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Iduoku K, Ngongang M, Kulathunga J, Daghighi A, Casanola-Martin G, Simsek S, Rasulev B. Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study. Foods 2024; 13:2147. [PMID: 38998653 PMCID: PMC11241027 DOI: 10.3390/foods13132147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 06/23/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024] Open
Abstract
The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.
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Affiliation(s)
- Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58102, USA
| | - Marvellous Ngongang
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Jayani Kulathunga
- Cereal Science Graduate Program, Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA (S.S.)
- Department of Multidisciplinary Studies, Faculty of Urban and Aquatic Bioresources, University of Sri Jayewardenepura, Gangodawila, Nugegoda 10250, Sri Lanka
| | - Amirreza Daghighi
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58102, USA
| | - Gerardo Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Senay Simsek
- Cereal Science Graduate Program, Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA (S.S.)
- Whistler Center for Carbohydrate Research, Department of Food Science, Purdue University, West Lafayette, IN 47907, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58102, USA
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2
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Dang L. Classification Model of Pesticide Toxicity in Americamysis bahia Based on Quantum Chemical Descriptors. ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2024; 87:69-77. [PMID: 38937321 DOI: 10.1007/s00244-024-01077-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 06/19/2024] [Indexed: 06/29/2024]
Abstract
A set of quantum chemical descriptors (molecular polarization, heat capacity, entropy, Mulliken net charge of the most positive hydrogen atom, APT charge of the most negative atom and APT charge of the most positive atom with hydrogen summed into heavy atoms) was successfully used to establish the classification models for the toxicity pLC50 of pesticides in Americamysis bahia. The optimal random forest model (Class Model A) yielded predictive accuracy of 100% (training set of 217 pesticides), 95.8% (test set of 72 pesticides) and 99.0% (total set of 289 pesticides), which were very satisfactory, compared with previous classification models reported for the toxicity of compounds in aquatic organisms. Therefore, it is reasonable to apply the quantum chemical descriptors associated with molecular structural information on molecular bulk, chemical reactivity and weak interactions, to develop classification models for the toxicity pLC50 of pesticides in A. bahia.
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Affiliation(s)
- Limin Dang
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, 411104, Hunan, China.
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3
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Daghighi A, Casanola-Martin GM, Iduoku K, Kusic H, González-Díaz H, Rasulev B. Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10116-10127. [PMID: 38797941 DOI: 10.1021/acs.est.4c01017] [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: 05/29/2024]
Abstract
In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.
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Affiliation(s)
- Amirreza Daghighi
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, Zagreb 10000, Croatia
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa 48940, Spain
- BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, Leioa 48940, Spain
- IKERBASQUE, Basque Foundation for Science,Bilbao, Biscay 48011, Spain
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
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4
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Xu B, Cui W, Tao L, Yang L, Zhao X. Risk mitigation strategy and mechanism analysis of neonicotinoid pesticides on earthworms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123719. [PMID: 38458525 DOI: 10.1016/j.envpol.2024.123719] [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: 02/12/2024] [Revised: 03/03/2024] [Accepted: 03/04/2024] [Indexed: 03/10/2024]
Abstract
Neonicotinoid insecticides (NNIs) are a new class of widely used insecticides with certain risks to non-target organisms, like earthworms. The gray correlation method was used to calculate the comprehensive risk effect value of acute toxicity (LC50) and bioaccumulation (logKow) of NNIs on earthworms. A comprehensive effects three-dimensional quantitative structure-activity relationship (3D-QSAR) model was constructed, using NNIs molecular structures and the comprehensive effect value as the independent and dependent variables, respectively. One of the representatives guadipyr (GUA) was selected as the template molecule for the molecular design and modification. A total of 63 NNIs alternatives were designed with a reduced comprehensive value higher than 10%, and as high as 42%. After screening, 15 NNIs alternatives were screened with decreased acute toxicity to earthworms, bioaccumulation effects and improved functional property. The calculated primary acute risk quotient of earthworms shows that the designed NNIs alternatives have lower earthworm risks (reduction of 70.48-99.99%). Results also found that the electronic, geometric and topological parameters of NNIs are the key descriptors that affect NNIs alternatives' toxicity. The number of hydrophobic interaction amino acid residues in NNIs molecules also contributes to the acute toxicity and the bioaccumulation of NNIs alternatives on earthworms. This study aims to design and screen functionally improved and environmentally friendly NNIs alternatives that have low risk to earthworms and provide theoretical methods and new ideas for the risk control and development of pesticides represented by NNIs.
