51
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Keshavarz MH, Shirazi Z, Sayehvand F. A novel approach for assessment of antitrypanosomal activity of sesquiterpene lactones through additive and non-additive molecular structure parameters. Mol Divers 2022:10.1007/s11030-022-10495-5. [DOI: 10.1007/s11030-022-10495-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/06/2022] [Indexed: 11/30/2022]
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52
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Bukhari SNA, Elsherif MA, Junaid K, Ejaz H, Alam P, Samad A, Jawarkar RD, Masand VH. Perceiving the Concealed and Unreported Pharmacophoric Features of the 5-Hydroxytryptamine Receptor Using Balanced QSAR Analysis. Pharmaceuticals (Basel) 2022; 15:ph15070834. [PMID: 35890133 PMCID: PMC9316833 DOI: 10.3390/ph15070834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/12/2022] [Accepted: 06/25/2022] [Indexed: 02/04/2023] Open
Abstract
The 5-hydroxytryptamine receptor 6 (5-HT6) has gained attention as a target for developing therapeutics for Alzheimer’s disease, schizophrenia, cognitive dysfunctions, anxiety, and depression, to list a few. In the present analysis, a larger and diverse dataset of 1278 molecules covering a broad chemical and activity space was used to identify visual and concealed structural features associated with binding affinity for 5-HT6. For this, quantitative structure–activity relationships (QSAR) and molecular docking analyses were executed. This led to the development of a statistically robust QSAR model with a balance of excellent predictivity (R2tr = 0.78, R2ex = 0.77), the identification of unreported aspects of known features, and also novel mechanistic interpretations. Molecular docking and QSAR provided similar as well as complementary results. The present analysis indicates that the partial charges on ring carbons present within four bonds from a sulfur atom, the occurrence of sp3-hybridized carbon atoms bonded with donor atoms, and a conditional occurrence of lipophilic atoms/groups from nitrogen atoms, which are prominent but unreported pharmacophores that should be considered while optimizing a molecule for 5-HT6. Thus, the present analysis led to identification of some novel unreported structural features that govern the binding affinity of a molecule. The results could be beneficial in optimizing the molecules for 5-HT6.
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Affiliation(s)
- Syed Nasir Abbas Bukhari
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka 72388, Saudi Arabia
| | | | - Kashaf Junaid
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Hasan Ejaz
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Pravej Alam
- Department of Biology, College of Science and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati 444603, Maharashtra, India
| | - Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444602, Maharashtra, India
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Keshavarz MH, Shirazi Z, Mohajeri M. Simple method for assessment of activities of thrombin inhibitors through their molecular structure parameters. Comput Biol Med 2022; 146:105640. [DOI: 10.1016/j.compbiomed.2022.105640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/28/2022] [Accepted: 05/02/2022] [Indexed: 11/16/2022]
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Aalizadeh R, Nikolopoulou V, Alygizakis NA, Thomaidis NS. First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography-Atmospheric Pressure Chemical Ionization-Quadrupole Time of Flight-Mass Spectrometry. Anal Chem 2022; 94:9766-9774. [PMID: 35760399 PMCID: PMC9280717 DOI: 10.1021/acs.analchem.2c01432] [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] [Indexed: 12/05/2022]
Abstract
![]()
The ionization efficiency
of emerging contaminants was modeled
for the first time in gas chromatography-high-resolution mass spectrometry
(GC-HRMS) which is coupled to an atmospheric pressure chemical ionization
source (APCI). The recent chemical space has been expanded in environmental
samples such as soil, indoor dust, and sediments thanks to recent
use of high-resolution mass spectrometric techniques; however, many
of these chemicals have remained unquantified. Chemical exposure in
dust can pose potential risk to human health, and semiquantitative
analysis is potentially of need to semiquantify these newly identified
substances and assist with their risk assessment and environmental
fate. In this study, a rigorously tested semiquantification workflow
was proposed based on GC-APCI-HRMS ionization efficiency measurements
of 78 emerging contaminants. The mechanism of ionization of compounds
in the APCI source was discussed via a simple connectivity index and
topological structure. The quantitative structure–property
relationship (QSPR)-based model was also built to predict the APCI
ionization efficiencies of unknowns and later use it for their quantification
analyses. The proposed semiquantification method could be transferred
into the household indoor dust sample matrix, and it could include
the effect of recovery and matrix in the predictions of actual concentrations
of analytes. A suspect compound, which falls inside the application
domain of the tool, can be semiquantified by an online web application,
free of access at http://trams.chem.uoa.gr/semiquantification/.
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Affiliation(s)
- Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Varvara Nikolopoulou
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Nikiforos A Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.,Environmental Institute, Okružná 784/42, 97241 Koš, Slovak Republic
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
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55
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NBİL B, NADJİ N, KHEROUF S, NOURİ L, BOUDJEMAA A, BACHARİ K, MESSADİ D. QSER modeling of half-wave oxidation potential of indolizines by theoretical descriptors. JOURNAL OF THE TURKISH CHEMICAL SOCIETY, SECTION A: CHEMISTRY 2022. [DOI: 10.18596/jotcsa.1065043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Indolizine derivatives hold essential biological functions and have been researched for hypoglycemic, antibacterial, anti-inflammatory, analgesic, and anti-tumor actions. Indolizine scaffold has intrigued conjecture and continuous attention and has become an effective parent system for generating powerful novel medication candidates. This research focused on applying the quantitative structure-electrochemistry relationship (QSER) approach to the half-wave potential (E1/2) for Indolizine derivatives using theoretical molecular descriptors. After calculating the descriptors and splitting the data into both sets, training and prediction. The QSER model was constructed using the Genetic Algorithm/Multiple Linear Regression (GA/MLR) technique, which was used to choose the optimal descriptors for the model. A four-parameter model has been established. Many assessment procedures, including cross-validation, external validation, and Y-scrambling testing, were used to assess the model's performance. Furthermore, the applicability domain (AD) was investigated using the Williams and Insubria graphs to assess the correctness of the established model's predictions. The constructed model exhibits great goodness-of-fit to experimental data, as well as high stability (R²=0.893, Q²LOO= 0.851, Q²LMO=0.843 RMSEtr= 0.052, s= 0.056). Prediction results show a good agreement with the experimental data of E1/2 (R²ext= 0.912, Q²F1= 0.883, Q²F2= 0.883, Q²F3= 0.919, CCCext= 0.942, RMSEext=0.045).
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Affiliation(s)
- Bouarra NBİL
- Centre de Recherche Scientifique et Technique en Analyses Physico-Chimiques
| | - Nawel NADJİ
- 1Centre de Recherche Scientifique et Technique en Analyses Physico-Chimiques
| | | | - Loubna NOURİ
- Centre de Recherche Scientifique et Technique en Analyses Physico-Chimiques
| | - Amel BOUDJEMAA
- Centre de Recherche Scientifique et Technique en Analyses Physico-Chimiques
| | - Khaldoun BACHARİ
- Centre de Recherche Scientifique et Technique en Analyses Physico-Chimiques
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56
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Mechanistic Analysis of Chemically Diverse Bromodomain-4 Inhibitors Using Balanced QSAR Analysis and Supported by X-ray Resolved Crystal Structures. Pharmaceuticals (Basel) 2022; 15:ph15060745. [PMID: 35745664 PMCID: PMC9231298 DOI: 10.3390/ph15060745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022] Open
Abstract
Bromodomain-4 (BRD-4) is a key enzyme in post-translational modifications, transcriptional activation, and many other cellular processes. Its inhibitors find their therapeutic usage in cancer, acute heart failure, and inflammation to name a few. In the present study, a dataset of 980 molecules with a significant diversity of structural scaffolds and composition was selected to develop a balanced QSAR model possessing high predictive capability and mechanistic interpretation. The model was built as per the OECD (Organisation for Economic Co-operation and Development) guidelines and fulfills the endorsed threshold values for different validation parameters (R2tr = 0.76, Q2LMO = 0.76, and R2ex = 0.76). The present QSAR analysis identified that anti-BRD-4 activity is associated with structural characters such as the presence of saturated carbocyclic rings, the occurrence of carbon atoms near the center of mass of a molecule, and a specific combination of planer or aromatic nitrogen with ring carbon, donor, and acceptor atoms. The outcomes of the present analysis are also supported by X-ray-resolved crystal structures of compounds with BRD-4. Thus, the QSAR model effectively captured salient as well as unreported hidden pharmacophoric features. Therefore, the present study successfully identified valuable novel pharmacophoric features, which could be beneficial for the future optimization of lead/hit compounds for anti-BRD-4 activity.
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57
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Chatterjee M, Roy K. Application of cross-validation strategies to avoid overestimation of performance of 2D-QSAR models for the prediction of aquatic toxicity of chemical mixtures. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:463-484. [PMID: 35638563 DOI: 10.1080/1062936x.2022.2081255] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
The quantitative structure-activity relationship (QSAR) modelling of mixtures is not as simple as that for individual chemicals, and it needs additional care to avoid overestimation of the performance. In this research, we have developed a 2D-QSAR model using only 2D interpretable and reproducible descriptors to predict the aquatic toxicity of mixtures of polar and non-polar narcotic substances present in the environment. Partial least squares (PLS) regression has been used to model the response variable (log 1/EC50 against Photobacterium phosphoreum) and the structural features of 84 binary mixtures of polar and nonpolar narcotic toxicants complying with the Organization of Economic Co-operation and Development (OECD) protocols. The model was cross-validated by mixtures-out and compounds-out cross-validation to nullify the developmental bias. The reliability of prediction of the model has been judged by the Prediction Reliability Indicator (PRI) tool using a newly designed set. The new model is robust, reproducible, extremely predictive, easily interpretable, and can be used for reliable prediction of aquatic toxicity of any untested chemical mixtures within the applicability domain. We have additionally used a machine learning-based chemical read-across algorithm in this study to improve the quality of predictions for the toxicity of the mixtures with the modelled descriptors.
