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Zhong S, Shengyu Liu, Xin Shi, Zhang X, Li K, Liu G, Li L, Tao S, Zheng B, Sheng W, Ye Z, Xing Q, Zhai Q, Ren L, Wu Y, Bao Y. Disulfiram in glioma: Literature review of drug repurposing. Front Pharmacol 2022; 13:933655. [PMID: 36091753 PMCID: PMC9448899 DOI: 10.3389/fphar.2022.933655] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Gliomas are the most common malignant brain tumors. High-grade gliomas, represented by glioblastoma multiforme (GBM), have a poor prognosis and are prone to recurrence. The standard treatment strategy is tumor removal combined with radiotherapy and chemotherapy, such as temozolomide (TMZ). However, even after conventional treatment, they still have a high recurrence rate, resulting in an increasing demand for effective anti-glioma drugs. Drug repurposing is a method of reusing drugs that have already been widely approved for new indication. It has the advantages of reduced research cost, safety, and increased efficiency. Disulfiram (DSF), originally approved for alcohol dependence, has been repurposed for adjuvant chemotherapy in glioma. This article reviews the drug repurposing method and the progress of research on disulfiram reuse for glioma treatment.
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2
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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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3
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Gao B, Wang L, Zhang N, Han M, Zhang Y, Liu H, Sun D, Liu Y. Screening Novel Drug Candidates for Kidney Renal Clear Cell Carcinoma Treatment: A Study on Differentially Expressed Genes through the Connectivity Map Database. Kidney Blood Press Res 2021; 46:702-713. [PMID: 34818247 DOI: 10.1159/000518437] [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: 04/09/2021] [Accepted: 07/13/2021] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE Kidney renal clear cell carcinoma (KIRC) is a common cancer with high morbidity and mortality in renal cancer. Thus, the transcriptome data of KIRC patients in The Cancer Genome Atlas (TCGA) database were analyzed and drug candidates for the treatment of KIRC were explored through the connectivity map (CMap) database. METHODS The transcriptome data of KIRC patients were downloaded from TCGA database, and KIRC-associated hub genes were screened out through differential analysis and protein-protein interaction (PPI) network analysis. Afterward, the CMap database was used to select drug candidates for KIRC treatment, and the drug-targeted genes were obtained through the STITCH database. A PPI network was constructed by combining drug-targeted genes with hub genes that affected the pathogenesis of KIRC to obtain final hub genes. Finally, combining hub genes and KIRC-associated hub genes, the pathways affected by drugs were explored by pathway enrichment analysis. RESULTS A total of 2,312 differentially expressed genes were found in patients, which were concentrated in immune cell activity, cytokine, and chemokine secretion pathways. Drug screening disclosed 5 drug candidates for KIRC treatment: fedratinib, Ly344864, geldanamycin, AS-605240, and luminespib. Based on drug-targeted genes and KIRC-associated hub genes, 16 hub genes were screened out. Pathway enrichment analysis revealed that drugs mainly affected pathways such as neuroactive ligand pathways, cell adhesion, and chemokines. CONCLUSION The above results indicated that fedratinib, LY 344864, geldanamycin, AS-605240, and luminespib could be used as candidates for KIRC therapy. The findings from this study will make contributions to the treatment of KIRC in the future.
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Affiliation(s)
- Bin Gao
- Department of Urology, Tangshan Central Hospital, Tangshan, China
| | - Lijuan Wang
- Department of Urology, Tangshan Central Hospital, Tangshan, China
| | - Na Zhang
- Department of Urology, Tangshan Central Hospital, Tangshan, China
| | - Miaomiao Han
- Department of Urology, Tangshan Central Hospital, Tangshan, China
| | - Yubo Zhang
- Department of Urology, Tangshan Central Hospital, Tangshan, China
| | - Huancai Liu
- Department of Urology, Tangshan Central Hospital, Tangshan, China
| | - Dongli Sun
- Department of Urology, Tangshan Central Hospital, Tangshan, China
| | - Yifei Liu
- Department of Urology, Tangshan Central Hospital, Tangshan, China
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Azami M, Beheshtizadeh N. Identification of regeneration-involved growth factors in cartilage engineering procedure promotes its reconstruction. Regen Med 2021; 16:719-731. [PMID: 34287065 DOI: 10.2217/rme-2021-0028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Aim: To fabricate mature cartilage for implantation, developmental biological processes and proteins should be understood and employed. Methods: A systems biology study of all protein-coding genes participating in cartilage regeneration resulted in a network graph with 11 nodes and 28 edges. Gene ontology and centrality analysis were performed based on the degree index. Results: The four most crucial biological processes along with the seven most interactive proteins involved in cartilage regeneration were identified. Some proteins, which are under serious discussion in cartilage developmental and disease processes, are included in regeneration. Conclusions: Findings positively correlate with the literature, supporting the use of the four most impressive proteins as growth factors applicable to cartilage tissue engineering, including COL2A1, SOX9, CTGF and TGFβ1.