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Affiliation(s)
- Bohan Xu
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
| | - Weihan Cui
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
| | - Li Tao
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
| | - Luze Yang
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
| | - Xingmin Zhao
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
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5
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Etemadi S, Khashei M. Etemadi regression in chemometrics: Reliability-based procedures for modeling and forecasting. Heliyon 2024; 10:e26399. [PMID: 38434293 PMCID: PMC10907519 DOI: 10.1016/j.heliyon.2024.e26399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/18/2023] [Accepted: 02/12/2024] [Indexed: 03/05/2024] Open
Abstract
The creation of predictive models with a high degree of generalizability in chemical analysis and process optimization is of paramount importance. Nonetheless, formulating a prediction model based on collected data from chemical measurements that maximize quantitative generalizability remains a challenging task for chemometrics experts. To tackle this challenge, a range of forecasting models with varying characteristics, structures, and capabilities has been developed, utilizing either accuracy-based or reliability-based modeling strategies. While the majority of models follow the accuracy-based approach, a recently proposed reliability-based approach, known as the Etemadi approach, has shown impressive performance across various scientific fields. The Etemadi models were constructed through a reliability-based parameter estimation process in such a manner that maximizes the models' reliability. However, the foundation of modeling procedures for chemometrics purposes is built upon the assumption that high generalizability in inaccessible/test data is achieved through the accuracy-based training procedure in which errors in available historical/training data are minimized. After conducting a thorough review of the current literature, we have found that none of the forecasting models for chemometrics purposes incorporate reliability into their modeling procedures. Given the dynamic and highly sensitive nature of chemistry experiments and processes, implementing a reliable model that controls performance criteria variation is a promising strategy for achieving stable and robust forecasts. To address this research gap, this paper introduces several key innovations, which can be highlighted as follows: (1) Proposing a general design structure based on a new optimal reliability-based parameter estimation process. (2) Introducing a novel risk-based modeling strategy that minimizes the performance variation of models implemented under different conditions in chemical laboratory experiments, to generate a more generalizable model for diverse applications in chemometrics. (3) Specifying the degree of influence that each reliability and accuracy factor has in enhancing the generalizability and uncertainty modeling of chemometric models. Empirical evidence confirms the effectiveness and superior performance of reliability-based models compared to accuracy-based models in 78.95% of the cases across various fields, including Pharmacology, Biochemistry, Agrochemical, Geochemical, Biological, Pollutants, Physicochemical Properties, and Gases Experiment. Furthermore, the study's findings demonstrate that the reliability-based modeling approach outperforms the accuracy-based strategy in terms of MAE, MSE, ARV, and RMSE by an average of 4.697%, 5.646%, 5.646%, and 4.342%, respectively. It is also statistically proven that reliability has a more significant impact on improving the generalizability of chemometric models than accuracy. This emphasizes the importance of including reliability as a crucial factor in chemometrics modeling, a consideration that has been overlooked in traditional modeling processes. Consequently, reliability-based modeling approaches can be regarded as a viable alternative to conventional accuracy-based modeling methods for chemical modeling purposes.
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Affiliation(s)
- Sepideh Etemadi
- Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, 84156-83111, Iran
| | - Mehdi Khashei
- Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, 84156-83111, Iran
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6
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Ghosh V, Bhattacharjee A, Kumar A, Ojha PK. q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:11-30. [PMID: 38193248 DOI: 10.1080/1062936x.2023.2298452] [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: 09/01/2023] [Accepted: 12/16/2023] [Indexed: 01/10/2024]
Abstract
A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure-Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r2, Q2F1 and Q2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.
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Affiliation(s)
- V Ghosh
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - A Bhattacharjee
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - A Kumar
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - P K Ojha
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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7
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Diem-Tran PT, Ho TT, Tuan NV, Bao LQ, Phuong HT, Chau TTG, Minh HTB, Nguyen CT, Smanova Z, Casanola-Martin GM, Rasulev B, Pham-The H, Cuong LCV. Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands. TOXICS 2023; 11:595. [PMID: 37505560 PMCID: PMC10383909 DOI: 10.3390/toxics11070595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/02/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβML) and potentiometric sensitivity (PSML) of 200 ligands in complexes with the heavy metal ions Cu2+, Cd2+, and Pb2+. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R2) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R2 of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.
