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Wanas AS, Radwan MM, Marzouk AA, Elkaeed EB, Alsfouk BA, Mostafa AE, Eissa IH, Metwaly AM, ElSohly MA. Isolation and in silico investigation of cannflavins from Cannabis sativa leaves as potential anti-SARS-CoV-2 agents targeting the Papain-Like Protease. Nat Prod Res 2025; 39:1081-1094. [PMID: 38100380 DOI: 10.1080/14786419.2023.2294111] [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: 06/15/2023] [Revised: 11/28/2023] [Accepted: 12/03/2023] [Indexed: 12/17/2023]
Abstract
This study aimed to isolate and identify three prenylflavonoids (cannflavin A, B, and C) from Cannabis sativa leaves using different chromatographic techniques. The potential of the isolated compounds against SARS-CoV-2 was suggested through several in silico analysis. Structural similarity studies against nine co-crystallized ligands of SARS-CoV-2's proteins indicated the similarities of the isolated cannflavins with the SARS-CoV-2 Papain-Like Protease (PLP) ligand, Y95. Then, flexible allignment study confirmed this similarity. Docking experiments showed successful binding of all cannflavins within the active pocket of PLP, with energies comparable to Y95. Among them, cannflavin A demonstrated the most similar binding mode, while cannflavin C exhibited the best energy. Molecular dynamics (MD) simulations and MM-GPSA confirmed the accurate binding of cannflavin A to the PLP. In silico ADMET studies indicated favourable drug-like properties for all three compounds, suggesting their potential as anti-SARS-CoV-2 agents. Further In vitro and In vivo investigations are necessary to validate these findings and establish their efficacy and safety profiles.
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Affiliation(s)
- Amira S Wanas
- National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, Mississippi, USA
- Department of Pharmacognosy, Faculty of Pharmacy, Minia University, Minia, Egypt
| | - Mohamed M Radwan
- National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, Mississippi, USA
| | - Adel A Marzouk
- National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, Mississippi, USA
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Al-Azhar University, Assiut, Egypt
| | - Eslam B Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
| | - Bshra A Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Ahmad E Mostafa
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
| | - Ibrahim H Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
| | - Ahmed M Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt
| | - Mahmoud A ElSohly
- National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, Mississippi, USA
- Department of Pharmaceutics and Drug Delivery, School of Pharmacy, University, Mississippi, USA
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Karunakaran K, Muniyan R. Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors. J Biomol Struct Dyn 2025; 43:1893-1914. [PMID: 38095558 DOI: 10.1080/07391102.2023.2293265] [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: 08/21/2023] [Accepted: 11/29/2023] [Indexed: 02/01/2025]
Abstract
Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are searching for new allosteric inhibitors as orthosteric inhibitors are non-selective and exert off-target effects. In the current study, we proposed an integrated computational workflow for identifying allosteric AKT1 inhibitors as this isoform is highly correlated with poor prognosis and survival. To achieve this objective, 84 classification QSAR models with six different machine learning algorithms were developed. The models created with RDKit_RF and RDKit_kstar outperformed internal and test set validation with an ROC of 0.98. The outperformed models were then used to screen Chembl, which contains over a million compounds, for AKT1 inhibitors. The Tanimoto similarity search approach identified the compounds structurally resembling AKT allosteric inhibitors. The filtered compounds were further subjected to docking phases, molecular dynamic simulation and mmpbsa to verify the binding mode of selected ones. All these analyses suggested hit 5 (CHEMBL3948083) as the potential allosteric inhibitor of AKT1 as the stability parameters, favourable binding affinity (-107.78 ± 11.56 KJ/mol) and ligand interaction were better in comparison to other compounds and reference compound. The residual analysis demonstrated that allosteric and isoform-specific residues such as Trp80 and Val270 contributed the larger energy for ligand binding. The proposed integrated approach in this study might achieve a futuristic outcome when employed in a pharmaceutical scheme different from the conventional method.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Keerthana Karunakaran
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Rajiniraja Muniyan
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
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Yu X, Chen Y, Chen L, Li W, Wang Y, Tang Y, Liu G. GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction. Mol Inform 2025; 44:e202400169. [PMID: 39421969 DOI: 10.1002/minf.202400169] [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: 05/14/2024] [Revised: 09/23/2024] [Accepted: 09/24/2024] [Indexed: 10/19/2024]
Abstract
In silico methods for prediction of chemical toxicity can decrease the cost and increase the efficiency in the early stage of drug discovery. However, due to low accessibility of sufficient and reliable toxicity data, constructing robust and accurate prediction models is challenging. Contrastive learning, a type of self-supervised learning, leverages large unlabeled data to obtain more expressive molecular representations, which can boost the prediction performance on downstream tasks. While molecular graph contrastive learning has gathered growing attentions, current models neglect the quality of negative data set. Here, we proposed a self-supervised pretraining deep learning framework named GCLmf. We first utilized molecular fragments that meet specific conditions as hard negative samples to boost the quality of the negative set and thus increase the difficulty of the proxy tasks during pre-training to learn informative representations. GCLmf has shown excellent predictive power on various molecular property benchmarks and demonstrates high performance in 33 toxicity tasks in comparison with multiple baselines. In addition, we further investigated the necessity of introducing hard negatives in model building and the impact of the proportion of hard negatives on the model.
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Affiliation(s)
- Xinxin Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Yuanting Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Long Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Yuhao Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
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Connor S, Li T, Qu Y, Roberts RA, Tong W. Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity. Drug Discov Today 2024; 29:103938. [PMID: 38432353 DOI: 10.1016/j.drudis.2024.103938] [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: 01/05/2024] [Revised: 02/16/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Drug-induced renal injury (DIRI) causes >1.5 million adverse events annually in the USA alone. Although standard biomarkers exist for DIRI, they lack the sensitivity or specificity to detect nephrotoxicity before the significant loss of renal function. In this study, we describe the creation of DIRIL - a list of drugs associated with DIRI and nephrotoxicity - from two literature datasets with DIRI annotation, confirmed using FDA drug labeling. DIRIL comprises 317 orally administered drugs covering all 14 anatomical, therapeutic and chemical (ATC) classification categories. Of the 317 drugs, 171 were DIRI-positive and 146 were DIRI-negative. DIRIL will be a relevant and invaluable resource for discovery of new approach methods (NAMs) to predict the occurrence and possible severity of DIRI earlier in drug development.
