1
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Oh KK, Yoon SJ, Song SH, Park JH, Kim JS, Kim MJ, Kim DJ, Suk KT. The unfolded features on the synchronized fashion of gut microbiota and Drynaria rhizome against obesity via integrated pharmacology. Food Chem 2024; 460:140616. [PMID: 39094340 DOI: 10.1016/j.foodchem.2024.140616] [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: 05/08/2024] [Revised: 06/30/2024] [Accepted: 07/22/2024] [Indexed: 08/04/2024]
Abstract
Drynaria rhizome (DR) is used as a natural remedy to ameliorate obesity (OB) in East Asia; in parallel, the gut microbiota (GM) might exert a positive impact on OB through their metabolites. This study elucidates the orchestrated effects of DR and GM on OB. DR-GM, - a key signaling pathway-target-metabolite (DGSTM) networks were used to unveil the relationship between DR and GM, and Molecular Docking Test (MDT) and Density Functional Theory (DFT) were adopted to underpin the uppermost molecules. The NR1H3 (target) - 3-Epicycloeucalenol (ligand), and PPARG (target) - Clionasterol (ligand) conjugates from DR, FABP3 (target) - Ursodeoxycholic acid, FABP4 (target) - Lithocholic acid (ligand) or Deoxycholic acid (ligand), PPARA (target) - Equol (ligand), and PPARD (target) - 2,3-Bis(3,4-dihydroxybenzyl)butyrolactone (ligand) conjugates from GM formed the most stable conformers via MDT and DFT. Overall, these findings suggest that DR-GM might be a promising ameliorator on PPAR signaling pathway against OB.
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Affiliation(s)
- Ki-Kwang Oh
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea.
| | - Sang-Jun Yoon
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
| | - Seol Hee Song
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
| | - Jeong Ha Park
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
| | - Jeong Su Kim
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
| | - Min Ju Kim
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
| | - Dong Joon Kim
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
| | - Ki-Tae Suk
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea.
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2
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Thorman AW, Reigle J, Chutipongtanate S, Yang J, Shamsaei B, Pilarczyk M, Fazel-Najafabadi M, Adamczak R, Kouril M, Bhatnagar S, Hummel S, Niu W, Morrow AL, Czyzyk-Krzeska MF, McCullumsmith R, Seibel W, Nassar N, Zheng Y, Hildeman DA, Medvedovic M, Herr AB, Meller J. Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking. SCIENCE ADVANCES 2024; 10:eadj3010. [PMID: 39213358 PMCID: PMC11364105 DOI: 10.1126/sciadv.adj3010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 07/26/2024] [Indexed: 09/04/2024]
Abstract
We present an in silico approach for drug discovery, dubbed connectivity enhanced structure activity relationship (ceSAR). Building on the landmark LINCS library of transcriptional signatures of drug-like molecules and gene knockdowns, ceSAR combines cheminformatic techniques with signature concordance analysis to connect small molecules and their targets and further assess their biophysical compatibility using molecular docking. Candidate compounds are first ranked in a target structure-independent manner, using chemical similarity to LINCS analogs that exhibit transcriptomic concordance with a target gene knockdown. Top candidates are subsequently rescored using docking simulations and machine learning-based consensus of the two approaches. Using extensive benchmarking, we show that ceSAR greatly reduces false-positive rates, while cutting run times by multiple orders of magnitude and further democratizing drug discovery pipelines. We further demonstrate the utility of ceSAR by identifying and experimentally validating inhibitors of BCL2A1, an important antiapoptotic target in melanoma and preterm birth-associated inflammation.
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Affiliation(s)
- Alexander W. Thorman
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - James Reigle
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
- Department of Biostatistics, Health Informatics and Data Sciences, University of Cincinnati, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Somchai Chutipongtanate
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
- Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Juechen Yang
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
- Department of Biostatistics, Health Informatics and Data Sciences, University of Cincinnati, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Behrouz Shamsaei
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
- Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Marcin Pilarczyk
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Mehdi Fazel-Najafabadi
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Rafal Adamczak
- Department of Informatics, Faculty of Physics, Astronomy an Informatics, Nicolaus Copernicus University, Toruń, Poland
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Surbhi Bhatnagar
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Sarah Hummel
- Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Wen Niu
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Ardythe L. Morrow
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Maria F. Czyzyk-Krzeska
- Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Veterans Affairs, Cincinnati Veteran Affairs Medical Center, Cincinnati, OH, USA
| | | | - William Seibel
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nicolas Nassar
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Experimental Hematology and Cancer Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Yi Zheng
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Experimental Hematology and Cancer Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - David A. Hildeman
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Mario Medvedovic
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
- Department of Biostatistics, Health Informatics and Data Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Andrew B. Herr
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Division of Infectious Diseases, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Jarek Meller
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
- Department of Biostatistics, Health Informatics and Data Sciences, University of Cincinnati, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Informatics, Faculty of Physics, Astronomy an Informatics, Nicolaus Copernicus University, Toruń, Poland
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
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3
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Mervin L, Voronov A, Kabeshov M, Engkvist O. QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design. J Chem Inf Model 2024; 64:5365-5374. [PMID: 38950185 DOI: 10.1021/acs.jcim.4c00457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular properties of small molecules are increasingly deployed within the design-make-test-analyze (DMTA) drug design cycle to predict molecular properties of interest. Despite this uptake, there are only a few automated packages to aid their development and deployment that also support uncertainty estimation, model explainability, and other key aspects of model usage. This represents a key unmet need within the field, and the large number of molecular representations and algorithms (and associated parameters) means it is nontrivial to robustly optimize, evaluate, reproduce, and deploy models. Here, we present QSARtuna, a molecule property prediction modeling pipeline, written in Python and utilizing the Optuna, Scikit-learn, RDKit, and ChemProp packages, which enables the efficient and automated comparison between molecular representations and machine learning models. The platform was developed by considering the increasingly important aspect of model uncertainty quantification and explainability by design. We provide details for our framework and provide illustrative examples to demonstrate the capability of the software when applied to simple molecular property, reaction/reactivity prediction, and DNA encoded library enrichment classification. We hope that the release of QSARtuna will further spur innovation in automatic ML modeling and provide a platform for education of best practices in molecular property modeling. The code for the QSARtuna framework is made freely available via GitHub.
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Affiliation(s)
- Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Mikhail Kabeshov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
- Department of Computer Science and Engineering, University of Gothenburg, Chalmers University of Technology, Gothenburg 412 96, Sweden
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4
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Zhang W, Huang RS. Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data. Expert Opin Drug Discov 2024; 19:841-853. [PMID: 38860709 DOI: 10.1080/17460441.2024.2365370] [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: 03/14/2024] [Accepted: 06/04/2024] [Indexed: 06/12/2024]
Abstract
INTRODUCTION Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs. AREAS COVERED Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination. EXPERT OPINION Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.
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Affiliation(s)
- Weijie Zhang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
| | - R Stephanie Huang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
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5
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Zhang W, Maeser D, Lee A, Huang Y, Gruener RF, Abdelbar IG, Jena S, Patel AG, Huang RS. Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors. Cancer Res 2024; 84:2021-2033. [PMID: 38581448 PMCID: PMC11178452 DOI: 10.1158/0008-5472.can-23-3005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/18/2023] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors. SIGNIFICANCE A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Robert F. Gruener
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Israa G. Abdelbar
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
- Clinical Pharmacy Practice Department, The British University in Egypt, El Sherouk, 11837, Egypt
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Anand G. Patel
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN 38105
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105
| | - R. Stephanie Huang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
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6
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Manoj M, Sowmyanarayan S, Kowshik AV, Chatterjee J. Identification of Potentially Repurposable Drugs for Lewy Body Dementia Using a Network-Based Approach. J Mol Neurosci 2024; 74:21. [PMID: 38363395 DOI: 10.1007/s12031-024-02199-2] [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: 01/01/2024] [Accepted: 02/06/2024] [Indexed: 02/17/2024]
Abstract
The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.
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Affiliation(s)
- Megha Manoj
- Department of Biotechnology, PES University, Bangalore, 560085, India
| | | | - Arjun V Kowshik
- Department of Biotechnology, PES University, Bangalore, 560085, India
| | - Jhinuk Chatterjee
- Department of Biotechnology, PES University, Bangalore, 560085, India.
