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Herzler M, Abedini J, Allen DG, Germolec D, Gordon J, Ko HS, Matheson J, Reinke E, Strickland J, Thierse HJ, To K, Truax J, Vanselow JT, Kleinstreuer N. Use of human predictive patch test (HPPT) data for the classification of skin sensitization hazard and potency. Arch Toxicol 2024; 98:1253-1269. [PMID: 38483583 DOI: 10.1007/s00204-023-03656-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 11/30/2023] [Indexed: 03/27/2024]
Abstract
Since the 1940s, patch tests in healthy volunteers (Human Predictive Patch Tests, HPPTs) have been used to identify chemicals that cause skin sensitization in humans. Recently, we reported the results of a major curation effort to support the development of OECD Guideline 497 on Defined Approaches (DAs) for skin sensitization (OECD in Guideline No. 497: Defined Approaches on Skin Sensitisation, 2021a. https://doi.org/10.1787/b92879a4-en ). In the course of this work, we compiled and published a database of 2277 HPPT results for 1366 unique test substances (Strickland et al. in Arch Toxicol 97:2825-2837, 2023. https://doi.org/10.1007/s00204-023-03530-3 ). Here we report a detailed analysis of the value of HPPT data for classification of chemicals as skin sensitizers under the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (GHS). As a result, we propose the dose per skin area (DSA) used for classification by the GHS to be replaced by or complemented with a dose descriptor that may better reflect sensitization incidence [e.g., the DSA causing induction of sensitization in one individual (DSA1+) or the DSA leading to an incidence of induction in 5% of the tested individuals (DSA05)]. We also propose standardized concepts and workflows for assessing individual HPPT results, for integrating multiple HPPT results and for using them in concert with Local Lymph Node Assay (LLNA) data in a weight of evidence (WoE) assessment. Overall, our findings show that HPPT results are often not sufficient for deriving unambiguous classifications on their own. However, where they are, the resulting classifications are reliable and reproducible and can be integrated well with those from other skin sensitization data, such as the LLNA.
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Affiliation(s)
- Matthias Herzler
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany.
| | | | | | - Dori Germolec
- Systems Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Research Triangle Park, NC, USA
| | - John Gordon
- United States Consumer Product Safety Commission, Rockville, MD, USA
| | - Hon-Sum Ko
- United States Food and Drug Administration, Silver Spring, MD, USA
| | - Joanna Matheson
- United States Consumer Product Safety Commission, Rockville, MD, USA
| | | | | | | | - Kim To
- Inotiv, Inc., Morrisville, NC, USA
| | | | - Jens T Vanselow
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), Predictive Toxicology Branch, Division of Translational Toxicology, NIEHS/NIH, Research Triangle Park, NC, USA
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Kleinstreuer N, Hartung T. Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine). Arch Toxicol 2024; 98:735-754. [PMID: 38244040 PMCID: PMC10861653 DOI: 10.1007/s00204-023-03666-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/12/2023] [Indexed: 01/22/2024]
Abstract
The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured-a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.
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Affiliation(s)
| | - Thomas Hartung
- Bloomberg School of Public Health, Doerenkamp-Zbinden Chair for Evidence-Based Toxicology, Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, MD, USA.
- CAAT-Europe, University of Konstanz, Constance, Germany.
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Bhuller Y, Karmaus A, Kleinstreuer N, Seidle T, Schlatter H, Wade M, Chandrasekera PC. Examining animal testing for risk assessment: A WC-12 workshop report. Regul Toxicol Pharmacol 2024; 147:105564. [PMID: 38182013 DOI: 10.1016/j.yrtph.2024.105564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/03/2024] [Indexed: 01/07/2024]
Abstract
In toxicology and regulatory testing, the use of animal methods has been both a cornerstone and a subject of intense debate. To continue this discourse a panel and audience representing scientists from various sectors and countries convened at a workshop held during the 12th World Congress on Alternatives and Animal Use in the Life Sciences (WC-12). The ensuing discussion focused on the scientific and ethical considerations surrounding the necessity and responsibility of defending the creation of new animal data in regulatory testing. The primary aim was to foster an open dialogue between the panel members and the audience while encouraging diverse perspectives on the responsibilities and obligations of various stakeholders (including industry, regulatory bodies, technology developers, research scientists, and animal welfare NGOs) in defending the development and subsequent utilization of new animal data. This workshop summary report captures the key elements from this critical dialogue and collective introspection. It describes the intersection of scientific progress and ethical responsibility as all sectors seek to accelerate the pace of 21st century predictive toxicology and new approach methodologies (NAMs) for the protection of human health and the environment.
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Affiliation(s)
| | | | - Nicole Kleinstreuer
- NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), Durham, NC, USA
| | - Troy Seidle
- Humane Society International, Toronto, ON, Canada
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Foster C, Wignall J, Kovach S, Choksi N, Allen D, Trgovcich J, Rochester JR, Ceger P, Daniel A, Hamm J, Truax J, Blake B, McIntyre B, Sutherland V, Stout MD, Kleinstreuer N. Standardizing Extracted Data Using Automated Application of Controlled Vocabularies. Environ Health Perspect 2024; 132:27006. [PMID: 38349723 PMCID: PMC10863721 DOI: 10.1289/ehp13215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 02/15/2024]
Abstract
BACKGROUND Extraction of toxicological end points from primary sources is a central component of systematic reviews and human health risk assessments. To ensure optimal use of these data, consistent language should be used for end point descriptions. However, primary source language describing treatment-related end points can vary greatly, resulting in large labor efforts to manually standardize extractions before data are fit for use. OBJECTIVES To minimize these labor efforts, we applied an augmented intelligence approach and developed automated tools to support standardization of extracted information via application of preexisting controlled vocabularies. METHODS We created and applied a harmonized controlled vocabulary crosswalk, consisting of Unified Medical Language System (UMLS) codes, German Federal Institute for Risk Assessment (BfR) DevTox harmonized terms, and The Organization for Economic Co-operation and Development (OECD) end point vocabularies, to roughly 34,000 extractions from prenatal developmental toxicology studies conducted by the National Toxicology Program (NTP) and 6,400 extractions from European Chemicals Agency (ECHA) prenatal developmental toxicology studies, all recorded based on the original study report language. RESULTS We automatically applied standardized controlled vocabulary terms to 75% of the NTP extracted end points and 57% of the ECHA extracted end points. Of all the standardized extracted end points, about half (51%) required manual review for potential extraneous matches or inaccuracies. Extracted end points that were not mapped to standardized terms tended to be too general or required human logic to find a good match. We estimate that this augmented intelligence approach saved > 350 hours of manual effort and yielded valuable resources including a controlled vocabulary crosswalk, organized related terms lists, code for implementing an automated mapping workflow, and a computationally accessible dataset. DISCUSSION Augmenting manual efforts with automation tools increased the efficiency of producing a findable, accessible, interoperable, and reusable (FAIR) dataset of regulatory guideline studies. This open-source approach can be readily applied to other legacy developmental toxicology datasets, and the code design is customizable for other study types. https://doi.org/10.1289/EHP13215.
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Affiliation(s)
| | | | | | - Neepa Choksi
- ILS, Research Triangle Park, North Carolina, USA
| | - Dave Allen
- ILS, Research Triangle Park, North Carolina, USA
| | | | | | | | - Amber Daniel
- ILS, Research Triangle Park, North Carolina, USA
| | - Jon Hamm
- ILS, Research Triangle Park, North Carolina, USA
| | - Jim Truax
- ILS, Research Triangle Park, North Carolina, USA
| | - Bevin Blake
- Division of Translational Toxicology (DTT), NIEHS, NIH, Research Triangle Park, North Carolina, USA
| | - Barry McIntyre
- Division of Translational Toxicology (DTT), NIEHS, NIH, Research Triangle Park, North Carolina, USA
| | - Vicki Sutherland
- Division of Translational Toxicology (DTT), NIEHS, NIH, Research Triangle Park, North Carolina, USA
| | - Matthew D. Stout
- Division of Translational Toxicology (DTT), NIEHS, NIH, Research Triangle Park, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), DTT, NIEHS, NIH, Research Triangle Park, North Carolina, USA
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To KT, Strickland J, Reinke E, Borrel A, Truax J, Maldonado H, Allen D, Kleinstreuer N. Computational application of internationally harmonized defined approaches to skin sensitization: DASS App. BMC Bioinformatics 2024; 25:4. [PMID: 38166637 PMCID: PMC10763465 DOI: 10.1186/s12859-023-05617-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Chemically induced skin sensitization, or allergic contact dermatitis, is a common occupational and public health issue. Regulatory authorities require an assessment of potential to cause skin sensitization for many chemical products. Defined approaches for skin sensitization (DASS) identify potential chemical skin sensitizers by integrating data from multiple non-animal tests based on human cells, molecular targets, and computational model predictions using standardized data interpretation procedures. While several DASS are internationally accepted by regulatory agencies, the data interpretation procedures vary in logical complexity, and manual application can be time-consuming or prone to error. RESULTS We developed the DASS App, an open-source web application, to facilitate user application of three regulatory testing strategies for skin sensitization assessment: the Two-out-of-Three (2o3), the Integrated Testing Strategy (ITS), and the Key Event 3/1 Sequential Testing Strategy (KE 3/1 STS) without the need for software downloads or computational expertise. The application supports upload and analysis of user-provided data, includes steps to identify inconsistencies and formatting issues, and provides predictions in a downloadable format. CONCLUSION This open-access web-based implementation of internationally harmonized regulatory guidelines for an important public health endpoint is designed to support broad user uptake and consistent, reproducible application. The DASS App is freely accessible via https://ntp.niehs.nih.gov/go/952311 and all scripts are available on GitHub ( https://github.com/NIEHS/DASS ).
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Affiliation(s)
- Kimberly T To
- Predictive Toxicology and Information Sciences Group, Discovery and Safety Assessment Division, Inotiv, Inc., Morrisville, NC, 27560, USA.
| | - Judy Strickland
- Predictive Toxicology and Information Sciences Group, Discovery and Safety Assessment Division, Inotiv, Inc., Morrisville, NC, 27560, USA
| | - Emily Reinke
- Predictive Toxicology and Information Sciences Group, Discovery and Safety Assessment Division, Inotiv, Inc., Morrisville, NC, 27560, USA
| | - Alexandre Borrel
- Predictive Toxicology and Information Sciences Group, Discovery and Safety Assessment Division, Inotiv, Inc., Morrisville, NC, 27560, USA
| | - Jim Truax
- Predictive Toxicology and Information Sciences Group, Discovery and Safety Assessment Division, Inotiv, Inc., Morrisville, NC, 27560, USA
| | - Heather Maldonado
- Predictive Toxicology and Information Sciences Group, Discovery and Safety Assessment Division, Inotiv, Inc., Morrisville, NC, 27560, USA
| | - Dave Allen
- Predictive Toxicology and Information Sciences Group, Discovery and Safety Assessment Division, Inotiv, Inc., Morrisville, NC, 27560, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of Translational Toxicology, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 27709, USA
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Sanvido O, Basketter DA, Berthet A, Bloch D, Ezendam J, Hopf NB, Kleinstreuer N, Merolla LL, Uter W, Wiemann C, Wilks MF. Quantitative risk assessment of skin sensitising pesticides: Clinical and toxicological considerations. Regul Toxicol Pharmacol 2023; 144:105493. [PMID: 37717614 DOI: 10.1016/j.yrtph.2023.105493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/14/2023] [Accepted: 09/08/2023] [Indexed: 09/19/2023]
Abstract
Like many other consumer and occupational products, pesticide formulations may contain active ingredients or co-formulants which have the potential to cause skin sensitisation. Currently, there is little evidence they do, but that could just reflect lack of clinical investigation. Consequently, it is necessary to carry out a safety evaluation process, quantifying risks so that they can be properly managed. A workshop on this topic in 2022 discussed how best to undertake quantitative risk assessment (QRA) for pesticide products, including learning from the experience of industries, notably cosmetics, that already undertake such a process routinely. It also addressed ways to remedy the matter of clinical investigation, even if only to demonstrate the absence of a problem. Workshop participants concluded that QRA for skin sensitisers in pesticide formulations was possible, but required careful justification of any safety factors applied, as well as improvements to the estimation of skin exposure. The need for regulations to stay abreast of the science was also noted. Ultimately, the success of any risk assessment/management for skin sensitisers must be judged by the clinical picture. Accordingly, the workshop participants encouraged the development of more active skin health monitoring amongst groups most exposed to the products.
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Affiliation(s)
- Olivier Sanvido
- State Secretariat for Economic Affairs SECO, Holzikofenweg 36, 3003, Bern, Switzerland.
| | | | - Aurélie Berthet
- Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Route de La Corniche 2, 1066, Epalinges, Lausanne, Switzerland
| | - Denise Bloch
- German Federal Institute for Risk Assessment, Department of Pesticides Safety, Max-Dohrn-Straße 8-10, 10589, Berlin, Germany
| | - Janine Ezendam
- National Institute for Public Health and the Environment (RIVM), Centre for Health Protection, Antonie van Leeuwenhoeklaan 9, 3721, MA, Bilthoven, the Netherlands
| | - Nancy B Hopf
- Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Route de La Corniche 2, 1066, Epalinges, Lausanne, Switzerland
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for Evaluation of Alternative Toxicological Methods (NICEATM), Research Triangle Park, NC, 27711, USA
| | | | - Wolfgang Uter
- Friedrich-Alexander Universität, Department of Medical Informatics, Biometry and Epidemiology, Erlangen, Germany
| | | | - Martin F Wilks
- University of Basel, Swiss Centre for Applied Human Toxicology, Missionsstrasse 64, CH-4055, Basel, Switzerland
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Schmeisser S, Miccoli A, von Bergen M, Berggren E, Braeuning A, Busch W, Desaintes C, Gourmelon A, Grafström R, Harrill J, Hartung T, Herzler M, Kass GEN, Kleinstreuer N, Leist M, Luijten M, Marx-Stoelting P, Poetz O, van Ravenzwaay B, Roggeband R, Rogiers V, Roth A, Sanders P, Thomas RS, Marie Vinggaard A, Vinken M, van de Water B, Luch A, Tralau T. New approach methodologies in human regulatory toxicology - Not if, but how and when! Environ Int 2023; 178:108082. [PMID: 37422975 PMCID: PMC10858683 DOI: 10.1016/j.envint.2023.108082] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/30/2023] [Accepted: 07/01/2023] [Indexed: 07/11/2023]
Abstract
The predominantly animal-centric approach of chemical safety assessment has increasingly come under pressure. Society is questioning overall performance, sustainability, continued relevance for human health risk assessment and ethics of this system, demanding a change of paradigm. At the same time, the scientific toolbox used for risk assessment is continuously enriched by the development of "New Approach Methodologies" (NAMs). While this term does not define the age or the state of readiness of the innovation, it covers a wide range of methods, including quantitative structure-activity relationship (QSAR) predictions, high-throughput screening (HTS) bioassays, omics applications, cell cultures, organoids, microphysiological systems (MPS), machine learning models and artificial intelligence (AI). In addition to promising faster and more efficient toxicity testing, NAMs have the potential to fundamentally transform today's regulatory work by allowing more human-relevant decision-making in terms of both hazard and exposure assessment. Yet, several obstacles hamper a broader application of NAMs in current regulatory risk assessment. Constraints in addressing repeated-dose toxicity, with particular reference to the chronic toxicity, and hesitance from relevant stakeholders, are major challenges for the implementation of NAMs in a broader context. Moreover, issues regarding predictivity, reproducibility and quantification need to be addressed and regulatory and legislative frameworks need to be adapted to NAMs. The conceptual perspective presented here has its focus on hazard assessment and is grounded on the main findings and conclusions from a symposium and workshop held in Berlin in November 2021. It intends to provide further insights into how NAMs can be gradually integrated into chemical risk assessment aimed at protection of human health, until eventually the current paradigm is replaced by an animal-free "Next Generation Risk Assessment" (NGRA).
