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Egorova KS, Kibardin AV, Posvyatenko AV, Ananikov VP. Mechanisms of Biological Effects of Ionic Liquids: From Single Cells to Multicellular Organisms. Chem Rev 2024; 124:4679-4733. [PMID: 38621413 DOI: 10.1021/acs.chemrev.3c00420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
The review presents a detailed discussion of the evolving field studying interactions between ionic liquids (ILs) and biological systems. Originating from molten salt electrolytes to present multiapplication substances, ILs have found usage across various fields due to their exceptional physicochemical properties, including excellent tunability. However, their interactions with biological systems and potential influence on living organisms remain largely unexplored. This review examines the cytotoxic effects of ILs on cell cultures, biomolecules, and vertebrate and invertebrate organisms. Our understanding of IL toxicity, while growing in recent years, is yet nascent. The established findings include correlations between harmful effects of ILs and their ability to disturb cellular membranes, their potential to trigger oxidative stress in cells, and their ability to cause cell death via apoptosis. Future research directions proposed in the review include studying the distribution of various ILs within cellular compartments and organelles, investigating metabolic transformations of ILs in cells and organisms, detailed analysis of IL effects on proteins involved in oxidative stress and apoptosis, correlation studies between IL doses, exposure times and resulting adverse effects, and examination of effects of subtoxic concentrations of ILs on various biological objects. This review aims to serve as a critical analysis of the current body of knowledge on IL-related toxicity mechanisms. Furthermore, it can guide researchers toward the design of less toxic ILs and the informed use of ILs in drug development and medicine.
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Affiliation(s)
- Ksenia S Egorova
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russia
| | - Alexey V Kibardin
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Ministry of Health of Russian Federation, Moscow 117198, Russia
| | - Alexandra V Posvyatenko
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russia
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Ministry of Health of Russian Federation, Moscow 117198, Russia
| | - Valentine P Ananikov
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russia
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2
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Zhang R, Chen Y, Fan D, Liu T, Ma Z, Dai Y, Wang Y, Zhu Z. Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:789-803. [PMID: 37722394 DOI: 10.1080/1062936x.2023.2255517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 08/30/2023] [Indexed: 09/20/2023]
Abstract
Deep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results through feature engineering (FE), the model regression results based on the CNN model for feature extraction have been substantially improved. The results showed that all six models (FE-SVM, FE-RF, FE-MLP, CNN-SVM, CNN-RF, and CNN-MLP) had good prediction accuracy, but the results based on the CNN model were better. The hyperparameters of six models were optimized by grid search and the 10-fold cross validation. Compared with the existing models in the literature, the model performance has been further improved. The model could be used as an intelligent tool to guide the design or screening of low-toxicity ILs.
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Affiliation(s)
- R Zhang
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, People's Republic of China
| | - Y Chen
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, People's Republic of China
| | - D Fan
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, People's Republic of China
| | - T Liu
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, People's Republic of China
| | - Z Ma
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, People's Republic of China
| | - Y Dai
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, People's Republic of China
| | - Y Wang
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, People's Republic of China
| | - Z Zhu
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, People's Republic of China
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Singh A, Kumar S, Kapoor A, Kumar P, Kumar A. Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors. Toxicol Mech Methods 2023; 33:222-232. [PMID: 36042574 DOI: 10.1080/15376516.2022.2118092] [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: 10/14/2022]
Abstract
The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R2 = 0.8053, 0.7591, and 0.8283) and robustness (Q2LOO = 0.7507, 0.7227, and 0.8194; Q2LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R2EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.