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7
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Ali HO, Elkheir LYM, Fahal AH. The use of artificial intelligence to improve mycetoma management. PLoS Negl Trop Dis 2024; 18:e0011914. [PMID: 38329930 PMCID: PMC10852264 DOI: 10.1371/journal.pntd.0011914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024] Open
Affiliation(s)
- Hyam Omar Ali
- Mycetoma Research Centre, University of Khartoum, Khartoum, Sudan
- The Faculty of Mathematical Sciences, University of Khartoum, Khartoum, Sudan
| | - Lamis Yahia Mohamed Elkheir
- Mycetoma Research Centre, University of Khartoum, Khartoum, Sudan
- The Faculty of Pharmacy, University of Khartoum, Khartoum, Sudan
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8
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B S N, P K KN, Akey KS, Sankaran S, Raman RK, Natarajan J, Selvaraj J. Vitamin D analog calcitriol for breast cancer therapy; an integrated drug discovery approach. J Biomol Struct Dyn 2023; 41:11017-11043. [PMID: 37054526 DOI: 10.1080/07391102.2023.2199866] [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: 07/25/2022] [Accepted: 12/11/2022] [Indexed: 04/15/2023]
Abstract
As breast cancer remains leading cause of cancer death globally, it is essential to develop an affordable breast cancer therapy in underdeveloped countries. Drug repurposing offers potential to address gaps in breast cancer treatment. Molecular networking studies were performed for drug repurposing approach by using heterogeneous data. The PPI networks were built to select the target genes from the EGFR overexpression signaling pathway and its associated family members. The selected genes EGFR, ErbB2, ErbB4 and ErbB3 were allowed to interact with 2637 drugs, leads to PDI network construction of 78, 61, 15 and 19 drugs, respectively. As drugs approved for treating non cancer-related diseases or disorders are clinically safe, effective, and affordable, these drugs were given considerable attention. Calcitriol had shown significant binding affinities with all four receptors than standard neratinib. The RMSD, RMSF, and H-bond analysis of protein-ligand complexes from molecular dynamics simulation (100 ns), confirmed the stable binding of calcitriol with ErbB2 and EGFR receptors. In addition, MMGBSA and MMP BSA also affirmed the docking results. These in-silico results were validated with in-vitro cytotoxicity studies in SK-BR-3 and Vero cells. The IC50 value of calcitriol (43.07 mg/ml) was found to be lower than neratinib (61.50 mg/ml) in SK-BR-3 cells. In Vero cells the IC50 value of calcitriol (431.05 mg/ml) was higher than neratinib (404.95 mg/ml). It demonstrates that calcitriol suggestively downregulated the SK-BR-3 cell viability in a dose-dependent manner. These implications revealed calcitriol has shown better cytotoxicity and decreased the proliferation rate of breast cancer cells than neratinib.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Nagaraj B S
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Krishnan Namboori P K
- Amrita Molecular Modeling and Synthesis (AMMAS) Research lab, Amrita Vishwavidyapeetham, Coimbatore, Tamilnadu, India
| | - Krishna Swaroop Akey
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Sathianarayanan Sankaran
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India
| | - Rajesh Kumar Raman
- Department of Pharmaceutical Biotechnology, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Jawahar Natarajan
- Department of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Jubie Selvaraj
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
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9
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Halip L, Avram S, Curpan R, Borota A, Bora A, Bologa C, Oprea TI. Exploring DrugCentral: from molecular structures to clinical effects. J Comput Aided Mol Des 2023; 37:681-694. [PMID: 37707619 PMCID: PMC10692006 DOI: 10.1007/s10822-023-00529-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/14/2023] [Indexed: 09/15/2023]
Abstract
DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.
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Affiliation(s)
- Liliana Halip
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Sorin Avram
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ramona Curpan
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ana Borota
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Alina Bora
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Cristian Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.
- Expert Systems Inc, San Diego, CA, USA.
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10
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Zhang W, Maeser D, Lee A, Huang Y, Gruener RF, Abdelbar IG, Jena S, Patel AG, Huang RS. Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.29.564598. [PMID: 37961545 PMCID: PMC10634928 DOI: 10.1101/2023.10.29.564598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Robert F Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Israa G Abdelbar
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
- Clinical Pharmacy Practice Department, The British University in Egypt, El Sherouk, 11837, Egypt
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Anand G Patel
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN 38105
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105
| | - R Stephanie Huang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
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11
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Urakov AL, Shabanov PD. Physical-chemical repurposing of drugs. History of its formation in Russia. REVIEWS ON CLINICAL PHARMACOLOGY AND DRUG THERAPY 2023; 21:231-242. [DOI: 10.17816/rcf567782] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/26/2024]
Abstract
It is reported that the traditional scheme of finding and developing a new drug and conducting the whole complex of preclinical studies requires several thousand chemical compounds, hundreds of millions of US dollars and more than 12 years of work. It is shown that physicochemical pharmacology was born in Russia at the end of the 20th century, which in our days has been transformed into physicochemical repurposing of known medicines. The first successfully repurposed known drug was a solution of 4% potassium chloride, which had previously traditionally belonged to the group of macro- and microelements, used by intravenous injections to regulate acid-base balance and rhythmic activity of the heart. In 1983, it was stated that this medicinal solution, when heated to 3942C and applied topically by irrigation of the bleeding surface, could be classified as a vasoconstrictor and hemostatic drug. Hyperthermia was used as a physico-chemical reprofiling factor, which, according to the Arrhenius law, accelerated and intensified, on the one hand, the spastic action of K+ cations on the gaping blood vessels (formation of hyperkalium contracture in the smooth muscles of the vascular wall) and, on the other hand, the blood clotting process in the wound. In subsequent years, the promise of physicochemical repurposing of known drugs was shown on the example of water, hydrogen peroxide, sodium chloride and sodium bicarbonate by purposefully changing their temperature, acid, osmotic activity, as well as the amount and quality of gas content (passing). A chronology of the physicochemical repurposing of known drug solutions and tablets is described and the essence of such new groups of drugs as bleachers of bruises and pyolytics is given. It is shown that both groups of drugs were discovered in Russia and are intended for local use to bleach bruises (blood stains) and dissolve thick mucus, sputum, pus, blood clots, meconium and other dense biological tissues containing the enzyme catalase. It is pointed out that the advantage and at the same time the limitation of the known drugs repurposed according to this scheme is their local application, since their new pharmacological activity is caused mainly by the physical and chemical principle of action, which is manifested by local interaction with the selected area of the patients organism.
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Imakura T, Sato S, Koyama K, Ogawa H, Niimura T, Murakami K, Yamashita Y, Haji K, Naito N, Kagawa K, Kawano H, Zamami Y, Ishizawa K, Nishioka Y. A polo-like kinase inhibitor identified by computational repositioning attenuates pulmonary fibrosis. Respir Res 2023; 24:148. [PMID: 37269004 DOI: 10.1186/s12931-023-02446-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/08/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND Idiopathic pulmonary fibrosis (IPF) is a fatal fibrotic lung disease with few effective therapeutic options. Recently, drug repositioning, which involves identifying novel therapeutic potentials for existing drugs, has been popularized as a new approach for the development of novel therapeutic reagents. However, this approach has not yet been fully utilized in the field of pulmonary fibrosis. METHODS The present study identified novel therapeutic options for pulmonary fibrosis using a systematic computational approach for drug repositioning based on integration of public gene expression signatures of drug and diseases (in silico screening approach). RESULTS Among the top compounds predicted to be therapeutic for IPF by the in silico approach, we selected BI2536, a polo-like kinase (PLK) 1/2 inhibitor, as a candidate for treating pulmonary fibrosis using an in silico analysis. However, BI2536 accelerated mortality and weight loss rate in an experimental mouse model of pulmonary fibrosis. Because immunofluorescence staining revealed that PLK1 expression was dominant in myofibroblasts while PLK2 expression was dominant in lung epithelial cells, we next focused on the anti-fibrotic effect of the selective PLK1 inhibitor GSK461364. Consequently, GSK461364 attenuated pulmonary fibrosis with acceptable mortality and weight loss in mice. CONCLUSIONS These findings suggest that targeting PLK1 may be a novel therapeutic approach for pulmonary fibrosis by inhibiting lung fibroblast proliferation without affecting lung epithelial cells. In addition, while in silico screening is useful, it is essential to fully determine the biological activities of candidates by wet-lab validation studies.
