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Peer Muhamed Noorani KR, Flora G, Surendarnath S, Mary Stephy G, Amesho KTT, Chinglenthoiba C, Thajuddin N. Recent advances in remediation strategies for mitigating the impacts of emerging pollutants in water and ensuring environmental sustainability. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119674. [PMID: 38061098 DOI: 10.1016/j.jenvman.2023.119674] [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: 07/08/2023] [Revised: 11/01/2023] [Accepted: 11/20/2023] [Indexed: 01/14/2024]
Abstract
The proliferation of emerging pollutants (EPs), encompassing a range of substances such as phthalates, phenolics, pharmaceuticals, pesticides, personal care products, surfactants, and disinfection agents, has become a significant global concern due to their potential risks to the environment and human well-being. Over the past two decades, numerous research studies have investigated the presence of EPs in wastewater and aquatic ecosystems, with the United States Environmental Protection Agency (USEPA) categorizing these newly introduced chemical compounds as emerging contaminants due to their poorly understood impact. EPs have been linked to adverse health effects in humans, including genotoxic and cytotoxic effects, as well as conditions such as obesity, diabetes, cardiovascular disease, and reproductive abnormalities, often associated with their estrogenic action. Microalgae have shown promise in the detoxification of both inorganic and organic contaminants, and several large-scale microalgal systems for wastewater treatment have been developed. However, the progress of algal bioremediation can be influenced by accidental contaminations and operational challenges encountered in pilot-scale research. Microalgae employ various processes, such as bioadsorption, biouptake, and biodegradation, to effectively remediate EPs. During microalgal biodegradation, complex chemical compounds are transformed into simpler substances through catalytic metabolic degradation. Integrating algal bioremediation with existing treatment methodologies offers a viable approach for efficiently eliminating EPs from wastewater. This review focuses on the use of algal-based biological remediation processes for wastewater treatment, the environmental impacts of EPs, and the challenges associated with implementing algal bioremediation systems to effectively remove emerging pollutants.
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Affiliation(s)
- Kalilur Rahman Peer Muhamed Noorani
- National Repository for Microalgae and Cyanobacteria - Freshwater (NRMC-F), (Sponsored by DBT, Govt. of India), Department of Microbiology, Bharathidasan University, Tiruchirappalli, 620 024, India
| | - G Flora
- PG and Research Department of Botany, St. Mary's College (Autonomous), Thoothukudi, Tamil Nadu, India
| | - S Surendarnath
- Department of Mechanical Engineering, DVR & Dr. HS MIC College of Technology (A), Vijayawada, 521 180, Andhra Pradesh, India
| | - G Mary Stephy
- PG and Research Department of Botany, St. Mary's College (Autonomous), Thoothukudi, Tamil Nadu, India
| | - Kassian T T Amesho
- Institute of Environmental Engineering, National Sun Yat-Sen University, Kaohsiung, 804, Taiwan; Center for Emerging Contaminants Research, National Sun Yat-Sen University, Kaohsiung, 804, Taiwan; The International University of Management, Centre for Environmental Studies, Main Campus, Dorado Park Ext 1, Windhoek, Namibia; Destinies Biomass Energy and Farming Pty Ltd, P.O.Box 7387, Swakomund, Namibia
| | | | - Nooruddin Thajuddin
- National Repository for Microalgae and Cyanobacteria - Freshwater (NRMC-F), (Sponsored by DBT, Govt. of India), Department of Microbiology, Bharathidasan University, Tiruchirappalli, 620 024, India; School of Life Sciences, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600048, India.
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Yang L, Tian R, Li Z, Ma X, Wang H, Sun W. Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach. CHEMOSPHERE 2023; 328:138433. [PMID: 36963572 DOI: 10.1016/j.chemosphere.2023.138433] [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: 01/11/2023] [Revised: 03/02/2023] [Accepted: 03/15/2023] [Indexed: 06/18/2023]
Abstract
Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.
