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Wang Y, Wang P, Fan T, Ren T, Zhang N, Zhao L, Zhong R, Sun G. From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: Dual computational toxicological approaches for prioritization. JOURNAL OF HAZARDOUS MATERIALS 2024; 476:134945. [PMID: 38905984 DOI: 10.1016/j.jhazmat.2024.134945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 06/03/2024] [Accepted: 06/15/2024] [Indexed: 06/23/2024]
Abstract
The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles.
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Affiliation(s)
- Yutong Wang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Peng Wang
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China; Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Ting Ren
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Na Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China.
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2
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Mousavi H, Zeynizadeh B, Hasanpour Galehban M. Ni II-containing l-glutamic acid cross-linked chitosan anchored on Fe 3O 4/ f-MWCNT: a sustainable catalyst for the green reduction and one-pot two-step reductive Schotten-Baumann-type acetylation of nitroarenes. NANOSCALE ADVANCES 2024; 6:3961-3977. [PMID: 39050942 PMCID: PMC11265578 DOI: 10.1039/d4na00160e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 05/28/2024] [Indexed: 07/27/2024]
Abstract
In this research, new and eye-catching catalytic applications of the nickelII (NiII) nanoparticles (NPs)-containing l-glutamic acid cross-linked chitosan anchored on magnetic carboxylic acid-functionalized multi-walled carbon nanotube (Fe3O4/f-MWCNT-CS-Glu/NiII) system, which was characterized by Fourier transform infrared (FT-IR), powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), SEM-based energy-dispersive X-ray (EDX) and elemental mapping, inductively coupled plasma-optical emission spectrometry (ICP-OES), thermogravimetric analysis (TGA), differential thermal analysis (DTA), and vibrating sample magnetometry (VSM), have been introduced for the environmentally benign and efficient reduction and one-pot two-step reductive Schotten-Baumann-type acetylation of nitroarenes in water at 60 °C under an air atmosphere. It is worth noting that the NiII-containing hybrid nanocatalyst, in the mentioned organic reactions, showed short reaction times, high yields of the desired products, acceptable turnover numbers (TONs) and turnover frequencies (TOFs), and also satisfactory magnetic recycling and reusability performance even after ten times of reuse. As another significant point, all the titled organic transformations have been carried out in water as an entirely favorable and green solvent for chemical reactions.
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Affiliation(s)
- Hossein Mousavi
- Department of Organic Chemistry, Faculty of Chemistry, Urmia University Urmia Iran
| | - Behzad Zeynizadeh
- Department of Organic Chemistry, Faculty of Chemistry, Urmia University Urmia Iran
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3
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Lv X, He M, Wei J, Li Q, Nie F, Shao Z, Wang Z, Tian L. Development of an effective QSAR-based hazard threshold prediction model for the ecological risk assessment of aromatic hydrocarbon compounds. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:47220-47236. [PMID: 38990260 DOI: 10.1007/s11356-024-34016-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 06/12/2024] [Indexed: 07/12/2024]
Abstract
The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure-activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC5) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and Vm, displayed excellent performance, as reflected by an optimal goodness of fit (R2adj = 0.918), robustness (Q2LOO = 0.869), and external prediction ability (Q2F1 = 0.760, Q2F2 = 0.782, and Q2F3 = 0.774). The hazard thresholds estimated using the optimal QSAR model were approximately close to the published water quality criteria developed by different countries and regions. The quantitative structure-toxicity relationship demonstrated that the molecular descriptors associated with electrophilicity and topological and electrotopological properties were important factors that affected the risks of AHCs. A new and reliable approach to estimate the hazard threshold of ecological risk assessment for various aromatic hydrocarbon pollutants was provided in this study, which can be widely popularised to similar contaminants with diverse structures.
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Affiliation(s)
- Xiudi Lv
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
- School of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Mei He
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
- School of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Jiajia Wei
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
- School of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Qiang Li
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
- School of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Fan Nie
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology Co., Ltd, Beijing, 102206, China
| | - Zhiguo Shao
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology Co., Ltd, Beijing, 102206, China
| | - Zhansheng Wang
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology Co., Ltd, Beijing, 102206, China
| | - Lei Tian
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China.
- School of Petroleum Engineering, Yangtze University, Wuhan, 430100, China.
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Li F, Wang P, Fan T, Zhang N, Zhao L, Zhong R, Sun G. Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133410. [PMID: 38185092 DOI: 10.1016/j.jhazmat.2023.133410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/09/2024]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.
