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Meng L, Zhou B, Liu H, Chen Y, Yuan R, Chen Z, Luo S, Chen H. Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174201. [PMID: 38936709 DOI: 10.1016/j.scitotenv.2024.174201] [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: 01/18/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 06/29/2024]
Abstract
Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.
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Affiliation(s)
- Lingxuan Meng
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Beihai Zhou
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haijun Liu
- School of Resources and Environment, Anqing Normal University, Anqing, China.
| | - Yuefang Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Rongfang Yuan
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhongbing Chen
- Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic.
| | - Shuai Luo
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Huilun Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
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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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Li T, Cui L, Xu Z, Liu H, Cui X, Fantke P. Micro- and nanoplastics in soil: Linking sources to damage on soil ecosystem services in life cycle assessment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166925. [PMID: 37689210 DOI: 10.1016/j.scitotenv.2023.166925] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/15/2023] [Accepted: 09/06/2023] [Indexed: 09/11/2023]
Abstract
Soil ecosystems are crucial for providing vital ecosystem services (ES), and are increasingly pressured by the intensification and expansion of human activities, leading to potentially harmful consequences for their related ES provision. Micro- and nanoplastics (MNPs), associated with releases from various human activities, have become prevalent in various soil ecosystems and pose a global threat. Life Cycle Assessment (LCA), a tool for evaluating environmental performance of product and technology life cycles, has yet to adequately include MNPs-related damage to soil ES, owing to factors like uncertainties in MNPs environmental fate and ecotoxicological effects, and characterizing related damage on soil species loss, functional diversity, and ES. This study aims to address this gap by providing as a first step an overview of the current understanding of MNPs in soil ecosystems and proposing a conceptual approach to link MNPs impacts to soil ES damage. We find that MNPs pervade soil ecosystems worldwide, introduced through various pathways, including wastewater discharge, urban runoff, atmospheric deposition, and degradation of larger plastic debris. MNPs can inflict a range of ecotoxicity effects on soil species, including physical harm, chemical toxicity, and pollutants bioaccumulation. Methods to translate these impacts into damage on ES are under development and typically focus on discrete, yet not fully integrated aspects along the impact-to-damage pathway. We propose a conceptual framework for linking different MNPs effects on soil organisms to damage on soil species loss, functional diversity loss and loss of ES, and elaborate on each link. Proposed underlying approaches include the Threshold Indicator Taxa Analysis (TITAN) for translating ecotoxicological effects associated with MNPs into quantitative measures of soil species diversity damage; trait-based approaches for linking soil species loss to functional diversity loss; and ecological networks and Bayesian Belief Networks for linking functional diversity loss to soil ES damage. With the proposed conceptual framework, our study constitutes a starting point for including the characterization of MNPs-related damage on soil ES in LCA.
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Affiliation(s)
- Tong Li
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark; School of Environment and Science, Centre for Planetary Health and Food Security, Griffith University, Nathan, Brisbane, QLD 4111, Australia
| | - Lizhen Cui
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhihong Xu
- School of Environment and Science, Centre for Planetary Health and Food Security, Griffith University, Nathan, Brisbane, QLD 4111, Australia
| | - Hongdou Liu
- School of Environment and Science, Centre for Planetary Health and Food Security, Griffith University, Nathan, Brisbane, QLD 4111, Australia.
| | - Xiaoyong Cui
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark.
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Saleh O, Otim FN, Otim O. Application of supervised learning classification modeling for predicting benthic sediment toxicity in the southern California bight: A test of concept. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 901:165946. [PMID: 37541495 DOI: 10.1016/j.scitotenv.2023.165946] [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: 06/06/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/06/2023]
Abstract
Benthic sediment toxicity is linked to harmful effects in marine organisms and humans, and an understanding of the link would require, in part, a comprehensive and exhaustive analysis of sediment toxicity data already in hand. One tool which could aid in the process is machine learning (ML), a supervised classification modeling technique that has transformed how actionable insight are acquired from large datasets. The current study is a test of concept in which an ML classifier is sought that can accurately extrapolate the characteristics of a 5437 California-wide coastal training dataset (assembled from 1635 samples) to predict sediment toxicity in southern California bight (SCB). Twelve classifiers were trained to recognize sediment toxicity using 70 % of the dataset and among them, a Gradient Boosting Classifier (GBC) model using latitude, longitude, and water depth was found to be the most accurate at predicting toxicity (83 %). Among the variables, latitude was found to be the most significant driver of prediction by GBC in this test ecosystem. The performance of the model was verified with the remaining 30 % of the dataset and found to be 83 % accurate. Presented with 884 unfamiliar data points assembled from 854 measurements at 346 stations across SCB, GBC was 87 % accurate post-training, thus demonstrating a role supervised learning can play in the southern California environmental analytics.
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Affiliation(s)
- Omar Saleh
- Department of Humanities and Sciences, University of California - Los Angeles, Los Angeles, CA 90024, USA
| | - Francesca Nyega Otim
- Department of Anthropology, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA
| | - Ochan Otim
- Department of Humanities and Sciences, University of California - Los Angeles, Los Angeles, CA 90024, USA; Environmental Monitoring Division, City of Los Angeles, Playa Del Rey, CA 90293, USA.
