1
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Saluja S, Lennon R. Exploring novel therapeutic opportunities for hypertension: a paradigm-shifting approach via integrative multiomic analysis, pioneering the path to precision medicine. J Hypertens 2024; 42:1147-1149. [PMID: 38818837 DOI: 10.1097/hjh.0000000000003738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Affiliation(s)
- Sushant Saluja
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester
- Division of Medicine and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust Manchester
| | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
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2
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Al-Odat OS, Nelson E, Budak-Alpdogan T, Jonnalagadda SC, Desai D, Pandey MK. Discovering Potential in Non-Cancer Medications: A Promising Breakthrough for Multiple Myeloma Patients. Cancers (Basel) 2024; 16:2381. [PMID: 39001443 PMCID: PMC11240591 DOI: 10.3390/cancers16132381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
MM is a common type of cancer that unfortunately leads to a significant number of deaths each year. The majority of the reported MM cases are detected in the advanced stages, posing significant challenges for treatment. Additionally, all MM patients eventually develop resistance or experience relapse; therefore, advances in treatment are needed. However, developing new anti-cancer drugs, especially for MM, requires significant financial investment and a lengthy development process. The study of drug repurposing involves exploring the potential of existing drugs for new therapeutic uses. This can significantly reduce both time and costs, which are typically a major concern for MM patients. The utilization of pre-existing non-cancer drugs for various myeloma treatments presents a highly efficient and cost-effective strategy, considering their prior preclinical and clinical development. The drugs have shown promising potential in targeting key pathways associated with MM progression and resistance. Thalidomide exemplifies the success that can be achieved through this strategy. This review delves into the current trends, the challenges faced by conventional therapies for MM, and the importance of repurposing drugs for MM. This review highlights a noncomprehensive list of conventional therapies that have potentially significant anti-myeloma properties and anti-neoplastic effects. Additionally, we offer valuable insights into the resources that can help streamline and accelerate drug repurposing efforts in the field of MM.
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Affiliation(s)
- Omar S. Al-Odat
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA; (O.S.A.-O.); (E.N.)
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA;
| | - Emily Nelson
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA; (O.S.A.-O.); (E.N.)
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA;
| | | | | | - Dhimant Desai
- Department of Pharmacology, Penn State Neuroscience Institute, Penn State College of Medicine, Hershey, PA 17033, USA;
| | - Manoj K. Pandey
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA; (O.S.A.-O.); (E.N.)
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3
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Santos AS, Costa VAF, Freitas VAQ, Dos Anjos LRB, de Almeida Santos ES, Arantes TD, Costa CR, de Sene Amâncio Zara AL, do Rosário Rodrigues Silva M, Neves BJ. Drug to genome to drug: a computational large-scale chemogenomics screening for novel drug candidates against sporotrichosis. Braz J Microbiol 2024:10.1007/s42770-024-01406-x. [PMID: 38888692 DOI: 10.1007/s42770-024-01406-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/28/2024] [Indexed: 06/20/2024] Open
Abstract
Sporotrichosis is recognized as the predominant subcutaneous mycosis in South America, attributed to pathogenic species within the Sporothrix genus. Notably, in Brazil, Sporothrix brasiliensis emerges as the principal species, exhibiting significant sapronotic, zoonotic and enzootic epidemic potential. Consequently, the discovery of novel therapeutic agents for the treatment of sporotrichosis is imperative. The present study is dedicated to the repositioning of pharmaceuticals for sporotrichosis therapy. To achieve this goal, we designed a pipeline with the following steps: (a) compilation and preparation of Sporothrix genome data; (b) identification of orthologous proteins among the species; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Sporothrix essential targets using validated genes from Saccharomyces cerevisiae; (e) molecular modeling studies; and (f) experimental validation of selected candidates. Based on this approach, we were able to prioritize eight drugs for in vitro experimental validation. Among the evaluated compounds, everolimus and bifonazole demonstrated minimum inhibitory concentration (MIC) values of 0.5 µg/mL and 4.0 µg/mL, respectively. Subsequently, molecular docking studies suggest that bifonazole and everolimus may target specific proteins within S. brasiliensis- namely, sterol 14-α-demethylase and serine/threonine-protein kinase TOR, respectively. These findings shed light on the potential binding affinities and binding modes of bifonazole and everolimus with their probable targets, providing a preliminary understanding of the antifungal mechanism of action of these compounds. In conclusion, our research advances the understanding of the therapeutic potential of bifonazole and everolimus, supporting their further investigation as antifungal agents for sporotrichosis in prospective hit-to-lead and preclinical investigations.
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Affiliation(s)
- Andressa Santana Santos
- Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Goiás, Brazil
- Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
| | | | | | - Laura Raniere Borges Dos Anjos
- Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Goiás, Brazil
- Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
| | | | - Thales Domingos Arantes
- Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Goiás, Brazil
| | - Carolina Rodrigues Costa
- Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Goiás, Brazil
| | - Ana Laura de Sene Amâncio Zara
- Postgraduate Program in Health Technology Assistance and Assessment (PPG-AAS), Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
| | | | - Bruno Junior Neves
- Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil.
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4
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Bui NL, Hoang DA, Ho QA, Nguyen Thi TN, Singh V, Chu DT. Drug repurposing for metabolic disorders: Scientific, technological and economic issues. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:321-336. [PMID: 38942542 DOI: 10.1016/bs.pmbts.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Obesity, diabetes, and other metabolic disorders place a huge burden on both the physical health and financial well-being of the community. While the need for effective treatment of metabolic disorders remains urgent and the reality is that traditional drug development involves high costs and a very long time with many pre-clinical and clinical trials, the need for drug repurposing has emerged as a potential alternative. Scientific evidence has shown the anti-diabetic and anti-obesity effects of old drugs, which were initially utilized for the treatment of inflammation, depression, infections, and even cancers. The drug library used modern technological methods to conduct drug screening. Computational molecular docking, genome-wide association studies, or omics data mining are advantageous and unavoidable methods for drug repurposing. Drug repurposing offers a promising avenue for economic efficiency in healthcare, especially for less common metabolic diseases, despite the need for rigorous research and validation. In this chapter, we aim to explore the scientific, technological, and economic issues surrounding drug repurposing for metabolic disorders. We hope to shed light on the potential of this approach and the challenges that need to be addressed to make it a viable option in the treatment of metabolic disorders, especially in the future fight against metabolic disorders.
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Affiliation(s)
- Nhat-Le Bui
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam; Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Vietnam
| | - Duc-Anh Hoang
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Quang-Anh Ho
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Thao-Nguyen Nguyen Thi
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, India
| | - Dinh-Toi Chu
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam; Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Vietnam.
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5
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Park JH, Cho YR. Computational drug repositioning with attention walking. Sci Rep 2024; 14:10072. [PMID: 38698208 PMCID: PMC11066070 DOI: 10.1038/s41598-024-60756-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 04/26/2024] [Indexed: 05/05/2024] Open
Abstract
Drug repositioning aims to identify new therapeutic indications for approved medications. Recently, the importance of computational drug repositioning has been highlighted because it can reduce the costs, development time, and risks compared to traditional drug discovery. Most approaches in this area use networks for systematic analysis. Inferring drug-disease associations is then defined as a link prediction problem in a heterogeneous network composed of drugs and diseases. In this article, we present a novel method of computational drug repositioning, named drug repositioning with attention walking (DRAW). DRAW proceeds as follows: first, a subgraph enclosing the target link for prediction is extracted. Second, a graph convolutional network captures the structural features of the labeled nodes in the subgraph. Third, the transition probabilities are computed using attention mechanisms and converted into random walk profiles. Finally, a multi-layer perceptron takes random walk profiles and predicts whether a target link exists. As an experiment, we constructed two heterogeneous networks with drug-drug similarities based on chemical structures and anatomical therapeutic chemical classification (ATC) codes. Using 10-fold cross-validation, DRAW achieved an area under the receiver operating characteristic (ROC) curve of 0.903 and outperformed state-of-the-art methods. Moreover, we demonstrated the results of case studies for selected drugs and diseases to further confirm the capability of DRAW to predict drug-disease associations.
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Affiliation(s)
- Jong-Hoon Park
- Division of Software, Yonsei University Mirae Campus, Wonju-si, 26493, Gangwon-do, Korea
| | - Young-Rae Cho
- Division of Software, Yonsei University Mirae Campus, Wonju-si, 26493, Gangwon-do, Korea.
- Division of Digital Healthcare, Yonsei University Mirae Campus, Wonju-si, 26493, Gangwon-do, Korea.
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6
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Xia Y, Sun M, Huang H, Jin WL. Drug repurposing for cancer therapy. Signal Transduct Target Ther 2024; 9:92. [PMID: 38637540 PMCID: PMC11026526 DOI: 10.1038/s41392-024-01808-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.
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Affiliation(s)
- Ying Xia
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China
- The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, 550001, PR China
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China
- Division of Gastroenterology and Hepatology, Department of Medicine and, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Ming Sun
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China
| | - Hai Huang
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China.
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China.
| | - Wei-Lin Jin
- Institute of Cancer Neuroscience, Medical Frontier Innovation Research Center, The First Hospital of Lanzhou University, The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, PR China.