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Affiliation(s)
- Feifan Li
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment, Beijing 100029, China
| | - Peng Wang
- Department of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Na Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
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Pandey NK, Murmu A, Banjare P, Matore BW, Singh J, Roy PP. Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:12371-12386. [PMID: 38228952 DOI: 10.1007/s11356-024-31872-7] [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: 09/26/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024]
Abstract
In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR). All developed QSAR and q-RASAR models were equally robust (R2 = 0.680-0.762, Q2Loo = 0.593-0.693, Q2F1 = 0.680-0.860) and find their superiority in either oat or corn model, respectively, based on MAE criteria. AD and PRI had been performed which confirm the reliability and predictability of the models. The mechanistic interpretation reveals that the symmetrical arrangement of electronegative atoms and polar groups directly influences the toxicity of compounds. The final phytotoxicity and prioritization are performed by the consensus approach which results into selection of 15 most toxic compounds for both species.
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Affiliation(s)
- Nilesh Kumar Pandey
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Anjali Murmu
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | | | - Balaji Wamanrao Matore
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Jagadish Singh
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Partha Pratim Roy
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India.
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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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Wang Y, Zhang P. Prediction of histone deacetylase inhibition by triazole compounds based on artificial intelligence. Front Pharmacol 2023; 14:1260349. [PMID: 38035010 PMCID: PMC10684768 DOI: 10.3389/fphar.2023.1260349] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
A quantitative structure-activity relationship (QSAR) study was conducted to predict the anti-colon cancer and HDAC inhibition of triazole-containing compounds. Four descriptors were selected from 579 descriptors which have the most obvious effect on the inhibition of histone deacetylase (HDAC). Four QSAR models were constructed using heuristic algorithm (HM), random forest (RF), radial basis kernel function support vector machine (RBF-SVM) and support vector machine optimized by particle swarm optimization (PSO-SVM). Furthermore, the robustness of four QSAR models were verified by K-fold cross-validation method, which was described by Q 2. In addition, the R 2 of the four models are greater than 0.8, which indicates that the four descriptors selected are reasonable. Among the four models, model based on PSO-SVM method has the best prediction ability and robustness with R 2 of 0.954, root mean squared error (RMSE) of 0.019 and Q 2 of 0.916 for the training set and R 2 of 0.965, RMSE of 0.017 and Q 2 of 0.907 for the test set. In this study, four key descriptors were discovered, which will help to screen effective new anti-colon cancer drugs in the future.
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Affiliation(s)
| | - Peijian Zhang
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong Province, China
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8
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Zhang Y, Xie L, Zhang D, Xu X, Xu L. Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants. Molecules 2023; 28:7457. [PMID: 38005179 PMCID: PMC10673120 DOI: 10.3390/molecules28227457] [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: 10/07/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
Persistent organic pollutants (POPs) are ubiquitous and bioaccumulative, posing potential and long-term threats to human health and the ecological environment. Quantitative structure-activity relationship (QSAR) studies play a guiding role in analyzing the toxicity and environmental fate of different organic pollutants. In the current work, five molecular descriptors are utilized to construct QSAR models for predicting the mean and maximum air half-lives of POPs, including specifically the energy of the highest occupied molecular orbital (HOMO_Energy_DMol3), a component of the dipole moment along the z-axis (Dipole_Z), fragment contribution to SAscore (SAscore_Fragments), subgraph counts (SC_3_P), and structural information content (SIC). The QSAR models were achieved through the application of three machine learning methods: partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA). The determination coefficients (R2) and relative errors (RE) for the mean air half-life of each model are 0.916 and 3.489% (PLS), 0.939 and 5.048% (MLR), 0.938 and 5.131% (GFA), respectively. Similarly, the determination coefficients (R2) and RE for the maximum air half-life of each model are 0.915 and 5.629% (PLS), 0.940 and 10.090% (MLR), 0.939 and 11.172% (GFA), respectively. Furthermore, the mechanisms that elucidate the significant factors impacting the air half-lives of POPs have been explored. The three regression models show good predictive and extrapolation abilities for POPs within the application domain.
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Affiliation(s)
| | | | | | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; (Y.Z.); (D.Z.)
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; (Y.Z.); (D.Z.)