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von Borries K, Holmquist H, Kosnik M, Beckwith KV, Jolliet O, Goodman JM, Fantke P. Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18259-18270. [PMID: 37914529 PMCID: PMC10666540 DOI: 10.1021/acs.est.3c05300] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 11/03/2023]
Abstract
Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is still limited, and their potential for addressing a wide range of chemicals is unknown. We prioritized chemical-related parameters for chemical toxicity characterization to inform ML model development based on two criteria: (1) each parameter's relevance to robustly characterize chemical toxicity described by the uncertainty in characterization results attributable to each parameter and (2) the potential for ML-based approaches to predict parameter values for a wide range of chemicals described by the availability of chemicals with measured parameter data. We prioritized 13 out of 38 parameters for developing ML-based approaches, while flagging another nine with critical data gaps. For all prioritized parameters, we performed a chemical space analysis to assess further the potential for ML-based approaches to predict data for diverse chemicals considering the structural diversity of available measured data, showing that ML-based approaches can potentially predict 8-46% of marketed chemicals based on 1-10% with available measured data. Our results can systematically inform future ML model development efforts to address data gaps in chemical toxicity characterization.
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Affiliation(s)
- Kerstin von Borries
- Quantitative
Sustainability Assessment, Department of Environmental and Resource
Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
| | - Hanna Holmquist
- IVL
Swedish Environmental Research Institute, Aschebergsgatan 44, 411 33 Göteborg, Sweden
| | - Marissa Kosnik
- Quantitative
Sustainability Assessment, Department of Environmental and Resource
Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
| | - Katie V. Beckwith
- Centre
for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United
Kingdom
| | - Olivier Jolliet
- Quantitative
Sustainability Assessment, Department of Environmental and Resource
Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
| | - Jonathan M. Goodman
- Centre
for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United
Kingdom
| | - Peter Fantke
- Quantitative
Sustainability Assessment, Department of Environmental and Resource
Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
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Schür C, Gasser L, Perez-Cruz F, Schirmer K, Baity-Jesi M. A benchmark dataset for machine learning in ecotoxicology. Sci Data 2023; 10:718. [PMID: 37853023 PMCID: PMC10584858 DOI: 10.1038/s41597-023-02612-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/28/2023] [Indexed: 10/20/2023] Open
Abstract
The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, which we consider a barrier of entry for this kind of research. Additionally, model performances can only be compared across studies when the same dataset, cleaning, and splittings were used. Therefore, we provide ADORE, an extensive and well-described dataset on acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species as well as chemical properties and molecular representations. Apart from challenging other researchers to try and achieve the best model performances across the whole dataset, we propose specific relevant challenges on subsets of the data and include datasets and splittings corresponding to each of these challenge as well as in-depth characterization and discussion of train-test splitting approaches.
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Affiliation(s)
- Christoph Schür
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.
| | - Lilian Gasser
- Swiss Data Science Center (SDSC), Zürich, Switzerland
| | - Fernando Perez-Cruz
- Swiss Data Science Center (SDSC), Zürich, Switzerland
- ETH Zürich: Department of Computer Science, Zürich, Switzerland
| | - Kristin Schirmer
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
- ETH Zürich: Department of Environmental Systems Science, Zürich, Switzerland
- EPF Lausanne, School of Architecture, Civil and Environmental Engineering, Lausanne, Switzerland
| | - Marco Baity-Jesi
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
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Wlodkowic D, Jansen M. High-throughput screening paradigms in ecotoxicity testing: Emerging prospects and ongoing challenges. CHEMOSPHERE 2022; 307:135929. [PMID: 35944679 DOI: 10.1016/j.chemosphere.2022.135929] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 06/09/2022] [Accepted: 07/31/2022] [Indexed: 06/15/2023]
Abstract
The rapidly increasing number of new production chemicals coupled with stringent implementation of global chemical management programs necessities a paradigm shift towards boarder uses of low-cost and high-throughput ecotoxicity testing strategies as well as deeper understanding of cellular and sub-cellular mechanisms of ecotoxicity that can be used in effective risk assessment. The latter will require automated acquisition of biological data, new capabilities for big data analysis as well as computational simulations capable of translating new data into in vivo relevance. However, very few efforts have been so far devoted into the development of automated bioanalytical systems in ecotoxicology. This is in stark contrast to standardized and high-throughput chemical screening and prioritization routines found in modern drug discovery pipelines. As a result, the high-throughput and high-content data acquisition in ecotoxicology is still in its infancy with limited examples focused on cell-free and cell-based assays. In this work we outline recent developments and emerging prospects of high-throughput bioanalytical approaches in ecotoxicology that reach beyond in vitro biotests. We discuss future importance of automated quantitative data acquisition for cell-free, cell-based as well as developments in phytotoxicity and in vivo biotests utilizing small aquatic model organisms. We also discuss recent innovations such as organs-on-a-chip technologies and existing challenges for emerging high-throughput ecotoxicity testing strategies. Lastly, we provide seminal examples of the small number of successful high-throughput implementations that have been employed in prioritization of chemicals and accelerated environmental risk assessment.
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Affiliation(s)
- Donald Wlodkowic
- The Neurotox Lab, School of Science, RMIT University, Melbourne, VIC, 3083, Australia.
| | - Marcus Jansen
- LemnaTec GmbH, Nerscheider Weg 170, 52076, Aachen, Germany
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