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Affiliation(s)
- Lu Yang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Ruya Tian
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhoujing Li
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiaomin Ma
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Hongyan Wang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Wei Sun
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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Anand U, Adelodun B, Cabreros C, Kumar P, Suresh S, Dey A, Ballesteros F, Bontempi E. Occurrence, transformation, bioaccumulation, risk and analysis of pharmaceutical and personal care products from wastewater: a review. ENVIRONMENTAL CHEMISTRY LETTERS 2022; 20:3883-3904. [PMID: 35996725 PMCID: PMC9385088 DOI: 10.1007/s10311-022-01498-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 07/22/2022] [Indexed: 05/02/2023]
Abstract
UNLABELLED Almost all aspects of society from food security to disease control and prevention have benefited from pharmaceutical and personal care products, yet these products are a major source of contamination that ends up in wastewater and ecosystems. This issue has been sharply accentuated during the coronavirus disease pandemic 2019 (COVID-19) due to the higher use of disinfectants and other products. Here we review pharmaceutical and personal care products with focus on their occurrence in the environment, detection, risk, and removal. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s10311-022-01498-7.
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Affiliation(s)
- Uttpal Anand
- Ben-Gurion University of the Negev, 84105 Beer-Sheva, Israel
- Zuckerberg Institute for Water Research, Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Midreshet Ben Gurion, 8499000, Israel
| | - Bashir Adelodun
- Department of Agricultural and Biosystems Engineering, University of Ilorin, PMB 1515, Ilorin, Nigeria
- Department of Agricultural Civil Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Carlo Cabreros
- Environmental Engineering Program, National Graduate School of Engineering, University of the Philippines, 1101 Diliman, Quezon City, Philippines
| | - Pankaj Kumar
- Agro-Ecology and Pollution Research Laboratory, Department of Zoology and Environmental Science, Gurukula Kangri (Deemed to Be University), Haridwar, Uttarakhand 249404 India
| | - S. Suresh
- Department of Chemical Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh 462 003 India
| | - Abhijit Dey
- Department of Life Sciences, Presidency University, 86/1 College Street, Kolkata, West Bengal 700073 India
| | - Florencio Ballesteros
- Environmental Engineering Program, National Graduate School of Engineering, University of the Philippines, 1101 Diliman, Quezon City, Philippines
| | - Elza Bontempi
- INSTM and Chemistry for Technologies Laboratory, University of Brescia, Via Branze 38, 25123 Brescia, Italy
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Pharmaceutical and Personal Care Products in Different Matrices: Occurrence, Pathways, and Treatment Processes. WATER 2021. [DOI: 10.3390/w13091159] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The procedures for analyzing pharmaceuticals and personal care products (PPCPs) are typically tedious and expensive and thus, it is necessary to synthesize all available information from previously conducted research. An extensive collection of PPCP data from the published literature was compiled to determine the occurrence, pathways, and the effectiveness of current treatment technologies for the removal of PPCPs in water and wastewater. Approximately 90% of the compiled published papers originated from Asia, Europe, and the North American regions. The incomplete removal of PPCPs in different water and wastewater treatment processes was widely reported, thus resulting in the occurrence of PPCP compounds in various environmental compartments. Caffeine, carbamazepine, diclofenac, ibuprofen, triclosan, and triclocarban were among the most commonly reported compounds detected in water and solid matrices. Trace concentrations of PPCPs were also detected on plants and animal tissues, indicating the bioaccumulative properties of some PPCP compounds. A significant lack of studies regarding the presence of PPCPs in animal and plant samples was identified in the review. Furthermore, there were still knowledge gaps on the ecotoxicity, sub-lethal effects, and effective treatment processes for PPCPs. The knowledge gaps identified in this study can be used to devise a more effective research paradigm and guidelines for PPCP management.
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Banjare P, Singh J, Roy PP. Predictive classification-based QSTR models for toxicity study of diverse pesticides on multiple avian species. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:17992-18003. [PMID: 33410022 DOI: 10.1007/s11356-020-11713-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 11/16/2020] [Indexed: 06/12/2023]
Abstract
Protection and restoration of different endangered bird species from pesticide exposure is crucial from the point of safety assessment of ecosystem. Toxicity predictions or risk assessment of pesticides by chemometric tools is one of the challenging fields in recent era. In the present study, classification-based quantitative structure toxicity relationship (QSTR) models were developed for a large dataset (516) of diverse pesticides on multiple avian species mallard duck, bobwhite quail, and zebra finch according to the Organization for Economic Co-operation and Development guidelines. The QSTR models were developed by linear discriminant analysis method with genetic algorithm for feature selection from 2D descriptors using QSAR-Co software. Different statistical metrics assured the reliability and robustness of the developed models. External compound prediction highlighted predictive nature of the models. The mechanistic interpretation suggested that presence of phosphate, halogens (Cl, Br), ether linkage, and NCOO influence the avian toxicity. Furthermore, model reliability was checked by the application of the standardization approach of the applicability domain (AD). Finally, the developed models provided a priori toxic and non-toxic classification for unknown pesticides (inside AD), with particular emphasis on organophosphate pesticides. The interspecies toxicity correlation and predictions encouraged for their further applicability for the fulfilment of data gaps in vital missing species.