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9
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Sun G, Bai P, Fan T, Zhao L, Zhong R, McElhinney RS, McMurry TBH, Donnelly DJ, McCormick JE, Kelly J, Margison GP. QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency. Pharmaceutics 2023; 15:2170. [PMID: 37631385 PMCID: PMC10458236 DOI: 10.3390/pharmaceutics15082170] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/16/2023] [Accepted: 08/19/2023] [Indexed: 08/27/2023] Open
Abstract
O6-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine O6-alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [3H]-methylated-DNA-based MGMT inactivation assays and calculated the IC50 values. Using the results of 370 compounds, we performed quantitative structure-activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC50 values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability (R2pr = 0.7474, Q2Fn = 0.7375-0.7437, CCCpr = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model (R2pr = 0.7528, Q2Fn = 0.7387-0.7449, CCCpr = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment.
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Affiliation(s)
- Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
| | - Peiying Bai
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
- Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
| | - R. Stanley McElhinney
- Chemistry Department, Trinity College, D02 PN40 Dublin, Ireland; (T.B.H.M.); (D.J.D.)
| | - T. Brian H. McMurry
- Chemistry Department, Trinity College, D02 PN40 Dublin, Ireland; (T.B.H.M.); (D.J.D.)
| | - Dorothy J. Donnelly
- Chemistry Department, Trinity College, D02 PN40 Dublin, Ireland; (T.B.H.M.); (D.J.D.)
| | - Joan E. McCormick
- Chemistry Department, Trinity College, D02 PN40 Dublin, Ireland; (T.B.H.M.); (D.J.D.)
| | - Jane Kelly
- Carcinogenesis Department, Paterson Institute for Cancer Research, Manchester M20 9BX, UK;
| | - Geoffrey P. Margison
- Carcinogenesis Department, Paterson Institute for Cancer Research, Manchester M20 9BX, UK;
- Epidemiology and Public Health Group, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PG, UK
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10
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Liu Y, Gao J, Zhu Q, Zhou X, Chu W, Huang J, Liu C, Yang B, Yang M. Zerovalent Iron/Cu Combined Degradation of Halogenated Disinfection Byproducts and Quantitative Structure-Activity Relationship Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:11241-11250. [PMID: 37461144 DOI: 10.1021/acs.est.3c01960] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Previous studies have reported that zerovalent iron (ZVI) can reduce several aliphatic groups of disinfection byproducts (DBPs) (e.g., haloacetic acids and haloacetamides) effectively, and the removal efficiency can be significantly improved by metallic copper. Information regarding ZVI/Cu combined degradation of different types of halogenated DBPs can help understand the fate of overall DBPs in drinking water distribution and storage systems consisting of unlined cast iron/copper pipes and related potential control strategies. In this study, we found that, besides aliphatic DBPs, many groups of new emerging aromatic DBPs formed in chlorinated and chloraminated drinking water can be effectively degraded by ZVI/Cu; meanwhile, total organic halogen and total ion intensity were reduced significantly after treatment. Moreover, a robust quantitative structure-activity relationship model was developed and validated based on the ZVI/Cu combined degradation rate constants of 14 typical aromatic DBPs; it can predict the degradation rate constants of other aromatic DBPs for screening and comparative purposes, and the optimized descriptors indicate that DBPs possessing a lower value of the lowest unoccupied molecular orbital energy and a higher value of dipole moment tend to present higher degradation rate constants. In addition, toxicity data of 47 DBPs (belonging to 18 groups) were predicted by two previously established toxicity models, demonstrating that, although most DBPs exhibit higher toxicity than their dehalogenated products, some DBPs show lower toxicity than their lowly halogenated analogs.
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Affiliation(s)
- Yan Liu
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Jianfa Gao
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Qingyao Zhu
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Xi Zhou
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Wenhai Chu
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Jingxiong Huang
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Changkun Liu
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Bo Yang
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Mengting Yang
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
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Aci-Sèche S, Bourg S, Bonnet P, Rebehmed J, de Brevern AG, Diharce J. A perspective on the sharing of docking data. Data Brief 2023; 49:109386. [PMID: 37492229 PMCID: PMC10365938 DOI: 10.1016/j.dib.2023.109386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/17/2023] [Accepted: 07/03/2023] [Indexed: 07/27/2023] Open
Abstract
Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.