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Affiliation(s)
- Skylar Connor
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - Ting Li
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - Yanyan Qu
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - Ruth A Roberts
- ApconiX, Alderley Park, Alderley Edge SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Weida Tong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
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Elkaeed EB, Alsfouk BA, Ibrahim TH, Arafa RK, Elkady H, Ibrahim IM, Eissa IH, Metwaly AM. Computer-assisted drug discovery of potential natural inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase through a multi-phase in silico approach. Antivir Ther 2023; 28:13596535231199838. [PMID: 37669909 DOI: 10.1177/13596535231199838] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
BACKGROUND The COVID-19 pandemic has led to significant loss of life and economic disruption worldwide. Currently, there are limited effective treatments available for this disease. SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp) has been identified as a potential target for drug development against COVID-19. Natural products have been shown to possess antiviral properties, making them a promising source for developing drugs against SARS-CoV-2. OBJECTIVES The objective of this study is to identify the most effective natural inhibitors of SARS-CoV-2 RdRp among a set of 4924 African natural products using a multi-phase in silico approach. METHODS The study utilized remdesivir (RTP), the co-crystallized ligand of RdRp, as a starting point to select compounds that have the most similar chemical structures among the examined set of compounds. Molecular fingerprints and structure similarity studies were carried out in the first part of the study. The second part of the study included molecular docking against SARS-CoV-2 RdRp (PDB ID: 7BV2) and Molecular Dynamics (MD) simulations including the calculation of RMSD, RMSF, Rg, SASA, hydrogen bonding, and PLIP. Moreover, the calculations of Molecular mechanics with generalised Born and surface area solvation (MM-GBSA) Lennard-Jones and Columbic electrostatic interaction energies have been conducted. Additionally, in silico ADMET and toxicity studies were performed to examine the drug likeness degrees of the selected compounds. RESULTS Eight compounds were identified as the most effective natural inhibitors of SARS-CoV-2 RdRp. These compounds are kaempferol 3-galactoside, kaempferol 3-O-β-D-glucopyranoside, mangiferin methyl ether, luteolin 7-O-β-D-glucopyranoside, quercetin-O-β-D-3-glucopyranoside, 1-methoxy-3-indolylmethyl glucosinolate, naringenin, and asphodelin A 4'-O-β-D-glucopyranoside. CONCLUSION The results of this study provide valuable information for the development of natural product-based drugs against COVID-19. However, the elected compounds should be further studied in vitro and in vivo to confirm their efficacy in treating COVID-19.
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Affiliation(s)
- Eslam B Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
| | - Bshra A Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Tuqa H Ibrahim
- Drug Design and Discovery Lab, Zewail City of Science and Technology, Cairo, Egypt
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Cairo, Egypt
| | - Reem K Arafa
- Drug Design and Discovery Lab, Zewail City of Science and Technology, Cairo, Egypt
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Cairo, Egypt
| | - Hazem Elkady
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
| | - Ibrahim M Ibrahim
- Biophysics Department, Faculty of Science, Cairo University, Giza, Egypt
| | - Ibrahim H Eissa
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
| | - Ahmed M Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt
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Moreno-Sánchez PA. Improvement of a prediction model for heart failure survival through explainable artificial intelligence. Front Cardiovasc Med 2023; 10:1219586. [PMID: 37600061 PMCID: PMC10434534 DOI: 10.3389/fcvm.2023.1219586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Cardiovascular diseases and their associated disorder of heart failure (HF) are major causes of death globally, making it a priority for doctors to detect and predict their onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnoses and treatments. Specifically, "eXplainable AI" (XAI) offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an explainability analysis and evaluation of two HF survival prediction models using a dataset that includes 299 patients who have experienced HF. The first model utilizes survival analysis, considering death events and time as target features, while the second model approaches the problem as a classification task to predict death. The model employs an optimization data workflow pipeline capable of selecting the best machine learning algorithm as well as the optimal collection of features. Moreover, different post hoc techniques have been used for the explainability analysis of the model. The main contribution of this paper is an explainability-driven approach to select the best HF survival prediction model balancing prediction performance and explainability. Therefore, the most balanced explainable prediction models are Survival Gradient Boosting model for the survival analysis and Random Forest for the classification approach with a c-index of 0.714 and balanced accuracy of 0.74 (std 0.03) respectively. The selection of features by the SCI-XAI in the two models is similar where "serum_creatinine", "ejection_fraction", and "sex" are selected in both approaches, with the addition of "diabetes" for the survival analysis model. Moreover, the application of post hoc XAI techniques also confirm common findings from both approaches by placing the "serum_creatinine" as the most relevant feature for the predicted outcome, followed by "ejection_fraction". The explainable prediction models for HF survival presented in this paper would improve the further adoption of clinical prediction models by providing doctors with insights to better understand the reasoning behind usually "black-box" AI clinical solutions and make more reasonable and data-driven decisions.
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Elkaeed EB, Khalifa MM, Alsfouk BA, Alsfouk AA, El-Attar AAMM, Eissa IH, Metwaly AM. The Discovery of Potential SARS-CoV-2 Natural Inhibitors among 4924 African Metabolites Targeting the Papain-like Protease: A Multi-Phase In Silico Approach. Metabolites 2022; 12:1122. [PMID: 36422263 PMCID: PMC9693093 DOI: 10.3390/metabo12111122] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 09/10/2024] Open
Abstract
Four compounds, hippacine, 4,2'-dihydroxy-4'-methoxychalcone, 2',5'-dihydroxy-4-methoxychalcone, and wighteone, were selected from 4924 African natural metabolites as potential inhibitors against SARS-CoV-2 papain-like protease (PLpro, PDB ID: 3E9S). A multi-phased in silico approach was employed to select the most similar metabolites to the co-crystallized ligand (TTT) of the PLpro through molecular fingerprints and structural similarity studies. Followingly, to examine the binding of the selected metabolites with the PLpro (molecular docking. Further, to confirm this binding through molecular dynamics simulations. Finally, in silico ADMET and toxicity studies were carried out to prefer the most convenient compounds and their drug-likeness. The obtained results could be a weapon in the battle against COVID-19 via more in vitro and in vivo studies.
