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Sweeney LM, Sterner TR. Toxicity reference values for force health protection: Provisional occupational exposure guidelines. Regul Toxicol Pharmacol 2024; 152:105686. [PMID: 39151720 DOI: 10.1016/j.yrtph.2024.105686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/30/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
Force Health Protection programs in the U.S. Air Force endeavor to sustain the operational readiness of the warfighters. We have previously identified hundreds of chemical substances of interest and toxicity reference value (TRV) knowledge gaps that constrain risk based-decision-making for potential exposures. Multiple approaches to occupational TRV estimation were used to generate possible guideline values for 84 compounds (18% of the substances of interest). These candidate TRVs included values from international databases, chemical similarity (nearest neighbor) approaches, empirical adjustments to account for duration differences, quantitative activity relationships, and thresholds of toxicological concern. This present work describes derivation of provisional TRVs from these candidate values. Rodent bioassay-derived long-term worker Derived No-Effect Levels (DNELs) were deemed presumptively the most reliable, but only 19 such DNELs were available for the 84 substances with TRV gaps. In the absence of DNELs, the quality of the approaches and consistency among candidate values were key elements of the weight of evidence used to select the most suitable guideline values. The use of novel nearest-neighbor approaches, empirical adjustment of short term TRVs, and occupational exposure bands were found to be options that would allow occupational TRV estimation with reasonable confidence for nearly all substances evaluated.
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Affiliation(s)
- Lisa M Sweeney
- UES, Inc., 4401 Dayton Xenia Road, Dayton, OH, 45432, USA.
| | - Teresa R Sterner
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., 2728 Q Street, Building 837, Wright-Patterson Air Force Base, OH, 45433, USA
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2
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Sweeney LM, Sterner TR. Toxicity reference values (TRVs) for force health protection: Gap identification and TRV prediction. Regul Toxicol Pharmacol 2024; 152:105685. [PMID: 39147262 DOI: 10.1016/j.yrtph.2024.105685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/30/2024] [Accepted: 08/13/2024] [Indexed: 08/17/2024]
Abstract
The mission of the Force Health Protection (FHP) program of the U.S. Air Force (USAF), sustaining the readiness of warfighters, relies on determinations of acceptable levels of exposure to a wide array of substances that USAF personnel may encounter. In many cases, exposure details are limited or authoritative toxicity reference values (TRVs) are unavailable. To address some of the TRV gaps, we are integrating several approaches to generate health protective exposure guidelines. Descriptions are provided for identification of chemicals of interest for USAF FHP (467 to date), synthesis of multiple TRVs to derive Operational Exposure Limits (OpELs), and strategies for identifying and developing candidate values for provisional OpELs when authoritative TRVs are lacking. Rodent bioassay-derived long-term Derived No Effect Levels (DNELs) for workers were available only for a minority of the substances with occupational TRV gaps (19 of 84). Additional occupational TRV estimation approaches were found to be straightforward to implement: Tier 1 Occupational Exposure Bands, cheminformatics approaches (multiple linear regression and novel nearest-neighbor approaches), and empirical adjustment of short term TRVs. Risk assessors working in similar contexts may benefit from application of the resources referenced and developed in this work.
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Affiliation(s)
- Lisa M Sweeney
- UES, Inc., 4401 Dayton Xenia Road, Dayton, OH, 45432, USA.
| | - Teresa R Sterner
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., 2728 Q Street, Building 837, Wright-Patterson Air Force Base, OH, 45433, USA
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3
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Mansouri K, Taylor K, Auerbach S, Ferguson S, Frawley R, Hsieh JH, Jahnke G, Kleinstreuer N, Mehta S, Moreira-Filho JT, Parham F, Rider C, Rooney AA, Wang A, Sutherland V. Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:85002. [PMID: 39106156 DOI: 10.1289/ehp14001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2024]
Abstract
BACKGROUND The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications. OBJECTIVES This commentary aims to deepen the understanding of class-based approaches by elucidating the pivotal role of chemical similarity (structural and biological) in clustering and classification approaches (CCAs). It addresses the dichotomy between general end point-agnostic similarity, often entailing unsupervised analysis, and end point-specific similarity necessitating supervised learning. The goal is to highlight the nuances of these approaches, their applications, and common misuses. DISCUSSION Understanding similarity is pivotal in toxicological research involving CCAs. The effectiveness of these approaches depends on the right definition and measure of similarity, which varies based on context and objectives of the study. This choice is influenced by how chemical structures are represented and the respective labels indicating biological activity, if applicable. The distinction between unsupervised clustering and supervised classification methods is vital, requiring the use of end point-agnostic vs. end point-specific similarity definition. Separate use or combination of these methods requires careful consideration to prevent bias and ensure relevance for the goal of the study. Unsupervised methods use end point-agnostic similarity measures to uncover general structural patterns and relationships, aiding hypothesis generation and facilitating exploration of datasets without the need for predefined labels or explicit guidance. Conversely, supervised techniques demand end point-specific similarity to group chemicals into predefined classes or to train classification models, allowing accurate predictions for new chemicals. Misuse can arise when unsupervised methods are applied to end point-specific contexts, like analog selection in read-across, leading to erroneous conclusions. This commentary provides insights into the significance of similarity and its role in supervised classification and unsupervised clustering approaches. https://doi.org/10.1289/EHP14001.