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Affiliation(s)
- Samia Aci-Sèche
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311, Université d'Orléans BP 6759, Orléans Cedex 2, 45067, France
| | - Stéphane Bourg
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311, Université d'Orléans BP 6759, Orléans Cedex 2, 45067, France
| | - Pascal Bonnet
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311, Université d'Orléans BP 6759, Orléans Cedex 2, 45067, France
| | - Joseph Rebehmed
- Department of Computer Science and Mathematics, Lebanese, American University, Beirut, Lebanon
| | - Alexandre G. de Brevern
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, Biologie Intégrée du Globule Rouge, UMR_S 1134, DSIMB Bioinformatics team, 75014 Paris, France
| | - Julien Diharce
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, Biologie Intégrée du Globule Rouge, UMR_S 1134, DSIMB Bioinformatics team, 75014 Paris, France
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12
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Hao N, Sun P, Zhao W, Li X. Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 255:114806. [PMID: 36948010 DOI: 10.1016/j.ecoenv.2023.114806] [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: 10/23/2022] [Revised: 03/04/2023] [Accepted: 03/16/2023] [Indexed: 06/18/2023]
Abstract
Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.
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Affiliation(s)
- Ning Hao
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Peixuan Sun
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Wenjin Zhao
- College of New Energy and Environment, Jilin University, Changchun 130012, China.
| | - Xixi Li
- State Environmental Protection Key Laboratory of Ecological Effect and Risk Assessment of Chemicals, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, A1B 3×5, Canada.
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13
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Naidu A, Nayak SS, Lulu S S, Sundararajan V. Advances in computational frameworks in the fight against TB: The way forward. Front Pharmacol 2023; 14:1152915. [PMID: 37077815 PMCID: PMC10106641 DOI: 10.3389/fphar.2023.1152915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.
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Affiliation(s)
| | | | | | - Vino Sundararajan
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, India
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14
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Goyal S, Rani P, Chahar M, Hussain K, Kumar P, Sindhu J. Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies. J Biomol Struct Dyn 2023; 41:13616-13631. [PMID: 37010991 DOI: 10.1080/07391102.2023.2193991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/03/2023] [Indexed: 04/04/2023]
Abstract
Endocrine disrupter chemicals (EDCs) are both natural and man-made chemicals that mimic, block or interfere with human hormonal system. In the present manuscript, QSAR modeling was performed for the androgen disruptors that interfere with biosynthesis, metabolism or action of androgens that causes adverse effects on male reproductive system. A set of 96 EDCs that exhibited affinity towards androgen receptors (Log RBA) in rats were employed for carrying out QSAR studies using Hybrid descriptors (combination of HFG and SMILES) through Monte Carlo Optimization. Using index of ideality of correlation (TF2), five splits were formed and predictability of five models resulting from these splits was assessed by various validation parameters. Models resulted from first split was the top most one with R2validation = 0.7878. Structural attributes responsible for change in endpoint were studied by employing correlation weights of structural attributes. In order to further validate the model, new EDCs were designed using these attributes. In silico molecular modelling studies were performed to assess the detailed interactions with the receptor. The binding energies of all the designed compounds were observed to be better than lead and are in the range of -10.46 to -14.80. Molecular dynamics simulation of 100 ns was performed for ED01 and NED05. The results revealed that the protein-ligand complex bearing NED05 was more stable than lead ED01 exhibiting better interactions with the receptor. Further, in an attempt to assess their metabolism, ADME studies were evaluated using SwissADME. The developed model enables to predict the characteristics of designed compounds in an authentic way.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Surbhi Goyal
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Payal Rani
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - Monika Chahar
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Khalid Hussain
- Department of AS&H, Mewat Engineering College, Palla, Nuh, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
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15
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Wang K, Geng T, Xu H. The synthesis of triazine‐based conjugated microporous polymers via nucleophilic substitution reactions for fluorescence sensing to
o
‐nitrophenol. J Appl Polym Sci 2023. [DOI: 10.1002/app.53707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Kang Wang
- Anhui Province Key Laboratory of Optoelectronic and Magnetism Functional Materials, School of Chemistry and Chemical Engineering Anqing Normal University Anqing China
| | - Tong‐Mou Geng
- Anhui Province Key Laboratory of Optoelectronic and Magnetism Functional Materials, School of Chemistry and Chemical Engineering Anqing Normal University Anqing China
| | - Heng Xu
- Anhui Province Key Laboratory of Optoelectronic and Magnetism Functional Materials, School of Chemistry and Chemical Engineering Anqing Normal University Anqing China
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16
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Piekuś-Słomka N, Zapadka M, Kupcewicz B. Methoxy and methylthio-substituted trans-stilbene derivatives as CYP1B1 inhibitors – QSAR study with detailed interpretation of molecular descriptors. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.104204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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17
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Hentabli H, Bengherbia B, Saeed F, Salim N, Nafea I, Toubal A, Nasser M. Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction. Int J Mol Sci 2022; 23:13230. [PMID: 36362018 PMCID: PMC9657591 DOI: 10.3390/ijms232113230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/22/2022] [Accepted: 10/27/2022] [Indexed: 10/15/2023] Open
Abstract