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Affiliation(s)
- Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh 13713, Saudi Arabia
| | - Mohamed M. Khalifa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
| | - Bshra A. Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Aisha A. Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abdul-Aziz M. M. El-Attar
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, Nasr City, Cairo 11884, Egypt
| | - Ibrahim H. Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
| | - Ahmed M. Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria 21934, Egypt
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Elkaeed EB, Metwaly AM, Alesawy MS, Saleh AM, Alsfouk AA, Eissa IH. Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach. Life (Basel) 2022; 12:1407. [PMID: 36143445 PMCID: PMC9505301 DOI: 10.3390/life12091407] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/01/2022] [Accepted: 09/05/2022] [Indexed: 11/17/2022] Open
Abstract
As an extension of our research against COVID-19, a multiphase in silico approach was applied in the selection of the three most common inhibitors (Glycyrrhizoflavone (76), Arctigenin (94), and Thiangazole (298)) against papain-like protease, PLpro (PDB ID: 4OW0), among 310 metabolites of natural origin. All compounds of the exam set were reported as antivirals. The structural similarity between the examined compound set and S88, the co-crystallized ligand of PLpro, was examined through structural similarity and fingerprint studies. The two experiments pointed to Brevicollin (28), Cryptopleurine (41), Columbamine (46), Palmatine (47), Glycyrrhizoflavone (76), Licochalcone A (87), Arctigenin (94), Termilignan (98), Anolignan B (99), 4,5-dihydroxy-6″-deoxybromotopsentin (192), Dercitin (193), Tryptanthrin (200), 6-Cyano-5-methoxy-12-methylindolo [2, 3A] carbazole (211), Thiangazole (298), and Phenoxan (300). The binding ability against PLpro was screened through molecular docking, disclosing the favorable binding modes of six metabolites. ADMET studies expected molecules 28, 76, 94, 200, and 298 as the most favorable metabolites. Then, molecules 76, 94, and 298 were chosen through in silico toxicity studies. Finally, DFT studies were carried out on glycyrrhizoflavone (76) and indicated a high level of similarity in the molecular orbital analysis. The obtained data can be used in further in vitro and in vivo studies to examine and confirm the inhibitory effect of the filtered metabolites against PLpro and SARS-CoV-2.
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Affiliation(s)
- Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh 13713, Saudi Arabia
| | - Ahmed M. Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria 21934, Egypt
| | - Mohamed S. Alesawy
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
| | - Abdulrahman M. Saleh
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
| | - Aisha A. Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Ibrahim H. Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
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Structure-Based Virtual Screening, Docking, ADMET, Molecular Dynamics, and MM-PBSA Calculations for the Discovery of Potential Natural SARS-CoV-2 Helicase Inhibitors from the Traditional Chinese Medicine. J CHEM-NY 2022. [DOI: 10.1155/2022/7270094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Continuing our antecedent work against COVID-19, a set of 5956 compounds of traditional Chinese medicine have been virtually screened for their potential against SARS-CoV-2 helicase (PDB ID: 5RMM). Initially, a fingerprint study with VXG, the ligand of the target enzyme, disclosed the similarity of 187 compounds. Then, a molecular similarity study declared the most similar 40 compounds. Subsequently, molecular docking studies were carried out to examine the binding modes and energies. Then, the most appropriate 26 compounds were subjected to in silico ADMET and toxicity studies to select the most convenient inhibitors to be: (1R,2S)-ephedrine (57), (1R,2S)-norephedrine (59), 2-(4-(pyrrolidin-1-yl)phenyl)acetic acid (84), 1-phenylpropane-1,2-dione (195), 2-methoxycinnamic acid (246), 2-methoxybenzoic acid (364), (R)-2-((R)-5-oxopyrrolidin-3-yl)-2-phenylacetic acid (405), (Z)-6-(3-hydroxy-4-methoxystyryl)-4-methoxy-2H-pyran-2-one (533), 8-chloro-2-(2-phenylethyl)-5,6,7-trihydroxy-5,6,7,8-tetrahydrochromone (637), 3-((1R,2S)-2-(dimethylamino)-1-hydroxypropyl)phenol (818), (R)-2-ethyl-4-(1-hydroxy-2-(methylamino)ethyl)phenol (5159), and (R)-2-((1S,2S,5S)-2-benzyl-5-hydroxy-4-methylcyclohex-3-en-1-yl)propane-1,2-diol (5168). Among the selected 12 compounds, the metabolites, compound 533 showed the best docking scores. Interestingly, the MD simulation studies for compound 533, the one with the highest docking score, over 100 ns showed its correct binding to SARS-CoV-2 helicase with low energy and optimum dynamics. Finally, MM-PBSA studies showed that 533 bonded favorably to SARS-CoV-2 helicase with a free energy value of −83 kJ/mol. Further, the free energy decomposition study determined the essential amino acid residues that contributed favorably to the binding process. The obtained results give a huge hope to find a cure for COVID-19 through further in vitro and in vivo studies for the selected compounds.