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Affiliation(s)
- Ayushi Singh
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Sunil Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Archana Kapoor
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
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Makarov D, Fadeeva Y, Safonova E, Shmukler L. Predictive modeling of antibacterial activity of ionic liquids by machine learning methods. Comput Biol Chem 2022; 101:107775. [DOI: 10.1016/j.compbiolchem.2022.107775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/24/2022] [Accepted: 10/03/2022] [Indexed: 11/03/2022]
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5
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CORAL: Quantitative Structure Retention Relationship (QSRR) of flavors and fragrances compounds studied on the stationary phase methyl silicone OV-101 column in gas chromatography using correlation intensity index and consensus modelling. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.133437] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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6
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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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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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Toropov AA, Di Nicola MR, Toropova AP, Roncaglioni A, Carnesecchi E, Kramer NI, Williams AJ, Ortiz-Santaliestra ME, Benfenati E, Dorne JLCM. A regression-based QSAR-model to predict acute toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica): Calibration, validation, and future developments to support risk assessment of chemicals in amphibians. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 830:154795. [PMID: 35341855 PMCID: PMC9535814 DOI: 10.1016/j.scitotenv.2022.154795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/16/2022] [Accepted: 03/20/2022] [Indexed: 04/15/2023]
Abstract
Amphibian populations are undergoing a global decline worldwide. Such decline has been attributed to their unique physiology, ecology, and exposure to multiple stressors including chemicals, temperature, and biological hazards such as fungi of the Batrachochytrium genus, viruses such as Ranavirus, and habitat reduction. There are limited toxicity data for chemicals available for amphibians and few quantitative structure-activity relationship (QSAR) models have been developed and are publicly available. Such QSARs provide important tools to assess the toxicity of chemicals particularly in a data poor context. QSARs provide important tools to assess the toxicity of chemicals particularly when no toxicological data are available. This manuscript provides a description and validation of a regression-based QSAR model to predict, in a quantitative manner, acute lethal toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica). QSAR models for acute median lethal molar concentrations (LC50-12 h) of waterborne chemicals using the Monte Carlo method were developed. The statistical characteristics of the QSARs were described as average values obtained from five random distributions into training and validation sets. Predictions from the model gave satisfactory results for the overall training set (R2 = 0.72 and RMSE = 0.33) and were even more robust for the validation set (R2 = 0.96 and RMSE = 0.11). Further development of QSAR models in amphibians, particularly for other life stages and species, are discussed.
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Affiliation(s)
- Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Matteo R Di Nicola
- Unit of Dermatology and Cosmetology, IRCCS San Raffaele Hospital, Via Olgettina 60, 20132 Milan, Italy; Toxicology Division, Wageningen University, PO Box 8000, 6700 EA Wageningen, the Netherlands.
| | - Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Alessandra Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Edoardo Carnesecchi
- Institute of Risk Assessment, Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Evidence Management Unit, European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy.
| | - Nynke I Kramer
- Toxicology Division, Wageningen University, PO Box 8000, 6700 EA Wageningen, the Netherlands; Institute of Risk Assessment, Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands.
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, Durham, USA.
| | - Manuel E Ortiz-Santaliestra
- Instituto de Investigación en Recursos Cinegéticos (IREC) UCLM-CSIC-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain.
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Jean-Lou C M Dorne
- Methodology and Scientific Support Unit, European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy.