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Affiliation(s)
- Phan Thi Diem-Tran
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Tue-Tam Ho
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Nguyen-Van Tuan
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Le-Quang Bao
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Ha Tran Phuong
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Trinh Thi Giao Chau
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Hoang Thi Binh Minh
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Cong-Truong Nguyen
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Zulayho Smanova
- Department of Chemistry, National University of Uzbekistan after Mirzo Ulugbek, Tashkent 100012, Uzbekistan
| | | | - Bakhtiyor Rasulev
- Department of Chemistry, National University of Uzbekistan after Mirzo Ulugbek, Tashkent 100012, Uzbekistan
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Hai Pham-The
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Le Canh Viet Cuong
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
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8
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Jia Q, Wang S, Yu M, Wang Q, Yan F. Two QSAR models for predicting the toxicity of chemicals towards Tetrahymena pyriformis based on topological-norm descriptors and spatial-norm descriptors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:147-161. [PMID: 36749040 DOI: 10.1080/1062936x.2023.2171478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Quantitative structure-activity relationship (QSAR) is important for safe, rapid and effective risk assessment of chemicals. In this study, two QSAR models were established with 1230 chemicals to predict toxicity towards Tetrahymena pyriformis using multiple linear regression (MLR) method. The topological(T)-QSAR model was developed by using topological-norm descriptors generated from the topological structure, and the spatial(S)-QSAR model were built with spatial-norm descriptors obtained from the three-dimensional structure of molecules and topological-norm descriptors. The r2training and r2test are 0.8304 and 0.8338 for the T-QSAR model, and 0.8485 and 0.8585 for the S-QSAR model, which means that T-QSAR model and S-QSAR model can be used to predict toxicity quickly and accurately. In addition, we also conducted validation on the developed models. Satisfying validation results and statistical parameters demonstrated that QSAR models based on the topological-norm descriptors and spatial-norm descriptors proposed in this paper could be further utilized to estimate the toxicity of chemicals towards Tetrahymena pyriformis.
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Affiliation(s)
- Q Jia
- School of Marine and Environmental Science, Tianjin Marine Environmental Protection and Restoration Technology Engineering Center, Tianjin University of Science and Technology, Tianjin, PR China
| | - S Wang
- School of Marine and Environmental Science, Tianjin Marine Environmental Protection and Restoration Technology Engineering Center, Tianjin University of Science and Technology, Tianjin, PR China
| | - M Yu
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, Tianjin, PR China
| | - Q Wang
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, Tianjin, PR China
| | - F Yan
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, Tianjin, PR China
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9
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Diéguez-Santana K, Nachimba-Mayanchi MM, Puris A, Gutiérrez RT, González-Díaz H. Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches. ENVIRONMENTAL RESEARCH 2022; 214:113984. [PMID: 35981614 DOI: 10.1016/j.envres.2022.113984] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/19/2022] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quantitative Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia. The most influential descriptors included in the MLR model are RBF, JGI2, nCbH, nRCOOR, nRSR, nPO4 and 'Cl-090', with positive contributions to the dependent variable (negative decimal logarithm of median lethal concentration at 96-h). The Random Forest (RF) regression model was superior amongst the five ML models. We observed higher values of R2 (0.812) and lower values of RMSE (0.595) and MAE (0.462) in the cross-validation training set and external validation set. Similarly, this study had a high level of fitness and was internally robust and externally predictive compared to models presented in similar studies. The results suggest that the developed QSTR models are suitable for reliably predicting the aquatic toxicity of structurally diverse pesticides and can be used for screening, prioritising new pesticides, filling data gaps and overcoming the limitations of in vivo and in vitro tests.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940, Leioa, Spain; Universidad Regional Amazónica Ikiam, Tena, Ecuador.
| | | | - Amilkar Puris
- Facultad de Ciencias de la Ingeniería, Universidad Técnica Estatal de Quevedo, Ecuador
| | | | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940, Leioa, Spain; Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940, Leioa, Spain; IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Biscay, Spain
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10
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Fang Z, Yu X, Zeng Q. Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis. Toxicology 2022; 480:153325. [PMID: 36115645 DOI: 10.1016/j.tox.2022.153325] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 12/01/2022]
Abstract
The random forest (RF) algorithm, together with ten Dragon descriptors, was used to develop a quantitative structure-toxicity/activity relationship (QSTR/QSAR) model for a larger data set of 1792 chemical toxicity pIGC50 towards Tetrahymena pyriformis. The optimal RF (ntree =300 and mtry =3) model yielded root mean square (rms) errors of 0.261 for the training set (1434 chemicals) and 0.348 for the test set (358 chemicals). Compared with other QSTR models reported in the literature, the optimal RF model in this paper is more accurate. The feasibility of applying the RF algorithm to predict chemical toxicity pIGC50 towards Tetrahymena pyriformis has been verified.