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Isolation and In Silico Inhibitory Potential against SARS-CoV-2 RNA Polymerase of the Rare Kaempferol 3-O-(6″-O-acetyl)-Glucoside from Calligonum tetrapterum. PLANTS 2022; 11:plants11152072. [PMID: 35956550 PMCID: PMC9370365 DOI: 10.3390/plants11152072] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 11/17/2022]
Abstract
The phytochemical constituents of Calligonum tetrapterum Jaub. & Spach (Family Polygonaceae) were studied for the first time. The study resulted in the isolation of the rare flavonol glycoside, kaempferol 3-O-(6″-O-acetyl)-glucoside,(K3G-A). The potential inhibitive activity of K3G-A toward SARS-CoV-2 was investigated utilizing several in silico approaches. First, molecular fingerprints and structural similarity experiments were carried out for K3G-A against nine co-crystallized ligands of nine proteins of SARS-CoV-2 to reveal if there is a structural similarity with any of them. The conducted studies showed the high similarity of K3G-A and remdesivir, the co-crystallized ligand of SARS-CoV-2 RNA-dependent RNA polymerase (PDB ID: 7BV2), RdRp. To validate these findings, a DFT study was conducted and confirmed the proposed similarity on the electronic and orbital levels. The binding of K3G-A against RdRp was confirmed through molecular docking studies exhibiting a binding energy of −27.43 kcal/mol, which was higher than that of remdesivir. Moreover, the RdRp-K3G-A complex was subjected to several MD studies at 100 ns that authenticated the accurate mode of binding and the correct dynamic behavior. Finally, in silico ADMET and toxicity evaluation of K3G-A was conducted and denoted the safety and the drug-likeness of K3G-A. In addition to K3G-A, two other metabolites were isolated and identified to be kaempferol (K) and β-sitosterol (β-S).
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The Computational Preventive Potential of the Rare Flavonoid, Patuletin, Isolated from Tagetes patula, against SARS-CoV-2. PLANTS 2022; 11:plants11141886. [PMID: 35890520 PMCID: PMC9323967 DOI: 10.3390/plants11141886] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/16/2022] [Accepted: 07/18/2022] [Indexed: 01/14/2023]
Abstract
The rare flavonoid, patuletin, was isolated from the flowers of Tagetes patula growing in Egypt. The rarity of the isolated compound inspired us to scrutinize its preventive effect against COVID-19 utilizing a multi-step computational approach. Firstly, a structural similarity study was carried out against nine ligands of nine SARS-CoV-2 proteins. The results showed a large structural similarity between patuletin and F86, the ligand of SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). Then, a 3D-Flexible alignment study of patuletin and F86 verified the proposed similarity. To determine the binding opportunity, patuletin was docked against the RdRp showing a correct binding inside its active pocket with an energy of −20 kcal/mol that was comparable to that of F86 (−23 kcal/mol). Following, several MD simulations as well as MM-PBSA studies authenticated the accurate binding of patuletin in the RdRp via the correct dynamic and energetic behaviors over 100 ns. Additionally, in silico ADMET studies showed the general safety and drug-likeness of patuletin.
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The Assessment of Anticancer and VEGFR-2 Inhibitory Activities of a New 1H-Indole Derivative: In Silico and In Vitro Approaches. Processes (Basel) 2022. [DOI: 10.3390/pr10071391] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Corresponding to the reported features of anti-VEGFR-2-approved compounds, a new 1H-indole derivative (compound 7) was designed. The inhibitory potential of the designed compound was revealed via a molecular docking study that showed the appropriate binding. Then, MD simulation (six studies) over a period of 100 ns was performed to confirm the precise binding and optimum energy. Additionally, MM-GBSA reaffirmed the perfect binding, exhibiting a total precise energy of −40.38 Kcal/Mol. The MM-GBSA experiments named the essential amino acids in the protein–ligand interaction, employing the binding energy decomposition and revealing the diversity of interactions of compound 7 inside the VEGFR-2 enzyme. As compound 7 is new, DFT experiments were utilized for molecular structure optimization. Additionally, the DFT results validated the coherent interaction of compound 7 with the VEGFR-2 enzyme. A good value of drug-likeness of compound 7 was acknowledged via in silico ADMET studies. Interestingly, the experimental in vitro prohibitory potential of compound 7 was better than that of sorafenib, demonstrating an IC50 value of 25 nM. Notably, the strong inhibitory effects of compound 10 against two cancer cell lines (MCF-7 and HCT 116) were established with IC50 values of 12.93 and 11.52 μM, disclosing high selectivity indexes of 6.7 and 7.5, respectively.
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13
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Multi-Step In Silico Discovery of Natural Drugs against COVID-19 Targeting Main Protease. Int J Mol Sci 2022; 23:ijms23136912. [PMID: 35805916 PMCID: PMC9266348 DOI: 10.3390/ijms23136912] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/15/2022] [Accepted: 06/15/2022] [Indexed: 02/04/2023] Open
Abstract
In continuation of our antecedent work against COVID-19, three natural compounds, namely, Luteoside C (130), Kahalalide E (184), and Streptovaricin B (278) were determined as the most promising SARS-CoV-2 main protease (Mpro) inhibitors among 310 naturally originated antiviral compounds. This was performed via a multi-step in silico method. At first, a molecular structure similarity study was done with PRD_002214, the co-crystallized ligand of Mpro (PDB ID: 6LU7), and favored thirty compounds. Subsequently, the fingerprint study performed with respect to PRD_002214 resulted in the election of sixteen compounds (7, 128, 130, 156, 157, 158, 180, 184, 203, 204, 210, 237, 264, 276, 277, and 278). Then, results of molecular docking versus Mpro PDB ID: 6LU7 favored eight compounds (128, 130, 156, 180, 184, 203, 204, and 278) based on their binding affinities. Then, in silico toxicity studies were performed for the promising compounds and revealed that all of them have good toxicity profiles. Finally, molecular dynamic (MD) simulation experiments were carried out for compounds 130, 184, and 278, which exhibited the best binding modes against Mpro. MD tests revealed that luteoside C (130) has the greatest potential to inhibit SARS-CoV-2 main protease.