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Affiliation(s)
- Kamel Mansouri
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Kyla Taylor
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Scott Auerbach
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Stephen Ferguson
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Rachel Frawley
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Jui-Hua Hsieh
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Gloria Jahnke
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Nicole Kleinstreuer
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Suril Mehta
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - José T Moreira-Filho
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Fred Parham
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Cynthia Rider
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Andrew A Rooney
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Amy Wang
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Vicki Sutherland
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
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Banerjee A, Roy K. ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2024; 26:991-1007. [PMID: 38743054 DOI: 10.1039/d4em00173g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Due to the lack of experimental toxicity data for environmental chemicals, there arises a need to fill data gaps by in silico approaches. One of the most commonly used in silico approaches for toxicity assessment of small datasets is the Quantitative Structure-Activity Relationship (QSAR), which generates predictive models for the efficient prediction of query compounds. However, the reliability of the predictions from QSARs derived from small datasets is often questionable from a statistical point of view. This is due to the presence of a larger number of descriptors as compared to the number of training compounds, which reduces the degree of freedom of the developed model. To reduce the overall prediction error for a particular QSAR model, we have proposed here the computation of the novel Arithmetic Residuals in K-groups Analysis (ARKA) descriptors. We have reduced the number of modeling descriptors in a supervised manner by partitioning them into K classes (K = 2 here) depending on the higher mean normalized values of the descriptors to a particular response class, thus preventing the loss of chemical information. A scatter plot of the data points using the values of two ARKA descriptors (ARKA_2 vs. ARKA_1) can potentially identify activity cliffs, less confident data points, and less modelable data points. We have used here five representative environmentally relevant endpoints (skin sensitization, earthworm toxicity, milk/plasma partitioning, algal toxicity, and rodent carcinogenicity of hazardous chemicals) with graded responses to which the ARKA framework was applied for classification modeling. On comparing the performance of the models generated using conventional QSAR descriptors and the ARKA descriptors, the prediction quality of the models derived from ARKA descriptors was found, based on multiple graded-data validation metrics-derived decision criteria, much better than the models derived from QSAR descriptors signifying the potential of ARKA descriptors in ecotoxicological classification modeling of small data sets. Additionally, this holds true for the Read-Across approach as well, since the Read-Across predictions using ARKA descriptors supersede the predictions generated from QSAR descriptors. For the ease of users, a Java-based expert system has been developed that computes the ARKA descriptors from the input of QSAR descriptors.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India.
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India.