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7
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Mishra A, Vasanthan M, Malliappan SP. Drug Repurposing: A Leading Strategy for New Threats and Targets. ACS Pharmacol Transl Sci 2024; 7:915-932. [PMID: 38633585 PMCID: PMC11019736 DOI: 10.1021/acsptsci.3c00361] [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: 12/13/2023] [Revised: 03/01/2024] [Accepted: 03/06/2024] [Indexed: 04/19/2024]
Abstract
Less than 6% of rare illnesses have an appropriate treatment option. Repurposed medications for new indications are a cost-effective and time-saving strategy that results in excellent success rates, which may significantly lower the risk associated with therapeutic development for rare illnesses. It is becoming a realistic alternative to repurposing "conventional" medications to treat joint and rare diseases considering the significant failure rates, high expenses, and sluggish stride of innovative medication advancement. This is due to delisted compounds, cheaper research fees, and faster development time frames. Repurposed drug competitors have been developed using strategic decisions based on data analysis, interpretation, and investigational approaches, but technical and regulatory restrictions must also be considered. Combining experimental and computational methodologies generates innovative new medicinal applications. It is a one-of-a-kind strategy for repurposing human-safe pharmaceuticals to treat uncommon and difficult-to-treat ailments. It is a very effective method for discovering and creating novel medications. Several pharmaceutical firms have developed novel therapies by repositioning old medications. Repurposing drugs is practical, cost-effective, and speedy and generally involves lower risks when compared to developing a new drug from the beginning.
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Affiliation(s)
- Ashish
Sriram Mishra
- Department
of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, 603202, Tamil Nadu, India
| | - Manimaran Vasanthan
- Department
of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, 603202, Tamil Nadu, India
| | - Sivakumar Ponnurengam Malliappan
- School
of Medicine and Pharmacy, Duy Tan University, Da Nang Vietnam, Institute
of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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8
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Sahu M, Vashishth S, Kukreti N, Gulia A, Russell A, Ambasta RK, Kumar P. Synergizing drug repurposing and target identification for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:111-169. [PMID: 38789177 DOI: 10.1016/bs.pmbts.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shrutikirti Vashishth
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Neha Kukreti
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashima Gulia
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashish Russell
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Rashmi K Ambasta
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India.
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9
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Handa Y, Okuwaki K, Kawashima Y, Hatada R, Mochizuki Y, Komeiji Y, Tanaka S, Furuishi T, Yonemochi E, Honma T, Fukuzawa K. Prediction of Binding Pose and Affinity of Nelfinavir, a SARS-CoV-2 Main Protease Repositioned Drug, by Combining Docking, Molecular Dynamics, and Fragment Molecular Orbital Calculations. J Phys Chem B 2024; 128:2249-2265. [PMID: 38437183 PMCID: PMC10946393 DOI: 10.1021/acs.jpcb.3c05564] [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: 08/17/2023] [Revised: 02/09/2024] [Accepted: 02/14/2024] [Indexed: 03/06/2024]
Abstract
A novel in silico drug design procedure is described targeting the Main protease (Mpro) of the SARS-CoV-2 virus. The procedure combines molecular docking, molecular dynamics (MD), and fragment molecular orbital (FMO) calculations. The binding structure and properties of Mpro were predicted for Nelfinavir (NFV), which had been identified as a candidate compound through drug repositioning, targeting Mpro. Several poses of the Mpro and NFV complexes were generated by docking, from which four docking poses were selected by scoring with FMO energy. Then, each pose was subjected to MD simulation, 100 snapshot structures were sampled from each of the generated MD trajectories, and the structures were evaluated by FMO calculations to rank the pose based on binding energy. Several residues were found to be important in ligand recognition, including Glu47, Asp48, Glu166, Asp187, and Gln189, all of which interacted strongly with NFV. Asn142 is presumably regarded to form hydrogen bonds or CH/π interaction with NFV; however, in the present calculation, their interactions were transient. Moreover, the tert-butyl group of NFV had no interaction with Mpro. Identifying such strong and weak interactions provides candidates for maintaining and substituting ligand functional groups and important suggestions for drug discovery using drug repositioning. Besides the interaction between NFV and the amino acid residues of Mpro, the desolvation effect of the binding pocket also affected the ranking order. A similar procedure of drug design was applied to Lopinavir, and the calculated interaction energy and experimental inhibitory activity value trends were consistent. Our approach provides a new guideline for structure-based drug design starting from a candidate compound whose complex crystal structure has not been obtained.
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Affiliation(s)
- Yuma Handa
- Department
of Physical Chemistry, School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
- Graduate
School of Pharmaceutical Sciences, Osaka
University, 1-6 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Koji Okuwaki
- Department
of Physical Chemistry, School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
- Department
of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Yusuke Kawashima
- Department
of Physical Chemistry, School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
| | - Ryo Hatada
- Department
of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Yuji Mochizuki
- Department
of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
- Institute
of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Yuto Komeiji
- Graduate
School of Pharmaceutical Sciences, Osaka
University, 1-6 Yamadaoka, Suita, Osaka 565-0871, Japan
- Department
of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
- Health
and Medical Research Institute, AIST, Tsukuba Central 6, Tsukuba, Ibaraki 305-8566, Japan
- RIKEN
Center
for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Shigenori Tanaka
- Graduate
School of System Informatics, Department of Computational Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Takayuki Furuishi
- Department
of Physical Chemistry, School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
| | - Etsuo Yonemochi
- Department
of Physical Chemistry, School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
| | - Teruki Honma
- RIKEN
Center
for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kaori Fukuzawa
- Department
of Physical Chemistry, School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
- Graduate
School of Pharmaceutical Sciences, Osaka
University, 1-6 Yamadaoka, Suita, Osaka 565-0871, Japan
- Department
of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, 6-6-11 Aoba, Aramaki, Aoba-ku, Sendai 980-8579, Japan
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10
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Lei S, Lei X, Chen M, Pan Y. Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity. Interdiscip Sci 2024; 16:160-175. [PMID: 38103130 DOI: 10.1007/s12539-023-00593-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 12/17/2023]
Abstract
Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug-disease heterogeneous networks to extract drug-disease features. However, these methods faced difficulty when we are using structurally simple models to deal with complex heterogeneous networks. Therefore, in this study, the researchers introduced a drug repositioning method named DRDSA. The method utilizes a deep sparse autoencoder and integrates drug-disease similarities. First, the researchers constructed a drug-disease feature network by incorporating information from drug chemical structure, disease semantic data, and existing known drug-disease associations. Then, we learned the low-dimensional representation of the feature network using a deep sparse autoencoder. Finally, we utilized a deep neural network to make predictions on new drug-disease associations based on the feature representation. The experimental results show that our proposed method has achieved optimal results on all four benchmark datasets, especially on the CTD dataset where AUC and AUPR reached 0.9619 and 0.9676, respectively, outperforming other baseline methods. In the case study, the researchers predicted the top ten antiviral drugs for COVID-19. Remarkably, six out of these predictions were subsequently validated by other literature sources.
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Affiliation(s)
- Song Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
| | - Ming Chen
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
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11
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Fatemi N, Karimpour M, Bahrami H, Zali MR, Chaleshi V, Riccio A, Nazemalhosseini-Mojarad E, Totonchi M. Current trends and future prospects of drug repositioning in gastrointestinal oncology. Front Pharmacol 2024; 14:1329244. [PMID: 38239190 PMCID: PMC10794567 DOI: 10.3389/fphar.2023.1329244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 12/11/2023] [Indexed: 01/22/2024] Open
Abstract
Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.
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Affiliation(s)
- Nayeralsadat Fatemi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Karimpour
- Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hoda Bahrami
- Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Vahid Chaleshi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Andrea Riccio
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies (DiSTABiF), Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy
- Institute of Genetics and Biophysics (IGB) “Adriano Buzzati-Traverso”, Consiglio Nazionale delle Ricerche (CNR), Naples, Italy
| | - Ehsan Nazemalhosseini-Mojarad
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehdi Totonchi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies (DiSTABiF), Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
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12
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Muniyappan S, Rayan AXA, Varrieth GT. EGeRepDR: An enhanced genetic-based representation learning for drug repurposing using multiple biomedical sources. J Biomed Inform 2023; 147:104528. [PMID: 37858852 DOI: 10.1016/j.jbi.2023.104528] [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: 07/13/2023] [Revised: 09/11/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023]
Abstract
MOTIVATION Drug repurposing (DR) is an imminent approach for identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical industry due to the high cost and time of launching new drugs to the market through traditional drug development. DR task majorly depends on genetic information since the drugs revert the modified Gene Expression (GE) of diseases to normal. Many of the existing studies have not considered the genetic importance of predicting the potential candidates. METHOD We proposed a novel multimodal framework that utilizes genetic aspects of drugs and diseases such as genes, pathways, gene signatures, or expression to enhance the performance of DR using various data sources. Firstly, the heterogeneous biological network (HBN) is constructed with three types of nodes namely drug, disease, and gene, and 4 types of edges similarities (drug, gene, and disease), drug-gene, gene-disease, and drug-disease. Next, a modified graph auto-encoder (GAE*) model is applied to learn the representation of drug and disease nodes using the topological structure and edge information. Secondly, the HBN is enhanced with the information extracted from biomedical literature and ontology using a novel semi-supervised pattern embedding-based bootstrapping model and novel DR perspective representation learning respectively to improve the prediction performance. Finally, our proposed system uses a neural network model to generate the probability score of drug-disease pairs. RESULTS We demonstrate the efficiency of the proposed model on various datasets and achieved outstanding performance in 5-fold cross-validation (AUC = 0.99, AUPR = 0.98). Further, we validated the top-ranked potential candidates using pathway analysis and proved that the known and predicted candidates share common genes in the pathways.
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Affiliation(s)
- Saranya Muniyappan
- Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India.