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14
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Suleimen YM, Jose RA, Suleimen RN, Ishmuratova MY, Toppet S, Dehaen W, Alsfouk AA, Elkaeed EB, Eissa IH, Metwaly AM. Isolation and In Silico SARS-CoV-2 Main Protease Inhibition Potential of Jusan Coumarin, a New Dicoumarin from Artemisia glauca. Molecules 2022; 27:2281. [PMID: 35408682 PMCID: PMC9000794 DOI: 10.3390/molecules27072281] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 12/31/2022] Open
Abstract
A new dicoumarin, jusan coumarin, (1), has been isolated from Artemisia glauca aerial parts. The chemical structure of jusan coumarin was estimated, by 1D, 2D NMR as well as HR-Ms spectroscopic methods, to be 7-hydroxy-6-methoxy-3-[(2-oxo-2H-chromen-6-yl)oxy]-2H-chromen-2-one. As the first time to be introduced in nature, its potential against SARS-CoV-2 has been estimated using various in silico methods. Molecular similarity and fingerprints experiments have been utilized for 1 against nine co-crystallized ligands of COVID-19 vital proteins. The results declared a great similarity between Jusan Coumarin and X77, the ligand of COVID-19 main protease (PDB ID: 6W63), Mpro. To authenticate the obtained outputs, a DFT experiment was achieved to confirm the similarity of X77 and 1. Consequently, 1 was docked against Mpro. The results clarified that 1 bonded in a correct way inside Mpro active site, with a binding energy of -18.45 kcal/mol. Furthermore, the ADMET and toxicity profiles of 1 were evaluated and showed the safety of 1 and its likeness to be a drug. Finally, to confirm the binding and understand the thermodynamic characters between 1 and Mpro, several molecular dynamics (MD) simulations studies have been administered. Additionally, the known coumarin derivative, 7-isopentenyloxycoumarin (2), has been isolated as well as β-sitosterol (3).
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Affiliation(s)
- Yerlan M. Suleimen
- The International Centre for Interdisciplinary Solutions on Antibiotics and Secondary Metabolites, Republican Collection of Microorganisms, Nur-Sultan 010000, Kazakhstan;
- The Laboratory of Engineering Profile of NMR Spectroscopy, Sh. Ualikhanov Kokshetau University, Kokshetau 020000, Kazakhstan
| | - Rani A. Jose
- Molecular Design & Synthesis, Department of Chemistry, Catholic University of Leuven, B-3001 Leuven, Belgium; (R.A.J.); (S.T.); (W.D.)
- Department of Chemistry, St. Dominic’s College, Mahatma Gandhi University, Kanjirappally 686512, India
| | - Raigul N. Suleimen
- Department of Technical Physics, Faculty of Physics and Technology, L.N. Gumilyov Eurasian National University, Nur-Sultan 010010, Kazakhstan
| | | | - Suzanne Toppet
- Molecular Design & Synthesis, Department of Chemistry, Catholic University of Leuven, B-3001 Leuven, Belgium; (R.A.J.); (S.T.); (W.D.)
| | - Wim Dehaen
- Molecular Design & Synthesis, Department of Chemistry, Catholic University of Leuven, B-3001 Leuven, Belgium; (R.A.J.); (S.T.); (W.D.)
| | - Aisha A. Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh 13713, Saudi Arabia;
| | - Ibrahim H. Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt;
| | - Ahmed M. Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
- Biopharmaceutical Product Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications, Alexandria 21934, Egypt
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15
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Eissa IH, Alesawy MS, Saleh AM, Elkaeed EB, Alsfouk BA, El-Attar AAMM, Metwaly AM. Ligand and Structure-Based In Silico Determination of the Most Promising SARS-CoV-2 nsp16-nsp10 2'- o-Methyltransferase Complex Inhibitors among 3009 FDA Approved Drugs. Molecules 2022; 27:2287. [PMID: 35408684 PMCID: PMC9000629 DOI: 10.3390/molecules27072287] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/27/2022] [Accepted: 03/28/2022] [Indexed: 12/15/2022] Open
Abstract
As a continuation of our earlier work against SARS-CoV-2, seven FDA-approved drugs were designated as the best SARS-CoV-2 nsp16-nsp10 2'-o-methyltransferase (2'OMTase) inhibitors through 3009 compounds. The in silico inhibitory potential of the examined compounds against SARS-CoV-2 nsp16-nsp10 2'-o-methyltransferase (PDB ID: (6W4H) was conducted through a multi-step screening approach. At the beginning, molecular fingerprints experiment with SAM (S-Adenosylmethionine), the co-crystallized ligand of the targeted enzyme, unveiled the resemblance of 147 drugs. Then, a structural similarity experiment recommended 26 compounds. Therefore, the 26 compounds were docked against 2'OMTase to reveal the potential inhibitory effect of seven promising compounds (Protirelin, (1187), Calcium folinate (1913), Raltegravir (1995), Regadenoson (2176), Ertapenem (2396), Methylergometrine (2532), and Thiamine pyrophosphate hydrochloride (2612)). Out of the docked ligands, Ertapenem (2396) showed an ideal binding mode like that of the co-crystallized ligand (SAM). It occupied all sub-pockets of the active site and bound the crucial amino acids. Accordingly, some MD simulation experiments (RMSD, RMSF, Rg, SASA, and H-bonding) have been conducted for the 2'OMTase-Ertapenem complex over 100 ns. The performed MD experiments verified the correct binding mode of Ertapenem against 2'OMTase exhibiting low energy and optimal dynamics. Finally, MM-PBSA studies indicated that Ertapenem bonded advantageously to the targeted protein with a free energy value of -43 KJ/mol. Furthermore, the binding free energy analysis revealed the essential amino acids of 2'OMTase that served positively to the binding. The achieved results bring hope to find a treatment for COVID-19 via in vitro and in vivo studies for the pointed compounds.
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Affiliation(s)
- Ibrahim H. Eissa
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt; (M.S.A.); (A.M.S.)
| | - Mohamed S. Alesawy
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt; (M.S.A.); (A.M.S.)
| | - Abdulrahman M. Saleh
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt; (M.S.A.); (A.M.S.)
| | - Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, Almaarefa University, Riyadh 13713, Saudi Arabia;
| | - Bshra A. Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Abdul-Aziz M. M. El-Attar
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, Cairo 11884, Egypt;
| | - Ahmed M. Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria 21934, Egypt
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16
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Multi-Phase In Silico Discovery of Potential SARS-CoV-2 RNA-Dependent RNA Polymerase Inhibitors among 3009 Clinical and FDA-Approved Related Drugs. Processes (Basel) 2022. [DOI: 10.3390/pr10030530] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Proceeding our prior studies of SARS-CoV-2, the inhibitory potential against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) has been investigated for a collection of 3009 clinical and FDA-approved drugs. A multi-phase in silico approach has been employed in this study. Initially, a molecular fingerprint experiment of Remdesivir (RTP), the co-crystallized ligand of the examined protein, revealed the most similar 150 compounds. Among them, 30 compounds were selected after a structure similarity experiment. Subsequently, the most similar 30 compounds were docked against SARS-CoV-2 RNA-dependent RNA polymerase (PDB ID: 7BV2). Aloin 359, Baicalin 456, Cefadroxil 1273, Sophoricoside 1459, Hyperoside 2109, and Vitexin 2286 exhibited the most precise binding modes, as well as the best binding energies. To confirm the obtained results, MD simulations experiments have been conducted for Hyperoside 2109, the natural flavonoid glycoside that exhibited the best docking scores, against RdRp (PDB ID: 7BV2) for 100 ns. The achieved results authenticated the correct binding of 2109, showing low energy and optimum dynamics. Our team presents these outcomes for scientists all over the world to advance in vitro and in vivo examinations against COVID-19 for the promising compounds.