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Affiliation(s)
- Mahmoud Azami
- Department of Tissue Engineering & Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Regenerative Medicine group (REMED), Universal Scientific Education & Research Network (USERN), Tehran, Iran
| | - Nima Beheshtizadeh
- Department of Tissue Engineering & Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Regenerative Medicine group (REMED), Universal Scientific Education & Research Network (USERN), Tehran, Iran
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5
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Samart K, Tuyishime P, Krishnan A, Ravi J. Reconciling multiple connectivity scores for drug repurposing. Brief Bioinform 2021; 22:6278144. [PMID: 34013329 PMCID: PMC8597919 DOI: 10.1093/bib/bbab161] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 12/16/2022] Open
Abstract
The basis of several recent methods for drug repurposing is the key principle that an
efficacious drug will reverse the disease molecular ‘signature’ with minimal side effects.
This principle was defined and popularized by the influential ‘connectivity map’ study in
2006 regarding reversal relationships between disease- and drug-induced gene expression
profiles, quantified by a disease-drug ‘connectivity score.’ Over the past 15 years,
several studies have proposed variations in calculating connectivity scores toward
improving accuracy and robustness in light of massive growth in reference drug profiles.
However, these variations have been formulated inconsistently using various notations and
terminologies even though they are based on a common set of conceptual and statistical
ideas. Therefore, we present a systematic reconciliation of multiple disease-drug
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}{}$EWCos$\end{document}) and connectivity scores
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}{}$EMUDRA$\end{document}) by defining them using consistent
notation and terminology. In addition to providing clarity and deeper insights, this
coherent definition of connectivity scores and their relationships provides a unified
scheme that newer methods can adopt, enabling the computational drug-development community
to compare and investigate different approaches easily. To facilitate the continuous and
transparent integration of newer methods, this article will be available as a live
document (https://jravilab.github.io/connectivity_scores) coupled with a GitHub
repository (https://github.com/jravilab/connectivity_scores) that any researcher can
build on and push changes to.
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Affiliation(s)
- Kewalin Samart
- Computational Mathematics, and Computational Math, Science & Engineering at Michigan State University, East Lansing, MI, USA
| | - Phoebe Tuyishime
- College of Agriculture and Natural Resources at Michigan State University, East Lansing, MI, USA
| | - Arjun Krishnan
- Departments of Computational Math, Science & Engineering, and Biochemistry & Molecular Biology at Michigan State University, East Lansing, MI, USA
| | - Janani Ravi
- Pathobiology and Diagnostic Investigation at Michigan State University, East Lansing, MI, USA
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Beheshtizadeh N, Asgari Y, Nasiri N, Farzin A, Ghorbani M, Lotfibakhshaiesh N, Azami M. A network analysis of angiogenesis/osteogenesis-related growth factors in bone tissue engineering based on in-vitro and in-vivo data: A systems biology approach. Tissue Cell 2021; 72:101553. [PMID: 33975231 DOI: 10.1016/j.tice.2021.101553] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 04/01/2021] [Accepted: 04/27/2021] [Indexed: 12/18/2022]
Abstract
The principal purpose of tissue engineering is to stimulate the injured or unhealthy tissues to revive their primary function through the simultaneous use of chemical agents, cells, and biocompatible materials. Still, choosing the appropriate protein as a growth factor (GF) for tissue engineering is vital to fabricate artificial tissues and accelerate the regeneration procedure. In this study, the angiogenesis and osteogenesis-related proteins' interactions are studied using their related network. Three major biological processes, including osteogenesis, angiogenesis, and angiogenesis regulation, were investigated by creating a protein-protein interaction (PPI) network (45 nodes and 237 edges) of bone regeneration efficient proteins. Furthermore, a gene ontology and a centrality analysis were performed to identify essential proteins within a network. The higher degree in this network leads to higher interactions between proteins and causes a considerable effect. The most highly connected proteins in the PPI network are the most remarkable for their employment. The results of this study showed that three significant proteins including prostaglandin endoperoxide synthase 2 (PTGS2), TEK receptor tyrosine kinase (TEK), and fibroblast growth factor 18 (FGF18) were involved simultaneously in osteogenesis, angiogenesis, and their positive regulatory. Regarding the available literature, the results of this study confirmed that PTGS2 and FGF18 could be used as a GF in bone tissue engineering (BTE) applications to promote angiogenesis and osteogenesis. Nevertheless, TEK was not used in BTE applications until now and should be considered in future works to be examined in-vitro and in-vivo.
