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Kumar A, Ojha PK, Roy K. The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2024; 26:870-881. [PMID: 38652036 DOI: 10.1039/d4em00059e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a short/long time which depends on the nature and amount of chemicals consumed. Therefore, the maximum acceptable daily intake of chemicals must be calculated to prevent these risks. In the present work, regression-based quantitative structure-activity relationship (QSAR) models were developed using 39 pesticides with maximum acceptable daily intake (MADI) for humans as the endpoint. From the statistical results (R2 = 0.674-0.712, QLOO2 = 0.553-0.580, Q(F1)2 = 0.544-0.611, and Q(F2)2 = 0.531-0.599), it can be inferred that the developed models were robust, reliable, reproducible, accurate, and predictive. Intelligent Consensus Prediction (ICP) was employed to improve the external predictivity (Q(F1)2 =0.579-0.657 and Q(F2)2 = 0.563-0.647) of the models. Some of the chemical markers responsible for toxicity enhancement are the presence of unsaturated bonds, lipophilicity, presence of C< (double bond-single bond-single bonded carbon), and the presence of sulphur and phosphate bonds at the topological distances 1 and 6, while the presence of hydrophilic groups and short chain fragments reduces the toxicity. The Pesticide Properties Database (PPDB) (1694 pesticides) was also screened with the developed models. Hence, this research work will be helpful for the toxicity assessment of pesticides before their synthesis, the development of eco-friendly and safer pesticides, and data-gap filling reducing the time, cost, and animal experimentation. Thus, this study might hold promise for future potential MADI assessment of pesticides and provide a meaningful contribution to the field of risk assessment.
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Affiliation(s)
- Ankur Kumar
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
| | - Probir Kumar Ojha
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
| | - Kunal Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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Chitre TS, Mandot AM, Bhagwat RD, Londhe ND, Suryawanshi AR, Hirode PV, Bhatambrekar AL, Choudhari SY. 2,4,6-Trimethoxy chalcone derivatives: an integrated study for redesigning novel chemical entities as anticancer agents through QSAR, molecular docking, ADMET prediction, and computational simulation. J Biomol Struct Dyn 2024:1-24. [PMID: 38321946 DOI: 10.1080/07391102.2024.2309644] [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: 08/11/2023] [Accepted: 01/18/2024] [Indexed: 02/08/2024]
Abstract
QSAR, an efficient and successful approach for optimizing lead compounds in drug design, was employed to study a reported series of compounds derived from 2,4,6-trimethoxy chalcone derivatives. The ability of these compounds to inhibit CDK1 was examined, with the help of QSARINS software for model development. The generated QSAR model revealed three significant descriptors, exhibiting strong correlations with impressive statistical values: cross-validation leave-one-out correlation coefficient (Q2LOO) = 0.6663, coefficient of determination (R2) = 0.7863, external validation coefficient (R2ext) = 0.7854, cross-validation leave-many-out correlation coefficient (Q2LMO) = 0.6256, Concordance Correlation Coefficient for cross-validation (CCCcv) = 0.8150, CCCtr = 0.8804, and CCCext = 0.8750. From the key structural findings and the insights gained from the descriptors, ETA_dPsi_A, WTPT-5, and GATS7s, new lead molecules were designed. The designed molecules were then evaluated for their CDK1 inhibitory activity using the three-descriptor model developed in this study. To evaluate their drug likeliness, in-silico ADMET predictions were made using Schrodinger's Software. Molecular docking was carried out to determine the interactions of designed compounds with the target protein. The designed compounds having excellent binding pocket molecular stability and anticancer effectiveness was substantiated by the findings of the molecular dynamics simulation. The results of this work point out important properties and crucial interactions necessary for efficient protein inhibition, suggesting lead candidates for further development as novel anticancer agents.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Trupti S Chitre
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Aayush M Mandot
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Ramali D Bhagwat
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Nikhil D Londhe
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Atharva R Suryawanshi
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Purvaj V Hirode
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Aniket L Bhatambrekar
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Somdutta Y Choudhari
- Department of Pharmaceutical Chemistry, Modern College of Pharmacy, Pune, Maharashtra, India
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Yang S, Kar S. First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals: Toxicity data gap filling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167991. [PMID: 37898216 DOI: 10.1016/j.scitotenv.2023.167991] [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: 08/30/2023] [Revised: 10/02/2023] [Accepted: 10/19/2023] [Indexed: 10/30/2023]
Abstract
The Toxic Substances Control Act (TSCA) mandates the Environmental Protection Agency (EPA) to document chemicals entering the US. Due to the vast range of toxicity endpoints, experimental toxicological study for all chemicals is impossible to conduct. To address this, in silico methods like QSAR and read-across are strategically used to prioritize testing for chemicals lacking ecotoxicity data. Aquatic toxicity is one of the most critical endpoints directly related to aquatic species, mainly fish, followed by direct to indirect effects on humans through drinking water and fish as food, respectively. Therefore, we have employed the ToxValDB database to curate acute LC50 toxicity data for three Tilapia species covering two different genera, an ideal species for aquatic toxicity testing. Employing the curated dataset, we have developed multiple robust and predictive QSAR and quantitative read-across structure-activity relationship (q-RASAR) models for Tilapia zillii, Oreochromis niloticus, and Oreochromis mossambicus which helped to understand the toxicological mode of action (MoA) of the modeled chemicals and predict the aquatic toxicity of new untested chemicals followed by toxicity data gap filling. The best three QSAR models showed encouraging statistical quality in terms of determination coefficient R2 (0.94, 0.74, and 0.77), cross-validated leave-one-out Q2 (0.90, 0.67 and 0.70), and predictive capability in terms of R2pred (0.95, 0.77, and 0.74) for T. zillii, O. niloticus, and O. mossambicus datasets, respectively. The developed best mathematical models were used for the prediction of aquatic toxicity in terms of pLC50 for 297 untested organic chemicals across three major Tilapia species ranging from 1.841 to 8.561 M in terms of environmental risk assessment.