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17
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Suleimen YM, Jose RA, Suleimen RN, Arenz C, Ishmuratova MY, Toppet S, Dehaen W, Alsfouk BA, Elkaeed EB, Eissa IH, Metwaly AM. Jusanin, a New Flavonoid from Artemisia commutata with an In Silico Inhibitory Potential against the SARS-CoV-2 Main Protease. Molecules 2022; 27:1636. [PMID: 35268738 PMCID: PMC8911936 DOI: 10.3390/molecules27051636] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 01/07/2023] Open
Abstract
A new flavonoid, Jusanin, (1) has been isolated from the aerial parts of Artemisia commutata. The chemical structure of Jusanin has been elucidated using 1D, 2D NMR, and HR-Ms spectroscopic methods to be 5,2',4'-trihydroxy-6,7,5'-trimethoxyflavone. Being new in nature, the inhibition potential of 1 has been estimated against SARS-CoV-2 using different in silico techniques. Firstly, molecular similarity and fingerprint studies have been conducted for Jusanin against co-crystallized ligands of eight different SARS-CoV-2 essential proteins. The studies indicated the similarity between 1 and X77, the co-crystallized ligand SARS-CoV-2 main protease (PDB ID: 6W63). To confirm the obtained results, a DFT study was carried out and indicated the similarity of (total energy, HOMO, LUMO, gap energy, and dipole moment) between 1 and X77. Accordingly, molecular docking studies of 1 against the target enzyme have been achieved and showed that 1 bonded correctly in the protein's active site with a binding energy of -19.54 Kcal/mol. Additionally, in silico ADMET in addition to the toxicity evaluation of Jusanin against seven models have been preceded and indicated the general safety and the likeness of Jusanin to be a drug. Finally, molecular dynamics simulation studies were applied to investigate the dynamic behavior of the Mpro-Jusanin complex and confirmed the correct binding at 100 ns. In addition to 1, three other metabolites have been isolated and identified to be сapillartemisin A (2), methyl-3-[S-hydroxyprenyl]-cumarate (3), and β-sitosterol (4).
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Affiliation(s)
- Yerlan M. Suleimen
- The International Centre for Interdisciplinary Solutions on Antibiotics and Secondary Metabolites, Republican Collection of Microorganisms, Nur-Sultan 010000, Kazakhstan;
- The Laboratory of Engineering Profile of NMR Spectroscopy, Sh. Ualikhanov Kokshetau University, Kokshetau 020000, Kazakhstan
| | - Rani A. Jose
- Molecular Design & Synthesis, Department of Chemistry, Catholic University of Leuven, B-3001 Leuven, Belgium; (R.A.J.); (S.T.); (W.D.)
- Department of Chemistry, St. Dominic’s College, Mahatma Gandhi University, Kanjirappally 686512, India
| | - Raigul N. Suleimen
- Department of Technical Physics, Faculty of Physics and Technology, L.N. Gumilyov Eurasian National University, Nur-Sultan 010010, Kazakhstan
| | - Christoph Arenz
- Institut für Chemie der Humboldt-Universität zu, D-12489 Berlin, Germany;
| | | | - Suzanne Toppet
- Molecular Design & Synthesis, Department of Chemistry, Catholic University of Leuven, B-3001 Leuven, Belgium; (R.A.J.); (S.T.); (W.D.)
| | - Wim Dehaen
- Molecular Design & Synthesis, Department of Chemistry, Catholic University of Leuven, B-3001 Leuven, Belgium; (R.A.J.); (S.T.); (W.D.)
| | - Bshra A. Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh 13713, Saudi Arabia;
| | - Ibrahim H. Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt;
| | - Ahmed M. Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications, Alexandria 21934, Egypt
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18
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Suleimen YM, Jose RA, Suleimen RN, Arenz C, Ishmuratova M, Toppet S, Dehaen W, Alsfouk AA, Elkaeed EB, Eissa IH, Metwaly AM. Isolation and In Silico Anti-SARS-CoV-2 Papain-Like Protease Potentialities of Two Rare 2-Phenoxychromone Derivatives from Artemisia spp. Molecules 2022; 27:1216. [PMID: 35209006 PMCID: PMC8879996 DOI: 10.3390/molecules27041216] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/02/2022] [Accepted: 02/06/2022] [Indexed: 02/06/2023] Open
Abstract
Two rare 2-phenoxychromone derivatives, 6-demethoxy-4`-O-capillarsine (1) and tenuflorin C (2), were isolated from the areal parts of Artemisia commutata and A. glauca, respectively, for the first time. Being rare in nature, the inhibition potentialities of 1 and 2 against SARS-CoV-2 was investigated using multistage in silico techniques. At first, molecular similarity and fingerprint studies were conducted for 1 and 2 against co-crystallized ligands of eight different COVID-19 enzymes. The carried-out studies indicated the similarity of 1 and 2 with TTT, the co-crystallized ligand of COVID-19 Papain-Like Protease (PLP), (PDB ID: 3E9S). Therefore, molecular docking studies of 1 and 2 against the PLP were carried out and revealed correct binding inside the active site exhibiting binding energies of -18.86 and -18.37 Kcal/mol, respectively. Further, in silico ADMET in addition to toxicity evaluation of 1 and 2 against seven models indicated the general safety and the likeness of 1 and 2 to be drugs. Lastly, to authenticate the binding and to investigate the thermodynamic characters, molecular dynamics (MD) simulation studies were conducted on 1 and PLP.
