1
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Mei S. Transferring knowledge across aquatic species via clustering techniques to unravel patterns of pesticide toxicity. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 950:175385. [PMID: 39122048 DOI: 10.1016/j.scitotenv.2024.175385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/28/2024] [Accepted: 08/06/2024] [Indexed: 08/12/2024]
Abstract
In silico modelling takes the advantage of accelerating ecotoxicological assessments on hazardous chemicals without conducting risky in vivo experiments under ethic regulation. To date, the prevailing strategy of one model for one species cannot be well generalized to multi-species modelling. In this work, we propose a new strategy of one model for multiple species to facilitate knowledge transfer across aquatic species. The available lethal concentration values of 4952 pesticides on 651 fish species are aggregated into one toxicity response matrix, purely through which we attempt to unravel fish toxicosis-phylogenesis relationships and pesticide toxicity-structure relationships via clustering techniques including non-negative matrix factorization (NMF) and hierarchical clustering. The clustering results suggest that (1) close NMF weights indicate close species-toxicosis and pesticide-toxicity profiles; (2) and that species toxicosis patterns are related with species phylogenetic relationships; (3) and that close pesticide-toxicity profiles indicate similar atom-pair structural fingerprints. These environmental, chemical and biological insights can be used as expert knowledge for environmentalists to manually gain knowledge about untested species/pesticides from tested species/pesticides, and meanwhile provide support for us to build in silico models from species phylogenetic and pesticide structural points of view. Besides unravelling the mechanisms behind toxicity response, we also adopt stratified cross validation and external test to validate the reliability of using NMF to predict missing toxicity values. Independent test on external data shows that NMF achieves 0.8404-0.9397 R2 on four fish species. In the context of toxicity prediction, non-negative matrix factorization can be viewed as a model based on quantitative activity-activity relationships (QAAR), and provides an alternative approach of inferring toxicity values on untested species from tested species.
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Affiliation(s)
- Suyu Mei
- Software College, Shenyang Normal University, Shenyang 110034, China.
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2
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Banerjee A, Kar S, Roy K, Patlewicz G, Charest N, Benfenati E, Cronin MTD. Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning. Crit Rev Toxicol 2024; 54:659-684. [PMID: 39225123 DOI: 10.1080/10408444.2024.2386260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/25/2024] [Accepted: 07/25/2024] [Indexed: 09/04/2024]
Abstract
This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure-activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.
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Affiliation(s)
- Arkaprava Banerjee
- Department of Pharmaceutical Technology, Drug Theoretics and Cheminformatics (DTC) Laboratory, Jadavpur University, Kolkata, India
| | - Supratik Kar
- Department of Chemistry and Physics, Chemometrics & Molecular Modeling Laboratory, Kean University, Union, NJ, USA
| | - Kunal Roy
- Department of Pharmaceutical Technology, Drug Theoretics and Cheminformatics (DTC) Laboratory, Jadavpur University, Kolkata, India
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Nathaniel Charest
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
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3
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Ninomiya Y, Watanabe H, Yamagishi T, Maruyama-Komoda T, Yamada T, Yamamoto H. Prediction of chronic toxicity of pharmaceuticals in Daphnia magna by combining ortholog prediction, pharmacological effects, and quantitative structure-activity relationship. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 282:116737. [PMID: 39047365 DOI: 10.1016/j.ecoenv.2024.116737] [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: 03/06/2024] [Revised: 06/26/2024] [Accepted: 07/12/2024] [Indexed: 07/27/2024]
Abstract
To develop a method for predicting chronic toxicity of pharmaceuticals in Daphnia, we investigated the feasibility of combining the presence of drug-target orthologs in Daphnia magna, classification based on pharmacological effects, and ecotoxicity quantitative structure-activity relationship (QSAR) prediction. We established datasets on the chronic toxicity of pharmaceuticals in Daphnia, including information on therapeutic categories, target proteins, and the presence or absence of drug-target orthologs in D. magna, using literature and databases. Chronic toxicity was predicted using ecotoxicity prediction QSAR (Ecological Structure Activity Relationship and Kashinhou Tool for Ecotoxicity), and the differences between the predicted and measured values and the presence or absence of drug-target orthologs were examined. For pharmaceuticals without drug-target orthologs in D. magna or without expected specific actions, the ecotoxicity prediction QSAR analysis yielded acceptable predictions of the chronic toxicity of pharmaceuticals. In addition, a workflow model to assess the chronic toxicity of pharmaceuticals in Daphnia was proposed based on these evaluations and verified using an additional dataset. The addition of biological aspects such as drug-target orthologs and pharmacological effects would support the use of QSARs for predicting the chronic toxicity of pharmaceuticals in Daphnia.