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Affiliation(s)
- Purusottam Banjare
- Department of Medicinal and Pharmaceutical Chemistry, Institute of Pharmaceutical Sciences, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Jagadish Singh
- Department of Medicinal and Pharmaceutical Chemistry, Institute of Pharmaceutical Sciences, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Partha Pratim Roy
- Department of Medicinal and Pharmaceutical Chemistry, Institute of Pharmaceutical Sciences, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009, India.
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Gajewicz-Skretna A, Kar S, Piotrowska M, Leszczynski J. The kernel-weighted local polynomial regression (KwLPR) approach: an efficient, novel tool for development of QSAR/QSAAR toxicity extrapolation models. J Cheminform 2021; 13:9. [PMID: 33579384 PMCID: PMC7881668 DOI: 10.1186/s13321-021-00484-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 01/11/2021] [Indexed: 11/10/2022] Open
Abstract
The ability of accurate predictions of biological response (biological activity/property/toxicity) of a given chemical makes the quantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models unique among the in silico tools. In addition, experimental data of selected species can also be used as an independent variable along with other structural as well as physicochemical variables to predict the response for different species formulating quantitative activity–activity relationship (QAAR)/quantitative structure–activity–activity relationship (QSAAR) approach. Irrespective of the models' type, the developed model's quality, and reliability need to be checked through multiple classical stringent validation metrics. Among the validation metrics, error-based metrics are more significant as the basic idea of a good predictive model is to improve the predictions' quality by lowering the predicted residuals for new query compounds. Following the concept, we have checked the predictive quality of the QSAR and QSAAR models employing kernel-weighted local polynomial regression (KwLPR) approach over the traditional linear and non-linear regression-based approaches tools such as multiple linear regression (MLR) and k nearest neighbors (kNN). Five datasets which were previously modeled using linear and non-linear regression method were considered to implement the KwPLR approach, followed by comparison of their validation metrics outcomes. For all five cases, the KwLPR based models reported better results over the traditional approaches. The present study's focus is not to develop a better or improved QSAR/QSAAR model over the previous ones, but to demonstrate the advantage, prediction power, and reliability of the KwLPR algorithm and establishing it as a novel, powerful cheminformatic tool. To facilitate the use of the KwLPR algorithm for QSAR/QSPR/QSTR/QSAAR modeling, the authors provide an in-house developed KwLPR.RMD script under the open-source R programming language. ![]()
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Affiliation(s)
- Agnieszka Gajewicz-Skretna
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland.
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1400 J. R. Lynch Street, P. O. Box 17910, Jackson, MS, 39217, USA
| | - Magdalena Piotrowska
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1400 J. R. Lynch Street, P. O. Box 17910, Jackson, MS, 39217, USA
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Bouhedjar K, Benfenati E, Nacereddine AK. Modelling quantitative structure activity-activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:785-801. [PMID: 32878491 DOI: 10.1080/1062936x.2020.1810770] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/12/2020] [Indexed: 06/11/2023]
Abstract
Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, Daphnia magna, and algae from the VEGA-Hub, as well as Tetrahymena pyriformis and Vibrio fischeri. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.
