the small data set, our study demonstrates a proof-of-concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147-164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
| | - Raoul Wolf
- Norwegian Institute for Water Research (NIVA)OsloNorway
| | - Li Xie
- Norwegian Institute for Water Research (NIVA)OsloNorway
- Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), ÅsNorway
- Centre for Environmental Radioactivity, Norwegian University of Life Sciences (NMBU), ÅsNorway
| | - Wayne G Landis
- Institute of Environmental Toxicology, Huxley College of the EnvironmentWestern Washington UniversityBellinghamWashingtonUSA
| | | | - Knut Erik Tollefsen
- Norwegian Institute for Water Research (NIVA)OsloNorway
- Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), ÅsNorway
- Centre for Environmental Radioactivity, Norwegian University of Life Sciences (NMBU), ÅsNorway
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Moné MJ, Pallocca G, Escher SE, Exner T, Herzler M, Bennekou SH, Kamp H, Kroese ED, Leist M, Steger-Hartmann T, van de Water B. Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience. Arch Toxicol 2020; 94:3581-3592. [PMID: 32886186 PMCID: PMC7502065 DOI: 10.1007/s00204-020-02866-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 08/12/2020] [Indexed: 01/22/2023]
Abstract
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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Affiliation(s)
- M J Moné
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - G Pallocca
- CAAT-Europe at the University of Konstanz, Constance, Germany
| | - S E Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Hannover, Germany
| | - T Exner
- Edelweiss Connect GmbH, Basel, Switzerland
| | - M Herzler
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | | | - H Kamp
- BASF SE, Ludwigshafen, Germany
| | - E D Kroese
- TNO Innovation for Life, Utrecht, The Netherlands
| | - Marcel Leist
- CAAT-Europe at the University of Konstanz, Constance, Germany.
- In Vitro Toxicology and Biomedicine, Department Inaugurated By the Doerenkamp-Zbinden Foundation at the University of Konstanz, University of Konstanz, 78457, Constance, Germany.
| | - T Steger-Hartmann
- Investigational Toxicology, Bayer AG, Pharmaceuticals, Berlin, Germany
| | - B van de Water
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
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Stainforth R, Schuemann J, McNamara AL, Wilkins RC, Chauhan V. Challenges in the quantification approach to a radiation relevant adverse outcome pathway for lung cancer. Int J Radiat Biol 2020; 97:85-101. [PMID: 32909875 DOI: 10.1080/09553002.2020.1820096] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
PURPOSE Adverse outcome pathways (AOPs) provide a modular framework for describing sequences of biological key events (KEs) and key event relationships (KERs) across levels of biological organization. Empirical evidence across KERs can support construction of quantified AOPs (qAOPs). Using an example AOP of energy deposition from ionizing radiation onto DNA leading to lung cancer incidence, we investigate the feasibility of quantifying data from KERs supported by all types of stressors. The merits and challenges of this process in the context of AOP construction are discussed. MATERIALS AND METHODS Empirical evidence across studies of dose-response from four KERs of the AOP were compiled independently for quantification. Three upstream KERs comprised of evidence from various radiation types in line with AOP guidelines. For these three KERs, a focused analysis of data from alpha-particle studies was undertaken to better characterize the process to the adverse outcome (AO) for a radon gas stressor. Numerical information was extracted from tables and graphs to plot and tabulate the response of KEs. To complement areas of the AOP quantification process, Monte Carlo (MC) simulations in TOPAS-nBio were performed to model exposure conditions relevant to the AO for an example bronchial compartment of the lung with secretory cell nuclei targets. RESULTS Quantification of AOP KERs highlighted the relevance of radiation types under the stressor-agnostic intent of AOP design, motivating a focus on specific types. For a given type, significant differences of KE response indicate meaningful data to derive linkages from the MIE to the AO is lacking and that better response-response focused studies are required. The MC study estimates the linear energy transfer (LET) of alpha-particles emitted by radon-222 and its progeny in the secretory cell nuclei of the example lung compartment to range from 94 - 5 + 5 to 192 - 18 + 15 keV/µm. CONCLUSION Quantifying AOP components provides a means to assemble empirical evidence across different studies. This highlights challenges in the context of studies examining similar endpoints using different radiation types. Data linking KERs to a MIE of 'deposition of energy' is shown to be non-compatible with the stressor-agnostic principles of AOP design. Limiting data to that describing response-response relationships between adjacent KERs may better delineate studies relevant to the damage that drives a pathway to the next KE and still support an 'all hazards' approach. Such data remains limited and future investigations in the radiation field may consider this approach when designing experiments and reporting their results and outcomes.
