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Gao YY, Zhao W, Huang YQ, Kumar V, Zhang X, Hao GF. In silico environmental risk assessment improves efficiency for pesticide safety management. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:167878. [PMID: 37858821 DOI: 10.1016/j.scitotenv.2023.167878] [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: 08/03/2023] [Revised: 10/09/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
Pesticides are indispensable to maintain crop quality and food production worldwide, but their use also poses environmental risks. Pesticide risk assessment involves a series of complex, expensive and time-consuming toxicity tests. To improve the efficiency and accuracy for assessing the environmental impact of pesticides, numerous computational tools have been developed. However, there is a notable deficiency in critical analysis or a systematic summary of environmental risk assessment tools and their applicable contexts. Here, many of the current approaches and tools for assessing environmental risks posed by pesticides are reviewed, and the question of whether these tools are fit for use on complex multicomponent scenarios is discussed. We analyze the adaptations of these tools to aquatic and terrestrial ecosystems, followed by the provision of resources for predicting pesticide concentrations in environmental medias, including air, soil and water. The successful application of computational tools for risk assessment and interpretation of predicted results will also be discussed. This assessment serves as a valuable resource, enabling scientists to utilize suitable models to enhance the robustness of pesticides risk assessments.
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Affiliation(s)
- Yang-Yang Gao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Wei Zhao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Yuan-Qin Huang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Vinit Kumar
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Xiao Zhang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China; National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan 430079, PR China.
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2
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Janicka M, Sztanke M, Sztanke K. Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology. Molecules 2024; 29:287. [PMID: 38257200 PMCID: PMC11154582 DOI: 10.3390/molecules29020287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024] Open
Abstract
Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.
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Affiliation(s)
- Małgorzata Janicka
- Department of Physical Chemistry, Faculty of Chemistry, Institute of Chemical Science, Maria Curie-Skłodowska University, 20-031 Lublin, Poland;
| | - Małgorzata Sztanke
- Department of Medical Chemistry, Medical University of Lublin, 4A Chodźki Street, 20-093 Lublin, Poland;
| | - Krzysztof Sztanke
- Laboratory of Bioorganic Compounds Synthesis and Analysis, Medical University of Lublin, 4A Chodźki Street, 20-093 Lublin, Poland
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3
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Peña-Chora G, Toledo-Hernández E, Sotelo-Leyva C, Damian-Blanco P, Villanueva-Flores AG, Alvarez-Fitz P, Palemón-Alberto F, Ortega-Acosta SÁ. Presence and distribution of pests and diseases of Apis mellifera (Hymenoptera: Apidae) in Mexico: a review. THE EUROPEAN ZOOLOGICAL JOURNAL 2023. [DOI: 10.1080/24750263.2023.2182920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Affiliation(s)
- G. Peña-Chora
- Centro de Investigaciones Biológicas, Universidad Autónoma del Estado de Morelos, Cuernavaca, México
| | - E. Toledo-Hernández
- Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Chilpancingo, México
| | - C. Sotelo-Leyva
- Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Chilpancingo, México
| | - P. Damian-Blanco
- Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Chilpancingo, México
| | - A. G. Villanueva-Flores
- Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Chilpancingo, México
| | - P. Alvarez-Fitz
- Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Chilpancingo, México
| | - F. Palemón-Alberto
- Facultad de Ciencias Agropecuarias y Ambientales, Universidad Autónoma de Guerrero, Iguala de la Independencia, México
| | - S. Á. Ortega-Acosta
- Facultad de Ciencias Agropecuarias y Ambientales, Universidad Autónoma de Guerrero, Iguala de la Independencia, México
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4
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Leon-Borges JA, Aguirre-García GJ, Silva VM, Lizardi-Jiménez MA. Hydrocarbons and other risks in a beekeeping area of México: the precautionary principle for prevention and biotechnology for remediation. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:69499-69513. [PMID: 37140869 DOI: 10.1007/s11356-023-27370-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 04/27/2023] [Indexed: 05/05/2023]
Abstract
The Yucatan Peninsula is the most important beekeeping region. However, the presence of hydrocarbons and pesticides violates the human right to a healthy environment twice over; it can affect human beings directly due to its toxicological characteristics, but it also constitutes a risk, not very well dimensioned, regarding the loss of biodiversity of the ecosystem via the impact on pollination. On the other hand, the precautionary principle obliges the authorities to prevent damage to the ecosystem that may be caused by the productive activity of individuals. Although there are studies that separately warn about the decrease of bees in the Yucatan due to industrial activity, this work has the novelty of presenting an intersectoral analysis of the risk that includes the soy industry, the swine industry and the tourist industry. The latter incorporates a new risk not considered until now, which is the presence of hydrocarbons in the ecosystem. Additionally, we can demonstrate that hydrocarbons, such as diesel and gasoline, should be avoided when using no genetically modified organisms (GMOs) in bioreactors. The objective of this work was to propose the precautionary principle around the risks in a beekeeping area and to propose biotechnology without using GMOs.