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13
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Li V, Lee Y, Lee C, Kim H. Repurposing existing drugs for monkeypox: applications of virtual screening methods. Genes Genomics 2023; 45:1347-1355. [PMID: 37713070 PMCID: PMC10587275 DOI: 10.1007/s13258-023-01449-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Monkeypox is endemic to African region and has become of Global concern recently due to its outbreaks in non-endemic countries. Although, the disease was first recorded in 1970, no monkeypox specific drug or vaccine exists as of now. METHODS We applied drug repositioning method, testing effectiveness of currently approved drugs against emerging disease, as one of the most affordable approaches for discovering novel treatment measures. Techniques such as virtual ligand-based and structure-based screening were applied to identify potential drug candidates against monkeypox. RESULTS We narrowed down our results to 6 antiviral and 20 anti-tumor drugs that exhibit theoretically higher potency than tecovirimat, the currently approved drug for monkeypox disease. CONCLUSIONS Our results indicated that selected drug compounds displayed strong binding affinity for p37 receptor of monkeypox virus and therefore can potentially be used in future studies to confirm their effectiveness against the disease.
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Affiliation(s)
- Vladimir Li
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Youngho Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Chul Lee
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
| | - Heebal Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Department of Agricultural Biotechnology, Research Institute for Agriculture and Life Sciences, Seoul National University, Gwanak-gu 1, Gwanak-ro, Seoul, 08826, Republic of Korea.
- eGnome, Seoul, Republic of Korea.
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14
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Zhong J, Cui P, Zhu Y, Xiao Q, Qu Z. DAHNGC: A Graph Convolution Model for Drug-Disease Association Prediction by Using Heterogeneous Network. J Comput Biol 2023; 30:1019-1033. [PMID: 37702623 DOI: 10.1089/cmb.2023.0135] [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] [Indexed: 09/14/2023] Open
Abstract
In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjacent nodes within the homogeneous network for characterizing information. However, this method lacks node attribute information from heterogeneous networks, which could hardly provide valuable insights for predicting drug-disease associations. In this study, a novel drug-disease association prediction model called DAHNGC is proposed, which is based on a graph convolutional neural network. This model includes two feature extraction methods that are specifically designed to extract the attribute characteristics of drugs and diseases from both homogeneous and heterogeneous networks. First, the DropEdge technique is added to the graph convolutional neural network to alleviate the oversmoothing problem and obtain the characteristics of the same nodes of drugs or diseases in the homogeneous network. Then, an automatic feature extraction method in the heterogeneous network is designed to obtain the features of drugs or diseases at different nodes. Finally, the obtained features are put into the fully connected network for nonlinear transformation, and the potential drug-disease pairs are obtained by bilinear decoding. Experimental results demonstrate that the DAHNGC model exhibits good predictive performance for drug-disease associations.
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Affiliation(s)
- Jiancheng Zhong
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Pan Cui
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yihong Zhu
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qiu Xiao
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zuohang Qu
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
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15
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Ai C, Yang H, Ding Y, Tang J, Guo F. Low Rank Matrix Factorization Algorithm Based on Multi-Graph Regularization for Detecting Drug-Disease Association. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3033-3043. [PMID: 37159322 DOI: 10.1109/tcbb.2023.3274587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Detecting potential associations between drugs and diseases plays an indispensable role in drug development, which has also become a research hotspot in recent years. Compared with traditional methods, some computational approaches have the advantages of fast speed and low cost, which greatly accelerate the progress of predicting the drug-disease association. In this study, we propose a novel similarity-based method of low-rank matrix decomposition based on multi-graph regularization. On the basis of low-rank matrix factorization with L2 regularization, the multi-graph regularization constraint is constructed by combining a variety of similarity matrices from drugs and diseases respectively. In the experiments, we analyze the difference in the combination of different similarities, resulting that combining all the similarity information on drug space is unnecessary, and only a part of the similarity information can achieve the desired performance. Then our method is compared with other existing models on three data sets (Fdataset, Cdataset and LRSSLdataset) and have a good advantage in the evaluation measurement of AUPR. Besides, a case study experiment is conducted and showing that the superior ability for predicting the potential disease-related drugs of our model. Finally, we compare our model with some methods on six real world datasets, and our model has a good performance in detecting real world data.
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16
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Huang Z, Chen S, Yu L. Predicting new drug indications based on double variational autoencoders. Comput Biol Med 2023; 164:107261. [PMID: 37487382 DOI: 10.1016/j.compbiomed.2023.107261] [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: 05/24/2023] [Revised: 06/29/2023] [Accepted: 07/16/2023] [Indexed: 07/26/2023]
Abstract
Experimental drug development is costly, complex, and time-consuming, and the number of drugs that have been put into application treatment is small. The identification of drug-disease correlations can provide important information for drug discovery and drug repurposing. Computational drug repurposing is an important and effective method that can be used to determine novel treatments for diseases. In recent years, an increasing number of large databases have been utilized for biological data research, particularly in the fields of drugs and diseases. Consequently, researchers have begun to explore the application of deep neural networks in biological data development. One particularly promising method for unsupervised learning is the deep generative model, with the variational autoencoder (VAE) being among the mainstream models. Here, we propose a drug indication prediction algorithm called DIDVAE (predicting new drug indications based on double variational autoencoders), which generates new data by learning the latent variable distribution of known data to achieve the goal of predicting drug-disease associations. In the experiment, we compared the DIDVAE algorithm with the BBNR, DrugNet, MBiRW and DRRS algorithms on a unified dataset. The comprehensive experimental results show that, compared with these prediction algorithms, the DIDVAE algorithm provides an overall improved prediction. In addition, further analysis and verification of the predicted unknown drug-disease association also proved the practicality of the method.
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Affiliation(s)
- Zhaoyang Huang
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Shengjian Chen
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
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17
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Chen L, Chen K, Zhou B. Inferring drug-disease associations by a deep analysis on drug and disease networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:14136-14157. [PMID: 37679129 DOI: 10.3934/mbe.2023632] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Kaiyu Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Bo Zhou
- Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
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18
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McChord J, Pereyra VM, Froebel S, Bekeredjian R, Schwab M, Ong P. Drug repurposing-a promising approach for patients with angina but non-obstructive coronary artery disease (ANOCA). Front Cardiovasc Med 2023; 10:1156456. [PMID: 37396593 PMCID: PMC10313125 DOI: 10.3389/fcvm.2023.1156456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 05/30/2023] [Indexed: 07/04/2023] Open
Abstract
In today's era of individualized precision medicine drug repurposing represents a promising approach to offer patients fast access to novel treatments. Apart from drug repurposing in cancer treatments, cardiovascular pharmacology is another attractive field for this approach. Patients with angina pectoris without obstructive coronary artery disease (ANOCA) report refractory angina despite standard medications in up to 40% of cases. Drug repurposing also appears to be an auspicious option for this indication. From a pathophysiological point of view ANOCA patients frequently suffer from vasomotor disorders such as coronary spasm and/or impaired microvascular vasodilatation. Consequently, we carefully screened the literature and identified two potential therapeutic targets: the blockade of the endothelin-1 (ET-1) receptor and the stimulation of soluble guanylate cyclase (sGC). Genetically increased endothelin expression results in elevated levels of ET-1, justifying ET-1 receptor blockers as drug candidates to treat coronary spasm. sGC stimulators may be beneficial as they stimulate the NO-sGC-cGMP pathway leading to GMP-mediated vasodilatation.
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Affiliation(s)
- Johanna McChord
- Department of Cardiology and Angiology, Robert-Bosch-Krankenhaus, Stuttgart, Germany
| | | | - Sarah Froebel
- Department of Cardiology and Angiology, Robert-Bosch-Krankenhaus, Stuttgart, Germany
| | - Raffi Bekeredjian
- Department of Cardiology and Angiology, Robert-Bosch-Krankenhaus, Stuttgart, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- Departments of Clinical Pharmacology, and Biochemistry and Pharmacy, University Tübingen, Tübingen, Germany
| | - Peter Ong
- Department of Cardiology and Angiology, Robert-Bosch-Krankenhaus, Stuttgart, Germany
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19
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Santos AS, Borges Dos Anjos LR, Costa VAF, Freitas VAQ, Zara ALDSA, Costa CR, Neves BJ, Silva MDRR. In silico-chemogenomic repurposing of new chemical scaffolds for histoplasmosis treatment. J Mycol Med 2023; 33:101363. [PMID: 36842411 DOI: 10.1016/j.mycmed.2023.101363] [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: 09/27/2022] [Revised: 01/10/2023] [Accepted: 02/10/2023] [Indexed: 02/13/2023]
Abstract
BACKGROUND Histoplasmosis is a systemic form of endemic mycosis to the American continent and may be lethal to people living with HIV/AIDS. The drugs available for treating histoplasmosis are limited, costly, and highly toxic. New drug development is time-consuming and costly; hence, drug repositioning is an advantageous strategy for discovering new therapeutic options. OBJECTIVE This study was conducted to identify drugs that can be repositioned for treating histoplasmosis in immunocompromised patients. METHODS Homologous proteins among Histoplasma capsulatum strains were selected and used to search for homologous targets in the DrugBank and Therapeutic Target Database. Essential genes were selected using Saccharomyces cerevisiae as a model, and functional regions of the therapeutic targets were analyzed. The antifungal activity of the selected drugs was verified, and homology modeling and molecular docking were performed to verify the interactions between the drugs with low inhibitory concentration values and their corresponding targets. RESULTS We selected 149 approved drugs with potential activity against histoplasmosis, among which eight were selected for evaluating their in vitro activity. For drugs with low minimum inhibitory concentration values, such as mebendazole, everolimus, butenafine, and bifonazole, molecular docking studies were performed. A chemogenomic framework revealed lanosterol 14-α-demethylase, squalene monooxygenase, serine/threonine-protein kinase mTOR, and the β-4B tubulin chain of H. capsulatum, respectively, as the protein targets of the drugs. CONCLUSIONS Our strategy can be used to identify promising antifungal targets, and drugs with repositioning potential for treating H. capsulatum.