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Affiliation(s)
- M Chatterjee
- 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
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58
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Wu YW, Ta GH, Lung YC, Weng CF, Leong MK. In Silico Prediction of Skin Permeability Using a Two-QSAR Approach. Pharmaceutics 2022; 14:961. [PMID: 35631545 PMCID: PMC9143389 DOI: 10.3390/pharmaceutics14050961] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/23/2022] [Accepted: 04/26/2022] [Indexed: 11/17/2022] Open
Abstract
Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.
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Affiliation(s)
- Yu-Wen Wu
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (Y.-W.W.); (G.H.T.); (Y.-C.L.)
| | - Giang Huong Ta
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (Y.-W.W.); (G.H.T.); (Y.-C.L.)
| | - Yi-Chieh Lung
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (Y.-W.W.); (G.H.T.); (Y.-C.L.)
| | - Ching-Feng Weng
- Institute of Respiratory Disease and Functional Physiology Section, Department of Basic Medical Science, Xiamen Medical College, Xiamen 361023, China;
| | - Max K. Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (Y.-W.W.); (G.H.T.); (Y.-C.L.)
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59
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Aalizadeh R, Nikolopoulou V, Alygizakis N, Slobodnik J, Thomaidis NS. A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS. Anal Bioanal Chem 2022; 414:7435-7450. [PMID: 35471250 DOI: 10.1007/s00216-022-04084-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/31/2022] [Accepted: 04/11/2022] [Indexed: 11/29/2022]
Abstract
There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .
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Affiliation(s)
- Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece.
| | - Varvara Nikolopoulou
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - Nikiforos Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
- Environmental Institute, Okružná 784/42, 97241, Koš, Slovak Republic
| | | | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece.
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60
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Liman W, Oubahmane M, Hdoufane I, Bjij I, Villemin D, Daoud R, Cherqaoui D, El Allali A. Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus. Molecules 2022; 27:molecules27092729. [PMID: 35566079 PMCID: PMC9099611 DOI: 10.3390/molecules27092729] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/13/2022] [Accepted: 04/21/2022] [Indexed: 11/16/2022] Open
Abstract
Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.
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Affiliation(s)
- Wissal Liman
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco; (W.L.); (R.D.)
| | - Mehdi Oubahmane
- Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco; (M.O.); (I.H.); (D.C.)
| | - Ismail Hdoufane
- Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco; (M.O.); (I.H.); (D.C.)
| | - Imane Bjij
- Institut Supérieur des Professions Infirmières et Techniques de Santé (ISPITS), Dakhla 73000, Morocco;
| | - Didier Villemin
- Ecole Nationale Supérieure d’Ingénieurs (ENSICAEN) Laboratoire de Chimie Moléculaire et Thioorganique, UMR 6507 CNRS, INC3M, FR3038, Labex EMC3, Labex SynOrg ENSICAEN & Université de Caen, 14118 Caen, France;
| | - Rachid Daoud
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco; (W.L.); (R.D.)
| | - Driss Cherqaoui
- Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco; (M.O.); (I.H.); (D.C.)
| | - Achraf El Allali
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco; (W.L.); (R.D.)
- Correspondence:
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61
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de Faria AC, Daré JK, da Cunha EFF, Freitas MP. Computer-Assisted Improvement of Sulfonylureas with Antifungal Properties and Limited Herbicidal Activity: Potential Application in Forage Conservation. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:3321-3330. [PMID: 35230107 DOI: 10.1021/acs.jafc.1c07352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This work reports studies at the molecular level of a series of modified sulfonylureas to determine the chemophoric sites responsible for their antifungal and herbicidal activities. For forage conservation, high antifungal potency and low phytotoxicity are required. A molecular modeling study based on multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) was performed to model these properties, as well as to guide the design of new agrochemical candidates. As a result, the MIA-QSAR models were reliable, robust, and predictive; for antifungal activity, the averages of the main validation parameters were r2 = 0.936, q2 = 0.741, and r2pred = 0.720, and for herbicidal activity, the model was very predictive (r2pred = 0.981 and r2m = 0.944). From the interpretation of the MIA-plots, 46 novel sulfonylureas with likely improved performance were proposed, from which 9 presented promising calculated selectivity indexes. Docking studies were performed to validate the QSAR predictions and to understand the interaction mode of the proposed ligands with the acetohydroxyacid synthase enzyme.
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Affiliation(s)
- Adriana C de Faria
- Departamento de Química, Instituto de Ciências Naturais, Universidade Federal de Lavras, Lavras, Minas Gerais 37200-900, Brazil
| | - Joyce K Daré
- Departamento de Química, Instituto de Ciências Naturais, Universidade Federal de Lavras, Lavras, Minas Gerais 37200-900, Brazil
| | - Elaine F F da Cunha
- Departamento de Química, Instituto de Ciências Naturais, Universidade Federal de Lavras, Lavras, Minas Gerais 37200-900, Brazil
| | - Matheus P Freitas
- Departamento de Química, Instituto de Ciências Naturais, Universidade Federal de Lavras, Lavras, Minas Gerais 37200-900, Brazil
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62
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Altaf R, Nadeem H, Iqbal MN, Ilyas U, Ashraf Z, Imran M, Muhammad SA. Synthesis, Biological Evaluation, 2D-QSAR, and Molecular Simulation Studies of Dihydropyrimidinone Derivatives as Alkaline Phosphatase Inhibitors. ACS OMEGA 2022; 7:7139-7154. [PMID: 35252705 PMCID: PMC8892665 DOI: 10.1021/acsomega.1c06833] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/08/2022] [Indexed: 05/09/2023]
Abstract
The presence of alkaline phosphatases has been observed in several species and has been known to play a crucial role in various biological functions. Higher expressions of alkaline phosphatase have been found in several multifactorial disorders and cancer patients, which has led it to be an interesting target for drug discovery. A strong structural similarity exists between intestinal alkaline phosphatases (IAPs) and tissue-nonspecific alkaline phosphatases (TNAPs), which has led to the discovery of only a few selective inhibitors. Therefore, a series of 22 derivatives of 6-(chloromethyl)-4-(4-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (1) and ethyl 6-(chloromethyl)-4-(2-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (2) were synthesized to evaluate the anticancer potential of these compounds against breast cancer. The compounds were characterized through spectral and elemental analyses. The inhibitory effect of dihydropyrimidinone derivatives on alkaline phosphatases was evaluated using the calf alkaline phosphatase assay. The antioxidant activity of these compounds was performed to study the radical scavenging effect. In silico molecular docking and molecular dynamic simulations were performed to elucidate the binding mode of active compounds. Moreover, the two-dimensional qualitative-structure-activity relationship (2D-QSAR) was performed to study the structural requirements for enzyme inhibition. The calf alkaline phosphatase inhibitory assay revealed significant inhibition of the enzyme by compound 4d with IC50 1.27 μM at 0.1 mM concentration as compared to standard KH2PO4 having IC50 2.80 μM. The compounds 4f, 4e, and 4i also showed very good inhibition with IC50 values of 2.502, 2.943, and 2.132 μM, respectively, at the same concentration. The antioxidant assay revealed efficient radical scavenging activity of compounds 4f, 4e, and 4g at 100 μg/mL with IC50 values of 0.48, 0.61, and 0.75 μg/mL, respectively. The molecular docking and simulation studies revealed efficient binding of active compounds in the active binding site of the target enzyme. The final QSAR equation revealed good predictivity and statistical validation having R 2 = 0.958 and Q 2 = 0.903, respectively, for the generated model. The compound 4d showed the highest inhibitory activity with stable binding modes acting as a future lead for identifying alkaline phosphatase inhibitors. The molecular simulations suggested the stable binding of this compound, and the QSAR studies revealed the importance of autocorrelated descriptors in the inhibition of alkaline phosphatase. The investigated compounds may serve as potential pharmacophores for potent and selective alkaline phosphatase inhibitors. We intend to further investigate the biological activities of these compounds as alkaline phosphatase inhibitors.