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Affiliation(s)
| | - Jan Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Aimee L McNamara
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Ruth C Wilkins
- Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Canada
| | - Vinita Chauhan
- Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Canada
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Tebby C, van der Voet H, de Sousa G, Rorije E, Kumar V, de Boer W, Kruisselbrink JW, Bois FY, Faniband M, Moretto A, Brochot C. A generic PBTK model implemented in the MCRA platform: Predictive performance and uses in risk assessment of chemicals. Food Chem Toxicol 2020; 142:111440. [PMID: 32473292 DOI: 10.1016/j.fct.2020.111440] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/07/2020] [Accepted: 05/15/2020] [Indexed: 12/13/2022]
Abstract
Physiologically-based toxicokinetic (PBTK) models are important tools for in vitro to in vivo or inter-species extrapolations in health risk assessment of foodborne and non-foodborne chemicals. Here we present a generic PBTK model implemented in the EuroMix toolbox, MCRA 9 and predict internal kinetics of nine chemicals (three endocrine disrupters, three liver steatosis inducers, and three developmental toxicants), in data-rich and data-poor conditions, when increasingly complex levels of parametrization are applied. At the first stage, only QSAR models were used to determine substance-specific parameters, then some parameter values were refined by estimates from substance-specific or high-throughput in vitro experiments. At the last stage, elimination or absorption parameters were calibrated based on available in vivo kinetic data. The results illustrate that parametrization plays a capital role in the output of the PBTK model, as it can change how chemicals are prioritized based on internal concentration factors. In data-poor situations, estimates can be far from observed values. In many cases of chronic exposure, the PBTK model can be summarized by an external to internal dose factor, and interspecies concentration factors can be used to perform interspecies extrapolation. We finally discuss the implementation and use of the model in the MCRA risk assessment platform.
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Affiliation(s)
- Cleo Tebby
- INERIS, Unit Models for Ecotoxicology and Toxicology (METO), Verneuil-en-Halatte, France.
| | | | | | - Emiel Rorije
- RIVM, Centre for Safety of Substances and Products, Department for Consumers and Product Safety, P.O. Box 1, 3720, BA Bilthoven, Netherlands
| | - Vikas Kumar
- URV, Universitat Rovira i Virgili, C/ Països Catalans, nº 26, 43007, Tarragona (Tarragona- Catalonia), Spain
| | - Waldo de Boer
- Wageningen University & Research, Biometris, Wageningen, Netherlands
| | | | - Frédéric Y Bois
- INERIS, Unit Models for Ecotoxicology and Toxicology (METO), Verneuil-en-Halatte, France
| | - Moosa Faniband
- Division of Occupational and Environmental Medicine, Faculty of Medicine, Lund University, Sweden
| | - Angelo Moretto
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Italy
| | - Céline Brochot
- INERIS, Unit Models for Ecotoxicology and Toxicology (METO), Verneuil-en-Halatte, France
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Spinu N, Cronin MTD, Enoch SJ, Madden JC, Worth AP. Quantitative adverse outcome pathway (qAOP) models for toxicity prediction. Arch Toxicol 2020; 94:1497-1510. [PMID: 32424443 PMCID: PMC7261727 DOI: 10.1007/s00204-020-02774-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/04/2020] [Indexed: 01/06/2023]
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.