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Affiliation(s)
- Andressa Santana Santos
- Institute of Tropical Pathology and Public Health (IPTSP), Federal University of Goiás, Goiânia, Brazil
| | | | | | | | | | - Carolina Rodrigues Costa
- Institute of Tropical Pathology and Public Health (IPTSP), Federal University of Goiás, Goiânia, Brazil
| | - Bruno Junior Neves
- Laboratory of Cheminformatics (LabChem), Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
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20
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Bruggemann L, Falls Z, Mangione W, Schwartz SA, Battaglia S, Aalinkeel R, Mahajan SD, Samudrala R. Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver KRAS Mutations in Non-Small Cell Lung Cancer. Int J Mol Sci 2023; 24:ijms24020997. [PMID: 36674513 PMCID: PMC9867122 DOI: 10.3390/ijms24020997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/04/2022] [Accepted: 11/06/2022] [Indexed: 01/06/2023] Open
Abstract
Pharmacogenomics is a rapidly growing field with the goal of providing personalized care to every patient. Previously, we developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to screen optimal compounds for any indication/disease by performing analytics on their interactions using large protein libraries. We implemented a comprehensive precision medicine drug discovery pipeline within the CANDO platform to determine which drugs are most likely to be effective against mutant phenotypes of non-small cell lung cancer (NSCLC) based on the supposition that drugs with similar interaction profiles (or signatures) will have similar behavior and therefore show synergistic effects. CANDO predicted that osimertinib, an EGFR inhibitor, is most likely to synergize with four KRAS inhibitors.Validation studies with cellular toxicity assays confirmed that osimertinib in combination with ARS-1620, a KRAS G12C inhibitor, and BAY-293, a pan-KRAS inhibitor, showed a synergistic effect on decreasing cellular proliferation by acting on mutant KRAS. Gene expression studies revealed that MAPK expression is strongly correlated with decreased cellular proliferation following treatment with KRAS inhibitor BAY-293, but not treatment with ARS-1620 or osimertinib. These results indicate that our precision medicine pipeline may be used to identify compounds capable of synergizing with inhibitors of KRAS G12C, and to assess their likelihood of becoming drugs by understanding their behavior at the proteomic/interactomic scales.
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Affiliation(s)
- Liana Bruggemann
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
| | - Zackary Falls
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
| | - William Mangione
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
| | | | | | | | - Supriya D. Mahajan
- Department of Medicine, University at Buffalo, Buffalo, NY 14260, USA
- Correspondence: (S.D.M.); (R.S.)
| | - Ram Samudrala
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
- Correspondence: (S.D.M.); (R.S.)
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21
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Orozco Morales ML, Rinaldi CA, de Jong E, Lansley SM, Lee YCG, Zemek RM, Bosco A, Lake RA, Lesterhuis WJ. Geldanamycin treatment does not result in anti-cancer activity in a preclinical model of orthotopic mesothelioma. PLoS One 2023; 18:e0274364. [PMID: 37146029 PMCID: PMC10162533 DOI: 10.1371/journal.pone.0274364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 03/26/2023] [Indexed: 05/07/2023] Open
Abstract
Mesothelioma is characterised by its aggressive invasive behaviour, affecting the surrounding tissues of the pleura or peritoneum. We compared an invasive pleural model with a non-invasive subcutaneous model of mesothelioma and performed transcriptomic analyses on the tumour samples. Invasive pleural tumours were characterised by a transcriptomic signature enriched for genes associated with MEF2C and MYOCD signaling, muscle differentiation and myogenesis. Further analysis using the CMap and LINCS databases identified geldanamycin as a potential antagonist of this signature, so we evaluated its potential in vitro and in vivo. Nanomolar concentrations of geldanamycin significantly reduced cell growth, invasion, and migration in vitro. However, administration of geldanamycin in vivo did not result in significant anti-cancer activity. Our findings show that myogenesis and muscle differentiation pathways are upregulated in pleural mesothelioma which may be related to the invasive behaviour. However, geldanamycin as a single agent does not appear to be a viable treatment for mesothelioma.
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Affiliation(s)
- M Lizeth Orozco Morales
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
- National Centre for Asbestos Related Diseases, Nedlands, Western Australia, Australia
- Institute for Respiratory Health, Nedlands, Western Australia, Australia
| | - Catherine A Rinaldi
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
- National Centre for Asbestos Related Diseases, Nedlands, Western Australia, Australia
- Centre for Microscopy Characterisation and Analysis, Nedlands, Western Australia, Australia
| | - Emma de Jong
- Telethon Kids Institute, The University of Western Australia, Nedlands, Western Australia, Australia
| | - Sally M Lansley
- Institute for Respiratory Health, Nedlands, Western Australia, Australia
| | - Y C Gary Lee
- Institute for Respiratory Health, Nedlands, Western Australia, Australia
| | - Rachael M Zemek
- Telethon Kids Institute, The University of Western Australia, Nedlands, Western Australia, Australia
| | - Anthony Bosco
- Telethon Kids Institute, The University of Western Australia, Nedlands, Western Australia, Australia
| | - Richard A Lake
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
- National Centre for Asbestos Related Diseases, Nedlands, Western Australia, Australia
- Institute for Respiratory Health, Nedlands, Western Australia, Australia
| | - W Joost Lesterhuis
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
- National Centre for Asbestos Related Diseases, Nedlands, Western Australia, Australia
- Institute for Respiratory Health, Nedlands, Western Australia, Australia
- Telethon Kids Institute, The University of Western Australia, Nedlands, Western Australia, Australia
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22
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Tan GSQ, Sloan EK, Lambert P, Kirkpatrick CMJ, Ilomäki J. Drug repurposing using real-world data. Drug Discov Today 2023; 28:103422. [PMID: 36341896 DOI: 10.1016/j.drudis.2022.103422] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/18/2022] [Accepted: 10/25/2022] [Indexed: 02/02/2023]
Abstract
The use of real-world data in drug repurposing has emerged due to well-established advantages of drug repurposing in supplementing de novo drug discovery and incentives in incorporating real-world evidence in regulatory approvals. We conducted a scoping review to characterize repurposing studies using real-world data and discuss their potential challenges and solutions. A total of 250 studies met the inclusion criteria, of which 36 were original studies on hypothesis generation, 101 on hypothesis validation, and seven on safety assessment. Key challenges that should be addressed for future progress in using real-world data for repurposing include isolated data sources with poor clinical granularity, false-positive signals from data mining, the sensitivity of hypothesis validation to bias and confounding, and the lack of clear regulatory guidance.
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Affiliation(s)
- George S Q Tan
- Centre for Medicine Use and Safety, Monash University, Parkville, Victoria, Australia
| | - Erica K Sloan
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Pete Lambert
- Drug Delivery Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Carl M J Kirkpatrick
- Centre for Medicine Use and Safety, Monash University, Parkville, Victoria, Australia.
| | - Jenni Ilomäki
- Centre for Medicine Use and Safety, Monash University, Parkville, Victoria, Australia.
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23
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Interaction of Thioflavin T (ThT) and 8-anilino-1-naphthalene sulfonic acid (ANS) with macromolecular crowding agents and their monomers: Biophysical analysis using in vitro and computational approaches. J Mol Liq 2023. [DOI: 10.1016/j.molliq.2023.121270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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24
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Yang J, Zhang D, Cai Y, Yu K, Li M, Liu L, Chen X. Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:256-265. [PMID: 35239490 DOI: 10.1109/tcbb.2022.3155453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypic effects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs. In this study, first, we constructed quantitative associations of substructure-domain, domain-ADR, and domain-ATC (Anatomical Therapeutic Chemical Classification System code) through L1LOG and L1SVM machine learning models. These associations represent relationships between phenotypes (ADRs and ATCs) and local structures of drugs and proteins. Then, based on these established associations, substructure-phenotype relationships were constructed which were utilized to quantify drug-phenotype relationships. Thus, this approach could achieve high-throughput and effective evaluations of the druggability of NDCs by referring to the established substructure-phenotype relationships and structural information of NDCs without additional prior knowledge. Using this computational pipeline, 83,205 drug-ATC relationships (including 1,479 drugs and 178 ATCs) and 306,421 drug-ADR relationships (including 1,752 drugs and 454 ADRs) were predicted in total. The prediction results were validated at four levels: five-fold cross validation, public databases, literature, and molecular docking. Furthermore, three case studies demonstrated the feasibility of our method. 79 ATCs and 269 ADRs were predicted to be related to Maraviroc, an approved drug, including the existing antiviral effect in clinical use. Additionally, we also found risk substructures of severe ADRs, for example, SUB215 (>= 1, saturated or only aromatic carbon ring size 7) can result in shock. And we analyzed the mechanism of action (MOA) of interested drugs based on the established drug-substructure-domain-protein associations. In a word, this approach through establishing drug-substructure-phenotype relationships can achieve quantitative prediction of phenotypes for a given NDC or drug without any prior knowledge except its structure information. Using that way, we can directly obtain the relationships between substructure and phenotype of a compound, which is more convenient to analyze the phenotypic mechanism of drugs and accelerate the process of rational drug design.