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Affiliation(s)
- Yoshikazu Ninomiya
- Department of Natural Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-8563, Japan
| | - Haruna Watanabe
- Department of Natural Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-8563, Japan; Health and Environmental Risk Division, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Takahiro Yamagishi
- Department of Natural Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-8563, Japan; Health and Environmental Risk Division, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Taeko Maruyama-Komoda
- Division of Risk Assessment, Center for Biological and Safety Research, National Institute of Health Science (NIHS), 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan
| | - Takashi Yamada
- Division of Risk Assessment, Center for Biological and Safety Research, National Institute of Health Science (NIHS), 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan
| | - Hiroshi Yamamoto
- Department of Natural Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-8563, Japan; Health and Environmental Risk Division, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan.
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4
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de Vries P. ECOTOXr: An R package for reproducible and transparent retrieval of data from EPA's ECOTOX database. CHEMOSPHERE 2024; 364:143078. [PMID: 39181462 DOI: 10.1016/j.chemosphere.2024.143078] [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: 03/18/2024] [Revised: 07/24/2024] [Accepted: 08/09/2024] [Indexed: 08/27/2024]
Abstract
The US EPA ECOTOX database provides key ecotoxicological data that are crucial in environmental risk assessment. It can be used for computational predictions of toxicity or indications of hazard in a wide range of situations. There is no standardised or formalised method for extracting and subsetting data from the database for these purposes. Consequently, results in such meta-analyses are difficult to reproduce. The present study introduces the software package ECOTOXr, which provides the means to formalise data retrieval from the ECOTOX database in the R scripting language. Three cases are presented to evaluate the performance of the package in relation to earlier data extractions and searches on the website. These cases demonstrate that the package can reproduce data sets relatively well. Furthermore, they illustrate how future studies can further improve traceability and reproducibility by applying the package and adhering to some simple guidelines. This contributes to the FAIR principles, credibility and acceptance of research that uses data from the ECOTOX database.
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Affiliation(s)
- Pepijn de Vries
- Dept. Wageningen Marine Research, Wageningen University and Research Center, P.O. Box 57, Den Helder, 1780AB, the Netherlands.
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5
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Joseph NK, Koshy VJ, Aravind UK, Aravindakumar CT. Photo-transformation of ofloxacin in natural aquatic conditions: Impact of indirect photolysis on the product profile and transformation mechanism. CHEMOSPHERE 2024; 361:142484. [PMID: 38830465 DOI: 10.1016/j.chemosphere.2024.142484] [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: 02/27/2024] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 06/05/2024]
Abstract
The natural phototransformation of organic pollutants in the environment depends on several water constituents, including inorganic ions, humic substances, and pH. However, the literature information concerning the influence of various water components on the amount of phototransformation and their impact on the development of various transformation products (TPs) is minimal. This study investigated the phototransformation of ofloxacin (OFL), a fluoroquinolone antibiotic, in the presence of various water components such as cations (K+, Na+, Ca2+, NH4+, Mg2+), anions (NO3-, SO42-, HCO3-, CO32-, PO43-), pH, and humic substances when exposed to natural sunlight. The study reveals that neutral pH levels (0.39374 min⁻1) enhance the phototransformation of OFL in aquatic environments. Carbonate, among anions, shows the highest rate constant (2.89966 min⁻1), significantly influencing OFL phototransformation, while all anions exhibit a notable impact. In aquatic environments, indirect phototransformation of OFL, driven by increased reactive oxygen species, expedites light-induced reactions, potentially enhancing OFL phototransformation. A clear difference was visible in the type of transformation products (TPs) formed during direct and indirect photolysis. The impact of indirect photolysis in the product profile was evaluated by examining the unique properties of TPs in direct and indirect photolysis. The primary transformation products were generated by oxidation and cleavage processes directed towards the ofloxacin piperazinyl, oxazine, and carboxyl groups. The toxicity assessment of TPs derived from OFL revealed that among the 26 identified TPs, TP3 (demethylated product), TP7 and TP8 (decarboxylated products), and TP15 (piperazine ring cleaved product) could potentially have some toxicological effects. These findings suggest that the phototransformation of OFL in the presence of various water components is necessary when assessing this antibiotic's environmental fate.