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Affiliation(s)
- Reem Altaf
- Department
of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Riphah International University, Islamabad 44000, Pakistan
- . Tel: 00923175638109
| | - Humaira Nadeem
- Department
of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Riphah International University, Islamabad 44000, Pakistan
| | - Muhammad Nasir Iqbal
- Department
of Biosciences, COMSATS University Islamabad, Sahiwal Campus, COMSATS University
Road, Off GT Road, Sahiwal, Sahiwal
District, Punjab 57000, Pakistan
| | - Umair Ilyas
- Department
of Pharmaceutics, Faculty of Pharmaceutical Sciences, Riphah International University, Islamabad 44000, Pakistan
| | - Zaman Ashraf
- Department
of Chemistry, Allama Iqbal Open University, Islamabad 747424, Pakistan
| | - Muhammad Imran
- Department
of Pharmaceutical Sciences, Iqra University, Islamabad Campus, Islamabad 44000, Pakistan
| | - Syed Aun Muhammad
- Institute
of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan 60800, Pakistan
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63
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Exploring the Prominent and Concealed Inhibitory Features for Cytoplasmic Isoforms of Hsp90 Using QSAR Analysis. Pharmaceuticals (Basel) 2022; 15:ph15030303. [PMID: 35337101 PMCID: PMC8953649 DOI: 10.3390/ph15030303] [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: 01/21/2022] [Revised: 02/19/2022] [Accepted: 02/23/2022] [Indexed: 11/24/2022] Open
Abstract
Cancer is a major life-threatening disease with a high mortality rate in many countries. Even though different therapies and options are available, patients generally prefer chemotherapy. However, serious side effects of anti-cancer drugs compel us to search for a safer drug. To achieve this target, Hsp90 (heat shock protein 90), which is responsible for stabilization of many oncoproteins in cancer cells, is a promising target for developing an anti-cancer drug. The QSAR (Quantitative Structure–Activity Relationship) could be useful to identify crucial pharmacophoric features to develop a Hsp90 inhibitor. Therefore, in the present work, a larger dataset encompassing 1141 diverse compounds was used to develop a multi-linear QSAR model with a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The new developed six-parameter model satisfies the recommended values for a good number of validation parameters such as R2tr = 0.78, Q2LMO = 0.77, R2ex = 0.78, and CCCex = 0.88. The present analysis reveals that the Hsp90 inhibitory activity is correlated with different types of nitrogen atoms and other hidden structural features such as the presence of hydrophobic ring/aromatic carbon atoms within a specific distance from the center of mass of the molecule, etc. Thus, the model successfully identified a variety of reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with Hsp90.
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64
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Zhu T, Tao C. Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor. JOURNAL OF HAZARDOUS MATERIALS 2022; 423:127037. [PMID: 34530267 DOI: 10.1016/j.jhazmat.2021.127037] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Polydimethylsiloxane-air partition coefficient (KPDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict KPDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the Radj2, QBOOT2 and Qext2 are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental KPDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
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Li Y, Yu X, Chen X, Yin J, Zhong W, Zhu L. Underlying mechanisms for the impacts of molecular structures and water chemistry on the enrichment of poly/perfluoroalkyl substances in aqueous aerosol. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 803:150003. [PMID: 34492487 DOI: 10.1016/j.scitotenv.2021.150003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 08/23/2021] [Accepted: 08/25/2021] [Indexed: 06/13/2023]
Abstract
Enrichment of ionic poly/perfluoroalkyl substances (PFASs) in aqueous aerosol (AA) is an important pathway for them to enter atmosphere. In this study, the enrichment behaviors of 12 legacy and emerging PFASs in AA in both single solute and mixed solutions were investigated. The enrichment factors (EF) displayed a general increasing trend with the fluorinated carbon chain length. For the first time, a robust Quantitative Structure-Property Relationship (QSPR) model coupled with partial least-square method was established with fifteen quantum chemical descriptors. Four molecular descriptors, including dipole moment (μ), molecular weight (MW), the maximal value of the molecular surface potential (Vs, max) and molecular volume (V) were identified as the key structural variables affecting the PFASs enrichment. Inorganic salts and humic acid (HA) which are common in seawater, facilitated the PFASs enrichment as a result of enhanced hydrophobicity and the bridging effect caused by divalent cations. The typical cationic and anionic surfactants, cetyltrimethylammonium bromide and sodium dodecyl sulfate, both inhibited the enrichment due to the competition between PFASs and surfactants. It is interesting that 6:2 chlorinated polyfluorinated ether sulfonate (F53B) had the highest EF among the 12 PFASs, implying its strong potential of atmosphere transport.
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Affiliation(s)
- Yao Li
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Xiaoyong Yu
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Xin Chen
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Jun Yin
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Wenjue Zhong
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Lingyan Zhu
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China.
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66
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A critical evaluation of novel demulsifying agents based on acrylic terpolymers for Mexican heavy crude oils dehydration. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2021.119878] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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67
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Chayawan, Selvestrel G, Baderna D, Toma C, Caballero Alfonso AY, Gamba A, Benfenati E. Skin sensitization quantitative QSAR models based on mechanistic structural alerts. Toxicology 2022; 468:153111. [DOI: 10.1016/j.tox.2022.153111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/05/2022] [Accepted: 01/26/2022] [Indexed: 10/19/2022]
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Zhu T, Chen W, Jafvert CT, Fu D, Cheng H, Chen M, Wang Y. Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 291:118223. [PMID: 34583266 DOI: 10.1016/j.envpol.2021.118223] [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] [Received: 06/23/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Knowledge about partitioning constants of hydrophobic organic compounds (HOCs) between the polymer and aqueous phases is critical for assessing chemical environmental fate and transport. The conventional experimental method is characterized by large discrepancies in the measured values due to the limited water solubility of HOCs and other associated issues. In the current work, a novel three-phase partitioning system was evaluated to determine accurate low-density polyethylene (LDPE)-water partition coefficients (KPE-w). By adding sufficient surfactant (Brij 30) to form the micellar pseudo-phase within the polymer/water system, the KPE-w values were obtained from a combination of two experimentally measured values, that is, the micelle-water partition coefficient (Kmic-w) and the LDPE-micelle partition coefficient (KPE-mic). The method presented here is capable of shortening the equilibration time to half a month, and avoiding defects of the traditional method with respect to directly measured aqueous phase concentrations. Herein, the KPE-w values were determined for HOCs with little errors. Meanwhile, based on the 120 experimental KPE-w data, several in silico models were also developed as valid extrapolation tools to estimate missing or uncertain values. Analysis of the underlying solubility interactions in the nonionic surfactant micelles were investigated, providing additional support for the reliability of the proposed method.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Chad T Jafvert
- Lyles School of Civil Engineering, and Environmental & Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Dafang Fu
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Yajun Wang
- School of Civil Engineering, Lanzhou University of Technology, 287 Langongping, Lanzhou, 730050, China
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69
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Zhu T, Chen W, Gu Y, Jafvert CT, Fu D. Polyethylene-water partition coefficients for polychlorinated biphenyls: Application of QSPR predictions models with experimental validation. WATER RESEARCH 2021; 207:117799. [PMID: 34731669 DOI: 10.1016/j.watres.2021.117799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 10/01/2021] [Accepted: 10/20/2021] [Indexed: 06/13/2023]
Abstract
The water environmental recalcitrance and ecotoxicity caused by polychlorinated biphenyls (PCBs) are international issues of common concern. The partition coefficients with PCBs between low-density polyethylene (LDPE) and water (KPE-w) are significant to assess their environmental transport and/or fate in aquatic environment. Even moderately hydrophobic PCBs, however, possess large KPE-w values, which makes directly experimental measurement labored. Here, based on the combination of quantitative structure-property relationships (QSPRs) and machine-learning algorithms, 10 in-silico models are developed to provide a quick estimate of KPE-w. These models exhibit good goodness-of-fit (R2adj: 0.919-0.975), robustness (Q2LOO: 0.870-0.954) and external prediction performances (Q2ext: 0.880-0.971), providing a speedy feasibility to close data gaps for limited or absent experimental information, especially the RF-2 model. Particularly, an additional experimental verification is performed for models by a rapid and accurate three-phase system (aqueous phase, surfactant micelles and LDPE). The results of the experiments for 16 PCBs show the modeling results agree well with experimental values, within or approaching the residuals of ± 0.3 log unit. Mechanism interpretations imply that the number of chlorine atoms and ortho-substituted chlorines are the great effect parameters for KPE-w. This result also heightens interest in measuring and predicting the KPE-w values of chemicals containing halogen atoms in water.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, P.R.China.
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, P.R.China
| | - Yuanyuan Gu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, P.R.China
| | - Chad T Jafvert
- Lyles School of Civil Engineering, and Environmental & Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Dafang Fu
- School of Civil Engineering, Southeast University, Nanjing, 210096, P.R.China
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70
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Bhujbal SP, Hah JM. Generation of Non-Nucleotide CD73 Inhibitors Using a Molecular Docking and 3D-QSAR Approach. Int J Mol Sci 2021; 22:ijms222312745. [PMID: 34884548 PMCID: PMC8657903 DOI: 10.3390/ijms222312745] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/23/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022] Open
Abstract
Radiotherapy and chemotherapy are conventional cancer treatments. Around 60% of all patients who are diagnosed with cancer receive radio- or chemotherapy in combination with surgery during their disease. Only a few patients respond to the blockage of immune checkpoints alone, or in combination therapy, because their tumours might not be immunogenic. Under these circumstances, an increasing level of extracellular adenosine via the activation of ecto-5’-nucleotidase (CD73) and consequent adenosine receptor signalling is a typical mechanism that tumours use to evade immune surveillance. CD73 is responsible for the conversion of adenosine monophosphate to adenosine. CD73 is overexpressed in various tumour types. Hence, targetting CD73’s signalling is important for the reversal of adenosine-facilitated immune suppression. In this study, we selected a potent series of the non-nucleotide small molecule inhibitors of CD73. Molecular docking studies were performed in order to examine the binding mode of the inhibitors inside the active site of CD73 and 3D-QSAR was used to study the structure–activity relationship. The obtained CoMFA (q2 = 0.844, ONC = 5, r2 = 0.947) and CoMSIA (q2 = 0.804, ONC = 4, r2 = 0.954) models showed reasonable statistical values. The 3D-QSAR contour map analysis revealed useful structural characteristics that were needed to modify non-nucleotide small molecule inhibitors. We used the structural information from the overall docking and 3D-QSAR results to design new, potent CD73 non-nucleotide inhibitors. The newly designed CD73 inhibitors exhibited higher activity (predicted pIC50) than the most active compound of all of the derivatives that were selected for this study. Further experimental studies are needed in order to validate the new CD73 inhibitors.
