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Akgüller Ö, Balcı MA, Cioca G. Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships. Int J Mol Sci 2024; 25:6890. [PMID: 38999999 PMCID: PMC11240958 DOI: 10.3390/ijms25136890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/14/2024] [Accepted: 06/21/2024] [Indexed: 07/14/2024] Open
Abstract
This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.
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Affiliation(s)
- Ömer Akgüller
- Department of Mathematics, Faculty of Science, Mugla Sitki Kocman University, 48000 Mugla, Turkey;
| | - Mehmet Ali Balcı
- Department of Mathematics, Faculty of Science, Mugla Sitki Kocman University, 48000 Mugla, Turkey;
| | - Gabriela Cioca
- Preclinical Department, Faculty of Medicine, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania;
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2
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Elhadi A, Zhao D, Ali N, Sun F, Zhong S. Multi-method computational evaluation of the inhibitors against leucine-rich repeat kinase 2 G2019S mutant for Parkinson's disease. Mol Divers 2024:10.1007/s11030-024-10808-w. [PMID: 38396210 DOI: 10.1007/s11030-024-10808-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 01/07/2024] [Indexed: 02/25/2024]
Abstract
Leucine-rich repeat kinase 2 G2019S mutant (LRRK2 G2019S) is a potential target for Parkinson's disease therapy. In this work, the computational evaluation of the LRRK2 G2019S inhibitors was conducted via a combined approach which contains a preliminary screening of a large database of compounds via similarity and pharmacophore, a secondary selection via structure-based affinity prediction and molecular docking, and a rescoring treatment for the final selection. MD simulations and MM/GBSA calculations were performed to check the agreement between different prediction methods for these inhibitors. 331 experimental ligands were collected, and 170 were used to build the structure-activity relationship. Eight representative ligand structural models were employed in similarity searching and pharmacophore screening over 14 million compounds. The process for selecting proper molecular descriptors provides a successful sample which can be used as a general strategy in QSAR modelling. The rescoring used in this work presents an alternative useful treatment for ranking and selection.
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Affiliation(s)
- Ahmed Elhadi
- School of Bioengineering, Dalian University of Technology, Dalian, 116024, Liaoning, People's Republic of China
| | - Dan Zhao
- School of Bioengineering, Dalian University of Technology, Dalian, 116024, Liaoning, People's Republic of China
| | - Noman Ali
- School of Bioengineering, Dalian University of Technology, Dalian, 116024, Liaoning, People's Republic of China
| | - Fusheng Sun
- School of Bioengineering, Dalian University of Technology, Dalian, 116024, Liaoning, People's Republic of China
| | - Shijun Zhong
- School of Bioengineering, Dalian University of Technology, Dalian, 116024, Liaoning, People's Republic of China.
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3
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Moulishankar A, Thirugnanasambandam S. Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents. J Recept Signal Transduct Res 2023; 43:83-92. [PMID: 37990804 DOI: 10.1080/10799893.2023.2281671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/03/2023] [Indexed: 11/23/2023]
Abstract
This study aims to develop a QSAR model for Antitubercular activity. The quantitative structure-activity relationship (QSAR) approach predicted the thiazolidine-4-ones derivative's Antitubercular activity. For the QSAR study, 53 molecules with Antitubercular activity on H37Rv were collected from the literature. Compound structures were drawn by ACD/Labs ChemSketch. The energy minimization of the 2D structure was done using the MM2 force field in Chem3D pro. PaDEL Descriptor software was used to construct the molecular descriptors. QSARINS software was used in this work to develop the 2D QSAR model. A series of thiazolidine 4-one with MIC data were taken from the literature to develop the QSAR model. These compounds were split into a training set (43 compounds) and a test set (10 compounds). The PaDEL software calculated 2300 descriptors for this series of thiazolidine 4-one derivatives. The best predictive Model 4, which has R2 of 0.9092, R2adj of 0.8950 and LOF parameter of 0.0289 identify a preferred fit. The QSAR study resulted in a stable, predictive, and robust model representing the original dataset. In the QSAR equation, the molecular descriptor of MLFER_S, GATSe2, Shal, and EstateVSA 6 positively correlated with Antitubercular activity. While the SpMAD_Dzs 6 is negatively correlated with Antitubercular activity. The high polarizability, Electronegativities, Surface area contributions and number of Halogen atoms in the thiazolidine 4-one derivatives will increase the Antitubercular activity.
