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He H, Xie J, Huang D, Zhang M, Zhao X, Ying Y, Wang J. DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network. J Mol Graph Model 2024; 130:108783. [PMID: 38677034 DOI: 10.1016/j.jmgm.2024.108783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/21/2024] [Accepted: 04/23/2024] [Indexed: 04/29/2024]
Abstract
Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.
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Affiliation(s)
- Hongjian He
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jiang Xie
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Dingkai Huang
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Mengfei Zhang
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Xuyu Zhao
- School of Life Sciences,Shanghai University, Shanghai, China
| | - Yiwei Ying
- School of Life Sciences,Shanghai University, Shanghai, China
| | - Jiao Wang
- School of Life Sciences,Shanghai University, Shanghai, China.
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2
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Wang QX, Cai J, Chen ZJ, Liu JC, Wang JJ, Zhou H, Li QQ, Wang ZX, Wang YB, Tong ZJ, Yang J, Wei TH, Zhang MY, Zhou Y, Dai WC, Ding N, Leng XJ, Yin XY, Sun SL, Yu YC, Li NG, Shi ZH. Exploring drug repositioning possibilities of kinase inhibitors via molecular simulation. Mol Inform 2024:e202300336. [PMID: 39031899 DOI: 10.1002/minf.202300336] [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: 12/06/2023] [Revised: 04/09/2024] [Accepted: 04/28/2024] [Indexed: 07/22/2024]
Abstract
Kinases, a class of enzymes controlling various substrates phosphorylation, are pivotal in both physiological and pathological processes. Although their conserved ATP binding pockets pose challenges for achieving selectivity, this feature offers opportunities for drug repositioning of kinase inhibitors (KIs). This study presents a cost-effective in silico prediction of KIs drug repositioning via analyzing cross-docking results. We established the KIs database (278 unique KIs, 1834 bioactivity data points) and kinases database (357 kinase structures categorized by the DFG motif) for carrying out cross-docking. Comparative analysis of the docking scores and reported experimental bioactivity revealed that the Atypical, TK, and TKL superfamilies are suitable for drug repositioning. Among these kinase superfamilies, Olverematinib, Lapatinib, and Abemaciclib displayed enzymatic activity in our focused AKT-PI3K-mTOR pathway with IC50 values of 3.3, 3.2 and 5.8 μM. Further cell assays showed IC50 values of 0.2, 1.2 and 0.6 μM in tumor cells. The consistent result between prediction and validation demonstrated that repositioning KIs via in silico method is feasible.
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Affiliation(s)
- Qing-Xin Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jiao Cai
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zi-Jun Chen
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jia-Chuan Liu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jing-Jing Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Hai Zhou
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Qing-Qing Li
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zi-Xuan Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Yi-Bo Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zhen-Jiang Tong
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jin Yang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Tian-Hua Wei
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Meng-Yuan Zhang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Yun Zhou
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Wei-Chen Dai
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, 211198, Nanjing, Jiangsu, China
| | - Ning Ding
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Xue-Jiao Leng
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Xiao-Ying Yin
- School of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, 201620, Shanghai, China
| | - Shan-Liang Sun
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Yan-Cheng Yu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Nian-Guang Li
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zhi-Hao Shi
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, 211198, Nanjing, Jiangsu, China
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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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Zhang Y, Sui X, Pan F, Yu K, Li K, Tian S, Erdengasileng A, Han Q, Wang W, Wang J, Wang J, Sun D, Chung H, Zhou J, Zhou E, Lee B, Zhang P, Qiu X, Zhao T, Zhang J. BioKG: a comprehensive, large-scale biomedical knowledge graph for AI-powered, data-driven biomedical research. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.13.562216. [PMID: 38168218 PMCID: PMC10760044 DOI: 10.1101/2023.10.13.562216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
To cope with the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have emerged as a powerful data structure for integrating large volumes of heterogeneous data to facilitate accurate and efficient information retrieval and automated knowledge discovery (AKD). However, transforming unstructured content from scientific literature into KGs has remained a significant challenge, with previous methods unable to achieve human-level accuracy. In this study, we utilized an information extraction pipeline that won first place in the LitCoin NLP Challenge to construct a largescale KG using all PubMed abstracts. The quality of the large-scale information extraction rivals that of human expert annotations, signaling a new era of automatic, high-quality database construction from literature. Our extracted information markedly surpasses the amount of content in manually curated public databases. To enhance the KG's comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. The comprehensive KG enabled rigorous performance evaluation of AKD, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and achieved unprecedented results for drug target identification and drug repurposing. Taking lung cancer as an example, we found that 40% of drug targets reported in literature could have been predicted by our algorithm about 15 years ago in a retrospective study, demonstrating that substantial acceleration in scientific discovery could be achieved through automated hypotheses generation and timely dissemination. A cloud-based platform (https://www.biokde.com) was developed for academic users to freely access this rich structured data and associated tools.
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Affiliation(s)
- Yuan Zhang
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | - Xin Sui
- Insilicom LLC, Tallahassee, FL 32303
| | - Feng Pan
- Insilicom LLC, Tallahassee, FL 32303
| | | | - Keqiao Li
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | - Shubo Tian
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | | | - Qing Han
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | - Wanjing Wang
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | | | - Jian Wang
- 977 Wisteria Ter., Sunnyvale, CA 94086
| | | | | | - Jun Zhou
- Insilicom LLC, Tallahassee, FL 32303
| | - Eric Zhou
- Insilicom LLC, Tallahassee, FL 32303
| | - Ben Lee
- Insilicom LLC, Tallahassee, FL 32303
| | - Peili Zhang
- Forward Informatics, Winchester, Massachusetts, 01890
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642
| | - Tingting Zhao
- Department of Geography, Florida State University, Tallahassee, FL 32306
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL 32306
- Insilicom LLC, Tallahassee, FL 32303
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Boudin M, Diallo G, Drancé M, Mougin F. The OREGANO knowledge graph for computational drug repurposing. Sci Data 2023; 10:871. [PMID: 38057380 PMCID: PMC10700660 DOI: 10.1038/s41597-023-02757-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 11/16/2023] [Indexed: 12/08/2023] Open
Abstract
Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).
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Affiliation(s)
- Marina Boudin
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France.
| | - Gayo Diallo
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
| | - Martin Drancé
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
| | - Fleur Mougin
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
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6
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Kumar S, Roy V. Repurposing Drugs: An Empowering Approach to Drug Discovery and Development. Drug Res (Stuttg) 2023; 73:481-490. [PMID: 37478892 DOI: 10.1055/a-2095-0826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Drug discovery and development is a time-consuming and costly procedure that necessitates a substantial effort. Drug repurposing has been suggested as a method for developing medicines that takes less time than developing brand new medications and will be less expensive. Also known as drug repositioning or re-profiling, this strategy has been in use from the time of serendipitous drug discoveries to the modern computer aided drug designing and use of computational chemistry. In the light of the COVID-19 pandemic too, drug repurposing emerged as a ray of hope in the dearth of available medicines. Data availability by electronic recording, libraries, and improvements in computational techniques offer a vital substrate for systemic evaluation of repurposing candidates. In the not-too-distant future, it could be possible to create a global research archive for us to access, thus accelerating the process of drug development and repurposing. This review aims to present the evolution, benefits and drawbacks including current approaches, key players and the legal and regulatory hurdles in the field of drug repurposing. The vast quantities of available data secured in multiple drug databases, assisting in drug repurposing is also discussed.
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Affiliation(s)
- Sahil Kumar
- Pharmacology, ESIC Dental College and Hospital, New Delhi, India
| | - Vandana Roy
- Pharmacology, Maulana Azad Medical College, New Delhi, India
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Zhang ML, Zhao BW, Su XR, He YZ, Yang Y, Hu L. RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction. BMC Bioinformatics 2022; 23:516. [PMID: 36456957 PMCID: PMC9713188 DOI: 10.1186/s12859-022-05069-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs. METHODS In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations. RESULTS To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.