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Affiliation(s)
- Siyun Yang
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ 07083, USA
| | - Supratik Kar
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ 07083, USA.
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Chitre TS, Hirode PV, Lokwani DK, Bhatambrekar AL, Hajare SG, Thorat SB, Priya D, Pradhan KB, Asgaonkar KD, Jain SP. In-silico studies of 2-aminothiazole derivatives as anticancer agents by QSAR, molecular docking, MD simulation and MM-GBSA approaches. J Biomol Struct Dyn 2023:1-19. [PMID: 37811574 DOI: 10.1080/07391102.2023.2262594] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 09/17/2023] [Indexed: 10/10/2023]
Abstract
Targeting Hec1/Nek2 is considered as crucial target for cancer treatment due to its significant role in cell proliferation. In pursuit of this, a series of twenty-five 2-aminothiazoles derivatives, along with their Hec1/Nek2 inhibitory activities were subjected to QSAR studies utilizing QSARINS software. The significant three descriptor QSAR model was generated, showing noteworthy statistical parameters: a correlation coefficient of cross validation leave one out (Q2LOO) = 0.7965, coefficient of determination (R2) = 0.8436, (R2ext) = 0.6308, cross validation leave many out (Q2LMO) = 0.7656, Concordance Correlation Coefficient (CCCCV = 0.8875), CCCtr = 0.9151, and CCCext = 0.0.7241. The descriptors integral to generated QSAR model include Moreau-Broto autocorrelation, which represents the spatial autocorrelation of a property along the molecular graph's topological structure (ATSC1i), Moran autocorrelation at lag 8, which is weighted by charges (MATS8c) and RPSA representing the total molecular surface area. It was noted that these descriptors significantly influence Hec1/Nek2 inhibitory activity of 2-aminothiazoles derivatives. New lead molecules were designed and predicted for their Hec1/Nek2 inhibitory activity based on the developed three descriptor model. Further, the ADMET and Molecular docking studies were carried out for these designed molecules. The three molecules were selected based on their docking score and further subjected for MD simulation studies. Post-MD MM-GBSA analysis were also performed to predicted the free binding energies of molecules. The study helped us to understand the key interactions between 2-aminothiazoles derivatives and Hec1/Nek2 protein that may be necessary to develop new lead molecules against cancer.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Trupti S Chitre
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Purvaj V Hirode
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Deepak K Lokwani
- Rajarshi Shahu College of Pharmacy, Buldhana, Maharashtra, India
| | - Aniket L Bhatambrekar
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Sayli G Hajare
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Shubhangi B Thorat
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - D Priya
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRMIST, Kattankulathur, Tamilnadu, India
| | - Kunal B Pradhan
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Kalyani D Asgaonkar
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, Maharashtra, India
| | - Shirish P Jain
- Rajarshi Shahu College of Pharmacy, Buldhana, Maharashtra, India
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Chen Y, Dong Y, Li L, Jiao J, Liu S, Zou X. Toxicity Rank Order (TRO) As a New Approach for Toxicity Prediction by QSAR Models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:701. [PMID: 36613021 PMCID: PMC9819504 DOI: 10.3390/ijerph20010701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Quantitative Structure-Activity Relationship (QSAR) models are commonly used for risk assessment of emerging contaminants. The objective of this study was to use a toxicity rank order (TRO) as an integrating parameter to improve the toxicity prediction by QSAR models. TRO for each contaminant was calculated from collected toxicity data including acute toxicity concentration and no observed effect concentration. TRO values associated with toxicity mechanisms were used to classify pollutants into three modes of action consisting of narcosis, transition and reactivity. The selection principle of parameters for QSAR models was established and verified. It showed a reasonable prediction of toxicities caused by organophosphates and benzene derivatives, especially. Compared with traditional procedures, incorporating TRO showed an improved correlation coefficient of QSAR models by approximately 10%. Our study indicated that the proposed procedure can be used for screening modeling parameter data and improve the toxicity prediction by QSAR models, and this could facilitate prediction and evaluation of environmental contaminant toxicity.