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Affiliation(s)
- Nima Beheshtizadeh
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Iran; Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran; School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia
| | - Yazdan Asgari
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Iran
| | - Noushin Nasiri
- School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia
| | - Ali Farzin
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Iran; Regenerative Medicine Group (REMED), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Mohammad Ghorbani
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Iran
| | - Nasrin Lotfibakhshaiesh
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Iran; Regenerative Medicine Group (REMED), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Mahmoud Azami
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Iran; Regenerative Medicine Group (REMED), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
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7
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Jain P, Jain SK, Jain M. Harnessing Drug Repurposing for Exploration of New Diseases: An Insight to Strategies and Case Studies. Curr Mol Med 2021; 21:111-132. [PMID: 32560606 DOI: 10.2174/1566524020666200619125404] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/12/2020] [Accepted: 05/13/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Traditional drug discovery is time consuming, costly, and risky process. Owing to the large investment, excessive attrition, and declined output, drug repurposing has become a blooming approach for the identification and development of new therapeutics. The method has gained momentum in the past few years and has resulted in many excellent discoveries. Industries are resurrecting the failed and shelved drugs to save time and cost. The process accounts for approximately 30% of the new US Food and Drug Administration approved drugs and vaccines in recent years. METHODS A systematic literature search using appropriate keywords were made to identify articles discussing the different strategies being adopted for repurposing and various drugs that have been/are being repurposed. RESULTS This review aims to describe the comprehensive data about the various strategies (Blinded search, computational approaches, and experimental approaches) used for the repurposing along with success case studies (treatment for orphan diseases, neglected tropical disease, neurodegenerative diseases, and drugs for pediatric population). It also inculcates an elaborated list of more than 100 drugs that have been repositioned, approaches adopted, and their present clinical status. We have also attempted to incorporate the different databases used for computational repurposing. CONCLUSION The data presented is proof that drug repurposing is a prolific approach circumventing the issues poised by conventional drug discovery approaches. It is a highly promising approach and when combined with sophisticated computational tools, it also carries high precision. The review would help researches in prioritizing the drugrepositioning method much needed to flourish the drug discovery research.
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Affiliation(s)
- Priti Jain
- Department of Pharmaceutical Chemistry and Computational Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Dhule (425405) Maharashtra, India
| | - Shreyans K Jain
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India
| | - Munendra Jain
- SVKM's Department of Sciences, Narsee Monjee Institute of Management Studies, Indore, Madhya Pradesh, India
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8
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Jiang H, Hu C, Chen M. The Advantages of Connectivity Map Applied in Traditional Chinese Medicine. Front Pharmacol 2021; 12:474267. [PMID: 33776757 PMCID: PMC7991830 DOI: 10.3389/fphar.2021.474267] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 01/11/2021] [Indexed: 01/11/2023] Open
Abstract
Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.
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Affiliation(s)
- Huimin Jiang
- School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China.,CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Cheng Hu
- School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China.,The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
| | - Meijuan Chen
- School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res 2021; 49:D1388-D1395. [PMID: 33151290 DOI: 10.1093/nar/gkaa971(2020)] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 05/28/2023] Open
Abstract
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
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Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jie Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Asta Gindulyte
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jia He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Siqian He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Qingliang Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Benjamin A Shoemaker
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Paul A Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Bo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Leonid Zaslavsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
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10
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Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res 2021; 49:D1388-D1395. [PMID: 33151290 PMCID: PMC7778930 DOI: 10.1093/nar/gkaa971] [Citation(s) in RCA: 1817] [Impact Index Per Article: 605.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 02/06/2023] Open
Abstract
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
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Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jie Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Asta Gindulyte
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jia He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Siqian He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Qingliang Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Benjamin A Shoemaker
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Paul A Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Bo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Leonid Zaslavsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
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11
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Huang L, Luo H, Li S, Wu FX, Wang J. Drug-drug similarity measure and its applications. Brief Bioinform 2020; 22:5956929. [PMID: 33152756 DOI: 10.1093/bib/bbaa265] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/13/2020] [Accepted: 09/14/2020] [Indexed: 02/01/2023] Open
Abstract
Drug similarities play an important role in modern biology and medicine, as they help scientists gain deep insights into drugs' therapeutic mechanisms and conduct wet labs that may significantly improve the efficiency of drug research and development. Nowadays, a number of drug-related databases have been constructed, with which many methods have been developed for computing similarities between drugs for studying associations between drugs, human diseases, proteins (drug targets) and more. In this review, firstly, we briefly introduce the publicly available drug-related databases. Secondly, based on different drug features, interaction relationships and multimodal data, we summarize similarity calculation methods in details. Then, we discuss the applications of drug similarities in various biological and medical areas. Finally, we evaluate drug similarity calculation methods with common evaluation metrics to illustrate the important roles of drug similarity measures on different applications.