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Affiliation(s)
- Nicoleta Spinu
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Andrew P Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
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Chauhan V, Stricklin D, Cool D. The integration of the adverse outcome pathway framework to radiation risk assessment. Int J Radiat Biol 2020; 97:60-67. [PMID: 32397918 DOI: 10.1080/09553002.2020.1761570] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
BACKGROUND A large body of radiobiological data has been generated over the past century using in vitro, animal and epidemiological models. This information represents global efforts to understand the mechanistic basis of radiation-induced health effects. However, it has been difficult to effectively integrate this data to derive meaningful information for refining the guidance on chronic, low dose radiation exposure for workers and the public. METHODS To increase our understanding of radiation risks and the biological processes that contribute to those risks, a paradigm shift is needed that will enable integration of information across levels of biological organization from a 'stressor' centric to an 'adverse outcome' approach to risk assessment. In chemical and ecological toxicity, a framework has been developed that captures available biologically-based knowledge in the literature and links it to outcomes of relevance to chemical toxicity, resulting in an adverse outcome pathway (AOP). RESULTS In this paper, we discuss the AOP concept, how it can be applied to the radiation field, and our vision for the next steps. For the approach to be successful, the radiation research community will need to work collaboratively to vet the vast amount of literature and harness the data in a systematic manner for incorporation into a framework based on the AOP approach. CONCLUSION We anticipate that AOPs could be adopted as a method to synthesize current available information to facilitate the identification of knowledge gaps, better co-ordinate research and qualitatively and quantitatively link key events to an adverse outcome. This can further assist in identifying biomarkers relevant to radiation exposures, refining risk from co-exposures and understanding critical events at the molecular, cellular, tissue, organ and whole animal level related to low dose/dose-rate exposures.
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Affiliation(s)
- Vinita Chauhan
- Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, ON, Canada
| | | | - Donald Cool
- Electric Power Research Institute, Charlotte, NC, USA
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26
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Romeo D, Salieri B, Hischier R, Nowack B, Wick P. An integrated pathway based on in vitro data for the human hazard assessment of nanomaterials. ENVIRONMENT INTERNATIONAL 2020; 137:105505. [PMID: 32014789 DOI: 10.1016/j.envint.2020.105505] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/13/2019] [Accepted: 01/17/2020] [Indexed: 05/23/2023]
Abstract
In line with the 3R concept, nanotoxicology is shifting from a phenomenological to a mechanistic approach based on in vitro and in silico methods, with a consequent reduction in animal testing. Risk Assessment (RA) and Life Cycle Assessment (LCA) methodologies, which traditionally rely on in vivo toxicity studies, will not be able to keep up with the pace of development of new nanomaterials unless they adapt to use this new type of data. While tools and models are already available and show a great potential for future use in RA and LCA, currently none is able alone to quantitatively assess human hazards (i.e. calculate chronic NOAEL or ED50 values). By highlighting which models and approaches can be used in a quantitative way with the available knowledge and data, we propose an integrated pathway for the use of in vitro data in RA and LCA. Starting with the characterization of nanoparticles' properties, the pathway then investigates how to select relevant in vitro human data, and how to bridge in vitro dose-response relationships to in vivo effects. If verified, this approach would allow RA and LCA to stir up the development of nanotoxicology by giving indications about the data and quality requirements needed in risk methodologies.
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Affiliation(s)
- Daina Romeo
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Particles-Biology Interactions Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
| | - Beatrice Salieri
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Technology and Society Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
| | - Roland Hischier
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Technology and Society Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
| | - Bernd Nowack
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Technology and Society Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
| | - Peter Wick
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Particles-Biology Interactions Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
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Vinken M, Kramer N, Allen TEH, Hoffmans Y, Thatcher N, Levorato S, Traussnig H, Schulte S, Boobis A, Thiel A, Rietjens IMCM. The use of adverse outcome pathways in the safety evaluation of food additives. Arch Toxicol 2020; 94:959-966. [PMID: 32065296 DOI: 10.1007/s00204-020-02670-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 02/11/2020] [Indexed: 12/16/2022]
Abstract
In the last decade, adverse outcome pathways have been introduced in the fields of toxicology and risk assessment of chemicals as pragmatic tools with broad application potential. While their use in the pharmaceutical and cosmetics sectors has been well documented, their application in the food area remains largely unexplored. In this respect, an expert group of the International Life Sciences Institute Europe has recently explored the use of adverse outcome pathways in the safety evaluation of food additives. A key activity was the organization of a workshop, gathering delegates from the regulatory, industrial and academic areas, to discuss the potentials and challenges related to the application of adverse outcome pathways in the safety assessment of food additives. The present paper describes the outcome of this workshop followed by a number of critical considerations and perspectives defined by the International Life Sciences Institute Europe expert group.