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Affiliation(s)
- Nisha K Joseph
- Inter-University Instrumentation Centre (IUIC), Mahatma Gandhi University (MGU), Kottayam, 686560, Kerala, India
| | - Valsamma J Koshy
- Inter-University Instrumentation Centre (IUIC), Mahatma Gandhi University (MGU), Kottayam, 686560, Kerala, India
| | - Usha K Aravind
- School of Environmental Studies, Cochin University of Science & Technology (CUSAT), Kochi, 682022, Kerala, India
| | - Charuvila T Aravindakumar
- Inter-University Instrumentation Centre (IUIC), Mahatma Gandhi University (MGU), Kottayam, 686560, Kerala, India; School of Environmental Sciences, Mahatma Gandhi University (MGU), Kottayam, 686560, Kerala, India.
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6
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Zhang M, Wang D, Ma H, Wei H, Wang G. Oxygen vacancy based WO 3/SnO 2-x promote electrochemical H 2O 2 accumulation by two-electron water oxidation reaction and toxic uniform dimethylhydrazine degradation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 924:171383. [PMID: 38462003 DOI: 10.1016/j.scitotenv.2024.171383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/22/2024] [Accepted: 02/28/2024] [Indexed: 03/12/2024]
Abstract
The key to constructing an anodic electro-Fenton system hinges on two pivotal criteria: enhancing the catalyst activity and selectivity in water oxidation reaction (WOR), while simultaneously inhibiting the decomposition of hydrogen peroxide (H2O2) which is on-site electrosynthesized at the anode. To address the issues, we synthesized novel WO3/SnO2-x electrocatalysts, enriched with oxygen vacancies, capitalize on the combined activity and selectivity advantages of both WO3 and SnO2-x for the two-electron pathway electrocatalytic production of H2O2. Moreover, the introduction of oxygen vacancies plays a critical role in impeding the decomposition of H2O2. This innovative design ensures that the Faraday efficiency and yield of H2O2 are maintained at over 80 %, with a noteworthy production rate of 0.2 mmol h-1 cm-2. We constructed a novel electro-Fenton system that operates using only H2O as its feedstock and applied it to treat highly toxic uniform dimethylhydrazine (UDMH) from rocket launch effluent. Our experiments revealed a substantial total organic carbon (TOC) removal, achieving approximately 90 % after 120 mins of treatment. Additionally, the toxicity of N-nitrosodimethylamine (NDMA), a byproduct of great concern, was shown to be effectively mitigated, as evidenced by acute toxicity evaluations using zebrafish embryos. The degradation mechanism of UDMH is predominantly characterized by the advanced oxidative action of H2O2 and hydroxyl radicals, as well as by complex electron transfer processes that warrant further investigation.
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Affiliation(s)
- Mengqiong Zhang
- School of Light Industry and Chemical Engineering, Dalian Polytechnic University, No. 1 Qinggongyuan, Ganjinzi District, Dalian 116034, PR China
| | - Dong Wang
- College of Marine Science-Technology and Environment, Dalian Ocean University, No. 52 Heishijiao, Shahekou District, Dalian 116023, PR China
| | - Hongchao Ma
- School of Light Industry and Chemical Engineering, Dalian Polytechnic University, No. 1 Qinggongyuan, Ganjinzi District, Dalian 116034, PR China
| | - Huangzhao Wei
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China.
| | - Guowen Wang
- School of Light Industry and Chemical Engineering, Dalian Polytechnic University, No. 1 Qinggongyuan, Ganjinzi District, Dalian 116034, PR China.
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7
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Lam TK, Law JCF, Leung KSY. Hybrid radical coupling during MnO 2-mediated transformation of a mixture of organic UV filters: Chemistry and toxicity assessment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:170121. [PMID: 38232841 DOI: 10.1016/j.scitotenv.2024.170121] [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: 11/21/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 01/19/2024]
Abstract
Manganese oxide (MnO2) is one of the most abundant metal oxides, and it is renowned for its ability to degrade various phenolic micropollutants. However, under MnO2-mediated transformation, BP-3 transforms into 12 different radical-coupled transformation products (TPs) out of 15 identified TPs. These radical-coupled TPs are reported with adverse environmental impacts. This study explored the effects of MnO2 on organic UV filter mixtures and different water constituents (i.e., bicarbonate ion (HCO3-), humic acid (HA) and halide ions) in terms of degradation efficiency and transformation chemistry. When a mixture of benzophenone-3 (BP-3) and avobenzone (AVO) underwent transformation by MnO2, hybrid radical-coupled TPs derived from both organic UV filters were generated. These hybrid radical-coupled TPs were evaluated by an in silico prediction tool and Vibrio fischeri bioluminescence inhibition assay (VFBIA). Results showed that these TPs were potentially toxic to aquatic organisms, even more so than their parent compounds. The higher the concentration of HCO3-, HA, chloride ion (Cl-) and bromide ion (Br-), the greater the reduction in the efficiencies of degrading BP-3 and AVO. Contrastingly, in the presence of iodide ion (I-), degradation efficiencies of BP-3 and AVO were enhanced; however, iodinated TPs and iodinated radical-coupled TPs were formed, with questionable toxicity. This study has revealed the environmental risks of hybrid radical-coupled TPs, iodinated TPs and iodinated radical-coupled TPs when the organic UV filters BP-3 and AVO are transformed by MnO2.