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Affiliation(s)
- Takeshi Imakura
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Seidai Sato
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Kazuya Koyama
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hirohisa Ogawa
- Department of Pathology and Laboratory Medicine, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Takahiro Niimura
- Department of Clinical Pharmacology and Therapeutics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Kojin Murakami
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yuya Yamashita
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Keiko Haji
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Nobuhito Naito
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Kozo Kagawa
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hiroshi Kawano
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yoshito Zamami
- Department of Clinical Pharmacology and Therapeutics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
- Department of Pharmacy, Okayama University Hospital, Okayama, Japan
| | - Keisuke Ishizawa
- Department of Clinical Pharmacology and Therapeutics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Yasuhiko Nishioka
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.
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13
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Mteremko D, Chilongola J, Paluch AS, Chacha M. Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water. J Mol Graph Model 2023; 118:108348. [PMID: 36257147 DOI: 10.1016/j.jmgm.2022.108348] [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: 06/07/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 11/29/2022]
Abstract
A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.
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Affiliation(s)
- Denis Mteremko
- The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania.
| | - Jaffu Chilongola
- Kilimanjaro Christian Medical University College, Moshi, Tanzania
| | - Andrew S Paluch
- Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, OH, 45056, USA
| | - Musa Chacha
- The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania; Arusha Technical College, Arusha, Tanzania
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14
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Minadakis G, Tomazou M, Dietis N, Spyrou GM. Vir2Drug: a drug repurposing framework based on protein similarities between pathogens. Brief Bioinform 2022; 24:6895455. [PMID: 36513376 PMCID: PMC9851336 DOI: 10.1093/bib/bbac536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/25/2022] [Accepted: 11/08/2022] [Indexed: 12/15/2022] Open
Abstract
We draw from the assumption that similarities between pathogens at both pathogen protein and host protein level, may provide the appropriate framework to identify and rank candidate drugs to be used against a specific pathogen. Vir2Drug is a drug repurposing tool that uses network-based approaches to identify and rank candidate drugs for a specific pathogen, combining information obtained from: (a) ranked pathogen-to-pathogen networks based on protein similarities between pathogens, (b) taxonomy distance between pathogens and (c) drugs targeting specific pathogen's and host proteins. The underlying pathogen networks are used to screen drugs by means of specific methodologies that account for either the host or pathogen's protein targets. Vir2Drug is a useful and yet informative tool for drug repurposing against known or unknown pathogens especially in periods where the emergence for repurposed drugs plays significant role in handling viral outbreaks, until reaching a vaccine. The web tool is available at: https://bioinformatics.cing.ac.cy/vir2drug, https://vir2drug.cing-big.hpcf.cyi.ac.cy.
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Affiliation(s)
- George Minadakis
- Corresponding author: George Minadakis, Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, PO Box 23462, 1683 Nicosia, Cyprus. Tel.: +357-22-392852; Fax: +357-22-358238; E-mail:
| | - Marios Tomazou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus
- PO Box 23462, 1683 Nicosia, Cyprus,The Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371 Ayios Dometios, PO Box 23462, 1683 Nicosia, Cyprus
| | - Nikolas Dietis
- Medical School, University of Cyprus, Nicosia 1678, Cyprus
| | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus
- PO Box 23462, 1683 Nicosia, Cyprus,The Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371 Ayios Dometios, PO Box 23462, 1683 Nicosia, Cyprus
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15
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Basheeruddin M, Khan S, Ahmed N, Jamal S. Effect of pH on Diclofenac-Lysozyme Interaction: Structural and Functional Aspect. Front Mol Biosci 2022; 9:872905. [PMID: 35898307 PMCID: PMC9309515 DOI: 10.3389/fmolb.2022.872905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 06/03/2022] [Indexed: 11/15/2022] Open
Abstract
As a nonsteroidal antiinflammatory drug, diclofenac (DCF) is used in the treatment of a variety of human ailments. It has already been reported that the use of this class of drugs for a longer duration is associated with numerous side effects such as cardiovascular implications, reno-medullary complications, etc. In the present study, the effect of DCF on the structure, stability, and function of lysozyme was studied. The study was designed to examine the effect of DCF only at various pH values. Heat-induced denaturation of lysozyme was analyzed in the presence and absence of various molar concentrations of DCF at different pH values. The values of thermodynamic parameters, the midpoint of denaturation (T m), enthalpy change at T m (ΔH m), constant pressure heat capacity change (ΔC p), and Gibbs energy change at 25°C (ΔG D o), thus obtained under a given set of conditions (pH and molar concentration of DCF), demonstrated the following 1) DCF destabilized lysozyme with respect of T m and ΔG D o at all the pH values, 2) the magnitude of protein destabilization is lesser at acidic pH than at physiological pH, 3) structural changes in lysozyme are less projecting at pH 2.0 than at pH 7.0, and 4) quenching is observed at both pH values. Furthermore, the process of protein destabilization in the presence of DCF is entropically driven.
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Affiliation(s)
| | | | | | - Shazia Jamal
- School of Life Sciences, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
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16
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Chavda VP, Kapadia C, Soni S, Prajapati R, Chauhan SC, Yallapu MM, Apostolopoulos V. A global picture: therapeutic perspectives for COVID-19. Immunotherapy 2022; 14:351-371. [PMID: 35187954 PMCID: PMC8884157 DOI: 10.2217/imt-2021-0168] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 01/19/2022] [Indexed: 02/06/2023] Open
Abstract
The COVID-19 pandemic is a lethal virus outbreak by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has severely affected human lives and the global economy. The most vital part of the research and development of therapeutic agents is to design drug products to manage COVID-19 efficiently. Numerous attempts have been in place to determine the optimal drug dose and combination of drugs to treat the disease on a global scale. This article documents the information available on SARS-CoV-2 and its life cycle, which will aid in the development of the potential treatment options. A consolidated summary of several natural and repurposed drugs to manage COVID-19 is depicted with summary of current vaccine development. People with high age, comorbity and concomitant illnesses such as overweight, metabolic disorders, pulmonary disease, coronary heart disease, renal failure, fatty liver and neoplastic disorders are more prone to create serious COVID-19 and its consequences. This article also presents an overview of post-COVID-19 complications in patients.
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Affiliation(s)
- Vivek P Chavda
- Department of Pharmaceutics & Pharmaceutical Technology, L.M. College of Pharmacy, Ahmedabad, Gujarat, 380009, India
- Department of Pharmaceutics, K B Institute of Pharmaceutical Education & Research, Kadi Sarva Vishwavidhyalaya, Gandhinagar, Gujarat, 382023, India
| | - Carron Kapadia
- Department of Pharmaceutics & Pharmaceutical Technology, L.M. College of Pharmacy, Ahmedabad, Gujarat, 380009, India
| | - Shailvi Soni
- Department of Pharmaceutics & Pharmaceutical Technology, L.M. College of Pharmacy, Ahmedabad, Gujarat, 380009, India
| | - Riddhi Prajapati
- Department of Pharmaceutics & Pharmaceutical Technology, L.M. College of Pharmacy, Ahmedabad, Gujarat, 380009, India
| | - Subhash C Chauhan
- Department of Immunology & Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, TX 78503, USA
- South Texas Center of Excellence in Cancer Research, School of Medicine, University of Texas Rio Grande Valley, McAllen, TX 78503, USA
| | - Murali M Yallapu
- Department of Immunology & Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, TX 78503, USA
- South Texas Center of Excellence in Cancer Research, School of Medicine, University of Texas Rio Grande Valley, McAllen, TX 78503, USA
| | - Vasso Apostolopoulos
- Institute for Health & Sport, Victoria University, Melbourne, VIC, 3030, Australia
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17
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Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases. Int J Mol Sci 2022; 23:ijms23073870. [PMID: 35409235 PMCID: PMC8999005 DOI: 10.3390/ijms23073870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/15/2022] [Accepted: 03/15/2022] [Indexed: 02/04/2023] Open
Abstract
Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug–disease association prediction methods focused on data about drugs and diseases from multiple sources. However, they did not deeply integrate the neighbor topological information of drug and disease nodes from various meta-path perspectives. We propose a prediction method called NAPred to encode and integrate meta-path-level neighbor topologies, multiple kinds of drug attributes, and drug-related and disease-related similarities and associations. The multiple kinds of similarities between drugs reflect the degrees of similarity between two drugs from different perspectives. Therefore, we constructed three drug–disease heterogeneous networks according to these drug similarities, respectively. A learning framework based on fully connected neural networks and a convolutional neural network with an attention mechanism is proposed to learn information of the neighbor nodes of a pair of drug and disease nodes. The multiple neighbor sets composed of different kinds of nodes were formed respectively based on meta-paths with different semantics and different scales. We established the attention mechanisms at the neighbor-scale level and at the neighbor topology level to learn enhanced neighbor feature representations and enhanced neighbor topological representations. A convolutional-autoencoder-based module is proposed to encode the attributes of the drug–disease pair in three heterogeneous networks. Extensive experimental results indicated that NAPred outperformed several state-of-the-art methods for drug–disease association prediction, and the improved recall rates demonstrated that NAPred was able to retrieve more actual drug–disease associations from the top-ranked candidates. Case studies on five drugs further demonstrated the ability of NAPred to identify potential drug-related disease candidates.