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Kumar P, Kumar A, Singh D. CORAL: Development of a hybrid descriptor based QSTR model to predict the toxicity of dioxins and dioxin-like compounds with correlation intensity index and consensus modelling. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2022; 93:103893. [PMID: 35654373 DOI: 10.1016/j.etap.2022.103893] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/21/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
In the present study, ninety-five halogenated dioxins and related chemicals (dibenzo-p-dioxins, dibenzofurans, biphenyls, and naphthalene) with endpoint pEC50 were used to develop twelve quantitative structure toxicity relationship (QSTR) models using inbuilt Monte Carlo algorithm of CORAL software. The hybrid optimal descriptor of correlation weights (DCW) using a combination of SMILES and HSG (hydrogen suppressed graph) was employed to generate QSTR models. Three target functions i.e. TF1 (WIIC=WCII=0), TF2 (WIIC= 0.3 & WCII=0) and TF3 (WIIC= 0.0 &WCII=0.3) were employed to develop robust QSTR models and the statistical outcomes of each target function were compared with each other. The correlation intensity index (CII) was found a reliable benchmark of the predictive potential for QSTR models. The numerical value of the determination coefficient of the validation set of split 1 computed by TF3 was found highest (RValid2=0.8438). The fragments responsible for the toxicity of dioxins and related chemicals were also identified in terms of the promoter of increase/decrease for pEC50. Three random splits (Split 1, Split 2 and Split 4) were selected for the extraction of the promoter of increase/decrease for pEC50. In the last, consensus modelling was performed using the intelligent consensus tool of DTC lab (https://dtclab.webs.com/software-tools). The original consensus model, which was created by combining four distinct models employing the split 4 arrangement, was more predictive for the validation set and the numerical value of the determination coefficient of the test set (validation set) was increased from 0.8133 to 0.9725. For the validation set of split 4, the mean absolute error (MAE 100%) was also lowered from 0.513 to 0.2739.
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Affiliation(s)
- Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana 136119, India.
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, Haryana 125001, India.
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, Haryana 124001, India
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Toropova AP, Toropov AA, Lombardo A, Lavado G, Benfenati E. Paradox of 'ideal correlations': improved model for air half-life of persistent organic pollutants. ENVIRONMENTAL TECHNOLOGY 2022; 43:2510-2515. [PMID: 33502960 DOI: 10.1080/09593330.2021.1882588] [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: 12/14/2020] [Accepted: 01/22/2021] [Indexed: 06/12/2023]
Abstract
The persistence of organic pollutants is an important environmental property due to the extended possibility to have an impact of corresponding substances. In many cases, the experimental values of the thousands of contaminants are missing. The object of the study is novel computational modelling for air pollutions. Quantitative structure-property relationship (QSPR) for air half-life has been built using the Monte Carlo method with applying the index of ideality of correlation (IIC). The basis of the predictive model of air half-life is the representation of the molecular structure by simplifying molecular input-line entry system (SMILES) and numerical data on the above endpoint (expressed by hours) converted to a decimal logarithm. The statistical quality of the model has been checked up with different validation metrics and is quite good. Paradoxically, the improvement of the statistical quality via the IIC for the validation set is done in detriment to the training set. The new model has performed better than those obtained previously on the same set of compounds, for the prediction of new compounds in the validation set. Some semi-quantitative indicators for the mechanistic interpretation of the model are suggested.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Anna Lombardo
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Giovanna Lavado
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
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Avinash Patil N, Macchindra Gore P, Shanmugrajan D, Patil H, Kudav M, Kandasubramanian B. Functionalized non-woven surfaces for combating the spread of the COVID-19 pandemic. Interface Focus 2022; 12:20210040. [PMID: 34956609 PMCID: PMC8662388 DOI: 10.1098/rsfs.2021.0040] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 11/09/2021] [Indexed: 12/29/2022] Open
Abstract
The worldwide outbreak of SARS-CoV-2 infection has necessitated mandatory use of face masks, personal protective equipment and intake of a healthy diet for immunity boosting. As per WHO's recommendation, continuous use of masks has been proven effective in decreasing the SARS-CoV-2 infection rate. The present study reports on the bacterial filtration efficacy (BFE) of a novel 4-ply functionalized non-woven face mask. We synthesized a polypropylene-based fabric with inner layers of melt-blown fine fibres coated with polylactic acid and immune-boosting herbal phytochemicals. Experimental studies on the synthesized face mask demonstrated a BFE of greater than 99% against Staphylococcus aureus (a bacterium species frequently found in mammalian respiratory tract). A thorough computational analysis using LibDock algorithm demonstrated an effective docking performance of herbal phytochemicals against harmful virus structures. More importantly, the face mask also showed sufficient and stable breathability as per regulatory standards. A breathing resistance of 30 Pa at an aerosol flow rate of 30 l h−1 was reported under standard temperature and pressure conditions, indicating a high potential for real-world applications.