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Affiliation(s)
- Lan Huang
- Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, China
| | - Huimin Luo
- School of Computer and Information Engineering at Henan University, Kaifeng, China
| | - Suning Li
- Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Fang-Xiang Wu
- College of Engineering and Department of Computer Sciences, University of Saskatchewan, Saskatoon, Canada
| | - Jianxin Wang
- Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, China
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12
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Lin CH, Chiu SI, Chen TF, Jang JSR, Chiu MJ. Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model. Int J Mol Sci 2020; 21:ijms21186914. [PMID: 32967146 PMCID: PMC7555120 DOI: 10.3390/ijms21186914] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/15/2022] Open
Abstract
Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.
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Affiliation(s)
- Chin-Hsien Lin
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100225, Taiwan; (C.-H.L.); (T.-F.C.)
| | - Shu-I Chiu
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan; (S.-I.C.); (J.-S.R.J.)
- Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan
| | - Ta-Fu Chen
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100225, Taiwan; (C.-H.L.); (T.-F.C.)
| | - Jyh-Shing Roger Jang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan; (S.-I.C.); (J.-S.R.J.)
| | - Ming-Jang Chiu
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100225, Taiwan; (C.-H.L.); (T.-F.C.)
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
- Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei 100233, Taiwan
- Graduate Institue of Psychology, National Taiwan University, Taipei 10617, Taiwan
- Correspondence: ; Tel.: +886-2-23123456 (ext. 65339)
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13
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Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform 2020; 12:46. [PMID: 33431024 PMCID: PMC7374666 DOI: 10.1186/s13321-020-00450-7] [Citation(s) in RCA: 148] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/13/2020] [Indexed: 01/13/2023] Open
Abstract
Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.
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Affiliation(s)
- Tamer N Jarada
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Jon G Rokne
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Reda Alhajj
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
- Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey.
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14
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Patel H, Iniesta R, Stahl D, Dobson RJ, Newhouse SJ. Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease. J Alzheimers Dis 2020; 74:545-561. [PMID: 32065794 PMCID: PMC7175937 DOI: 10.3233/jad-191163] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/13/2020] [Indexed: 11/15/2022]
Abstract
BACKGROUND The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific. OBJECTIVE Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature. METHODS Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls. RESULTS The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4). CONCLUSIONS This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.
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Affiliation(s)
- Hamel Patel
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
| | - Raquel Iniesta
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Richard J.B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Stephen J. Newhouse
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
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15
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Romanelli MM, da Costa-Silva TA, Cunha-Junior E, Dias Ferreira D, Guerra JM, Galisteo AJ, Pinto EG, Barbosa LRS, Torres-Santos EC, Tempone AG. Sertraline Delivered in Phosphatidylserine Liposomes Is Effective in an Experimental Model of Visceral Leishmaniasis. Front Cell Infect Microbiol 2019; 9:353. [PMID: 31737574 PMCID: PMC6828611 DOI: 10.3389/fcimb.2019.00353] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 09/30/2019] [Indexed: 12/17/2022] Open
Abstract
Liposomes containing phosphatidylserine (PS) has been used for the delivery of drugs into the intramacrophage milieu. Leishmania (L.) infantum parasites live inside macrophages and cause a fatal and neglected viscerotropic disease, with a toxic treatment. Sertraline was studied as a free formulation (SERT) and also entrapped into phosphatidylserine liposomes (LP-SERT) against intracellular amastigotes and in a murine model of visceral leishmaniasis. LP-SERT showed a potent activity against intracellular amastigotes with an EC50 value of 2.5 μM. The in vivo efficacy of SERT demonstrated a therapeutic failure. However, when entrapped into negatively charged liposomes (−58 mV) of 125 nm, it significantly reduced the parasite burden in the mice liver by 89% at 1 mg/kg, reducing the serum levels of the cytokine IL-6 and upregulating the levels of the chemokine MCP-1. Histopathological studies demonstrated the presence of an inflammatory infiltrate with the development of granulomas in the liver, suggesting the resolution of the infection in the treated group. Delivery studies showed fluorescent-labeled LP-SERT in the liver and spleen of mice even after 48 h of administration. This study demonstrates the efficacy of PS liposomes containing sertraline in experimental VL. Considering the urgent need for VL treatments, the repurposing approach of SERT could be a promising alternative.