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Affiliation(s)
- Mathieu Vinken
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Nynke Kramer
- Institute for Risk Assessment Sciences, Utrecht University, PO Box 80177, 3508 TD, Utrecht, The Netherlands
| | - Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Yvette Hoffmans
- Wageningen University and Research, Droevendaalsesteeg 4, 6708 PB, Wageningen, The Netherlands
| | - Natalie Thatcher
- Mondelēz International, Bournville Place, Bournville Ln, Birmingham, B30 2LU, UK
| | - Sara Levorato
- Unilever, Safety and Environmental Assurance Centre, Sharnbrook, Bedford, MK441LQ, UK
| | - Heinz Traussnig
- Mayr-Melnhof Karton Gesellschaft m.b.H., Frohnleiten Mill, Wannersdorf 80, 8130, Frohnleiten, Austria
| | - Stefan Schulte
- Department of Product Safety, BASF SE, 67056, Ludwigshafen, Germany
| | - Alan Boobis
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
| | - Anette Thiel
- DSM Nutritional Products Ltd., Wurmisweg 576, 4303, Kaiseraugst, Switzerland
| | - Ivonne M C M Rietjens
- Division of Toxicology, Wageningen University, Stippeneng 4, 6708 WE, Wageningen, The Netherlands
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Hines DE, Conolly RB, Jarabek AM. A Quantitative Source-to-Outcome Case Study To Demonstrate the Integration of Human Health and Ecological End Points Using the Aggregate Exposure Pathway and Adverse Outcome Pathway Frameworks. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:11002-11012. [PMID: 31436975 DOI: 10.1021/acs.est.9b04639] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Exposure to environmental contaminants can lead to adverse outcomes in both human and nonhuman receptors. The Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) frameworks can mechanistically inform cumulative risk assessment for human health and ecological end points by linking together environmental transport and transformation, external exposure, toxicokinetics, and toxicodynamics. This work presents a case study of a hypothetical contaminated site to demonstrate a quantitative approach for implementing the AEP framework and linking this framework to AOPs. We construct an AEP transport and transformation model and then quantify external exposure pathways for humans, fishes, and small herbivorous mammals at the hypothetical site. A Monte Carlo approach was used to address parameter variability. Source apportionment was quantified for each species, and published pharmacokinetic models were used to estimate internal target site exposure from external exposures. Published dose-response data for a multispecies AOP network were used to interpret AEP results in the context of species-specific effects. This work demonstrates (1) the construction, analysis, and application of a quantitative AEP model, (2) the utility of AEPs for organizing mechanistic exposure data and highlighting data gaps, and (3) the advantages provided by a source-to-outcome construct for leveraging exposure data and to aid transparency regarding assumptions.
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Affiliation(s)
- David E Hines
- U.S. Environmental Protection Agency , Office of Research and Development, National Health and Environmental Effects Research Laboratory, Integrated Systems Toxicology Division , Research Triangle Park, Durham , North Carolina 27709 , United States
| | - Rory B Conolly
- U.S. Environmental Protection Agency , Office of Research and Development, National Health and Environmental Effects Research Laboratory, Integrated Systems Toxicology Division , Research Triangle Park, Durham , North Carolina 27709 , United States
| | - Annie M Jarabek
- U.S. Environmental Protection Agency , Office of Research and Development, National Center for Environmental Assessment , Research Triangle Park, Durham , North Carolina 27709 , United States
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Spinu N, Bal-Price A, Cronin MTD, Enoch SJ, Madden JC, Worth AP. Development and analysis of an adverse outcome pathway network for human neurotoxicity. Arch Toxicol 2019; 93:2759-2772. [DOI: 10.1007/s00204-019-02551-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Accepted: 08/14/2019] [Indexed: 12/21/2022]
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30
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Battistoni M, Di Renzo F, Menegola E, Bois FY. Quantitative AOP based teratogenicity prediction for mixtures of azole fungicides. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2019.03.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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31
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Mechanistic integration of exposure and effects: advances to apply systems toxicology in support of regulatory decision-making. CURRENT OPINION IN TOXICOLOGY 2019. [DOI: 10.1016/j.cotox.2019.09.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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