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Affiliation(s)
| | | | - Violeta Mendezcarlo Silva
- Universidad Autónoma de San Luis Potosí, Sierra Leona 550, 2da. Sección, C. P. 78210, San Luis Potosí , San Luis Potosí, Mexico
| | - Manuel Alejandro Lizardi-Jiménez
- CONACyT-Universidad Autónoma de San Luis Potosí, MDH, LGAC Estudios Sociales, Sierra Leona 550, 2da. Sección, C. P. 78210, San Luis Potosí, San Luis Potosí, Mexico.
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5
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Combined Micellar Liquid Chromatography Technique and QSARs Modeling in Predicting the Blood-Brain Barrier Permeation of Heterocyclic Drug-like Compounds. Int J Mol Sci 2022; 23:ijms232415887. [PMID: 36555527 PMCID: PMC9786067 DOI: 10.3390/ijms232415887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 12/15/2022] Open
Abstract
The quantitative structure-activity relationship (QSAR) methodology was used to predict the blood-brain permeability (log BB) for 65 synthetic heterocyclic compounds tested as promising drug candidates. The compounds were characterized by different descriptors: lipophilicity, parachor, polarizability, molecular weight, number of hydrogen bond acceptors, number of rotatable bonds, and polar surface area. Lipophilic properties of the compounds were evaluated experimentally by micellar liquid chromatography (MLC). In the experiments, sodium dodecyl sulfate (SDS) as the effluent component and the ODS-2 column were used. Using multiple linear regression and leave-one-out cross-validation, we derived the statistically significant and highly predictive quantitative structure-activity relationship models. Thus, this study provides valuable information on the expected properties of the substances that can be used as a support tool in the design of new therapeutic agents.
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Raha FK, Hasan J, Ali A, Fakayode SO, Halim MA. Exploring the molecular level interaction of Xenoestrogen phthalate plasticisers with oestrogen receptor alpha (ERα) Y537S mutant. MOLECULAR SIMULATION 2022. [DOI: 10.1080/08927022.2022.2101675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Fahmida Khanam Raha
- Division of Molecular Cancer, The Red-Green Research Centre, BICCB, Dhaka, Bangladesh
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Jahid Hasan
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Ackas Ali
- Division of Molecular Cancer, The Red-Green Research Centre, BICCB, Dhaka, Bangladesh
| | - Sayo O. Fakayode
- Department of Chemistry, Physics & Astronomy, Georgia College & State University, Milledgeville, GA, USA
| | - Mohammad A. Halim
- Department of Chemistry and Biochemistry, Kennesaw State University, Kennesaw, GA, USA
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7
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Yang P, Henle EA, Fern XZ, Simon CM. Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels. J Chem Phys 2022; 157:034102. [DOI: 10.1063/5.0090573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a dataset of 382 molecules with toxicity labels from honey bee exposure experiments, we train a support vector machine (SVM) to predict the toxicity of pesticides to honey bees. We compare two representations of the pesticide molecules: (i) a random walk feature vector listing counts of length- L walks on the molecular graph with each vertex- and edge-label sequence and (ii) the Molecular ACCess System (MACCS) structural key fingerprint (FP), a bit vector indicating the presence/absence of a list of pre-defined subgraph patterns in the molecular graph. We explicitly construct the MACCS FPs but rely on the fixed-length- L random walk graph kernel (RWGK) in place of the dot product for the random walk representation. The L-RWGK-SVM achieves an accuracy, precision, recall, and F1 score (mean over 2000 runs) of 0.81, 0.68, 0.71, and 0.69, respectively, on the test data set—with L = 4 being the mode optimal walk length. The MACCS-FP-SVM performs on par/marginally better than the L-RWGK-SVM, lends more interpretability, but varies more in performance. We interpret the MACCS-FP-SVM by illuminating which subgraph patterns in the molecules tend to strongly push them toward the toxic/non-toxic side of the separating hyperplane.
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Affiliation(s)
- Ping Yang
- School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, Oregon 97331, USA
| | - E. Adrian Henle
- School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, Oregon 97331, USA
| | - Xiaoli Z. Fern
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, USA
| | - Cory M. Simon
- School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, Oregon 97331, USA
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Larras F, Charles S, Chaumot A, Pelosi C, Le Gall M, Mamy L, Beaudouin R. A critical review of effect modeling for ecological risk assessment of plant protection products. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:43448-43500. [PMID: 35391640 DOI: 10.1007/s11356-022-19111-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 02/03/2022] [Indexed: 06/14/2023]
Abstract
A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).