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Affiliation(s)
- Meng-Long Zhang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Yi-Zhou He
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Yue Yang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
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8
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Taweel B, Marson AG, Mirza N. A systems medicine strategy to predict the efficacy of drugs for monogenic epilepsies. Epilepsia 2022; 63:3125-3133. [PMID: 36196775 PMCID: PMC10092251 DOI: 10.1111/epi.17429] [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: 08/19/2022] [Revised: 10/03/2022] [Accepted: 10/03/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVE Monogenic epilepsies are rare but often severe. Because of their rarity, they are neglected by traditional drug developers. Hence, many lack effective treatments. Treatments for a disease can be discovered more quickly and economically by computationally predicting drugs that can be repurposed for it. We aimed to create a computational method to predict the efficacy of drugs for monogenic epilepsies, and to use the method to predict drugs for Dravet syndrome, as (1) it is the archetypal monogenic catastrophic epilepsy; (2) few antiseizure medications are efficacious in Dravet syndrome; and (3) predicting the effect of drugs on Dravet syndrome is challenging, because Dravet syndrome is typically caused by an SCN1A mutation, but some antiseizure medications that are efficacious in Dravet syndrome do not affect SCN1A, and some antiseizure medications that affect SCN1A aggravate seizures in Dravet syndrome. METHODS We have devised a computational method to predict drugs that could be repurposed for a monogenic epilepsy, based on a combined measure of drugs' effects upon (1) the function of the disease's causal gene and other genes predicted to influence its phenotype, (2) the transcriptomic dysregulation induced by the casual gene mutation, and (3) clinical phenotypes. RESULTS Our method correctly predicts drugs that are more effective, less effective, ineffective, and aggravating for seizures in people with Dravet syndrome. Our method correctly predicts the positive "hits" from large-scale screening of compounds in an animal model of Dravet syndrome. We predict the relative efficacy of 1462 drugs. At least 38 drugs are ranked higher than one or more of the antiseizure drugs currently used for Dravet syndrome and have existing evidence of antiseizure efficacy in animal models. SIGNIFICANCE Our predictions are a novel resource for identifying new treatments for seizures in Dravet syndrome, and our method can be adapted for other monogenic epilepsies.
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Affiliation(s)
- Basel Taweel
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Anthony G Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Nasir Mirza
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
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Ahmed F, Lee JW, Samantasinghar A, Kim YS, Kim KH, Kang IS, Memon FH, Lim JH, Choi KH. SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19. Front Public Health 2022; 10:902123. [PMID: 35784208 PMCID: PMC9244710 DOI: 10.3389/fpubh.2022.902123] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022] Open
Abstract
The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Jae Wook Lee
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
- BioSpero, Inc., Jeju, South Korea
| | | | | | - Kyung Hwan Kim
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - In Suk Kang
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Fida Hussain Memon
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Jong Hwan Lim
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
- BioSpero, Inc., Jeju, South Korea
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10
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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11
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Peng Z, Xu R, You Q. Role of Traditional Chinese Medicine in Bone Regeneration and Osteoporosis. Front Bioeng Biotechnol 2022; 10:911326. [PMID: 35711635 PMCID: PMC9194098 DOI: 10.3389/fbioe.2022.911326] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 05/12/2022] [Indexed: 12/21/2022] Open
Abstract
According to World Health Organization (WHO), osteoporosis is a systematic bone disability marked by reduced bone mass and microarchitectural degeneration of osseous cells, which leads to increased bones feebleness and fractures vulnerability. It is a polygenetic, physiological bone deformity that frequently leads to osteoporotic fractures and raises the risk of fractures in minimal trauma. Additionally, the molecular changes that cause osteoporosis are linked to decreased fracture repair and delayed bone regeneration. Bones have the ability to regenerate as part of the healing mechanism after an accident or trauma, including musculoskeletal growth and ongoing remodeling throughout adulthood. The principal treatment approaches for bone loss illnesses, such as osteoporosis, are hormone replacement therapy (HRT) and bisphosphonates. In this review, we searched literature regarding the Traditional Chinese medicines (TCM) in osteoporosis and bone regeneration. The literature results are summarized in this review for osteoporosis and bone regeneration. Traditional Chinese medicines (TCM) have grown in popularity as a result of its success in curing ailments while causing minimal adverse effects. Natural Chinese medicine has already been utilized to cure various types of orthopedic illnesses, notably osteoporosis, bone fractures and rheumatism with great success. TCM is a discipline of conventional remedy that encompasses herbal medication, massage (tui na), acupuncture, food, and exercise (qigong) therapy. It is based on more than 2,500 years of Chinese healthcare profession. This article serves as a comprehensive review summarizing the osteoporosis, bone regeneration and the traditional Chinese medicines used since ancient times for the management of osteoporosis and bone regeneration.
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12
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Kenneth K W To, Cho WCS. Drug repurposing for cancer therapy in the era of precision medicine. Curr Mol Pharmacol 2022; 15:895-903. [PMID: 35156588 DOI: 10.2174/1874467215666220214104530] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/15/2021] [Accepted: 11/07/2021] [Indexed: 11/22/2022]
Abstract
Drug repurposing refers to the identification of clinically approved drugs, with the known safety profiles and defined pharmacokinetic properties, to new indications. Despite the advances in oncology research, cancers are still associated with the most unmet medical needs. Drug repurposing has emerged as a useful approach for the search for effective and durable cancer treatment. It may also represent a promising strategy to facilitate precision cancer treatment and to overcome drug resistance. The repurposing of non-cancer drugs for precision oncology effectively extends the inventory of actionable molecular targets and thus increases the number of patients who may benefit from precision cancer treatment. In cancer types where genetic heterogeneity is so high that it is not feasible to identify strong repurposed drug candidates for standard treatment, the precision oncology approach offers individual patients access to novel treatment options. For repurposed candidates with low potency, a combination of multiple repurposed drugs may produce a synergistic therapeutic effect. Precautions should be taken when combining repurposed drugs with anticancer agents to avoid detrimental drug-drug interactions and unwanted side effects. New multifactorial data analysis and artificial intelligence methods are needed to untangle the complex association of molecular signatures influencing specific cancer subtypes to facilitate drug repurposing in precision oncology.
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Affiliation(s)
- Kenneth K W To
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - William C S Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
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13
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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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14
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Present and future challenges in therapeutic designing using computational approaches. COMPUTATIONAL APPROACHES FOR NOVEL THERAPEUTIC AND DIAGNOSTIC DESIGNING TO MITIGATE SARS-COV-2 INFECTION 2022. [PMCID: PMC9300749 DOI: 10.1016/b978-0-323-91172-6.00020-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Currently, various computational methods are being used for the purpose of therapeutic design. The advent of the Coronavirus disease-2019 (COVID-19) pandemic has created a lot of problems due to which the development of effective treatment options is urgently needed. Computational intelligence is used in the control, prevention, prediction, diagnosis, and treatment of the disease. Several important drug targets have been identified in severe acute respiratory syndrome-Coronavirus-2 using in silico methods. Computer-aided drug design includes a variety of theoretical and computational approaches that are part of modern drug discovery. Advances in machine learning methods and their applications speed up the drug discovery process. Exploration of nucleic acid-based therapeutics is playing an important role in healthcare also. But a lot of challenges have also been seen that complicate the therapeutic design. Therefore, investigation of challenges associated with therapeutic design is important, and the present chapter is aimed to cover various therapeutic design approaches and challenges associated with them. Moreover, the role of computational strategies in the exploration of potential therapeutics against COVID-19 has been investigated.