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Affiliation(s)
- Zhengjun Fang
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, China
| | - Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, China.
| | - Qun Zeng
- Department of Neurosurgery, Xiangtan Central Hospital, Xiangtan, Hunan 411100, China
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11
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Diéguez-Santana K, Casañola-Martin GM, Torres R, Rasulev B, Green JR, González-Díaz H. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds. Mol Pharm 2022; 19:2151-2163. [PMID: 35671399 PMCID: PMC9986951 DOI: 10.1021/acs.molpharmaceut.2c00029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States.,Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Roldan Torres
- Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Humbert González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, 48940 Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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12
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Support vector machine-based model for toxicity of organic compounds against fish. Regul Toxicol Pharmacol 2021; 123:104942. [PMID: 33940084 DOI: 10.1016/j.yrtph.2021.104942] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/27/2021] [Accepted: 04/26/2021] [Indexed: 11/22/2022]
Abstract
Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity pLC50 of organic chemicals against various fish species. Only six descriptors were used to develop the QSTR model, by applying support vector machine (SVM) together with genetic algorithm. The QSTR model was trained and established on a sufficiently large data set of 840 organic compounds and evaluated with a test set (281 compounds). Compared with other QSTRs reported in the literature, the optimal SVM model for fish toxicity produces better statistical results with determination coefficients R2 above 0.70 for both the training set and test set, although the QSTR model in this work possesses fewer molecular descriptors. Applying SVM and genetic algorithm to develop the QSTR model for pLC50 of organic compounds against various fish species is successful.
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13
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Extended Regression Modeling of the Toxicity of Phenol Derivatives to <i>Tetrahymena pyriformis</i> Using the Electronic-Structure Informatics Descriptor. JOURNAL OF COMPUTER AIDED CHEMISTRY 2021. [DOI: 10.2751/jcac.22.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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14
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Yu X. Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2020; 224:105496. [PMID: 32408003 DOI: 10.1016/j.aquatox.2020.105496] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 04/16/2020] [Accepted: 04/19/2020] [Indexed: 06/11/2023]
Abstract
Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure-activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC10 of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a test set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R2 of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R2 of 0.75 and MAE of 0.61 for the test set. Compared with other models reported in the literature, our SVM model for the toxicity pEC10 shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the test set. A QSTR model for pEC50 of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.
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Affiliation(s)
- Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan, 411104, China.
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15
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Yu X. Prediction of chemical toxicity to Tetrahymena pyriformis with four-descriptor models. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 190:110146. [PMID: 31923753 DOI: 10.1016/j.ecoenv.2019.110146] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 12/27/2019] [Accepted: 12/28/2019] [Indexed: 06/10/2023]
Abstract
A quantitative structure-toxicity relationship (QSTR) model based on four descriptors was successfully developed for 1163 chemical toxicants against Tetrahymena pyriformis by applying general regression neural network (GRNN). The training set consisting of 600 organic compounds was used to train GRNN models that were evaluated with the test set of 563 compounds. For the optimal GRNN model, the training set possesses the coefficient of determination R2 of 0.86 and root mean square (rms) error of 0.41, and the test set has R2 of 0.80 and rms of 0.41. Investigated results indicate that the optimal GRNN model is accurate, although the GRNN model has only four descriptor and more samples in the test set.
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Affiliation(s)
- Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Chemistry and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan, 411104, China.
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16
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Ye Z, Yang J, Zhong N, Tu X, Jia J, Wang J. Tackling environmental challenges in pollution controls using artificial intelligence: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 699:134279. [PMID: 33736193 DOI: 10.1016/j.scitotenv.2019.134279] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 09/02/2019] [Accepted: 09/03/2019] [Indexed: 06/12/2023]
Abstract
This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.
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Affiliation(s)
- Zhiping Ye
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Jiaqian Yang
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Na Zhong
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Xin Tu
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, United Kingdom
| | - Jining Jia
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Jiade Wang
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, PR China.