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25
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Mokou M, Narayanasamy S, Stroggilos R, Balaur IA, Vlahou A, Mischak H, Frantzi M. A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures. Methods Mol Biol 2023; 2684:59-99. [PMID: 37410228 DOI: 10.1007/978-1-0716-3291-8_4] [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] [Indexed: 07/07/2023]
Abstract
Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.
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Affiliation(s)
- Marika Mokou
- Department of Biomarker Research, Mosaiques Diagnostics, Hannover, Germany.
| | - Shaman Narayanasamy
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rafael Stroggilos
- Systems Biology Center, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Irina-Afrodita Balaur
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Antonia Vlahou
- Systems Biology Center, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Harald Mischak
- Department of Biomarker Research, Mosaiques Diagnostics, Hannover, Germany
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Maria Frantzi
- Department of Biomarker Research, Mosaiques Diagnostics, Hannover, Germany
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Mobashir M, Turunen SP, Izhari MA, Ashankyty IM, Helleday T, Lehti K. An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation. Cells 2022; 11:cells11244121. [PMID: 36552885 PMCID: PMC9777290 DOI: 10.3390/cells11244121] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes'/proteins' connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein-protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways' components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment.
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Affiliation(s)
- Mohammad Mobashir
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solnavägen 9, Solna 17165, Sweden
- Correspondence: ; Tel.: +46-70-872-3675
| | - S. Pauliina Turunen
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solnavägen 9, Solna 17165, Sweden
| | - Mohammad Asrar Izhari
- Faculty of Applied Medical Sciences, University of Al-Baha, Al-Baha 65528, Saudi Arabia
| | - Ibraheem Mohammed Ashankyty
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 22233, Saudi Arabia
| | - Thomas Helleday
- SciLifeLab, Department of Oncology and Pathology, Karolinska Institutet, P.O. Box 1031, 17121 Stockholm, Sweden
| | - Kaisa Lehti
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solnavägen 9, Solna 17165, Sweden
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27
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Wang MN, Xie XJ, You ZH, Ding DW, Wong L. A weighted non-negative matrix factorization approach to predict potential associations between drug and disease. J Transl Med 2022; 20:552. [PMID: 36463215 PMCID: PMC9719187 DOI: 10.1186/s12967-022-03757-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 11/06/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Associations of drugs with diseases provide important information for expediting drug development. Due to the number of known drug-disease associations is still insufficient, and considering that inferring associations between them through traditional in vitro experiments is time-consuming and costly. Therefore, more accurate and reliable computational methods urgent need to be developed to predict potential associations of drugs with diseases. METHODS In this study, we present the model called weighted graph regularized collaborative non-negative matrix factorization for drug-disease association prediction (WNMFDDA). More specifically, we first calculated the drug similarity and disease similarity based on the chemical structures of drugs and medical description information of diseases, respectively. Then, to extend the model to work for new drugs and diseases, weighted [Formula: see text] nearest neighbor was used as a preprocessing step to reconstruct the interaction score profiles of drugs with diseases. Finally, a graph regularized non-negative matrix factorization model was used to identify potential associations between drug and disease. RESULTS During the cross-validation process, WNMFDDA achieved the AUC values of 0.939 and 0.952 on Fdataset and Cdataset under ten-fold cross validation, respectively, which outperforms other competing prediction methods. Moreover, case studies for several drugs and diseases were carried out to further verify the predictive performance of WNMFDDA. As a result, 13(Doxorubicin), 13(Amiodarone), 12(Obesity) and 12(Asthma) of the top 15 corresponding candidate diseases or drugs were confirmed by existing databases. CONCLUSIONS The experimental results adequately demonstrated that WNMFDDA is a very effective method for drug-disease association prediction. We believe that WNMFDDA is helpful for relevant biomedical researchers in follow-up studies.
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Affiliation(s)
- Mei-Neng Wang
- grid.449868.f0000 0000 9798 3808School of Mathematics and Computer Science, Yichun University, Yichun, 336000 Jiangxi China
| | - Xue-Jun Xie
- grid.449868.f0000 0000 9798 3808School of Mathematics and Computer Science, Yichun University, Yichun, 336000 Jiangxi China
| | - Zhu-Hong You
- grid.440588.50000 0001 0307 1240School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072 China
| | - De-Wu Ding
- grid.449868.f0000 0000 9798 3808School of Mathematics and Computer Science, Yichun University, Yichun, 336000 Jiangxi China
| | - Leon Wong
- grid.9227.e0000000119573309Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
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Gnilopyat S, DePietro PJ, Parry TK, McLaughlin WA. The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases. Biomolecules 2022; 12:1559. [PMID: 36358909 PMCID: PMC9687941 DOI: 10.3390/biom12111559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 08/13/2023] Open
Abstract
We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.
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Affiliation(s)
| | | | | | - William A. McLaughlin
- Department of Medical Education, Geisinger Commonwealth School of Medicine, 525 Pine Street, Scranton, PA 18509, USA
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29
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Kakoti BB, Bezbaruah R, Ahmed N. Therapeutic drug repositioning with special emphasis on neurodegenerative diseases: Threats and issues. Front Pharmacol 2022; 13:1007315. [PMID: 36263141 PMCID: PMC9574100 DOI: 10.3389/fphar.2022.1007315] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 09/12/2022] [Indexed: 11/21/2022] Open
Abstract
Drug repositioning or repurposing is the process of discovering leading-edge indications for authorized or declined/abandoned molecules for use in different diseases. This approach revitalizes the traditional drug discovery method by revealing new therapeutic applications for existing drugs. There are numerous studies available that highlight the triumph of several drugs as repurposed therapeutics. For example, sildenafil to aspirin, thalidomide to adalimumab, and so on. Millions of people worldwide are affected by neurodegenerative diseases. According to a 2021 report, the Alzheimer's disease Association estimates that 6.2 million Americans are detected with Alzheimer's disease. By 2030, approximately 1.2 million people in the United States possibly acquire Parkinson's disease. Drugs that act on a single molecular target benefit people suffering from neurodegenerative diseases. Current pharmacological approaches, on the other hand, are constrained in their capacity to unquestionably alter the course of the disease and provide patients with inadequate and momentary benefits. Drug repositioning-based approaches appear to be very pertinent, expense- and time-reducing strategies for the enhancement of medicinal opportunities for such diseases in the current era. Kinase inhibitors, for example, which were developed for various oncology indications, demonstrated significant neuroprotective effects in neurodegenerative diseases. This review expounds on the classical and recent examples of drug repositioning at various stages of drug development, with a special focus on neurodegenerative disorders and the aspects of threats and issues viz. the regulatory, scientific, and economic aspects.
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Affiliation(s)
- Bibhuti Bhusan Kakoti
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India
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30
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Xie G, Xu H, Li J, Gu G, Sun Y, Lin Z, Zhu Y, Wang W, Wang Y, Shao J. DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19. Comput Chem Eng 2022; 166:107947. [PMID: 35942213 PMCID: PMC9349049 DOI: 10.1016/j.compchemeng.2022.107947] [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: 09/27/2021] [Revised: 04/13/2022] [Accepted: 07/27/2022] [Indexed: 12/25/2022]
Abstract
Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.
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Affiliation(s)
- Guobo Xie
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Haojie Xu
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Jianming Li
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Guosheng Gu
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China,Corresponding author
| | - Yuping Sun
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Zhiyi Lin
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Yinting Zhu
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Weiming Wang
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
| | - Youfu Wang
- Huaneng Qinghai Power Generation Co., Ltd. New Energy Branch, Xining 810000, China
| | - Jiang Shao
- School of Architecture & Design, China University of Mining and Technology, Xuzhou 221116, China
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31
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Inaam ul haq M, Li Q, Hou J, Iftekhar A. Detecting the research structure and topic trends of social media using static and dynamic probabilistic topic models. ASLIB J INFORM MANAG 2022. [DOI: 10.1108/ajim-02-2022-0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeA huge volume of published research articles is available on social media which evolves because of the rapid scientific advances and this paper aims to investigate the research structure of social media.Design/methodology/approachThis study employs an integrated topic modeling and text mining-based approach on 30381 Scopus index titles, abstracts, and keywords published between 2006 and 2021. It combines analytical analysis of top-cited reviews with topic modeling as means of semantic validation. The output sequences of the dynamic model are further analyzed using the statistical techniques that facilitate the extraction of topic clusters, communities, and potential inter-topic research directions.FindingsThis paper brings into vision the research structure of social media in terms of topics, temporal topic evolutions, topic trends, emerging, fading, and consistent topics of this domain. It also traces various shifts in topic themes. The hot research topics are the application of the machine or deep learning towards social media in general, alcohol consumption in different regions and its impact, Social engagement and media platforms. Moreover, the consistent topics in both models include food management in disaster, health study of diverse age groups, and emerging topics include drug violence, analysis of social media news for misinformation, and problems of Internet addiction.Originality/valueThis study extends the existing topic modeling-based studies that analyze the social media literature from a specific disciplinary viewpoint. It focuses on semantic validations of topic-modeling output and correlations among the topics and also provides a two-stage cluster analysis of the topics.