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Affiliation(s)
- Nikhil Avinash Patil
- Nanofibre and Nano Surface Texturing Laboratory, Department of Metallurgical and Materials Engineering, Defence Institute of Advanced Technology, Ministry of Defence, Girinagar, Pune, Maharashtra 411025, India
| | - Prakash Macchindra Gore
- Nanofibre and Nano Surface Texturing Laboratory, Department of Metallurgical and Materials Engineering, Defence Institute of Advanced Technology, Ministry of Defence, Girinagar, Pune, Maharashtra 411025, India.,Institute for Frontier Materials, Deakin University, Waurn Ponds Campus, Geelong 3216, Victoria, Australia
| | - Dhivya Shanmugrajan
- Department of Life Sciences, Altem Technologies, Platinum Partner of Dassault Systemes, Bangalore 560095, Karnataka, India
| | - Harshal Patil
- Venus Safety and Health Pvt Ltd, New Mumbai 410208, Maharashtra, India
| | - Mahesh Kudav
- Venus Safety and Health Pvt Ltd, New Mumbai 410208, Maharashtra, India
| | - Balasubramanian Kandasubramanian
- Nanofibre and Nano Surface Texturing Laboratory, Department of Metallurgical and Materials Engineering, Defence Institute of Advanced Technology, Ministry of Defence, Girinagar, Pune, Maharashtra 411025, India.,Institute for Frontier Materials, Deakin University, Waurn Ponds Campus, Geelong 3216, Victoria, Australia
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12
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Singh R, Kumar P, Devi M, Lal S, Kumar A, Sindhu J, Toropova AP, Toropov AA, Singh D. Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors. NEW J CHEM 2022. [DOI: 10.1039/d2nj03515d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Monte Carlo optimization based QSGFEAR model development using CII results in the formation of more reliable, robust and predictive models.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Alla P. Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Andrey A. Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India
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Kumar P, Kumar A. Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling. Nanotoxicology 2021; 15:1199-1214. [PMID: 34961428 DOI: 10.1080/17435390.2021.2008039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.
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Affiliation(s)
- Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
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14
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Kumar P, Kumar A. Correlation intensity index (CII) as a benchmark of predictive potential: Construction of quantitative structure activity relationship models for anti-influenza single-stranded DNA aptamers using Monte Carlo optimization. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.131205] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Ahmadi S, Lotfi S, Afshari S, Kumar P, Ghasemi E. CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:1013-1031. [PMID: 34875951 DOI: 10.1080/1062936x.2021.2003429] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/03/2021] [Indexed: 06/13/2023]
Abstract
Global QSAR modelling was performed to predict the pIC50 values of 233 diverse heterocyclic compounds as BTK inhibitors with the Monte Carlo algorithm of CORAL software using the DCW hybrid descriptors extracted from SMILES notations of molecules. The dataset of 233 BTK inhibitors was randomly split into training, invisible training, calibration and validation sets. The index of ideality of correlation was also applied to build and judge the predictability of the QSAR models. Eight global QSAR models based on the hybrid optimal descriptor using two target functions, i.e. TF1 (WIIC = 0) and TF2 (WIIC = 0.2) have been constructed. The statistical parameters of QSAR models computed by TF2 are more reliable and robust and were used to predict the pIC50 values. The model constructed for split 4 via TF2 is regarded as the best model and the numerical values of r2Train, r2Valid, Q2Train and Q2Valid are equal to 0.7981, 0.7429, 0.7898 and 0.6784, respectively. By internal and external validation techniques, the predictability and reliability of the designed models have been assessed. The structural attributes responsible for the increase and decrease of pIC50 of BTK inhibitors were also identified.