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Affiliation(s)
- Anguraj Moulishankar
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu 603203, India
| | - Sundarrajan Thirugnanasambandam
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu 603203, India
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4
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Azimi S, Mohammadi B, Babadoust S, Vessally E. In Silico study and design of some new potent threonine tyrosine kinase inhibitors using molecular docking simulation. MOLECULAR SIMULATION 2023. [DOI: 10.1080/08927022.2023.2172192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Saeid Azimi
- Department of Chemistry, Payame Noor University, P. O. BOX 19395-3697, Tehran, Iran
| | - Bagher Mohammadi
- Department of Chemistry, Payame Noor University, P. O. BOX 19395-3697, Tehran, Iran
| | - Shahram Babadoust
- Department of Chemistry, Payame Noor University, P. O. BOX 19395-3697, Tehran, Iran
| | - Esmail Vessally
- Department of Chemistry, Payame Noor University, P. O. BOX 19395-3697, Tehran, Iran
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5
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Dhingra N, Kapoor K, Sharma S, Saxena A. Towards further understanding the structural insights of isoxazoles analogues against leishmaniasis using QSAR, molecular docking and molecular dynamics model. J INDIAN CHEM SOC 2022. [DOI: 10.1016/j.jics.2022.100847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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6
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Banerjee A, De P, Kumar V, Kar S, Roy K. Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across. CHEMOSPHERE 2022; 309:136579. [PMID: 36174732 DOI: 10.1016/j.chemosphere.2022.136579] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, United States
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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Identification of sulfonamide-based butyrylcholinesterase inhibitors using machine learning. Future Med Chem 2022; 14:1049-1070. [PMID: 35707942 DOI: 10.4155/fmc-2021-0325] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Aim: This study reports the designing of BChE inhibitors through machine learning (ML), followed by in silico and in vitro evaluations. Methodology: ML technique was used to predict the virtual hit, and its derivatives were synthesized and characterized. The compounds were evaluated by using various in vitro tests and in silico methods. Results: The gradient boosting classifier predicted N-phenyl-4-(phenylsulfonamido) benzamide as an active BChE inhibitor. The derivatives of the inhibitor, i.e., compounds 34, 37 and 54 were potent BChE inhibitors and displayed blood-brain barrier permeability with no significant AChE inhibition. Conclusion: The ML prediction was effective, and the synthesized compounds showed the BChE inhibitory activity, which was also supported by the in silico studies.
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8
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Kumar V, Kar S, De P, Roy K, Leszczynski J. Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:357-386. [PMID: 35380087 DOI: 10.1080/1062936x.2022.2055140] [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: 02/10/2022] [Accepted: 03/14/2022] [Indexed: 06/14/2023]
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion - toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.
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Affiliation(s)
- V Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S Kar
- Department of Chemistry, Physics and Atmospheric Sciences interdisciplinary Center for Nontoxicity, Jackson State University, Jackson, MS, USA
| | - P De
- 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
| | - J Leszczynski
- Department of Chemistry, Physics and Atmospheric Sciences interdisciplinary Center for Nontoxicity, Jackson State University, Jackson, MS, USA
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9
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Singh R, Ganeshpurkar A, Ghosh P, Pokle AV, Kumar D, Singh RB, Singh SK, Kumar A. Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods. Chem Biol Drug Des 2021; 98:1079-1097. [PMID: 34592057 DOI: 10.1111/cbdd.13965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 09/18/2021] [Accepted: 09/26/2021] [Indexed: 11/28/2022]
Abstract
The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) at the β-site. The sequential proteolytic cleavage of APP, first by β-secretase and then by γ-secretase complex, leads to the production and release of amyloid-β peptide, a pathological hallmark of Alzheimer's disease (AD). BACE1 inhibitors are reported to possess considerable potential in decreasing the level of amyloid-β in brain and preventing the progression of AD. A classification study has been conducted on 3536 diverse BACE1 inhibitors, obtained from Binding DB database, by extracting two types of descriptors, that is molecular property (Mordred) and fingerprints (Pubchem, MACCS and KRFP). Furthermore, based on the descriptors, various machine learning algorithms such as Naïve Bayesian (NB), nearest known neighbours (kNN), support vector machine (SVM), random forest (RF) and gradient-boosted algorithms (XGB) were applied to develop classification models. The performance of models was evaluated by using accuracy, precision, recall and F1 score of test set. The best NB, kNN, SVM, RF and XGB classifiers had F1 score of 0.74, 0.85, 0.86, 0.87 and 0.87, respectively. The diverse 3536 BACE1 inhibitors were clustered into 11 subsets, and the structural features of each subset were evaluated. The important fragments present in active and inactive compounds were also identified. The model developed in the study would serve as a valuable tool for the designing of BACE1 inhibitors, and also in virtual screening of molecules to identify these.
