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Serafini MM, Sepehri S, Midali M, Stinckens M, Biesiekierska M, Wolniakowska A, Gatzios A, Rundén-Pran E, Reszka E, Marinovich M, Vanhaecke T, Roszak J, Viviani B, SenGupta T. Recent advances and current challenges of new approach methodologies in developmental and adult neurotoxicity testing. Arch Toxicol 2024; 98:1271-1295. [PMID: 38480536 DOI: 10.1007/s00204-024-03703-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/06/2024] [Indexed: 03/27/2024]
Abstract
Adult neurotoxicity (ANT) and developmental neurotoxicity (DNT) assessments aim to understand the adverse effects and underlying mechanisms of toxicants on the human nervous system. In recent years, there has been an increasing focus on the so-called new approach methodologies (NAMs). The Organization for Economic Co-operation and Development (OECD), together with European and American regulatory agencies, promote the use of validated alternative test systems, but to date, guidelines for regulatory DNT and ANT assessment rely primarily on classical animal testing. Alternative methods include both non-animal approaches and test systems on non-vertebrates (e.g., nematodes) or non-mammals (e.g., fish). Therefore, this review summarizes the recent advances of NAMs focusing on ANT and DNT and highlights the potential and current critical issues for the full implementation of these methods in the future. The status of the DNT in vitro battery (DNT IVB) is also reviewed as a first step of NAMs for the assessment of neurotoxicity in the regulatory context. Critical issues such as (i) the need for test batteries and method integration (from in silico and in vitro to in vivo alternatives, e.g., zebrafish, C. elegans) requiring interdisciplinarity to manage complexity, (ii) interlaboratory transferability, and (iii) the urgent need for method validation are discussed.
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Affiliation(s)
- Melania Maria Serafini
- Department of Pharmacological and Biomolecular Sciences, "Rodolfo Paoletti", Università degli Studi di Milano, Milan, Italy.
| | - Sara Sepehri
- Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussels, Brussels, Belgium
| | - Miriam Midali
- Department of Pharmacological and Biomolecular Sciences, "Rodolfo Paoletti", Università degli Studi di Milano, Milan, Italy
| | - Marth Stinckens
- Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussels, Brussels, Belgium
| | - Marta Biesiekierska
- Department of Translational Research, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Anna Wolniakowska
- Department of Translational Research, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Alexandra Gatzios
- Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussels, Brussels, Belgium
| | - Elise Rundén-Pran
- The Climate and Environmental Research Institute NILU, Kjeller, Norway
| | - Edyta Reszka
- Department of Translational Research, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Marina Marinovich
- Department of Pharmacological and Biomolecular Sciences, "Rodolfo Paoletti", Università degli Studi di Milano, Milan, Italy
- Center of Research on New Approach Methodologies (NAMs) in chemical risk assessment (SAFE-MI), Università degli Studi di Milano, Milan, Italy
| | - Tamara Vanhaecke
- Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussels, Brussels, Belgium
| | - Joanna Roszak
- Department of Translational Research, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Barbara Viviani
- Department of Pharmacological and Biomolecular Sciences, "Rodolfo Paoletti", Università degli Studi di Milano, Milan, Italy
- Center of Research on New Approach Methodologies (NAMs) in chemical risk assessment (SAFE-MI), Università degli Studi di Milano, Milan, Italy
| | - Tanima SenGupta
- The Climate and Environmental Research Institute NILU, Kjeller, Norway
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Pitzer EM, Shafer TJ, Herr DW. Identification of neurotoxicology (NT)/developmental neurotoxicology (DNT) adverse outcome pathways and key event linkages with in vitro DNT screening assays. Neurotoxicology 2023; 99:184-194. [PMID: 37866692 DOI: 10.1016/j.neuro.2023.10.007] [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: 04/14/2023] [Revised: 08/14/2023] [Accepted: 10/13/2023] [Indexed: 10/24/2023]
Abstract
There is a need to assess compounds reliably and quickly for neurotoxicity (NT) and developmental neurotoxicity (DNT). Adverse outcome pathways (AOPs) enable the mapping of molecular events to an apical endpoint in a chemical agnostic manner and have begun to be applied in NT and DNT testing frameworks. We assessed the status of NT/DNT AOPs in the AOP-Wiki (ca. 2/1/23; https://aopwiki.org/), to characterize the state of AOP development, identify strengths and knowledge gaps, elucidate areas for improvement, and describe areas for future focus. AOPs in the Wiki database were assessed for inclusion of NT/DNT molecular events and endpoints, AOP development and endorsement, as well as the linkages of key neurodevelopmental processes with in vitro new approach methods (NAMs). This review found that 41 AOPs have been proposed detailing NT/DNT, of which eight were endorsed by working parties in OECD. Further, this review determined that learning and memory is included as an adverse outcome in eight NT/DNT AOPS, often without distinction regarding the varying forms of learning and memory, regional specification, temporal dynamics, or acquisition mechanisms involved. There is also an overlap with key events (KEs) and in vitro NAMs, which synaptogenesis appeared as a common process. Overall, progress on NT/DNT AOPs could be expanded, adding in modes of action that are missing, improvement in defining apical endpoints, as well as utilizing NAMs further to develop AOPs and identify gaps in current knowledge.
