1
|
Khan K, Kar S, Roy K. Are we ready to combat the ecotoxicity of COVID-19 pharmaceuticals? An in silico aquatic risk assessment. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 256:106416. [PMID: 36758333 PMCID: PMC9898056 DOI: 10.1016/j.aquatox.2023.106416] [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: 01/05/2023] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
To fight COVID-19 with uncountable medications and bioproducts throughout the world has taken us to another challenge of ecotoxicity. The indiscriminate usage followed by improper disposal of unused antibacterials, antivirals, antimalarials, immunomodulators, angiotensin II receptor blockers, corticosteroids, anthelmintics, anticoagulants etc. can lead us to an unimaginable ecotoxicity in the long run. A series of studies already identified active pharmaceutical ingredients (APIs) of the mentioned therapeutic classes and their metabolites in aquatic bodies as well as in wastewater treatment plants. Therefore, an initial ecotoxicity assessment of the majorly used pharmaceuticals is utmost requirement of the present time. The present in silico risk assessment study is focused on the aquatic toxicity prediction of 81 pharmaceuticals where 77 are most-used pharmaceuticals for COVID-19 throughout the world based on the literature along with one drug nirmatrelvir [PF-07321332] approved for emergency use by US-FDA and three other molecules under clinical trial. The ecotoxicity of the studied compounds were predicted based on the three aquatic species fish, algae and crustaceans employing the highest quality QSAR models available from the literature as well as using ECOSAR and QSAR Toolbox. To compare the toxicity thresholds, we have also used 4 control pharmaceuticals based on the worldwide occurrence from river, lake, STP, WWTPs, influent and effluent followed by high reported aquatic toxicity over the years as per the literature. Based on the statistical comparison, we have proposed top 3 pharmaceuticals used for the COVID-19 most toxic to the aquatic environment. The study will provide confident predictions of aquatic ecotoxicity data related to abundant use of COVID-19 drugs. The major aim of the study is to fill up the aquatic ecotoxicity data gap of major medications used for COVID-19.
Collapse
Affiliation(s)
- Kabiruddin Khan
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, Kolkata 700032, India
| | - Supratik Kar
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, Union, NJ 07083, USA.
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, Kolkata 700032, India.
| |
Collapse
|
2
|
Xiao L, Deng J, Yang L, Huang X, Yu X. Random forest algorithm-based accurate prediction of rat acute oral toxicity. Mol Phys 2022. [DOI: 10.1080/00268976.2022.2140083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Linrong Xiao
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, People’s Republic of China
| | - Jiyong Deng
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, People’s Republic of China
| | - Liping Yang
- Shenzhen Expressway Environment Co., Ltd., Shenzhen, People’s Republic of China
| | - Xianwei Huang
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, People’s Republic of China
| | - Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, People’s Republic of China
| |
Collapse
|
3
|
Shavalieva G, Papadokonstantakis S, Peters G. Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity. J Chem Inf Model 2022; 62:4018-4031. [PMID: 35998659 PMCID: PMC9472271 DOI: 10.1021/acs.jcim.1c01079] [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] [Indexed: 11/30/2022]
Abstract
![]()
Early assessment of the potential impact of chemicals
on health
and the environment requires toxicological properties of the molecules.
Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is
often scarce and uncertain. One of the ways to advance the predictive
modeling procedure might be the use of knowledge existing in the field.
Scientific publications contain a vast amount of knowledge. However,
the amount of manual work required to process the enormous volumes
of information gathered in scientific articles might hinder its utilization.
This work explores the opportunity of semiautomated knowledge extraction
from scientific papers and investigates a few potential ways of its
use for predictive modeling. The knowledge extraction and predictive
modeling are applied to the field of acute aquatic toxicity. Acute
aquatic toxicity is an important parameter of the safety assessment
of chemicals. The extensive amount of diverse information existing
in the field makes acute aquatic toxicity an attractive area for investigation
of knowledge use for predictive modeling. The work demonstrates that
the knowledge collection and classification procedure could be useful
in hybrid modeling studies concerning the model and predictor selection,
addressing data gaps, and evaluation of models’ performance.