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Goetz A, Ryan N, Sauve-Ciencewicki A, Lord CC, Hilton GM, Wolf DC. Assessing human carcinogenicity risk of agrochemicals without the rodent cancer bioassay. FRONTIERS IN TOXICOLOGY 2024; 6:1394361. [PMID: 38933090 PMCID: PMC11200232 DOI: 10.3389/ftox.2024.1394361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024] Open
Abstract
The rodent cancer bioassays are conducted for agrochemical safety assessment yet they often do not inform regulatory decision-making. As part of a collaborative effort, the Rethinking Carcinogenicity Assessment for Agrochemicals Project (ReCAAP) developed a reporting framework to guide a weight of evidence (WOE)-based carcinogenicity assessment that demonstrates how to fulfill the regulatory requirements for chronic risk estimation without the need to conduct lifetime rodent bioassays. The framework is the result of a multi-stakeholder collaboration that worked through an iterative process of writing case studies (in the form of waivers), technical peer reviews of waivers, and an incorporation of key learnings back into the framework to be tested in subsequent case study development. The example waivers used to develop the framework were written retrospectively for registered agrochemical active substances for which the necessary data and information could be obtained through risk assessment documents or data evaluation records from the US EPA. This exercise was critical to the development of a framework, but it lacked authenticity in that the stakeholders reviewing the waiver already knew the outcome of the rodent cancer bioassay(s). Syngenta expanded the evaluation of the ReCAAP reporting framework by writing waivers for three prospective case studies for new active substances where the data packages had not yet been submitted for registration. The prospective waivers followed the established framework considering ADME, potential exposure, subchronic toxicity, genotoxicity, immunosuppression, hormone perturbation, mode of action (MOA), and all relevant information available for read-across using a WOE assessment. The point of departure was estimated from the available data, excluding the cancer bioassay results, with a proposed use for the chronic dietary risk assessment. The read-across assessments compared data from reliable registered chemical analogues to strengthen the prediction of chronic toxicity and/or tumorigenic potential. The prospective case studies represent a range of scenarios, from a new molecule in a well-established chemical class with a known MOA to a molecule with a new pesticidal MOA (pMOA) and limited read-across to related molecules. This effort represents an important step in establishing criteria for a WOE-based carcinogenicity assessment without the rodent cancer bioassay(s) while ensuring a health protective chronic dietary risk assessment.
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Affiliation(s)
- Amber Goetz
- Syngenta Crop Protection LLCGreensboro, NC, United States
| | - Natalia Ryan
- Syngenta Crop Protection LLCGreensboro, NC, United States
| | | | - Caleb C. Lord
- Syngenta Crop Protection LLCGreensboro, NC, United States
| | - Gina M. Hilton
- PETA Science Consortium International e.V., Stuttgart, Germany
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Guerrero-Limón G, Zappia J, Muller M. A realistic mixture of ubiquitous persistent organic pollutants affects bone and cartilage development in zebrafish by interaction with nuclear receptor signaling. PLoS One 2024; 19:e0298956. [PMID: 38547142 PMCID: PMC10977810 DOI: 10.1371/journal.pone.0298956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/01/2024] [Indexed: 04/02/2024] Open
Abstract
"Persistent organic pollutants (POPs)" have a plethora of deleterious effects on humans and the environment due to their bioaccumulative, persistent, and mimicking properties. Individually, each of these chemicals has been tested and its effects measured, however they are rather found as parts of complex mixtures of which we do not fully grasp the extent of their potential consequences. Here we studied the effects of realistic, environmentally relevant mixtures of 29 POPs on cartilage and bone development using zebrafish as a model species. We observed developmental issues in cartilage, in the form of diverse malformations such as micrognathia, reduced size of the Meckel's and other structures. Also, mineralized bone formation was disrupted, hence impacting the overall development of the larvae at later life stages. Assessment of the transcriptome revealed disruption of nuclear receptor pathways, such as androgen, vitamin D, and retinoic acid, that may explain the mechanisms of action of the compounds within the tested mixtures. In addition, clustering of the compounds using their chemical signatures revealed structural similarities with the model chemicals vitamin D and retinoic acid that can explain the effects and/or enhancing the phenotypes we witnessed. Further mechanistic studies will be required to fully understand this kind of molecular interactions and their repercussions in organisms. Our results contribute to the already existing catalogue of deleterious effects caused by exposure to POPs and help to understand the potential consequences in at risk populations.