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15
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Yang B, Wang X, Dong D, Pan Y, Wu J, Liu J. Existing Drug Repurposing for Glioblastoma to Discover Candidate Drugs as a New a Approach. LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180818666210509141735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aims:
Repurposing of drugs has been hypothesized as a means of identifying novel
treatment methods for certain diseases.
Background:
Glioblastoma (GB) is an aggressive type of human cancer; the most effective treatment
for glioblastoma is chemotherapy, whereas, when repurposing drugs, a lot of time and money can be
saved.
Objective:
Repurposing of the existing drug may be used to discover candidate drugs for individualized
treatments of GB.
Method:
We used the bioinformatics method to obtain the candidate drugs. In addition, the drugs
were verified by MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation experiments,
as well as statistical analysis.
Result:
We obtained 4 candidate drugs suitable for the treatment of glioma, camptothecin, doxorubicin,
daunorubicin and mitoxantrone, by the expression spectrum data IPAS algorithm analysis and
drug-pathway connectivity analysis. These validation experiments showed that camptothecin was
more effective in treating the GB, such as MTT assay, Transwell® assays, TUNEL staining, and in
vivo tumor formation.
Conclusion:
With regard to personalized treatment, this present study may be used to guide the research
of new drugs via verification experiments and tumor formation. The present study also provides
a guide to systematic, individualized drug discovery for complex diseases and may contribute
to the future application of individualized treatments.
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Affiliation(s)
- Bo Yang
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Xiande Wang
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Dong Dong
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Yunqing Pan
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Junhua Wu
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Jianjian Liu
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
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16
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Groza V, Udrescu M, Bozdog A, Udrescu L. Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug-Gene Interactions. Pharmaceutics 2021; 13:2117. [PMID: 34959398 PMCID: PMC8709282 DOI: 10.3390/pharmaceutics13122117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 12/14/2022] Open
Abstract
Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug-gene interaction data. We obtained drug-gene interaction data from an earlier version of DrugBank, built a drug-gene interaction network, and projected it as a drug-drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug-gene interaction data to generate a comprehensive drug repurposing hint list.
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Affiliation(s)
- Vlad Groza
- Department of Computer and Information Technology, University Politehnica of Timişoara, 300223 Timişoara, Romania; (V.G.); (A.B.)
| | - Mihai Udrescu
- Department of Computer and Information Technology, University Politehnica of Timişoara, 300223 Timişoara, Romania; (V.G.); (A.B.)
| | - Alexandru Bozdog
- Department of Computer and Information Technology, University Politehnica of Timişoara, 300223 Timişoara, Romania; (V.G.); (A.B.)
| | - Lucreţia Udrescu
- Department I—Drug Analysis, “Victor Babeş” University of Medicine and Pharmacy Timişoara, 300041 Timişoara, Romania;
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17
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Chong LC, Gandhi G, Lee JM, Yeo WWY, Choi SB. Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review. Int J Mol Sci 2021; 22:8962. [PMID: 34445667 PMCID: PMC8396480 DOI: 10.3390/ijms22168962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 01/27/2021] [Indexed: 01/02/2023] Open
Abstract
Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.
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Affiliation(s)
- Li Chuin Chong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Gayatri Gandhi
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Jian Ming Lee
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Wendy Wai Yeng Yeo
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Sy-Bing Choi
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
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18
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Cai L, Lu C, Xu J, Meng Y, Wang P, Fu X, Zeng X, Su Y. Drug repositioning based on the heterogeneous information fusion graph convolutional network. Brief Bioinform 2021; 22:6347207. [PMID: 34378011 DOI: 10.1093/bib/bbab319] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/30/2021] [Accepted: 07/21/2021] [Indexed: 11/13/2022] Open
Abstract
In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines. Therefore, there is a pressing need for computational drug repurposing methodologies to facilitate drug discovery. In this study, we propose a new method, called DRHGCN (Drug Repositioning based on the Heterogeneous information fusion Graph Convolutional Network), to discover potential drugs for a certain disease. To make full use of different topology information in different domains (i.e. drug-drug similarity, disease-disease similarity and drug-disease association networks), we first design inter- and intra-domain feature extraction modules by applying graph convolution operations to the networks to learn the embedding of drugs and diseases, instead of simply integrating the three networks into a heterogeneous network. Afterwards, we parallelly fuse the inter- and intra-domain embeddings to obtain the more representative embeddings of drug and disease. Lastly, we introduce a layer attention mechanism to combine embeddings from multiple graph convolution layers for further improving the prediction performance. We find that DRHGCN achieves high performance (the average AUROC is 0.934 and the average AUPR is 0.539) in four benchmark datasets, outperforming the current approaches. Importantly, we conducted molecular docking experiments on DRHGCN-predicted candidate drugs, providing several novel approved drugs for Alzheimer's disease (e.g. benzatropine) and Parkinson's disease (e.g. trihexyphenidyl and haloperidol).
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Affiliation(s)
- Lijun Cai
- Hunan University, Changsha, Hunan, 410082, China
| | | | - Junlin Xu
- Hunan University, Changsha, Hunan, 410082, China
| | - Yajie Meng
- Hunan University, Changsha, Hunan, 410082, China
| | - Peng Wang
- Hunan University, Changsha, Hunan, 410082, China
| | | | | | - Yansen Su
- Anhui University, Changsha, Hunan, 410082, China
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19
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Dotolo S, Marabotti A, Facchiano A, Tagliaferri R. A review on drug repurposing applicable to COVID-19. Brief Bioinform 2021; 22:726-741. [PMID: 33147623 PMCID: PMC7665348 DOI: 10.1093/bib/bbaa288] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/16/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022] Open
Abstract
Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.
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Affiliation(s)
| | | | | | - Roberto Tagliaferri
- Artificial Intelligence, Statistical Pattern Recognition, Clustering, Biomedical imaging and Bioinformatics
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20
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Chrétien B, Jourdan JP, Davis A, Fedrizzi S, Bureau R, Sassier M, Rochais C, Alexandre J, Lelong-Boulouard V, Dolladille C, Dallemagne P. Disproportionality analysis in VigiBase as a drug repositioning method for the discovery of potentially useful drugs in Alzheimer's disease. Br J Clin Pharmacol 2020; 87:2830-2837. [PMID: 33274491 DOI: 10.1111/bcp.14690] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 11/09/2020] [Accepted: 11/28/2020] [Indexed: 12/12/2022] Open
Abstract
Drug repositioning aims to propose new indications for marketed drugs. Although several methods exist, the utility of pharmacovigilance databases for this purpose is unclear. We conducted a disproportionality analysis in the World Health Organization pharmacovigilance database VigiBase to identify potential anticholinesterase drug candidates for repositioning in Alzheimer's disease (AD). METHODS Disproportionality analysis is a validated method for detecting significant associations between drugs and adverse events (AEs) in pharmacovigilance databases. We applied this approach in VigiBase to establish the safety profile displayed by the anticholinesterase drugs used in AD and searched the database for drugs with similar safety profiles. The detected drugs with potential activity against acetylcholinesterase and butyrylcholinesterases (BuChEs) were then evaluated to confirm their anticholinesterase potential. RESULTS We identified 22 drugs with safety profiles similar to AD medicines. Among these drugs, 4 (clozapine, aripiprazole, sertraline and S-duloxetine) showed a human BuChE inhibition rate of over 70% at 10-5 M. Their human BuChE half maximal inhibitory concentration values were compatible with clinical anticholinesterase action in humans at their normal doses. The most active human BuChE inhibitor in our study was S-duloxetine, with a half maximal inhibitory concentration of 1.2 μM. Combined with its ability to inhibit serotonin (5-HT) reuptake, the use of this drug could represent a novel multitarget directed ligand therapeutic strategy for AD. CONCLUSION We identified 4 drugs with repositioning potential in AD using drug safety profiles derived from a pharmacovigilance database. This method could be useful for future drug repositioning efforts.