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Affiliation(s)
- K Bouhedjar
- Laboratoire de Synthèse et Biocatalyse Organique, Département de Chimie, Faculté des Sciences, Université Badji Mokhtar Annaba , Annaba, Algeria
- Laboratoire Bioinformatique, Centre de Recherche en Biotechnologie (CRBt) , Constantine, Algeria
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS , Milano, Italy
| | - E Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS , Milano, Italy
| | - A K Nacereddine
- Laboratory of Physical Chemistry and Biology of Materials, Department of Physics and Chemistry, Higher Normal School of Technological Education-Skikda , Skikda, Algeria
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Sun H, Yang X, Li X, Jin X. Development of predictive models for silicone rubber-water partition coefficients of hydrophobic organic contaminants. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2019; 21:2020-2030. [PMID: 31589229 DOI: 10.1039/c9em00343f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The silicone rubber passive sampling technique is extensively applied to monitor the aqueous freely dissolved concentration of hydrophobic organic compounds (HOCs). The silicone rubber-water partition coefficient (Ksrw) is an important parameter to accurately measure the concentrations of chemicals using passive sampling devices. In this study, two theoretical linear solvation energy relationship (TLSER) models and a quantitative structure-property relationship (QSPR) model were developed for predicting the Ksrw of HOCs. The 119 model compounds studied here included 31 personal care products, such as musks, UV-filters, and organophosphate flame retardants, as well as "conventional" pollutants, such as polycyclic aromatic hydrocarbons and polychlorinated biphenyls. The statistical parameters indicated that the final QSPR model with seven descriptors for all 119 chemicals had a satisfactory goodness-of-fit (Radj2 = 0.898), robustness (QLOO2 = 0.881) and predictive ability (Qext-F1,2,32 = 0.897-0.926). In comparison, the results of one TLSER model with four descriptors for 113 chemicals (Radj2 = 0.826, QLOO2 = 0.790, Qext-F1,2,32 = 0.805-0.837) and another TLSER model with one descriptor for 5 chemicals (Radj2 = 0.747, QLOO2 = 0.647) were also acceptable. The applicability domains of the obtained models covered chemicals containing hydroxyl, imino groups, carbonyl groups, ether bonds, halogen atoms, sulfur atoms, phosphorus atoms, nitro groups, and cyano groups. In addition, the structural features governing the partition behavior of chemicals between silicone rubber and water were explored through interpretation of appropriate mechanisms.
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Affiliation(s)
- Huichao Sun
- School of Life Science, Liaoning Normal University, Dalian 116081, China.
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9
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Furuhama A, Hayashi TI, Yamamoto H. Development of QSAAR and QAAR models for predicting fish early-life stage toxicity with a focus on industrial chemicals. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:825-846. [PMID: 31607178 DOI: 10.1080/1062936x.2019.1669707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 09/16/2019] [Indexed: 06/10/2023]
Abstract
We developed models for predicting fish early-life stage (ELS) toxicities oriented to industrial chemicals. The training set was constructed without data from the Office of Pesticide Programs Pesticide Ecotoxicity Database, the main source for the pesticide-biased training set used in our previous work (SAR QSAR Environ. Res. 29:9, 725-742). In addition to the descriptors from the previous study, we also used water solubility to develop the new models, which were evaluated against the test set used in our previous study so that we could focus on the effects of the different training set and the additional descriptor. The statistics for the new models were hardly better than those for the previous models, which suggests, contrary to our expectations, that pesticide-biased data can successfully be used to develop models for predicting the fish ELS toxicities oriented to industrial chemicals. Acute Daphnia magna toxicity was important for the predictive QSAARs in both studies. A distance-based method for defining the applicability domains indicated that water solubility was a key indicator for detecting underestimated chemicals. The comparison of fish ELS toxicities for chemicals presented in different literatures revealed the uncertainty of the experimental data, which may lead to the low predictivity.
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Affiliation(s)
- A Furuhama
- Centre for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES) , Tsukuba , Japan
| | - T I Hayashi
- Centre for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES) , Tsukuba , Japan
| | - H Yamamoto
- Centre for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES) , Tsukuba , Japan
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Roy PP, Banjare P, Verma S, Singh J. Acute Rat and Mouse Oral Toxicity Determination of Anticholinesterase Inhibitor Carbamate Pesticides: A QSTR Approach. Mol Inform 2019; 38:e1800151. [DOI: 10.1002/minf.201800151] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 04/08/2019] [Indexed: 01/01/2023]
Affiliation(s)
- Partha Pratim Roy
- Institute of Pharmaceutical SciencesGuru Ghasidas VishwavidyalayaA central University) Bilaspur- 495009 India
| | - Purusottam Banjare
- Institute of Pharmaceutical SciencesGuru Ghasidas VishwavidyalayaA central University) Bilaspur- 495009 India
| | - Sandhya Verma
- Institute of Pharmaceutical SciencesGuru Ghasidas VishwavidyalayaA central University) Bilaspur- 495009 India
| | - Jagadish Singh
- Institute of Pharmaceutical SciencesGuru Ghasidas VishwavidyalayaA central University) Bilaspur- 495009 India
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Cao Q, Liu L, Yang H, Cai Y, Li W, Liu G, Lee PW, Tang Y. In silico estimation of chemical aquatic toxicity on crustaceans using chemical category methods. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2018; 20:1234-1243. [PMID: 30069560 DOI: 10.1039/c8em00220g] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
With industrial development and eventual commercial use, environmental chemicals through accidental spills and effluents appear more frequently in aquatic ecosystems and may produce an enormous effect on water, soil, wildlife and human health. Therefore, aquatic toxicity becomes an increasingly important endpoint in the evaluation of the environmental impact of chemicals. In this study, based on ECOTOX database, a large data set containing 824 diverse compounds with experimental 48 h EC50 values on crustaceans was compiled. A series of in silico models were then developed using six machine learning methods combined with seven types of molecular fingerprints. Performance of these models was measured by an external validation set, involving 246 molecules. The best model proposed is MACCS fingerprint and SVM algorithm with high accuracy of 0.87 for external validation set. Additionally, we proposed five structural alerts identified by information gain and substructure frequency analysis for mechanistic interpretation. The models and structural alerts can provide critical information and useful tools for a priori evaluation of chemical aquatic toxicity in environmental hazard assessment.