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Affiliation(s)
| | - Andrea Miccoli
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany; National Research Council, Ancona, Italy
| | - Martin von Bergen
- Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; University of Leipzig, Faculty of Life Sciences, Institute of Biochemistry, Leipzig, Germany
| | | | - Albert Braeuning
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Wibke Busch
- Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
| | - Christian Desaintes
- European Commission (EC), Directorate General for Research and Innovation (RTD), Brussels, Belgium
| | - Anne Gourmelon
- Organisation for Economic Cooperation and Development (OECD), Environment Directorate, Paris, France
| | | | - Joshua Harrill
- Center for Computational Toxicology and Exposure (CCTE), United States Environmental Protection Agency (US EPA), Durham, USA
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health Baltimore MD USA, CAAT-Europe, University of Konstanz, Konstanz, Germany
| | - Matthias Herzler
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | | | - Nicole Kleinstreuer
- NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences (NIEHS), Durham, USA
| | - Marcel Leist
- CAAT‑Europe and Department of Biology, University of Konstanz, Konstanz, Germany
| | - Mirjam Luijten
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | | | - Oliver Poetz
- NMI Natural and Medical Science Institute at the University of Tuebingen, Reutlingen, Germany; SIGNATOPE GmbH, Reutlingen, Germany
| | | | - Rob Roggeband
- European Partnership for Alternative Approaches to Animal Testing (EPAA), Procter and Gamble Services Company NV/SA, Strombeek-Bever, Belgium
| | - Vera Rogiers
- Scientific Committee on Consumer Safety (SCCS), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Adrian Roth
- F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Pascal Sanders
- Fougeres Laboratory, French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Fougères, France France
| | - Russell S Thomas
- Center for Computational Toxicology and Exposure (CCTE), United States Environmental Protection Agency (US EPA), Durham, USA
| | | | | | | | - Andreas Luch
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Tewes Tralau
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany
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To KT, Kleinstreuer N, Vasiliou V, Hogberg HT. New approach methodologies to address population variability and susceptibility. Hum Genomics 2023; 17:56. [PMID: 37381067 DOI: 10.1186/s40246-023-00502-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023] Open
Affiliation(s)
| | - Nicole Kleinstreuer
- NIH/NIEHS/DTT/NICEATM, RTP, Morrisville, NC, 27709, USA
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06520, USA
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Natsch A, Kleinstreuer N, Asturiol D. Reply to Comment on "Reduced specificity for the local lymph node assay for lipophilic chemicals: Implications for the validation of new approach methods for skin sensitization" by Roberts and Basketter. Regul Toxicol Pharmacol 2023; 140:105371. [PMID: 36898649 DOI: 10.1016/j.yrtph.2023.105371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 03/01/2023] [Indexed: 03/12/2023]
Affiliation(s)
- Andreas Natsch
- Fragrances S&T, Ingredients Research, Givaudan Schweiz AG, Kemptthal, Switzerland
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, RTP, NC, USA
| | - David Asturiol
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
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Ceger P, Allen D, Blankinship A, Choksi N, Daniel A, Eckel WP, Hamm J, Harwood DE, Johnson T, Kleinstreuer N, Sprankle CS, Truax J, Lowit M. Evaluation of the fish acute toxicity test for pesticide registration. Regul Toxicol Pharmacol 2023; 139:105340. [PMID: 36702196 DOI: 10.1016/j.yrtph.2023.105340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/01/2023] [Accepted: 01/21/2023] [Indexed: 01/25/2023]
Abstract
The U.S. Environmental Protection Agency (USEPA) uses the in vivo fish acute toxicity test to assess potential risk of substances to non-target aquatic vertebrates. The test is typically conducted on a cold and a warm freshwater species and a saltwater species for a conventional pesticide registration, potentially requiring upwards of 200 or more fish. A retrospective data evaluation was conducted to explore the potential for using fewer fish species to support conventional pesticide risk assessments. Lethal concentration 50% (LC50) values and experimental details were extracted and curated from 718 studies on fish acute toxicity submitted to USEPA. The LC50 data were analysed to determine, when possible, the relative sensitivity of the tested species to each pesticide. One of the tested freshwater species was most sensitive in 85% of those cases. The tested cold freshwater species was the most sensitive overall among cases with established relative sensitivity and was within 3X of the LC50 value of the most sensitive species tested in 98% of those cases. The results support potentially using fewer than three fish species to conduct ecological risk assessments for the registration of conventional pesticides.
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Affiliation(s)
- Patricia Ceger
- Inotiv, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - David Allen
- Inotiv, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - Amy Blankinship
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507M, 1200 Pennsylvania Ave. NW, Washington, DC, 20460, USA.
| | - Neepa Choksi
- Inotiv, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - Amber Daniel
- Inotiv, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - William P Eckel
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507M, 1200 Pennsylvania Ave. NW, Washington, DC, 20460, USA.
| | - Jon Hamm
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC, 27709, USA.
| | - D Ethan Harwood
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507M, 1200 Pennsylvania Ave. NW, Washington, DC, 20460, USA.
| | - Tamara Johnson
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507M, 1200 Pennsylvania Ave. NW, Washington, DC, 20460, USA.
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC, 27709, USA.
| | | | - James Truax
- Inotiv, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - Michael Lowit
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507M, 1200 Pennsylvania Ave. NW, Washington, DC, 20460, USA.
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11
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Natsch A, Kleinstreuer N, Asturiol D. Reduced specificity for the local lymph node assay for lipophilic chemicals: Implications for the validation of new approach methods for skin sensitization. Regul Toxicol Pharmacol 2023; 138:105333. [PMID: 36608925 PMCID: PMC9941753 DOI: 10.1016/j.yrtph.2023.105333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/11/2022] [Accepted: 01/03/2023] [Indexed: 01/05/2023]
Abstract
Meaningful and accurate reference data are crucial for the validation of New Approach Methodologies (NAMs) in toxicology. For skin sensitization, multiple reference datasets are available including human patch test data, guinea pig data and data from the mouse local lymph node assay (LLNA). When assessed against the LLNA, a reduced sensitivity has been reported for in vitro and in chemico assays for lipophilic chemicals with a LogP ≥3.5, resulting in reliability restrictions within the h-CLAT OECD test guideline. Here we address the question of whether LLNA data are an appropriate reference for chemicals in this physicochemical range. Analysis of LLNA vs human reference data indicates that the false-discovery rate of the LLNA is significantly higher for chemicals with LogP ≥3.5. We present a mechanistic hypothesis whereby irritation caused by testing lipophilic chemicals at high test doses leads to unspecific cell proliferation. The accompanying analysis indicates that for lipophilic chemicals with negative calls in in vitro and in chemico assays, resorting to the LLNA is not necessarily a better option. These results indicate that the validation of NAMs in this particular LogP range should be based on a more holistic evaluation of the reference data and not solely upon LLNA data.
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Affiliation(s)
- Andreas Natsch
- Fragrances S&T, Ingredients Research, Givaudan Schweiz AG, Kemptthal, Switzerland
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, RTP, NC, USA
| | - David Asturiol
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
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12
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Lee KM, Corley R, Jarabek AM, Kleinstreuer N, Paini A, Stucki AO, Bell S. Advancing New Approach Methodologies (NAMs) for Tobacco Harm Reduction: Synopsis from the 2021 CORESTA SSPT-NAMs Symposium. Toxics 2022; 10:760. [PMID: 36548593 PMCID: PMC9781465 DOI: 10.3390/toxics10120760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/05/2022] [Accepted: 11/05/2022] [Indexed: 06/17/2023]
Abstract
New approach methodologies (NAMs) are emerging chemical safety assessment tools consisting of in vitro and in silico (computational) methodologies intended to reduce, refine, or replace (3R) various in vivo animal testing methods traditionally used for risk assessment. Significant progress has been made toward the adoption of NAMs for human health and environmental toxicity assessment. However, additional efforts are needed to expand their development and their use in regulatory decision making. A virtual symposium was held during the 2021 Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) Smoke Science and Product Technology (SSPT) conference (titled "Advancing New Alternative Methods for Tobacco Harm Reduction"), with the goals of introducing the concepts and potential application of NAMs in the evaluation of potentially reduced-risk (PRR) tobacco products. At the symposium, experts from regulatory agencies, research organizations, and NGOs shared insights on the status of available tools, strengths, limitations, and opportunities in the application of NAMs using case examples from safety assessments of chemicals and tobacco products. Following seven presentations providing background and application of NAMs, a discussion was held where the presenters and audience discussed the outlook for extending the NAMs toxicological applications for tobacco products. The symposium, endorsed by the CORESTA In Vitro Tox Subgroup, Biomarker Subgroup, and NextG Tox Task Force, illustrated common ground and interest in science-based engagement across the scientific community and stakeholders in support of tobacco regulatory science. Highlights of the symposium are summarized in this paper.
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Affiliation(s)
| | - Richard Corley
- Greek Creek Toxicokinetics Consulting, LLC, Boise, ID 83714, USA
| | - Annie M. Jarabek
- Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for Evaluation of Alternative Toxicological Methods (NICEATM), Research Triangle Park, NC 27711, USA
| | - Alicia Paini
- European Commission Joint Research Center (EC JRC), 2749 Ispra, Italy
| | - Andreas O. Stucki
- PETA Science Consortium International e.V., 70499 Stuttgart, Germany
| | - Shannon Bell
- Inotiv-RTP, Research Triangle Park, NC 27709, USA
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13
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Strickland J, Allen DG, Germolec D, Kleinstreuer N, Johnson VJ, Gulledge T, Truax J, Lowit A, Dole T, McMahon T, Panger M, Facey J, Savage S. Application of Defined Approaches to Assess Skin Sensitization Potency of Isothiazolinone Compounds. Appl In Vitro Toxicol 2022; 8:117-128. [PMID: 36647556 PMCID: PMC9814110 DOI: 10.1089/aivt.2022.0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Isothiazolinones (ITs) are widely used as antimicrobial preservatives in cosmetics and as additives for preservation of consumer and industrial products to control bacteria, fungi, and algae. Although they are effective biocides, they have the potential to produce skin irritation and sensitization, which poses a human health hazard. In this project, we evaluated nonanimal defined approaches (DAs) for skin sensitization that can provide point-of-departure estimates for use in quantitative risk assessment for ITs. MATERIALS AND METHODS The skin sensitization potential of six ITs was evaluated using three internationally harmonized nonanimal test methods: the direct peptide reactivity assay, KeratinoSens™, and the human cell line activation test. Results from these test methods were then applied to two versions of the Shiseido Artificial Neural Network DA. RESULTS Sensitization hazard or potency predictions were compared with those of the in vivo murine local lymph node assay (LLNA). The nonanimal methods produced skin sensitization hazard and potency classifications concordant with those of the LLNA. EC3 values (the estimated concentration needed to produce a stimulation index of three, the threshold positive response) generated by the DAs had less variability than LLNA EC3 values, and confidence limits from the DAs overlapped those of the LLNA EC3 for most substances. CONCLUSION The application of in silico models to in chemico and in vitro skin sensitization data is a promising data integration procedure for DAs to support hazard and potency classification and quantitative risk assessment.
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Affiliation(s)
| | | | - Dori Germolec
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Methods, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Victor J. Johnson
- Burleson Research Technologies, Inc., Morrisville, North Carolina, USA
| | - Travis Gulledge
- Burleson Research Technologies, Inc., Morrisville, North Carolina, USA
| | - Jim Truax
- Inotiv, Inc., Morrisville, North Carolina, USA
| | - Anna Lowit
- United States Environmental Protection Agency, Office of Pollution Prevention and Toxics, Washington, District of Columbia, USA
| | - Timothy Dole
- United States Environmental Protection Agency, Office of Pesticide Programs, Washington, District of Columbia, USA
| | - Timothy McMahon
- United States Environmental Protection Agency, Office of Pesticide Programs, Washington, District of Columbia, USA
| | - Melissa Panger
- United States Environmental Protection Agency, Office of Pesticide Programs, Washington, District of Columbia, USA
| | - Judy Facey
- United States Environmental Protection Agency, Office of Pesticide Programs, Washington, District of Columbia, USA
| | - Stephen Savage
- United States Environmental Protection Agency, Office of Pesticide Programs, Washington, District of Columbia, USA
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14
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Daniel AB, Choksi N, Abedini J, Bell S, Ceger P, Cook B, Karmaus AL, Rooney J, To KT, Allen D, Kleinstreuer N. Data curation to support toxicity assessments using the Integrated Chemical Environment. Front Toxicol 2022; 4:987848. [PMID: 36408349 PMCID: PMC9669273 DOI: 10.3389/ftox.2022.987848] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 10/18/2022] [Indexed: 12/01/2023] Open
Abstract
Humans are exposed to large numbers of chemicals during their daily activities. To assess and understand potential health impacts of chemical exposure, investigators and regulators need access to reliable toxicity data. In particular, reliable toxicity data for a wide range of chemistries are needed to support development of new approach methodologies (NAMs) such as computational models, which offer increased throughput relative to traditional approaches and reduce or replace animal use. NAMs development and evaluation require chemically diverse data sets that are typically constructed by incorporating results from multiple studies into a single, integrated view; however, integrating data is not always a straightforward task. Primary study sources often vary in the way data are organized and reported. Metadata and information needed to support interoperability and provide context are often lacking, which necessitates literature research on the assay prior to attempting data integration. The Integrated Chemical Environment (ICE) was developed to support the development, evaluation, and application of NAMs. ICE provides curated toxicity data and computational tools to integrate and explore available information, thus facilitating knowledge discovery and interoperability. This paper describes the data curation workflow for integrating data into ICE. Data destined for ICE undergo rigorous harmonization, standardization, and formatting processes using both automated and manual expert-driven approaches. These processes improve the utility of the data for diverse analyses and facilitate application within ICE or a user's external workflow while preserving data integrity and context. ICE data curation provides the structure, reliability, and accessibility needed for data to support chemical assessments.