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Affiliation(s)
- Emily M Pitzer
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
| | - Timothy J Shafer
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - David W Herr
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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Collins SP, Barton-Maclaren TS. Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening. FRONTIERS IN TOXICOLOGY 2022; 4:981928. [PMID: 36204696 PMCID: PMC9530987 DOI: 10.3389/ftox.2022.981928] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/02/2022] [Indexed: 11/13/2022] Open
Abstract
An area of ongoing concern in toxicology and chemical risk assessment is endocrine disrupting chemicals (EDCs). However, thousands of legacy chemicals lack the toxicity testing required to assess their respective EDC potential, and this is where computational toxicology can play a crucial role. The US (United States) Environmental Protection Agency (EPA) has run two programs, the Collaborative Estrogen Receptor Activity Project (CERAPP) and the Collaborative Modeling Project for Receptor Activity (CoMPARA) which aim to predict estrogen and androgen activity, respectively. The US EPA solicited research groups from around the world to provide endocrine receptor activity Qualitative (or Quantitative) Structure Activity Relationship ([Q]SAR) models and then combined them to create consensus models for different toxicity endpoints. Random Forest (RF) models were developed to cover a broader range of substances with high predictive capabilities using large datasets from CERAPP and CoMPARA for estrogen and androgen activity, respectively. By utilizing simple descriptors from open-source software and large training datasets, RF models were created to expand the domain of applicability for predicting endocrine disrupting activity and help in the screening and prioritization of extensive chemical inventories. In addition, RFs were trained to conservatively predict the activity, meaning models are more likely to make false-positive predictions to minimize the number of False Negatives. This work presents twelve binary and multi-class RF models to predict binding, agonism, and antagonism for estrogen and androgen receptors. The RF models were found to have high predictive capabilities compared to other in silico modes, with some models reaching balanced accuracies of 93% while having coverage of 89%. These models are intended to be incorporated into evolving priority-setting workflows and integrated strategies to support the screening and selection of chemicals for further testing and assessment by identifying potential endocrine-disrupting substances.
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Crofton KM, Bassan A, Behl M, Chushak YG, Fritsche E, Gearhart JM, Marty MS, Mumtaz M, Pavan M, Ruiz P, Sachana M, Selvam R, Shafer TJ, Stavitskaya L, Szabo DT, Szabo ST, Tice RR, Wilson D, Woolley D, Myatt GJ. Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 22:100223. [PMID: 35844258 PMCID: PMC9281386 DOI: 10.1016/j.comtox.2022.100223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.
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Affiliation(s)
| | - Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova,
Italy
| | - Mamta Behl
- Division of the National Toxicology Program, National
Institutes of Environmental Health Sciences, Durham, NC 27709, USA
| | - Yaroslav G. Chushak
- Henry M Jackson Foundation for the Advancement of Military
Medicine, Wright-Patterson AFB, OH 45433, USA
| | - Ellen Fritsche
- IUF – Leibniz Research Institute for Environmental
Medicine & Medical Faculty Heinrich-Heine-University, Düsseldorf,
Germany
| | - Jeffery M. Gearhart
- Henry M Jackson Foundation for the Advancement of Military
Medicine, Wright-Patterson AFB, OH 45433, USA
| | | | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, US
Department of Health and Human Services, Atlanta, GA, USA
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova,
Italy
| | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, US
Department of Health and Human Services, Atlanta, GA, USA
| | - Magdalini Sachana
- Environment Health and Safety Division, Environment
Directorate, Organisation for Economic Co-Operation and Development (OECD), 75775
Paris Cedex 16, France
| | - Rajamani Selvam
- Office of Clinical Pharmacology, Office of Translational
Sciences, Center for Drug Evaluation and Research (CDER), U.S. Food and Drug
Administration (FDA), Silver Spring, MD 20993, USA
| | - Timothy J. Shafer
- Biomolecular and Computational Toxicology Division, Center
for Computational Toxicology and Exposure, US EPA, Research Triangle Park, NC,
USA
| | - Lidiya Stavitskaya
- Office of Clinical Pharmacology, Office of Translational
Sciences, Center for Drug Evaluation and Research (CDER), U.S. Food and Drug
Administration (FDA), Silver Spring, MD 20993, USA
| | | | | | | | - Dan Wilson
- The Dow Chemical Company, Midland, MI 48667, USA
| | | | - Glenn J. Myatt
- Instem, Columbus, OH 43215, USA
- Corresponding author.
(G.J. Myatt)
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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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Kamerlin N, Delcey MG, Manzetti S, van der Spoel D. Toward a Computational Ecotoxicity Assay. J Chem Inf Model 2020; 60:3792-3803. [PMID: 32648756 DOI: 10.1021/acs.jcim.0c00574] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.
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Affiliation(s)
- Natasha Kamerlin
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Mickaël G Delcey
- Department of Chemistry-Ångström Laboratory, Uppsala University, SE-75120 Uppsala, Sweden
| | - Sergio Manzetti
- Institute for Science and Technology, Fjordforsk A.S., Midtun, 6894 Vangsnes, Norway
| | - David van der Spoel
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
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Morger A, Mathea M, Achenbach JH, Wolf A, Buesen R, Schleifer KJ, Landsiedel R, Volkamer A. KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development. J Cheminform 2020; 12:24. [PMID: 33431007 PMCID: PMC7157991 DOI: 10.1186/s13321-020-00422-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/09/2020] [Indexed: 02/07/2023] Open
Abstract
Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.
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Affiliation(s)
- Andrea Morger
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | | | | | | | | | | | | | - Andrea Volkamer
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany.
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