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Affiliation(s)
| | | | - Edezio Cunha-Junior
- Fundação Oswaldo Cruz, Instituto Oswaldo Cruz, Pavilhão Leonidas Deane, Laboratório de Bioquímica de Tripanosomatídeos, Rio de Janeiro, Brazil
| | | | | | - Andres Jimenez Galisteo
- Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | | | - Leandro R S Barbosa
- Instituto de Física da Universidade de São Paulo, Cidade Universitária, São Paulo, Brazil
| | - Eduardo Caio Torres-Santos
- Fundação Oswaldo Cruz, Instituto Oswaldo Cruz, Pavilhão Leonidas Deane, Laboratório de Bioquímica de Tripanosomatídeos, Rio de Janeiro, Brazil
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16
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Turki T, Taguchi YH. Machine learning algorithms for predicting drugs–tissues relationships. EXPERT SYSTEMS WITH APPLICATIONS 2019. [DOI: 10.1016/j.eswa.2019.02.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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17
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Abstract
We present a bipartite graph-based approach to calculate drug pairwise similarity for identifying potential new indications of approved drugs. Both chemical and molecular features were used in drug similarity calculation. In this paper, we first extracted drug chemical structures and drug-target interactions. Second, we computed chemical structure similarity and drug- target profile similarity. Further, we constructed a bipartite graph model with known relationships between drugs and their target proteins. Finally, we weighted summing drug structure similarity with target profile similarity to derive drug pairwise similarity, so that we can predict potential indication of a drug from its similar drugs. In addition, we summarized some alternative strategies and variations follow-up to each section in the overall analysis.
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18
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Abstract
Background Drug repositioning, also known as drug repurposing, defines new indications for existing drugs and can be used as an alternative to drug development. In recent years, the accumulation of large volumes of information related to drugs and diseases has led to the development of various computational approaches for drug repositioning. Although herbal medicines have had a great impact on current drug discovery, there are still a large number of herbal compounds that have no definite indications. Results In the present study, we constructed a computational model to predict the unknown pharmacological effects of herbal compounds using machine learning techniques. Based on the assumption that similar diseases can be treated with similar drugs, we used four categories of drug-drug similarity (e.g., chemical structure, side-effects, gene ontology, and targets) and three categories of disease-disease similarity (e.g., phenotypes, human phenotype ontology, and gene ontology). Then, associations between drug and disease were predicted using the employed similarity features. The prediction models were constructed using classification algorithms, including logistic regression, random forest and support vector machine algorithms. Upon cross-validation, the random forest approach showed the best performance (AUC = 0.948) and also performed well in an external validation assessment using an unseen independent dataset (AUC = 0.828). Finally, the constructed model was applied to predict potential indications for existing drugs and herbal compounds. As a result, new indications for 20 existing drugs and 31 herbal compounds were predicted and validated using clinical trial data. Conclusions The predicted results were validated manually confirming the performance and underlying mechanisms – for example, irinotecan as a treatment for neuroblastoma. From the prediction, herbal compounds were considered to be drug candidates for related diseases which is important to be further developed. The proposed prediction model can contribute to drug discovery by suggesting drug candidates from herbal compounds which have potentials but few were studied. Electronic supplementary material The online version of this article (10.1186/s12859-019-2811-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Eunyoung Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea
| | - A-Sol Choi
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.
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19
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GNS HS, GR S, Murahari M, Krishnamurthy M. An update on Drug Repurposing: Re-written saga of the drug’s fate. Biomed Pharmacother 2019; 110:700-716. [DOI: 10.1016/j.biopha.2018.11.127] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 11/16/2018] [Accepted: 11/27/2018] [Indexed: 12/20/2022] Open
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20
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Brown AS, Patel CJ. A review of validation strategies for computational drug repositioning. Brief Bioinform 2018; 19:174-177. [PMID: 27881429 PMCID: PMC5862266 DOI: 10.1093/bib/bbw110] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Indexed: 12/15/2022] Open
Abstract
Repositioning of previously approved drugs is a promising methodology because it reduces the cost and duration of the drug development pipeline and reduces the likelihood of unforeseen adverse events. Computational repositioning is especially appealing because of the ability to rapidly screen candidates in silico and to reduce the number of possible repositioning candidates. What is unclear, however, is how useful such methods are in producing clinically efficacious repositioning hypotheses. Furthermore, there is no agreement in the field over the proper way to perform validation of in silico predictions, and in fact no systematic review of repositioning validation methodologies. To address this unmet need, we review the computational repositioning literature and capture studies in which authors claimed to have validated their work. Our analysis reveals widespread variation in the types of strategies, predictions made and databases used as ‘gold standards’. We highlight a key weakness of the most commonly used strategy and propose a path forward for the consistent analytic validation of repositioning techniques.
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Affiliation(s)
- Adam S Brown
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA, USA
- Corresponding author: Chirag J. Patel, Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115, USA. Tel.: (617) 432 1195; E-mail:
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21
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Shameer K, Glicksberg BS, Hodos R, Johnson KW, Badgeley MA, Readhead B, Tomlinson MS, O’Connor T, Miotto R, Kidd BA, Chen R, Ma’ayan A, Dudley JT. Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Brief Bioinform 2018; 19:656-678. [PMID: 28200013 PMCID: PMC6192146 DOI: 10.1093/bib/bbw136] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 11/29/2016] [Indexed: 12/22/2022] Open
Abstract
Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.