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Affiliation(s)
| | - Neepa Choksi
- Inotiv, Research Triangle Park, NC, United States
| | | | - Shannon Bell
- Inotiv, Research Triangle Park, NC, United States
| | | | - Bethany Cook
- Inotiv, Research Triangle Park, NC, United States
| | | | - John Rooney
- Inotiv, Research Triangle Park, NC, United States
| | | | - David Allen
- Inotiv, Research Triangle Park, NC, United States
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15
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Chang X, Palmer J, Lumen A, Lee UJ, Ceger P, Mansouri K, Sprankle C, Donley E, Bell S, Knudsen TB, Wambaugh J, Cook B, Allen D, Kleinstreuer N. Quantitative in vitro to in vivo extrapolation for developmental toxicity potency of valproic acid analogues. Birth Defects Res 2022; 114:1037-1055. [PMID: 35532929 PMCID: PMC9790683 DOI: 10.1002/bdr2.2019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/17/2022] [Accepted: 03/22/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND The developmental toxicity potential (dTP) concentration from the devTOX quickPredict (devTOXqP ) assay, a metabolomics-based human induced pluripotent stem cell assay, predicts a chemical's developmental toxicity potency. Here, in vitro to in vivo extrapolation (IVIVE) approaches were applied to address whether the devTOXqP assay could quantitatively predict in vivo developmental toxicity lowest effect levels (LELs) for the prototypical teratogen valproic acid (VPA) and a group of structural analogues. METHODS VPA and a series of structural analogues were tested with the devTOXqP assay to determine dTP concentration and we estimated the equivalent administered doses (EADs) that would lead to plasma concentrations equivalent to the in vitro dTP concentrations. The EADs were compared to the LELs in rat developmental toxicity studies, human clinical doses, and EADs reported using other in vitro assays. To evaluate the impact of different pharmacokinetic (PK) models on IVIVE outcomes, we compared EADs predicted using various open-source and commercially available PK and physiologically based PK (PBPK) models. To evaluate the effect of in vitro kinetics, an equilibrium distribution model was applied to translate dTP concentrations to free medium concentrations before subsequent IVIVE analyses. RESULTS The EAD estimates for the VPA analogues based on different PK/PBPK models were quantitatively similar to in vivo data from both rats and humans, where available, and the derived rank order of the chemicals was consistent with observed in vivo developmental toxicity. Different models were identified that provided accurate predictions for rat prenatal LELs and conservative estimates of human safe exposure. The impact of in vitro kinetics on EAD estimates is chemical-dependent. EADs from this study were within range of predicted doses from other in vitro and model organism data. CONCLUSIONS This study highlights the importance of pharmacokinetic considerations when using in vitro assays and demonstrates the utility of the devTOXqP human stem cell-based platform to quantitatively assess a chemical's developmental toxicity potency.
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Affiliation(s)
| | | | - Annie Lumen
- National Center for Toxicological ResearchU.S. Food and Drug AdministrationJeffersonArkansasUSA,Present address:
Clinical Pharmacology, Modeling and SimulationsAmgenSouth San FranciscoCaliforniaUSA
| | - Un Jung Lee
- National Center for Toxicological ResearchU.S. Food and Drug AdministrationJeffersonArkansasUSA,Present address:
Albert Einstein College of MedicineBronxNew YorkUSA
| | | | - Kamel Mansouri
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological MethodsNational Institute of Environmental Health SciencesResearch Triangle ParkNorth CarolinaUSA
| | | | | | | | - Thomas B. Knudsen
- Center for Computational Toxicology and ExposureEnvironmental Protection AgencyResearch Triangle ParkNorth CarolinaUSA
| | - John Wambaugh
- Center for Computational Toxicology and ExposureEnvironmental Protection AgencyResearch Triangle ParkNorth CarolinaUSA
| | | | | | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological MethodsNational Institute of Environmental Health SciencesResearch Triangle ParkNorth CarolinaUSA
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16
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Herzler M, Abedini J, Allen D, Api A, Germolec D, Gordon J, Ko HS, Matheson J, Strickland J, Thierse HJ, To K, Truax J, Vanselow J, Kleinstreuer N. SOC-V-06 New classification approach for Human Predictive Patch Test (HPPT) results under the UN GHS improves skin sensitisation potency sub-categorisation and weight-of-evidence assessments. Toxicol Lett 2022. [DOI: 10.1016/j.toxlet.2022.07.195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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17
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Casati S, Asturiol D, Browne P, Kleinstreuer N, Régimbald-Krnel M, Therriault P. Standardisation and international adoption of defined approaches for skin sensitisation. Front Toxicol 2022; 4:943152. [PMID: 36032790 PMCID: PMC9402929 DOI: 10.3389/ftox.2022.943152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Abstract
In the absence of stand-alone one-to-one replacements for existing animal tests, efforts were made to integrate data from in silico, in chemico and in vitro methods to ensure sufficient mechanistic coverage of the skin sensitisation Adverse Outcome Pathway (AOP) and generate predictions suitable for hazard identification and potency sub-categorisation. A number of defined approaches (DAs), using fixed data interpretation procedures (DIP) to integrate data from multiple non-animal information sources, were proposed and documented using a standard reporting template developed by the Organisation for Economic Co-operation and Development (OECD). Subsequent international activities focused on the extensive characterisation of three of these DAs with respect to the reference in vivo data, applicability domains, limitations, predictive performances and characterisations of the level of confidence associated with the predictions. The ultimate product of this project was an OECD Guideline that provides information equivalent to that provided by the animal studies and that can be used to satisfy countries’ regulatory data requirements for skin sensitisation. This Defined Approach Guideline was the first of its kind for the OECD, and provides an important precedent for regulatory adoption of human biology-relevant new approach methodologies with performances equivalent to, or better than, traditional animal tests. This mini review summarizes the principal features of the defined approaches described in OECD guideline 497.
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Affiliation(s)
- Silvia Casati
- European Commission, Joint Research Centre (JRC), Ispra, Italy
- *Correspondence: Silvia Casati,
| | - David Asturiol
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Patience Browne
- OECD Environment Directorate, Environment Health and Safety Division, Paris, France
| | - Nicole Kleinstreuer
- NIH/NIEHS/DNTP/The NTP Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, Durham, NC, United States
| | - Michèle Régimbald-Krnel
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Pierre Therriault
- Health Effects Division, Pest Management Regulatory Agency, Health Canada, Ottawa, ON, Canada
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18
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Alves VM, Borba JVB, Braga RC, Korn DR, Kleinstreuer N, Causey K, Tropsha A, Rua D, Muratov EN. PreS/MD: Predictor of Sensitization hazard for chemical substances released from Medical Devices. Toxicol Sci 2022; 189:250-259. [PMID: 35916740 PMCID: PMC9516038 DOI: 10.1093/toxsci/kfac078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
In the United States, a pre-market regulatory submission for any medical device that comes into contact with either a patient or the clinical practitioner must include an adequate toxicity evaluation of chemical substances that can be released from the device during its intended use. These substances, also referred to as extractables and leachables, must be evaluated for their potential to induce sensitization/allergenicity, which traditionally has been done in animal assays such as the guinea pig maximization test (GPMT). However, advances in basic and applied science are continuously presenting opportunities to employ New Approach Methodologies (NAMs), including computational methods which, when qualified, could replace animal testing methods to support regulatory submissions. Herein, we developed a new computational tool for rapid and accurate prediction of the GPMT outcome that we have named PreS/MD (Predictor of Sensitization for Medical Devices). To enable model development, we (i) collected, curated, and integrated the largest publicly available dataset for GPMT results; (ii) succeeded in developing externally predictive (balanced accuracy of 70-74% as evaluated by both 5-fold external cross-validation and testing of novel compounds) Quantitative Structure-Activity Relationships (QSAR) models for GPMT using machine learning algorithms, including Deep Learning; and (iii) developed a publicly accessible web portal integrating PreS/MD models that can predict GPMT outcomes for any molecule of interest. We expect that PreS/MD will be used by both industry and regulatory scientists in medical device safety assessments and help replace, reduce, or refine the use of animals in toxicity testing. PreS/MD is freely available at https://presmd.mml.unc.edu/.
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Affiliation(s)
- Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Joyce V B Borba
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | | | - Daniel R Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Nicole Kleinstreuer
- National Toxicology Program, Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Kevin Causey
- Predictive, LLC, Research Triangle Park, NC, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Predictive, LLC, Research Triangle Park, NC, USA
| | - Diego Rua
- Division of Biology, Chemistry, and Materials Science, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, 20993, USA
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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19
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Corton JC, Liu J, Kleinstreuer N, Gwinn MR, Ryan N. Towards replacement of animal tests with in vitro assays: a gene expression biomarker predicts in vitro and in vivo estrogen receptor activity. Chem Biol Interact 2022; 363:109995. [PMID: 35697134 DOI: 10.1016/j.cbi.2022.109995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/02/2022] [Accepted: 05/24/2022] [Indexed: 11/18/2022]
Abstract
High-throughput transcriptomics (HTTr) has the potential to support efforts to reduce or replace some animal tests. In past studies, we described a computational approach utilizing a gene expression biomarker consisting of 46 genes to predict estrogen receptor (ER) activity after chemical exposure in ER-positive human breast cancer cells including the MCF-7 cell line. We hypothesized that the biomarker model could identify ER activities of chemicals examined by Endocrine Disruptor Screening Program (EDSP) Tier 1 screening assays in which transcript profiles of the same chemicals were examined in MCF-7 cells. For the 62 chemicals examined including 5 chemicals examined in this study using RNA-Seq, the ER biomarker model accuracy was 1) 97% for in vitro reference chemicals, 2) 76-85% for guideline uterotrophic assays, and 3) 87-88% for guideline and nonguideline uterotrophic assays. For the same chemicals, these accuracies were similar or slightly better than those of the ToxCast ER model based on 18 in vitro assays. The performance of the ER biomarker model indicates that HTTr interpreted using the ER biomarker correctly identifies active and inactive ER reference chemicals. As part of the HTTr screening program the approach could rapidly identify chemicals with potential ER bioactivities for additional screening and testing.
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Affiliation(s)
- J Christopher Corton
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC, 27711, USA.
| | - Jie Liu
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC, 27711, USA.
| | - Nicole Kleinstreuer
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27711, USA.
| | - Maureen R Gwinn
- Center for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC, 27711, USA.
| | - Natalia Ryan
- Oak Ridge Institute for Science and Education (ORISE), Research Triangle Park, NC, 27711, USA.
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20
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Ceger P, Garcia-Reyero Vinas N, Allen D, Arnold E, Bloom R, Brennan JC, Clarke C, Eisenreich K, Fay K, Hamm J, Henry PFP, Horak K, Hunter W, Judkins D, Klein P, Kleinstreuer N, Koehrn K, LaLone CA, Laurenson JP, Leet JK, Lowit A, Lynn SG, Norberg-King T, Perkins EJ, Petersen EJ, Rattner BA, Sprankle CS, Steeger T, Warren JE, Winfield S, Odenkirchen E. Current ecotoxicity testing needs among selected U.S. federal agencies. Regul Toxicol Pharmacol 2022; 133:105195. [PMID: 35660046 PMCID: PMC9623878 DOI: 10.1016/j.yrtph.2022.105195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 05/19/2022] [Accepted: 05/25/2022] [Indexed: 10/18/2022]
Abstract
U.S. regulatory and research agencies use ecotoxicity test data to assess the hazards associated with substances that may be released into the environment, including but not limited to industrial chemicals, pharmaceuticals, pesticides, food additives, and color additives. These data are used to conduct hazard assessments and evaluate potential risks to aquatic life (e.g., invertebrates, fish), birds, wildlife species, or the environment. To identify opportunities for regulatory uses of non-animal replacements for ecotoxicity tests, the needs and uses for data from tests utilizing animals must first be clarified. Accordingly, the objective of this review was to identify the ecotoxicity test data relied upon by U.S. federal agencies. The standards, test guidelines, guidance documents, and/or endpoints that are used to address each of the agencies' regulatory and research needs regarding ecotoxicity testing are described in the context of their application to decision-making. Testing and information use, needs, and/or requirements relevant to the regulatory or programmatic mandates of the agencies taking part in the Interagency Coordinating Committee on the Validation of Alternative Methods Ecotoxicology Workgroup are captured. This information will be useful for coordinating efforts to develop and implement alternative test methods to reduce, refine, or replace animal use in chemical safety evaluations.