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32
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Zhang F, Hu W, Liu Y. GCMM: graph convolution network based on multimodal attention mechanism for drug repurposing. BMC Bioinformatics 2022; 23:372. [PMID: 36100897 PMCID: PMC9469552 DOI: 10.1186/s12859-022-04911-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/26/2022] [Indexed: 11/10/2022] Open
Abstract
Background The main focus of in silico drug repurposing, which is a promising area for using artificial intelligence in drug discovery, is the prediction of drug–disease relationships. Although many computational models have been proposed recently, it is still difficult to reliably predict drug–disease associations from a variety of sources of data. Results In order to identify potential drug–disease associations, this paper introduces a novel end-to-end model called Graph convolution network based on a multimodal attention mechanism (GCMM). In particular, GCMM incorporates known drug–disease relations, drug–drug chemical similarity, drug–drug therapeutic similarity, disease–disease semantic similarity, and disease–disease target-based similarity into a heterogeneous network. A Graph Convolution Network encoder is used to learn how diseases and drugs are embedded in various perspectives. Additionally, GCMM can enhance performance by applying a multimodal attention layer to assign various levels of value to various features and the inputting of multi-source information. Conclusion 5 fold cross-validation evaluations show that the GCMM outperforms four recently proposed deep-learning models on the majority of the criteria. It shows that GCMM can predict drug–disease relationships reliably and suggests improvement in the desired metrics. Hyper-parameter analysis and exploratory ablation experiments are also provided to demonstrate the necessity of each module of the model and the highest possible level of prediction performance. Additionally, a case study on Alzheimer’s disease (AD). Four of the five medications indicated by GCMM to have the highest potential correlation coefficient with AD have been demonstrated through literature or experimental research, demonstrating the viability of GCMM. All of these results imply that GCMM can provide a strong and effective tool for drug development and repositioning.
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Song Y, Cui H, Zhang T, Yang T, Li X, Xuan P. Prediction of Drug-Related Diseases Through Integrating Pairwise Attributes and Neighbor Topological Structures. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2963-2974. [PMID: 34133286 DOI: 10.1109/tcbb.2021.3089692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Identifying new disease indications for the approved drugs can help reduce the cost and time of drug development. Most of the recent methods focus on exploiting the various information related to drugs and diseases for predicting the candidate drug-disease associations. However, the previous methods failed to deeply integrate the neighborhood topological structure and the node attributes of an interested drug-disease node pair. We propose a new prediction method, ANPred, to learn and integrate pairwise attribute information and neighbor topology information from the similarities and associations related to drugs and diseases. First, a bi-layer heterogeneous network with intra-layer and inter-layer connections is established to combine the drug similarities, the disease similarities, and the drug-disease associations. Second, the embedding of a pair of drug and disease is constructed based on integrating multiple biological premises about drugs and diseases. The learning framework based on multi-layer convolutional neural networks is designed to learn the attribute representation of the pair of drug and disease nodes from its embedding. The sequences composed of neighbor nodes are formed based on random walk on the heterogeneous network. A framework based on fully-connected autoencoder and skip-gram module is constructed to learn the neighbor topological representations of nodes. The cross-validation results indicate the performance of ANPred is superior to several state-of-the-art methods. The case studies on 5 drugs further confirm the ability of ANPred in discovering the potential drug-disease association candidates.
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Mangrio GR, Maneengam A, Khalid Z, Jafar TH, Chanihoon GQ, Nassani R, Unar A. RP-HPLC Method Development, Validation, and Drug Repurposing of Sofosbuvir Pharmaceutical Dosage Form: A Multidimensional Study. ENVIRONMENTAL RESEARCH 2022; 212:113282. [PMID: 35487258 DOI: 10.1016/j.envres.2022.113282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 04/05/2022] [Accepted: 04/08/2022] [Indexed: 06/14/2023]
Abstract
A smooth, exceptionally sensitive, correct, and extra reproducible RP-HPLC technique was developed and demonstrated to estimate Sofosbuvir (SOF) in pharmaceutical dosage formulations. This process was carried out by Agilent High-Pressure Liquid Chromatograph 1260 with GI311C Quat. Pump, Phenomenex Luna C-18 (150 mm × 4.6 mm × 5 μm) (USA), and Photodiode Array Detector (PDA) G1315D. The cell section, including acetonitrile and methanol with 80:20 v/v and solution (B) 0.1% phosphoric acid (40:60), was used for the study. However, 10 μL of the sample was injected with a drift flow of 1 mL/min. The separation occurred at a column temperature of 30 °C, and the eluents used PDA set at 260 nm. The retention time of SOF was 5 min. The calibration curve was modified linearly within the range of 0.05-0.15 mg/mL with a correlation coefficient of 0.99 and genuine linear dating among top vicinity and consciousness in the calibration curve. The detection and quantification restrictions were 0.001 and 0.003 mg/mL, respectively. SOF recovery from pharmaceutical components ranged from 98% to 99%. The percentage assay of SOF was 99%. Analytical validation parameters, such as specificity, linearity, precision, accuracy, and selectivity, were studied, and the percentage relative standard deviation (%RSD) was less than 2%. All other key parameters were observed within the desired thresholds. Hence, the proposed RP-HPLC technique was proven effective for developing SOF in bulk and pharmaceutical pill dosage forms. SOF was found to interact with SARS-COV-2 nsp12, and molecular docking results revealed its high affinity and firm binding within the active site groove of nsp12. The key interacting residues include; LYS-72, GLN-75, MET-80 ALA-99, ASN-99, TRP-100, TYR-101 with ASN-99 and TRP-100 forming hydrogen bonds. Molecular Dynamics simulation of SOF and nsp12 complex elucidated that the system was stable throughout 20ns. Therefore, this drug repurposing strategy for SOF can be used for treating COVID-19 infections by performing animal experiments and accurate clinical trials in the future.
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Affiliation(s)
| | - Apichit Maneengam
- Department of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut's University of Technology North Bangkok, Wongsawang, Bangsue, Bangkok, 10800, Thailand
| | - Zunera Khalid
- School of Life Sciences, University of Science and Technology of China, Hefei, 230027, PR China
| | | | - Ghulam Qadir Chanihoon
- National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro, 76090, Pakistan
| | - Rayan Nassani
- Center for Computational Biology, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Ahsanullah Unar
- School of Life Sciences, University of Science and Technology of China, Hefei, 230027, PR China.
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35
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Zhong C, Ai J, Yang Y, Ma F, Sun W. Small Molecular Drug Screening Based on Clinical Therapeutic Effect. Molecules 2022; 27:molecules27154807. [PMID: 35956770 PMCID: PMC9369618 DOI: 10.3390/molecules27154807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022] Open
Abstract
Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard–Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future.
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Affiliation(s)
| | | | | | | | - Wei Sun
- Correspondence: ; Tel.: +86-10-64445826
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36
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Ahmed S, Mobashir M, Al-Keridis LA, Alshammari N, Adnan M, Abid M, Hassan MI. A Network-Guided Approach to Discover Phytochemical-Based Anticancer Therapy: Targeting MARK4 for Hepatocellular Carcinoma. Front Oncol 2022; 12:914032. [PMID: 35936719 PMCID: PMC9355243 DOI: 10.3389/fonc.2022.914032] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/15/2022] [Indexed: 12/15/2022] Open
Abstract
MAP/microtubule affinity-regulating kinase 4 (MARK4) is associated with various biological functions, including neuronal migration, cell polarity, microtubule dynamics, apoptosis, and cell cycle regulation, specifically in the G1/S checkpoint, cell signaling, and differentiation. It plays a critical role in different types of cancers. Hepatocellular carcinoma (HCC) is the one of the most common forms of liver cancer caused due to mutations, epigenetic aberrations, and altered gene expression patterns. Here, we have applied an integrated network biology approach to see the potential links of MARK4 in HCC, and subsequently identified potential herbal drugs. This work focuses on the naturally-derived compounds from medicinal plants and their properties, making them targets for potential anti-hepatocellular treatments. We further analyzed the HCC mutated genes from the TCGA database by using cBioPortal and mapped out the MARK4 targets among the mutated list. MARK4 and Mimosin, Quercetin, and Resveratrol could potentially interact with critical cancer-associated proteins. A set of the hepatocellular carcinoma altered genes is directly the part of infection, inflammation, immune systems, and cancer pathways. Finally, we conclude that among all these drugs, Gingerol and Fisetin appear to be the highly promising drugs against MARK4-based targets, followed by Quercetin, Resveratrol, and Apigenin.
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Affiliation(s)
- Sarfraz Ahmed
- Department of Biosciences, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, India
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Mohammad Mobashir
- Department of Biosciences, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, India
| | - Lamya Ahmed Al-Keridis
- Department of Biology, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Nawaf Alshammari
- Department of Biology, College of Science, University of Hail, Hail, Saudi Arabia
| | - Mohd Adnan
- Department of Biology, College of Science, University of Hail, Hail, Saudi Arabia
| | - Mohammad Abid
- Department of Biosciences, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, India
| | - Md Imtaiyaz Hassan
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
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Zong N, Li N, Wen A, Ngo V, Yu Y, Huang M, Chowdhury S, Jiang C, Fu S, Weinshilboum R, Jiang G, Hunter L, Liu H. BETA: a comprehensive benchmark for computational drug-target prediction. Brief Bioinform 2022; 23:6596989. [PMID: 35649342 PMCID: PMC9294420 DOI: 10.1093/bib/bbac199] [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: 01/14/2022] [Revised: 04/10/2022] [Accepted: 04/29/2022] [Indexed: 11/14/2022] Open
Abstract
Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.