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Affiliation(s)
- Tsz-Ki Lam
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong, PR China
| | - Japhet Cheuk-Fung Law
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong, PR China
| | - Kelvin Sze-Yin Leung
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong, PR China; HKBU Institute of Research and Continuing Education, Shenzhen Virtual University Park, Shenzhen, PR China.
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8
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Gustavsson M, Käll S, Svedberg P, Inda-Diaz JS, Molander S, Coria J, Backhaus T, Kristiansson E. Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms. SCIENCE ADVANCES 2024; 10:eadk6669. [PMID: 38446886 PMCID: PMC10917336 DOI: 10.1126/sciadv.adk6669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/30/2024] [Indexed: 03/08/2024]
Abstract
Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups-algae, aquatic invertebrates and fish-and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC50/EC10), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.
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Affiliation(s)
- Mikael Gustavsson
- Department of Economics, University of Gothenburg, Gothenburg, Sweden
| | - Styrbjörn Käll
- Department of Mathematical Sciences, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden
| | - Patrik Svedberg
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Juan S. Inda-Diaz
- Department of Mathematical Sciences, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden
| | - Sverker Molander
- Division of Environmental Systems Analysis, Department of Technology Management and Economics, Chalmers University of Technology, Gothenburg, Sweden
| | - Jessica Coria
- Department of Economics, University of Gothenburg, Gothenburg, Sweden
| | - Thomas Backhaus
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Erik Kristiansson
- Department of Mathematical Sciences, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden
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9
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Zhang S, Wang Z, Chen J, Luo X, Mai B. Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1944-1953. [PMID: 38240238 DOI: 10.1021/acs.est.3c08016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals.
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Affiliation(s)
- Shuying Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Zhongyu Wang
- Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment of the People's Republic of China, Beijing 100029, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xiaojun Luo
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Bixian Mai
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
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10
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Lam TK, Law JCF, Leung KSY. Hazardous radical-coupled transformation products of benzophenone-3 formed during manganese dioxide treatment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 901:166481. [PMID: 37611723 DOI: 10.1016/j.scitotenv.2023.166481] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/14/2023] [Accepted: 08/20/2023] [Indexed: 08/25/2023]
Abstract
Radical-coupled transformation products (TPs) have been identified as the byproducts of various transformation processes, including both natural attenuation and artificial treatments, of phenolic micropollutants. Benzophenone-3 (BP-3), an organic UV filter of emerging concern, has been previously reported with ubiquitous occurrence in the natural environment and water bodies. Current research has demonstrated how TPs are formed from BP-3 when it is treated with manganese oxide (MnO2). The ecological and toxicological risks of these TPs have also been assessed. Polymerization of BP-3 through radical coupling was observed as the major pathway by which BP-3 is transformed when treated with MnO2. These radical-coupled TPs haven't shown further degradation after formation, suggesting their potential persistence once occurred in the environment. In silico experiments predict the radical-coupled TPs will increase in mobility, persistence and ecotoxicity. If true, they also represent an ever-increasing threat to the environment, ecosystems and, most immediately, aquatic living organisms. In addition, radical-coupled TPs produced by MnO2 transformation of BP-3 have shown escalated estrogenic activity compared to the parent compound. This suggests that radical coupling amplifies the toxicological impacts of parent compound. These results provide strong evidence that radical-coupled TPs with larger molecular sizes are having potential adverse impacts on the ecosystem and biota.
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Affiliation(s)
- Tsz-Ki Lam
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong Special Administrative Region, P. R. China
| | - Japhet Cheuk-Fung Law
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong Special Administrative Region, P. R. China
| | - Kelvin Sze-Yin Leung
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong Special Administrative Region, P. R. China; HKBU Institute of Research and Continuing Education, Shenzhen Virtual University Park, Shenzhen, P. R. China.