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Affiliation(s)
- Floriane Larras
- INRAE, Directorate for Collective Scientific Assessment, Foresight and Advanced Studies, Paris, 75338, France
| | - Sandrine Charles
- University of Lyon, University Lyon 1, CNRS UMR 5558, Laboratory of Biometry and Evolutionary Biology, Villeurbanne Cedex, 69622, France
| | - Arnaud Chaumot
- INRAE, UR RiverLy, Ecotoxicology laboratory, Villeurbanne, F-69625, France
| | - Céline Pelosi
- Avignon University, INRAE, UMR EMMAH, Avignon, 84000, France
| | - Morgane Le Gall
- Ifremer, Information Scientifique et Technique, Bibliothèque La Pérouse, Plouzané, 29280, France
| | - Laure Mamy
- Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Thiverval-Grignon, 78850, France
| | - Rémy Beaudouin
- Ineris, Experimental Toxicology and Modelling Unit, UMR-I 02 SEBIO, Verneuil en Halatte, 65550, France.
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9
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Si-Hung L, Izumi Y, Nakao M, Takahashi M, Bamba T. Investigation of supercritical fluid chromatography retention behaviors using quantitative structure-retention relationships. Anal Chim Acta 2022; 1197:339463. [DOI: 10.1016/j.aca.2022.339463] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/02/2022] [Accepted: 01/06/2022] [Indexed: 12/11/2022]
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10
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Mukherjee RK, Kumar V, Roy K. Chemometric modeling of plant protection products (PPPs) for the prediction of acute contact toxicity against honey bees (A. mellifera): A 2D-QSAR approach. JOURNAL OF HAZARDOUS MATERIALS 2022; 423:127230. [PMID: 34844352 DOI: 10.1016/j.jhazmat.2021.127230] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 06/13/2023]
Abstract
Honey bees (Apis mellifera) are vital for economic, viable agriculture and for food safety. Although Plant Protection Products (PPPs) are of undeniable importance in the global agricultural system, these have become potential threats for non-target organisms like pollinators (e.g., honey bees etc.), resulting in the disruption of the ecological balance. In the current work, we have used the 113 PPP analogs to develop a 2D-QSAR model and explored the structural features modulating the toxic effects on honey bees, following the Organization for Economic Co-operation and Development (OECD) guidelines. The extensive validation of the developed model has been performed using internal and external validation metrics to make sure that the model is statistically sound and interpretable enough to be acceptable. The obtained results (R2 = 0.666, Q2 = 0.594, Q2F1 = 0.647 and Q2F2 = 0.646) determine the predictability and reliability of the developed model. This model should be useful for the predictions (acute contact toxicity (LD50)) of the new and untested compounds located inside the applicability domain of the developed model. Moreover, we have performed the in-silico prediction of toxicity against honey bees of a total of 709 compounds obtained from the pesticide properties database (PPDB) using the developed model.
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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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11
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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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12
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Li J, Luo D, Wen T, Liu Q, Mo Z. Representative feature selection of molecular descriptors in QSAR modeling. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.131249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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13
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Ouyang Y, Huang JJ, Wang YL, Zhong H, Song BA, Hao GF. In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness? JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:10761-10773. [PMID: 34516106 DOI: 10.1021/acs.jafc.1c01460] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Unfavorable bioavailability is an important aspect underlying the failure of drug candidates. Computational approaches for evaluating drug-likeness can minimize these risks. Over the past decades, computational approaches for evaluating drug-likeness have sped up the process of drug development and were also quickly derived to pesticide-likeness. As a result of many critical differences between drugs and pesticides, many kinds of methods for drug-likeness cannot be used for pesticide-likeness. Therefore, it is crucial to comprehensively compare and analyze the differences between drug-likeness and pesticide-likeness, which may provide a basis for solving the problems encountered during the evaluation of pesticide-likeness. Here, we systematically collected the recent advances of drug-likeness and pesticide-likeness and compared their characteristics. We also evaluated the current lack of studies on pesticide-likeness, the molecular descriptors and parameters adopted, the pesticide-likeness model on pesticide target organisms, and comprehensive analysis tools. This work may guide researchers to use appropriate methods for developing pesticide-likeness models. It may also aid non-specialists to understand some important concepts in drug-likeness and pesticide-likeness.