Collapse
Affiliation(s)
- Gulnara Shavalieva
- Department of Space, Earth and Environment, Division of Energy Technology, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Stavros Papadokonstantakis
- Department of Space, Earth and Environment, Division of Energy Technology, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.,Institute of Chemical, Environmental and Bioscience Engineering, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria
| | - Gregory Peters
- Department of Technology Management and Economics, Chalmers University of Technology, SE-411 33 Gothenburg, Sweden
| |
Collapse
|
4
|
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).
Collapse
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.
| |
Collapse
|
5
|
Lotfi S, Ahmadi S, Kumar P. Ecotoxicological prediction of organic chemicals toward Pseudokirchneriella subcapitata by Monte Carlo approach. RSC Adv 2022; 12:24988-24997. [PMID: 36199875 PMCID: PMC9434604 DOI: 10.1039/d2ra03936b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 08/19/2022] [Indexed: 11/21/2022] Open
Abstract
In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria. Green alga Pseudokirchneriella subcapitata has been used for ecotoxicological studies to assess the toxicity of different toxic chemicals in freshwater. Quantitative Structure Activity Relationships (QSAR) are mathematical models to relate chemical structure and activity/physicochemical properties of chemicals quantitatively. Herein, Quantitative Structure Toxicity Relationship (QSTR) modeling is applied to assess the toxicity of a data set of 334 different chemicals on Pseudokirchneriella subcapitata, in terms of EC10 and EC50 values. The QSTR models are established using CORAL software by utilizing the target function (TF2) with the index of ideality of correlation (IIC). A hybrid optimal descriptor computed from SMILES and molecular hydrogen-suppressed graphs (HSG) is employed to construct QSTR models. The results of various statistical parameters of the QSTR model developed for pEC10 and pEC50 range from excellent to good and are in line with the standard parameters. The models prepared with IIC for Split 3 are chosen as the best model for both endpoints (pEC10 and pEC50). The numerical value of the determination coefficient of the validation set of split 3 for the endpoint pEC10 is 0.7849 and for the endpoint pEC50, it is 0.8150. The structural fractions accountable for the toxicity of chemicals are also extracted. The hydrophilic attributes like 1…n…(… and S…(…
Created by potrace 1.16, written by Peter Selinger 2001-2019
]]>
… exert positive contributions to controlling the aquatic toxicity and reducing algal toxicity, whereas attributes such as c…c…c…, C…C…C… enhance lipophilicity of the molecules and consequently enhance algal toxicity. In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria.![]()
Collapse
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
| |
Collapse
|
6
|
Masand VH, Zaki MEA, Al-Hussain SA, Ghorbal AB, Akasapu S, Lewaa I, Ghosh A, Jawarkar RD. Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 239:105962. [PMID: 34525418 DOI: 10.1016/j.aquatox.2021.105962] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 08/10/2021] [Accepted: 09/01/2021] [Indexed: 06/13/2023]
Abstract
In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC50 for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R2 = 0.72, Q2LOO = 0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.
Collapse
Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, 444 602, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, College of Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, College of Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.
| | - Anis Ben Ghorbal
- Department of Mathematics and Statistics, Faculty of Science, College of Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.
| | | | - Israa Lewaa
- Assistant Lecturer of Statistics, Faculty of Business Administration, Department of Business Administration, Economics and Political Science, The British University in Egypt, Cairo, Egypt.