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11
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Schlender T, Viljanen M, van Rijn JN, Mohr F, Peijnenburg WJGM, Hoos HH, Rorije E, Wong A. The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17818-17830. [PMID: 37315216 PMCID: PMC10666535 DOI: 10.1021/acs.est.3c00334] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/25/2023] [Accepted: 05/25/2023] [Indexed: 06/16/2023]
Abstract
Toxicological information as needed for risk assessments of chemical compounds is often sparse. Unfortunately, gathering new toxicological information experimentally often involves animal testing. Simulated alternatives, e.g., quantitative structure-activity relationship (QSAR) models, are preferred to infer the toxicity of new compounds. Aquatic toxicity data collections consist of many related tasks─each predicting the toxicity of new compounds on a given species. Since many of these tasks are inherently low-resource, i.e., involve few associated compounds, this is challenging. Meta-learning is a subfield of artificial intelligence that can lead to more accurate models by enabling the utilization of information across tasks. In our work, we benchmark various state-of-the-art meta-learning techniques for building QSAR models, focusing on knowledge sharing between species. Specifically, we employ and compare transformational machine learning, model-agnostic meta-learning, fine-tuning, and multi-task models. Our experiments show that established knowledge-sharing techniques outperform single-task approaches. We recommend the use of multi-task random forest models for aquatic toxicity modeling, which matched or exceeded the performance of other approaches and robustly produced good results in the low-resource settings we studied. This model functions on a species level, predicting toxicity for multiple species across various phyla, with flexible exposure duration and on a large chemical applicability domain.
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Affiliation(s)
- Thalea Schlender
- Leiden
Institute of Advanced Computer Science, Leiden University, Leiden 2333 CA, The Netherlands
- National
Institute for Public Health and the Environment (RIVM), Bilthoven 3720 BA, The Netherlands
| | - Markus Viljanen
- National
Institute for Public Health and the Environment (RIVM), Bilthoven 3720 BA, The Netherlands
| | - Jan N. van Rijn
- Leiden
Institute of Advanced Computer Science, Leiden University, Leiden 2333 CA, The Netherlands
| | - Felix Mohr
- Universidad
de La Sabana, Chía 250001, Colombia
| | - Willie JGM. Peijnenburg
- National
Institute for Public Health and the Environment (RIVM), Bilthoven 3720 BA, The Netherlands
- Institute
of Environmental Sciences, Leiden University, Leiden 2333 CC, The Netherlands
| | - Holger H. Hoos
- Leiden
Institute of Advanced Computer Science, Leiden University, Leiden 2333 CA, The Netherlands
- Chair
for AI Methodology, RWTH Aaachen University, Aachen 52056, Germany
- Department
of Computer Science, The University of British
Columbia, Vancouver V6T 1Z4, Canada
| | - Emiel Rorije
- National
Institute for Public Health and the Environment (RIVM), Bilthoven 3720 BA, The Netherlands
| | - Albert Wong
- National
Institute for Public Health and the Environment (RIVM), Bilthoven 3720 BA, The Netherlands
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12
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Viljanen M, Minnema J, Wassenaar PNH, Rorije E, Peijnenburg W. What is the ecotoxicity of a given chemical for a given aquatic species? Predicting interactions between species and chemicals using recommender system techniques. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:765-788. [PMID: 37670728 DOI: 10.1080/1062936x.2023.2254225] [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: 04/21/2023] [Accepted: 08/27/2023] [Indexed: 09/07/2023]
Abstract
Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solving this apparent contradiction as they allow to generalize toxicity patterns across chemicals and species. However, despite the availability of large public toxicity datasets, the data is highly sparse, complicating model development. The aim of this study is to provide insights into how ML can predict toxicity using a large but sparse dataset. We developed models to predict LC50-values, based on experimental LC50-data covering 2431 organic chemicals and 1506 aquatic species from the ECOTOX-database. Several well-known ML techniques were evaluated and a new ML model was developed, inspired by recommender systems. This new model involves a simple linear model that learns low-rank interactions between species and chemicals using factorization machines. We evaluated the predictive performances of the developed models based on two validation settings: 1) predicting unseen chemical-species pairs, and 2) predicting unseen chemicals. The results of this study show that ML models can accurately predict LC50-values in both validation settings. Moreover, we show that the novel factorization machine approach can match well-tuned, complex, ML approaches.