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Affiliation(s)
- Yan Ouyang
- Guizhou Engineering Laboratory for Synthetic Drugs, Key Laboratory of Guizhou Fermentation Engineering and Biomedicine, School of Pharmaceutical Sciences, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
| | - Jun-Jie Huang
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
| | - Yu-Liang Wang
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Hang Zhong
- Guizhou Engineering Laboratory for Synthetic Drugs, Key Laboratory of Guizhou Fermentation Engineering and Biomedicine, School of Pharmaceutical Sciences, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
| | - Bao-An Song
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
| | - Ge-Fei Hao
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
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14
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Banerjee S, Mahmud F, Deng S, Ma L, Yun MK, Fakayode SO, Arnst KE, Yang L, Chen H, Wu Z, Lukka PB, Parmar K, Meibohm B, White SW, Wang Y, Li W, Miller DD. X-ray Crystallography-Guided Design, Antitumor Efficacy, and QSAR Analysis of Metabolically Stable Cyclopenta-Pyrimidinyl Dihydroquinoxalinone as a Potent Tubulin Polymerization Inhibitor. J Med Chem 2021; 64:13072-13095. [PMID: 34406768 DOI: 10.1021/acs.jmedchem.1c01202] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Small molecules that interact with the colchicine binding site in tubulin have demonstrated therapeutic efficacy in treating cancers. We report the design, syntheses, and antitumor efficacies of new analogues of pyridopyrimidine and hydroquinoxalinone compounds with improved drug-like characteristics. Eight analogues, 5j, 5k, 5l, 5m, 5n, 5r, 5t, and 5u, showed significant improvement in metabolic stability and demonstrated strong antiproliferative potency in a panel of human cancer cell lines, including melanoma, lung cancer, and breast cancer. We report crystal structures of tubulin in complex with five representative compounds, 5j, 5k, 5l, 5m, and 5t, providing direct confirmation for their binding to the colchicine site in tubulin. A quantitative structure-activity relationship analysis of the synthesized analogues showed strong ability to predict potency. In vivo, 5m (4 mg/kg) and 5t (5 mg/kg) significantly inhibited tumor growth as well as melanoma spontaneous metastasis into the lung and liver against a highly paclitaxel-resistant A375/TxR xenograft model.
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Affiliation(s)
- Souvik Banerjee
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States.,Department of Physical Sciences, College of STEM, University of Arkansas Fort Smith, Fort Smith, Arkansas 72913, United States
| | - Foyez Mahmud
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Shanshan Deng
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Lingling Ma
- Department of Respiratory and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Mi-Kyung Yun
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Sayo O Fakayode
- Department of Physical Sciences, College of STEM, University of Arkansas Fort Smith, Fort Smith, Arkansas 72913, United States
| | - Kinsie E Arnst
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Lei Yang
- Department of Chemical Biology and Therapeutics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Hao Chen
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Zhongzhi Wu
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Pradeep B Lukka
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Keyur Parmar
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Stephen W White
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Yuxi Wang
- Department of Respiratory and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wei Li
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Duane D Miller
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
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15
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Mahmud S, Islam MJ, Parves MR, Khan MA, Tabussum L, Ahmed S, Ali MA, Fakayode SO, Halim MA. Designing potent inhibitors against the multidrug resistance P-glycoprotein. J Biomol Struct Dyn 2021; 40:9403-9415. [PMID: 34060432 DOI: 10.1080/07391102.2021.1930159] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The multidrug transporter P-glycoprotein is an ATP binding cassette (ABC) exporter responsible for resistance to tumor cells during chemotherapy. This study was designed with computational approaches aimed at identifying the best potent inhibitors of P-glycoprotein. Although many compounds have been suggested to inhibit P-glycoprotein, however, their information on bioavailability, selectivity, ADMET properties, and molecular interactions has not been revealed. Molecular docking, ADMET analysis, molecular dynamics, Principal component analysis (PCA), and binding free energy calculations were performed. Two compounds D1 and D2 showed the best docking score against P-glycoprotein and both compounds have 4-thiazolidinone derivatives containing indolin-3 one moiety are novel anti-tumor compounds. ADMET calculation analysis predicted D1 and D2 to have acceptable pharmacokinetic properties. The MD simulation discloses that D1-P-glycoprotein and D2-P-glycoprotein complexes are in stable conformation as apo-form. Hydrophobic amino acid such as phenylalanine plays significant on the interactions of inhibitors. Principal component analysis shows that both complexes are relatively similar variables as apo-form except planarity and Columbo energy profile. In addition, Quantitative Structural Activity Relationship (QSAR) of the ligand candidates were subjected to the principal component analysis (PCA) for pattern recognition. Partial-least-square (PLS) regression analysis was further utilized to model drug candidates' QSAR for subsequent prediction of the binding energy of validated drug candidates. PCA revealed groupings of the drug candidates based on the similarity or differences in drug candidates QSAR. Moreover, the developed PLS regression accurately predicted the values of the binding energy of drug candidates, with low residual error of prediction.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shafi Mahmud
- Division of Computer Aided Drug-Design, The Red-Green Research Center, BICCB, Tejgaon, Dhaka, Bangladesh.,Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Jahirul Islam
- Division of Computer Aided Drug-Design, The Red-Green Research Center, BICCB, Tejgaon, Dhaka, Bangladesh