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Affiliation(s)
- Yuting Chen
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Yuying Dong
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Le Li
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Jian Jiao
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Sitong Liu
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Xuejun Zou
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
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De P, Kar S, Ambure P, Roy K. Prediction reliability of QSAR models: an overview of various validation tools. Arch Toxicol 2022; 96:1279-1295. [PMID: 35267067 DOI: 10.1007/s00204-022-03252-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 02/14/2022] [Indexed: 01/20/2023]
Abstract
The reliability of any quantitative structure-activity relationship (QSAR) model depends on multiple aspects such as the accuracy of the input dataset, selection of significant descriptors, the appropriate splitting process of the dataset, statistical tools used, and most notably on the measures of validation. Validation, the most crucial step in QSAR model development, confirms the reliability of the developed QSAR models and the acceptability of each step in the model development. The present review deals with various validation tools that involve multiple techniques that improve the model quality and robustness. The double cross-validation tool helps in building improved quality models using different combinations of the same training set in an inner cross-validation loop. This exhaustive method is also integrated for small datasets (< 40 compounds) in another tool, namely the small dataset modeler tool. The main aim of QSAR researchers is to improve prediction quality by lowering the prediction errors for the query compounds. 'Intelligent' selection of multiple models and consensus predictions integrated in the intelligent consensus predictor tool were found to be more externally predictive than individual models. Furthermore, another tool called Prediction Reliability Indicator was explained to understand the quality of predictions for a true external set. This tool uses a composite scoring technique to identify query compounds as 'good' or 'moderate' or 'bad' predictions. We have also discussed a quantitative read-across tool which predicts a chemical response based on the similarity with structural analogues. The discussed tools are freely available from https://dtclab.webs.com/software-tools or http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ and https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home (for read-across).
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Affiliation(s)
- Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA
| | | | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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Roy J, Roy K. Modeling and mechanistic understanding of cytotoxicity of metal oxide nanoparticles (MeOxNPs) to Escherichia coli: categorization and data gap filling for untested metal oxides. Nanotoxicology 2022; 16:152-164. [PMID: 35166631 DOI: 10.1080/17435390.2022.2038299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Metal oxide nanoparticles (MeOxNPs) production is expected to increase every year exponentially, and their potential to cause adverse effect to the environment and human health will also expand rapidly. Hence, risk assessment of nanoparticles (NPs) is necessary to design ecosafe products. However, experimental ecotoxicological assessments are time-consuming requiring a lot of resources. Therefore, researchers rely on alternative in silico approaches to predict the behavior of NPs in the biological system. Quantitative structure - toxicity relationship (QSTR) has been adopted as a potential method to predict the cytotoxicity of untested NPs. Hence, in the present study, multiple linear regression (MLR) models were developed using 17 MeOxNPs on Escherichia coli (E. coli) bacteria cells under both light and dark conditions. The models were developed applying Small Dataset Modeler software, version 1.0.0 (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) which generates models with a limited number of data points. Periodic table-based descriptors (both 1st and 2nd generation) were used for the modeling purpose. Two statistically significant MLR models based on photo-induced toxicity (Q(LOO)2= 0.612, R2 = 0.726) and dark-based toxicity (Q(LOO)2= 0.627, R2 = 0.770) were developed. From the developed models, we interpreted that increase in valency and oxidation state of the metal will decrease the cytotoxicity whereas the atomic radius of the metal and electronegativity of MeOxNPs influence the toxicity toward E. coli cells. The MLR models were validated using different internal validation metrics. Additionally, we have collected 42 MeOxNPs as an external set to observe the predictive power of the two developed MLR models and categorize them into toxic and non-toxic classes. The chemical features selected in the developed models are important for understanding the mechanisms of nanotoxicity. Thus, the developed models can be a scientific basis for designing safer NPs.