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17
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Pham-The H, Cabrera-Pérez MÁ, Nam NH, Castillo-Garit JA, Rasulev B, Le-Thi-Thu H, Casañola-Martin GM. In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling. Curr Top Med Chem 2019; 18:2209-2229. [PMID: 30499410 DOI: 10.2174/1568026619666181130140350] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 10/16/2018] [Accepted: 11/19/2018] [Indexed: 11/22/2022]
Abstract
One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.
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Affiliation(s)
- Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Miguel Á Cabrera-Pérez
- Unit of Modeling and Experimental Biopharmaceutics, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.,Department of Engineering, Area of Pharmacy and Pharmaceutical Technology, Miguel Hernández University, 03550 Sant Juan d'Alacant, Alicante, Spain
| | - Nguyen-Hai Nam
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Juan A Castillo-Garit
- Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas "Dr. Serafín Ruiz de Zarate Ruiz" de Villa Clara, Santa Clara, 50200, Villa Clara, Cuba
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymer Materials, North Dakota State University, Fargo, ND, 58102, United States
| | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, 144 Xuan Thuy, Hanoi, Vietnam
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymer Materials, North Dakota State University, Fargo, ND, 58102, United States
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18
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Ruan Y, Dou Y, Chen J, Warren A, Li J, Lin X. Evaluation of phenol-induced ecotoxicity in two model ciliate species: Population growth dynamics and antioxidant enzyme activity. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2018; 166:176-185. [PMID: 30269012 DOI: 10.1016/j.ecoenv.2018.09.091] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 09/08/2018] [Accepted: 09/21/2018] [Indexed: 06/08/2023]
Abstract
The application of identical exposure dosages in different species generally leads to a limited understanding of dose-response patterns because of species-specific factors. To evaluate phenol-induced ecotoxicity, antioxidant enzyme activity and population growth dynamics were compared in two model ciliates, the marine species Euplotes vannus and the freshwater species Paramecium multimicronucleatum. Dosage ranges of phenol exposure were based on tolerance limits of test ciliates as determined by their carrying capacity (K) and growth rate (r). When the exposure duration of phenol increased from 48 h to 96 h, the median effective dose (ED50) for P. multimicronucleatum decreased faster than that for E. vannus, and the ratio of the former to the latter declined from 2.75 to 0.30. When E. vannus was exposed to increasing concentrations of phenol (0-140 mg l-1), r rose initially and then dropped significantly at concentrations higher than 40 mg l-1, whereas K decreased linearly over the entire range. For P. multimicronucleatum, both r and K declined gradually over the range 0-200 mg l-1 phenol. Dose-response patterns of activities of three individual antioxidant enzymes, and the integrative index of the three enzymes, presented a biphasic (inverse U-shaped) curve at each of four durations of exposure, i.e. 12 h, 24 h, 36 h and 48 h. Cluster analyses and multidimensional scaling analyses of antioxidant enzyme activities revealed differences in the temporal succession of physiological states between the two model ciliates. In brief, combining ED50 with growth dynamic parameters is helpful for designing exposure dosages of toxicants in ecotoxicity tests.
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Affiliation(s)
- Yuanyuan Ruan
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, South China Normal University, Guangzhou 510631, China
| | - Yingfeng Dou
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, South China Normal University, Guangzhou 510631, China
| | - Jingyi Chen
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, South China Normal University, Guangzhou 510631, China
| | - Alan Warren
- Department of Life Sciences, Natural History Museum, Cromwell Road, London SW7 5BD, UK
| | - Jiqiu Li
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, South China Normal University, Guangzhou 510631, China.