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Affiliation(s)
- Patricia Ceger
- Integrated Laboratory Systems, LLC, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | | | - David Allen
- Integrated Laboratory Systems, LLC, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - Elyssa Arnold
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507P, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
| | - Raanan Bloom
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
| | - Jennifer C Brennan
- U.S. Environmental Protection Agency, Office of Pollution Prevention and Toxics, 7401M, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
| | - Carol Clarke
- U.S. Department of Agriculture, 1400 Independence Ave. SW, Washington, DC, 20250, USA.
| | - Karen Eisenreich
- U.S. Environmental Protection Agency, Office of Pollution Prevention and Toxics, 7401M, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
| | - Kellie Fay
- U.S. Environmental Protection Agency, Office of Pollution Prevention and Toxics, 7401M, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
| | - Jonathan Hamm
- Integrated Laboratory Systems, LLC, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - Paula F P Henry
- U.S. Geological Survey, Eastern Ecological Science Center, 12100 Beech Forest Rd, Laurel, MD, 20708, USA.
| | - Katherine Horak
- U.S. Department of Agriculture, Wildlife Services National Wildlife Research Center, 4101 LaPorte Ave. Fort Collins, CO, 80521, USA.
| | - Wesley Hunter
- U.S. Food and Drug Administration, Center for Veterinary Medicine, HFV-161, 7500 Standish Place, Rockville, MD, 20855, USA.
| | - Donna Judkins
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507P, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
| | - Patrice Klein
- U.S. Department of Agriculture, 1400 Independence Ave. SW, Washington, DC, 20250, USA.
| | - Nicole Kleinstreuer
- National Institute of Environmental Health Sciences, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, P.O. Box 12233, Research Triangle Park, NC, 27709, USA.
| | - Kara Koehrn
- U.S. Environmental Protection Agency, Office of Pollution Prevention and Toxics, 7401M, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
| | - Carlie A LaLone
- U.S. Environmental Protection Agency, Office of Research and Development, 8101R, 6201 Congdon Blvd., Duluth, MN, 55804, USA.
| | - James P Laurenson
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
| | - Jessica K Leet
- U.S. Geological Survey, Columbia Environmental Research Center (CERC), Columbia, MO, 65201, USA.
| | - Anna Lowit
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507P, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
| | - Scott G Lynn
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507P, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
| | - Teresa Norberg-King
- U.S. Environmental Protection Agency, Office of Research and Development, 8101R, 6201 Congdon Blvd., Duluth, MN, 55804, USA.
| | - Edward J Perkins
- U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Rd., Vicksburg, MS, 39180, USA.
| | - Elijah J Petersen
- U.S. Department of Commerce, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, 2089, USA.
| | - Barnett A Rattner
- U.S. Geological Survey, Eastern Ecological Science Center, 10300 Baltimore Ave, BARC-EAST Bldg. 308, Beltsville, MD, 20705, USA.
| | - Catherine S Sprankle
- Integrated Laboratory Systems, LLC, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - Thomas Steeger
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507P, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
| | - Jim E Warren
- U.S. Department of Agriculture, 1400 Independence Ave. SW, Washington, DC, 20250, USA.
| | - Sarah Winfield
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, 5001 Campus Drive, HFS-009, College Park, MD, 20740, USA.
| | - Edward Odenkirchen
- U.S. Environmental Protection Agency, Office of Pesticide Programs, MC7507P, 1200 Pennsylvania Avenue NW, Washington, DC, 20460, USA.
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21
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Strickland J, Truax J, Corvaro M, Settivari R, Henriquez J, McFadden J, Gulledge T, Johnson V, Gehen S, Germolec D, Allen DG, Kleinstreuer N. Application of Defined Approaches for Skin Sensitization to Agrochemical Products. Front Toxicol 2022; 4:852856. [PMID: 35586187 PMCID: PMC9108145 DOI: 10.3389/ftox.2022.852856] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Skin sensitization testing is a regulatory requirement for safety evaluations of pesticides in multiple countries. Globally harmonized test guidelines that include in chemico and in vitro methods reduce animal use, but no single assay is recommended as a complete replacement for animal tests. Defined approaches (DAs) that integrate data from multiple non-animal methods are accepted; however, the methods that comprise them have been evaluated using monoconstituent substances rather than mixtures or formulations. To address this data gap, we tested 27 agrochemical formulations in the direct peptide reactivity assay (DPRA), the KeratinoSens™ assay, and the human cell line activation test (h-CLAT). These data were used as inputs to evaluate three DAs for hazard classification of skin sensitization potential and two DAs for potency categorization. When compared to historical animal results, balanced accuracy for the DAs for predicting in vivo skin sensitization hazard (i.e., sensitizer vs. nonsensitizer) ranged from 56 to 78%. The best performing DA was the “2 out of 3 (2o3)” DA, in which the hazard classification was based on two concordant results from the DPRA, KeratinoSens, or h-CLAT. The KE 3/1 sequential testing strategy (STS), which uses h-CLAT and DPRA results, and the integrated testing strategy (ITSv2), which uses h-CLAT, DPRA, and an in silico hazard prediction from OECD QSAR Toolbox, had balanced accuracies of 56–57% for hazard classification. Of the individual test methods, KeratinoSens had the best performance for predicting in vivo hazard outcomes. Its balanced accuracy of 81% was similar to that of the 2o3 DA (78%). For predicting potency categories defined by the United Nations Globally Harmonized System of Classification and Labelling of Chemicals (GHS), the correct classification rate of the STS was 52% and that of the ITSv2 was 43%. These results demonstrate that non-animal test methods have utility for evaluating the skin sensitization potential of agrochemical formulations as compared to animal reference data. While additional data generation is needed, testing strategies such as DAs anchored to human biology and mechanistic information provide a promising approach for agrochemical formulation testing.
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Affiliation(s)
- Judy Strickland
- Integrated Laboratory Systems, LLC, Research Triangle Park, NC, United States
- *Correspondence: Judy Strickland,
| | - James Truax
- Integrated Laboratory Systems, LLC, Research Triangle Park, NC, United States
| | - Marco Corvaro
- Corteva Agriscience, Regulatory Sciences R&D, Rome, Italy
| | - Raja Settivari
- Corteva Agriscience, General, Genetic, and Molecular Toxicology, Newark, DE, United States
| | - Joseph Henriquez
- Corteva Agriscience, Regulatory Toxicology and Risk Group, Indianapolis, IN, United States
| | - Jeremy McFadden
- Corteva Agriscience, Regulatory Toxicology and Risk Group, Indianapolis, IN, United States
| | - Travis Gulledge
- Burleson Research Technologies, Inc., Morrisville, NC, United States
| | - Victor Johnson
- Burleson Research Technologies, Inc., Morrisville, NC, United States
| | - Sean Gehen
- Corteva Agriscience, Regulatory Toxicology and Risk Group, Indianapolis, IN, United States
| | - Dori Germolec
- Systems Toxicology Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States
| | - David G. Allen
- Integrated Laboratory Systems, LLC, Research Triangle Park, NC, United States
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States
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22
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Karmaus AL, Mansouri K, To KT, Blake B, Fitzpatrick J, Strickland J, Patlewicz G, Allen D, Casey W, Kleinstreuer N. Evaluation of Variability across Rat Acute Oral Systemic Toxicity Studies. Toxicol Sci 2022; 188:34-47. [PMID: 35426934 PMCID: PMC9237992 DOI: 10.1093/toxsci/kfac042] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Regulatory agencies rely upon rodent in vivo acute oral toxicity data to determine hazard categorization, require appropriate precautionary labeling, and perform quantitative risk assessments. As the field of toxicology moves toward animal-free new approach methodologies (NAMs), there is a pressing need to develop a reliable, robust reference data set to characterize the reproducibility and inherent variability in the in vivo acute oral toxicity test method, which would serve to contextualize results and set expectations regarding NAM performance. Such a data set is also needed for training and evaluating computational models. To meet these needs, rat acute oral LD50 data from multiple databases were compiled, curated, and analyzed to characterize variability and reproducibility of results across a set of up to 2441 chemicals with multiple independent study records. Conditional probability analyses reveal that replicate studies only result in the same hazard categorization on average at 60% likelihood. Although we did not have sufficient study metadata to evaluate the impact of specific protocol components (eg, strain, age, or sex of rat, feed used, treatment vehicle, etc.), studies were assumed to follow standard test guidelines. We investigated, but could not attribute, various chemical properties as the sources of variability (ie, chemical structure, physiochemical properties, functional use). Thus, we conclude that inherent biological or protocol variability likely underlies the variance in the results. Based on the observed variability, we were able to quantify a margin of uncertainty of ±0.24 log10 (mg/kg) associated with discrete in vivo rat acute oral LD50 values.
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Affiliation(s)
- Agnes L Karmaus
- Integrated Laboratory Systems, LLC, Morrisville, NC, 27560, USA
| | - Kamel Mansouri
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Kimberly T To
- Integrated Laboratory Systems, LLC, Morrisville, NC, 27560, USA
| | - Bevin Blake
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Jeremy Fitzpatrick
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Judy Strickland
- Integrated Laboratory Systems, LLC, Morrisville, NC, 27560, USA
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - David Allen
- Integrated Laboratory Systems, LLC, Morrisville, NC, 27560, USA
| | - Warren Casey
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
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23
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Hines DE, Bell S, Chang X, Mansouri K, Allen D, Kleinstreuer N. Application of an Accessible Interface for Pharmacokinetic Modeling and In Vitro to In Vivo Extrapolation. Front Pharmacol 2022; 13:864742. [PMID: 35496281 PMCID: PMC9043603 DOI: 10.3389/fphar.2022.864742] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/28/2022] [Indexed: 12/03/2022] Open
Abstract
Regulatory toxicology testing has traditionally relied on in vivo methods to inform decision-making. However, scientific, practical, and ethical considerations have led to an increased interest in the use of in vitro and in silico methods to fill data gaps. While in vitro experiments have the advantage of rapid application across large chemical sets, interpretation of data coming from these non-animal methods can be challenging due to the mechanistic nature of many assays. In vitro to in vivo extrapolation (IVIVE) has emerged as a computational tool to help facilitate this task. Specifically, IVIVE uses physiologically based pharmacokinetic (PBPK) models to estimate tissue-level chemical concentrations based on various dosing parameters. This approach is used to estimate the administered dose needed to achieve in vitro bioactivity concentrations within the body. IVIVE results can be useful to inform on metrics such as margin of exposure or to prioritize potential chemicals of concern, but the PBPK models used in this approach have extensive data requirements. Thus, access to input parameters, as well as the technical requirements of applying and interpreting models, has limited the use of IVIVE as a routine part of in vitro testing. As interest in using non-animal methods for regulatory and research contexts continues to grow, our perspective is that access to computational support tools for PBPK modeling and IVIVE will be essential for facilitating broader application and acceptance of these techniques, as well as for encouraging the most scientifically sound interpretation of in vitro results. We highlight recent developments in two open-access computational support tools for PBPK modeling and IVIVE accessible via the Integrated Chemical Environment (https://ice.ntp.niehs.nih.gov/), demonstrate the types of insights these tools can provide, and discuss how these analyses may inform in vitro-based decision making.
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Affiliation(s)
- David E. Hines
- Inotiv-RTP, Research Triangle Park, Durham, NC, United States
- *Correspondence: David E. Hines,
| | - Shannon Bell
- Inotiv-RTP, Research Triangle Park, Durham, NC, United States
| | - Xiaoqing Chang
- Inotiv-RTP, Research Triangle Park, Durham, NC, United States
| | - Kamel Mansouri
- NIH/NIEHS/DNTP/NICEATM, Research Triangle Park, Durham, NC, United States
| | - David Allen
- Inotiv-RTP, Research Triangle Park, Durham, NC, United States
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24
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Borba JV, Alves VM, Braga RC, Korn DR, Overdahl K, Silva AC, Hall SU, Overdahl E, Kleinstreuer N, Strickland J, Allen D, Andrade CH, Muratov EN, Tropsha A. STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity. Environ Health Perspect 2022; 130:27012. [PMID: 35192406 PMCID: PMC8863177 DOI: 10.1289/ehp9341] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 05/22/2023]
Abstract
BACKGROUND Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points. METHODS We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets. RESULTS In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays. CONCLUSIONS We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.
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Affiliation(s)
- Joyce V.B. Borba
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Daniel R. Korn
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Kirsten Overdahl
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| | - Arthur C. Silva
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Steven U.S. Hall
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Erik Overdahl
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Judy Strickland
- Integrated Laboratory Systems, LLC, Research Triangle Park, North Carolina, USA
| | - David Allen
- Integrated Laboratory Systems, LLC, Research Triangle Park, North Carolina, USA
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, Paraiba, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
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25
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Krishna S, Borrel A, Huang R, Zhao J, Xia M, Kleinstreuer N. High-Throughput Chemical Screening and Structure-Based Models to Predict hERG Inhibition. Biology (Basel) 2022; 11:biology11020209. [PMID: 35205076 PMCID: PMC8869358 DOI: 10.3390/biology11020209] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/18/2022] [Accepted: 01/21/2022] [Indexed: 12/23/2022]
Abstract
Simple Summary Cardiovascular disease is the leading cause of death for people of most ethnicities in the United States. The human ether-a-go-go-related gene (hERG) potassium channel plays a pivotal role in cardiac rhythm regulation, and cardiotoxicity associated with hERG inhibition by drug molecules and environmental chemicals is a major public health concern. An evaluation of the effect of environmental chemicals on hERG channel function can help inform the potential public health risks of these compounds. To assess the cardiotoxic effect of diverse drugs and environmental compounds, the Tox21 federal research program has screened a collection of 9667 chemicals for inhibitory activity against the hERG channel. A set of molecular descriptors covering physicochemical and structural properties of chemicals, self-organizing maps, and hierarchical clustering were applied to characterize the chemicals inhibiting hERG. Machine learning approaches were applied to build robust statistical models that can predict the probability of any new chemical to cause cardiotoxicity via this mechanism. Abstract Chemical inhibition of the human ether-a -go-go-related gene (hERG) potassium channel leads to a prolonged QT interval that can contribute to severe cardiotoxicity. The adverse effects of hERG inhibition are one of the principal causes of drug attrition in clinical and pre-clinical development. Preliminary studies have demonstrated that a wide range of environmental chemicals and toxicants may also inhibit the hERG channel and contribute to the pathophysiology of cardiovascular (CV) diseases. As part of the US federal Tox21 program, the National Center for Advancing Translational Science (NCATS) applied a quantitative high throughput screening (qHTS) approach to screen the Tox21 library of 10,000 compounds (~7871 unique chemicals) at 14 concentrations in triplicate to identify chemicals perturbing hERG activity in the U2OS cell line thallium flux assay platform. The qHTS cell-based thallium influx assay provided a robust and reliable dataset to evaluate the ability of thousands of drugs and environmental chemicals to inhibit hERG channel protein, and the use of chemical structure-based clustering and chemotype enrichment analysis facilitated the identification of molecular features that are likely responsible for the observed hERG activity. We employed several machine-learning approaches to develop QSAR prediction models for the assessment of hERG liabilities for drug-like and environmental chemicals. The training set was compiled by integrating hERG bioactivity data from the ChEMBL database with the Tox21 qHTS thallium flux assay data. The best results were obtained with the random forest method (~92.6% balanced accuracy). The data and scripts used to generate hERG prediction models are provided in an open-access format as key in vitro and in silico tools that can be applied in a translational toxicology pipeline for drug development and environmental chemical screening.