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18
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Hamed AA, Fandy TE, Tkaczuk KL, Verspoor K, Lee BS. COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments. Pharmaceutics 2022; 14:567. [PMID: 35335943 PMCID: PMC8955179 DOI: 10.3390/pharmaceutics14030567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/25/2022] [Accepted: 02/25/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. METHODS We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. RESULT AND CONCLUSIONS Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.
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Affiliation(s)
- Ahmed Abdeen Hamed
- School of Cybersecurity, Data Science and Computing, Norwich University, Northfield, VT 05663, USA
- Sano Centre for Computational Medicine, 30-072 Kraków, Poland;
| | - Tamer E. Fandy
- Department of Pharmaceutical and Administrative Sciences, University of Charleston, Charleston, WV 25304, USA;
| | | | - Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne 3001, Australia;
- School of Computing and Information Systems, The University of Melbourne, Melbourne 3010, Australia
| | - Byung Suk Lee
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA;
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19
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Allegretti M, Cesta MC, Zippoli M, Beccari A, Talarico C, Mantelli F, Bucci EM, Scorzolini L, Nicastri E. Repurposing the estrogen receptor modulator raloxifene to treat SARS-CoV-2 infection. Cell Death Differ 2022; 29:156-166. [PMID: 34404919 PMCID: PMC8370058 DOI: 10.1038/s41418-021-00844-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 07/09/2021] [Accepted: 07/25/2021] [Indexed: 12/15/2022] Open
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates strategies to identify prophylactic and therapeutic drug candidates to enter rapid clinical development. This is particularly true, given the uncertainty about the endurance of the immune memory induced by both previous infections or vaccines, and given the fact that the eradication of SARS-CoV-2 might be challenging to reach, given the attack rate of the virus, which would require unusually high protection by a vaccine. Here, we show how raloxifene, a selective estrogen receptor modulator with anti-inflammatory and antiviral properties, emerges as an attractive candidate entering clinical trials to test its efficacy in early-stage treatment COVID-19 patients.
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Affiliation(s)
| | | | | | | | | | | | - Enrico M Bucci
- Sbarro Health Research Organization, Biology Department CFT, Temple University, Philadelphia, PA, USA
| | - Laura Scorzolini
- Lazzaro Spallanzani National Institute for Infectious Diseases, IRCCS, Rome, Italy
| | - Emanuele Nicastri
- Lazzaro Spallanzani National Institute for Infectious Diseases, IRCCS, Rome, Italy
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20
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Repurposing approved therapeutics for new indication: Addressing unmet needs in psoriasis treatment. CURRENT RESEARCH IN PHARMACOLOGY AND DRUG DISCOVERY 2021; 2:100041. [PMID: 34909670 PMCID: PMC8663928 DOI: 10.1016/j.crphar.2021.100041] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/04/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Psoriasis is a chronic inflammatory autoimmune condition manifested by the hyperproliferation of keratinocytes with buildup of inflammatory red patches and scales on skin surfaces. The available treatment options for the management of psoriasis have various drawbacks, and the clinical need for effective therapeutics for this disease remain unmet; therefore, the approaches of drug repurposing or drug repositioning could potentially be used for treating indications of psoriasis. The undiscovered potential of drug repurposing or repositioning compensates for the limitations and hurdles in drug discovery and drug development processes. Drugs initially approved for other indications, including anticancer, antidiabetic, antihypertensive, and anti-arthritic activities, are being investigated for their potential in psoriasis management as a new therapeutic indication by using repurposing strategies. This article envisages the potential of various therapeutics for the management of psoriasis. Psoriasis is an autoimmune inflammatory skin disorder with complex physiology. Conventional treatments for psoriasis cause severe adverse effects; therefore an unmet need remains for safer and more effective therapies for psoriasis. Various drugs that effectively decrease the inflammation and proliferation of skin cells can be repurposed for the management of psoriasis. Repurposed drugs provide various incentives to the pharmaceutical industry.
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21
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Gao L, Cui H, Zhang T, Sheng N, Xuan P. Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets. Brief Bioinform 2021; 23:6446271. [PMID: 34850815 DOI: 10.1093/bib/bbab467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/23/2021] [Accepted: 10/13/2021] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION The development process of a new drug is time-consuming and costly. Thus, identifying new uses for approved drugs, named drug repositioning, is helpful for speeding up the drug development process and reducing development costs. Existing drug-related disease prediction methods mainly focus on single or multiple drug-disease heterogeneous networks. However, heterogeneous networks, and drug subnets and disease subnet contained in heterogeneous networks cover the common topology information between drug and disease nodes, the specific information between drug nodes and the specific information between disease nodes, respectively. RESULTS We design a novel model, CTST, to extract and integrate common and specific topologies in multiple heterogeneous networks and subnets. Multiple heterogeneous networks composed of drug and disease nodes are established to integrate multiple kinds of similarities and associations among drug and disease nodes. These heterogeneous networks contain multiple drug subnets and a disease subnet. For multiple heterogeneous networks and subnets, we then define the common and specific representations of drug and disease nodes. The common representations of drug and disease nodes are encoded by a graph convolutional autoencoder with sharing parameters and they integrate the topological relationships of all nodes in heterogeneous networks. The specific representations of nodes are learned by specific graph convolutional autoencoders, respectively, and they fuse the topology and attributes of the nodes in each subnet. We then propose attention mechanisms at common representation level and specific representation level to learn more informative common and specific representations, respectively. Finally, an integration module with representation feature level attention is built to adaptively integrate these two representations for final association prediction. Extensive experimental results confirm the effectiveness of CTST. Comparison with six latest methods and case studies on five drugs further verify CTST has the ability to discover potential candidate diseases.
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Affiliation(s)
- Ling Gao
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne 3083, Australia
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin 150080, China
| | - Nan Sheng
- College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
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22
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Gupta RK, Nwachuku EL, Zusman BE, Jha RM, Puccio AM. Drug repurposing for COVID-19 based on an integrative meta-analysis of SARS-CoV-2 induced gene signature in human airway epithelium. PLoS One 2021; 16:e0257784. [PMID: 34582497 PMCID: PMC8478222 DOI: 10.1371/journal.pone.0257784] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 09/09/2021] [Indexed: 12/24/2022] Open
Abstract
Drug repurposing has the potential to bring existing de-risked drugs for effective intervention in an ongoing pandemic-COVID-19 that has infected over 131 million, with 2.8 million people succumbing to the illness globally (as of April 04, 2021). We have used a novel `gene signature'-based drug repositioning strategy by applying widely accepted gene ranking algorithms to prioritize the FDA approved or under trial drugs. We mined publically available RNA sequencing (RNA-Seq) data using CLC Genomics Workbench 20 (QIAGEN) and identified 283 differentially expressed genes (FDR<0.05, log2FC>1) after a meta-analysis of three independent studies which were based on severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection in primary human airway epithelial cells. Ingenuity Pathway Analysis (IPA) revealed that SARS-CoV-2 activated key canonical pathways and gene networks that intricately regulate general anti-viral as well as specific inflammatory pathways. Drug database, extracted from the Metacore and IPA, identified 15 drug targets (with information on COVID-19 pathogenesis) with 46 existing drugs as potential-novel candidates for repurposing for COVID-19 treatment. We found 35 novel drugs that inhibit targets (ALPL, CXCL8, and IL6) already in clinical trials for COVID-19. Also, we found 6 existing drugs against 4 potential anti-COVID-19 targets (CCL20, CSF3, CXCL1, CXCL10) that might have novel anti-COVID-19 indications. Finally, these drug targets were computationally prioritized based on gene ranking algorithms, which revealed CXCL10 as the common and strongest candidate with 2 existing drugs. Furthermore, the list of 283 SARS-CoV-2-associated proteins could be valuable not only as anti-COVID-19 targets but also useful for COVID-19 biomarker development.