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Affiliation(s)
- Basile Chrétien
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France
| | - Jean-Pierre Jourdan
- Department of Pharmacy, Caen University Hospital, Caen, F-14000, France.,Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Audrey Davis
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Sophie Fedrizzi
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France
| | - Ronan Bureau
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Marion Sassier
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France
| | - Christophe Rochais
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Joachim Alexandre
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France.,EA4650, Signalisation, électrophysiologie et imagerie des lésions d'ischémie-reperfusion myocardique, Normandie Univ, UNICAEN, Caen, 14000, France
| | - Véronique Lelong-Boulouard
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,INSERM UMR 1075, COMETE-MOBILITES "Vieillissement, Pathologie, Santé", Normandie Univ, UNICAEN, Caen, 14000, France
| | - Charles Dolladille
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,EA4650, Signalisation, électrophysiologie et imagerie des lésions d'ischémie-reperfusion myocardique, Normandie Univ, UNICAEN, Caen, 14000, France
| | - Patrick Dallemagne
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
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21
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Zaza P, Matthieu R, Jean‐Luc C, Charles K. Drug repurposing in Raynaud's phenomenon through adverse event signature matching in the World Health Organization pharmacovigilance database. Br J Clin Pharmacol 2020; 86:2217-2222. [PMID: 32337731 PMCID: PMC7576623 DOI: 10.1111/bcp.14322] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 04/02/2020] [Accepted: 04/15/2020] [Indexed: 01/27/2023] Open
Abstract
AIMS Several pharmacological treatments are recommended for Raynaud's phenomenon (RP) secondary to systemic sclerosis, but they only have modest efficacy. A way to efficiently identify new drugs is drug repurposing, which can be based on signature matching. The signature could be derived from chemical structures, pharmacological affinity or adverse event profiles. We propose to use the World Health Organization (WHO) pharmacovigilance database to generate repositioning hypotheses for treatments of RP through adverse event signature matching. METHODS We first screened all drugs associated with at least 1 case of erythromelalgia, an adverse effect opposite to RP. In parallel, to define the adverse event signature of drugs recommended in secondary RP from the WHO pharmacovigilance database, we selected the 14 most representative adverse drug reactions (ADRs). Lastly, we performed a hierarchical cluster analysis to identify drugs with similar ADR signature to vasodilatory drugs used in RP. RESULTS In total, 179 drugs were associated with erythromelalgia; they were related to 860 334 adverse events representative of RP drugs in the WHO pharmacovigilance database. Hierarchical cluster analysis allowed identification of 6 clusters. The most stable cluster contained 7 drugs, among which 5 are recommended in secondary RP, or pertain to the same drug class: epoprostenol, nifedipine, nicardipine, lacidipine and israpidine. The 2 remaining drugs were alemtuzumab and fumaric acid. CONCLUSION Our ADR signature matching approach suggests that alemtuzumab and fumaric acid could be effective treatments of secondary RP. The latter is currently being investigated as a treatment of pulmonary hypertension in systemic sclerosis.
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Affiliation(s)
- Putkaradze Zaza
- Inserm CIC1406, clinical pharmacology departmentGrenoble Alpes university hospitalGrenobleFrance
| | - Roustit Matthieu
- Inserm CIC1406, clinical pharmacology departmentGrenoble Alpes university hospitalGrenobleFrance
- Inserm, UMR 1042‐HP2university Grenoble AlpesGrenobleFrance
| | - Cracowski Jean‐Luc
- Inserm CIC1406, clinical pharmacology departmentGrenoble Alpes university hospitalGrenobleFrance
- Inserm, UMR 1042‐HP2university Grenoble AlpesGrenobleFrance
| | - Khouri Charles
- Inserm CIC1406, clinical pharmacology departmentGrenoble Alpes university hospitalGrenobleFrance
- Inserm, UMR 1042‐HP2university Grenoble AlpesGrenobleFrance
- Pharmacovigilance unitGrenoble Alpes university hospitalGrenobleFrance
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22
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Rymbai E, Sugumar D, Saravanan J, Divakar S. Ropinirole, a potential drug for systematic repositioning based on side effect profile for management and treatment of breast cancer. Med Hypotheses 2020; 144:110156. [PMID: 32763725 DOI: 10.1016/j.mehy.2020.110156] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 07/18/2020] [Accepted: 07/29/2020] [Indexed: 01/11/2023]
Abstract
Drug repositioning offers two main advantages in drug discovery - the process is less tedious and less costly. In the past, many drugs like thalidomide and sildenafil were successfully repositioned but the process was entirely serendipitous. These days drug repositioning is widely accepted as an alternate method of drug discovery and the process is based on building a strong hypothesis guided by systematic computational and experimental methods. One of the methods used in drug repositioning is based on shared side effects by drugs of different pharmacological categories. This method rests on the principle that drugs that share side effects might also share common biological targets and therefore same pharmacological indications. Old drugs can be repositioned for new uses by identifying the shared side effects of existing drugs and by modulating their chemical structure if required. Breast cancer is the most common type of cancer in women and the second leading cause of death worldwide after lung cancer in both men and women. Letrozole, an aromatase inhibitor, is used in the treatment of advanced, recurrent and metastatic breast cancer in post-menopausal women. Identification of drugs that share side effects with letrozole might help us to identify a potential drug for repositioning in the treatment of breast cancer. Ropinirole, a dopaminergic agonist was found to share the maximum number of side effects with letrozole. Studies have proposed that dopaminergic agonists induce apoptosis in breast, colon, ovarian cancer cells and leukemia neuroblastoma. This is consistent with our hypothesis that ropinirole that shares the maximum number of side effects with letrozole might be effective in the management of breast cancer. This hypothesis was further validated by preliminary molecular docking and in-vitro cell-line studies.
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Affiliation(s)
- Emdormi Rymbai
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, The Nilgiris, Tamil Nadu, India
| | - Deepa Sugumar
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, The Nilgiris, Tamil Nadu, India
| | - J Saravanan
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, The Nilgiris, Tamil Nadu, India.
| | - S Divakar
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, The Nilgiris, Tamil Nadu, India
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23
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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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24
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Wang Z, Zhou M, Arnold C. Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing. Bioinformatics 2020; 36:i525-i533. [PMID: 32657387 PMCID: PMC7355266 DOI: 10.1093/bioinformatics/btaa437] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Mining drug-disease association and related interactions are essential for developing in silico drug repurposing (DR) methods and understanding underlying biological mechanisms. Recently, large-scale biological databases are increasingly available for pharmaceutical research, allowing for deep characterization for molecular informatics and drug discovery. However, DR is challenging due to the molecular heterogeneity of disease and diverse drug-disease associations. Importantly, the complexity of molecular target interactions, such as protein-protein interaction (PPI), remains to be elucidated. DR thus requires deep exploration of a multimodal biological network in an integrative context. RESULTS In this study, we propose BiFusion, a bipartite graph convolution network model for DR through heterogeneous information fusion. Our approach combines insights of multiscale pharmaceutical information by constructing a multirelational graph of drug-protein, disease-protein and PPIs. Especially, our model introduces protein nodes as a bridge for message passing among diverse biological domains, which provides insights into utilizing PPI for improved DR assessment. Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation. We offered an exploratory analysis for finding novel drug-disease associations. Extensive experiments showed that our approach achieved improved performance than multiple baselines for DR analysis. AVAILABILITY AND IMPLEMENTATION Source code and preprocessed datasets are at: https://github.com/zcwang0702/BiFusion.