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Affiliation(s)
- Qianqian Cao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
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Chen L, Zhuang C, Lu J, Jiang Y, Sheng C. Discovery of Novel KRAS-PDEδ Inhibitors by Fragment-Based Drug Design. J Med Chem 2018; 61:2604-2610. [PMID: 29510040 DOI: 10.1021/acs.jmedchem.8b00057] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Targeting KRAS-PDEδ protein-protein interactions with small molecules represents a promising opportunity for developing novel antitumor agents. However, current KRAS-PDEδ inhibitors are limited by poor cellular antitumor potency and the druggability of the target remains to be validated by new inhibitors. To tackle these challenges, herein, novel, highly potent KRAS-PDEδ inhibitors were identified by fragment-based drug design, providing promising lead compounds or chemical probes for investigating the biological functions and druggability of KRAS-PDEδ interaction.
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Affiliation(s)
- Long Chen
- School of Pharmacy , Second Military Medical University , 325 Guohe Road , Shanghai 200433 , China
| | - Chunlin Zhuang
- School of Pharmacy , Second Military Medical University , 325 Guohe Road , Shanghai 200433 , China
| | - Junjie Lu
- School of Pharmacy , Second Military Medical University , 325 Guohe Road , Shanghai 200433 , China
| | - Yan Jiang
- School of Pharmacy , Second Military Medical University , 325 Guohe Road , Shanghai 200433 , China
| | - Chunquan Sheng
- School of Pharmacy , Second Military Medical University , 325 Guohe Road , Shanghai 200433 , China
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Kar S, Roy K, Leszczynski J. Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach. Methods Mol Biol 2018; 1800:395-443. [PMID: 29934904 PMCID: PMC7120680 DOI: 10.1007/978-1-4939-7899-1_19] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
An extensive use of pharmaceuticals and the widespread practices of their erroneous disposal measures have made these products contaminants of emerging concern (CEC). Especially, active pharmaceutical ingredients (APIs) are ubiquitously detected in surface water and soil, mainly in the aquatic compartment, where they do affect the living systems. Unfortunately, there is a huge gap in the availability of ecotoxicological data on pharmaceuticals' environmental behavior and ecotoxicity which force EMEA (European Medicines Agency) to release guidelines for their risk assessment. In silico modeling approaches are vital tools to exploit the existing information to rapidly emphasize the potentially most hazardous and toxic pharmaceuticals and prioritize the most environmentally hazardous ones for focusing further on their experimental studies. The quantitative structure-activity relationship (QSAR) models are capable of predicting missing properties for toxic end-points required to prioritize existing, or newly synthesized chemicals for their potential hazard. This chapter reviews the information regarding occurrence and impact of pharmaceuticals and their metabolites in the environment along with their persistence, environmental fate, risk assessment, and risk management. A bird's eye view about the necessity of in silico methods for fate prediction of pharmaceuticals in the environment as well as existing successful models regarding ecotoxicity of pharmaceuticals are discussed. Available toxicity endpoints, ecotoxicity databases, and expert systems frequently used for ecotoxicity predictions of pharmaceuticals are also reported. The overall discussion justifies the requirement to build up additional in silico models for quick prediction of ecotoxicity of pharmaceuticals economically, without or involving only limited animal testing.