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Affiliation(s)
- Swapnil P. Bhujbal
- College of Pharmacy, Hanyang University, Ansan 426-791, Korea;
- Institute of Pharmaceutical Science and Technology, Hanyang University, Ansan 426-791, Korea
| | - Jung-Mi Hah
- College of Pharmacy, Hanyang University, Ansan 426-791, Korea;
- Institute of Pharmaceutical Science and Technology, Hanyang University, Ansan 426-791, Korea
- Correspondence: ; Tel.: +82-31-400-5803
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71
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Ghasedi N, Ahmadi S, Ketabi S, Almasirad A. DFT based QSAR study on quinolone-triazole derivatives as antibacterial agents. J Recept Signal Transduct Res 2021; 42:418-428. [PMID: 34693868 DOI: 10.1080/10799893.2021.1988971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
QSAR modeling was performed on 39 quinolone-triazole derivatives against gram-positive Staphylococcus aureus and gram-negative Pseudomonas aeruginosa bacteria. The molecular structures were optimized using the DFT/B3LYP method and 6-31 G basis set. Molecular descriptors were extracted using quantum mechanical calculations. The hierarchical cluster analysis was performed for a rational subset division. The initial dataset was divided into calibration and validation sets, and modeling was done by stepwise MLR method for each of the two bacteria. Internal and external validation methods confirmed the robustness and predictability of the obtained models. According to the obtained model for S. aureus (R2 = 0.889, R2ext = 0.938, Q2LOO = 0.853), the four descriptors- partial atomic charges for the N1 atom in triazole and C7 of the quinolone nucleus, 4-carbonyl bond length, and 13C-NMR chemical shift of 3-carboxylic acid- were found to be the descriptors controlling the activity. According to the obtained model for P. aeruginosa (R2 = 0.957, R2ext = 0.923, Q2LOO = 0.909), the O atom's partial charge in carbonyl, LUMO-HOMO energy gap, and logP were found to be the descriptors having the highest correlation with the antibacterial activity. Finally, some new compounds with higher activities were designed and proposed.
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Affiliation(s)
- Niloofar Ghasedi
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran medical sciences, Islamic Azad University, Tehran, Iran
| | - Sepideh Ketabi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran medical sciences, Islamic Azad University, Tehran, Iran
| | - Ali Almasirad
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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Yang X, Ou W, Zhao S, Wang L, Chen J, Kusko R, Hong H, Liu H. Human transthyretin binding affinity of halogenated thiophenols and halogenated phenols: An in vitro and in silico study. CHEMOSPHERE 2021; 280:130627. [PMID: 33964751 DOI: 10.1016/j.chemosphere.2021.130627] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/15/2021] [Accepted: 04/17/2021] [Indexed: 06/12/2023]
Abstract
Serious harmful effects have been reported for thiophenols, which are widely used industrial materials. To date, little information is available on whether such chemicals can elicit endocrine-related detrimental effects. Herein the potential binding affinity and underlying mechanism of action between human transthyretin (hTTR) and seven halogenated-thiophenols were examined experimentally and computationally. Experimental results indicated that the halogenated-thiophenols, except for pentafluorothiophenol, were powerful hTTR binders. The differentiated hTTR binding affinity of halogenated-thiophenols and halogenated-phenols were observed. The hTTR binding affinity of mono- and di-halo-thiophenols was higher than that of corresponding phenols; while the opposite relationship was observed for tri- and penta-halo-thiophenols and phenols. Our results also confirmed that the binding interactions were influenced by the degree of ligand dissociation. Molecular modeling results implied that the dominant noncovalent interactions in the molecular recognition processes between hTTR and halogenated-thiophenols were ionic pair, hydrogen bonds and hydrophobic interactions. Finally, a model with acceptable predictive ability was developed, which can be used to computationally predict the potential hTTR binding affinity of other halogenated-thiophenols and phenols. Taken together, our results highlighted that more research is needed to determine their potential endocrine-related harmful effects and appropriate management actions should be taken to promote their sustainable use.
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Affiliation(s)
- Xianhai Yang
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Wang Ou
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Songshan Zhao
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Lianjun Wang
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Rebeca Kusko
- Immuneering Corporation, Cambridge, MA, 02142, USA
| | - Huixiao Hong
- National Center for Toxicological Research US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Huihui Liu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
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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.
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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
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Lavado GJ, Baderna D, Gadaleta D, Ultre M, Roy K, Benfenati E. Ecotoxicological QSAR modeling of the acute toxicity of organic compounds to the freshwater crustacean Thamnocephalus platyurus. CHEMOSPHERE 2021; 280:130652. [PMID: 34162072 DOI: 10.1016/j.chemosphere.2021.130652] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/21/2021] [Accepted: 04/22/2021] [Indexed: 06/13/2023]
Abstract
Growing interest in environmental toxicity assessment using Thamnocephalus platyurus as organism has led to an increased availability of acute toxicity data. Despite this growing interest in tests with this organism, however, to the best of our knowledge there are no computational models to predict the acute toxicity in T. platyurus. In view of the limited number of in silico models for this crustacean, we developed Quantitative Structure-Activity Relationship (QSAR) models for the prediction of acute toxicity towards T. platyurus, reflected by the 24h LC50, using publicly available data according to the ISO 14380:2011 guideline. Two models were developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). We used partial least squares and gradient boosting machine techniques, which gave encouraging statistical quality in our data set.
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Affiliation(s)
- Giovanna J Lavado
- Laboratory of Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, via Mario Negri 2, 20156, Milan, Italy
| | - Diego Baderna
- Laboratory of Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, via Mario Negri 2, 20156, Milan, Italy.
| | - Domenico Gadaleta
- Laboratory of Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, via Mario Negri 2, 20156, Milan, Italy
| | - Marta Ultre
- ECOTOX LDS S.r.l., via G. Battista Vico 7, 20010, Milan, Italy
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India
| | - Emilio Benfenati
- Laboratory of Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, via Mario Negri 2, 20156, Milan, Italy
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75
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Gandhi A, Masand V, Zaki MEA, Al-Hussain SA, Ghorbal AB, Chapolikar A. QSAR analysis of sodium glucose co-transporter 2 (SGLT2) inhibitors for anti-hyperglycaemic lead development. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:731-744. [PMID: 34494464 DOI: 10.1080/1062936x.2021.1971295] [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: 06/15/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
QSAR (Quantitative Structure Activity Relationship) modelling was performed on a dataset of 90 sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors. The quantitative and explicative evaluations revealed some of the subtle and distinguished structural features that are responsible for the inhibitory potency of these compounds against SGLT2, such as less possible number of ring carbons at 8 Å from the lipophilic atoms in the molecule (fringClipo8A) and more possible value for the sum of the partial charges of the lipophilic atoms present within seven bonds from the donor atoms (lipo_don_7Bc). Multivariate GA-MLR (genetic algorithm-multi linear regression) and thorough validation methodology out-turned a statistically robust QSAR model with a very high predictability shown from various statistical parameters. A QSAR model with r2 = 0.83, F = 51.54, Q2LOO = 0.79, Q2LMO = 0.79, CCCcv = 0.88, Q2Fn = 0.76-0.81, r2ext = 0.77, CCCext = 0.85, and with RMSEtr < RMSEcv was proposed. This QSAR model will assist synthetic chemists in the development of the SGLT2 inhibitors as the antidiabetic leads.
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Affiliation(s)
- A Gandhi
- Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra, India
| | - V Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, India
| | - M E A Zaki
- Department of Chemistry, College of Science, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - S A Al-Hussain
- Department of Chemistry, College of Science, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - A Ben Ghorbal
- Department of Mathematics and Statistics, College of Sciences, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - A Chapolikar
- Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra, India
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Gandhi A, Masand V, Zaki MEA, Al-Hussain SA, Ghorbal AB, Chapolikar A. Quantitative Structure-Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads. Molecules 2021; 26:molecules26164795. [PMID: 34443383 PMCID: PMC8401583 DOI: 10.3390/molecules26164795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 11/23/2022] Open
Abstract
In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R2 = 0.79, Q2LOO = 0.77, Q2LMO = 0.76–0.77, Q2-Fn = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists.
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Affiliation(s)
- Ajaykumar Gandhi
- Department of Chemistry, Government College of Arts and Science, Aurangabad 431 004, Maharashtra, India;
- Correspondence: (A.G.); (M.E.A.Z.)
| | - Vijay Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444 602, Maharashtra, India;
| | - Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
- Correspondence: (A.G.); (M.E.A.Z.)