Determining and modeling the possible behaviour and actions of molecules requires investigating the basic structural features and physicochemical properties that determine their behaviour during chemical, physical, biological, and environmental processes. Computational approaches such as machine learning methods are alternatives to predicting the physiochemical properties of molecules based on their structures. However, the limited accuracy and high error rates of such predictions restrict their use. In this paper, a novel technique based on a deep learning convolutional neural network (CNN) for the prediction of chemical compounds' bioactivity is proposed and developed. The molecules are represented in the new matrix format Mol2mat, a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. To evaluate the performance of the proposed methods, a series of experiments were conducted using two standard datasets, namely the MDL Drug Data Report (MDDR) and Sutherland, datasets comprising 10 homogeneous and 14 heterogeneous activity classes. After analysing the eight fingerprints, all the probable combinations were investigated using the five best descriptors. The results showed that a combination of three fingerprints, ECFP4, EPFP4, and ECFC4, along with a CNN activity prediction process, achieved the highest performance of 98% AUC when compared to the state-of-the-art ML algorithms NaiveB, LSVM, and RBFN.
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Affiliation(s)
- Hamza Hentabli
- Laboratory of Advanced Electronics Systems (LSEA), University of Medea, Medea 26000, Algeria
- UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
| | - Billel Bengherbia
- Laboratory of Advanced Electronics Systems (LSEA), University of Medea, Medea 26000, Algeria
| | - Faisal Saeed
- UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
- DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK
| | - Naomie Salim
- UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
| | - Ibtehal Nafea
- College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia
| | - Abdelmoughni Toubal
- Laboratory of Advanced Electronics Systems (LSEA), University of Medea, Medea 26000, Algeria
| | - Maged Nasser
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
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18
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Kumar P, Singh R, Kumar A, Toropova AP, Toropov AA, Devi M, Lal S, Sindhu J, Singh D. Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:677-700. [PMID: 36093620 DOI: 10.1080/1062936x.2022.2120068] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r2VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.
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Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - R Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - A Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, India
| | - A P Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - A A Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - M Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - S Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - J Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - D Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, India
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19
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Zhu C, Huang H, Chen Y. Recent advances in biological removal of nitroaromatics from wastewater. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 307:119570. [PMID: 35667518 DOI: 10.1016/j.envpol.2022.119570] [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: 03/19/2022] [Revised: 05/16/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Various nitroaromatic compounds (NACs) released into the environment cause potential threats to humans and animals. Biological treatment is valued for cost-effectiveness, environmental friendliness, and availability when treating wastewater containing NACs. Considering the significance and wide use of NACs, this review focuses on recent advances in biological treatment systems for NACs removal from wastewater. Meanwhile, factors affecting biodegradation and methods to enhance removal efficiency of NACs are discussed. The selection of biological treatment system needs to consider NACs loading and cost, and its performance is affected by configuration and operation strategy. Generally, sequential anaerobic-aerobic biological treatment systems perform better in mineralizing NACs and removing co-pollutants. Future research on mechanism exploration of NACs biotransformation and performance optimization will facilitate the large-scale application of biological treatment systems.
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Affiliation(s)
- Cuicui Zhu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, China
| | - Haining Huang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, China
| | - Yinguang Chen
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, China.
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20
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First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability. Mol Divers 2022; 26:2847-2862. [PMID: 35767129 DOI: 10.1007/s11030-022-10478-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/03/2022] [Indexed: 12/21/2022]
Abstract
Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure-activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has so far been used only in case of graded predictions or classification modeling. In this work, we attempt, for the first time, to apply RASAR for quantitative predictions (q-RASAR) using a case study of androgen receptor binding affinity data. We have computed a number of error-based and similarity-based measures such as weighted standard deviation of the predicted values, coefficient of variation of the computed predictions, average similarity level of close training compounds for each query molecule, standard deviation and coefficient of variation of similarity levels, maximum similarity levels to positive and negative close training compounds, a concordance measure indicating similarity to positive, negative or both classes of close training compounds, etc. We have clubbed these additional measures along with the selected chemical descriptors from the previously developed QSAR model and redeveloped new partial least squares models from the training set, and predicted the endpoint using the query data set. Interestingly, these new models outperform the internal and external validation quality of the original QSAR model. In this study, we have also introduced a new similarity-based concordance measure (Banerjee-Roy coefficient) that can significantly contribute to the model quality. A q-RASAR model also has the advantage over read-across predictions in providing easy interpretation and indicating quantitative contributions of important chemical features. The strategy described here should be applicable to other biological/toxicological/property data modeling for enhanced quality of predictions, easy interpretability, and efficient transferability.