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Affiliation(s)
- Khader Shameer
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Benjamin S Glicksberg
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Rachel Hodos
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
- New York University, New York, NY, USA
| | - Kipp W Johnson
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Marcus A Badgeley
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Ben Readhead
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Max S Tomlinson
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | | | - Riccardo Miotto
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Brian A Kidd
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Rong Chen
- Clinical Genome Informatics, Icahn Institute of Genetics and Multiscale
Biology, Mount Sinai Health System, New York, NY
| | - Avi Ma’ayan
- Mount Sinai Center for Bioinformatics, Mount Sinai Health System, New York,
NY
| | - Joel T Dudley
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New
York, NY, USA
- Department of Population Health Science and Policy, Mount Sinai Health System,
New York, NY, USA
- Director of Biomedical Informatics, Icahn School of Medicine at Mount Sinai,
Mount Sinai Health System, New York, NY
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22
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Nguyen TM, Muhammad SA, Ibrahim S, Ma L, Guo J, Bai B, Zeng B. DeCoST: A New Approach in Drug Repurposing From Control System Theory. Front Pharmacol 2018; 9:583. [PMID: 29922160 PMCID: PMC5996185 DOI: 10.3389/fphar.2018.00583] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 05/15/2018] [Indexed: 01/19/2023] Open
Abstract
In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from "one: disease-gene-drug" to "multi: gene, dru" and from "lazy guilt-by-association" to "systematic model-based pattern matching," mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear-quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.).
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Affiliation(s)
- Thanh M Nguyen
- Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN, United States
| | - Syed A Muhammad
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
| | - Sara Ibrahim
- Department of Biology, School of Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN, United States
| | - Lin Ma
- The 1st School of Medicine and School of Information and Engineering, Wenzhou Medical University, Zhejiang, China
| | - Jinlei Guo
- The 1st School of Medicine and School of Information and Engineering, Wenzhou Medical University, Zhejiang, China
| | - Baogang Bai
- The 1st School of Medicine and School of Information and Engineering, Wenzhou Medical University, Zhejiang, China
| | - Bixin Zeng
- Institute of Lasers and Biomedical Photonics, Wenzhou Medical University, Wenzhou, China
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23
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Brown AS, Rasooly D, Patel CJ. Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:124-129. [PMID: 28941007 PMCID: PMC5824113 DOI: 10.1002/psp4.12258] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 09/07/2017] [Accepted: 09/19/2017] [Indexed: 12/13/2022]
Abstract
Computational drug repositioning methods can scalably nominate approved drugs for new diseases, with reduced risk of unforeseen side effects. The majority of methods eschew individual‐level phenotypes despite the promise of biomarker‐driven repositioning. In this study, we propose a framework for discovering serendipitous interactions between drugs and routine clinical phenotypes in cross‐sectional observational studies. Key to our strategy is the use of a healthy and nondiabetic population derived from the National Health and Nutrition Examination Survey, mitigating risk for confounding by indication. We combine complementary diagnostic phenotypes (fasting glucose and glucose response) and associate them with prescription drug usage. We then sought confirmation of phenotype‐drug associations in unidentifiable member claims data from the Aetna Insurance company using a retrospective self‐controlled case analysis approach. We identify bupropion as a plausible glucose lowering agent, suggesting that surveying otherwise healthy individuals in cross‐sectional studies can discover new drug repositioning hypotheses that have applicability to longitudinal clinical practice.