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Affiliation(s)
- Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, USA
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, USA
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14
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Bakire S, Yang X, Ma G, Wei X, Yu H, Chen J, Lin H. Developing predictive models for toxicity of organic chemicals to green algae based on mode of action. CHEMOSPHERE 2018; 190:463-470. [PMID: 29028601 DOI: 10.1016/j.chemosphere.2017.10.028] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 10/03/2017] [Accepted: 10/04/2017] [Indexed: 06/07/2023]
Abstract
Organic chemicals in the aquatic ecosystem may inhibit algae growth and subsequently lead to the decline of primary productivity. Growth inhibition tests are required for ecotoxicological assessments for regulatory purposes. In silico study is playing an important role in replacing or reducing animal tests and decreasing experimental expense due to its efficiency. In this work, a series of theoretical models was developed for predicting algal growth inhibition (log EC50) after 72 h exposure to diverse chemicals. In total 348 organic compounds were classified into five modes of toxic action using the Verhaar Scheme. Each model was established by using molecular descriptors that characterize electronic and structural properties. The external validation and leave-one-out cross validation proved the statistical robustness of the derived models. Thus they can be used to predict log EC50 values of chemicals that lack authorized algal growth inhibition values (72 h). This work systematically studied algal growth inhibition according to toxic modes and the developed model suite covers all five toxic modes. The outcome of this research will promote toxic mechanism analysis and be made applicable to structural diversity.
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Affiliation(s)
- Serge Bakire
- College of Geography and Environmental Sciences, Zhejiang Normal University, Yingbin Avenue 688, 321004, Jinhua, PR China
| | - Xinya Yang
- College of Geography and Environmental Sciences, Zhejiang Normal University, Yingbin Avenue 688, 321004, Jinhua, PR China
| | - Guangcai Ma
- College of Geography and Environmental Sciences, Zhejiang Normal University, Yingbin Avenue 688, 321004, Jinhua, PR China
| | - Xiaoxuan Wei
- College of Geography and Environmental Sciences, Zhejiang Normal University, Yingbin Avenue 688, 321004, Jinhua, PR China
| | - Haiying Yu
- College of Geography and Environmental Sciences, Zhejiang Normal University, Yingbin Avenue 688, 321004, Jinhua, PR China.
| | - Jianrong Chen
- College of Geography and Environmental Sciences, Zhejiang Normal University, Yingbin Avenue 688, 321004, Jinhua, PR China
| | - Hongjun Lin
- College of Geography and Environmental Sciences, Zhejiang Normal University, Yingbin Avenue 688, 321004, Jinhua, PR China
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Aalizadeh R, von der Ohe PC, Thomaidis NS. Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization-Support Vector Machine QSTR models. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2017; 19:438-448. [PMID: 28234392 DOI: 10.1039/c6em00679e] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
According to the European REACH Directive, the acute toxicity towards Daphnia magna should be assessed for any industrial chemical with a market volume of more than 1 t/a. Therefore, it is highly recommended to determine the toxicity at a certain confidence level, either experimentally or by applying reliable prediction models. To this end, a large dataset was compiled, with the experimental acute toxicity values (pLC50) of 1353 compounds in Daphnia magna after 48 h of exposure. A novel quantitative structure-toxicity relationship (QSTR) model was developed, using Ant Colony Optimization (ACO) to select the most relevant set of molecular descriptors, and Support Vector Machine (SVM) to correlate the selected descriptors with the toxicity data. The proposed model showed high performance (QLOO2 = 0.695, Rfitting2 = 0.920 and Rtest2 = 0.831) with low root mean square errors of 0.498 and 0.707 for the training and test set, respectively. It was found that, in addition to hydrophobicity, polarizability and summation of solute-hydrogen bond basicity affected toxicity positively, while minimum atom-type E-state of -OH influenced toxicity values in Daphnia magna inversely. The applicability domain of the proposed model was carefully studied, considering the effect of chemical structure and prediction error in terms of leverage values and standardized residuals. In addition, a new method was proposed to define the chemical space failure for a compound with unknown toxicity to avoid using these prediction results. The resulting ACO-SVM model was successfully applied on an additional evaluation set and the prediction results were found to be very accurate for those compounds that fall inside the defined applicability domain. In fact, compounds commonly found to be difficult to predict, such as quaternary ammonium compounds or organotin compounds were outside the applicability domain, while five representative homologues of LAS (non-ionic surfactants) were, on average, well predicted within one order of magnitude.
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Affiliation(s)
- Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
| | | | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
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