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21
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Wang Z, Zhang F, Wang DG. Predicting joint toxicity of chemicals by incorporating a weighted descriptor into a mixture model: Cases for binary antibiotics and binary nanoparticles. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 236:113472. [PMID: 35390689 DOI: 10.1016/j.ecoenv.2022.113472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/21/2022] [Accepted: 03/27/2022] [Indexed: 06/14/2023]
Abstract
A prediction method that integrated a mixture descriptor with an established mixture toxicology method was proposed for the joint toxicity of chemical pollutants. A weighted descriptor derived from the single descriptor of each component was employed to calculate a mixture descriptor, which was successfully embedded into the generalized concentration addition (GCA) model named the extended GCA (XGCA) model. To develop and validate the proposed approach, binary antibiotic mixtures (ciprofloxacin and oxytetracycline) and metal-oxide (copper oxide and zinc oxide) nanoparticle mixtures were selected to study their toxicity to freshwater green algae. The results showed that concentration-response curve (CRC) derived from the XGCA model was closer to the observed CRC than those from the GCA, Concentration Addition (CA), and Independent Action (IA) models. The difference between effect concentrations predicted by the XGCA model and observed did not exceed a factor of 1.6. The XGCA model was relatively more accurate at predicting joint toxicity (in terms of effect concentrations and effect errors) than the reference models, independent of component types and mixture ratios. The XGCA model predicts the joint toxicity through molecular structural or nanostructural characters, thus modes of toxic action are not preconditions for predicting the toxicity of the mixtures. This result demonstrates the practicability of using the XGCA method in toxicity assessments of mixture pollutants with unknown modes of action.
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Affiliation(s)
- Zhuang Wang
- School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science and Technology, Nanjing 210044, PR China.
| | - Fan Zhang
- School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science and Technology, Nanjing 210044, PR China
| | - De-Gao Wang
- College of Environmental Science and Engineering, Dalian Maritime University, Dalian 116026, PR China
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22
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Huang HJ, Lee YH, Chou CL, Zheng CM, Chiu HW. Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach. Comput Struct Biotechnol J 2022; 20:1876-1884. [PMID: 35521549 PMCID: PMC9052077 DOI: 10.1016/j.csbj.2022.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/02/2022] [Accepted: 04/11/2022] [Indexed: 11/15/2022] Open
Abstract
Drug-induced nephrotoxicity remains a common problem after exposure to medications and diagnostic agents, which may be heightened in the kidney microenvironment and deteriorate kidney function. In this study, the toxic effects of fourteen marked drugs with the individual chemical structure were evaluated in kidney cells. The quantitative structure–activity relationship (QSAR) approach was employed to investigate the potential structural descriptors of each drug-related to their toxic effects. The most reasonable equation of the QSAR model displayed that the estimated regression coefficients such as the number of ring assemblies, three-membered rings, and six-membered rings were strongly related to toxic effects on renal cells. Meanwhile, the chemical properties of the tested compounds including carbon atoms, bridge bonds, H-bond donors, negative atoms, and rotatable bonds were favored properties and promote the toxic effects on renal cells. Particularly, more numbers of rotatable bonds were positively correlated with strong toxic effects that displayed on the most toxic compound. The useful information discovered from our regression QSAR models may help to identify potential hazardous moiety to avoid nephrotoxicity in renal preventive medicine.
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23
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Li F, Fan T, Sun G, Zhao L, Zhong R, Peng Y. Systematic QSAR and iQCCR modelling of fused/non-fused aromatic hydrocarbons (FNFAHs) carcinogenicity to rodents: reducing unnecessary chemical synthesis and animal testing. GREEN CHEMISTRY 2022; 24:5304-5319. [DOI: 10.1039/d2gc00986b] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
The prediction of new or untested FNFAHs will reduce unnecessary chemical synthesis and animal testing, and contribute to the design of safer chemicals for production activities.
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Affiliation(s)
- Feifan Li
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
- Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
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