| | - Md Rimon Parves
- Division of Computer Aided Drug-Design, The Red-Green Research Center, BICCB, Tejgaon, Dhaka, Bangladesh.,Department of Biochemistry and Biotechnology, University of Science and Technology Chittagong (USTC), Chittagong, Bangladesh
| | - Md Arif Khan
- Division of Computer Aided Drug-Design, The Red-Green Research Center, BICCB, Tejgaon, Dhaka, Bangladesh.,Department of Biotechnology and Genetic Engineering, University of Development Alternative (UODA), Dhaka, Bangladesh
| | - Lamiya Tabussum
- Division of Computer Aided Drug-Design, The Red-Green Research Center, BICCB, Tejgaon, Dhaka, Bangladesh.,Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Sinthyia Ahmed
- Division of Computer Aided Drug-Design, The Red-Green Research Center, BICCB, Tejgaon, Dhaka, Bangladesh
| | - Md Ackas Ali
- Division of Computer Aided Drug-Design, The Red-Green Research Center, BICCB, Tejgaon, Dhaka, Bangladesh
| | - Sayo O Fakayode
- Department of Physical Sciences, University of Arkansas-Fort Smith, Fort Smith, Arkansas, USA
| | - Mohammad A Halim
- Department of Physical Sciences, University of Arkansas-Fort Smith, Fort Smith, Arkansas, USA
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16
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Support vector machine-based model for toxicity of organic compounds against fish. Regul Toxicol Pharmacol 2021; 123:104942. [PMID: 33940084 DOI: 10.1016/j.yrtph.2021.104942] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/27/2021] [Accepted: 04/26/2021] [Indexed: 11/22/2022]
Abstract
Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity pLC50 of organic chemicals against various fish species. Only six descriptors were used to develop the QSTR model, by applying support vector machine (SVM) together with genetic algorithm. The QSTR model was trained and established on a sufficiently large data set of 840 organic compounds and evaluated with a test set (281 compounds). Compared with other QSTRs reported in the literature, the optimal SVM model for fish toxicity produces better statistical results with determination coefficients R2 above 0.70 for both the training set and test set, although the QSTR model in this work possesses fewer molecular descriptors. Applying SVM and genetic algorithm to develop the QSTR model for pLC50 of organic compounds against various fish species is successful.
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17
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Yang L, Wang Y, Chang J, Pan Y, Wei R, Li J, Wang H. QSAR modeling the toxicity of pesticides against Americamysis bahia. CHEMOSPHERE 2020; 258:127217. [PMID: 32535437 DOI: 10.1016/j.chemosphere.2020.127217] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/24/2020] [Accepted: 05/24/2020] [Indexed: 06/11/2023]
Abstract
The widespread use of pesticides has received increasing attention in regulatory agencies because their extensive overuse and various adverse effects on all living organisms. Organizations such as EPA and ECHA have published laws that pesticides should be fully evaluated before bring them to market. In the present study, we evaluated the pesticides toxicity using the Quantitative Structural-Activity Relationship (QSAR) method. The models for the single class pesticides (herbicides, insecticides and fungicides) as well as the general class pesticides (the combined dataset plus some microbicides, molluscicides, etc.) were developed using the Genetic Algorithm and Multiple Linear Regression method. The internal and external validation results suggested that all the obtained models were stable and predictive. According to the modeling descriptors, the lipophilic descriptors contributed positively while all the electrotopological state descriptors showed a negative contribution, their presences in every model verified the conspicuous influence of molecular lipophilicity and hydrophilicity on the pesticides toxicity. However, the influence of topological structure descriptors was different and varies with the physiochemical information they encode. Finally, the models presented in this paper would help assess the pesticides toxicity against Americamysis bahia, shorten test time, and reduce the cost of pesticides risk assessment.
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Affiliation(s)
- Lu Yang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China; University of Chinese Academy of Sciences, Yuquan RD 19A, Beijing, 100049, China
| | - Yinghuan Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Jing Chang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Yifan Pan
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China; University of Chinese Academy of Sciences, Yuquan RD 19A, Beijing, 100049, China
| | - Ruojin Wei
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China; University of Chinese Academy of Sciences, Yuquan RD 19A, Beijing, 100049, China
| | - Jianzhong Li
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Huili Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China.
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18
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Banerjee S, Norman DD, Deng S, Fakayode SO, Lee SC, Parrill AL, Li W, Miller DD, Tigyi GJ. Molecular modelling guided design, synthesis and QSAR analysis of new small molecule non-lipid autotaxin inhibitors. Bioorg Chem 2020; 103:104188. [PMID: 32890995 PMCID: PMC8163515 DOI: 10.1016/j.bioorg.2020.104188] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/18/2020] [Accepted: 08/04/2020] [Indexed: 02/06/2023]
Abstract
The lysophospholipase D autotaxin (ATX) generates lysophosphatidic acid (LPA) that activates six cognate G-protein coupled receptors (GPCR) in cancerous cells, promoting their motility and invasion. Four novel compounds were generated aided by molecular docking guided design and synthesis techniques to obtain new dual inhibitors of ATX and the lysophosphatidic acid receptor subtype 1 (LPAR1). Biological evaluation of these compounds revealed two compounds, 10 and 11, as new ATX enzyme inhibitors with potencies in the range of 218-220 nM and water solubility (>100 µg/mL), but with no LPAR1 inhibitory activity. A QSAR model was generated that included four newly designed compounds and twenty-one additional compounds that we have reported previously. The QSAR model provided excellent predictability of the pharmacological activity and potency among structurally related drug candidates. This model will be highly useful in guiding the synthesis of new ATX inhibitors in the future.