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Affiliation(s)
- M Viljanen
- Department of Statistics, Data Science and Modelling, National Institute of Public Health and the Environment, Bilthoven, The Netherlands
| | - J Minnema
- Center for Safety of Substances and Products, National Institute of Public Health and the Environment, Bilthoven, The Netherlands
| | - P N H Wassenaar
- Center for Safety of Substances and Products, National Institute of Public Health and the Environment, Bilthoven, The Netherlands
| | - E Rorije
- Center for Safety of Substances and Products, National Institute of Public Health and the Environment, Bilthoven, The Netherlands
| | - W Peijnenburg
- Center for Safety of Substances and Products, National Institute of Public Health and the Environment, Bilthoven, The Netherlands
- Institute of Environmental Sciences (CML), Leiden University, Leiden, The Netherlands
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13
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Zubrod JP, Galic N, Vaugeois M, Dreier DA. Physiological variables in machine learning QSARs allow for both cross-chemical and cross-species predictions. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 263:115250. [PMID: 37487435 DOI: 10.1016/j.ecoenv.2023.115250] [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: 04/11/2023] [Revised: 06/23/2023] [Accepted: 07/09/2023] [Indexed: 07/26/2023]
Abstract
A major challenge in ecological risk assessment is estimating chemical-induced effects across taxa without species-specific testing. Where ecotoxicological data may be more challenging to gather, information on species physiology is more available for a broad range of taxa. Physiology is known to drive species sensitivity but understanding about the relative contribution of specific underlying processes is still elusive. Consequently, there remains a need to understand which physiological processes lead to differences in species sensitivity. The objective of our study was to utilize existing knowledge about organismal physiology to both understand and predict differences in species sensitivity. Machine learning models were trained to predict chemical- and species-specific endpoints as a function of both chemical fingerprints/descriptors and physiological properties represented by dynamic energy budget (DEB) parameters. We found that random forest models were able to predict chemical- and species-specific endpoints, and that DEB parameters were relatively important in the models, particularly for invertebrates. Our approach illuminates how physiological properties may drive species sensitivity, which will allow more realistic predictions of effects across species without the need for additional animal testing.
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Affiliation(s)
| | - Nika Galic
- Syngenta Crop Protection AG, Basel, Switzerland
| | - Maxime Vaugeois
- Syngenta Crop Protection, LLC, Greensboro, NC, United States
| | - David A Dreier
- Syngenta Crop Protection, LLC, Greensboro, NC, United States.
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14
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Gajewicz-Skretna A, Wyrzykowska E, Gromelski M. Quantitative multi-species toxicity modeling: Does a multi-species, machine learning model provide better performance than a single-species model for the evaluation of acute aquatic toxicity by organic pollutants? THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 861:160590. [PMID: 36473653 DOI: 10.1016/j.scitotenv.2022.160590] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/25/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
The toxicological profile of any chemical is defined by multiple endpoints and testing procedures, including representative test species from different trophic levels. While computer-aided methods play an increasingly important role in supporting ecotoxicology research and chemical hazard assessment, most of the recently developed machine learning models are directed towards a single, specific endpoint. To overcome this limitation and accelerate the process of identifying potentially hazardous environmental pollutants, we are introducing an effective approach for quantitative, multi-species modeling. The proposed approach is based on canonical correlation analysis that finds a pair(s) of uncorrelated, linear combinations of the original variables that best defines the overall variability within and between multiple biological responses and predictor variables. Its effectiveness was confirmed by the machine learning model for estimating acute toxicity of diverse organic pollutants in aquatic species from three trophic levels: algae (Pseudokirchneriella subcapitata), daphnia (Daphnia magna), and fish (Oryzias latipes). The multi-species model achieved a favorable predictive performance that were in line with predictive models derived for the aquatic organisms individually. The chemical bioavailability and reactivity parameters (n-octanol/water partition coefficient, chemical potential, and molecular size and volume) were important to accurately predict acute ecotoxicity to the three aquatic organisms. To facilitate the use of this approach, an open-source, Python-based script, named qMTM (quantitative Multi-species Toxicity Modeling) has been provided.
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Affiliation(s)
- Agnieszka Gajewicz-Skretna
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.
| | - Ewelina Wyrzykowska
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Maciej Gromelski
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
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15
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Lane TR, Harris J, Urbina F, Ekins S. Comparing LD 50/LC 50 Machine Learning Models for Multiple Species. ACS CHEMICAL HEALTH & SAFETY 2023. [DOI: 10.1021/acs.chas.2c00088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
- Thomas R. Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Joshua Harris
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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16
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Feng D, Baumgartner R. A closer look at the kernels generated by the decision and regression tree ensembles. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2150680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Dai Feng
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL, USA
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17
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Shavalieva G, Papadokonstantakis S, Peters G. Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity. J Chem Inf Model 2022; 62:4018-4031. [PMID: 35998659 PMCID: PMC9472271 DOI: 10.1021/acs.jcim.1c01079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Indexed: 11/30/2022]
Abstract
Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models' performance.