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Affiliation(s)
- Yerlan M. Suleimen
- The International Centre for Interdisciplinary Solutions on Antibiotics and Secondary Metabolites, Republican Collection of Microorganisms, Nur-Sultan 010000, Kazakhstan;
- The Laboratory of Engineering Profile of NMR Spectroscopy, Sh. Ualikhanov Kokshetau University, Kokshetau 020000, Kazakhstan
| | - Rani A. Jose
- Molecular Design & Synthesis, Department of Chemistry, Catholic University of Leuven, B-3001 Heverlee, Belgium; (R.A.J.); (S.T.); (W.D.)
| | - Raigul N. Suleimen
- Department of Natural Science, Faculty of Technical Physics, L.N. Gumilyov Eurasian National University, Nur-Sultan 010010, Kazakhstan
| | - Christoph Arenz
- Institut für Chemie der Humboldt, Universität zu Berlin, D-12489 Berlin, Germany;
| | - Margarita Ishmuratova
- Department of Botany, E.A. Buketov Karaganda University, Karaganda 100024, Kazakhstan;
| | - Suzanne Toppet
- Molecular Design & Synthesis, Department of Chemistry, Catholic University of Leuven, B-3001 Heverlee, Belgium; (R.A.J.); (S.T.); (W.D.)
| | - Wim Dehaen
- Molecular Design & Synthesis, Department of Chemistry, Catholic University of Leuven, B-3001 Heverlee, Belgium; (R.A.J.); (S.T.); (W.D.)
| | - Aisha A. Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia;
| | - Ibrahim H. Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt;
| | - Ahmed M. Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria 21934, Egypt
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19
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Shi Y, Hua Y, Wang B, Zhang R, Li X. In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity. Front Pharmacol 2022; 12:793332. [PMID: 35082675 PMCID: PMC8785686 DOI: 10.3389/fphar.2021.793332] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 11/23/2021] [Indexed: 12/11/2022] Open
Abstract
Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.
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Affiliation(s)
- Yinping Shi
- Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yuqing Hua
- Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China.,School of Pharmacy, Shandong First Medical University, Tai'an, China
| | - Baobao Wang
- Department of Nephrology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Ruiqiu Zhang
- Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China.,School of Pharmacy, Shandong First Medical University, Tai'an, China
| | - Xiao Li
- Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China.,Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
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20
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Bassan A, Alves VM, Amberg A, Anger LT, Beilke L, Bender A, Bernal A, Cronin MT, Hsieh JH, Johnson C, Kemper R, Mumtaz M, Neilson L, Pavan M, Pointon A, Pletz J, Ruiz P, Russo DP, Sabnis Y, Sandhu R, Schaefer M, Stavitskaya L, Szabo DT, Valentin JP, Woolley D, Zwickl C, Myatt GJ. In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 20:100188. [PMID: 35721273 PMCID: PMC9205464 DOI: 10.1016/j.comtox.2021.100188] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.
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Affiliation(s)
- Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Vinicius M. Alves
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, United States
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Lennart T. Anger
- Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, United States
| | - Andreas Bender
- AI and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United States
| | | | - Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Jui-Hua Hsieh
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, United States
| | | | - Raymond Kemper
- Nuvalent, One Broadway, 14th floor, Cambridge, MA 02142, United States
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, United States
| | - Louise Neilson
- Broughton Nicotine Services, Oak Tree House, West Craven Drive, Earby, Lancashire BB18 6JZ UK
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Amy Pointon
- Functional and Mechanistic Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Julia Pletz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, United States
| | - Daniel P. Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, United States
- Department of Chemistry, Rutgers University, Camden, NJ 08102, United States
| | - Yogesh Sabnis
- UCB Biopharma SRL, Chemin du Foriest, B-1420 Braine-l’Alleud, Belgium
| | - Reena Sandhu
- SafeDose Ltd., 20 Dundas Street West, Suite 921, Toronto, Ontario M5G2H1, Canada
| | - Markus Schaefer
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Lidiya Stavitskaya
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | | | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, United States
| | - Glenn J. Myatt
- Instem, 1393 Dublin Road, Columbus, OH 43215, United States
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21
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Eissa IH, Khalifa MM, Elkaeed EB, Hafez EE, Alsfouk AA, Metwaly AM. In Silico Exploration of Potential Natural Inhibitors against SARS-Cov-2 nsp10. Molecules 2021; 26:6151. [PMID: 34684735 PMCID: PMC8539059 DOI: 10.3390/molecules26206151] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 09/30/2021] [Accepted: 10/06/2021] [Indexed: 12/24/2022] Open
Abstract
In continuation of our previous effort, different in silico selection methods were applied to 310 naturally isolated metabolites that exhibited antiviral potentialities before. The applied selection methods aimed to pick the most relevant inhibitor of SARS-CoV-2 nsp10. At first, a structural similarity study against the co-crystallized ligand, S-Adenosyl Methionine (SAM), of SARS-CoV-2 nonstructural protein (nsp10) (PDB ID: 6W4H) was carried out. The similarity analysis culled 30 candidates. Secondly, a fingerprint study against SAM preferred compounds 44, 48, 85, 102, 105, 182, 220, 221, 282, 284, 285, 301, and 302. The docking studies picked 48, 182, 220, 221, and 284. While the ADMET analysis expected the likeness of the five candidates to be drugs, the toxicity study preferred compounds 48 and 182. Finally, a density-functional theory (DFT) study suggested vidarabine (182) to be the most relevant SARS-Cov-2 nsp10 inhibitor.