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Affiliation(s)
- Adam S Brown
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Danielle Rasooly
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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24
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Abstract
Background Much effort has been devoted to the discovery of specific mechanisms between drugs and single targets to date. However, as biological systems maintain homeostasis at the level of functional networks robustly controlling the internal environment, such networks commonly contain multiple redundant mechanisms designed to counteract loss or perturbation of a single member of the network. As such, investigation of therapeutics that target dysregulated pathways or processes, rather than single targets, may identify agents that function at a level of the biological organization more relevant to the pathology of complex diseases such as Parkinson’s Disease (PD). Genome-wide association studies (GWAS) in PD have identified common variants underlying disease susceptibility, while gene expression microarray data provide genome-wide transcriptional profiles. These genomic studies can illustrate upstream perturbations causing the dysfunction in signaling pathways and downstream biochemical mechanisms leading to the PD phenotype. We hypothesize that drugs acting at the level of a gene expression module specific to PD can overcome the lack of efficacy associated with targeting a single gene in polygenic diseases. Thus, this approach represents a promising new direction for module-based drug discovery in human diseases such as PD. Results We built a framework that integrates GWAS data with gene co-expression modules from tissues representing three brain regions—the frontal gyrus, the lateral substantia, and the medial substantia in PD patients. Using weighted gene correlation network analysis (WGCNA) software package in R, we conducted enrichment analysis of data from a GWAS of PD. This led to the identification of two over-represented PD-specific gene co-expression network modules: the Brown Module (Br) containing 449 genes and the Turquoise module (T) containing 905 genes. Further enrichment analysis identified four functional pathways within the Br module (cellular respiration, intracellular transport, energy coupled proton transport against the electrochemical gradient, and microtubule-based movement), and one functional pathway within the T module (M-phase). Next, we utilized drug-protein regulatory relationship databases (DMAP) and developed a Drug Effect Sum Score (DESS) to evaluate all candidate drugs that might restore gene expression to normal level across the Br and T modules. Among the drugs with the 12 highest DESS scores, 5 had been reported as potential treatments for PD and 6 hold potential repositioning applications. Conclusion In this study, we present a systems pharmacology framework which draws on genetic data from GWAS and gene expression microarray data to reposition drugs for PD. Our innovative approach integrates gene co-expression modules with biomolecular interaction network analysis to identify network modules critical to the PD pathway and disease mechanism. We quantify the positive effects of drugs in a DESS score that is based on known drug-target activity profiles. Our results illustrate that this modular approach is promising for repositioning drugs for use in polygenic diseases such as PD, and is capable of addressing challenges of the hindered gene target in drug repositioning approaches to date. Electronic supplementary material The online version of this article (10.1186/s12859-017-1889-0) contains supplementary material, which is available to authorized users.
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Bioinformatics in translational drug discovery. Biosci Rep 2017; 37:BSR20160180. [PMID: 28487472 PMCID: PMC6448364 DOI: 10.1042/bsr20160180] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 05/04/2017] [Accepted: 05/08/2017] [Indexed: 12/31/2022] Open
Abstract
Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.
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Brown AS, Patel CJ. MeSHDD: Literature-based drug-drug similarity for drug repositioning. J Am Med Inform Assoc 2017; 24:614-618. [PMID: 27678460 PMCID: PMC5391732 DOI: 10.1093/jamia/ocw142] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Revised: 08/17/2016] [Accepted: 08/23/2016] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms. METHODS We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs. RESULTS We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin. CONCLUSION Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/ ).
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Affiliation(s)
- Adam S Brown
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Brown AS, Patel CJ. A standard database for drug repositioning. Sci Data 2017; 4:170029. [PMID: 28291243 PMCID: PMC5349249 DOI: 10.1038/sdata.2017.29] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Accepted: 01/20/2017] [Indexed: 02/03/2023] Open
Abstract
Drug repositioning, the process of discovering, validating, and marketing previously approved drugs for new indications, is of growing interest to academia and industry due to reduced time and costs associated with repositioned drugs. Computational methods for repositioning are appealing because they putatively nominate the most promising candidate drugs for a given indication. Comparing the wide array of computational repositioning methods, however, is a challenge due to inconsistencies in method validation in the field. Furthermore, a common simplifying assumption, that all novel predictions are false, is intellectually unsatisfying and hinders reproducibility. We address this assumption by providing a gold standard database, repoDB, that consists of both true positives (approved drugs), and true negatives (failed drugs). We have made the full database and all code used to prepare it publicly available, and have developed a web application that allows users to browse subsets of the data (http://apps.chiragjpgroup.org/repoDB/).
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Affiliation(s)
- Adam S Brown
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, Massachusetts 02115, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, Massachusetts 02115, USA
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Abstract
Background The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously. Results We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC50) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC50 of 0.8–1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control. Conclusions DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3260-7) contains supplementary material, which is available to authorized users.
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Sun Y, Hameed PN, Verspoor K, Halgamuge S. A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning. BMC SYSTEMS BIOLOGY 2016; 10:128. [PMID: 28105946 PMCID: PMC5249043 DOI: 10.1186/s12918-016-0371-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background Drug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. It is challenging to reposition drugs as pharmacological data is large and complex. Subnetwork identification has already been used to simplify the visualization and interpretation of biological data, but it has not been applied to drug repositioning so far. In this paper, we fill this gap by proposing a new Physarum-inspired Prize-Collecting Steiner Tree algorithm to identify subnetworks for drug repositioning. Results Drug Similarity Networks (DSN) are generated using the chemical, therapeutic, protein, and phenotype features of drugs. In DSNs, vertex prizes and edge costs represent the similarities and dissimilarities between drugs respectively, and terminals represent drugs in the cardiovascular class, as defined in the Anatomical Therapeutic Chemical classification system. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed in this paper to identify subnetworks. We apply both the proposed algorithm and the widely-used GW algorithm to identify subnetworks in our 18 generated DSNs. In these DSNs, our proposed algorithm identifies subnetworks with an average Rand Index of 81.1%, while the GW algorithm can only identify subnetworks with an average Rand Index of 64.1%. We select 9 subnetworks with high Rand Index to find drug repositioning opportunities. 10 frequently occurring drugs in these subnetworks are identified as candidates to be repositioned for cardiovascular diseases. Conclusions We find evidence to support previous discoveries that nitroglycerin, theophylline and acarbose may be able to be repositioned for cardiovascular diseases. Moreover, we identify seven previously unknown drug candidates that also may interact with the biological cardiovascular system. These discoveries show our proposed Prize-Collecting Steiner Tree approach as a promising strategy for drug repositioning. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0371-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yahui Sun
- Department of Mechanical Engineering, University of Melbourne, Parkville, Melbourne, 3010, Australia
| | - Pathima Nusrath Hameed
- Department of Mechanical Engineering, University of Melbourne, Parkville, Melbourne, 3010, Australia.,Data61, Victoria Research Lab, West Melbourne, 3003, Australia.,Department of Computer Science, University of Ruhuna, Matara, 81000, Sri Lanka
| | - Karin Verspoor
- Department of Computing and Information Systems, University of Melbourne, Parkville, Melbourne, 3010, Australia
| | - Saman Halgamuge
- Research School of Engineering, College of Engineering & Computer Science, The Australian National University, Canberra, 2601, ACT, Australia.
