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Wan Z, Jiang N, Su M, Zhang X, Cao Y, Wu A, Zhang P, Jiang T. Multiscale fusion network drives the repurposing of anticancer drugs. Clin Transl Med 2024; 14:e1745. [PMID: 38924682 PMCID: PMC11199060 DOI: 10.1002/ctm2.1745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024] Open
Affiliation(s)
- Zhaoman Wan
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems MedicineChinese Academy of Medical Sciences & Peking Union Medical CollegeSuzhouChina
| | - Nan Jiang
- 4+4 Medical Doctor ProgramChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingChina
| | - Mingming Su
- Beijing Cloudna Technology Co., Ltd.BeijingChina
| | - Xinlei Zhang
- Beijing Cloudna Technology Co., Ltd.BeijingChina
| | - Yang Cao
- School of Biological SciencesSichuan UniversityChengduChina
| | - Aiping Wu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems MedicineChinese Academy of Medical Sciences & Peking Union Medical CollegeSuzhouChina
| | - Peng Zhang
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children’s HospitalCapital Medical University, National Center for Children's HealthBeijingChina
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Li X, Liu Z, Liao J, Chen Q, Lu X, Fan X. Network pharmacology approaches for research of Traditional Chinese Medicines. Chin J Nat Med 2023; 21:323-332. [PMID: 37245871 DOI: 10.1016/s1875-5364(23)60429-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Indexed: 05/30/2023]
Abstract
Pharmacodynamics material basis and effective mechanisms are the two main issues to decipher the mechnisms of action of Traditional Chinese medicines (TCMs) for the treatment of diseases. TCMs, in "multi-component, multi-target, multi-pathway" paradigm, show satisfactory clinical results in complex diseases. New ideas and methods are urgently needed to explain the complex interactions between TCMs and diseases. Network pharmacology (NP) provides a novel paradigm to uncover and visualize the underlying interaction networks of TCMs against multifactorial diseases. The development and application of NP has promoted the safety, efficacy, and mechanism investigations of TCMs, which then reinforces the credibility and popularity of TCMs. The current organ-centricity of medicine and the "one disease-one target-one drug" dogma obstruct the understanding of complex diseases and the development of effective drugs. Therefore, more attentions should be paid to shift from "phenotype and symptom" to "endotype and cause" in understanding and redefining current diseases. In the past two decades, with the advent of advanced and intelligent technologies (such as metabolomics, proteomics, transcriptomics, single-cell omics, and artificial intelligence), NP has been improved and deeply implemented, and presented its great value and potential as the next drug-discovery paradigm. NP is developed to cure causal mechanisms instead of treating symptoms. This review briefly summarizes the recent research progress on NP application in TCMs for efficacy research, mechanism elucidation, target prediction, safety evaluation, drug repurposing, and drug design.
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Affiliation(s)
- Xiang Li
- School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou 311399, China; Department of Chinese Medicine Science & Engineering, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Center in Zhejiang University, State Key Laboratory of Component-based Chinese Medicine, Hangzhou 310058, China
| | - Ziqi Liu
- Department of Chinese Medicine Science & Engineering, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jie Liao
- Department of Chinese Medicine Science & Engineering, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Center in Zhejiang University, State Key Laboratory of Component-based Chinese Medicine, Hangzhou 310058, China; Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China
| | - Qian Chen
- Department of Chinese Medicine Science & Engineering, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Center in Zhejiang University, State Key Laboratory of Component-based Chinese Medicine, Hangzhou 310058, China; Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China
| | - Xiaoyan Lu
- Department of Chinese Medicine Science & Engineering, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Center in Zhejiang University, State Key Laboratory of Component-based Chinese Medicine, Hangzhou 310058, China; Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China
| | - Xiaohui Fan
- Department of Chinese Medicine Science & Engineering, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Center in Zhejiang University, State Key Laboratory of Component-based Chinese Medicine, Hangzhou 310058, China; Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China.
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Qin S, Li W, Yu H, Xu M, Li C, Fu L, Sun S, He Y, Lv J, He W, Chen L. Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks. Int J Mol Sci 2023; 24:ijms24032244. [PMID: 36768566 PMCID: PMC9917231 DOI: 10.3390/ijms24032244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/05/2023] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.