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Affiliation(s)
- Rajaneesh K. Gupta
- Department of Neurosurgery, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Enyinna L. Nwachuku
- Department of Neurosurgery, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Benjamin E. Zusman
- Department of Neurosurgery, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Ruchira M. Jha
- Departments of Neurology, Neurobiology, Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Ava M. Puccio
- Department of Neurosurgery, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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Wang X, Liu M, Zhang Y, He S, Qin C, Li Y, Lu T. Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery. Brief Bioinform 2021; 22:6342939. [PMID: 34368838 DOI: 10.1093/bib/bbab289] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 07/03/2021] [Accepted: 07/06/2021] [Indexed: 01/17/2023] Open
Abstract
The advent of large-scale biomedical data and computational algorithms provides new opportunities for drug repurposing and discovery. It is of great interest to find an appropriate data representation and modeling method to facilitate these studies. The anatomical therapeutic chemical (ATC) classification system, proposed by the World Health Organization (WHO), is an essential source of information for drug repurposing and discovery. Besides, computational methods are applied to predict drug ATC classification. We conducted a systematic review of ATC computational prediction studies and revealed the differences in data sets, data representation, algorithm approaches, and evaluation metrics. We then proposed a deep fusion learning (DFL) framework to optimize the ATC prediction model, namely DeepATC. The methods based on graph convolutional network, inferring biological network and multimodel attentive fusion network were applied in DeepATC to extract the molecular topological information and low-dimensional representation from the molecular graph and heterogeneous biological networks. The results indicated that DeepATC achieved superior model performance with area under the curve (AUC) value at 0.968. Furthermore, the DFL framework was performed for the transcriptome data-based ATC prediction, as well as another independent task that is significantly relevant to drug discovery, namely drug-target interaction. The DFL-based model achieved excellent performance in the above-extended validation task, suggesting that the idea of aggregating the heterogeneous biological network and node's (molecule or protein) self-topological features will bring inspiration for broader drug repurposing and discovery research.
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Affiliation(s)
- Xiting Wang
- Life Science School, Beijing University of Chinese Medicine, Beijing, China
| | - Meng Liu
- Chinese Medicine School, Beijing University of Chinese Medicine, Beijing, China
| | - Yiling Zhang
- Beijing University of Chinese Medicine, Beijing, China
| | - Shuangshuang He
- Chinese Medicine School, Beijing University of Chinese Medicine, Beijing, China
| | - Caimeng Qin
- School of Life Sciences, Beijing University of Chinese Medicine and Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Yu Li
- Chinese Medicine School, Beijing University of Chinese Medicine, Beijing, China
| | - Tao Lu
- Integrative Medicine Center in School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
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24
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Jiang Z, Xu J, Yan A, Wang L. A comprehensive comparative assessment of 3D molecular similarity tools in ligand-based virtual screening. Brief Bioinform 2021; 22:6304389. [PMID: 34151363 DOI: 10.1093/bib/bbab231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 05/10/2021] [Accepted: 05/27/2021] [Indexed: 12/19/2022] Open
Abstract
Three-dimensional (3D) molecular similarity, one major ligand-based virtual screening (VS) method, has been widely used in the drug discovery process. A variety of 3D molecular similarity tools have been developed in recent decades. In this study, we assessed a panel of 15 3D molecular similarity programs against the DUD-E and LIT-PCBA datasets, including commercial ROCS and Phase, in terms of screening power and scaffold-hopping power. The results revealed that (1) SHAFTS, LS-align, Phase Shape_Pharm and LIGSIFT showed the best VS capability in terms of screening power. Some 3D similarity tools available to academia can yield relatively better VS performance than commercial ROCS and Phase software. (2) Current 3D similarity VS tools exhibit a considerable ability to capture actives with new chemotypes in terms of scaffold hopping. (3) Multiple conformers relative to single conformations will generally improve VS performance for most 3D similarity tools, with marginal improvement observed in area under the receiving operator characteristic curve values, enrichment factor in the top 1% and hit rate in the top 1% values showed larger improvement. Moreover, redundancy and complementarity analyses of hit lists from different query seeds and different 3D similarity VS tools showed that the combination of different query seeds and/or different 3D similarity tools in VS campaigns retrieved more (and more diverse) active molecules. These findings provide useful information for guiding choices of the optimal 3D molecular similarity tools for VS practices and designing possible combination strategies to discover more diverse active compounds.
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Affiliation(s)
- Zhenla Jiang
- South China University of Technology, Guangzhou 510006, China
| | - Jianrong Xu
- Shanghai Jiao Tong University School of Medicine and Shanghai University of Traditional Chinese Medicine, Guangzhou 510006, China
| | - Aixia Yan
- Beijing University of Chemical Technology, Guangzhou 510006, China
| | - Ling Wang
- South China University of Technology, Guangzhou 510006, China
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25
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Xuan P, Gao L, Sheng N, Zhang T, Nakaguchi T. Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations. IEEE J Biomed Health Inform 2021; 25:1793-1804. [PMID: 33216722 DOI: 10.1109/jbhi.2020.3039502] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods focused on the multi-source data related to drugs and diseases to predict the candidate associations between drugs and diseases. There are multiple kinds of similarities between drugs, and these similarities reflect how similar two drugs are from the different views, whereas most of the previous methods failed to deeply integrate these similarities. In addition, the topology structures of the multiple drug-disease heterogeneous networks constructed by using the different kinds of drug similarities are not fully exploited. We therefore propose GFPred, a method based on a graph convolutional autoencoder and a fully-connected autoencoder with an attention mechanism, to predict drug-related diseases. GFPred integrates drug-disease associations, disease similarities, three kinds of drug similarities and attributes of the drug nodes. Three drug-disease heterogeneous networks are constructed based on the different kinds of drug similarities. We construct a graph convolutional autoencoder module, and integrate the attributes of the drug and disease nodes in each network to learn the topology representations of each drug node and disease node. As the different kinds of drug attributes contribute differently to the prediction of drug-disease associations, we construct an attribute-level attention mechanism. A fully-connected autoencoder module is established to learn the attribute representations of the drug and disease nodes. Finally, the original features of the drug-disease node pairs are also important auxiliary information for their association prediction. A combined strategy based on a convolutional neural network is proposed to fully integrate the topology representations, the attribute representations, and the original features of the drug-disease pairs. The ablation studies showed the contributions of data related to three types of drug attributes. Comparison with other methods confirmed that GFPred achieved better performance than several state-of-the-art prediction methods. In particular, case studies confirmed that GFPred is able to retrieve more actual drug-disease associations in the top k part of the prediction results. It is helpful for biologists to discover real associations by wet-lab experiments.
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26
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Han Y, Wang Z, Ren J, Wei Z, Li J. Potential inhibitors for the novel coronavirus (SARS-CoV-2). Brief Bioinform 2021; 22:1225-1231. [PMID: 32942296 PMCID: PMC7543260 DOI: 10.1093/bib/bbaa209] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 07/27/2020] [Accepted: 08/12/2020] [Indexed: 01/03/2023] Open
Abstract
The lack of a vaccine or any effective treatment for the aggressive novel coronavirus disease (COVID-19) has created a sense of urgency for the discovery of effective drugs. Several repurposing pharmaceutical candidates have been reported or envisaged to inhibit the emerging infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but their binding sites, binding affinities and inhibitory mechanisms are still unavailable. In this study, we use the ligand-protein docking program and molecular dynamic simulation to ab initio investigate the binding mechanism and inhibitory ability of seven clinically approved drugs (Chloroquine, Hydroxychloroquine, Remdesivir, Ritonavir, Beclabuvir, Indinavir and Favipiravir) and a recently designed α-ketoamide inhibitor (13b) at the molecular level. The results suggest that Chloroquine has the strongest binding affinity with 3CL hydrolase (Mpro) among clinically approved drugs, indicating its effective inhibitory ability for SARS-CoV-2. However, the newly designed inhibitor 13b shows potentially improved inhibition efficiency with larger binding energy compared with Chloroquine. We further calculate the important binding site residues at the active site and demonstrate that the MET 165 and HIE 163 contribute the most for 13b, while the MET 165 and GLN 189 for Chloroquine, based on residual energy decomposition analysis. The proposed work offers a higher research priority for 13b to treat the infection of SARS-CoV-2 and provides theoretical basis for further design of effective drug molecules with stronger inhibition.