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Affiliation(s)
- Souvik Banerjee
- Department of Physical Sciences, University of Arkansas Fort Smith, Fort Smith, AR 72913, USA; Department of Physiology, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
| | - Derek D Norman
- Department of Physiology, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Shanshan Deng
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Sayo O Fakayode
- Department of Physical Sciences, University of Arkansas Fort Smith, Fort Smith, AR 72913, USA
| | - Sue Chin Lee
- Department of Physiology, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Abby L Parrill
- Department of Chemistry, Computational Research on Material Institute, The University of Memphis, Memphis, TN 38152, USA
| | - Wei Li
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Duane D Miller
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
| | - Gabor J Tigyi
- Department of Physiology, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
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19
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Carnesecchi E, Toma C, Roncaglioni A, Kramer N, Benfenati E, Dorne JLCM. Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 735:139243. [PMID: 32480144 DOI: 10.1016/j.scitotenv.2020.139243] [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: 04/01/2020] [Revised: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 06/11/2023]
Abstract
Honey bees (Apis mellifera) provide key ecosystem services as pollinators bridging agriculture, the food chain and ecological communities, thereby ensuring food production and security. Ecological risk assessment of single Plant Protection Products (PPPs) requires an understanding of the exposure and toxicity. In silico tools such as QSAR models can play a major role for the prediction of structural, physico-chemical and pharmacokinetic properties of chemicals as well as toxicity of single and multiple chemicals. Here, the first integrative honey bee QSAR model has been developed for PPPs using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD50) and ii) to profile the Mode of Action (MoA) of pesticides active substances. Three different classification-based and four regression-based models were developed and tested for their performance, thus identifying two models providing the most reliable predictions based on k-NN algorithm. The two-category QSAR model (toxic/non-toxic; n = 411) was validated using sensitivity (=0.93), specificity (=0.85), balanced accuracy (=0.90), and Matthews correlation coefficient (MCC = 0.78) as statistical parameters. The regression-based model (n = 113) was validated for its reliability and robustness (R2 = 0.74; MAE = 0.52). Current study proposes the MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD50s data points from three different databases. The classification allows to further define MoAs and the target site of PPPs active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals. Relevant future perspectives are briefly addressed to integrate MoA, adverse outcome pathways (AOPs) and toxicokinetic information for the refinement of single-chemical/combined toxicity predictions and risk estimates at different levels of biological organization in the bee health context.
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Affiliation(s)
- Edoardo Carnesecchi
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy.
| | - Cosimo Toma
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Alessandra Roncaglioni
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Nynke Kramer
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands
| | - Emilio Benfenati
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Jean Lou C M Dorne
- European Food Safety Authority (EFSA), Scientific Committee and Emerging Risks Unit, Via Carlo Magno 1A, 43126 Parma, Italy
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20
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Graph attention convolutional neural network model for chemical poisoning of honey bees' prediction. Sci Bull (Beijing) 2020; 65:1184-1191. [PMID: 36659148 DOI: 10.1016/j.scib.2020.04.006] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 02/03/2020] [Accepted: 03/24/2020] [Indexed: 01/21/2023]
Abstract
The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning (DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks (GACNN) with the combination of undirected graph (UG) and attention convolutional neural networks (ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus non-poisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7% Matthews Correlation Coefficient (MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications. In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform (http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment.
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21
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Yu X. Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2020; 224:105496. [PMID: 32408003 DOI: 10.1016/j.aquatox.2020.105496] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 04/16/2020] [Accepted: 04/19/2020] [Indexed: 06/11/2023]
Abstract
Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure-activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC10 of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a test set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R2 of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R2 of 0.75 and MAE of 0.61 for the test set. Compared with other models reported in the literature, our SVM model for the toxicity pEC10 shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the test set. A QSTR model for pEC50 of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.
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Affiliation(s)
- Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan, 411104, China.
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22
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In Silico Prediction of Critical Micelle Concentration (CMC) of Classic and Extended Anionic Surfactants from Their Molecular Structural Descriptors. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020. [DOI: 10.1007/s13369-020-04598-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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23
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Yang L, Wang Y, Hao W, Chang J, Pan Y, Li J, Wang H. Modeling pesticides toxicity to Sheepshead minnow using QSAR. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 193:110352. [PMID: 32120163 DOI: 10.1016/j.ecoenv.2020.110352] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 02/14/2020] [Accepted: 02/15/2020] [Indexed: 06/10/2023]
Abstract
Nowadays, the environmental risk caused by the widespread use of pesticides and their ubiquitous residuals has received more and more attention in academia and regulatory agencies. Due to the large number of pesticides used in agriculture and their adverse effects on all living organisms and the numerous end-points, it is necessary to employ the in silico tools to quickly highlight hazardous pesticides. In this study, we have evaluated the toxicity of pesticides against Sheepshead minnow with the Quantitative Structure-Activity Relationship (QSAR) approach. The models for the specific-type (insecticides, herbicides and fungicides) as well as the general-type (combing all the specific-type pesticides and some microbicides, nematicides, etc.) pesticides were developed using the Genetic Algorithm and the Multiple Linear Regression method, subsequently validated with various metrics. The validation results suggested that the obtained models were highly robust, externally predictive and characterized by a broad applicability domain. Considering the modeling descriptors, the toxicity of pesticides would increase with the lipophilicity and decrease with the polarity and hydrophilicity. Most electrotopological state descriptors contribute negatively to the toxicity, while the influence of topological structure descriptors mainly depends on the physiochemical information they encode. The models proposed in this paper would be useful in filling the data gaps, prioritizing and then focusing experiments on more hazardous pesticides.