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, Assam, 781014, India
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| |
Collapse
|
7
|
Kaneko H. Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables. Heliyon 2021; 7:e07356. [PMID: 34195450 PMCID: PMC8237311 DOI: 10.1016/j.heliyon.2021.e07356] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/02/2021] [Accepted: 06/16/2021] [Indexed: 11/24/2022] Open
Abstract
The selection of a descriptor, X, is crucial for improving the interpretation and prediction accuracy of a regression model. In this study, the prediction accuracy of models constructed using the selected X was determined and the results of variable selection, according to the number of selected X and number of selected variables that are unrelated to an objective variable, such as activities and properties (y), were investigated to evaluate the variable or feature selection methods. Variable selection methods include least absolute shrinkage and selection operator, genetic algorithm-based partial least squares, genetic algorithm-based support vector regression, and Boruta. Several regression analysis methods were used to test the prediction accuracy of the model constructed using the selected X. The characteristics of each variable selection method were analyzed using eight datasets. The results showed that even when variables unrelated to y were selected by variable selection and the number of unrelated variables was the same as the number of the original variables, a regression model with good accuracy, which ignores the influence of such noise variables, can be constructed by applying various regression analysis methods. Additionally, the variables related to y must not to be deleted. These findings provide a basis for improving the variable selection methods.
Collapse
Affiliation(s)
- Hiromasa Kaneko
- Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
| |
Collapse
|
8
|
Prediction of inhibitory constants of compounds against SARS-CoV 3CLpro enzyme with 2D-QSAR model. JOURNAL OF SAUDI CHEMICAL SOCIETY 2021. [PMCID: PMC8139336 DOI: 10.1016/j.jscs.2021.101262] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Developing broad-spectrum anti-coronavirus drugs is greatly important, since the novel SARS-CoV-2 has rapidly become a threat to the public health and economy worldwide. SARS-CoV 3-chymotrypsin-like protease (3CLpro), as highly conserved in betacoronavirus, is a viable target for anti-SARS drugs. A quantitative structure–activity relationship (QSAR) for inhibitory constants (pKi) of 89 compounds against SARS-CoV 3CLpro enzyme was developed by using support vector machine (SVM) and genetic algorithm. The optimal SVM model (C = 90.2339 and γ = 1.19826 × 10−5) based on six molecular descriptors has determination coefficients of 0.839 for the training set (65 compounds) and 0.747 for test set (24 compounds), and rms errors of 0.435 and 0.525, respectively. These results are accurate and acceptable compared with that in other models reported, although our SVM model deals with more samples in the dada set. The SVM model could be beneficial for search of novel 3CLpro enzyme inhibitors against SARS-CoV.
Collapse
|
9
|
Carve M, Nugegoda D, Allinson G, Shimeta J. A systematic review and ecological risk assessment for organic ultraviolet filters in aquatic environments. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 268:115894. [PMID: 33120145 DOI: 10.1016/j.envpol.2020.115894] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/22/2020] [Accepted: 10/15/2020] [Indexed: 05/21/2023]
Abstract
Organic ultraviolet filters (OUVFs) are used in a wide range of manufactured products including personal care (e.g. sunscreens) and plastic items. This review summarizes the available data regarding the toxic effects of OUVFs on marine and freshwater organisms and generates the predicted no-effect concentration (PNEC) values necessary for assessing ecological risk. Through a systematic search of the literature, 89 studies were identified and ecotoxicological data extracted. Collectively, these studies described toxicity testing with 39 OUVF from 10 structural classes, with derivatives of benzophenones (49%) and camphors (16%) most studied. There was a bias towards selecting freshwater species (61%), and evaluating single OUVF effects (87%) rather than OUVF mixtures. Short-term (acute) experimentation (58%) was marginally more common than long-term (chronic) testing (42%). Reproductive, developmental, genetic, and neurological toxicity were the most commonly identified effects in aquatic organism, and were associated with molecular interactions with steroid receptors, DNA, or the production of reactive oxygen species. Species sensitivity distribution and/or assessment factors were used to calculate PNECs for 22 OUVFs and the risk quotients for 12 OUVFs. When using maximum concentrations, high risk was observed for six OUVFs in marine environments (4-methylbenzylidene-camphor, octocrylene, padimate-O, benzophenone-1, and oxybenzone, ethylhexyl-4-methoxycinnamate), and for four OUVFs in freshwater environments (ethylhexyl-4-methoxycinnamate, octocrylene, avobenzone and oxybenzone). When using median concentrations, a risk to marine environments was observed for oxybenzone. The results of this review underline that there is limited knowledge of the pathological effects of OUVFs and their metabolites in aquatic environments, and this inhibits the development of informed water-quality guidelines.