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Affiliation(s)
| | | | - Jiahao Ren
- Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine
| | - Zhiyun Wei
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication; Key Laboratory for Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University
| | - Jinjin Li
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
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27
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Kumar S, Singh B, Kumari P, Kumar PV, Agnihotri G, Khan S, Kant Beuria T, Syed GH, Dixit A. Identification of multipotent drugs for COVID-19 therapeutics with the evaluation of their SARS-CoV2 inhibitory activity. Comput Struct Biotechnol J 2021; 19:1998-2017. [PMID: 33841751 PMCID: PMC8025584 DOI: 10.1016/j.csbj.2021.04.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/04/2021] [Accepted: 04/04/2021] [Indexed: 12/12/2022] Open
Abstract
The SARS-CoV2 is a highly contagious pathogen that causes COVID-19 disease. It has affected millions of people globally with an average lethality of ~3%. There is an urgent need of drugs for the treatment of COVID-19. In the current studies, we have used bioinformatics techniques to screen the FDA approved drugs against nine SARS-CoV2 proteins to identify drugs for repurposing. Additionally, we analyzed if the identified molecules can also affect the human proteins whose expression in lung changed during SARS-CoV2 infection. Targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 74 molecules that can bind to various SARS-CoV2 and human host proteins. We experimentally validated our in-silico predictions using vero E6 cells infected with SARS-CoV2 virus. Interestingly, many of our predicted molecules viz. capreomycin, celecoxib, mefloquine, montelukast, and nebivolol showed good activity (IC50) against SARS-CoV2. We hope that these studies may help in the development of new therapeutic options for the treatment of COVID-19.
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Affiliation(s)
- Sugandh Kumar
- Institute of Life Science, Nalco Square, Bhubaneswar, Odisha 751023, India
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT) University, Bhubaneswar, Odisha 751024, India
| | - Bharati Singh
- Institute of Life Science, Nalco Square, Bhubaneswar, Odisha 751023, India
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT) University, Bhubaneswar, Odisha 751024, India
| | - Pratima Kumari
- Institute of Life Science, Nalco Square, Bhubaneswar, Odisha 751023, India
- Regional Centre for Biotechnology (RCB), 3rd Milestone, Faridabad-Gurgaon, Haryana 121001, India
| | - Preethy V. Kumar
- Institute of Life Science, Nalco Square, Bhubaneswar, Odisha 751023, India
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT) University, Bhubaneswar, Odisha 751024, India
| | - Geetanjali Agnihotri
- School of Chemical Technology, Kalinga Institute of Industrial Technology (KIIT) University, Bhubaneswar, Odisha 751024, India
| | - Shaheerah Khan
- Institute of Life Science, Nalco Square, Bhubaneswar, Odisha 751023, India
- Regional Centre for Biotechnology (RCB), 3rd Milestone, Faridabad-Gurgaon, Haryana 121001, India
| | - Tushar Kant Beuria
- Institute of Life Science, Nalco Square, Bhubaneswar, Odisha 751023, India
| | - Gulam Hussain Syed
- Institute of Life Science, Nalco Square, Bhubaneswar, Odisha 751023, India
| | - Anshuman Dixit
- Institute of Life Science, Nalco Square, Bhubaneswar, Odisha 751023, India
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28
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Fang J, Pan Z, Yu H, Yang S, Hu X, Lu X, Li L. Regulatory Master Genes Identification and Drug Repositioning by Integrative mRNA-miRNA Network Analysis for Acute Type A Aortic Dissection. Front Pharmacol 2021; 11:575765. [PMID: 33551796 PMCID: PMC7861055 DOI: 10.3389/fphar.2020.575765] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 12/21/2020] [Indexed: 12/21/2022] Open
Abstract
Acute type A aortic dissection (ATAAD) is a life-threatening disease. The understanding of its pathogenesis and treatment approaches remains unclear. In the present work, differentially expressed genes (DEGs) from two ATAAD datasets GSE52093 and GSE98770 were filtered. Transcription factor TEAD4 was predicted as a key modulator in protein-protein interaction (PPI) network. Weighted correlation network analysis (WGCNA) identified five modules in GSE52093 and four modules in GSE98770 were highly correlated with ATAAD. 71 consensus DEGs of highly correlated modules were defined and functionally annotated. L1000CDS2 was executed to predict drug for drug repositioning in ATAAD treatment. Eight compounds were filtered as potential drugs. Integrative analysis revealed the interaction network of five differentially expressed miRNA and 16 targeted DEGs. Finally, master DEGs were validated in human ATAAD samples and AD cell model in vitro. TIMP3 and SORBS1 were downregulated in ATAAD samples and AD cell model, while PRUNE2 only decreased in vitro. Calcium channel blocker and glucocorticoid receptor agonist might be potential drugs for ATAAD. The present study offers potential targets and underlying molecular mechanisms ATAAD pathogenesis, prevention and drug discovery.
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Affiliation(s)
- Junjun Fang
- Surgical Intensive Critical Care Unit, The First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, China
| | - Zongfu Pan
- Department of Pharmacy, Zhejiang Provincial People's Hospital, Zhejiang, China
| | - Hao Yu
- Thoracic Surgery, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Zhejiang, China
| | - Si Yang
- Department of Clinical Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, China
| | - Xiaoping Hu
- Department of Pharmacy, Zhejiang Provincial People's Hospital, Zhejiang, China
| | - Xiaoyang Lu
- Department of Clinical Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, China
| | - Lu Li
- Department of Clinical Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, China
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29
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Yang M, Huang L, Xu Y, Lu C, Wang J. Heterogeneous graph inference with matrix completion for computational drug repositioning. Bioinformatics 2020; 36:5456-5464. [PMID: 33331887 DOI: 10.1093/bioinformatics/btaa1024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 11/23/2020] [Accepted: 11/26/2020] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Emerging evidence presents that traditional drug discovery experiment is time-consuming and high costs. Computational drug repositioning plays a critical role in saving time and resources for drug research and discovery. Therefore, developing more accurate and efficient approaches is imperative. Heterogeneous graph inference is a classical method in computational drug repositioning, which not only has high convergence precision, but also has fast convergence speed. However, the method has not fully considered the sparsity of heterogeneous association network. In addition, rough similarity measure can reduce the performance in identifying drug-associated indications. RESULTS In this article, we propose a heterogeneous graph inference with matrix completion (HGIMC) method to predict potential indications for approved and novel drugs. First, we use a bounded matrix completion (BMC) model to prefill a part of the missing entries in original drug-disease association matrix. This step can add more positive and formative drug-disease edges between drug network and disease network. Second, Gaussian radial basis function (GRB) is employed to improve the drug and disease similarities since the performance of heterogeneous graph inference more relies on similarity measures. Next, based on the updated drug-disease associations and new similarity measures of drug and disease, we construct a novel heterogeneous drug-disease network. Finally, HGIMC utilizes the heterogeneous network to infer the scores of unknown association pairs, and then recommend the promising indications for drugs. To evaluate the performance of our method, HGIMC is compared with five state-of-the-art approaches of drug repositioning in the 10-fold cross-validation and de novo tests. As the numerical results shown, HGIMC not only achieves a better prediction performance, but also has an excellent computation efficiency. In addition, cases studies also confirm the effectiveness of our method in practical application. AVAILABILITY The HGIMC software is freely available at https://github.com/BioinformaticsCSU/HGIMC, https://hub.docker.com/repository/docker/yangmy84/hgimc, and http://doi.org/10.5281/zenodo.4285640. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mengyun Yang
- The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, P.R.China.,School of Science, Shaoyang University, Shaoyang, P.R.China
| | - Lan Huang
- The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, P.R.China
| | - Yunpei Xu
- The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, P.R.China
| | - Chengqian Lu
- The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, P.R.China
| | - Jianxin Wang
- The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, P.R.China
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30
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Tuerkova A, Zdrazil B. A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19. J Cheminform 2020; 12:71. [PMID: 33250934 PMCID: PMC7686838 DOI: 10.1186/s13321-020-00474-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/09/2020] [Indexed: 01/01/2023] Open
Abstract
Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces (APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, we present a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basis of two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeted download of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data for GLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.