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Affiliation(s)
- Shimei Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hongzheng Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Manyi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Chao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shibin Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin 150001, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Correspondence: ; Tel.: +86-451-8667-4768
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Wu J, Li J, He Y, Huang J, Zhao X, Pan B, Wang Y, Cheng L, Han J. DrugSim2DR: systematic prediction of drug functional similarities in the context of specific disease for drug repurposing. Gigascience 2022; 12:giad104. [PMID: 38116825 PMCID: PMC10729734 DOI: 10.1093/gigascience/giad104] [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: 07/27/2023] [Revised: 10/23/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Traditional approaches to drug development are costly and involve high risks. The drug repurposing approach can be a valuable alternative to traditional approaches and has therefore received considerable attention in recent years. FINDINGS Herein, we develop a previously undescribed computational approach, called DrugSim2DR, which uses a network diffusion algorithm to identify candidate anticancer drugs based on a drug functional similarity network. The innovation of the approach lies in the drug-drug functional similarity network constructed in a manner that implicitly links drugs through their common biological functions in the context of a specific disease state, as the similarity relationships based on general states (e.g., network proximity or Jaccard index of drug targets) ignore disease-specific molecular characteristics. The drug functional similarity network may provide a reference for prediction of drug combinations. We describe and validate the DrugSim2DR approach through analysis of data on breast cancer and lung cancer. DrugSim2DR identified some US Food and Drug Administration-approved anticancer drugs, as well as some candidate drugs validated by previous studies in the literature. Moreover, DrugSim2DR showed excellent predictive performance, as evidenced by receiver operating characteristic analysis and multiapproach comparisons in various cancer datasets. CONCLUSIONS DrugSim2DR could accurately assess drug-drug functional similarity within a specific disease context and may more effectively prioritize disease candidate drugs. To increase the usability of our approach, we have developed an R-based software package, DrugSim2DR, which is freely available on CRAN (https://CRAN.R-project.org/package=DrugSim2DR).
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Affiliation(s)
- Jiashuo Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Ji Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yalan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Junling Huang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xilong Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Bingyue Pan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
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Shao K, Zhang Y, Wen Y, Zhang Z, He S, Bo X. DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph. Brief Bioinform 2022; 23:6563180. [PMID: 35380622 DOI: 10.1093/bib/bbac109] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/14/2022] [Accepted: 03/03/2022] [Indexed: 12/19/2022] Open
Abstract
Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and targets, experimental identification of DTIs is costly and time-consuming. In recent years, the emerging graph neural network (GNN) has been applied to DTI prediction because DTIs can be represented effectively using graphs. However, some of these methods are only based on homogeneous graphs, and some consist of two decoupled steps that cannot be trained jointly. To further explore GNN-based DTI prediction by integrating heterogeneous graph information, this study regards DTI prediction as a link prediction problem and proposes an end-to-end model based on HETerogeneous graph with Attention mechanism (DTI-HETA). In this model, a heterogeneous graph is first constructed based on the drug-drug and target-target similarity matrices and the DTI matrix. Then, the graph convolutional neural network is utilized to obtain the embedded representation of the drugs and targets. To highlight the contribution of different neighborhood nodes to the central node in aggregating the graph convolution information, a graph attention mechanism is introduced into the node embedding process. Afterward, an inner product decoder is applied to predict DTIs. To evaluate the performance of DTI-HETA, experiments are conducted on two datasets. The experimental results show that our model is superior to the state-of-the-art methods. Also, the identification of novel DTIs indicates that DTI-HETA can serve as a powerful tool for integrating heterogeneous graph information to predict DTIs.
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Affiliation(s)
| | | | - Yuqi Wen
- Beijing Institute of Radiation Medicine, Beijing, China
| | | | - Song He
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing, China
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Yan Y, Yang M, Zhao H, Duan G, Peng X, Wang J. Drug repositioning based on multi-view learning with matrix completion. Brief Bioinform 2022; 23:6548374. [PMID: 35289352 DOI: 10.1093/bib/bbac054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/14/2022] [Accepted: 01/31/2022] [Indexed: 12/21/2022] Open
Abstract
Determining drug indications is a critical part of the drug development process. However, traditional drug discovery is expensive and time-consuming. Drug repositioning aims to find potential indications for existing drugs, which is considered as an important alternative to the traditional drug discovery. In this article, we propose a multi-view learning with matrix completion (MLMC) method to predict the potential associations between drugs and diseases. Specifically, MLMC first learns the comprehensive similarity matrices from five drug similarity matrices and two disease similarity matrices based on the multi-view learning (ML) with Laplacian graph regularization, and updates the drug-disease association matrix simultaneously. Then, we introduce matrix completion (MC) to add some positive entries in original association matrix based on low-rank structure, and re-execute the multi-view learning algorithm for association prediction. At last, the prediction results of the above two operations are integrated as the final output. Evaluated by 10-fold cross-validation and de novo tests, MLMC achieves higher prediction accuracy than the current state-of-the-art methods. Moreover, case studies confirm the ability of our method in novel drug-disease association discovery. The codes of MLMC are available at https://github.com/BioinformaticsCSU/MLMC. Contact: jxwang@mail.csu.edu.cn.
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Affiliation(s)
- Yixin Yan
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Mengyun Yang
- Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang, Hunan 422000, China
| | - Haochen Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Guihua Duan
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Xiaoqing Peng
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410038, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
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Chen H, Zhang Z, Zhang J. In silico drug repositioning based on integrated drug targets and canonical correlation analysis. BMC Med Genomics 2022; 15:48. [PMID: 35249529 PMCID: PMC8898485 DOI: 10.1186/s12920-022-01203-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 03/02/2022] [Indexed: 01/21/2023] Open
Abstract
Background Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level. Methods Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug–target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug’s mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets. Results We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions. Conclusions To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01203-1.
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