Collapse
Affiliation(s)
- Megan Carve
- Centre for Environmental Sustainability and Remediation (EnSuRe), School of Science, RMIT University, Melbourne, Victoria, 3000, Australia.
| | - Dayanthi Nugegoda
- Centre for Environmental Sustainability and Remediation (EnSuRe), School of Science, RMIT University, Melbourne, Victoria, 3000, Australia
| | - Graeme Allinson
- Centre for Environmental Sustainability and Remediation (EnSuRe), School of Science, RMIT University, Melbourne, Victoria, 3000, Australia
| | - Jeff Shimeta
- Centre for Environmental Sustainability and Remediation (EnSuRe), School of Science, RMIT University, Melbourne, Victoria, 3000, Australia
| |
Collapse
|
10
|
Lunghini F, Marcou G, Azam P, Enrici MH, Van Miert E, Varnek A. Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:655-675. [PMID: 32799684 DOI: 10.1080/1062936x.2020.1797872] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 07/15/2020] [Indexed: 06/11/2023]
Abstract
We report new consensus models estimating acute toxicity for algae, Daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSEext = 0.56-0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/ColorAtom utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracy and data coverage. Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending the applicability domain and relevance for application in an industrial context. Generated models and collected public data are made freely available.
Collapse
Affiliation(s)
- F Lunghini
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
- Toxicological and Environmental Risk Assessment Unit , Solvay S.A., St. Fons, France
| | - G Marcou
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
| | - P Azam
- Toxicological and Environmental Risk Assessment Unit , Solvay S.A., St. Fons, France
| | - M H Enrici
- Toxicological and Environmental Risk Assessment Unit , Solvay S.A., St. Fons, France
| | - E Van Miert
- Toxicological and Environmental Risk Assessment Unit , Solvay S.A., St. Fons, France
| | - A Varnek
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
| |
Collapse
|
11
|
Pandey SK, Ojha PK, Roy K. Exploring QSAR models for assessment of acute fish toxicity of environmental transformation products of pesticides (ETPPs). CHEMOSPHERE 2020; 252:126508. [PMID: 32240857 DOI: 10.1016/j.chemosphere.2020.126508] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/14/2020] [Accepted: 03/14/2020] [Indexed: 06/11/2023]
Abstract
Environmental transformation products of pesticides (ETPPs) have a great deal of ecological impact owing to their ability to cause toxicity to the aquatic organisms, which can then be translated to the humans. The limited experimental data on biochemical and toxic effects of ETPPs, the high test costs together with regulatory limitations and the international push to reduce animal testing encourage greater dependence on predictive in silico techniques like quantitative structure-activity relationship (QSAR) models. The aim of the present work was to explore the key structural features, which regulate the toxicity towards fishes, for 85 ETPPs using a partial least squares (PLS) regression based chemometric model developed according to Organisation for Economic Co-operation and Development (OECD) guidelines. The model was extensively validated using both internal and external validation metrics, and the results so obtained justify the reliability and usefulness of the developed model (Q2 = 0.648, R2pred or Q2F1 = 0.734 and Q2F2 = 0.733). From the developed model, we can conclude that lipophilicity, polarity, presence of branching and the functional form of O-atom in the transformed structures of pesticides are the important features that are to be considered during ecotoxicity assessment of ETPPs. The information obtained from the descriptors of the developed model could be utilized in the future for assessing ETPPs with the benefit of providing an early warning of their potentially detrimental effect on fishes for regulatory purposes.