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Affiliation(s)
- Alzbeta Tuerkova
- Department of Pharmaceutical Chemistry, Division of Drug Design and Medicinal Chemistry, University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Barbara Zdrazil
- Department of Pharmaceutical Chemistry, Division of Drug Design and Medicinal Chemistry, University of Vienna, Althanstraße 14, 1090 Vienna, Austria
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31
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Kumar N, Gahlawat A, Kumar RN, Singh YP, Modi G, Garg P. Drug repurposing for Alzheimer’s disease: in silico and in vitro investigation of FDA-approved drugs as acetylcholinesterase inhibitors. J Biomol Struct Dyn 2020; 40:2878-2892. [DOI: 10.1080/07391102.2020.1844054] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Navneet Kumar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Mohali, Punjab, India
| | - Anuj Gahlawat
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Mohali, Punjab, India
| | - Rajaram Naresh Kumar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Mohali, Punjab, India
| | - Yash Pal Singh
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, India
| | - Gyan Modi
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Mohali, Punjab, India
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32
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Feldmann C, Yonchev D, Stumpfe D, Bajorath J. Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity. Mol Pharm 2020; 17:4652-4666. [PMID: 33151084 DOI: 10.1021/acs.molpharmaceut.0c00901] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alters ADMET characteristics. Features distinguishing between compounds with single- and multitarget activity are currently only little understood. On the basis of systematic data analysis, we have assembled large sets of promiscuous compounds with activity against related or functionally distinct targets and the corresponding compounds with single-target activity. Machine learning predicted promiscuous compounds with surprisingly high accuracy. Molecular similarity analysis combined with control calculations under varying conditions revealed that accurate predictions were largely determined by structural nearest-neighbor relationships between compounds from different classes. We also found that large proportions of promiscuous compounds with activity against related or unrelated targets and corresponding single-target compounds formed analog series with distinct chemical space coverage, which further rationalized the predictions. Moreover, compounds with activity against proteins from functionally distinct classes were often active against unique targets that were not covered by other promiscuous compounds. The results of our analysis revealed that nearest-neighbor effects determined the prediction of promiscuous compounds and that preferential partitioning of compounds with single- and multitarget activity into structurally distinct analog series was responsible for such effects, hence providing a rationale for the presence of different structure-promiscuity relationships.
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Affiliation(s)
- Christian Feldmann
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany
| | - Dimitar Yonchev
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany
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33
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Ávalos-Moreno M, López-Tejada A, Blaya-Cánovas JL, Cara-Lupiañez FE, González-González A, Lorente JA, Sánchez-Rovira P, Granados-Principal S. Drug Repurposing for Triple-Negative Breast Cancer. J Pers Med 2020; 10:E200. [PMID: 33138097 PMCID: PMC7711505 DOI: 10.3390/jpm10040200] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 10/20/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer which presents a high rate of relapse, metastasis, and mortality. Nowadays, the absence of approved specific targeted therapies to eradicate TNBC remains one of the main challenges in clinical practice. Drug discovery is a long and costly process that can be dramatically improved by drug repurposing, which identifies new uses for existing drugs, both approved and investigational. Drug repositioning benefits from improvements in computational methods related to chemoinformatics, genomics, and systems biology. To the best of our knowledge, we propose a novel and inclusive classification of those approaches whereby drug repurposing can be achieved in silico: structure-based, transcriptional signatures-based, biological networks-based, and data-mining-based drug repositioning. This review specially emphasizes the most relevant research, both at preclinical and clinical settings, aimed at repurposing pre-existing drugs to treat TNBC on the basis of molecular mechanisms and signaling pathways such as androgen receptor, adrenergic receptor, STAT3, nitric oxide synthase, or AXL. Finally, because of the ability and relevance of cancer stem cells (CSCs) to drive tumor aggressiveness and poor clinical outcome, we also focus on those molecules repurposed to specifically target this cell population to tackle recurrence and metastases associated with the progression of TNBC.
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Affiliation(s)
- Marta Ávalos-Moreno
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 18016 Granada, Spain; (M.Á.-M.); (A.L.-T.); (J.L.B.-C.); (F.E.C.-L.); (A.G.-G.); (J.A.L.)
| | - Araceli López-Tejada
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 18016 Granada, Spain; (M.Á.-M.); (A.L.-T.); (J.L.B.-C.); (F.E.C.-L.); (A.G.-G.); (J.A.L.)
- UGC de Oncología Médica, Complejo Hospitalario de Jaén, 23007 Jaén, Spain;
| | - Jose L. Blaya-Cánovas
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 18016 Granada, Spain; (M.Á.-M.); (A.L.-T.); (J.L.B.-C.); (F.E.C.-L.); (A.G.-G.); (J.A.L.)
- UGC de Oncología Médica, Complejo Hospitalario de Jaén, 23007 Jaén, Spain;
| | - Francisca E. Cara-Lupiañez
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 18016 Granada, Spain; (M.Á.-M.); (A.L.-T.); (J.L.B.-C.); (F.E.C.-L.); (A.G.-G.); (J.A.L.)
- UGC de Oncología Médica, Complejo Hospitalario de Jaén, 23007 Jaén, Spain;
| | - Adrián González-González
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 18016 Granada, Spain; (M.Á.-M.); (A.L.-T.); (J.L.B.-C.); (F.E.C.-L.); (A.G.-G.); (J.A.L.)
- UGC de Oncología Médica, Complejo Hospitalario de Jaén, 23007 Jaén, Spain;
| | - Jose A. Lorente
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 18016 Granada, Spain; (M.Á.-M.); (A.L.-T.); (J.L.B.-C.); (F.E.C.-L.); (A.G.-G.); (J.A.L.)
- Department of Legal Medicine, School of Medicine—PTS—University of Granada, 18016 Granada, Spain
| | | | - Sergio Granados-Principal
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 18016 Granada, Spain; (M.Á.-M.); (A.L.-T.); (J.L.B.-C.); (F.E.C.-L.); (A.G.-G.); (J.A.L.)
- UGC de Oncología Médica, Complejo Hospitalario de Jaén, 23007 Jaén, Spain;
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Fahimian G, Zahiri J, Arab SS, Sajedi RH. RepCOOL: computational drug repositioning via integrating heterogeneous biological networks. J Transl Med 2020; 18:375. [PMID: 33008415 PMCID: PMC7532104 DOI: 10.1186/s12967-020-02541-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Accepted: 09/21/2020] [Indexed: 01/07/2023] Open
Abstract
Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.
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Affiliation(s)
- Ghazale Fahimian
- Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Javad Zahiri
- Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
| | - Seyed Shahriar Arab
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Reza H Sajedi
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
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Biophysical Insight into the Interaction of Human Lysozyme with Anticancer Drug Anastrozole: A Multitechnique Approach. ScientificWorldJournal 2020; 2020:8363685. [PMID: 32908463 PMCID: PMC7468670 DOI: 10.1155/2020/8363685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 06/19/2020] [Indexed: 12/14/2022] Open
Abstract
In the present study, we employ fluorescence spectroscopy, dynamic light scattering, and molecular docking methods. Binding of anticancer drug anastrozole with human lysozyme (HL) is studied. Binding of anastrozole to HL is moderate but spontaneous. There is anastrozole persuaded hydrodynamic change in HL, leading to molecular compaction. Binding of anastrozole to HL also decreased in vitro lytic activity of HL. Molecular docking results suggest the electrostatic interactions and van der Waals forces played key role in binding interaction of anastrozole near the catalytic site. Binding interaction of anastrozole to proteins other than major transport proteins in blood can significantly affect pharmacokinetics of this molecule. Hence, rationalizing drug dosage is important. This study also points to unrelated effects that small molecules bring in the body that are considerable and need thorough investigation.
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Mazzolari A, Gervasoni S, Pedretti A, Fumagalli L, Matucci R, Vistoli G. Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays. Int J Mol Sci 2020; 21:ijms21175961. [PMID: 32825082 PMCID: PMC7503225 DOI: 10.3390/ijms21175961] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 08/14/2020] [Accepted: 08/16/2020] [Indexed: 12/12/2022] Open
Abstract
Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM2 muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM2. Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM2 ligands. Overall, the study enhances the understanding of the factors governing the hM2 allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties.
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Affiliation(s)
- Angelica Mazzolari
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; (A.M.); (S.G.); (A.P.); (L.F.)
| | - Silvia Gervasoni
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; (A.M.); (S.G.); (A.P.); (L.F.)
| | - Alessandro Pedretti
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; (A.M.); (S.G.); (A.P.); (L.F.)
| | - Laura Fumagalli
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; (A.M.); (S.G.); (A.P.); (L.F.)
| | - Rosanna Matucci
- Dipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), Sezione di Farmacologia e Tossicologia, Università degli Studi di Firenze, Viale Pieraccini 6, 50139 Firenze, Italy;
| | - Giulio Vistoli
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; (A.M.); (S.G.); (A.P.); (L.F.)