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
| | - Anis Ben Ghorbal
- Department of Mathematics and Statistics, College of Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
| | - Archana Chapolikar
- Department of Chemistry, Government College of Arts and Science, Aurangabad 431 004, Maharashtra, India;
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77
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Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity. Int J Mol Sci 2021; 22:ijms22158352. [PMID: 34361118 PMCID: PMC8348508 DOI: 10.3390/ijms22158352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/24/2021] [Accepted: 07/31/2021] [Indexed: 12/02/2022] Open
Abstract
Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.
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78
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Pereira IV, de Freitas MP. Double focus in the modelling of anti-influenza properties of 2-iminobenzimidazolines: pharmacology and toxicology. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:643-654. [PMID: 34282674 DOI: 10.1080/1062936x.2021.1950832] [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] [Received: 05/22/2021] [Accepted: 06/29/2021] [Indexed: 06/13/2023]
Abstract
Influenza affects millions of people globally and the appearance of drug-resistant strains is an ongoing problem. Therefore, this work reports the development of quantitative structure-activity relationship (QSAR) models to predict some biological properties of new 2-iminobenzimidazoline candidates for the treatment of the flu. A series of 2-iminobenzimidazoline derivatives with experimentally available values for cytotoxicity (pCC50) and anti-influenza activity (pIC50) was used for multivariate image analysis applied to QSAR (MIA-QSAR). The models were vigorously validated according to the best practices in QSAR and the chemical features responsible for the response variables were analysed based on MIA-plots, which assess the PLS regression coefficients and variable importance in projection scores. MIA descriptors encoding atomic properties (van der Waals radius and electronegativity) were capable of properly modelling the pCC50 and pIC50 data. The internally and externally validated models were used to predict the selectivity indexes (SI = pCC50/pIC50) of unprecedented analogues, which were designed upon analysis of the MIA-plots that show the substituent groups most affecting the biological data and by the combination of substructures of selected molecules. At least three promising anti-influenza candidates could be proposed from the predictive MIA-QSAR models.
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Affiliation(s)
- I V Pereira
- Departamento de Química, Instituto de Ciências Naturais, Universidade Federal de Lavras, 37200-900, Lavras, MG, Brazil
| | - M P de Freitas
- Departamento de Química, Instituto de Ciências Naturais, Universidade Federal de Lavras, 37200-900, Lavras, MG, Brazil
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79
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Biological Activities Related to Plant Protection and Environmental Effects of Coumarin Derivatives: QSAR and Molecular Docking Studies. Int J Mol Sci 2021; 22:ijms22147283. [PMID: 34298898 PMCID: PMC8303553 DOI: 10.3390/ijms22147283] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/01/2021] [Accepted: 07/02/2021] [Indexed: 01/31/2023] Open
Abstract
The aim was to study the inhibitory effects of coumarin derivatives on the plant pathogenic fungi, as well as beneficial bacteria and nematodes. The antifungal assay was performed on four cultures of phytopathogenic fungi by measuring the radial growth of the fungal colonies. Antibacterial activity was determined by the broth microdilution method performed on two beneficial soil organisms. Nematicidal activity was tested on two entomopathogenic nematodes. The quantitative structure-activity relationship (QSAR) model was generated by genetic algorithm, and toxicity was estimated by T.E.S.T. software. The mode of inhibition of enzymes related to the antifungal activity is elucidated by molecular docking. Coumarin derivatives were most effective against Macrophomina phaseolina and Sclerotinia sclerotiorum, but were not harmful against beneficial nematodes and bacteria. A predictive QSAR model was obtained for the activity against M. phaseolina (R2tr = 0.78; R2ext = 0.67; Q2loo = 0.67). A QSAR study showed that multiple electron-withdrawal groups, especially at position C-3, enhanced activities against M. phaseolina, while the hydrophobic benzoyl group at the pyrone ring, and –Br, –OH, –OCH3, at the benzene ring, may increase inhibition of S. sclerotiourum. Tested compounds possibly act inhibitory against plant wall-degrading enzymes, proteinase K. Coumarin derivatives are the potentially active ingredient of environmentally friendly plant-protection products.
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80
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Zhu T, Cao Z, Singh RP, Cheng H, Chen M. In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 289:112437. [PMID: 33812149 DOI: 10.1016/j.jenvman.2021.112437] [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] [Received: 01/25/2021] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 06/12/2023]
Abstract
Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (KPE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (KPE-W), LDPE-air (KPE-A) and LDPE-seawater (KPE-SW) partition coefficients. These developed models have satisfying predictability (R2adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSEtra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q2ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSEext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown KPE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Zaizhi Cao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | | | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
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81
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Kobayashi Y, Yoshida K. Development of QSAR models for prediction of fish bioconcentration factors using physicochemical properties and molecular descriptors with machine learning algorithms. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101285] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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82
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Vincze A, Dargó G, Rácz A, Balogh GT. A corneal-PAMPA-based in silico model for predicting corneal permeability. J Pharm Biomed Anal 2021; 203:114218. [PMID: 34166924 DOI: 10.1016/j.jpba.2021.114218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/14/2021] [Accepted: 06/15/2021] [Indexed: 12/23/2022]
Abstract
The capability to predict corneal permeability based on physicochemical parameters has always been a desirable objective of ophthalmic drug development. However, previous work has been limited to cases where either the diversity of compounds used was lacking or the performance of the models was poor. Our study provides extensive quantitative structure-property relationship (QSPR) models for corneal permeability predictions. The models involved in vitro corneal permeability measurements of 189 diverse compounds. Preliminary analysis of data showed that there is no significant correlation between corneal-PAMPA (Parallel Artificial Membrane Permeability Assay) permeability values and other pharmacokinetically relevant in silico drug transport parameters like Caco-2, jejunal permeability and blood-brain partition coefficient (logBB). Two different QSPR models were developed: one for corneal permeability and one for corneal membrane retention, based on experimental corneal-PAMPA permeability data. Partial least squares regression was applied for producing the models, which contained classical molecular descriptors and ECFP fingerprints in combination. A complex validation protocol (including internal and external validation) was carried out to provide robust and appropriate predictions for the permeability and membrane retention values. Both models had an overall fit of R2 > 0.90, including R2-values not lower than 0.85 for validation runs, and provide quick and accurate predictions of corneal permeability values for a diverse set of compounds.
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Affiliation(s)
- Anna Vincze
- Department of Chemical and Environmental Process Engineering, Budapest University of Technology and Economics, Műegyetem Rakpart 3., 1111, Budapest, Hungary
| | - Gergő Dargó
- Department of Chemical and Environmental Process Engineering, Budapest University of Technology and Economics, Műegyetem Rakpart 3., 1111, Budapest, Hungary
| | - Anita Rácz
- Institute of Materials and Environmental Chemistry, Research Centre for Natural Sciences, Magyar Tudósok Krt. 2., 1117, Budapest, Hungary.
| | - György T Balogh
- Department of Chemical and Environmental Process Engineering, Budapest University of Technology and Economics, Műegyetem Rakpart 3., 1111, Budapest, Hungary; Department of Pharmacodynamics and Biopharmacy, University of Szeged, Eötvös u. 6., 6720, Szeged, Hungary.
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83
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Oubahmane M, Hdoufane I, Bjij I, Jerves C, Villemin D, Cherqaoui D. COVID-19: In silico identification of potent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2. J Mol Struct 2021; 1244:130897. [PMID: 34149065 PMCID: PMC8205609 DOI: 10.1016/j.molstruc.2021.130897] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/09/2021] [Accepted: 06/11/2021] [Indexed: 12/23/2022]
Abstract
The COVID-19 has been creating a global crisis, causing countless deaths and unbearable panic. Despite the progress made in the development of the vaccine, there is an urge need for the discovery of antivirals that may better work at different stages of SARS-CoV-2 reproduction. The main protease (Mpro) of the SARS-CoV-2 is a crucial therapeutic target due to its critical function in virus replication. The α-ketoamide derivatives represent an important class of inhibitors against the Mpro of the SARS-CoV. While there is 99% sequence similarity between SARS-CoV and SARS-CoV-2 main proteases, anti-SARS-CoV compounds may have a huge demonstration's prospect of their effectiveness against the SARS-CoV-2. In this study, we applied various computational approaches to investigate the inhibition potency of novel designed α-ketoamide-based compounds. In this regard, a set of 21 α-ketoamides was employed to construct a QSAR model, using the genetic algorithm-multiple linear regression (GA-MLR), as well as a pharmacophore fit model. Based on the GA-MLR model, 713 new designed molecules were reduced to 150 promising hits, which were later subject to the established pharmacophore fit model. Among the 150 compounds, the best selected compounds (3 hits) with greater pharmacophore fit score were further studied via molecular docking, molecular dynamic simulations along with the Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. Our approach revealed that the three hit compounds could serve as potential inhibitors against the SARS-CoV-2 Mpro target.