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Affiliation(s)
- Ravi Singh
- Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Ankit Ganeshpurkar
- Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Powsali Ghosh
- Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Ankit Vyankatrao Pokle
- Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | | | - Ravi Bhushan Singh
- Institute of Pharmacy Harischandra PG College, Bawanbigha, Varanasi, India
| | - Sushil Kumar Singh
- Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Ashok Kumar
- Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
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10
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Ojo OA, Ojo AB, Okolie C, Nwakama MAC, Iyobhebhe M, Evbuomwan IO, Nwonuma CO, Maimako RF, Adegboyega AE, Taiwo OA, Alsharif KF, Batiha GES. Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer's Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches. Molecules 2021; 26:molecules26071996. [PMID: 33915968 PMCID: PMC8037217 DOI: 10.3390/molecules26071996] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/22/2021] [Accepted: 03/29/2021] [Indexed: 02/06/2023] Open
Abstract
Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC50 values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R2 = 0.86 and Q2 = 0.73), pls_38 (R2 = 0.77 and Q2 = 0.72), kpls_desc_44 (R2 = 0.81 and Q2 = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.
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Affiliation(s)
- Oluwafemi Adeleke Ojo
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
- Correspondence: ; Tel.: +234-703-782-4647
| | - Adebola Busola Ojo
- Department of Biochemistry, Faculty of Sciences, Ekiti State University, Ado-Ekiti PMB 5363, Nigeria;
| | - Charles Okolie
- Department of Microbiology, Landmark University, Omu-Aran PMB 1001, Nigeria; (C.O.); (I.O.E.)
| | - Mary-Ann Chinyere Nwakama
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
| | - Matthew Iyobhebhe
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
| | | | - Charles Obiora Nwonuma
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
| | - Rotdelmwa Filibus Maimako
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
| | | | | | - Khalaf F. Alsharif
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour, AlBeheira 22511, Egypt;
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11
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Bhatt S, Kumar V, Dogra A, Ojha PK, Wazir P, Sangwan PL, Singh G, Nandi U. Amalgamation of in-silico, in-vitro and in-vivo approach to establish glabridin as a potential CYP2E1 inhibitor. Xenobiotica 2021; 51:625-635. [PMID: 33539218 DOI: 10.1080/00498254.2021.1883769] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
CYP2E1 is directly or indirectly involved in the metabolism of ethanol and endogenous fatty acids but it plays a major role in the bio-activation of toxic substances that produce reactive metabolites leading to hepatotoxicity. Therefore, identification of CYP2E1 inhibitor from bioflavonoids class having useful pharmacological properties has dual benefit regarding avoidance of severe food-drug/nutraceutical-drug interaction and scope to develop a phytotherapeutics through an intended pharmacokinetic interaction.In the present study, we aimed to identify CYP2E1 inhibitor from experimental bioflavonoids which are unexplored for CYP2E1 inhibition till date using in-silico, in-vitro and in-vivo approaches.Results of in-vitro CYP2E1 inhibitory studies using CYP2E1-mediated chlorzoxazone 6-hydroxylation in human liver microsomes showed that glabridin have the highest potential than fisetin, epicatechin, nobiletin, and chrysin to inhibit CYP2E1 enzyme. Mechanistic investigations indicate that glabridin is a competitive CYP2E1 inhibitor. Molecular docking study results demonstrate that glabridin strongly interacted with the active site of human CYP2E1 enzyme. Pharmacokinetics of a CYP2E1 substrate in mice model indicates a significant alteration of chlorzoxazone and 6-hydroxychlorzoxazone plasma levels in the presence of glabridin. Further studies are needed to confirm the results at clinical level.Overall, glabridin is found to be a potential CYP2E1 inhibitor.