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Affiliation(s)
- Ibrahim H. Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt;
| | - Mohamed M. Khalifa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt;
| | - Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, Almaarefa University, Riyadh 13713, Saudi Arabia;
| | - Elsayed E. Hafez
- Department of Plant Protection and Biomolecular Diagnosis, ALCRI, City of Scientific Research and Technological Applications, New Borg El-Arab City 21934, Egypt;
| | - Aisha A. Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh 11564, Saudi Arabia;
| | - Ahmed M. Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
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22
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Venkatraman V. FP-ADMET: a compendium of fingerprint-based ADMET prediction models. J Cheminform 2021; 13:75. [PMID: 34583740 PMCID: PMC8479898 DOI: 10.1186/s13321-021-00557-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 09/20/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors. AVAILABILITY The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet .
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Affiliation(s)
- Vishwesh Venkatraman
- Norwegian University of Science and Technology, Realfagbygget, Gløshaugen, Høgskoleringen, 7491, Trondheim, Norway.
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23
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Xie Y, Meng WY, Li RZ, Wang YW, Qian X, Chan C, Yu ZF, Fan XX, Pan HD, Xie C, Wu QB, Yan PY, Liu L, Tang YJ, Yao XJ, Wang MF, Leung ELH. Early lung cancer diagnostic biomarker discovery by machine learning methods. Transl Oncol 2021; 14:100907. [PMID: 33217646 PMCID: PMC7683339 DOI: 10.1016/j.tranon.2020.100907] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/21/2020] [Accepted: 09/25/2020] [Indexed: 02/07/2023] Open
Abstract
Early diagnosis has been proved to improve survival rate of lung cancer patients. The availability of blood-based screening could increase early lung cancer patient uptake. Our present study attempted to discover Chinese patients' plasma metabolites as diagnostic biomarkers for lung cancer. In this work, we use a pioneering interdisciplinary mechanism, which is firstly applied to lung cancer, to detect early lung cancer diagnostic biomarkers by combining metabolomics and machine learning methods. We collected total 110 lung cancer patients and 43 healthy individuals in our study. Levels of 61 plasma metabolites were from targeted metabolomic study using LC-MS/MS. A specific combination of six metabolic biomarkers note-worthily enabling the discrimination between stage I lung cancer patients and healthy individuals (AUC = 0.989, Sensitivity = 98.1%, Specificity = 100.0%). And the top 5 relative importance metabolic biomarkers developed by FCBF algorithm also could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction. This research will provide strong support for the feasibility of blood-based screening, and bring a more accurate, quick and integrated application tool for early lung cancer diagnostic. The proposed interdisciplinary method could be adapted to other cancer beyond lung cancer.
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Affiliation(s)
- Ying Xie
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Wei-Yu Meng
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Run-Ze Li
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Yu-Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Xin Qian
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Chang Chan
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Zhi-Fang Yu
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Xing-Xing Fan
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Hu-Dan Pan
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Chun Xie
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Qi-Biao Wu
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Pei-Yu Yan
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Liang Liu
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Yi-Jun Tang
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Xiao-Jun Yao
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China.
| | - Mei-Fang Wang
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China.
| | - Elaine Lai-Han Leung
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China; Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China.
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24
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Wang MWH, Goodman JM, Allen TEH. Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models. Chem Res Toxicol 2020; 34:217-239. [PMID: 33356168 DOI: 10.1021/acs.chemrestox.0c00316] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.
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Affiliation(s)
- Marcus W H Wang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.,MRC Toxicology Unit, University of Cambridge, Hodgkin Building, Lancaster Road, Leicester LE1 7HB, United Kingdom
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25
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Developing novel computational prediction models for assessing chemical-induced neurotoxicity using naïve Bayes classifier technique. Food Chem Toxicol 2020; 143:111513. [PMID: 32621845 DOI: 10.1016/j.fct.2020.111513] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/02/2020] [Accepted: 06/04/2020] [Indexed: 02/08/2023]
Abstract
Development of reliable and efficient alternative in vivo methods for evaluation of the chemicals with potential neurotoxicity is an urgent need in the early stages of drug design. In this investigation, the computational prediction models for drug-induced neurotoxicity were developed by using the classical naïve Bayes classifier. Eight molecular properties closely relevant to neurotoxicity were selected. Then, 110 classification models were developed with using the eight important molecular descriptors and 10 types of fingerprints with 11 different maximum diameters. Among these 110 prediction models, the prediction model (NB-03) based on eight molecular descriptors combined with ECFP_10 fingerprints showed the best prediction performance, which gave 90.5% overall prediction accuracy for the training set and 82.1% concordance for the external test set. In addition, compared to naïve Bayes classifier, the recursive partitioning classifier displayed worse predictive performance for neurotoxicity. Therefore, the established NB-03 prediction model can be used as a reliable virtual screening tool to predict neurotoxicity in the early stages of drug design. Moreover, some structure alerts for characterizing neurotoxicity were identified in this research, which could give an important guidance for the chemists in structural modification and optimization to reduce the chemicals with potential neurotoxicity.
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26
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Cendoya X, Quevedo C, Ipiñazar M, Planes FJ. Computational approach for collection and prediction of molecular initiating events in developmental toxicity. Reprod Toxicol 2020; 94:55-64. [PMID: 32344110 DOI: 10.1016/j.reprotox.2020.03.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 03/04/2020] [Accepted: 03/20/2020] [Indexed: 02/06/2023]
Abstract
Developmental toxicity is defined as the occurrence of adverse effects on the developing organism as a result from exposure to a toxic agent. These alterations can have long-term acute effects. Current in vitro models present important limitations and the evaluation of toxicity is not entirely objective. In silico methods have also shown limited success, in part due to complex and varied mechanisms of action that mediate developmental toxicity, which are sometimes poorly understood. In this article, we compiled a dataset of compounds with developmental toxicity categories and annotated mechanisms of action for both toxic and non-toxic compounds (DVTOX). With it, we selected a panel of protein targets that might be part of putative Molecular Initiating Events (MIEs) of Adverse Outcome Pathways of developmental toxicity. The validity of this list of candidate MIEs was studied through the evaluation of new drug-target relationships that include such proteins, but were not part of the original database. Finally, an orthology analysis of this protein panel was conducted to select an appropriate animal model to assess developmental toxicity. We tested our approach using the zebrafish embryo toxicity test, finding positive results.
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Affiliation(s)
- Xabier Cendoya
- TECNUN, University of Navarra, San Sebastian, 20018, Spain
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