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Affiliation(s)
- Shagun Krishna
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle, NC 27560, USA;
| | | | - Ruili Huang
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Bethesda, MD 20892-4874, USA; (R.H.); (J.Z.); (M.X.)
| | - Jinghua Zhao
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Bethesda, MD 20892-4874, USA; (R.H.); (J.Z.); (M.X.)
| | - Menghang Xia
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Bethesda, MD 20892-4874, USA; (R.H.); (J.Z.); (M.X.)
| | - Nicole Kleinstreuer
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), Research Triangle, NC 27560, USA;
- Correspondence: ; Tel.: +1-984-287-3150
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26
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Silva AC, Borba JV, Alves VM, Hall SU, Furnham N, Kleinstreuer N, Muratov E, Tropsha A, Andrade CH. Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals. Artificial Intelligence in the Life Sciences 2021; 1. [PMID: 35935266 PMCID: PMC9355119 DOI: 10.1016/j.ailsci.2021.100028] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Eye irritation and corrosion are fundamental considerations in developing chemicals to be used in or near the eye, from cleaning products to ophthalmic solutions. Unfortunately, animal testing is currently the standard method to identify compounds that cause eye irritation or corrosion. Yet, there is growing pressure on the part of regulatory agencies both in the USA and abroad to develop New Approach Methodologies (NAMs) that help reduce the need for animal testing and address unmet need to modernize safety evaluation of chemical hazards. In furthering the development and applications of computational NAMs in chemical safety assessment, in this study we have collected the largest expertly curated dataset of compounds tested for eye irritation and corrosion, and employed this data to build and validate binary and multi-classification Quantitative Structure-Activity Relationships (QSAR) models that can reliably assess eye irritation/corrosion potential of novel untested compounds. QSAR models were generated with Random Forest (RF) and Multi-Descriptor Read Across (MuDRA) machine learning (ML) methods, and validated using a 5-fold external cross-validation protocol. These models demonstrated high balanced accuracy (CCR of 0.68–0.88), sensitivity (SE of 0.61–0.84), positive predictive value (PPV of 0.65–0.90), specificity (SP of 0.56–0.91), and negative predictive value (NPV of 0.68–0.85). Overall, MuDRA models outperformed RF models and were applied to predict compounds’ irritation/corrosion potential from the Inactive Ingredient Database, which contains components present in FDA-approved drug products, and from the Cosmetic Ingredient Database, the European Commission source of information on cosmetic substances. All models built and validated in this study are publicly available at the STopTox web portal (https://stoptox.mml.unc.edu/). These models can be employed as reliable tools for identifying potential eye irritant/corrosive compounds
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27
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Tan YM, Barton HA, Boobis A, Brunner R, Clewell H, Cope R, Dawson J, Domoradzki J, Egeghy P, Gulati P, Ingle B, Kleinstreuer N, Lowe K, Lowit A, Mendez E, Miller D, Minucci J, Nguyen J, Paini A, Perron M, Phillips K, Qian H, Ramanarayanan T, Sewell F, Villanueva P, Wambaugh J, Embry M. Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment. Regul Toxicol Pharmacol 2021; 127:105070. [PMID: 34718074 DOI: 10.1016/j.yrtph.2021.105070] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/18/2021] [Accepted: 10/25/2021] [Indexed: 02/08/2023]
Abstract
Top dose selection for repeated dose animal studies has generally focused on identification of apical endpoints, use of the limit dose, or determination of a maximum tolerated dose (MTD). The intent is to optimize the ability of toxicity tests performed in a small number of animals to detect effects for hazard identification. An alternative approach, the kinetically derived maximum dose (KMD), has been proposed as a mechanism to integrate toxicokinetic (TK) data into the dose selection process. The approach refers to the dose above which the systemic exposures depart from being proportional to external doses. This non-linear external-internal dose relationship arises from saturation or limitation of TK process(es), such as absorption or metabolism. The importance of TK information is widely acknowledged when assessing human health risks arising from exposures to environmental chemicals, as TK determines the amount of chemical at potential sites of toxicological responses. However, there have been differing opinions and interpretations within the scientific and regulatory communities related to the validity and application of the KMD concept. A multi-stakeholder working group, led by the Health and Environmental Sciences Institute (HESI), was formed to provide an opportunity for impacted stakeholders to address commonly raised scientific and technical issues related to this topic and, more specifically, a weight of evidence approach is recommended to inform design and dose selection for repeated dose animal studies. Commonly raised challenges related to the use of TK data for dose selection are discussed, recommendations are provided, and illustrative case examples are provided to address these challenges or refute misconceptions.
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Affiliation(s)
- Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Durham, NC, USA
| | | | | | - Rachel Brunner
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Durham, NC, USA
| | | | - Rhian Cope
- Australian Pesticides and Veterinary Medicines Authority, Sydney, NSW, Australia
| | - Jeffrey Dawson
- U.S. Environmental Protection Agency, Office of Chemical Safety and Pollution Prevention, Washington, DC, USA
| | | | - Peter Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Pankaj Gulati
- Australian Pesticides and Veterinary Medicines Authority, Sydney, NSW, Australia
| | - Brandall Ingle
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Durham, NC, USA
| | - Nicole Kleinstreuer
- National Toxicology Program, Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, NC, USA
| | - Kelly Lowe
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Anna Lowit
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Elizabeth Mendez
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - David Miller
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Jeffrey Minucci
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - James Nguyen
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Alicia Paini
- European Commission, Joint Research Centre, Ispra, Italy
| | - Monique Perron
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Katherine Phillips
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Hua Qian
- ExxonMobil Biomedical Sciences, Inc., Annandale, NJ, USA
| | | | - Fiona Sewell
- National Centre for the Replacement, Refinement, and Reduction of Animals in Research, London, UK
| | - Philip Villanueva
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - John Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Michelle Embry
- Health and Environmental Sciences Institute, Washington DC, USA.
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Kleinstreuer N, Holmes A. Harnessing the power of microphysiological systems for COVID-19 research. Drug Discov Today 2021; 26:2496-2501. [PMID: 34332095 PMCID: PMC8317448 DOI: 10.1016/j.drudis.2021.06.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/20/2021] [Accepted: 06/25/2021] [Indexed: 02/04/2023]
Abstract
The pharmaceutical industry is constantly striving for innovative ways to bridge the translational gap between preclinical and clinical drug development to reduce attrition. Substantial effort has focused on the preclinical application of human-based microphysiological systems (MPS) to better identify compounds not likely to be safe or efficacious in the clinic. The Coronavirus 2019 (COVID-19) pandemic provides a clear opportunity for assessing the utility of MPS models of the lungs and other organ systems affected by the disease in understanding the pathophysiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and in the development of effective therapeutics. Here, we review progress and describe the establishment of a global working group to coordinate activities around MPS and COVID-19 and to maximize their scientific, human health, and animal welfare impacts.
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Affiliation(s)
- Nicole Kleinstreuer
- NICEATM, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, RTP, NC, USA
| | - Anthony Holmes
- National Centre for the Replacement, Refinement and Reduction of Animals in Research, London, UK.
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29
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Hamm J, Allen D, Ceger P, Flint T, Lowit A, O'Dell L, Tao J, Kleinstreuer N. Performance of the GHS Mixtures Equation for Predicting Acute Oral Toxicity. Regul Toxicol Pharmacol 2021; 125:105007. [PMID: 34298086 DOI: 10.1016/j.yrtph.2021.105007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 07/01/2021] [Accepted: 07/13/2021] [Indexed: 11/26/2022]
Abstract
Acute oral toxicity classifications are based on the estimated chemical dose causing lethality in 50 % of laboratory animals tested (LD50). Given the large number of pesticide registration applications that require acute toxicity data, an alternative to the in vivo test could greatly reduce animal testing. The United Nations Globally Harmonized System of Classification and Labelling of Chemicals (GHS) Mixtures Equation estimates the acute toxicity of mixtures using the toxicities of mixture components. The goal of this study was to evaluate the concordance of LD50s predicted using the GHS Mixtures Equation and LD50s from the in vivo test results. Using the EPA classification system, concordance was 55 % for the full dataset (N = 671), 52 % for agrochemical formulations (N = 620), and 84 % for antimicrobial cleaning products (N = 51). Most discordant results were from substances LD50 > 2000 mg/kg (limit test) or 2000 < LD50 < 5000 mg/kg that were predicted as LD50 > 5000 mg/kg. A supplementary analysis combining all formulations with an LD50 > 500 mg/kg produced a concordance of 82 %. The lack of more toxic formulations in this dataset prevented a thorough evaluation of the GHS equation for such substances. Accordingly, our results suggest the GHS equation is helpful to predict the toxicity of mixtures, particularly those with lower toxicity.
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Affiliation(s)
- Jon Hamm
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - David Allen
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - Patricia Ceger
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - Tara Flint
- Office of Pesticide Programs, U.S. Environmental Protection Agency, 1200 Pennsylvania Ave, NW, Washington, DC, 20460, USA.
| | - Anna Lowit
- Office of Pesticide Programs, U.S. Environmental Protection Agency, 1200 Pennsylvania Ave, NW, Washington, DC, 20460, USA.
| | - Lindsay O'Dell
- Office of Pesticide Programs, U.S. Environmental Protection Agency, 1200 Pennsylvania Ave, NW, Washington, DC, 20460, USA.
| | - Jenny Tao
- Office of Pesticide Programs, U.S. Environmental Protection Agency, 1200 Pennsylvania Ave, NW, Washington, DC, 20460, USA.
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC, 27709, USA.
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Alves VM, Auerbach SS, Kleinstreuer N, Rooney JP, Muratov EN, Rusyn I, Tropsha A, Schmitt C. Curated Data In - Trustworthy In Silico Models Out: The Impact of Data Quality on the Reliability of Artificial Intelligence Models as Alternatives to Animal Testing. Altern Lab Anim 2021; 49:73-82. [PMID: 34233495 PMCID: PMC8609471 DOI: 10.1177/02611929211029635] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
New Approach Methodologies (NAMs) that employ artificial intelligence (AI) for predicting adverse effects of chemicals have generated optimistic expectations as alternatives to animal testing. However, the major underappreciated challenge in developing robust and predictive AI models is the impact of the quality of the input data on the model accuracy. Indeed, poor data reproducibility and quality have been frequently cited as factors contributing to the crisis in biomedical research, as well as similar shortcomings in the fields of toxicology and chemistry. In this article, we review the most recent efforts to improve confidence in the robustness of toxicological data and investigate the impact that data curation has on the confidence in model predictions. We also present two case studies demonstrating the effect of data curation on the performance of AI models for predicting skin sensitisation and skin irritation. We show that, whereas models generated with uncurated data had a 7-24% higher correct classification rate (CCR), the perceived performance was, in fact, inflated owing to the high number of duplicates in the training set. We assert that data curation is a critical step in building computational models, to help ensure that reliable predictions of chemical toxicity are achieved through use of the models.
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Affiliation(s)
- Vinicius M. Alves
- Office of Data Science, Division of the National Toxicology Program (DNTP), National Institute of Environmental Health Sciences (NIEHS), Durham, NC, USA
| | - Scott S. Auerbach
- Toxinformatics Group, Predictive Toxicology Branch, DNTP, NIEHS, Durham, NC, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Scientific Director's Office, DNTP, NIEHS, Durham, NC, USA
| | - John P. Rooney
- Integrated Laboratory Systems, LLC, Morrisville, NC, USA
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, NC, USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, Paraiba, Brazil
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, NC, USA
| | - Charles Schmitt
- Office of Data Science, Division of the National Toxicology Program (DNTP), National Institute of Environmental Health Sciences (NIEHS), Durham, NC, USA
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31
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Rooney JP, Choksi NY, Ceger P, Daniel AB, Truax J, Allen D, Kleinstreuer N. Analysis of variability in the rabbit skin irritation assay. Regul Toxicol Pharmacol 2021; 122:104920. [PMID: 33757807 DOI: 10.1016/j.yrtph.2021.104920] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/03/2021] [Accepted: 03/16/2021] [Indexed: 10/21/2022]
Abstract
The in vivo rabbit test is the benchmark against which new approach methodologies for skin irritation are usually compared. No alternative method offers a complete replacement of animal use for this endpoint for all regulatory applications. Variability in the animal reference data may be a limiting factor in identifying a replacement. We established a curated data set of 2624 test records, representing 990 substances, each tested at least twice, to characterize the reproducibility of the in vivo assay. Methodological deviations from guidelines were noted, and multiple data sets with differing tolerances for deviations were created. Conditional probabilities were used to evaluate the reproducibility of the in vivo method in identification of U.S. Environmental Protection Agency or Globally Harmonized System hazard categories. Chemicals classified as moderate irritants at least once were classified as mild or non-irritants at least 40% of the time when tested repeatedly. Variability was greatest between mild and moderate irritants, which both had less than a 50% likelihood of being replicated. Increased reproducibility was observed when a binary categorization between corrosives/moderate irritants and mild/non-irritants was used. This analysis indicates that variability present in the rabbit skin irritation test should be considered when evaluating nonanimal alternative methods as potential replacements.
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Affiliation(s)
- John P Rooney
- Integrated Laboratory Systems, LLC, 601 Keystone Park Dr, Suite 800, Morrisville, NC, 27560, USA.
| | - Neepa Y Choksi
- Integrated Laboratory Systems, LLC, 601 Keystone Park Dr, Suite 800, Morrisville, NC, 27560, USA
| | - Patricia Ceger
- Integrated Laboratory Systems, LLC, 601 Keystone Park Dr, Suite 800, Morrisville, NC, 27560, USA
| | - Amber B Daniel
- Integrated Laboratory Systems, LLC, 601 Keystone Park Dr, Suite 800, Morrisville, NC, 27560, USA
| | - James Truax
- Integrated Laboratory Systems, LLC, 601 Keystone Park Dr, Suite 800, Morrisville, NC, 27560, USA
| | - David Allen
- Integrated Laboratory Systems, LLC, 601 Keystone Park Dr, Suite 800, Morrisville, NC, 27560, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institutes of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
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Petersen EJ, Nguyen A, Brown J, Elliott JT, Clippinger A, Gordon J, Kleinstreuer N, Roesslein M. Characteristics to consider when selecting a positive control material for an in vitro assay. ALTEX 2021; 38:365-376. [PMID: 33637998 DOI: 10.14573/altex.2102111] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 02/18/2021] [Indexed: 11/23/2022]
Abstract
The use of in vitro assays to inform decision-making requires robust and reproducible results across studies, laboratories, and time. Experiments using positive control materials are an integral component of an assay procedure to demonstrate the extent to which the measurement system is performing as expected. This paper reviews ten characteristics that should be considered when selecting a positive control material for an in vitro assay: 1) the biological mechanism of action, 2) ease of preparation, 3) chemical purity, 4) verifiable physical properties, 5) stability, 6) ability to generate responses spanning the dynamic range of the assay, 7) technical or biological interference, 8) commercial availability, 9) user toxicity, and 10) disposability. Examples and a case study of the monocyte activation test are provided to demonstrate the application of these characteristics for identification and selection of potential positive control materials. Because specific positive control materials are often written into testing standards for in vitro assays, selection of the positive control material based on these characteristics can aid in ensuring the long-term relevance and usability of these standards.