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Affiliation(s)
- Gulnara Shavalieva
- Department
of Space, Earth and Environment, Division of Energy Technology, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Stavros Papadokonstantakis
- Department
of Space, Earth and Environment, Division of Energy Technology, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Institute
of Chemical, Environmental and Bioscience Engineering, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria
| | - Gregory Peters
- Department
of Technology Management and Economics, Chalmers University of Technology, SE-411 33 Gothenburg, Sweden
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18
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Hu S, Liu G, Zhang J, Yan J, Zhou H, Yan X. Linking electron ionization mass spectra of organic chemicals to toxicity endpoints through machine learning and experimentation. JOURNAL OF HAZARDOUS MATERIALS 2022; 431:128558. [PMID: 35228074 DOI: 10.1016/j.jhazmat.2022.128558] [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: 12/06/2021] [Revised: 01/25/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Quantitative structure-activity relationship (QSAR) modeling has been widely used to predict the potential harm of chemicals, in which the prediction heavily relies on the accurate annotation of chemical structures. However, it is difficult to determine the accurate structure of an unknown compound in many cases, such as in complex water environments. Here, we solved the above problem by linking electron ionization mass spectra (EI-MS) of organic chemicals to toxicity endpoints through various machine learning methods. The proposed method was verified by predicting 50% growth inhibition of Tetrahymena pyriformis (T. pyriformis) and liver toxicity. The optimal model performance obtained an R2 > 0.7 or balanced accuracy > 0.72 for both the training set and test set. External experimentation further verified the application potential of our proposed method in the toxicity prediction of unknown chemicals. Feature importance analysis allowed us to identify critical spectral features that were responsible for chemical-induced toxicity. Our approach has the potential for toxicity prediction in such fields that it is difficult to determine accurate chemical structures.
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Affiliation(s)
- Song Hu
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China; School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China
| | - Guohong Liu
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Jin Zhang
- School of Public Health, Guizhou Medical University, Guiyang 550025, China
| | - Jiachen Yan
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Hongyu Zhou
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Xiliang Yan
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China.
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19
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Xu M, Yang H, Liu G, Tang Y, Li W. In Silico Prediction of Chemical Aquatic Toxicity by Multiple Machine Learning and Deep Learning Approaches. J Appl Toxicol 2022; 42:1766-1776. [PMID: 35653511 DOI: 10.1002/jat.4354] [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: 04/12/2022] [Revised: 05/16/2022] [Accepted: 05/31/2022] [Indexed: 11/08/2022]
Abstract
Fish is one of the model animals used to evaluate the adverse effects of a chemical exposed to the ecosystem. However, its low throughput and relevantly high expense make it impossible to test all new chemicals in manufacture. Hence using in silico models to prioritize compounds to be tested has been widely applied in environmental risk assessment and drug discovery. In this study, we constructed the local predictive models for four fish species, including bluegill sunfish, rainbow trout, fathead minnow, and sheepshead minnow, and the global models with all four fish data. A total of 1874 unique compounds with their labels, i.e. toxic (LC50 < 10 ppm) or nontoxic were collected from ECOTOX and literature. Both conventional machine learning methods and the deep learning architecture, graph convolutional network (GCN), were used to build predictive models. The classification accuracy of the best local model for each fish species was higher than 0.83. For the global models, two strategies including consistency prediction and probability threshold were adopted to improve the predictive capability at the cost of limiting applicability domain. For 63% of compounds in domain, the accuracy was around 0.97. By comparison of the deep learning and machine learning methods, we found that the single-task GCN showed specific advantages in performance and multi-task GCN showed no advantages over the conventional machine learning methods. The data and models are available on GitHub (https://github.com/ChemPredict/ChemicalAquaticToxicity).
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Affiliation(s)
- Minjie Xu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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20
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In silico prediction models for thyroid peroxidase inhibitors and their application to synthetic flavors. Food Sci Biotechnol 2022; 31:483-495. [PMID: 35464247 PMCID: PMC8994803 DOI: 10.1007/s10068-022-01041-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/22/2022] [Accepted: 02/02/2022] [Indexed: 11/27/2022] Open
Abstract
AbstractSystematic toxicity tests are often waived for the synthetic flavors as they are added in a very small amount in foods. However, their safety for some endpoints such as endocrine disruption should be concerned as they are likely to be active in low levels. In this case, structure–activity-relationship (SAR) models are good alternatives. In this study, therefore, binary, ternary, and quaternary prediction models were designed using simple or complex machine-learning methods. Overall, hard-voting classifiers outperformed other methods. The test scores for the best binary, ternary, and quaternary models were 0.6635, 0.5083, and 0.5217, respectively. Along with model development, some substructures including primary aromatic amine, (enol)ether, phenol, heterocyclic sulfur, and heterocyclic nitrogen, dominantly occurred in the most highly active compounds. The best predicting models were applied to synthetic flavors, and 22 agents appeared to have a strong inhibitory potential towards TPO activities.
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21
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Kumar P, Kumar A. Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling. Nanotoxicology 2021; 15:1199-1214. [PMID: 34961428 DOI: 10.1080/17435390.2021.2008039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.