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Affiliation(s)
- Shipra Bhatt
- PK-PD, Toxicology and Formulation Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Vinay Kumar
- Drug Theoretics and Chemoinformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Ashish Dogra
- PK-PD, Toxicology and Formulation Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Probir Kumar Ojha
- Drug Theoretics and Chemoinformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Priya Wazir
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Payare Lal Sangwan
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.,Bio-Organic Chemistry Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India
| | - Gurdarshan Singh
- PK-PD, Toxicology and Formulation Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Utpal Nandi
- PK-PD, Toxicology and Formulation Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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12
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Elucidating the interactions of compounds identified from Aframomum melegueta seeds as promising candidates for the management of diabetes mellitus: A computational approach. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100720] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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13
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Tran TS, Le MT, Tran TD, Tran TH, Thai KM. Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches. Molecules 2020; 25:molecules25163644. [PMID: 32785161 PMCID: PMC7464027 DOI: 10.3390/molecules25163644] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/20/2020] [Accepted: 08/07/2020] [Indexed: 12/25/2022] Open
Abstract
Acetylcholinesterase (AChE) and beta-secretase (BACE-1) are the two crucial enzymes involved in the pathology of Alzheimer's disease. The former is responsible for many defects in cholinergic signaling pathway and the latter is the primary enzyme in the biosynthesis of beta-amyloid as the main component of the amyloid plaques. These both abnormalities are found in the brains of Alzheimer's patients. In this study, in silico models were developed, including 3D-pharmacophore, 2D-QSAR (two-dimensional quantitative structure-activity relationship), and molecular docking, to screen virtually a database of compounds for AChE and BACE-1 inhibitory activities. A combinatorial library containing more than 3 million structures of curcumin and flavonoid derivatives was generated and screened for drug-likeness and enzymatic inhibitory bioactivities against AChE and BACE-1 through the validated in silico models. A total of 47 substances (two curcumins and 45 flavonoids), with remarkable predicted pIC50 values against AChE and BACE-1 ranging from 4.24-5.11 (AChE) and 4.52-10.27 (BACE-1), were designed. The in vitro assays on AChE and BACE-1 were performed and confirmed the in silico results. The study indicated that, by using in silico methods, a series of curcumin and flavonoid structures were generated with promising predicted bioactivities. This would be a helpful foundation for the experimental investigations in the future. Designed compounds which were the most feasible for chemical synthesis could be potential candidates for further research and lead optimization.
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Affiliation(s)
- Thai-Son Tran
- Department of Medicinal Chemistry, Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam or (T.-S.T.); (T.-D.T.)
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, College of Medicine and Pharmacy, Hue University, Hue City 530000, Vietnam;
| | - Minh-Tri Le
- Department of Medicinal Chemistry, Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam or (T.-S.T.); (T.-D.T.)
- School of Medicine, Vietnam National University Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
- Correspondence: or (M.-T.L.); or (K.-M.T.); Tel.: +84-903-718-190 (M-T.L.); +84-28-3855-2225 or +84-909-680-385 (K-M.T.); Fax: +84-28-3822-5435 (K-M.T.)
| | - Thanh-Dao Tran
- Department of Medicinal Chemistry, Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam or (T.-S.T.); (T.-D.T.)
| | - The-Huan Tran
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, College of Medicine and Pharmacy, Hue University, Hue City 530000, Vietnam;
| | - Khac-Minh Thai
- Department of Medicinal Chemistry, Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam or (T.-S.T.); (T.-D.T.)
- Correspondence: or (M.-T.L.); or (K.-M.T.); Tel.: +84-903-718-190 (M-T.L.); +84-28-3855-2225 or +84-909-680-385 (K-M.T.); Fax: +84-28-3822-5435 (K-M.T.)
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Pandey SK, Ojha PK, Roy K. Exploring QSAR models for assessment of acute fish toxicity of environmental transformation products of pesticides (ETPPs). CHEMOSPHERE 2020; 252:126508. [PMID: 32240857 DOI: 10.1016/j.chemosphere.2020.126508] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/14/2020] [Accepted: 03/14/2020] [Indexed: 06/11/2023]
Abstract
Environmental transformation products of pesticides (ETPPs) have a great deal of ecological impact owing to their ability to cause toxicity to the aquatic organisms, which can then be translated to the humans. The limited experimental data on biochemical and toxic effects of ETPPs, the high test costs together with regulatory limitations and the international push to reduce animal testing encourage greater dependence on predictive in silico techniques like quantitative structure-activity relationship (QSAR) models. The aim of the present work was to explore the key structural features, which regulate the toxicity towards fishes, for 85 ETPPs using a partial least squares (PLS) regression based chemometric model developed according to Organisation for Economic Co-operation and Development (OECD) guidelines. The model was extensively validated using both internal and external validation metrics, and the results so obtained justify the reliability and usefulness of the developed model (Q2 = 0.648, R2pred or Q2F1 = 0.734 and Q2F2 = 0.733). From the developed model, we can conclude that lipophilicity, polarity, presence of branching and the functional form of O-atom in the transformed structures of pesticides are the important features that are to be considered during ecotoxicity assessment of ETPPs. The information obtained from the descriptors of the developed model could be utilized in the future for assessing ETPPs with the benefit of providing an early warning of their potentially detrimental effect on fishes for regulatory purposes.
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Affiliation(s)
- Sapna Kumari Pandey
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Probir Kumar Ojha
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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