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Affiliation(s)
- Mehdi Oubahmane
- Department of Chemistry, Faculty of Sciences Semlalia, BP, 2390 Marrakech, Morocco
| | - Ismail Hdoufane
- Department of Chemistry, Faculty of Sciences Semlalia, BP, 2390 Marrakech, Morocco
| | - Imane Bjij
- Department of Chemistry, Faculty of Sciences Semlalia, BP, 2390 Marrakech, Morocco
| | - Carola Jerves
- Departamento de Quimica, Faculdade de Ciencias da Universidade do Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal.,Facultad de Ciencias Químicas, Universidad de Cuenca, Cuenca, Ecuador
| | - Didier Villemin
- Ecole Nationale Supérieure d'Ingénieurs (ENSICAEN) Laboratoire de Chimie Moléculaire et Thioorganique. UMR 6507 CNRS, INC3M, FR3038, Labex EMC3, Labex SynOrg ENSICAEN & Université de Caen, France
| | - Driss Cherqaoui
- Department of Chemistry, Faculty of Sciences Semlalia, BP, 2390 Marrakech, Morocco
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84
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Agić D, Karnaš M, Šubarić D, Lončarić M, Tomić S, Karačić Z, Bešlo D, Rastija V, Molnar M, Popović BM, Lisjak M. Coumarin Derivatives Act as Novel Inhibitors of Human Dipeptidyl Peptidase III: Combined In Vitro and In Silico Study. Pharmaceuticals (Basel) 2021; 14:ph14060540. [PMID: 34198854 PMCID: PMC8229952 DOI: 10.3390/ph14060540] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/30/2021] [Accepted: 06/02/2021] [Indexed: 01/19/2023] Open
Abstract
Dipeptidyl peptidase III (DPP III), a zinc-dependent exopeptidase, is a member of the metalloproteinase family M49 with distribution detected in almost all forms of life. Although the physiological role of human DPP III (hDPP III) is not yet fully elucidated, its involvement in pathophysiological processes such as mammalian pain modulation, blood pressure regulation, and cancer processes, underscores the need to find new hDPP III inhibitors. In this research, five series of structurally different coumarin derivatives were studied to provide a relationship between their inhibitory profile toward hDPP III combining an in vitro assay with an in silico molecular modeling study. The experimental results showed that 26 of the 40 tested compounds exhibited hDPP III inhibitory activity at a concentration of 10 µM. Compound 12 (3-benzoyl-7-hydroxy-2H-chromen-2-one) proved to be the most potent inhibitor with IC50 value of 1.10 μM. QSAR modeling indicates that the presence of larger substituents with double and triple bonds and aromatic hydroxyl groups on coumarin derivatives increases their inhibitory activity. Docking predicts that 12 binds to the region of inter-domain cleft of hDPP III while binding mode analysis obtained by MD simulations revealed the importance of 7-OH group on the coumarin core as well as enzyme residues Ile315, Ser317, Glu329, Phe381, Pro387, and Ile390 for the mechanism of the binding pattern and compound 12 stabilization. The present investigation, for the first time, provides an insight into the inhibitory effect of coumarin derivatives on this human metalloproteinase.
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Affiliation(s)
- Dejan Agić
- Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.K.); (D.Š.); (D.B.); (V.R.); (M.L.)
- Correspondence:
| | - Maja Karnaš
- Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.K.); (D.Š.); (D.B.); (V.R.); (M.L.)
| | - Domagoj Šubarić
- Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.K.); (D.Š.); (D.B.); (V.R.); (M.L.)
| | - Melita Lončarić
- Faculty of Food Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.L.); (M.M.)
| | - Sanja Tomić
- Division of Organic Chemistry and Biochemistry, Ruđer Bošković Institute, 10000 Zagreb, Croatia; (S.T.); (Z.K.)
| | - Zrinka Karačić
- Division of Organic Chemistry and Biochemistry, Ruđer Bošković Institute, 10000 Zagreb, Croatia; (S.T.); (Z.K.)
| | - Drago Bešlo
- Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.K.); (D.Š.); (D.B.); (V.R.); (M.L.)
| | - Vesna Rastija
- Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.K.); (D.Š.); (D.B.); (V.R.); (M.L.)
| | - Maja Molnar
- Faculty of Food Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.L.); (M.M.)
| | - Boris M. Popović
- Department of Field and Vegetable Crops, Faculty of Agriculture, University of Novi Sad, 21000 Novi Sad, Serbia;
| | - Miroslav Lisjak
- Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.K.); (D.Š.); (D.B.); (V.R.); (M.L.)
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85
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Aalizadeh R, Panara A, Thomaidis NS. Development and Application of a Novel Semi-quantification Approach in LC-QToF-MS Analysis of Natural Products. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1412-1423. [PMID: 34027658 DOI: 10.1021/jasms.1c00032] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Use of high-resolution mass spectrometry (HRMS) including a MS calibration method has enabled simultaneous identification and quantification of knowns/unknowns. This has expanded our knowledge about the existing sample relevant chemical space in a way beyond reconciliation with a quantification task. This is largely due to fact that reference standards are not always available to achieve quantitative analysis. In this scenario, a semi-quantitative approach can fill the gap and provide a rough estimation of concentration. This research aimed to develop and compare several semi-quantification approaches based on chemical similarity or properties. The ionization efficiency scale was created for several groups of natural products. Advanced modeling approach based on a support vector machine was conducted to learn from the experimental ionization efficiency and apply it to unknowns or suspected compounds to predict their ionization efficiency in electrospray ionization mode. The developed semi-quantification workflows could be useful in most HRMS based "omics" areas, especially in natural products discovery.
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Affiliation(s)
- Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Anthi Panara
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
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86
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Wang L, Ding J, Shi P, Fu L, Pan L, Tian J, Cao D, Jiang H, Ding X. Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates. Arch Toxicol 2021; 95:2443-2457. [PMID: 33934188 DOI: 10.1007/s00204-021-03056-6] [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: 02/08/2021] [Accepted: 04/21/2021] [Indexed: 12/13/2022]
Abstract
Organophosphates (OPs) are hazardous chemicals widely used in industry and agriculture. Distribution of their residues in nature causes serious risks to humans, animals, and plants. To reduce hazards from OPs, quantitative structure-activity relationship (QSAR) models for predicting their acute oral toxicity in rats and mice and inhibition constants concerning human acetylcholinesterase were developed according to the bioactivity data of 456 unique OPs. Based on robust, two-dimensional molecular descriptors and quantum chemical descriptors, which accurately reflect OP electronic structures and reactivities, the influences of eight machine-learning algorithms on the prediction performance of the QSAR models were explored, and consensus QSAR models were constructed. Several strict model validation indices and the results of applicability domain evaluations show that the established consensus QSAR models exhibit good robustness, practical prediction abilities, and wide application scopes. Poor correlation was observed between acute oral toxicity at the mammalian level and the inhibition constants at the molecular level, indicating that the acute toxicity of OPs cannot be evaluated only by the experimental data of enzyme inhibitory activity, their toxicokinetic characteristics must also be considered. The constructed QSAR models described herein provide rapid, theoretical assessment of the bioactivity of unstudied or unknown OPs, as well as guidance for making decisions regarding their regulation.
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Affiliation(s)
- Liangliang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Junjie Ding
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Peichang Shi
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China
| | - Li Pan
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Jiahao Tian
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China. .,Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, People's Republic of China.
| | - Hui Jiang
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China.
| | - Xiaoqin Ding
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China.
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87
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Duchowicz PR, Bennardi DO, Ortiz EV, Comelli NC. QSAR models for insecticidal properties of plant essential oils on the housefly ( Musca domestica L.). SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:395-410. [PMID: 33870800 DOI: 10.1080/1062936x.2021.1905711] [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: 12/10/2020] [Accepted: 03/16/2021] [Indexed: 06/12/2023]
Abstract
The fumigant and topical activities exhibited by 27 plant-derived essentials oils (EOs) on adult M. domestica housefly are predicted through the Quantitative Structure-Activity Relationship (QSAR) theory. These molecular structure based calculations are performed on 253 structurally diverse compounds from the EOs, where the number of constituents in each essential oil mixture varies between 2 to 24. A large number of 86,048 non-conformational mixture descriptors are derived as linear combinations of the molecular descriptors of the EO components. Two strategies are compared for the mixture descriptor formulation, which consider or avoid the use of the chemical composition. The multivariable linear regression QSAR models of the present work are useful for fumigant and topical applications, describing predictive parallelisms for the insecticidal activity of the analysed complex mixtures.