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Affiliation(s)
- Zichen Wang
- Computational Diagnostics Lab, Departments of Radiology and Pathology, Los Angeles, CA 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
| | - Mu Zhou
- SenseBrain Research, CA 95131, USA
| | - Corey Arnold
- Computational Diagnostics Lab, Departments of Radiology and Pathology, Los Angeles, CA 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
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25
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Li X, Rousseau JF, Ding Y, Song M, Lu W. Understanding Drug Repurposing From the Perspective of Biomedical Entities and Their Evolution: Bibliographic Research Using Aspirin. JMIR Med Inform 2020; 8:e16739. [PMID: 32543442 PMCID: PMC7327595 DOI: 10.2196/16739] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 01/08/2020] [Accepted: 03/31/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Drug development is still a costly and time-consuming process with a low rate of success. Drug repurposing (DR) has attracted significant attention because of its significant advantages over traditional approaches in terms of development time, cost, and safety. Entitymetrics, defined as bibliometric indicators based on biomedical entities (eg, diseases, drugs, and genes) studied in the biomedical literature, make it possible for researchers to measure knowledge evolution and the transfer of drug research. OBJECTIVE The purpose of this study was to understand DR from the perspective of biomedical entities (diseases, drugs, and genes) and their evolution. METHODS In the work reported in this paper, we extended the bibliometric indicators of biomedical entities mentioned in PubMed to detect potential patterns of biomedical entities in various phases of drug research and investigate the factors driving DR. We used aspirin (acetylsalicylic acid) as the subject of the study since it can be repurposed for many applications. We propose 4 easy, transparent measures based on entitymetrics to investigate DR for aspirin: Popularity Index (P1), Promising Index (P2), Prestige Index (P3), and Collaboration Index (CI). RESULTS We found that the maxima of P1, P3, and CI are closely associated with the different repurposing phases of aspirin. These metrics enabled us to observe the way in which biomedical entities interacted with the drug during the various phases of DR and to analyze the potential driving factors for DR at the entity level. P1 and CI were indicative of the dynamic trends of a specific biomedical entity over a long time period, while P2 was more sensitive to immediate changes. P3 reflected the early signs of the practical value of biomedical entities and could be valuable for tracking the research frontiers of a drug. CONCLUSIONS In-depth studies of side effects and mechanisms, fierce market competition, and advanced life science technologies are driving factors for DR. This study showcases the way in which researchers can examine the evolution of DR using entitymetrics, an approach that can be valuable for enhancing decision making in the field of drug discovery and development.
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Affiliation(s)
- Xin Li
- Information Retrieval and Knowledge Mining Laboratory, School of Information Management, Wuhan University, Wuhan, China.,School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
| | - Justin F Rousseau
- Department of Population Health and Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
| | - Ying Ding
- School of Information, Dell Medical School, The University of Texas Austin, Austin, TX, United States
| | - Min Song
- Department of Library and Information Science, Yonsei University, Seoul, Republic of Korea
| | - Wei Lu
- Information Retrieval and Knowledge Mining Laboratory, School of Information Management, Wuhan University, Wuhan, China
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26
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Wu Y, Warner JL, Wang L, Jiang M, Xu J, Chen Q, Nian H, Dai Q, Du X, Yang P, Denny JC, Liu H, Xu H. Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing. JCO Clin Cancer Inform 2020; 3:1-9. [PMID: 31141421 PMCID: PMC6693869 DOI: 10.1200/cci.19.00001] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Drug development is becoming increasingly expensive and time consuming. Drug repurposing is one potential solution to accelerate drug discovery. However, limited research exists on the use of electronic health record (EHR) data for drug repurposing, and most published studies have been conducted in a hypothesis-driven manner that requires a predefined hypothesis about drugs and new indications. Whether EHRs can be used to detect drug repurposing signals is not clear. We want to demonstrate the feasibility of mining large, longitudinal EHRs for drug repurposing by detecting candidate noncancer drugs that can potentially be used for the treatment of cancer. PATIENTS AND METHODS By linking cancer registry data to EHRs, we identified 43,310 patients with cancer treated at Vanderbilt University Medical Center (VUMC) and 98,366 treated at the Mayo Clinic. We assessed the effect of 146 noncancer drugs on cancer survival using VUMC EHR data and sought to replicate significant associations (false discovery rate < .1) using the identical approach with Mayo Clinic EHR data. To evaluate replicated signals further, we reviewed the biomedical literature and clinical trials on cancers for corroborating evidence. RESULTS We identified 22 drugs from six drug classes (statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, β-blockers, nonsteroidal anti-inflammatory drugs, and α-1 blockers) associated with improved overall cancer survival (false discovery rate < .1) from VUMC; nine of the 22 drug associations were replicated at the Mayo Clinic. Literature and cancer clinical trial evaluations also showed very strong evidence to support the repurposing signals from EHRs. CONCLUSION Mining of EHRs for drug exposure–mediated survival signals is feasible and identifies potential candidates for antineoplastic repurposing. This study sets up a new model of mining EHRs for drug repurposing signals.
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Affiliation(s)
- Yonghui Wu
- The University of Texas Health Science Center at Houston, Houston, TX.,University of Florida, Gainesville, FL
| | | | | | - Min Jiang
- The University of Texas Health Science Center at Houston, Houston, TX
| | - Jun Xu
- The University of Texas Health Science Center at Houston, Houston, TX
| | - Qingxia Chen
- Vanderbilt University Medical Center, Nashville, TN
| | - Hui Nian
- Vanderbilt University Medical Center, Nashville, TN
| | - Qi Dai
- Vanderbilt University Medical Center, Nashville, TN.,Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN
| | - Xianglin Du
- The University of Texas Health Science Center at Houston, Houston, TX
| | | | | | | | - Hua Xu
- The University of Texas Health Science Center at Houston, Houston, TX
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27
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Ayed M, Lim H, Xie L. Biological representation of chemicals using latent target interaction profile. BMC Bioinformatics 2019; 20:674. [PMID: 31861982 PMCID: PMC6924142 DOI: 10.1186/s12859-019-3241-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Background Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery, and many other applications. Chemical fingerprints are a widely used feature to build machine learning models. However, the fingerprints that are derived from chemical structures ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in vivo activities than the chemical structure. Nevertheless, the scope of direct application of the chemical-target interaction profile is limited due to the severe incompleteness, biasness, and noisiness of bioassay data. Results To address the aforementioned problems, we developed a novel chemical representation method: Latent Target Interaction Profile (LTIP). LTIP embeds chemicals into a low dimensional continuous latent space that represents genome-scale chemical-target interactions. Subsequently LTIP can be used as a feature to build machine learning models. Using the drug sensitivity of cancer cell lines as a benchmark, we have shown that the LTIP robustly outperforms chemical fingerprints regardless of machine learning algorithms. Moreover, the LTIP is complementary with the chemical fingerprints. It is possible for us to combine LTIP with other fingerprints to further improve the performance of bioactivity prediction. Conclusions Our results demonstrate the potential of LTIP in particular and multi-scale modeling in general in predictive modeling of chemical modulation of biological activities.
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Affiliation(s)
- Mohamed Ayed
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA
| | - Hansaim Lim
- Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, NY, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, & The Graduate Center, The City University of New York, New York, NY, USA.