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Affiliation(s)
- Lu Yang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China; University of Chinese Academy of Sciences, Yuquan RD 19A, Beijing, 100049, China
| | - Yinghuan Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Weiyu Hao
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Jing Chang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Yifan Pan
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China; University of Chinese Academy of Sciences, Yuquan RD 19A, Beijing, 100049, China
| | - Jianzhong Li
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Huili Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China.
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24
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Carnesecchi E, Toropov AA, Toropova AP, Kramer N, Svendsen C, Dorne JL, Benfenati E. Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 704:135302. [PMID: 31810690 DOI: 10.1016/j.scitotenv.2019.135302] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 10/29/2019] [Accepted: 10/29/2019] [Indexed: 06/10/2023]
Abstract
Pollinators such as honey bees are of considerable importance, because of the crucial pollination services they provide for food crops and wild plants. Since bees are exposed to a wide range of multiple chemicals "mixtures" both of anthropogenic (e.g. plant protection products) and natural origin (e.g. plant toxins), understanding their combined toxicity is critical. Although honey bees are employed worldwide as surrogate species for Apis and non-Apis bees in toxicity tests, it is practically unfeasible to perform in vivo tests for all mixtures of chemicals. Therefore, Quantitative Structure-Activity Relationships (QSAR) models can be developed using available data and can provide useful tools to predict such combined toxicity. Here, three different QSAR models within the CORAL software have been calibrated and validated for honey bees (A. mellifera) to predict the acute contact mixtures potency (LD50-mix), in two regression based-models, and the nature of combined toxicity (synergism / non-synergism) in a classification-based model. Experimental data on binary mixtures (n = 123) (LD50-mix) including dose response data (n = 97) and corresponding Toxic Unit values were retrieved from EFSA databases. The models were built using the principle of extraction of attributes from SMILES (or quasi-SMILES) while calculating so-called correlation weights for these attributes using Monte Carlo techniques. The two regression models were validated for their reliability and robustness (R2 = 0.89, CCC = 0.92, Q2 = 0.81; R2 = 0.87, CCC = 0.89, Q2 = 0.75). The classification model was validated using sensitivity (=0.86), specificity (=1), accuracy (=0.96), and Matthews correlation coefficient (MCC = 0.90) as qualitative statistical validation parameters. Results indicate that these QSAR models successfully predict acute contact toxicity of binary mixtures in honey bees and can support prioritisation of multiple chemicals of concerns. Data gaps and further development of QSAR models for honey bees are highlighted particularly for chronic and sub-lethal effects.
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Affiliation(s)
- Edoardo Carnesecchi
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156 Milan, Italy; Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, The Netherlands.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156 Milan, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156 Milan, Italy
| | - Nynke Kramer
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, The Netherlands
| | - Claus Svendsen
- Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Wallingford, Oxfordshire OX10 8BB, UK
| | - Jean Lou Dorne
- European Food Safety Authority (EFSA), Scientific Committee and Emerging Risks Unit, Via Carlo Magno 1A, 43126 Parma, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156 Milan, Italy
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25
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Lu BQ, Liu SS, Wang ZJ, Xu YQ. Conlecs: A novel procedure for deriving the concentration limits of chemicals outside the criteria of human drinking water using existing criteria and species sensitivity distribution based on quantitative structure-activity relationship prediction. JOURNAL OF HAZARDOUS MATERIALS 2020; 384:121380. [PMID: 31614281 DOI: 10.1016/j.jhazmat.2019.121380] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/15/2019] [Accepted: 10/01/2019] [Indexed: 06/10/2023]
Abstract
Water quality criteria (WQC) for an increasing number of emerging chemicals need to be developed to protect human health and biological safety. Existing species sensitivity distribution (SSD) methods can only be used to help establish WQC for ecological protection, and cannot be extended to the protection of human beings from various hazards. In this study, a novel procedure called Conlecs is proposed to derive the concentration limits (ConLs) of pesticides outside the criteria for human drinking water (CHDW) using the existing criteria of pesticides and SSD integrated with the toxicity prediction achieved through robust QSAR models. Optimal SSD models of four pesticides (within the CHDW) and two pesticides (outside the CHDW) on 12 species were first constructed, and the existing ConLs of four pesticides within the CHDW were then utilized to select the most suitable species for the optimal proportions to avoid human hazards (PHH), allowing the ConLs of two pesticides outside the CHDW to be derived.