- Correspondence: ; Tel.: +39-02-5019349
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Abstract
The current global pandemic COVID-19 caused by the SARS-CoV-2 virus has already inflicted insurmountable damage both to the human lives and global economy. There is an immediate need for identification of effective drugs to contain the disastrous virus outbreak. Global efforts are already underway at a war footing to identify the best drug combination to address the disease. In this review, an attempt has been made to understand the SARS-CoV-2 life cycle, and based on this information potential druggable targets against SARS-CoV-2 are summarized. Also, the strategies for ongoing and future drug discovery against the SARS-CoV-2 virus are outlined. Given the urgency to find a definitive cure, ongoing drug repurposing efforts being carried out by various organizations are also described. The unprecedented crisis requires extraordinary efforts from the scientific community to effectively address the issue and prevent further loss of human lives and health.
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Affiliation(s)
- Ambrish Saxena
- Indian Institute of Technology Tirupati, Tirupati, India
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38
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Ibrahim MAA, Abdelrahman AHM, Hegazy MEF. In-silico drug repurposing and molecular dynamics puzzled out potential SARS-CoV-2 main protease inhibitors. J Biomol Struct Dyn 2020; 39:5756-5767. [PMID: 32684114 PMCID: PMC7441803 DOI: 10.1080/07391102.2020.1791958] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Herein, the DrugBank database which contains 10,036 approved and investigational drugs
was explored deeply for potential drugs that target SARS-CoV-2 main protease
(Mpro). Filtration process of the database was conducted using three levels
of accuracy for molecular docking calculations. The top 35 drugs with docking scores
> −11.0 kcal/mol were then subjected to 10 ns molecular dynamics (MD) simulations
followed by molecular mechanics–generalized Born surface area (MM-GBSA) binding energy
calculations. The results showed that DB02388 and Cobicistat (DB09065) exhibited potential
binding affinities towards Mpro over 100 ns MD simulations, with binding energy
values of −49.67 and −46.60 kcal/mol, respectively. Binding energy and structural analyses
demonstrated the higher stability of DB02388 over Cobicistat. The potency of DB02388 and
Cobicistat is attributed to their abilities to form several hydrogen bonds with the
essential amino acids inside the active site of Mpro. Compared to DB02388 and
Cobicistat, Darunavir showed a much lower binding affinity of −34.83 kcal/mol. The present
study highlights the potentiality of DB02388 and Cobicistat as anti-COVID-19 drugs for
clinical trials. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mahmoud A A Ibrahim
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia, Egypt
| | - Alaa H M Abdelrahman
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia, Egypt
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Bharadwaj S, Lee KE, Dwivedi VD, Kang SG. Computational insights into tetracyclines as inhibitors against SARS-CoV-2 M pro via combinatorial molecular simulation calculations. Life Sci 2020; 257:118080. [PMID: 32653520 PMCID: PMC7347340 DOI: 10.1016/j.lfs.2020.118080] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/06/2020] [Accepted: 07/07/2020] [Indexed: 01/17/2023]
Abstract
The COVID-19 pandemic raised by SARS-CoV-2 is a public health emergency. However, lack of antiviral drugs and vaccine against human coronaviruses demands a concerted approach to challenge the SARS-CoV-2 infection. Under limited resource and urgency, combinatorial computational approaches to identify the potential inhibitor from known drugs could be applied against risen COVID-19 pandemic. Thereof, this study attempted to purpose the potent inhibitors from the approved drug pool against SARS-CoV-2 main protease (Mpro). To circumvent the issue of lead compound from available drugs as antivirals, antibiotics with broad spectrum of viral activity, i.e. doxycycline, tetracycline, demeclocycline, and minocycline were chosen for molecular simulation analysis against native ligand N3 inhibitor in SARS-CoV-2 Mpro crystal structure. Molecular docking simulation predicted the docking score >−7 kcal/mol with significant intermolecular interaction at the catalytic dyad (His41 and Cys145) and other essential substrate binding residues of SARS-CoV-2 Mpro. The best ligand conformations were further studied for complex stability and intermolecular interaction profiling with respect to time under 100 ns classical molecular dynamics simulation, established the significant stability and interactions of selected antibiotics by comparison to N3 inhibitor. Based on combinatorial molecular simulation analysis, doxycycline and minocycline were selected as potent inhibitor against SARS-CoV-2 Mpro which can used in combinational therapy against SARS-CoV-2 infection.
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Affiliation(s)
- Shiv Bharadwaj
- Department of Biotechnology, Institute of Biotechnology, College of Life and Applied Sciences, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Republic of Korea
| | - Kyung Eun Lee
- Department of Biotechnology, Institute of Biotechnology, College of Life and Applied Sciences, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Republic of Korea
| | - Vivek Dhar Dwivedi
- Centre for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.
| | - Sang Gu Kang
- Department of Biotechnology, Institute of Biotechnology, College of Life and Applied Sciences, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Republic of Korea.
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Moustaqil M, Gambin Y, Sierecki E. Biophysical Techniques for Target Validation and Drug Discovery in Transcription-Targeted Therapy. Int J Mol Sci 2020; 21:E2301. [PMID: 32225120 PMCID: PMC7178067 DOI: 10.3390/ijms21072301] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/13/2020] [Accepted: 03/13/2020] [Indexed: 01/10/2023] Open
Abstract
In the post-genome era, pathologies become associated with specific gene expression profiles and defined molecular lesions can be identified. The traditional therapeutic strategy is to block the identified aberrant biochemical activity. However, an attractive alternative could aim at antagonizing key transcriptional events underlying the pathogenesis, thereby blocking the consequences of a disorder, irrespective of the original biochemical nature. This approach, called transcription therapy, is now rendered possible by major advances in biophysical technologies. In the last two decades, techniques have evolved to become key components of drug discovery platforms, within pharmaceutical companies as well as academic laboratories. This review outlines the current biophysical strategies for transcription manipulation and provides examples of successful applications. It also provides insights into the future development of biophysical methods in drug discovery and personalized medicine.
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Affiliation(s)
- Mehdi Moustaqil
- EMBL Australia Node in Single Molecule Science and School of Medical Sciences, UNSW Sydney, NSW 2052, Australia;
| | | | - Emma Sierecki
- EMBL Australia Node in Single Molecule Science and School of Medical Sciences, UNSW Sydney, NSW 2052, Australia;
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Olgen S. A Prospective Overview of Drug Repurposing in Drug Discovery and Development. Curr Med Chem 2019; 26:5338-5339. [DOI: 10.2174/092986732628191025094454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Sureyya Olgen
- Department of Pharmaceutical Chemistry Faculty of Pharmacy, Biruni University Istanbul, Turkey
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Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing. J Mol Biol 2019; 431:2423-2433. [PMID: 31125569 DOI: 10.1016/j.jmb.2019.05.024] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 05/13/2019] [Accepted: 05/14/2019] [Indexed: 01/02/2023]
Abstract
The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (Ka, Kd, Ki, IC50) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein-ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.
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Mizuno T, Kinoshita S, Ito T, Maedera S, Kusuhara H. Development of Orthogonal Linear Separation Analysis (OLSA) to Decompose Drug Effects into Basic Components. Sci Rep 2019; 9:1824. [PMID: 30755704 PMCID: PMC6372619 DOI: 10.1038/s41598-019-38528-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 12/27/2018] [Indexed: 01/06/2023] Open
Abstract
Drugs have multiple, not single, effects. Decomposition of drug effects into basic components helps us to understand the pharmacological properties of a drug and contributes to drug discovery. We have extended factor analysis and developed a novel profile data analysis method: orthogonal linear separation analysis (OLSA). OLSA contracted 11,911 genes to 118 factors from transcriptome data of MCF7 cells treated with 318 compounds in a Connectivity Map. Ontology of the main genes constituting the factors detected significant enrichment of the ontology in 65 of 118 factors and similar results were obtained in two other data sets. In further analysis of the Connectivity Map data set, one factor discriminated two Hsp90 inhibitors, geldanamycin and radicicol, while clustering analysis could not. Doxorubicin and other topoisomerase inhibitors were estimated to inhibit Na+/K+ ATPase, one of the suggested mechanisms of doxorubicin-induced cardiotoxicity. Based on the factor including PI3K/AKT/mTORC1 inhibition activity, 5 compounds were predicted to be novel inducers of autophagy, and other analyses including western blotting revealed that 4 of the 5 actually induced autophagy. These findings indicate the potential of OLSA to decompose the effects of a drug and identify its basic components.
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Affiliation(s)
- Tadahaya Mizuno
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Setsuo Kinoshita
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
- ProMedico Co., Ltd., Ota-ku, Tokyo, 143-0023, Japan
| | - Takuya Ito
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Shotaro Maedera
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Hiroyuki Kusuhara
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
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