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Affiliation(s)
- Elijah J Petersen
- Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA
| | - Andrew Nguyen
- PETA Science Consortium International e.V., Stuttgart, Germany
| | - Jeffrey Brown
- PETA Science Consortium International e.V., Stuttgart, Germany
| | - John T Elliott
- Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA
| | - Amy Clippinger
- PETA Science Consortium International e.V., Stuttgart, Germany
| | - John Gordon
- US Consumer Products Safety Commission (CPSC), Rockville, MD, USA
| | - Nicole Kleinstreuer
- National Institute of Environmental Health Sciences (NIEHS), National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, NC, USA
| | - Matthias Roesslein
- Empa, Swiss Federal Laboratories for Material Testing and Research, Particles-Biology Interactions Laboratory, St. Gallen, Switzerland
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Jain S, Siramshetty VB, Alves VM, Muratov EN, Kleinstreuer N, Tropsha A, Nicklaus MC, Simeonov A, Zakharov AV. Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods. J Chem Inf Model 2021; 61:653-663. [PMID: 33533614 DOI: 10.1021/acs.jcim.0c01164] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.
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Affiliation(s)
- Sankalp Jain
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Vishal B Siramshetty
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Vinicius M Alves
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Nicole Kleinstreuer
- Division of Intramural Research, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 T.W. Alexander Drive, Durham, North Carolina 27709, United States.,National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, 111 T.W. Alexander Drive, Durham, North Carolina 27709, United States
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Marc C Nicklaus
- Computer-Aided Drug Design (CADD) Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick, 376 Boyles Street, Frederick, Maryland 21702, United States
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
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Abstract
Cardiovascular (CV) disease is one of the most prevalent public health concerns, and mounting evidence supports the contribution of environmental chemicals to CV disease burden. In this study, we performed cardiotoxicity profiling for the Tox21 chemical library by focusing on high-throughput screening (HTS) assays whose targets are associated with adverse events related to CV failure modes. Our objective was to develop new hypotheses around environmental chemicals of potential interest for adverse CV outcomes using Tox21/ToxCast HTS data. Molecular and cellular events linked to six failure modes of CV toxicity were cross-referenced with 1399 Tox21/ToxCast assays to identify cardio-relevant bioactivity signatures. The resulting 40 targets, measured in 314 assays, were integrated via a ToxPi visualization tool and ranking system to prioritize 1138 chemicals based upon formal integration across multiple domains of information. Filtering was performed based on cytotoxicity and generalized cell stress endpoints to try and isolate chemicals with effects specific to CV biology, and bioactivity- and structure-based clustering identified subgroups of chemicals preferentially affecting targets such as ion channels and vascular tissue biology. Our approach identified drugs with known cardiotoxic effects, such as estrogenic modulators like clomiphene and raloxifene, anti-arrhythmic drugs like amiodarone and haloperidol, and antipsychotic drugs like chlorpromazine. Several classes of environmental chemicals such as organotins, bisphenol-like chemicals, pesticides, and quaternary ammonium compounds demonstrated strong bioactivity against CV targets; these were compared to existing data in the literature (e.g., from cardiomyocytes, animal data, or human epidemiological studies) and prioritized for further testing.
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Affiliation(s)
- Shagun Krishna
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, 530 Davis Drive, Research Triangle Park, North Carolina 27560, United States
| | - Brian Berridge
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, 530 Davis Drive, Research Triangle Park, North Carolina 27560, United States
| | - Nicole Kleinstreuer
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, 530 Davis Drive, Research Triangle Park, North Carolina 27560, United States
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35
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Smith MT, Guyton KZ, Kleinstreuer N, Borrel A, Cardenas A, Chiu WA, Felsher DW, Gibbons CF, Goodson WH, Houck KA, Kane AB, La Merrill MA, Lebrec H, Lowe L, McHale CM, Minocherhomji S, Rieswijk L, Sandy MS, Sone H, Wang A, Zhang L, Zeise L, Fielden M. The Key Characteristics of Carcinogens: Relationship to the Hallmarks of Cancer, Relevant Biomarkers, and Assays to Measure Them. Cancer Epidemiol Biomarkers Prev 2020; 29:1887-1903. [PMID: 32152214 PMCID: PMC7483401 DOI: 10.1158/1055-9965.epi-19-1346] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 01/15/2020] [Accepted: 03/04/2020] [Indexed: 12/21/2022] Open
Abstract
The key characteristics (KC) of human carcinogens provide a uniform approach to evaluating mechanistic evidence in cancer hazard identification. Refinements to the approach were requested by organizations and individuals applying the KCs. We assembled an expert committee with knowledge of carcinogenesis and experience in applying the KCs in cancer hazard identification. We leveraged this expertise and examined the literature to more clearly describe each KC, identify current and emerging assays and in vivo biomarkers that can be used to measure them, and make recommendations for future assay development. We found that the KCs are clearly distinct from the Hallmarks of Cancer, that interrelationships among the KCs can be leveraged to strengthen the KC approach (and an understanding of environmental carcinogenesis), and that the KC approach is applicable to the systematic evaluation of a broad range of potential cancer hazards in vivo and in vitro We identified gaps in coverage of the KCs by current assays. Future efforts should expand the breadth, specificity, and sensitivity of validated assays and biomarkers that can measure the 10 KCs. Refinement of the KC approach will enhance and accelerate carcinogen identification, a first step in cancer prevention.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."
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Affiliation(s)
- Martyn T Smith
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California.
| | - Kathryn Z Guyton
- Monographs Programme, International Agency for Research on Cancer, Lyon, France
| | - Nicole Kleinstreuer
- Division of Intramural Research, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
| | - Alexandre Borrel
- Division of Intramural Research, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina
| | - Andres Cardenas
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California
| | - Weihsueh A Chiu
- Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas
| | - Dean W Felsher
- Division of Oncology, Departments of Medicine and Pathology, Stanford University School of Medicine, Stanford, California
| | - Catherine F Gibbons
- Office of Research and Development, US Environmental Protection Agency, Washington, D.C
| | - William H Goodson
- California Pacific Medical Center Research Institute, San Francisco, California
| | - Keith A Houck
- Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Agnes B Kane
- Department of Pathology and Laboratory Medicine, Alpert Medical School, Brown University, Providence, Rhode Island
| | - Michele A La Merrill
- Department of Environmental Toxicology, University of California, Davis, California
| | - Herve Lebrec
- Comparative Biology & Safety Sciences, Amgen Research, Amgen Inc., Thousand Oaks, California
| | - Leroy Lowe
- Getting to Know Cancer, Truro, Nova Scotia, Canada
| | - Cliona M McHale
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California
| | - Sheroy Minocherhomji
- Comparative Biology & Safety Sciences, Amgen Research, Amgen Inc., Thousand Oaks, California
| | - Linda Rieswijk
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California
- Institute of Data Science, Maastricht University, Maastricht, the Netherlands
| | - Martha S Sandy
- Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, California
| | - Hideko Sone
- Yokohama University of Pharmacy and National Institute for Environmental Studies, Tsukuba Ibaraki, Japan
| | - Amy Wang
- Office of the Report on Carcinogens, Division of National Toxicology Program, The National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
| | - Luoping Zhang
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California
| | - Lauren Zeise
- Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, California
| | - Mark Fielden
- Expansion Therapeutics Inc, San Diego, California
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Bell S, Abedini J, Ceger P, Chang X, Cook B, Karmaus AL, Lea I, Mansouri K, Phillips J, McAfee E, Rai R, Rooney J, Sprankle C, Tandon A, Allen D, Casey W, Kleinstreuer N. An integrated chemical environment with tools for chemical safety testing. Toxicol In Vitro 2020; 67:104916. [PMID: 32553663 PMCID: PMC7393692 DOI: 10.1016/j.tiv.2020.104916] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 05/29/2020] [Accepted: 06/10/2020] [Indexed: 12/27/2022]
Abstract
Moving toward species-relevant chemical safety assessments and away from animal testing requires access to reliable data to develop and build confidence in new approaches. The Integrated Chemical Environment (ICE) provides tools and curated data centered around chemical safety assessment. This article describes updates to ICE, including improved accessibility and interpretability of in vitro data via mechanistic target mapping and enhanced interactive tools for in vitro to in vivo extrapolation (IVIVE). Mapping of in vitro assay targets to toxicity endpoints of regulatory importance uses literature-based mode-of-action information and controlled terminology from existing knowledge organization systems to support data interoperability with external resources. The most recent ICE update includes Tox21 high-throughput screening data curated using analytical chemistry data and assay-specific parameters to eliminate potential artifacts or unreliable activity. Also included are physicochemical/ADME parameters for over 800,000 chemicals predicted by quantitative structure-activity relationship models. These parameters are used by the new ICE IVIVE tool in combination with the U.S. Environmental Protection Agency's httk R package to estimate in vivo exposures corresponding to in vitro bioactivity concentrations from stored or user-defined assay data. These new ICE features allow users to explore the applications of an expanded data space and facilitate building confidence in non-animal approaches.
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Affiliation(s)
- Shannon Bell
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Jaleh Abedini
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Patricia Ceger
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Xiaoqing Chang
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Bethany Cook
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Agnes L Karmaus
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Isabel Lea
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Kamel Mansouri
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Jason Phillips
- Sciome LLC, 2 Davis Dr., Research Triangle Park, NC 27709, USA.
| | - Eric McAfee
- Sciome LLC, 2 Davis Dr., Research Triangle Park, NC 27709, USA.
| | - Ruhi Rai
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - John Rooney
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Catherine Sprankle
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Arpit Tandon
- Sciome LLC, 2 Davis Dr., Research Triangle Park, NC 27709, USA.
| | - David Allen
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Warren Casey
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
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37
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Judson R, Houck K, Paul Friedman K, Brown J, Browne P, Johnston PA, Close DA, Mansouri K, Kleinstreuer N. Selecting a minimal set of androgen receptor assays for screening chemicals. Regul Toxicol Pharmacol 2020; 117:104764. [PMID: 32798611 PMCID: PMC8356084 DOI: 10.1016/j.yrtph.2020.104764] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/03/2020] [Accepted: 08/07/2020] [Indexed: 01/01/2023]
Abstract
Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modeling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. Agonist batteries of as few as six assays and antagonist batteries of as few as five assays can yield balanced accuracies of 95% or better relative to the full model. Balanced accuracy for predicting reference chemicals is 100%. An approach is outlined for researchers to develop their own subset batteries to accurately detect AR activity using assays that map to the pathway of key molecular and cellular events involved in chemical-mediated AR activation and transcriptional activity. This work indicates in vitro bioactivity and in silico predictions that map to the AR pathway could be used in an integrated approach to testing and assessment for identifying chemicals that interact directly with the mammalian AR.
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Affiliation(s)
| | - Keith Houck
- U.S. Environmental Protection Agency, RTP, NC, USA
| | | | - Jason Brown
- U.S. Environmental Protection Agency, RTP, NC, USA
| | | | - Paul A Johnston
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | - David A Close
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kamel Mansouri
- Integrated Laboratory Systems, Inc., Morrisville, NC, USA
| | - Nicole Kleinstreuer
- NTP Interagency Center for the Evaluation of Alternative Toxicological Methods, RTP, NC, USA
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38
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Borba JVB, Braga RC, Alves VM, Muratov EN, Kleinstreuer N, Tropsha A, Andrade CH. Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers. Chem Res Toxicol 2020; 34:258-267. [PMID: 32673477 DOI: 10.1021/acs.chemrestox.0c00186] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay, and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure-activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.
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Affiliation(s)
- Joyce V B Borba
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil.,Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | | | - Vinicius M Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.,Department of Pharmaceutical Sciences, Federal University of Paraíba, João Pessoa, Paraíba 58059, Brazil
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil
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39
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Comess S, Akbay A, Vasiliou M, Hines RN, Joppa L, Vasiliou V, Kleinstreuer N. Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations. Front Artif Intell 2020; 3. [PMID: 33184612 PMCID: PMC7654840 DOI: 10.3389/frai.2020.00031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Understanding the role that the environment plays in influencing public health often involves collecting and studying large, complex data sets. There have been a number of private and public efforts to gather sufficient information and confront significant unknowns in the field of environmental public health, yet there is a persistent and largely unmet need for findable, accessible, interoperable, and reusable (FAIR) data. Even when data are readily available, the ability to create, analyze, and draw conclusions from these data using emerging computational tools, such as augmented and artificial inteligence (AI) and machine learning, requires technical skills not currently implemented on a programmatic level across research hubs and academic institutions. We argue that collaborative efforts in data curation and storage, scientific computing, and training are of paramount importance to empower researchers within environmental sciences and the broader public health community to apply AI approaches and fully realize their potential. Leaders in the field were asked to prioritize challenges in incorporating big data in environmental public health research: inconsistent implementation of FAIR principles in data collection and sharing, a lack of skilled data scientists and appropriate cyber-infrastructures, and limited understanding of possibilities and communication of benefits were among those identified. These issues are discussed, and actionable recommendations are provided.
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Affiliation(s)
- Saskia Comess
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States.,Department of Statistics and Data Science, Yale University, New Haven, CT, United States
| | - Alexia Akbay
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States.,Symbrosia Inc, Kailua-Kona, HI, United States
| | - Melpomene Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States
| | - Ronald N Hines
- US Environmental Protection Agency, Center for Public Health and Environmental Assessment, Research Triangle Park, NC, United States
| | - Lucas Joppa
- Microsoft Corporation, AI for Earth, Redmond, WA, United States
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States
| | - Nicole Kleinstreuer
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States.,National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States
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40
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Rovida C, Barton-Maclaren T, Benfenati E, Caloni F, Chandrasekera PC, Chesné C, Cronin MTD, De Knecht J, Dietrich DR, Escher SE, Fitzpatrick S, Flannery B, Herzler M, Hougaard Bennekou S, Hubesch B, Kamp H, Kisitu J, Kleinstreuer N, Kovarich S, Leist M, Maertens A, Nugent K, Pallocca G, Pastor M, Patlewicz G, Pavan M, Presgrave O, Smirnova L, Schwarz M, Yamada T, Hartung T. Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology. ALTEX 2020; 37:579-606. [PMID: 32369604 PMCID: PMC9201788 DOI: 10.14573/altex.1912181] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 04/28/2020] [Indexed: 11/23/2022]
Abstract
Read-across (RAx) translates available information from well-characterized chemicals to a substance for which there is a toxicological data gap. The OECD is working on case studies to probe general applicability of RAx, and several regulations (e.g., EU-REACH) already allow this procedure to be used to waive new in vivo tests. The decision to prepare a review on the state of the art of RAx as a tool for risk assessment for regulatory purposes was taken during a workshop with international experts in Ranco, Italy in July 2018. Three major issues were identified that need optimization to allow a higher regulatory acceptance rate of the RAx procedure: (i) the definition of similarity of source and target, (ii) the translation of biological/toxicological activity of source to target in the RAx procedure, and (iii) how to deal with issues of ADME that may differ between source and target. The use of new approach methodologies (NAM) was discussed as one of the most important innovations to improve the acceptability of RAx. At present, NAM data may be used to confirm chemical and toxicological similarity. In the future, the use of NAM may be broadened to fully characterize the hazard and toxicokinetic properties of RAx compounds. Concerning available guidance, documents on Good Read-Across Practice (GRAP) and on best practices to perform and evaluate the RAx process were identified. Here, in particular, the RAx guidance, being worked out by the European Commission’s H2020 project EU-ToxRisk together with many external partners with regulatory experience, is given.