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Affiliation(s)
- Bing-Qing Lu
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Shu-Shen Liu
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China.
| | - Ze-Jun Wang
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Ya-Qian Xu
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
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Ahmed S, Islam N, Shahinozzaman M, Fakayode SO, Afrin N, Halim MA. Virtual screening, molecular dynamics, density functional theory and quantitative structure activity relationship studies to design peroxisome proliferator-activated receptor-γ agonists as anti-diabetic drugs. J Biomol Struct Dyn 2020; 39:728-742. [DOI: 10.1080/07391102.2020.1714482] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Sinthyia Ahmed
- Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Nazrul Islam
- Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Md Shahinozzaman
- Department of Bioscience and Biotechnology, Faculty of Agriculture, University of the Ryukyus, Okinawa, Japan
| | - Sayo O. Fakayode
- Department of Physical Sciences, University of Arkansas-Fort Smith, Fort Smith, Arkansas, USA
| | - Nadia Afrin
- Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Mohammad A. Halim
- Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
- Department of Physical Sciences, University of Arkansas-Fort Smith, Fort Smith, Arkansas, USA
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Abstract
Pollination services are vital for agriculture, food security and biodiversity. Although many insect species provide pollination services, honeybees are thought to be the major provider of this service to agriculture. However, the importance of wild bees in this respect should not be overlooked. Whilst regulatory risk assessment processes have, for a long time, included that for pollinators, using honeybees (Apis mellifera) as a protective surrogate, there are concerns that this approach may not be sufficiently adequate particularly because of global declines in pollinating insects. Consequently, risk assessments are now being expanded to include wild bee species such as bumblebees (Bombus spp.) and solitary bees (Osmia spp.). However, toxicity data for these species is scarce and are absent from the main pesticide reference resources. The aim of the study described here was to collate data relating to the acute toxicity of pesticides to wild bee species (both topical and dietary exposure) from published regulatory documents and peer reviewed literature, and to incorporate this into one of the main online resources for pesticide risk assessment data: The Pesticide Properties Database, thus ensuring that the data is maintained and continuously kept up to date. The outcome of this study is a dataset collated from 316 regulatory and peer reviewed articles that contains 178 records covering 120 different pesticides and their variants which includes 142 records for bumblebees and a further 115 records for other wild bee species.
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Khan PM, Roy K, Benfenati E. Chemometric modeling of Daphnia magna toxicity of agrochemicals. CHEMOSPHERE 2019; 224:470-479. [PMID: 30831498 DOI: 10.1016/j.chemosphere.2019.02.147] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/21/2019] [Accepted: 02/22/2019] [Indexed: 06/09/2023]
Abstract
Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.
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Affiliation(s)
- Pathan Mohsin Khan
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India; Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy.
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy
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Bora A, Suzuki T, Funar-Timofei S. Neonicotinoid insecticide design: molecular docking, multiple chemometric approaches, and toxicity relationship with Cowpea aphids. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2019; 26:14547-14561. [PMID: 30877540 DOI: 10.1007/s11356-019-04662-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 02/19/2019] [Indexed: 06/09/2023]
Abstract
Neonicotinoids are the fastest-growing class of insecticides successfully applied in plant protection, human and animal health care. The significant resistance increases led to the urgent need for alternative new neonicotinoids, with improved insecticidal activity. We performed molecular docking to describe a common binding mode of neonicotinoids into the nicotinic acetylcholine receptor, and to select the appropriate conformations to derive models. These were further used in a QSAR study employing both linear and nonlinear approaches to model the inhibitory activity against the Cowpea aphids. Linear modeling was performed by multiple linear regression and partial least squares and nonlinear modeling by artificial neural networks and support vector machine methods. The OECD principles were considered for QSAR models validation. Robust models with predictive power were found for neonicotinoid diverse structures. Based on our QSAR and docking outcomes, five new insecticides were predicted, according to the model applicability domain, the ligand efficiencies, and the binding mode. Therefore, the developed models can be confidently used for the prediction of the insecticidal activity of new chemicals, saving a substantial amount of time and money and, also, contributing to the chemical risk assessment.
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Affiliation(s)
- Alina Bora
- Institute of Chemistry Timisoara of the Romanian Academy, 24 Mihai Viteazul Av., 300223, Timisoara, Romania
| | - Takahiro Suzuki
- Natural Science Laboratory, Toyo University, 5-28-20 Hakusan, Bunkyo-ku, Tokyo, 112-8606, Japan
| | - Simona Funar-Timofei
- Institute of Chemistry Timisoara of the Romanian Academy, 24 Mihai Viteazul Av., 300223, Timisoara, Romania.
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