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Affiliation(s)
- S Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - S Lotfi
- Department of Chemistry, Payame Noor University (PNU), Tehran, Iran
| | - S Afshari
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - E Ghasemi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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Ahmadi S, Lotfi S, Kumar P. Quantitative structure-toxicity relationship models for predication of toxicity of ionic liquids towards Leukemia rat cell line IPC-81 based on index of ideality of correlation. Toxicol Mech Methods 2021; 32:302-312. [PMID: 34724871 DOI: 10.1080/15376516.2021.2000686] [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] [Indexed: 10/19/2022]
Abstract
The application of ion liquids (ILs) as green solvents has attracted the attention of the scientific community. However, ILs may play the role of toxins. Even though ionic liquids may assist to minimise air pollution, but their discharge into aquatic ecosystems might result in significant water pollution due to their potential toxicity and inaccessibility to biodegradation. Recently, more attention has been paid to the toxicity of ILs on plants, bacteria, and humans. Here, a quantitative structure-toxicity relationship study (QSTR) based on the Monte Carlo method of CORAL software has been applied to estimate the logarithm of the half-maximal effective concentration of toxicity of ILs against leukemia rat cell line IPC-81 (logEC50). A hybrid optimal descriptor is used to build QSTR models for a large set of 304 diverse ILs including ammonium, imidazolium, morpholinium, phosphonium, piperidinium, pyridinium, pyrrolidinium, quinolinium, sulfonium, and protic ILs. The SMILES notations of Ils are utilized to compute the descriptor correlation weight (DCW). Four splits are made from the whole dataset and each split is randomly divided into four sets (training subsets and validation set). The index of ideality of correlation (IIC) is applied to evaluate the authenticity and robustness of the QSTR models. A QSTR model with statistical parameters R2=0.85, CCC =0.92, Q2=0.84, and MAE =0.25 for the validation set of the best split is considered as a prime model. The outliers and promoters of increase/decrease of logEC50 are extracted and the mechanistic interpretation of effective descriptors for the model is also offered.HighlightsGlobal SMILES-based QSAR model was developed to predict the toxicity of ILs.The CORAL software is used to model the ILs toxicity on IPC-81 leukemia rat cell lineIIC is tested as a criterion of predictive potential.The toxicological effects of ILs are discussed based on the proposed model.
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Affiliation(s)
- Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran medical sciences, Islamic Azad University, Tehran, Iran
| | - Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU), 19395-4697 Tehran, Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
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Lotfi S, Ahmadi S, Kumar P. The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors. RSC Adv 2021; 11:33849-33857. [PMID: 35497322 PMCID: PMC9042335 DOI: 10.1039/d1ra06861j] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/11/2021] [Indexed: 12/17/2022] Open
Abstract
Ionic liquids (ILs) have captured intensive attention owing to their unique properties such as high thermal stability, negligible vapour pressure, high dissolution capacity and high ionic conductivity as well as their wide applications in various scientific fields including organic synthesis, catalysis, and industrial extraction processes. Many applications of ionic liquids (ILs) rely on the melting point (Tm). Therefore, in the present manuscript, the melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs. The Monte Carlo algorithm of CORAL software is applied to build up a robust QSPR model to calculate the values Tm of 353 imidazolium ILs. Using a combination of SMILES and hydrogen-suppressed molecular graphs (HSGs), the hybrid optimal descriptor is computed and used to generate the QSPR models. Internal and external validation parameters are also employed to evaluate the predictability and reliability of the QSPR model. Four splits are prepared from the dataset and each split is randomly distributed into four sets i.e. training set (≈33%), invisible training set (≈31%), calibration set (≈16%) and validation set (≈20%). In QSPR modelling, the numerical values of various statistical features of the validation sets such as RValidation2, QValidation2, and IICValidation are found to be in the range of 0.7846–0.8535, 0.7687–0.8423 and 0.7424–0.8982, respectively. For mechanistic interpretation, the structural attributes which are responsible for the increase/decrease of Tm are also extracted. The melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs.![]()