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28
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Yang K, Zhao X, Waxman D, Zhao XM. Predicting drug-disease associations with heterogeneous network embedding. CHAOS (WOODBURY, N.Y.) 2019; 29:123109. [PMID: 31893652 DOI: 10.1063/1.5121900] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 11/14/2019] [Indexed: 06/10/2023]
Abstract
The prediction of drug-disease associations holds great potential for precision medicine in the era of big data and is important for the identification of new indications for existing drugs. The associations between drugs and diseases can be regarded as a complex heterogeneous network with multiple types of nodes and links. In this paper, we propose a method, namely HED (Heterogeneous network Embedding for Drug-disease association), to predict potential associations between drugs and diseases based on a drug-disease heterogeneous network. Specifically, with the heterogeneous network constructed from known drug-disease associations, HED employs network embedding to characterize drug-disease associations and then trains a classifier to predict novel potential drug-disease associations. The results on two real datasets show that HED outperforms existing popular approaches. Furthermore, some of our predictions have been verified by evidence from literature. For instance, carvedilol, a drug that was originally used for heart failure, left ventricular dysfunction, and hypertension, is predicted to be useful for atrial fibrillation by HED, which is supported by clinical trials.
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Affiliation(s)
- Kai Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, People's Republic of China
| | - Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, People's Republic of China
| | - David Waxman
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, People's Republic of China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, People's Republic of China
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29
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Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets. Semin Cancer Biol 2019; 68:59-74. [PMID: 31562957 DOI: 10.1016/j.semcancer.2019.09.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/24/2019] [Accepted: 09/24/2019] [Indexed: 12/14/2022]
Abstract
Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.
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30
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Gan D, Xu X, Chen D, Feng P, Xu Z. Network Pharmacology-Based Pharmacological Mechanism of the Chinese Medicine Rhizoma drynariae Against Osteoporosis. Med Sci Monit 2019; 25:5700-5716. [PMID: 31368456 PMCID: PMC6688518 DOI: 10.12659/msm.915170] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Rhizoma drynariae is the main traditional Chinese medicine used for the treatment of osteoporosis, but its anti-osteoporotic targeting mechanism has not been fully elucidated due to the complexity of its active ingredients. In this study, the pharmacological mechanism of action of Rhizoma drynariae against osteoporosis was studied by integrating pharmacological concepts. The pharmacokinetic characteristics of selected major active constituents of Rhizoma drynariae and the SMILES structural similarity were used to predict related targets. A literature search was conducted to identify known osteoporosis treatment targets, which were then combined with the predicted targets to construct the direct or indirect target interaction network map of Rhizoma drynariae against osteoporosis. Finally, data on the key targets of the interactions, ranked according to relevant node parameters obtained through pathway enrichment analysis and screening of key targets and active ingredients of Rhizoma drynariae, were used to perform molecular docking simulation. We screened 16 active ingredients of Rhizoma drynariae, and 7 key targets with direct or indirect effects with a high frequency were obtained. These main pathways were found to play important roles in the PI3k-akt signaling pathway, osteoclast differentiation, Wnt signaling pathway, and estrogen signaling pathway. Molecular docking showed that most active ingredients of Rhizoma drynariae had strong binding efficiency with key targets. Based on network pharmacology, we conclude that Rhizoma drynariae plays an anti-osteoporotic role by directly or indirectly targeting multiple major signaling pathways and influencing the proliferation and differentiation of multiple types of cells.
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Affiliation(s)
- Donghao Gan
- First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland)
| | - Xiaowei Xu
- First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland)
| | - Deqiang Chen
- First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland).,Department of Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland)
| | - Peng Feng
- First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland)
| | - Zhanwang Xu
- First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland).,Department of Orthopaedics, Affilited Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland)
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31
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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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32
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Musa A, Tripathi S, Dehmer M, Yli-Harja O, Kauffman SA, Emmert-Streib F. Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks. Sci Rep 2019; 9:7849. [PMID: 31127155 PMCID: PMC6534546 DOI: 10.1038/s41598-019-44291-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 05/08/2019] [Indexed: 02/01/2023] Open
Abstract
Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.
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Affiliation(s)
- Aliyu Musa
- Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
- Institute of Biosciences and Medical Technology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
| | - Shailesh Tripathi
- Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
- Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Wehrgrabengasse 1-3, 4400, Steyr, Austria
| | - Matthias Dehmer
- Department for Biomedical Computer Science and Mechatronics, UMIT - The Health and Lifesciences University, Eduard Wallnoefer Zentrum 1, 6060, Hall in Tyrol, Austria
- College of Computer and Control Engineering, Nankai University, Tianjin, 300350, P.R. China
- Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Wehrgrabengasse 1-3, 4400, Steyr, Austria
| | - Olli Yli-Harja
- Institute of Biosciences and Medical Technology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
- Computational Systems Biology Lab, Tampere University of Technology, Korkeakoulunkatu 10, 33720, Tampere, Finland
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | | | - Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland.
- Institute of Biosciences and Medical Technology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland.
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33
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Mining heterogeneous network for drug repositioning using phenotypic information extracted from social media and pharmaceutical databases. Artif Intell Med 2019; 96:80-92. [DOI: 10.1016/j.artmed.2019.03.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 02/24/2019] [Accepted: 03/05/2019] [Indexed: 01/09/2023]
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34
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Lotfi Shahreza M, Ghadiri N, Mousavi SR, Varshosaz J, Green JR. A review of network-based approaches to drug repositioning. Brief Bioinform 2019; 19:878-892. [PMID: 28334136 DOI: 10.1093/bib/bbx017] [Citation(s) in RCA: 169] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Indexed: 01/17/2023] Open
Abstract
Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.
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Affiliation(s)
- Maryam Lotfi Shahreza
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Nasser Ghadiri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | | | - Jaleh Varshosaz
- Drug Delivery Systems Research Center of Isfahan University of Medical Sciences
| | - James R Green
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
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35
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Zhang M, Zhang M, Ge C, Liu Q, Wang J, Wei J, Zhu KQ. Automatic discovery of adverse reactions through Chinese social media. Data Min Knowl Discov 2019. [DOI: 10.1007/s10618-018-00610-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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36
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Wang Y, Yella J, Jegga AG. Transcriptomic Data Mining and Repurposing for Computational Drug Discovery. Methods Mol Biol 2019; 1903:73-95. [PMID: 30547437 DOI: 10.1007/978-1-4939-8955-3_5] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Conventional drug discovery in general is costly and time-consuming with extremely low success and relatively high attrition rates. The disparity between high cost of drug discovery and vast unmet medical needs resulted in advent of an increasing number of computational approaches that can "connect" disease with a candidate therapeutic. This includes computational drug repurposing or repositioning wherein the goal is to discover a new indication for an approved drug. Computational drug discovery approaches that are commonly used are similarity-based wherein network analysis or machine learning-based methods are used. One such approach is matching gene expression signatures from disease to those from small molecules, commonly referred to as connectivity mapping. In this chapter, we will focus on how publicly available existing transcriptomic data from diseases can be reused to identify novel candidate therapeutics and drug repositioning candidates. To elucidate these, we will present two case studies: (1) using transcriptional signature similarity or positive correlation to identify novel small molecules that are similar to an approved drug and (2) identifying candidate therapeutics via reciprocal connectivity or negative correlation between transcriptional signatures from a disease and small molecule.
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Affiliation(s)
- Yunguan Wang
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jaswanth Yella
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH, USA
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. .,Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH, USA.
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37
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Abstract
Drug repurposing is a powerful technique which has been recently employed in both industry and academia to discover and validate previously approved drugs for new indications. It provides the quickest possible transition from bench to bedside. In essence, computational strategies are appealing because they putatively nominate the most encouraging candidate for a given indication. A wide range of computational methods exist for repositioning. In this chapter we present the guidelines for performing integrated drug repurposing strategy by combining disease-disease association and molecular simulation analysis.