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Lu J, Carlson HA. ChemTreeMap: an interactive map of biochemical similarity in molecular datasets. Bioinformatics 2016; 32:3584-3592. [PMID: 27515740 DOI: 10.1093/bioinformatics/btw523] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Revised: 07/18/2016] [Accepted: 08/07/2016] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION What if you could explain complex chemistry in a simple tree and share that data online with your collaborators? Computational biology often incorporates diverse chemical data to probe a biological question, but the existing tools for chemical data are ill-suited for the very large datasets inherent to bioinformatics. Furthermore, existing visualization methods often require an expert chemist to interpret the patterns. Biologists need an interactive tool for visualizing chemical information in an intuitive, accessible way that facilitates its integration into today's team-based biological research. RESULTS ChemTreeMap is an interactive, bioinformatics tool designed to explore chemical space and mine the relationships between chemical structure, molecular properties, and biological activity. ChemTreeMap synergistically combines extended connectivity fingerprints and a neighbor-joining algorithm to produce a hierarchical tree with branch lengths proportional to molecular similarity. Compound properties are shown by leaf color, size and outline to yield a user-defined visualization of the tree. Two representative analyses are included to demonstrate ChemTreeMap's capabilities and utility: assessing dataset overlap and mining structure-activity relationships. AVAILABILITY AND IMPLEMENTATION The examples from this paper may be accessed at http://ajing.github.io/ChemTreeMap/ Code for the server and client are available in the Supplementary Information, at the aforementioned github site, and on Docker Hub (https://hub.docker.com) with the nametag ajing/chemtreemap. CONTACT carlsonh@umich.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jing Lu
- Department of Computational Medicine and Bioinformatics
| | - Heather A Carlson
- Department of Computational Medicine and Bioinformatics.,Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
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Brown AS, Kong SW, Kohane IS, Patel CJ. ksRepo: a generalized platform for computational drug repositioning. BMC Bioinformatics 2016; 17:78. [PMID: 26860211 PMCID: PMC4746802 DOI: 10.1186/s12859-016-0931-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 01/29/2016] [Indexed: 01/22/2023] Open
Abstract
Background Repositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in repositioning, however, the majority of available methodologies require highly specific data inputs that preclude the use of many datasets and databases. There is a clear unmet need for a generalized methodology that enables the integration of multiple types of both gene expression data and database schema. Results ksRepo eliminates the need for a single microarray platform as input and allows for the use of a variety of drug and chemical exposure databases. We tested ksRepo’s performance on a set of five prostate cancer datasets using the Comparative Toxicogenomics Database (CTD) as our database of gene-compound interactions. ksRepo successfully predicted significance for five frontline prostate cancer therapies, representing a significant enrichment from over 7000 CTD compounds, and achieved specificity similar to other repositioning methods. Conclusions We present ksRepo, which enables investigators to use any data inputs for computational drug repositioning. ksRepo is implemented in a series of four functions in the R statistical environment under a BSD3 license. Source code is freely available at http://github.com/adam-sam-brown/ksRepo. A vignette is provided to aid users in performing ksRepo analysis.
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Affiliation(s)
- Adam S Brown
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
| | - Sek Won Kong
- Boston Children's Hospital, Boston, MA, 02115, USA.
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
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Wren JD, Thakkar S, Homayouni R, Johann DJ, Dozmorov MG. Proceedings of the 2015 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference. BMC Bioinformatics 2015; 16 Suppl 13:S1. [PMID: 26424691 PMCID: PMC4596983 DOI: 10.1186/1471-2105-16-s13-s1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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