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Affiliation(s)
- P R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, La Plata, Argentina
| | - D O Bennardi
- Cátedra de Química Orgánica, Facultad de Ciencias Agrarias y Forestales, La Plata, Argentina
| | - E V Ortiz
- Instituto de Monitoreo y Control de la Degradación Geoambiental (IMCoDeG), CONICET, Facultad de Tecnología y Ciencias Aplicadas, Universidad Nacional de Catamarca, Catamarca, Argentina
| | - N C Comelli
- Centro de Investigaciones y Transferencia de Catamarca (CITCA), CONICET, Universidad Nacional de Catamarca, Catamarca, Argentina
- Facultad de Ciencias Agrarias, Universidad Nacional de Catamarca, Catamarca, Argentina
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88
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Kobayashi Y, Yoshida K. Quantitative structure-property relationships for the calculation of the soil adsorption coefficient using machine learning algorithms with calculated chemical properties from open-source software. ENVIRONMENTAL RESEARCH 2021; 196:110363. [PMID: 33148423 DOI: 10.1016/j.envres.2020.110363] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/11/2020] [Accepted: 10/20/2020] [Indexed: 06/11/2023]
Abstract
The soil adsorption coefficient (Koc) is an environmental fate parameter that is essential for environmental risk assessment. However, obtaining Koc requires a significant amount of time and enormous expenditure. Thus, it is necessary to efficiently estimate Koc in the early stages of a chemical's development. In this study, a quantitative structure-property relationship (QSPR) model was developed using calculated physicochemical properties and molecular descriptors with the OPEn structure-activity/property Relationship App (OPERA) and Mordred software using the largest available Koc dataset. Specifically, we compared the accuracies of the model using the light gradient boosted machine (LightGBM), a gradient boosting decision tree (GBDT) algorithm, with those of previous models. The experimental results suggested the potential to develop a QSPR model that will produce highly accurate Koc values using molecular descriptors and physicochemical properties. Unlike previous studies, the use of a combination of LightGBM, OPERA and Mordred enables the prediction of Koc for many chemicals with high accuracy. In this study, OPERA was used to calculate the physicochemical properties, and Mordred was used to calculate molecular descriptors. The wide range of chemicals covered by OPERA and Mordred enables the analysis of a diverse range of chemical compounds. We also report a method to tune the LightBGM program. The use of fast-processing software, such as LightGBM, enables parameter tuning of a method required to obtain best performance. Our research represents one of the few studies in the field of environmental chemistry to use LightGBM. Using physicochemical properties as well as molecular descriptors, we could develop highly accurate Koc prediction models when compared to prior studies. In addition, our QSPR models may be useful for preliminary environmental risk assessment without incurring significant costs during the early chemical developmental stage.
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Affiliation(s)
- Yoshiyuki Kobayashi
- Graduate School of Business Sciences, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, 112-0012, Tokyo, Japan.
| | - Kenichi Yoshida
- Graduate School of Business Sciences, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, 112-0012, Tokyo, Japan
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89
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Chatterjee M, Roy K. Prediction of aquatic toxicity of chemical mixtures by the QSAR approach using 2D structural descriptors. JOURNAL OF HAZARDOUS MATERIALS 2021; 408:124936. [PMID: 33387719 DOI: 10.1016/j.jhazmat.2020.124936] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/17/2020] [Accepted: 12/20/2020] [Indexed: 06/12/2023]
Abstract
The rapid industrialization has led to the generation of various organic chemicals and multi-component mixtures which affect the environment adversely. Although organic chemicals are often exposed to the environment as a form of chemical mixtures rather than individual compounds, there is insufficient toxicity data available for the chemical mixtures due to the associated complexities. Most importantly, the nature of toxicity of mixtures is completely different from the individual chemicals, which makes the evaluation more difficult and challenging. In this paper, we have developed QSAR models for various individual and mixture data sets for the prediction of the aquatic toxicity. We have used Partial Least Squares (PLS) regression as a statistical tool to build the models. The various structural features of the individual chemicals and the mixture components have been modeled against the toxicity end point pEC50 (negative logarithm of median effective concentration in molar scale) of the aquatic organisms Photobacterium phosphoreum (marine bacterium) and Selenastrum capricornutum (freshwater algae). The mixture descriptors have been calculated by the weighted descriptor generation approach. The models were developed in accordance with OECD guidelines, and the quality of each model has been adjudged by strict validation parameters. The final models are robust, extremely predictive and interpretable mechanistically which can be used for the prediction of toxicity of untested chemical mixtures under the domain of applicability of the developed models.
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Affiliation(s)
- Mainak Chatterjee
- 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.
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90
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Banjare P, Matore B, Singh J, Roy PP. In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides. In Silico Pharmacol 2021; 9:28. [PMID: 33868896 PMCID: PMC8019672 DOI: 10.1007/s40203-021-00087-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 03/15/2021] [Indexed: 11/30/2022] Open
Abstract
The persistent and accumulative nature of the pesticide of indiscriminate use emerged as ecotoxicological hazards. The bioconcentration factor (BCF) is one of the key elements for environmental assessments of the aquatic compartment. Limitations of prediction accuracy of global model facilitate the use of local predictive models in toxicity modeling of emerging compounds. The BCF data of diverse organophosphate (n = 55) was collected from the Pesticide Properties Database and used as a model data set in the present study to explore physicochemical properties and structural alert concerning BCF. The structures were downloaded from Pubchem, ChemSpider database. Two splitting techniques (biological sorting and structure-based) were used to divide the whole dataset into training and test set compounds. The QSAR study was carried out with two-dimensional descriptors (2D) calculated from PaDEL by applying genetic algorithm (GA) as chemometric tools using QSARINS software. The models were statistically robust enough both internally as well as externally (Q2: 0.709-0.722, Q2 Ext: 0.717-0.903, CCC: 0.857-0.880). Overall molecular mass, presence of fused, and heterocyclic ring with electron-withdrawing groups affect the BCF value. The developed models reflected extended applicability domain (AD) and reliable predictions than the reported models for the studied chemical class. Finally, predictions of unknown organophosphate pesticides and the toxic nature of unknown organophosphate pesticides were commented on. These findings may be useful for the scientific community in prioritizing high potential pesticides of organophosphate class.
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Affiliation(s)
- Purusottam Banjare
- Department of Pharmacy, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009 India
| | - Balaji Matore
- Department of Pharmacy, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009 India
| | - Jagadish Singh
- Department of Pharmacy, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009 India
| | - Partha Pratim Roy
- Department of Pharmacy, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009 India
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91
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Zaki MEA, Al-Hussain SA, Masand VH, Akasapu S, Lewaa I. QSAR and Pharmacophore Modeling of Nitrogen Heterocycles as Potent Human N-Myristoyltransferase (Hs-NMT) Inhibitors. Molecules 2021; 26:molecules26071834. [PMID: 33805223 PMCID: PMC8038050 DOI: 10.3390/molecules26071834] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 03/12/2021] [Accepted: 03/19/2021] [Indexed: 11/24/2022] Open
Abstract
N-myristoyltransferase (NMT) is an important eukaryotic monomeric enzyme which has emerged as an attractive target for developing a drug for cancer, leishmaniasis, ischemia-reperfusion injury, malaria, inflammation, etc. In the present work, statistically robust machine leaning models (QSAR (Quantitative Structure–Activity Relationship) approach) for Human NMT (Hs-NMT) inhibitory has been performed for a dataset of 309 Nitrogen heterocycles screened for NMT inhibitory activity. Hundreds of QSAR models were derived. Of these, the model 1 and 2 were chosen as they not only fulfil the recommended values for a good number of validation parameters (e.g., R2 = 0.77–0.79, Q2LMO = 0.75–0.76, CCCex = 0.86–0.87, Q2-F3 = 0.74–0.76, etc.) but also provide useful insights into the structural features that sway the Hs-NMT inhibitory activity of Nitrogen heterocycles. That is, they have an acceptable equipoise of descriptive and predictive qualities as per Organisation for Economic Co-operation and Development (OECD) guidelines. The developed QSAR models identified a good number of molecular descriptors like solvent accessible surface area of all atoms having specific partial charge, absolute surface area of Carbon atoms, etc. as important features to be considered in future optimizations. In addition, pharmacophore modeling has been performed to get additional insight into the pharmacophoric features, which provided additional results.
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Affiliation(s)
- Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
- Correspondence: (M.E.A.Z.); (V.H.M.)
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
| | - Vijay H. Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444 602, Maharashtra, India
- Correspondence: (M.E.A.Z.); (V.H.M.)
| | | | - Israa Lewaa
- Department of Business Administration, Faculty of Business Administration, Economics and Political Science, British University in Egypt, Cairo 11837, Egypt;
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92
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Lozynski M, Rusinska-Roszak D. Finding the direct energy-structure correlations in intramolecular aromaticity assisted hydrogen bonding (AAHB). J Mol Graph Model 2021; 105:107884. [PMID: 33725643 DOI: 10.1016/j.jmgm.2021.107884] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/25/2021] [Accepted: 02/25/2021] [Indexed: 11/25/2022]
Abstract
A predictive model for intramolecular hydrogen bond energy (EHB) calculation of polyaromatic ortho-hydroxyaldehydes based on a set of small, functionalized hydrocarbons is developed. The complete data set of 18 compounds was used for this study. The model is based on one of four optional categories of molecular descriptors: geometric, spectroscopic, bond order and topological indices. The model of Wiberg bond indices (WBIs) as descriptors of the CC involved bond based on stepwise regression has acceptable prediction abilities for 14 structures of ortho-hydroxyformylobenzo[a]pyrene derivatives already at the semiempirical level. The presented correlation enables a significantly more rapid and quantitative description of the hydrogen bonding strength than the much more time-consuming MTA method. Thus, WBIs are shown to provide a reliable means for fast prescreening of the energy of chelate hydrogen bonds potentially for any polyaromatic derivatives.
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Affiliation(s)
- Marek Lozynski
- Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60-965, Poznan, Poland
| | - Danuta Rusinska-Roszak
- Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60-965, Poznan, Poland.
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93
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Zhu T, Gu L, Chen M, Sun F. Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches. CHEMOSPHERE 2021; 266:128962. [PMID: 33218721 DOI: 10.1016/j.chemosphere.2020.128962] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 06/11/2023]
Abstract
Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (KPUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the KPUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Liming Gu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Feng Sun
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
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94
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Ta GH, Jhang CS, Weng CF, Leong MK. Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability. Pharmaceutics 2021; 13:pharmaceutics13020174. [PMID: 33525340 PMCID: PMC7911528 DOI: 10.3390/pharmaceutics13020174] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 01/09/2021] [Accepted: 01/21/2021] [Indexed: 12/26/2022] Open
Abstract
Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.