| | - Xiaofeng Lin
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, South China Normal University, Guangzhou 510631, China
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19
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Lv L, Li W, Yu Y, Meng L, Qin W, Wu C. Predicting acute toxicity of traditional Chinese medicine wastewater using UV absorption and volatile fatty acids as surrogates. CHEMOSPHERE 2018; 194:211-219. [PMID: 29207353 DOI: 10.1016/j.chemosphere.2017.11.170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 11/19/2017] [Accepted: 11/29/2017] [Indexed: 06/07/2023]
Abstract
In this study, the applicability of UV absorbance at 254 nm (UV254) and volatile fatty acids (VFAs) to serve as reliable surrogates to predict acute toxicity of traditional Chinese medicine (TCM) wastewater was investigated. The medicine residues and VFAs were identified as main components of the TCM wastewater, and their individual and joint toxicity assays were operated with luminescent bacteria. The median effective concentration (EC50) values of medicine residues and VFAs were in the range of 26.46-165.55 mg/L and 11.45-20.58 g/L, respectively. The joint toxicity action modes of medicine residues, VFAs and medicine residues-VFAs were identified as additive, additive and synergistic respectively. UV254 and VFAs showed better correlations with acute toxicity according to the correlation analysis, compared with other conventional parameters. The regression model was a good fit for toxic unit (TU50) as a function of UV254 and VFAs according to the stepwise regression method (adjusted R2 = 0.836). Validation of the model to the pilot-scale samples provided satisfactory prediction results in the influent and hydrolysis acidification effluent samples tests, but for EGSB effluent and final effluent samples, the model needed further optimization. Surrogates prediction using UV254 and VFAs provided a valuable and cost-saving tool for rapid or on-line monitoring of acute toxicity of TCM wastewater.
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Affiliation(s)
- Longyi Lv
- School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, PR China
| | - Weiguang Li
- School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, PR China; State Key Laboratory of Urban Water Resource and Environment, Harbin, 150090, PR China.
| | - Yang Yu
- School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, PR China
| | - Liqiang Meng
- School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, PR China
| | - Wen Qin
- School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, PR China
| | - Chuandong Wu
- School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, PR China
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20
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Hao SY, Hou SX, Van Hecke K, Cui GH. Construction of noninterpenetrating and interpenetrating Co(ii) networks with halogenated carboxylate modulated by auxiliary N-donor co-ligands: structural diversity, electrochemical and photocatalytic properties. Dalton Trans 2017; 46:1951-1964. [DOI: 10.1039/c6dt04516b] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Six Co(ii) complexes with 2,5-dichloroterephthalate and bis(benzimidazole) were synthesized and characterized, which manifest photocatalytic activities.
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Affiliation(s)
- Shao Yun Hao
- College of Chemical Engineering
- Hebei Key Laboratory for Environment Photocatalytic and Electrocatalytic Materials
- North China University of Science and Technology
- Tangshan Hebei 063009
- P. R. China
| | - Suo Xia Hou
- College of Chemical Engineering
- Hebei Key Laboratory for Environment Photocatalytic and Electrocatalytic Materials
- North China University of Science and Technology
- Tangshan Hebei 063009
- P. R. China
| | - Kristof Van Hecke
- XStruct
- Department of Inorganic and Physical Chemistry
- Ghent University
- 9000 Ghent
- Belgium
| | - Guang Hua Cui
- College of Chemical Engineering
- Hebei Key Laboratory for Environment Photocatalytic and Electrocatalytic Materials
- North China University of Science and Technology
- Tangshan Hebei 063009
- P. R. China
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21
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Zhou W, Fan Y, Cai X, Xiang Y, Jiang P, Dai Z, Chen Y, Tan S, Yuan Z. High-accuracy QSAR models of narcosis toxicities of phenols based on various data partition, descriptor selection and modelling methods. RSC Adv 2016. [DOI: 10.1039/c6ra21076g] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The environmental protection agency thinks that quantitative structure–activity relationship (QSAR) analysis can better replace toxicity tests.
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Affiliation(s)
- Wei Zhou
- Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests
- Hunan Agricultural University
- Changsha 410128
- P. R. China
- Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization
| | - Yanjun Fan
- Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests
- Hunan Agricultural University
- Changsha 410128
- P. R. China
| | - Xunhui Cai
- Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests
- Hunan Agricultural University
- Changsha 410128
- P. R. China
| | - Yan Xiang
- Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests
- Hunan Agricultural University
- Changsha 410128
- P. R. China
| | - Peng Jiang
- Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests
- Hunan Agricultural University
- Changsha 410128
- P. R. China
| | - Zhijun Dai
- Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests
- Hunan Agricultural University
- Changsha 410128
- P. R. China
| | - Yuan Chen
- Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests
- Hunan Agricultural University
- Changsha 410128
- P. R. China
| | - Siqiao Tan
- Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests
- Hunan Agricultural University
- Changsha 410128
- P. R. China
| | - Zheming Yuan
- Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests
- Hunan Agricultural University
- Changsha 410128
- P. R. China
- Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization
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