Collapse
Affiliation(s)
- Sapna Kumari Pandey
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Probir Kumar Ojha
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
| |
Collapse
|
12
|
Yu X. Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2020; 224:105496. [PMID: 32408003 DOI: 10.1016/j.aquatox.2020.105496] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 04/16/2020] [Accepted: 04/19/2020] [Indexed: 06/11/2023]
Abstract
Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure-activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC10 of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a test set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R2 of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R2 of 0.75 and MAE of 0.61 for the test set. Compared with other models reported in the literature, our SVM model for the toxicity pEC10 shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the test set. A QSTR model for pEC50 of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.
Collapse
Affiliation(s)
- Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan, 411104, China.
| |
Collapse
|
13
|
Kumar V, Roy K. Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:511-526. [PMID: 32543892 DOI: 10.1080/1062936x.2020.1776388] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 05/27/2020] [Indexed: 06/11/2023]
Abstract
In the context of recently emerged pandemic of COVID-19, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) modelling using SARS-CoV-3CLpro enzyme inhibitors for the development of a multiple linear regression (MLR) based model. We have used 2D descriptors with an aim to develop an easily interpretable, transferable and reproducible model which may be used for quick prediction of SAR-CoV-3CLpro inhibitory activity for query compounds in the screening process. Based on the insights obtained from the developed 2D-QSAR model, we have identified the structural features responsible for the enhancement of the inhibitory activity against 3CLpro enzyme. Moreover, we have performed the molecular docking analysis using the most and least active molecules from the dataset to understand the molecular interactions involved in binding, and the results were then correlated with the essential structural features obtained from the 2D-QSAR model. Additionally, we have performed in silico predictions of SARS-CoV 3CLpro enzyme inhibitory activity of a total of 50,437 compounds obtained from two anti-viral drug databases (CAS COVID-19 antiviral candidate compound database and another recently reported list of prioritized compounds from the ZINC15 database) using the developed model and provided prioritized compounds for experimental detection of their performance for SARS-CoV 3CLpro enzyme inhibition.
Collapse
Affiliation(s)
- V Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University , Kolkata, India
| | - K Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University , Kolkata, India
| |
Collapse
|
14
|
Jillella GK, Khan K, Roy K. Application of QSARs in identification of mutagenicity mechanisms of nitro and amino aromatic compounds against Salmonella typhimurium species. Toxicol In Vitro 2020; 65:104768. [PMID: 31926304 DOI: 10.1016/j.tiv.2020.104768] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Revised: 12/19/2019] [Accepted: 01/06/2020] [Indexed: 11/25/2022]
Abstract
In an attempt to describe the underlying causes of mutagenicity mainly due to organic chemicals, quantitative structure-activity relationship (QSAR) models have been developed using two different Salmonella typhimurium mutagenicity endpoints with or without presence of liver metabolic microsomal enzymes (S9) namely TA98-S9 and TA98 + S9. The models were developed using simple 2D variables having definite physicochemical meaning calculated from Dragon, SiRMS, and PaDEL-descriptor software tools. Stepwise regression followed by partial least squares (PLS) regression was used in model development following the strict OECD guidelines for QSAR model development and validation. The models were validated using coefficient of determination R2, cross-validation coefficient Q2LOO (leave one out) while the test set predictions were analyzed using Q2F1 (coefficient of determination for the test set). Several other internationally accepted validation metrics like MAE95%train, average rm(LOO)2 and Δrm(LOO)2 (for the training set) were used to check model robustness while predictive efficiency was evaluated using MAE95%test, average rm2 and Δrm2 (for the test set). The scope of predictions was defined by applicability domain analysis using the DModX approach, a recommended tool for PLS models. The major contributing features related to mutagenicity include lipophilicity, electronegativity, branching and unsaturation, etc. The present manuscript is the first attempt to undertake modeling of two different endpoints (TA98-S9 and TA98 + S9) in order to explore major contributing molecular features linked directly or indirectly to mutagenicity.
Collapse
Affiliation(s)
- Gopala Krishna Jillella
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India
| | - Kabiruddin Khan
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India.
| |
Collapse
|