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38
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Lee T, Yoon Y. Drug repositioning using drug-disease vectors based on an integrated network. BMC Bioinformatics 2018; 19:446. [PMID: 30463505 PMCID: PMC6249928 DOI: 10.1186/s12859-018-2490-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 11/12/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Diverse interactions occur between biomolecules, such as activation, inhibition, expression, or repression. However, previous network-based studies of drug repositioning have employed interaction on the binary protein-protein interaction (PPI) network without considering the characteristics of the interactions. Recently, some studies of drug repositioning using gene expression data found that associations between drug and disease genes are useful information for identifying novel drugs to treat diseases. However, the gene expression profiles for drugs and diseases are not always available. Although gene expression profiles of drugs and diseases are available, existing methods cannot use the drugs or diseases, when differentially expressed genes in the profiles are not included in their network. RESULTS We developed a novel method for identifying candidate indications of existing drugs considering types of interactions between biomolecules based on known drug-disease associations. To obtain associations between drug and disease genes, we constructed a directed network using protein interaction and gene regulation data obtained from various public databases providing diverse biological pathways. The network includes three types of edges depending on relationships between biomolecules. To quantify the association between a target gene and a disease gene, we explored the shortest paths from the target gene to the disease gene and calculated the types and weights of the shortest paths. For each drug-disease pair, we built a vector consisting of values for each disease gene influenced by the drug. Using the vectors and known drug-disease associations, we constructed classifiers to identify novel drugs for each disease. CONCLUSION We propose a method for exploring candidate drugs of diseases using associations between drugs and disease genes derived from a directed gene network instead of gene regulation data obtained from gene expression profiles. Compared to existing methods that require information on gene relationships and gene expression data, our method can be applied to a greater number of drugs and diseases. Furthermore, to validate our predictions, we compared the predictions with drug-disease pairs in clinical trials using the hypergeometric test, which showed significant results. Our method also showed better performance compared to existing methods for the area under the receiver operating characteristic curve (AUC).
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Affiliation(s)
- Taekeon Lee
- Department of Computer Engineering, Gachon University, 5-22Ho, IT college, 1324 Seongnam-daero, Seongnam-si, 13120 South Korea
| | - Youngmi Yoon
- Department of Computer Engineering, Gachon University, 5-22Ho, IT college, 1324 Seongnam-daero, Seongnam-si, 13120 South Korea
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McGarry K, Graham Y, McDonald S, Rashid A. RESKO: Repositioning drugs by using side effects and knowledge from ontologies. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.06.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Ibrahim SJA, Thangamani M. Prediction of Novel Drugs and Diseases for Hepatocellular Carcinoma Based on Multi-Source Simulated Annealing Based Random Walk. J Med Syst 2018; 42:188. [PMID: 30173379 DOI: 10.1007/s10916-018-1038-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 08/20/2018] [Indexed: 01/09/2023]
Abstract
Computational techniques for foreseeing drug-disease associations by means of incorporating gene expression as well as biological network give high intuitions to the composite associations amongst targets, drugs, disease genes in addition to the diseases at a system level. Hepatocellular Carcinoma (HCC) is a malevolent tumor containing a greater rate of sickness as well as mortality. In the present work, an Integrative framework is presented with the aim of resolving this problem, for identifying new Drugs for HCC dependent upon Multi-Source Random Walk (PD-MRW), in which score the complete drugs by means of building the drug-drug similarity network. On the other hand, the collection of clinical phenotypes as well as drug side effects in combination with patient-specific genetic info. As a result, the formation of disease-drug networks that denotes the prescriptions, which are allotted to treat those diseases that are not concentrated by means of PD-MRW model. With the aim of overcoming this issue, this research offers an integrative framework for foreseeing new drugs as well as diseases for HCC dependent upon Multi-Source Simulated Annealing based Random Walk (PDD-MSSARW). Primarily, build a Gene-Gene Weighted Interaction Network (GWIN), dependent upon the gene expression as well as protein interaction network. After that, construct a drug-drug similarity network, dependent upon multi-source random walk in GWIN, disease-drug similarity network with the help of Similarity Weighted Bipartite Graph Network (SWBGN) that is build up in which the nodes are drugs as well as association among one node to another node that explains the disease diagnoses. Lastly, dependent upon the known drugs for HCC, score the entire drugs in the similarity networks. The sturdiness of the likelihoods, their overlap with those stated in Comparative Toxicogenomics Database (CTD) as well as kinds of literature, and their enhanced KEGG pathway illustrate PDD-MSSARW method be capable of efficiently find out novel drug signs.
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Affiliation(s)
| | - M Thangamani
- Kongu Engineering College, Perundurai, Tamilnadu, India
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Zhou C, Liu L, Zhuang J, Wei J, Zhang T, Gao C, Liu C, Li H, Si H, Sun C. A Systems Biology-Based Approach to Uncovering Molecular Mechanisms Underlying Effects of Traditional Chinese Medicine Qingdai in Chronic Myelogenous Leukemia, Involving Integration of Network Pharmacology and Molecular Docking Technology. Med Sci Monit 2018; 24:4305-4316. [PMID: 29934492 PMCID: PMC6049014 DOI: 10.12659/msm.908104] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Background The method of multiple targets overall control is increasingly used to predict the main active ingredient and potential target group of Chinese traditional medicines and to determine the mechanisms involved in their curative effects. Qingdai is the main traditional Chinese medicine used in the treatment of chronic myelogenous leukemia (CML), but the complex active ingredients and antitumor targets in treatment of CML have not been clearly defined in previous studies. Material/Methods We constructed a protein-protein interaction network diagram of CML with 638 nodes (proteins) and 1830 edges, based on the biological function of chronic myelocytic leukemia by use of Cytoscape, and we determined 19 key gene nodes in the CML molecule by network topological properties analysis in a data bank. Then, we used the Surflex-dock plugin in SYBYL7.3 docking and acquired the protein crystal structures of key genes involved in CML from the chemical composition of the traditional Chinese medicine Qingdai with key proteins in CML networks. Results According to the score and the spatial structure, the pharmacodynamically active ingredients of Qingdai are Isdirubin, Isoindigo, N-phenyl-2-naphthylamine, and Isatin, among which Isdirubin is the most important. We further screened the most effective activity key protein structures of CML to find the best pharmacodynamically active ingredients of Qingdai, according to the binding interactions of the inhibitors at the catalytic site performed in best docking combinations. Conclusions The results suggest that Isdirubin plays a role in resistance to CML by altering the expressions of PIK3CA, MYC, JAK2, and TP53 target proteins. Network pharmacology and molecular docking technology can be used to search for possible reactive molecules in traditional chinese medicines (TCM) and to elucidate their molecular mechanisms.
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Affiliation(s)
- Chao Zhou
- Cancer Center, WeiFang Traditional Chinese Hospital, Weifang, Shandong, China (mainland)
| | - LiJuan Liu
- Cancer Center, WeiFang Traditional Chinese Hospital, Weifang, Shandong, China (mainland)
| | - Jing Zhuang
- Cancer Center, WeiFang Traditional Chinese Hospital, Weifang, Shandong, China (mainland)
| | - JunYu Wei
- Cancer Center, WeiFang Traditional Chinese Hospital, Weifang, Shandong, China (mainland)
| | - TingTing Zhang
- Clinical Institute, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland)
| | - ChunDi Gao
- Clinical Institute, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland)
| | - Cun Liu
- Clinical Institute, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland)
| | - HuaYao Li
- Clinical Institute, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland)
| | - HongZong Si
- Department of Public Health, Qingdao University, Qingdao, Shandong, China (mainland)
| | - ChangGang Sun
- Department of Oncology, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, China (mainland)
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Yella JK, Yaddanapudi S, Wang Y, Jegga AG. Changing Trends in Computational Drug Repositioning. Pharmaceuticals (Basel) 2018; 11:E57. [PMID: 29874824 PMCID: PMC6027196 DOI: 10.3390/ph11020057] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 06/01/2018] [Accepted: 06/02/2018] [Indexed: 12/12/2022] Open
Abstract
Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated-"The most fruitful basis for the discovery of a new drug is to start with an old drug". However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of "serendipity", discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules-pharmacologically relevant sub-networks of biomolecules and/or pathways-from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.