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Affiliation(s)
- Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU) 19395-4697 Tehran Iran
| | - Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University Tehran Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University Kurukshetra Haryana 136119 India
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Kumar A, Kumar P. Prediction of power conversion efficiency of phenothiazine-based dye-sensitized solar cells using Monte Carlo method with index of ideality of correlation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:817-834. [PMID: 34530657 DOI: 10.1080/1062936x.2021.1973095] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Simplified molecular-input line-entry system (SMILES) notation and inbuilt Monte Carlo algorithm of CORAL software were employed to construct generative and prediction QSPR models for the analysis of the power conversion efficiency (PCE) of 215 phenothiazine derivatives. The dataset was divided into four splits and each split was further divided into four sets. A hybrid descriptor, a combination of SMILES and hydrogen suppressed graph (HSG), was employed to build reliable and robust QSPR models. The role of the index of ideality of correlation (IIC) was also studied in depth. We performed a comparative study to predict PCE using two target functions (TF1 without IIC and TF2 with IIC). Eight QSPR models were developed and the models developed with TF2 was shown robust and reliable. The QSPR model generated from split 4 was considered a leading model. The different statistical benchmarks were computed for the lead model and these were rtraining set2=0.7784; rinvisible training set2=0.7955; rcalibration set2=0.7738; rvalidation set2=0.7506; Qtraining set2=0.7691; Qinvisible training set2=0.7850; Qcalibration set2=0.7501; Qvalidation set2=0.7085; IICtraining set = 0.8590; IICinvisible training set = 0.8297; IICcalibration set = 0.8796; IICvalidation set = 0.8293, etc. The promoters of increase and decrease of endpoint PCE were also extracted.
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Affiliation(s)
- A Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
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Lotfi S, Ahmadi S, Kumar P. A hybrid descriptor based QSPR model to predict the thermal decomposition temperature of imidazolium ionic liquids using Monte Carlo approach. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116465] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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The predictive model for band gap prediction of metal oxide nanoparticles based on quasi-SMILES. Struct Chem 2021. [DOI: 10.1007/s11224-021-01748-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Predicting the end point potential break values: A case of potentiometric titration of lipophilic anions with cetylpyridinium chloride. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105758] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Papadiamantis AG, Klaessig FC, Exner TE, Hofer S, Hofstaetter N, Himly M, Williams MA, Doganis P, Hoover MD, Afantitis A, Melagraki G, Nolan TS, Rumble J, Maier D, Lynch I. Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E2033. [PMID: 33076428 PMCID: PMC7602672 DOI: 10.3390/nano10102033] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/08/2020] [Accepted: 10/11/2020] [Indexed: 12/15/2022]
Abstract
The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.
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Affiliation(s)
- Anastasios G. Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- Novamechanics Ltd., 1065 Nicosia, Cyprus; (A.A.); (G.M.)
| | | | | | - Sabine Hofer
- Department of Biosciences, Paris Lodron University of Salzburg, 5020 Salzburg, Austria; (S.H.); (N.H.); (M.H.)
| | - Norbert Hofstaetter
- Department of Biosciences, Paris Lodron University of Salzburg, 5020 Salzburg, Austria; (S.H.); (N.H.); (M.H.)
| | - Martin Himly
- Department of Biosciences, Paris Lodron University of Salzburg, 5020 Salzburg, Austria; (S.H.); (N.H.); (M.H.)
| | - Marc A. Williams
- U.S. Army Public Health Center (APHC), Aberdeen Proving Ground—South, Aberdeen, MD 21010, USA;
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece;
| | | | | | | | - Tracy S. Nolan
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - John Rumble
- R&R Data Services, Gaithersburg, MD 20877, USA;
- CODATA-VAMAS Working Group on Nanomaterials, 75016 Paris, France
| | | | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
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