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Affiliation(s)
- Costanza Rovida
- Center for Alternatives to Animal Testing, CAAT-Europe, University of Konstanz, Konstanz, Germany
| | | | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Francesca Caloni
- Università degli Studi di Milano, Department of Veterinary Medicine (DIMEVET) Milan, Italy
| | | | | | - Mark T D Cronin
- Liverpool John Moores University, School of Pharmacy and Biomolecular Sciences, Liverpool, UK
| | - Joop De Knecht
- Centre for Safety of Substances and Products, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Daniel R Dietrich
- Human and Environmental Toxicology, University of Konstanz, Konstanz, Germany
| | - Sylvia E Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Hannover, Germany
| | - Suzanne Fitzpatrick
- US Food and Drug Administration, Center for Food Safety and Applied Nutrition, MD, USA
| | - Brenna Flannery
- US Food and Drug Administration, Center for Food Safety and Applied Nutrition, MD, USA
| | - Matthias Herzler
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Susanne Hougaard Bennekou
- Danish Environmental Protection Agency, Copenhagen, Denmark / Danish Technical University, FOOD, Lyngby, Denmark
| | - Bruno Hubesch
- European Chemical Industry Council (Cefic), Brussels, Belgium
| | - Hennicke Kamp
- Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany
| | - Jaffar Kisitu
- In vitro Toxicology and Biomedicine, Dept inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Konstanz, Germany
| | - Nicole Kleinstreuer
- NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States
| | | | - Marcel Leist
- Center for Alternatives to Animal Testing, CAAT-Europe, University of Konstanz, Konstanz, Germany.,In vitro Toxicology and Biomedicine, Dept inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Konstanz, Germany
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, MD, USA
| | - Kerry Nugent
- Australian Government Department of Health, Canberra, Australia
| | - Giorgia Pallocca
- Center for Alternatives to Animal Testing, CAAT-Europe, University of Konstanz, Konstanz, Germany
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Grace Patlewicz
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | | | - Octavio Presgrave
- Departamento de Farmacologia e Toxicologia, Instituto Nacional de Controle da Qualidade em Saúde, Fundação Oswaldo Cruz (Fiocruz), Rio de Janeiro, Brazil
| | - Lena Smirnova
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, MD, USA
| | | | | | - Thomas Hartung
- Center for Alternatives to Animal Testing, CAAT-Europe, University of Konstanz, Konstanz, Germany.,Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, MD, USA
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41
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Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, Farag S, Fourches D, García-Sosa AT, Gramatica P, Grisoni F, Grulke CM, Hong H, Horvath D, Hu X, Huang R, Jeliazkova N, Li J, Li X, Liu H, Manganelli S, Mangiatordi GF, Maran U, Marcou G, Martin T, Muratov E, Nguyen DT, Nicolotti O, Nikolov NG, Norinder U, Papa E, Petitjean M, Piir G, Pogodin P, Poroikov V, Qiao X, Richard AM, Roncaglioni A, Ruiz P, Rupakheti C, Sakkiah S, Sangion A, Schramm KW, Selvaraj C, Shah I, Sild S, Sun L, Taboureau O, Tang Y, Tetko IV, Todeschini R, Tong W, Trisciuzzi D, Tropsha A, Van Den Driessche G, Varnek A, Wang Z, Wedebye EB, Williams AJ, Xie H, Zakharov AV, Zheng Z, Judson RS. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. Environ Health Perspect 2020; 128:27002. [PMID: 32074470 DOI: 10.23645/epacomptox.5176876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
BACKGROUND Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.
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Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
- ScitoVation LLC, Research Triangle Park, North Carolina, USA
- Integrated Laboratory Systems, Inc., Morrisville, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Ahmed M Abdelaziz
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Domenico Alberga
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Vinicius M Alves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Carolina H Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
| | - Fang Bai
- School of Pharmacy, Lanzhou University, China
| | - Ilya Balabin
- Information Systems & Global Solutions (IS&GS), Lockheed Martin, USA
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche "Mario Negri", IRCCS, Milan, Italy
| | - Barun Bhhatarai
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Scott Boyer
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Jingwen Chen
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Chris M Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Dragos Horvath
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Xin Hu
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Jiazhong Li
- School of Pharmacy, Lanzhou University, China
| | - Xuehua Li
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | | | - Serena Manganelli
- Istituto di Ricerche Farmacologiche "Mario Negri", IRCCS, Milan, Italy
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Todd Martin
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Nikolai G Nikolov
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Ulf Norinder
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Michel Petitjean
- Computational Modeling of Protein-Ligand Interactions (CMPLI)-INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Pavel Pogodin
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Xianliang Qiao
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Ann M Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | | | - Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Chetan Rupakheti
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
- Department of Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Alessandro Sangion
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Karl-Werner Schramm
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Lixia Sun
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Olivier Taboureau
- Computational Modeling of Protein-Ligand Interactions (CMPLI)-INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Yun Tang
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Igor V Tetko
- BIGCHEM GmbH, Neuherberg, Germany
- Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Zhongyu Wang
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Eva B Wedebye
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Hongbin Xie
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ziye Zheng
- Chemistry Department, Umeå University, Umeå, Sweden
| | - Richard S Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
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Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, Farag S, Fourches D, García-Sosa AT, Gramatica P, Grisoni F, Grulke CM, Hong H, Horvath D, Hu X, Huang R, Jeliazkova N, Li J, Li X, Liu H, Manganelli S, Mangiatordi GF, Maran U, Marcou G, Martin T, Muratov E, Nguyen DT, Nicolotti O, Nikolov NG, Norinder U, Papa E, Petitjean M, Piir G, Pogodin P, Poroikov V, Qiao X, Richard AM, Roncaglioni A, Ruiz P, Rupakheti C, Sakkiah S, Sangion A, Schramm KW, Selvaraj C, Shah I, Sild S, Sun L, Taboureau O, Tang Y, Tetko IV, Todeschini R, Tong W, Trisciuzzi D, Tropsha A, Van Den Driessche G, Varnek A, Wang Z, Wedebye EB, Williams AJ, Xie H, Zakharov AV, Zheng Z, Judson RS. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. Environ Health Perspect 2020; 128:27002. [PMID: 32074470 PMCID: PMC7064318 DOI: 10.1289/ehp5580] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/27/2019] [Accepted: 12/05/2019] [Indexed: 05/04/2023]
Abstract
BACKGROUND Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.
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Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
- ScitoVation LLC, Research Triangle Park, North Carolina, USA
- Integrated Laboratory Systems, Inc., Morrisville, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Ahmed M. Abdelaziz
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Domenico Alberga
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
| | - Fang Bai
- School of Pharmacy, Lanzhou University, China
| | - Ilya Balabin
- Information Systems & Global Solutions (IS&GS), Lockheed Martin, USA
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche “Mario Negri”, IRCCS, Milan, Italy
| | - Barun Bhhatarai
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Scott Boyer
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Jingwen Chen
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Chris M. Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Dragos Horvath
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Xin Hu
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Jiazhong Li
- School of Pharmacy, Lanzhou University, China
| | - Xuehua Li
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | | | - Serena Manganelli
- Istituto di Ricerche Farmacologiche “Mario Negri”, IRCCS, Milan, Italy
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Todd Martin
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Nikolai G. Nikolov
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Ulf Norinder
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Michel Petitjean
- Computational Modeling of Protein-Ligand Interactions (CMPLI)–INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Pavel Pogodin
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Xianliang Qiao
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Ann M. Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | | | - Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Chetan Rupakheti
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
- Department of Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Alessandro Sangion
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Karl-Werner Schramm
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Lixia Sun
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Olivier Taboureau
- Computational Modeling of Protein-Ligand Interactions (CMPLI)–INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Yun Tang
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Igor V. Tetko
- BIGCHEM GmbH, Neuherberg, Germany
- Helmholtz Zentrum Muenchen – German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Zhongyu Wang
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Eva B. Wedebye
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Antony J. Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Hongbin Xie
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ziye Zheng
- Chemistry Department, Umeå University, Umeå, Sweden
| | - Richard S. Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
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43
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Kojima H, Yamaguchi H, Sozu T, Kleinstreuer N, Chae-Hyung L, Chen W, Watanabe M, Fukuda T, Yamashita K, Takezawa T. Multi-laboratory Validation Study of the Vitrigel-Eye Irritancy Test Method as an Alternative to In Vivo Eye Irritation Testing. Altern Lab Anim 2019; 47:140-157. [DOI: 10.1177/0261192919886665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Collagen vitrigel membranes (CVMs) comprising high-density collagen fibrils equivalent to in vivo connective tissues have been widely used in cell culture applications. A human corneal epithelium (hCE) model was previously developed by the Takezawa group, by culturing HCE-T cells (derived from hCE cells) on a CVM scaffold in a chamber that provided an air–liquid interface culture system. This hCE model was used to establish a new test method, known as the Vitrigel-Eye Irritancy Test (Vitrigel-EIT) method, which can be used to estimate the ocular irritation potential of test chemicals by analysing relative changes in transepithelial electrical resistance (TEER) over time. The current study was conducted in order to assess the reliability and relevance of the Vitrigel-EIT method at three participating laboratories by determining the method’s within-laboratory reproducibility and between-laboratory reproducibility, as well as its capacity for distinguishing non-irritants from irritants in a bottom-up approach. The initial test sample size was found to be too low to evaluate the predictive capacity of the test method, and so it was evaluated with additional in-house data for a total of 93 test chemicals. The results showed 80–100% within-laboratory reproducibility and an excellent between-laboratory reproducibility that met the acceptance criteria of 80%. However, the method’s predictive capacity for distinguishing non-irritants (test chemicals not requiring classification and labelling for eye irritation or serious eye damage, i.e. United Nations Globally Harmonised System of Classification and Labelling of Chemicals (GHS) No Category) from irritants (GHS Categories 1 and 2) in a bottom-up approach was unacceptable because of false negative rates as high as 16.7%. After considerable review of the data with a view to using the method for regulatory purposes, it was determined that a more defined applicability domain, excluding test chemical solutions with a pH of 5 or less and solid test chemicals, improved the false negative rate to 4.2%. These results suggested that, within this carefully defined applicability domain, the Vitrigel-EIT method could be a useful alternative for distinguishing test chemicals that are ocular non-irritants from those that are irritants as part of a bottom-up approach.
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Affiliation(s)
- Hajime Kojima
- Japanese Center for the Validation of Alternative Methods (JaCVAM), National Institute of Health Sciences (NIHS), Kawasaki, Kanagawa, Japan
| | | | - Takashi Sozu
- Tokyo University of Science, Katsushika-ku, Tokyo, Japan
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM)/Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), Research Triangle Park, NC, USA
| | - Lim Chae-Hyung
- Korean Center for the Validation of Alternative Methods (KoCVAM), National Institute of Food and Drug Safety Evaluation, Osong-eup, Chungcheongbuk-do, South Korea
| | - Wannhsin Chen
- Industrial Technology Research Institute, (ITRI), Hsinchu, Taiwan
| | - Mika Watanabe
- Hatano Research Institute, Food and Drug Safety Center (FDSC), Hadano, Kanagawa, Japan
| | | | | | - Toshiaki Takezawa
- Institute of Agrobiological Sciences (NIAS), National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
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44
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Furman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, Ferrucci L, Gilroy DW, Fasano A, Miller GW, Miller AH, Mantovani A, Weyand CM, Barzilai N, Goronzy JJ, Rando TA, Effros RB, Lucia A, Kleinstreuer N, Slavich GM. Chronic inflammation in the etiology of disease across the life span. Nat Med 2019; 25:1822-1832. [PMID: 31806905 DOI: 10.1038/s41591-019-0675-0] [Citation(s) in RCA: 1881] [Impact Index Per Article: 376.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 10/31/2019] [Indexed: 02/06/2023]
Abstract
Although intermittent increases in inflammation are critical for survival during physical injury and infection, recent research has revealed that certain social, environmental and lifestyle factors can promote systemic chronic inflammation (SCI) that can, in turn, lead to several diseases that collectively represent the leading causes of disability and mortality worldwide, such as cardiovascular disease, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease and autoimmune and neurodegenerative disorders. In the present Perspective we describe the multi-level mechanisms underlying SCI and several risk factors that promote this health-damaging phenotype, including infections, physical inactivity, poor diet, environmental and industrial toxicants and psychological stress. Furthermore, we suggest potential strategies for advancing the early diagnosis, prevention and treatment of SCI.