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Affiliation(s)
- Nansu Zong
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Ning Li
- Center for Structure Biology, Center for Cancer Research, National Cancer Institute, Frederick, MD
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Victoria Ngo
- Betty Irene Moore School of Nursing, University of California Davis Health, Sacramento, CA.,Stanford Health Policy, Stanford School of Medicine and Freeman Spogli Institute for International Studies, Palo Alto, CA
| | - Yue Yu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Ming Huang
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Shaika Chowdhury
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Chao Jiang
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN
| | - Guoqian Jiang
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Lawrence Hunter
- Department of Pharmacology, University of Colorado Denver, Aurora, CO
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
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Feng Y, Cheng X, Wu S, Mani Saravanan K, Liu W. Hybrid drug-screening strategy identifies potential SARS-CoV-2 cell-entry inhibitors targeting human transmembrane serine protease. Struct Chem 2022; 33:1503-1515. [PMID: 35571866 PMCID: PMC9091140 DOI: 10.1007/s11224-022-01960-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/28/2022] [Indexed: 11/21/2022]
Abstract
The spread of coronavirus infectious disease (COVID-19) is associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has risked public health more than any other infectious disease. Researchers around the globe use multiple approaches to identify an effective approved drug (drug repurposing) that treats viral infections. Most of the drug repurposing approaches target spike protein or main protease. Here we use transmembrane serine protease 2 (TMPRSS2) as a target that can prevent the virus entry into the cell by interacting with the surface receptors. By hypothesizing that the TMPRSS2 binders may help prevent the virus entry into the cell, we performed a systematic drug screening over the current approved drug database. Furthermore, we screened the Enamine REAL fragments dataset against the TMPRSS2 and presented nine potential drug-like compounds that give us clues about which kinds of groups the pocket prefers to bind, aiding future structure-based drug design for COVID-19. Also, we employ molecular dynamics simulations, binding free energy calculations, and well-tempered metadynamics to validate the obtained candidate drug and fragment list. Our results suggested three potential FDA-approved drugs against human TMPRSS2 as a target. These findings may pave the way for more drugs to be exposed to TMPRSS2, and testing the efficacy of these drugs with biochemical experiments will help improve COVID-19 treatment.
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In Silico Drug Discovery Strategies Identified ADMET Properties of Decoquinate RMB041 and Its Potential Drug Targets against Mycobacterium tuberculosis. Microbiol Spectr 2022; 10:e0231521. [PMID: 35352998 PMCID: PMC9045315 DOI: 10.1128/spectrum.02315-21] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
The highly adaptive cellular response of Mycobacterium tuberculosis to various antibiotics and the high costs for clinical trials, hampers the development of novel antimicrobial agents with improved efficacy and safety. Subsequently, in silico drug screening methods are more commonly being used for the discovery and development of drugs, and have been proven useful for predicting the pharmacokinetics, toxicities, and targets, of prospective new antimicrobial agents. In this investigation we used a reversed target fishing approach to determine potential hit targets and their possible interactions between M. tuberculosis and decoquinate RMB041, a propitious new antituberculosis compound. Two of the 13 identified targets, Cyp130 and BlaI, were strongly proposed as optimal drug-targets for dormant M. tuberculosis, of which the first showed the highest comparative binding affinity to decoquinate RMB041. The metabolic pathways associated with the selected target proteins were compared to previously published molecular mechanisms of decoquinate RMB041 against M. tuberculosis, whereby we confirmed disrupted metabolism of proteins, cell wall components, and DNA. We also described the steps within these pathways that are inhibited and elaborated on decoquinate RMB041’s activity against dormant M. tuberculosis. This compound has previously showed promising in vitro safety and good oral bioavailability, which were both supported by this in silico study. The pharmacokinetic properties and toxicity of this compound were predicted and investigated using the online tools pkCSM and SwissADME, and Discovery Studio software, which furthermore supports previous safety and bioavailability characteristics of decoquinate RMB041 for use as an antimycobacterial medication. IMPORTANCE This article elaborates on the mechanism of action of a novel antibiotic compound against both, active and dormant Mycobacterium tuberculosis and describes its pharmacokinetics (including oral bioavailability and toxicity). Information provided in this article serves useful during the search for drugs that shorten the treatment regimen for Tuberculosis and cause minimal adverse effects.
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40
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Singh S, Weiss A, Goodman J, Fisk M, Kulkarni S, Lu I, Gray J, Smith R, Sommer M, Cheriyan J. Niclosamide - a promising treatment for COVID-19. Br J Pharmacol 2022; 179:3250-3267. [PMID: 35348204 PMCID: PMC9111792 DOI: 10.1111/bph.15843] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/09/2022] [Accepted: 02/23/2022] [Indexed: 12/15/2022] Open
Abstract
Vaccines have reduced the transmission and severity of COVID‐19, but there remains a paucity of efficacious treatment for drug‐resistant strains and more susceptible individuals, particularly those who mount a suboptimal vaccine response, either due to underlying health conditions or concomitant therapies. Repurposing existing drugs is a timely, safe and scientifically robust method for treating pandemics, such as COVID‐19. Here, we review the pharmacology and scientific rationale for repurposing niclosamide, an anti‐helminth already in human use as a treatment for COVID‐19. In addition, its potent antiviral activity, niclosamide has shown pleiotropic anti‐inflammatory, antibacterial, bronchodilatory and anticancer effects in numerous preclinical and early clinical studies. The advantages and rationale for nebulized and intranasal formulations of niclosamide, which target the site of the primary infection in COVID‐19, are reviewed. Finally, we give an overview of ongoing clinical trials investigating niclosamide as a promising candidate against SARS‐CoV‐2.
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Affiliation(s)
- Shivani Singh
- Division of Pulmonary and Critical Care Medicine, NYU School of Medicine, New York, USA
| | - Anne Weiss
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.,UNION Therapeutics Research Services, Hellerup, Denmark
| | - James Goodman
- Department of Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Marie Fisk
- Department of Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Spoorthy Kulkarni
- Department of Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Ing Lu
- Department of Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Joanna Gray
- Department of Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Rona Smith
- Department of Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.,Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Morten Sommer
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.,UNION Therapeutics, Hellerup, Denmark
| | - Joseph Cheriyan
- Department of Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.,Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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Huang W, Zhang L, Li Z. Advances in computer-aided drug design for type 2 diabetes. Expert Opin Drug Discov 2022; 17:461-472. [PMID: 35254188 DOI: 10.1080/17460441.2022.2047644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing. AREAS COVERED In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies. EXPERT OPINION There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.
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Affiliation(s)
- Wanqiu Huang
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, PR China.,Key Laboratory of New Drug Discovery and Evaluation, Guangdong Pharmaceutical University, Guangzhou, PR China.,Guangzhou Key Laboratory of Construction and Application of New Drug Screening Model Systems, Guangdong Pharmaceutical University, Guangzhou, PR China
| | - Luyong Zhang
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, PR China.,Key Laboratory of New Drug Discovery and Evaluation, Guangdong Pharmaceutical University, Guangzhou, PR China.,Guangzhou Key Laboratory of Construction and Application of New Drug Screening Model Systems, Guangdong Pharmaceutical University, Guangzhou, PR China.,Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing, PR China
| | - Zheng Li
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, PR China.,Key Laboratory of New Drug Discovery and Evaluation, Guangdong Pharmaceutical University, Guangzhou, PR China
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42
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Ratajczak F, Joblin M, Ringsquandl M, Hildebrandt M. Task-driven knowledge graph filtering improves prioritizing drugs for repurposing. BMC Bioinformatics 2022; 23:84. [PMID: 35246025 PMCID: PMC8894843 DOI: 10.1186/s12859-022-04608-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 12/09/2021] [Indexed: 02/07/2023] Open
Abstract
Background Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency. Results We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied. Conclusion Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04608-y.
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Affiliation(s)
- Florin Ratajczak
- Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany. .,Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany.
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43
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Nguyen TM, Nguyen T, Le TM, Tran T. GEFA: Early Fusion Approach in Drug-Target Affinity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:718-728. [PMID: 34197324 DOI: 10.1109/tcbb.2021.3094217] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA)problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug binding effects. In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel graph-in-graph neural network with attention mechanism to address the changes in target representation because of the binding effects. Specifically, a drug is modeled as a graph of atoms, which then serves as a node in a larger graph of residues-drug complex. The resulting model is an expressive deep nested graph neural network. We also use pre-trained protein representation powered by the recent effort of learning contextualized protein representation. The experiments are conducted under different settings to evaluate scenarios such as novel drugs or targets. The results demonstrate the effectiveness of the pre-trained protein embedding and the advantages our GEFA in modeling the nested graph for drug-target interaction.
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44
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Bahmad HF, Demus T, Moubarak MM, Daher D, Alvarez Moreno JC, Polit F, Lopez O, Merhe A, Abou-Kheir W, Nieder AM, Poppiti R, Omarzai Y. Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing. Med Sci (Basel) 2022; 10:medsci10010015. [PMID: 35225948 PMCID: PMC8883996 DOI: 10.3390/medsci10010015] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/12/2022] [Accepted: 02/16/2022] [Indexed: 12/12/2022] Open
Abstract
Prostate cancer (PCa) is the second most common cancer in men. Common treatments include active surveillance, surgery, or radiation. Androgen deprivation therapy and chemotherapy are usually reserved for advanced disease or biochemical recurrence, such as castration-resistant prostate cancer (CRPC), but they are not considered curative because PCa cells eventually develop drug resistance. The latter is achieved through various cellular mechanisms that ultimately circumvent the pharmaceutical’s mode of action. The need for novel therapeutic approaches is necessary under these circumstances. An alternative way to treat PCa is by repurposing of existing drugs that were initially intended for other conditions. By extrapolating the effects of previously approved drugs to the intracellular processes of PCa, treatment options will expand. In addition, drug repurposing is cost-effective and efficient because it utilizes drugs that have already demonstrated safety and efficacy. This review catalogues the drugs that can be repurposed for PCa in preclinical studies as well as clinical trials.