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Affiliation(s)
- Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
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22
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Cordero JA, He K, Janya K, Echigo S, Itoh S. Predicting formation of haloacetic acids by chlorination of organic compounds using machine-learning-assisted quantitative structure-activity relationships. JOURNAL OF HAZARDOUS MATERIALS 2021; 408:124466. [PMID: 33191030 DOI: 10.1016/j.jhazmat.2020.124466] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 10/30/2020] [Accepted: 10/31/2020] [Indexed: 06/11/2023]
Abstract
The presence of disinfection byproducts (DBPs) in drinking water is a major public health concern, and an effective strategy to limit the formation of these DBPs is to prevent their precursors. In silico prediction from chemical structure would allow rapid identification of precursors and could be used as a prescreening tool to prioritize testing. We present models using machine learning algorithms (i.e., support vector regressor, random forest regressor, and multilayer perceptron regressor) and chemical descriptors as features to predict the formation of haloacetic acids (HAAs). A robust model with good predictivity (i.e., leave-one-out cross-validated Q2 > 0.5) to predict the formation of trichloroacetic acid (TCAA) was developed using a random forest regressor. The number of aromatic bonds, hydrophilicity, and electrotopological descriptors related to electrostatic interactions and the atomic distribution of electronegativity were identified as important predictors of TCAA formation potentials (FPs). However, the prediction of dichloroacetic acid was less accurate, which is congruent with the presence of different types of precursors exhibiting distinct mechanisms. This study demonstrates that nonlinear combinations of general chemical descriptors can adequately estimate HAAFPs, and we hope that our study can be used to predict precursors of other disinfection byproducts based on chemical structures using a similar workflow.
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Affiliation(s)
- José Andrés Cordero
- Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Nishikyo, Kyoto 6158540, Japan
| | - Kai He
- Research Center for Environmental Quality Management, Kyoto University, 1-2 Yumihama, Otsu, Shiga 5200811, Japan.
| | - Kanjira Janya
- Department of Chemical Engineering, Faculty of Engineering, Mahidol University, Nakorn Pathom 73170, Thailand
| | - Shinya Echigo
- Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Nishikyo, Kyoto 6158540, Japan
| | - Sadahiko Itoh
- Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Nishikyo, Kyoto 6158540, Japan
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23
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Zhou L, Fan D, Yin W, Gu W, Wang Z, Liu J, Xu Y, Shi L, Liu M, Ji G. Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals. BMC Bioinformatics 2021; 22:151. [PMID: 33761866 PMCID: PMC7992851 DOI: 10.1186/s12859-020-03903-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 11/24/2020] [Indexed: 10/30/2022] Open
Abstract
BACKGROUND A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset. RESULTS In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools. CONCLUSION QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.
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Affiliation(s)
- Linjun Zhou
- Nanjing Tech University, Nanjing, 211816, China
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China
| | - Deling Fan
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China
| | - Wei Yin
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China
| | - Wen Gu
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China
| | - Zhen Wang
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China
| | - Jining Liu
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China
| | - Yanhua Xu
- Nanjing Tech University, Nanjing, 211816, China.
| | - Lili Shi
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China.
| | - Mingqing Liu
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China
| | - Guixiang Ji
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China
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24
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Jiao Z, Hu P, Xu H, Wang Q. Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications. ACS CHEMICAL HEALTH & SAFETY 2020. [DOI: 10.1021/acs.chas.0c00075] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Zeren Jiao
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Pingfan Hu
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Hongfei Xu
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Qingsheng Wang
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
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25
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Lunghini F, Marcou G, Azam P, Enrici MH, Van Miert E, Varnek A. Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:655-675. [PMID: 32799684 DOI: 10.1080/1062936x.2020.1797872] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 07/15/2020] [Indexed: 06/11/2023]
Abstract
We report new consensus models estimating acute toxicity for algae, Daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSEext = 0.56-0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/ColorAtom utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracy and data coverage. Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending the applicability domain and relevance for application in an industrial context. Generated models and collected public data are made freely available.
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Affiliation(s)
- F Lunghini
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
- Toxicological and Environmental Risk Assessment Unit , Solvay S.A., St. Fons, France
| | - G Marcou
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
| | - P Azam
- Toxicological and Environmental Risk Assessment Unit , Solvay S.A., St. Fons, France
| | - M H Enrici
- Toxicological and Environmental Risk Assessment Unit , Solvay S.A., St. Fons, France
| | - E Van Miert
- Toxicological and Environmental Risk Assessment Unit , Solvay S.A., St. Fons, France
| | - A Varnek
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
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