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Affiliation(s)
- David Furman
- Buck Institute for Research on Aging, Novato, CA, USA. .,Stanford 1000 Immunomes Project, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA. .,Institute for Research in Translational Medicine, Universidad Austral, CONICET, Pilar, Buenos Aires, Argentina. .,Iuve Inc., San Mateo, CA, USA.
| | - Judith Campisi
- Buck Institute for Research on Aging, Novato, CA, USA.,Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Eric Verdin
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Pedro Carrera-Bastos
- Center for Primary Health Care Research, Lund University/Region Skåne, Skåne University Hospital, Malmö, Sweden
| | - Sasha Targ
- Iuve Inc., San Mateo, CA, USA.,Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA
| | - Claudio Franceschi
- IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy.,Department of Applied Mathematics and Laboratory of Systems Biology of Aging, Lobachevsky University, Nizhny Novgorod, Russia
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Derek W Gilroy
- Centre for Clinical Pharmacology and Therapeutics, Division of Medicine, University College London, London, UK
| | - Alessio Fasano
- MassGeneral Hospital for Children, Harvard Medical School, Boston, MA, USA
| | - Gary W Miller
- Department of Environmental Health Sciences, School of Public Health, Columbia University Medical Center, New York, NY, USA
| | - Andrew H Miller
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Alberto Mantovani
- Humanitas Clinical and Research Center, Rozzano, Milan, Italy.,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.,William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary University, London, UK
| | - Cornelia M Weyand
- Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Nir Barzilai
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Jorg J Goronzy
- Paul F. Glenn Center for the Biology of Aging, Stanford University School of Medicine, Stanford, CA, USA
| | - Thomas A Rando
- Paul F. Glenn Center for the Biology of Aging, Stanford University School of Medicine, Stanford, CA, USA.,Center for Tissue Regeneration, Repair and Restoration, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.,Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Rita B Effros
- Department of Pathology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Alejandro Lucia
- Faculty of Sport Sciences, Universidad Europea de Madrid, Madrid, Spain.,Research Institute of the Hospital 12 de Octubre (i+12), Madrid, Spain
| | - Nicole Kleinstreuer
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA.,NTP Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - George M Slavich
- Cousins Center for Psychoneuroimmunology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
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45
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Rooney JP, Chorley B, Kleinstreuer N, Corton JC. Identification of Androgen Receptor Modulators in a Prostate Cancer Cell Line Microarray Compendium. Toxicol Sci 2019; 166:146-162. [PMID: 30085300 DOI: 10.1093/toxsci/kfy187] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
High-throughput transcriptomic (HTTr) technologies are increasingly being used to screen environmental chemicals in vitro to identify molecular targets and provide mechanistic context for regulatory testing. Here, we describe the development and validation of a novel gene expression biomarker to identify androgen receptor (AR)-modulating chemicals using a pattern matching method. Androgen receptor biomarker genes were identified by their consistent expression after exposure to 4 AR agonists and 4 AR antagonists and included only those genes that were regulated by AR. The 51 gene biomarker was evaluated as a predictive tool using the fold-change, rank-based Running Fisher algorithm. Using 158 comparisons from cells treated with 95 chemicals, the biomarker gave balanced accuracies for prediction of AR activation or AR suppression of 97% or 98%, respectively. The biomarker correctly classified 16 out of the 17 AR reference antagonists including those that are "weak" and "very weak". Predictions based on microarray profiles from AR-positive LAPC-4 cells treated with 28 chemicals in antagonist mode were compared with those from an AR pathway model which used 11 in vitro HT assays. The balanced accuracy for suppression was 93%. Using our approach, we identified conditions in which AR was modulated in a large collection of microarray profiles from prostate cancer cell lines including (1) constitutively active mutants or knockdown of AR, (2) decreases in availability of androgens by castration or removal from media, and (3) exposure to chemical modulators that work through indirect mechanisms including suppression of AR expression. These results demonstrate that the AR gene expression biomarker could be a useful tool in HTTr to identify AR modulators.
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Affiliation(s)
- John P Rooney
- Oak Ridge Institute for Science and Education (ORISE), Research Triangle Park, North Carolina 27711.,Integrated Systems Toxicology Division, US-EPA, Research Triangle Park, North Carolina 27711
| | - Brian Chorley
- Integrated Systems Toxicology Division, US-EPA, Research Triangle Park, North Carolina 27711
| | - Nicole Kleinstreuer
- NTP Interagency Center for the Evaluation of Alternative Toxicological Methods, NTP, NIEHS, NIH, DHHS, Research Triangle Park, North Carolina
| | - J Christopher Corton
- Integrated Systems Toxicology Division, US-EPA, Research Triangle Park, North Carolina 27711
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46
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Solecki R, Rauch M, Gall A, Buschmann J, Kellner R, Kucheryavenko O, Schmitt A, Delrue N, Li W, Hu J, Fujiwara M, Kuwagata M, Mantovani A, Makris SL, Paumgartten F, Schönfelder G, Schneider S, Vogl S, Kleinstreuer N, Schneider M, Schulze F, Fritsche E, Clark R, Shiota K, Chahoud I. Update of the DevTox data database for harmonized risk assessment and alternative methodologies in developmental toxicology: Report of the 9th Berlin Workshop on Developmental Toxicity. Reprod Toxicol 2019; 89:124-129. [DOI: 10.1016/j.reprotox.2019.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/07/2019] [Accepted: 07/02/2019] [Indexed: 01/24/2023]
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47
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Patlewicz G, Lizarraga LE, Rua D, Allen DG, Daniel AB, Fitzpatrick SC, Garcia-Reyero N, Gordon J, Hakkinen P, Howard AS, Karmaus A, Matheson J, Mumtaz M, Richarz AN, Ruiz P, Scarano L, Yamada T, Kleinstreuer N. Exploring current read-across applications and needs among selected U.S. Federal Agencies. Regul Toxicol Pharmacol 2019; 106:197-209. [PMID: 31078681 PMCID: PMC6814248 DOI: 10.1016/j.yrtph.2019.05.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 04/27/2019] [Accepted: 05/08/2019] [Indexed: 10/26/2022]
Abstract
Read-across is a well-established data gap-filling technique applied for regulatory purposes. In US Environmental Protection Agency's New Chemicals Program under TSCA, read-across has been used extensively for decades, however the extent of application and acceptance of read-across among U.S. federal agencies is less clear. In an effort to build read-across capacity, raise awareness of the state of the science, and work towards a harmonization of read-across approaches across U.S. agencies, a new read-across workgroup was established under the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM). This is one of several ad hoc groups ICCVAM has convened to implement the ICCVAM Strategic Roadmap. In this article, we outline the charge and scope of the workgroup and summarize the current applications, tools used, and needs of the agencies represented on the workgroup for read-across. Of the agencies surveyed, the Environmental Protection Agency had the greatest experience in using read-across whereas other agencies indicated that they would benefit from gaining a perspective of the landscape of the tools and available guidance. Two practical case studies are also described to illustrate how the read-across approaches applied by two agencies vary on account of decision context.
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Affiliation(s)
- Grace Patlewicz
- (a)National Center for Computational Toxicology, U.S. Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC, 27709, USA.
| | - Lucina E Lizarraga
- (b)National Center for Environmental Assessment, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, OH, 45268, USA
| | - Diego Rua
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - David G Allen
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - Amber B Daniel
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - Suzanne C Fitzpatrick
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5100 Paint Branch Parkway, College Park, MD, 20740, USA
| | - Natàlia Garcia-Reyero
- Environmental Laboratory, U.S. Army Engineer Research and Developmental Center, 3909 Halls Ferry Rd., Vicksburg, MS, 39180, USA
| | - John Gordon
- U.S. Consumer Product Safety Commission, 5 Research Place, Rockville, MD, 20850, USA
| | - Pertti Hakkinen
- National Library of Medicine, 6707 Democracy Blvd., Bethesda, MD, 20892, USA
| | | | - Agnes Karmaus
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - Joanna Matheson
- U.S. Consumer Product Safety Commission, 5 Research Place, Rockville, MD, 20850, USA
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, 1600 Clifton Rd., Chamblee, GA, 30341, USA
| | | | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, 1600 Clifton Rd., Chamblee, GA, 30341, USA
| | - Louis Scarano
- Office of Pollution Prevention and Toxics, U.S. Environmental Protection Agency, 1200 Pennsylvania Ave. NW, Washington, DC, 20460, USA
| | - Takashi Yamada
- Division of Risk Assessment, Biological Safety Research Center, National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa, 210-9501, Japan
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC, 27709, USA
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48
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Burgdorf T, Piersma AH, Landsiedel R, Clewell R, Kleinstreuer N, Oelgeschläger M, Desprez B, Kienhuis A, Bos P, de Vries R, de Wit L, Seidle T, Scheel J, Schönfelder G, van Benthem J, Vinggaard AM, Eskes C, Ezendam J. Workshop on the validation and regulatory acceptance of innovative 3R approaches in regulatory toxicology - Evolution versus revolution. Toxicol In Vitro 2019; 59:1-11. [PMID: 30946968 DOI: 10.1016/j.tiv.2019.03.039] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 03/28/2019] [Accepted: 03/28/2019] [Indexed: 12/21/2022]
Abstract
At a joint workshop organized by RIVM and BfR, international experts from governmental institutes, regulatory agencies, industry, academia and animal welfare organizations discussed and provided recommendations for the development, validation and implementation of innovative 3R approaches in regulatory toxicology. In particular, an evolutionary improvement of our current approach of test method validation in the context of defined approaches or integrated testing strategies was discussed together with a revolutionary approach based on a comprehensive description of the physiological responses of the human body to chemical exposure and the subsequent definition of relevant and predictive in vitro, in chemico or in silico methods. A more comprehensive evaluation of biological relevance, scientific validity and regulatory purpose of new test methods and assessment strategies together with case studies that provide practical experience with new approaches were discussed as essential steps to build up the necessary confidence to facilitate regulatory acceptance.
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Affiliation(s)
- T Burgdorf
- German Centre for the Protection of Laboratory Animals (Bf3R), German Federal Institute for Risk Assessment, Berlin, Germany
| | - A H Piersma
- National Institute for Public Health and the Environment (RIVM), Center for Health Protection, Bilthoven, Netherlands; Institute for Risk Assessment Sciences, Utrecht University, Netherlands
| | | | - R Clewell
- 21(st) Century Tox Consulting, Chapel Hill, NC 27515, USA
| | | | - M Oelgeschläger
- German Centre for the Protection of Laboratory Animals (Bf3R), German Federal Institute for Risk Assessment, Berlin, Germany.
| | | | - A Kienhuis
- National Institute for Public Health and the Environment (RIVM), Center for Health Protection, Bilthoven, Netherlands
| | - P Bos
- National Institute for Public Health and the Environment (RIVM), Centre for Nutrition, Prevention and Health Services, Bilthoven, Netherlands
| | - R de Vries
- Evidence-based Toxicology Collaboration, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA & SYRCLE, Department for Health Evidence, Radboud Institute for Health Sciences, Radboudumc, Nijmegen, the Netherlands
| | - L de Wit
- National Institute for Public Health and the Environment (RIVM), Centre for Nutrition, Prevention and Health Services, Bilthoven, Netherlands
| | - T Seidle
- Humane Society International, Toronto, Canada
| | - J Scheel
- Evonik Performance Materials GmbH, Darmstadt, Germany
| | - G Schönfelder
- German Centre for the Protection of Laboratory Animals (Bf3R), German Federal Institute for Risk Assessment, Berlin, Germany; Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
| | - J van Benthem
- National Institute for Public Health and the Environment (RIVM), Center for Health Protection, Bilthoven, Netherlands
| | - A M Vinggaard
- National Food Institute, Technical University of Denmark, Kemitorvet building 202, DK-2800 Kgs.Lyngby, Denmark
| | - C Eskes
- Swiss 3R Competence Centre (3RCC), Switzerland
| | - J Ezendam
- National Institute for Public Health and the Environment (RIVM), Center for Health Protection, Bilthoven, Netherlands
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Strickland J, Daniel AB, Allen D, Aguila C, Ahir S, Bancos S, Craig E, Germolec D, Ghosh C, Hudson NL, Jacobs A, Lehmann DM, Matheson J, Reinke EN, Sadrieh N, Vukmanovic S, Kleinstreuer N. Skin sensitization testing needs and data uses by US regulatory and research agencies. Arch Toxicol 2018; 93:273-291. [PMID: 30377734 DOI: 10.1007/s00204-018-2341-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 10/23/2018] [Indexed: 12/16/2022]
Abstract
United States regulatory and research agencies may rely upon skin sensitization test data to assess the sensitization hazards associated with dermal exposure to chemicals and products. These data are evaluated to ensure that such substances will not cause unreasonable adverse effects to human health when used appropriately. The US Consumer Product Safety Commission, the US Environmental Protection Agency, the US Food and Drug Administration, the Occupational Safety and Health Administration, the National Institute for Occupational Safety and Health, and the US Department of Defense are member agencies of the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM). ICCVAM seeks to identify opportunities for the use of non-animal replacements to satisfy these testing needs and requirements. This review identifies the standards, test guidelines, or guidance documents that are applicable to satisfy each of these agency's needs; the current use of animal testing and flexibility for using alternative methodologies; information needed from alternative tests to fulfill the needs for skin sensitization data; and whether data from non-animal alternative approaches are accepted by these US federal agencies.
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Affiliation(s)
- Judy Strickland
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA.
| | - Amber B Daniel
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - David Allen
- ILS, P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - Cecilia Aguila
- Center for Veterinary Medicine, US Food and Drug Administration (FDA), HFV-153, 7500 Standish Place, Rockville, MD, 20855, USA
| | - Surender Ahir
- US Occupational Safety and Health Administration, 200 Constitution Ave. NW, Washington, DC, 20210, USA
| | - Simona Bancos
- Center for Devices and Radiological Health, FDA, White Oak Office Building 66, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Evisabel Craig
- Office of Pesticide Programs, US Environmental Protection Agency, 1200 Pennsylvania Ave. NW, Washington, DC, 20460, USA
| | - Dori Germolec
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC, 27709, USA
| | - Chandramallika Ghosh
- Center for Devices and Radiological Health, FDA, White Oak Office Building 66, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Naomi L Hudson
- National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, 1150 Tusculum Ave, Cincinnati, OH, 45226, USA
| | - Abigail Jacobs
- Center for Drug Evaluation and Research, FDA, White Oak Office Building 22, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - David M Lehmann
- Office of Research and Development, US Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Joanna Matheson
- US Consumer Product Safety Commission, 5 Research Place, Rockville, MD, 20850, USA
| | - Emily N Reinke
- US Army Public Health Center, 5158 Blackhawk Rd, Aberdeen Proving Ground, MD, 21010, USA
| | - Nakissa Sadrieh
- Center for Food Safety and Applied Nutrition, FDA, Harvey W. Wiley Building, 5100 Paint Branch Parkway, College Park, MD, 20740, USA
| | - Stanislav Vukmanovic
- Center for Food Safety and Applied Nutrition, FDA, Harvey W. Wiley Building, 5100 Paint Branch Parkway, College Park, MD, 20740, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC, 27709, USA
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Nguyen J, Maier A, Ovesen J, Kleinstreuer N, Judson R, Krishan M. A proof-of-concept study: evaluating the applicability of high-throughput screening data and read-across tools for food relevant chemicals. Toxicol Lett 2018. [DOI: 10.1016/j.toxlet.2018.06.725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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