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Affiliation(s)
- Jaswanth K Yella
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Suryanarayana Yaddanapudi
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Yunguan Wang
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.
- Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH 45219, USA.
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Lu L, Yu H. DR2DI: a powerful computational tool for predicting novel drug-disease associations. J Comput Aided Mol Des 2018; 32:633-642. [DOI: 10.1007/s10822-018-0117-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 04/01/2018] [Indexed: 01/01/2023]
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Zheng M, Zhao J, Cui C, Fu Z, Li X, Liu X, Ding X, Tan X, Li F, Luo X, Chen K, Jiang H. Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies. Med Res Rev 2018; 38:914-950. [DOI: 10.1002/med.21483] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 12/13/2017] [Accepted: 12/15/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Mingyue Zheng
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Jihui Zhao
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Chen Cui
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Zunyun Fu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xutong Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaohong Liu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Xiaoyu Ding
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaoqin Tan
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Fei Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- Department of Chemistry, College of Sciences; Shanghai University; Shanghai China
| | - Xiaomin Luo
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Kaixian Chen
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
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Yu L, Su R, Wang B, Zhang L, Zou Y, Zhang J, Gao L. Prediction of Novel Drugs for Hepatocellular Carcinoma Based on Multi-Source Random Walk. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:966-977. [PMID: 27076463 DOI: 10.1109/tcbb.2016.2550453] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Computational approaches for predicting drug-disease associations by integrating gene expression and biological network provide great insights to the complex relationships among drugs, targets, disease genes, and diseases at a system level. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high rate of morbidity and mortality. We provide an integrative framework to predict novel d rugs for HCC based on multi-source random walk (PD-MRW). Firstly, based on gene expression and protein interaction network, we construct a gene-gene weighted i nteraction network (GWIN). Then, based on multi-source random walk in GWIN, we build a drug-drug similarity network. Finally, based on the known drugs for HCC, we score all drugs in the drug-drug similarity network. The robustness of our predictions, their overlap with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched KEGG pathway demonstrate our approach can effectively identify new drug indications. Specifically, regorafenib (Rank = 9 in top-20 list) is proven to be effective in Phase I and II clinical trials of HCC, and the Phase III trial is ongoing. And, it has 11 overlapping pathways with HCC with lower p-values. Focusing on a particular disease, we believe our approach is more accurate and possesses better scalability.
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Cha Y, Erez T, Reynolds IJ, Kumar D, Ross J, Koytiger G, Kusko R, Zeskind B, Risso S, Kagan E, Papapetropoulos S, Grossman I, Laifenfeld D. Drug repurposing from the perspective of pharmaceutical companies. Br J Pharmacol 2017; 175:168-180. [PMID: 28369768 DOI: 10.1111/bph.13798] [Citation(s) in RCA: 228] [Impact Index Per Article: 32.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 03/06/2017] [Accepted: 03/08/2017] [Indexed: 02/01/2023] Open
Abstract
Drug repurposing holds the potential to bring medications with known safety profiles to new patient populations. Numerous examples exist for the identification of new indications for existing molecules, most stemming from serendipitous findings or focused recent efforts specifically limited to the mode of action of a specific drug. In recent years, the need for new approaches to drug research and development, combined with the advent of big data repositories and associated analytical methods, has generated interest in developing systematic approaches to drug repurposing. A variety of innovative computational methods to enable systematic repurposing screens, experimental as well as through in silico approaches, have emerged. An efficient drug repurposing pipeline requires the combination of access to molecular data, appropriate analytical expertise to enable robust insights, expertise and experimental set-up for validation and clinical development know-how. In this review, we describe some of the main approaches to systematic repurposing and discuss the various players in this field and the need for strategic collaborations to increase the likelihood of success in bringing existing molecules to new indications, as well as the current advantages, considerations and challenges in repurposing as a drug development strategy pursued by pharmaceutical companies. LINKED ARTICLES This article is part of a themed section on Inventing New Therapies Without Reinventing the Wheel: The Power of Drug Repurposing. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.2/issuetoc.
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Affiliation(s)
- Y Cha
- Immuneering Corporation, Cambridge, MA, USA
| | - T Erez
- Global Research and Development, Teva Pharmaceutical Industries, Netanya, Israel
| | - I J Reynolds
- Global Research and Development, Teva Pharmaceutical Industries, West Chester, PA, USA
| | - D Kumar
- Immuneering Corporation, Cambridge, MA, USA
| | - J Ross
- Immuneering Corporation, Cambridge, MA, USA
| | - G Koytiger
- Immuneering Corporation, Cambridge, MA, USA
| | - R Kusko
- Immuneering Corporation, Cambridge, MA, USA
| | - B Zeskind
- Immuneering Corporation, Cambridge, MA, USA
| | - S Risso
- Global Research and Development, Teva Pharmaceutical Industries, West Chester, PA, USA
| | - E Kagan
- Global Research and Development, Teva Pharmaceutical Industries, Netanya, Israel
| | - S Papapetropoulos
- Global Research and Development, Teva Pharmaceutical Industries, Frazer, PA, USA
| | - I Grossman
- Global Research and Development, Teva Pharmaceutical Industries, Netanya, Israel
| | - D Laifenfeld
- Global Research and Development, Teva Pharmaceutical Industries, Netanya, Israel
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Sun P, Guo J, Winnenburg R, Baumbach J. Drug repurposing by integrated literature mining and drug–gene–disease triangulation. Drug Discov Today 2017; 22:615-619. [DOI: 10.1016/j.drudis.2016.10.008] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Revised: 09/26/2016] [Accepted: 10/18/2016] [Indexed: 01/18/2023]
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Lotfi Shahreza M, Ghadiri N, Mousavi SR, Varshosaz J, Green JR. Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning. J Biomed Inform 2017; 68:167-183. [DOI: 10.1016/j.jbi.2017.03.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 02/09/2017] [Accepted: 03/10/2017] [Indexed: 12/14/2022]
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Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome. Artif Intell Med 2017; 77:53-63. [DOI: 10.1016/j.artmed.2017.03.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Revised: 01/06/2017] [Accepted: 03/17/2017] [Indexed: 01/16/2023]
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Karuppasamy R, Verma K, Sequeira VM, Basavanna LN, Veerappapillai S. An Integrative Drug Repurposing Pipeline: Switching Viral Drugs to Breast Cancer. J Cell Biochem 2017; 118:1412-1422. [PMID: 27859674 DOI: 10.1002/jcb.25799] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 11/15/2016] [Indexed: 11/07/2022]
Abstract
The growing incidence rate of breast cancer, coupled with cellular chemotherapeutic resistance, has made this disease one of the most prevalent cancers among women worldwide. Despite the recent efforts to understand the underlying cause of the resistance due to mutation, there are no feasible tactics to overcome this bottleneck. This issue could be addressed by the concept of polypharmacology-disguising drugs present in the pharmacopeia for novel purposes (drug repurposing). Of note, we have proposed a multi-modal computational drug-repositioning stratagem to predict drugs possessing anti-proliferative effect. Our results suggest that Ombitasvir, a Hepatitis C NS5B polymerase inhibitor, could be "repurposed" for the control and prevention of beta-tubulin-driven breast cancers. J. Cell. Biochem. 118: 1412-1422, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Ramanathan Karuppasamy
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Kanika Verma
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Velin Marita Sequeira
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Lokapriya Nandan Basavanna
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Shanthi Veerappapillai
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
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