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Affiliation(s)
- Hisham F. Bahmad
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
- Correspondence: or ; Tel.: +1-786-961-0216
| | - Timothy Demus
- Division of Urology, Columbia University, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (T.D.); (A.M.N.)
| | - Maya M. Moubarak
- Department of Anatomy, Cell Biology, and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut 1107-2020, Lebanon; (M.M.M.); (W.A.-K.)
- CNRS, IBGC, UMR5095, Universite de Bordeaux, F-33000 Bordeaux, France
| | - Darine Daher
- Faculty of Medicine, American University of Beirut, Beirut 1107-2020, Lebanon;
| | - Juan Carlos Alvarez Moreno
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
| | - Francesca Polit
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
| | - Olga Lopez
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
| | - Ali Merhe
- Department of Urology, Jackson Memorial Hospital, University of Miami, Leonard M. Miller School of Medicine, Miami, FL 33136, USA;
| | - Wassim Abou-Kheir
- Department of Anatomy, Cell Biology, and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut 1107-2020, Lebanon; (M.M.M.); (W.A.-K.)
| | - Alan M. Nieder
- Division of Urology, Columbia University, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (T.D.); (A.M.N.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
| | - Robert Poppiti
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
| | - Yumna Omarzai
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
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Zhang H, Cui H, Zhang T, Cao Y, Xuan P. Learning multi-scale heterogenous network topologies and various pairwise attributes for drug-disease association prediction. Brief Bioinform 2022; 23:6523412. [PMID: 35136910 DOI: 10.1093/bib/bbac009] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/19/2021] [Accepted: 01/07/2022] [Indexed: 01/18/2023] Open
Abstract
MOTIVATION Identifying new therapeutic effects for the approved drugs is beneficial for effectively reducing the drug development cost and time. Most of the recent computational methods concentrate on exploiting multiple kinds of information about drugs and disease to predict the candidate associations between drugs and diseases. However, the drug and disease nodes have neighboring topologies with multiple scales, and the previous methods did not fully exploit and deeply integrate these topologies. RESULTS We present a prediction method, multi-scale topology learning for drug-disease (MTRD), to integrate and learn multi-scale neighboring topologies and the attributes of a pair of drug and disease nodes. First, for multiple kinds of drug similarities, multiple drug-disease heterogenous networks are constructed respectively to integrate the similarities and associations related to drugs and diseases. Moreover, each heterogenous network has its specific topology structure, which is helpful for learning the corresponding specific topology representation. We formulate the topology embeddings for each drug node and disease node by random walking on each heterogeneous network, and the embeddings cover the neighboring topologies with different scopes. Because the multi-scale topology embeddings have context relationships, we construct Bi-directional long short-term memory-based module to encode these embeddings and their relationships and learn the neighboring topology representation. We also design the attention mechanisms at feature level and at scale level to obtain the more informative pairwise features and topology embeddings. A module based on multi-layer convolutional networks is constructed to learn the representative attributes of the drug-disease node pair according to their related similarity and association information. Comprehensive experimental results indicate that MTRD achieves the superior performance than several state-of-the-art methods for predicting drug-disease associations. MTRD also retrieves more actual drug-disease associations in the top-ranked candidates of the prediction result. Case studies on five drugs further demonstrate MTRD's ability in discovering the potential candidate diseases for the interested drugs.
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Affiliation(s)
- Hongda Zhang
- 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
| | - Yangkun Cao
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
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46
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Park N, Jeon JY, Jeong E, Kim S, Yoon D. Drug Repositioning Using Temporal Trajectories of Accompanying Comorbidities in Diabetes Mellitus. Endocrinol Metab (Seoul) 2022; 37:65-73. [PMID: 35144331 PMCID: PMC8901955 DOI: 10.3803/enm.2021.1275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/22/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Most studies of systematic drug repositioning have used drug-oriented data such as chemical structures, gene expression patterns, and adverse effect profiles. As it is often difficult to prove repositioning candidates' effectiveness in real-world clinical settings, we used patient-centered real-world data for screening repositioning candidate drugs for multiple diseases simultaneously, especially for diabetic complications. METHODS Using the National Health Insurance Service-National Sample Cohort (2002 to 2013), we analyzed claims data of 43,048 patients with type 2 diabetes mellitus (age ≥40 years). To find repositioning candidate disease-drug pairs, a nested case-control study was used for 29 pairs of diabetic complications and the drugs that met our criteria. To validate this study design, we conducted an external validation for a selected candidate pair using electronic health records. RESULTS We found 24 repositioning candidate disease-drug pairs. In the external validation study for the candidate pair cerebral infarction and glycopyrrolate, we found that glycopyrrolate was associated with decreased risk of cerebral infarction (hazard ratio, 0.10; 95% confidence interval, 0.02 to 0.44). CONCLUSION To reduce risks of diabetic complications, it would be possible to consider these candidate drugs instead of other drugs, given the same indications. Moreover, this methodology could be applied to diseases other than diabetes to discover their repositioning candidates, thereby offering a new approach to drug repositioning.
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Affiliation(s)
- Namgi Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Korea
| | - Ja Young Jeon
- Department of Endocrinology and Metabolism, Ajou University School of Medicine, Suwon, Korea
| | - Eugene Jeong
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Soyeon Kim
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Dukyong Yoon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea
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Transcription Factor Activation Profiles (TFAP) identify compounds promoting differentiation of Acute Myeloid Leukemia cell lines. Cell Death Dis 2022; 8:16. [PMID: 35013135 PMCID: PMC8748454 DOI: 10.1038/s41420-021-00811-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/22/2021] [Accepted: 12/13/2021] [Indexed: 11/26/2022]
Abstract
Repurposing of drugs for new therapeutic use has received considerable attention for its potential to limit time and cost of drug development. Here we present a new strategy to identify chemicals that are likely to promote a desired phenotype. We used data from the Connectivity Map (CMap) to produce a ranked list of drugs according to their potential to activate transcription factors that mediate myeloid differentiation of leukemic progenitor cells. To validate our strategy, we tested the in vitro differentiation potential of candidate compounds using the HL-60 human cell line as a myeloid differentiation model. Ten out of 22 compounds, which were ranked high in the inferred list, were confirmed to promote significant differentiation of HL-60. These compounds may be considered candidate for differentiation therapy. The method that we have developed is versatile and it can be adapted to different drug repurposing projects.
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48
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Wang S, Li J, Wang Y. M2PP: a novel computational model for predicting drug-targeted pathogenic proteins. BMC Bioinformatics 2022; 23:7. [PMID: 34983358 PMCID: PMC8728953 DOI: 10.1186/s12859-021-04522-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/07/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Detecting pathogenic proteins is the origin way to understand the mechanism and resist the invasion of diseases, making pathogenic protein prediction develop into an urgent problem to be solved. Prediction for genome-wide proteins may be not necessarily conducive to rapidly cure diseases as developing new drugs specifically for the predicted pathogenic protein always need major expenditures on time and cost. In order to facilitate disease treatment, computational method to predict pathogenic proteins which are targeted by existing drugs should be exploited. RESULTS In this study, we proposed a novel computational model to predict drug-targeted pathogenic proteins, named as M2PP. Three types of features were presented on our constructed heterogeneous network (including target proteins, diseases and drugs), which were based on the neighborhood similarity information, drug-inferred information and path information. Then, a random forest regression model was trained to score unconfirmed target-disease pairs. Five-fold cross-validation experiment was implemented to evaluate model's prediction performance, where M2PP achieved advantageous results compared with other state-of-the-art methods. In addition, M2PP accurately predicted high ranked pathogenic proteins for common diseases with public biomedical literature as supporting evidence, indicating its excellent ability. CONCLUSIONS M2PP is an effective and accurate model to predict drug-targeted pathogenic proteins, which could provide convenience for the future biological researches.
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Affiliation(s)
- Shiming Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
| | - Jie Li
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
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49
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Schuler J, Falls Z, Mangione W, Hudson ML, Bruggemann L, Samudrala R. Evaluating the performance of drug-repurposing technologies. Drug Discov Today 2022; 27:49-64. [PMID: 34400352 PMCID: PMC10014214 DOI: 10.1016/j.drudis.2021.08.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 06/20/2021] [Accepted: 08/08/2021] [Indexed: 01/22/2023]
Abstract
Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.
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Affiliation(s)
- James Schuler
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Matthew L Hudson
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Liana Bruggemann
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
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50
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Yang Y, Chen L. Identification of Drug-Disease Associations by Using Multiple Drug and
Disease Networks. Curr Bioinform 2022. [DOI: 10.2174/1574893616666210825115406] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Drug repositioning is a new research area in drug development. It aims to discover
novel therapeutic uses of existing drugs. It could accelerate the process of designing novel drugs
for some diseases and considerably decrease the cost. The traditional method to determine novel therapeutic
uses of an existing drug is quite laborious. It is alternative to design computational methods to
overcome such defect.
Objective:
This study aims to propose a novel model for the identification of drug–disease associations.
Method:
Twelve drug networks and three disease networks were built, which were fed into a powerful
network-embedding algorithm called Mashup to produce informative drug and disease features. These
features were combined to represent each drug–disease association. Classic classification algorithm,
random forest, was used to build the model.
Results:
Tenfold cross-validation results indicated that the MCC, AUROC, and AUPR were 0.7156,
0.9280, and 0.9191, respectively.
Conclusion:
The proposed model showed good performance. Some tests indicated that a small dimension
of drug features and a large dimension of disease features were beneficial for constructing the
model. Moreover, the model was quite robust even if some drug or disease properties were not available.
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Affiliation(s)
- Ying Yang
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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