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Affiliation(s)
- Giang Huong Ta
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (G.H.T.); (C.-S.J.)
| | - Cin-Syong Jhang
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (G.H.T.); (C.-S.J.)
| | - Ching-Feng Weng
- Department of Physiology, School of Basic Medical Science, Xiamen Medical College, Xiamen 361023, China;
| | - Max K. Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (G.H.T.); (C.-S.J.)
- Correspondence: ; Tel.: +886-3-890-3609
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95
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Bhujbal SP, Keretsu S, Cho SJ. Molecular Modelling Studies on Pyrazole Derivatives for the Design of Potent Rearranged during Transfection Kinase Inhibitors. Molecules 2021; 26:691. [PMID: 33525725 PMCID: PMC7865942 DOI: 10.3390/molecules26030691] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/23/2021] [Accepted: 01/24/2021] [Indexed: 01/14/2023] Open
Abstract
RET (rearranged during transfection) kinase, one of the receptor tyrosine kinases, plays a crucial role in the development of the human nervous system. It is also involved in various cell signaling networks responsible for the normal cell division, growth, migration, and survival. Previously reported clinical studies revealed that deregulation or aberrant activation of RET signaling can cause several types of human cancer. For example, medullary thyroid carcinoma (MTC) and multiple endocrine neoplasia (MEN2A, MEN2B) occur due to sporadic mutation or germline RET mutation. A number of RET kinase inhibitors have been approved by the FDA for the treatment of cancer, such as cabozantinib, vandetanib, lenvatinib, and sorafenib. However, each of these drugs is a multikinase inhibitor. Hence, RET is an important therapeutic target for cancer drug design. In this work, we have performed various molecular modelling studies, such as molecular docking and dynamics simulation for the most active compound of the pyrazole series as RET kinase inhibitors. Furthermore, molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) free energy calculation and 3-dimensional quantitative structure-activity relationship (3D-QSAR) were performed using g_mmpbsa and SYBYL-X 2.1 package. The results of this study revealed the crucial binding site residues at the active site of RET kinase and contour map analysis showed important structural characteristics for the design of new highly active inhibitors. Therefore, we have designed ten RET kinase inhibitors, which showed higher inhibitory activity than the most active compound of the series. The results of our study provide insights to design more potent and selective RET kinase inhibitors.
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Affiliation(s)
- Swapnil P. Bhujbal
- Department of Biomedical Sciences, College of Medicine, Chosun University, Gwangju 501-759, Korea; (S.P.B.); (S.K.)
| | - Seketoulie Keretsu
- Department of Biomedical Sciences, College of Medicine, Chosun University, Gwangju 501-759, Korea; (S.P.B.); (S.K.)
| | - Seung Joo Cho
- Department of Biomedical Sciences, College of Medicine, Chosun University, Gwangju 501-759, Korea; (S.P.B.); (S.K.)
- Department of Cellular Molecular Medicine, College of Medicine, Chosun University, Gwangju 501-759, Korea
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96
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Lata S, Vikas. Concentration-dependent adsorption of organic contaminants by graphene nanosheets: quantum-mechanical models. J Mol Model 2021; 27:48. [PMID: 33496822 DOI: 10.1007/s00894-021-04686-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 01/19/2021] [Indexed: 10/22/2022]
Abstract
Adsorption is the key process in the expression of environmentally relevant physicochemical and toxicological properties of carbon nanomaterials. However, the adsorption of organic contaminants on to nanomaterials is a highly complex phenomenon, owing to the heterogeneity of adsorption sites, for example, on graphene surface as well as due to multiple factors operative during the adsorption, particularly, at the quantum-mechanical level. For predicting the concentration-dependent adsorption coefficients of organic contaminants by carbon nanomaterials, one option has been to rely on the existing linear-solvation energy relationship (LSER) models. The present work on the adsorption of aromatic and aliphatic organic contaminants by graphene nanosheets reveals that the existing LSER models are prone to failure when tested for internal and external validation using an external prediction set of compounds unknown to the model. As an alternative to the LSERs, the present work reports pure quantum-mechanical models developed using computational only quantum-mechanical descriptors. The reliability of the quantum-mechanical models was tested using state-of-the-art validation procedures employing an external prediction set of compounds. The proposed quantum-mechanical models reveal mean polarizability, zero-point vibrational energy, and its electron-correlation contribution to be the key descriptors in the prediction of adsorption coefficients of organic contaminants by graphene nanosheets.
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Affiliation(s)
- Suman Lata
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh, 160014, India
| | - Vikas
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh, 160014, India.
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97
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Wang LL, Ding JJ, Pan L, Fu L, Tian JH, Cao DS, Jiang H, Ding XQ. Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice. JOURNAL OF HAZARDOUS MATERIALS 2021; 401:123724. [PMID: 33113726 DOI: 10.1016/j.jhazmat.2020.123724] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 07/29/2020] [Accepted: 08/13/2020] [Indexed: 06/11/2023]
Abstract
Organophosphates (OPs) are highly toxic compounds, with widespread application in agricultural and chemical industries, whose introduction into the environment poses serious hazards to humans and ecological systems. To assess and ultimately mitigate these hazards, this study predicted the acute toxicity of OPs according to their chemical structure and administration route. The acute toxicity data of 161 OPs in two species via six different administration routes were manually collected and used to develop a series of quantitative structure-toxicity relationship (QSTR) models with robust and practical predictive abilities. The random forest algorithm was used to develop the models, employing both quantum chemical and two-dimensional descriptors according to OECD guidelines. Correlation results and feature similarities indicated that whereas acute toxicity data from rats and mice via the same administration route were combinable for modeling, data from different routes were not. Six QSTR models for each route in a single species and two QSTR models for a single route in the two species were constructed, achieving practical predictive performance. Despite significant variances in their datasets, the prediction models could predict the acute toxicity of novel or unknown OPs, realize rapid assessment, and provide guidance for regulatory decisions to reduce the hazards of OPs.
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Affiliation(s)
- Liang-Liang Wang
- Beijing Institute of Pharmaceutical Chemistry, Beijing, 102205, PR China
| | - Jun-Jie Ding
- Beijing Institute of Pharmaceutical Chemistry, Beijing, 102205, PR China
| | - Li Pan
- Beijing Institute of Pharmaceutical Chemistry, Beijing, 102205, PR China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, PR China
| | - Jia-Hao Tian
- Beijing Institute of Pharmaceutical Chemistry, Beijing, 102205, PR China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, PR China; Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, PR China.
| | - Hui Jiang
- Beijing Institute of Pharmaceutical Chemistry, Beijing, 102205, PR China.
| | - Xiao-Qin Ding
- Beijing Institute of Pharmaceutical Chemistry, Beijing, 102205, PR China.
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98
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Fayet G, Rotureau P. Chemoinformatics for the Safety of Energetic and Reactive Materials at Ineris. Mol Inform 2020; 41:e2000190. [PMID: 33283975 DOI: 10.1002/minf.202000190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 12/06/2020] [Indexed: 11/07/2022]
Abstract
The characterization of physical hazards of substances is a key information to manage the risks associated to their use, storage and transport. With decades of work in this area, Ineris develops and implements cutting-edge experimental facilities allowing such characterizations at different scales and under various conditions to study all of the dreaded accident scenarios. This review presents the efforts engaged by Ineris more recently in the field of chemoinformatics to develop and use new predictive methods for the anticipation and management of industrials risks associated to energetic and reactive materials as a complement to experiments. An overview of the methods used for the development of Quantitative Structure-Property Relationships for physical hazards are presented and discussed regarding the specificities associated to this class of properties. A review of models developed at Ineris is also provided from the first tentative models on the explosivity of nitro compounds to the successful application to the flammability of organic mixtures. Then, a discussion is proposed on the use of QSPR models. Good practices for robust use for QSPR models are recalled with specific comments related to physical hazards, notably for regulatory purpose. Dissemination and training efforts engaged by Ineris are also presented. The potential offered by these predictive methods in terms of in silico design and for the development of new intrinsically safer technologies in safety-by-design strategies is finally discussed. At last, challenges and perspectives to extend the application of chemoinformatics in the field of safety and in particular for the physical hazards of energetic and reactive substances are proposed.
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Affiliation(s)
- Guillaume Fayet
- Ineris, Accidental Risk Division, Parc Technologique Alata, 60550, Verneuil-en-Halatte, France
| | - Patricia Rotureau
- Ineris, Accidental Risk Division, Parc Technologique Alata, 60550, Verneuil-en-Halatte, France
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99
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Marondedze EF, Govender KK, Govender PP. Ligand-based pharmacophore modelling and virtual screening for the identification of amyloid-beta diagnostic molecules. J Mol Graph Model 2020; 101:107711. [DOI: 10.1016/j.jmgm.2020.107711] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 07/17/2020] [Accepted: 08/04/2020] [Indexed: 01/26/2023]
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100
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QSAR models for the fumigant activity prediction of essential oils. J Mol Graph Model 2020; 101:107751. [DOI: 10.1016/j.jmgm.2020.107751] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 08/20/2020] [Accepted: 09/04/2020] [Indexed: 12/23/2022]
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