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Affiliation(s)
- Gustavo Guerrero-Limón
- Laboratory for Organogenesis and Regeneration, GIGA Institute, University of Liège, Liège, Belgium
| | - Jérémie Zappia
- Bone and Cartilage Research Unit, Arthropôle Liège, Center for Interdisciplinary Research on Medicines (CIRM) Liège, Institute of Pathology, CHU-Sart Tilman, University of Liège, Liège, Belgium
| | - Marc Muller
- Laboratory for Organogenesis and Regeneration, GIGA Institute, University of Liège, Liège, Belgium
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Lee S, Ok SY, Moon HB, Seo SC, Ra JS. Developing a Novel Read-Across Concept for Ecotoxicological Risk Assessment of Phosphate Chemicals: A Case Study. TOXICS 2024; 12:96. [PMID: 38276731 PMCID: PMC10818528 DOI: 10.3390/toxics12010096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
This study introduces a novel concept approach for a read-across assessment, considering species sensitivity differences among phosphate chemicals within structurally similar compound groups. Twenty-five organic chemicals, with a log Kow of 5 or less, were categorized into three functional groups based on acetylcholinesterase (AChE) inhibition as a specific mode of action (MOA). The short-term aquatic toxicity data (LC50) for fish, crustaceans, and insects were collected from the U.S. EPA Ecotoxicology (ECOTOX) Knowledgebase. A geometric mean calculation method was applied for multiple toxic endpoints. Performance metrics for the new read-across concept, including correlation coefficient, bias, precision, and accuracy, were calculated. Overall, a slightly higher overestimation (49.2%) than underestimation (48.4%) in toxicity predictions was observed in two case studies. In Case study I, a strong positive correlation (r = 0.93) between the predicted and known toxicity values of target chemicals was observed, while in Case study II, with limited information on species and their ecotoxicity, showed a moderate correlation (r = 0.75). Overall, the bias and precision for Case study I were 0.32 ± 0.01, while Case study II showed 0.65 ± 0.06; however, the relative bias (%) increased from 37.65% (Case study I) to 91.94% (Case study II). Bland-Altman plots highlight the mean differences of 1.33 (Case study I) and 1.24 (Case study II), respectively. The new read-across concept, focusing on AChE inhibition and structural similarity, demonstrated good reliability, applicability, and accuracy with minimal bias. Future studies are needed to evaluate various types of chemical substances, diverse modes of action, functional groups, toxic endpoints, and test species to ensure overall comprehensiveness and robustness in toxicity predictions.
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Affiliation(s)
- Seokwon Lee
- Geum River Environment Research Center, National Institute of Environmental Research, Okcheon-gun 29027, Chungbuk, Republic of Korea;
| | - Seung-Yeop Ok
- Department of Environmental Fate and Modelling, Knoell Korea Ltd., Seoul 07327, Republic of Korea;
- Department of Marine Sciences and Convergent Engineering, Hanyang University, Ansan 15588, Republic of Korea;
| | - Hyo-Bang Moon
- Department of Marine Sciences and Convergent Engineering, Hanyang University, Ansan 15588, Republic of Korea;
| | - Sung-Chul Seo
- Department of Nano, Chemical and Biological Engineering, College of Engineering, Seokyeong University, Seoul 02173, Republic of Korea
| | - Jin-Sung Ra
- Regulatory Chemical Analysis & Risk Assessment Center, Korea Institute of Industrial Technology (KITECH), Ansan 15588, Republic of Korea
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Muncke J, Andersson AM, Backhaus T, Belcher SM, Boucher JM, Carney Almroth B, Collins TJ, Geueke B, Groh KJ, Heindel JJ, von Hippel FA, Legler J, Maffini MV, Martin OV, Peterson Myers J, Nadal A, Nerin C, Soto AM, Trasande L, Vandenberg LN, Wagner M, Zimmermann L, Thomas Zoeller R, Scheringer M. A vision for safer food contact materials: Public health concerns as drivers for improved testing. ENVIRONMENT INTERNATIONAL 2023; 180:108161. [PMID: 37758599 DOI: 10.1016/j.envint.2023.108161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/17/2023] [Accepted: 08/17/2023] [Indexed: 09/29/2023]
Abstract
Food contact materials (FCMs) and food contact articles are ubiquitous in today's globalized food system. Chemicals migrate from FCMs into foodstuffs, so called food contact chemicals (FCCs), but current regulatory requirements do not sufficiently protect public health from hazardous FCCs because only individual substances used to make FCMs are tested and mostly only for genotoxicity while endocrine disruption and other hazard properties are disregarded. Indeed, FCMs are a known source of a wide range of hazardous chemicals, and they likely contribute to highly prevalent non-communicable diseases. FCMs can also include non-intentionally added substances (NIAS), which often are unknown and therefore not subject to risk assessment. To address these important shortcomings, we outline how the safety of FCMs may be improved by (1) testing the overall migrate, including (unknown) NIAS, of finished food contact articles, and (2) expanding toxicological testing beyond genotoxicity to multiple endpoints associated with non-communicable diseases relevant to human health. To identify mechanistic endpoints for testing, we group chronic health outcomes associated with chemical exposure into Six Clusters of Disease (SCOD) and we propose that finished food contact articles should be tested for their impacts on these SCOD. Research should focus on developing robust, relevant, and sensitive in-vitro assays based on mechanistic information linked to the SCOD, e.g., through Adverse Outcome Pathways (AOPs) or Key Characteristics of Toxicants. Implementing this vision will improve prevention of chronic diseases that are associated with hazardous chemical exposures, including from FCMs.