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Affiliation(s)
- Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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Lavado GJ, Baderna D, Carnesecchi E, Toropova AP, Toropov AA, Dorne JLCM, Benfenati E. QSAR models for soil ecotoxicity: Development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida. JOURNAL OF HAZARDOUS MATERIALS 2022; 423:127236. [PMID: 34844354 DOI: 10.1016/j.jhazmat.2021.127236] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 06/13/2023]
Abstract
Soil pollution is a critical environmental challenge: the substances released in the soil can adversely affect humans and the ecosystem. Several bioassays were developed to investigate the soil ecotoxicity of chemicals with soil microbes, plants, invertebrates and vertebrates. The 28-day collembolan reproduction test with the springtail Folsomia candida is a recently introduced bioassay described by OECD guideline 232. Although the importance of springtails for maintaining soil quality, toxicity data for Collembola are still limited. We have developed two QSAR models for the prediction of reproductive toxicity induced by organic compounds in Folsomia candida using 28 days NOEC data. We assembled a dataset with the highest number of compounds available so far: 54 compounds were collected from publicly available sources, including plant protection products, reactive intermediates and industrial chemicals, household and cosmetic ingredients, drugs, environmental transformation products and polycyclic aromatic hydrocarbons. The models were developed using partial least squares regression (PLS) and the Monte Carlo technique with respectively the open source tools Small Dataset Modeler and CORAL software. Both QSAR models gave good predictive performance even though based on a small dataset, so they could serve for the ecological risk assessment of chemicals for terrestrial organisms.
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Affiliation(s)
- Giovanna J Lavado
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy
| | - Diego Baderna
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy.
| | - Edoardo Carnesecchi
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy
| | - Jean Lou C M Dorne
- Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, Parma, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy
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Mukherjee RK, Kumar V, Roy K. Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:335-348. [PMID: 34905924 DOI: 10.1021/acs.est.1c05732] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The ever-increasing use of pesticides in response to the rising agricultural demand has threatened the existence of nontarget organisms like avian species, disrupting the global ecological integrity. Therefore, it is critical to protect and restore different endangered bird species from the perspective of ecosystem safety. In the present work, we have developed regression-based two-dimensional quantitative structure toxicity relationship (2D QSTR) and quantitative structure toxicity-toxicity relationship (QSTTR) models to estimate the toxicity of pesticides on five different avian species following the Organization for Economic Co-operation and Development (OECD) guidelines. Rigorous validation has been performed using different statistical internal and external validation parameters to ensure the robustness and interpretability of the developed models. From the developed models, it can be stated that the presence of electronegative and lipophilic features greatly enhance pesticide toxicity, whereas the hydrophilic characters are shown to have a detrimental impact on the toxicity of pesticides. Moreover, the developed QSTTR models have been employed to the in silico toxicity prediction of 124, 154, and 250 pesticides against bobwhite quail, ring-necked pheasant, and mallard duck species, respectively, extracted from the Office of Pesticides Program (OPP) Pesticide Ecotoxicity Database. The information obtained from the modeled descriptors might be used for pesticide risk assessment in the future, with the added benefit of providing an early caution of their possible negative impact on birds for regulatory purposes.
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Affiliation(s)
- Rajendra Kumar Mukherjee
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
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Adhikari N, Banerjee S, Baidya SK, Ghosh B, Jha T. Ligand-based quantitative structural assessments of SARS-CoV-2 3CL pro inhibitors: An analysis in light of structure-based multi-molecular modeling evidences. J Mol Struct 2021; 1251:132041. [PMID: 34866654 PMCID: PMC8627846 DOI: 10.1016/j.molstruc.2021.132041] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 11/10/2021] [Accepted: 11/26/2021] [Indexed: 12/11/2022]
Abstract
Due to COVID-19, the whole world is undergoing a devastating situation, but treatment with no such drug candidates still has been established exclusively. In that context, 69 diverse chemicals with potential SARS-CoV-2 3CLpro inhibitory property were taken into consideration for building different internally and externally validated linear (SW-MLR and GA-MLR), non-linear (ANN and SVM) QSAR, and HQSAR models to identify important structural and physicochemical characters required for SARS-CoV-2 3CLpro inhibition. Importantly, 2-oxopyrrolidinyl methyl and benzylester functions, and methylene (hydroxy) sulphonic acid warhead group, were crucial for retaining higher SARS-CoV-2 3CLpro inhibition. These GA-MLR and HQSAR models were also applied to predict some already repurposed drugs. As per the GA-MLR model, curcumin, ribavirin, saquinavir, sepimostat, and remdesivir were found to be the potent ones, whereas according to the HQSAR model, lurasidone, saquinavir, lopinavir, elbasvir, and paritaprevir were the highly effective SARS-CoV-2 3CLpro inhibitors. The binding modes of those repurposed drugs were also justified by the molecular docking, molecular dynamics (MD) simulation, and binding energy calculations conducted by several groups of researchers. This current work, therefore, may be able to find out important structural parameters to accelerate the COVID-19 drug discovery processes in the future.
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Affiliation(s)
- Nilanjan Adhikari
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Suvankar Banerjee
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Sandip Kumar Baidya
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Balaram Ghosh
- Epigenetic Research Laboratory, Birla Institute of Technology and Science-Pilani Hyderabad Campus, Shamirpet, Hyderabad, India, 500078
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
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