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Affiliation(s)
- Jane Muncke
- Food Packaging Forum Foundation, Zurich, Switzerland.
| | - Anna-Maria Andersson
- Dept. of Growth and Reproduction, Rigshospitalet and Centre for Research and Research Training in Male Reproduction and Child Health (EDMaRC), Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Thomas Backhaus
- Dept of Biological and Environmental Sciences, University of Gothenburg, Sweden
| | - Scott M Belcher
- Dept. of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | | | | | | | - Birgit Geueke
- Food Packaging Forum Foundation, Zurich, Switzerland
| | - Ksenia J Groh
- Department of Environmental Toxicology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Jerrold J Heindel
- Healthy Environment and Endocrine Disruptor Strategies, Durham, NC, USA
| | - Frank A von Hippel
- Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Juliette Legler
- Dept. of Population Health Sciences, Faculty of Veterinary Medicine, University of Utrecht, Netherlands
| | | | - Olwenn V Martin
- Plastic Waste Innovation Hub, Department of Arts and Science, University College London, UK
| | - John Peterson Myers
- Dept. of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA; Environmental Health Sciences, Charlottesville, VA, USA
| | - Angel Nadal
- IDiBE and CIBERDEM, Miguel Hernández University of Elche, Alicante, Spain
| | - Cristina Nerin
- Dept. of Analytical Chemistry, I3A, University of Zaragoza, Zaragoza, Spain
| | - Ana M Soto
- Department of Immunology, Tufts University School of Medicine, Boston, MA, USA; Centre Cavaillès, Ecole Normale Supérieure, Paris, France
| | - Leonardo Trasande
- College of Global Public Health and Grossman School of Medicine and Wagner School of Public Service, New York University, New York, NY, USA
| | - Laura N Vandenberg
- Department of Environmental Health Sciences, School of Public Health & Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Martin Wagner
- Dept. of Biology, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - R Thomas Zoeller
- Department of Environmental Health Sciences, School of Public Health & Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Martin Scheringer
- RECETOX, Masaryk University, Brno, Czech Republic; Department of Environmental Systems Science, ETH Zurich, Switzerland.
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Yang C, Rathman JF, Mostrag A, Ribeiro JV, Hobocienski B, Magdziarz T, Kulkarni S, Barton-Maclaren T. High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge. Chem Res Toxicol 2023. [PMID: 37399585 DOI: 10.1021/acs.chemrestox.3c00062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Read-across is an in silico method applied in chemical risk assessment for data-poor chemicals. The read-across outcomes for repeated-dose toxicity end points include the no-observed-adverse-effect level (NOAEL) and estimated uncertainty for a particular category of effects. We have previously developed a new paradigm for estimating NOAELs based on chemoinformatics analysis and experimental study qualities from selected analogues, not relying on quantitative structure-activity relationships (QSARs) or rule-based SAR systems, which are not well-suited to end points for which the underpinning data are weakly grounded in specific chemical-biological interactions. The central hypothesis of this approach is that similar compounds have similar toxicity profiles and, hence, similar NOAEL values. Analogue quality (AQ) quantifies the suitability of an analogue candidate for reading across to the target by considering similarity from structure, physicochemical, ADME (absorption, distribution, metabolism, excretion), and biological perspectives. Biological similarity is based on experimental data; assay vectors derived from aggregations of ToxCast/Tox21 data are used to derive machine learning (ML) hybrid rules that serve as biological fingerprints to capture target-analogue similarity relevant to specific effects of interest, for example, hormone receptors (ER/AR/THR). Once one or more analogues have been qualified for read-across, a decision theory approach is used to estimate confidence bounds for the NOAEL of the target. The confidence interval is dramatically narrowed when analogues are constrained to biologically related profiles. Although this read-across process works well for a single target with several analogues, it can become unmanageable when, for example, screening multiple targets (e.g., virtual screening library) or handling a parent compound having numerous metabolites. To this end, we have established a digitalized framework to enable the assessment of a large number of substances, while still allowing for human decisions for filtering and prioritization. This workflow was developed and validated through a use case of a large set of bisphenols and their metabolites.
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Affiliation(s)
| | - James F Rathman
- MN-AM, Columbus, Ohio 43215, United States
- Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
| | | | | | | | | | - Sunil Kulkarni
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada
| | - Tara Barton-Maclaren
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada
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