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Biswal S, Mallick B. Unlocking the potential of signature-based drug repurposing for anticancer drug discovery. Arch Biochem Biophys 2024; 761:110150. [PMID: 39265695 DOI: 10.1016/j.abb.2024.110150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 08/01/2024] [Accepted: 09/09/2024] [Indexed: 09/14/2024]
Abstract
Cancer is the leading cause of death worldwide and is often associated with tumor relapse even after chemotherapeutics. This reveals malignancy is a complex process, and high-throughput omics strategies in recent years have contributed significantly in decoding the molecular mechanisms of these complex events in cancer. Further, the omics studies yield a large volume of cancer-specific molecular signatures that promote the discovery of cancer therapy drugs by a method termed signature-based drug repurposing. The drug repurposing method identifies new uses for approved drugs beyond their intended initial therapeutic use, and there are several approaches to it. In this review, we discuss signature-based drug repurposing in cancer, how cancer omics have revolutionized this method of drug discovery, and how one can use the cancer signature data for repurposed drug identification by providing a step-by-step procedural handout. This modern approach maximizes the use of existing therapeutic agents for cancer therapy or combination therapy to overcome chemotherapeutics resistance, making it a pragmatic and efficient alternative to traditional resource-intensive and time-consuming methods.
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Affiliation(s)
- Sruti Biswal
- RNAi and Functional Genomics Lab., Department of Life Science, National Institute of Technology Rourkela, Rourkela-769008, Odisha, India
| | - Bibekanand Mallick
- RNAi and Functional Genomics Lab., Department of Life Science, National Institute of Technology Rourkela, Rourkela-769008, Odisha, India.
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2
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Jin Q, Zhang X, Huo D, Xie H, Zhang D, Liu L, Zhao Y, Chen X. Predicting drug synergy using a network propagation inspired machine learning framework. Brief Funct Genomics 2024; 23:429-440. [PMID: 38183214 DOI: 10.1093/bfgp/elad056] [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: 05/12/2023] [Revised: 10/14/2023] [Accepted: 12/04/2023] [Indexed: 01/07/2024] Open
Abstract
Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability. Here, we proposed a novel network propagation-based machine learning framework to predict synergistic drug combinations. Based on the topological information of a comprehensive drug-drug association network, we innovatively introduced an affinity score between drug pairs as one of the features to train machine learning models. We applied network-based strategy to evaluate their therapeutic potential to different cancer types. Finally, we identified 17 specific-, 21 general- and 40 broad-spectrum antitumor drug combinations, in which 69% drug combinations were validated by vitro cellular experiments, 83% drug combinations were validated by literature reports and 100% drug combinations were validated by biological function analyses. By quantifying the network relationships between drug targets and cancer-related driver genes in the human protein-protein interactome, we show the existence of four distinct patterns of drug-drug-disease relationships. We also revealed that 32 biological pathways were correlated with the synergistic mechanism of broad-spectrum antitumor drug combinations. Overall, our model offers a powerful scalable screening framework for cancer treatments.
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Affiliation(s)
- Qing Jin
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Xianze Zhang
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Diwei Huo
- Department of General Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hongbo Xie
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Denan Zhang
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Lei Liu
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
| | - Yashuang Zhao
- Department of Epidemiology, College of Public Health, Harbin Medical University, Harbin, China
| | - Xiujie Chen
- Department of Pharmacogenomics, College of Bioinformatics and Science Technology, Harbin Medical University, Harbin, China
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3
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Kang H, Hou L, Gu Y, Lu X, Li J, Li Q. Drug-disease association prediction with literature based multi-feature fusion. Front Pharmacol 2023; 14:1205144. [PMID: 37284317 PMCID: PMC10239876 DOI: 10.3389/fphar.2023.1205144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/09/2023] [Indexed: 06/08/2023] Open
Abstract
Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge. Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism. Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916. Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.
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Affiliation(s)
- Hongyu Kang
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li Hou
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yaowen Gu
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiao Lu
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jiao Li
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qin Li
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
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4
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He H, Duo H, Hao Y, Zhang X, Zhou X, Zeng Y, Li Y, Li B. Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications. Comput Biol Med 2023; 155:106671. [PMID: 36805225 DOI: 10.1016/j.compbiomed.2023.106671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.
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Affiliation(s)
- Hao He
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yujie Zeng
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yinghong Li
- The Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
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5
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Torab-Miandoab A, Poursheikh Asghari M, Hashemzadeh N, Ferdousi R. Analysis and identification of drug similarity through drug side effects and indications data. BMC Med Inform Decis Mak 2023; 23:35. [PMID: 36788528 PMCID: PMC9926629 DOI: 10.1186/s12911-023-02133-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND The measurement of drug similarity has many potential applications for assessing drug therapy similarity, patient similarity, and the success of treatment modalities. To date, a family of computational methods has been employed to predict drug-drug similarity. Here, we announce a computational method for measuring drug-drug similarity based on drug indications and side effects. METHODS The model was applied for 2997 drugs in the side effects category and 1437 drugs in the indications category. The corresponding binary vectors were built to determine the Drug-drug similarity for each drug. Various similarity measures were conducted to discover drug-drug similarity. RESULTS Among the examined similarity methods, the Jaccard similarity measure was the best in overall performance results. In total, 5,521,272 potential drug pair's similarities were studied in this research. The offered model was able to predict 3,948,378 potential similarities. CONCLUSION Based on these results, we propose the current method as a robust, simple, and quick approach to identifying drug similarity.
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Affiliation(s)
- Amir Torab-Miandoab
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Mehdi Poursheikh Asghari
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Nastaran Hashemzadeh
- grid.412888.f0000 0001 2174 8913Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran ,grid.412888.f0000 0001 2174 8913Research Center for Pharmaceutical Nanotechnology, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Ferdousi
- Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711, Iran.
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Askr H, Elgeldawi E, Aboul Ella H, Elshaier YAMM, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artif Intell Rev 2022; 56:5975-6037. [PMID: 36415536 PMCID: PMC9669545 DOI: 10.1007/s10462-022-10306-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/18/2022]
Abstract
Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.
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Affiliation(s)
- Heba Askr
- Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City, Egypt
| | - Enas Elgeldawi
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Heba Aboul Ella
- Faculty of Pharmacy and Drug Technology, Chinese University in Egypt (CUE), Cairo, Egypt
| | | | - Mamdouh M. Gomaa
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt
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7
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Literature-based drug-drug similarity for drug repurposing: impact of Medical Subject Headings term refinement and hierarchical clustering. Future Med Chem 2022; 14:1309-1323. [PMID: 36017692 DOI: 10.4155/fmc-2022-0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background: We describe herein, an improved procedure for drug repurposing based on refined Medical Subject Headings (MeSH) terms and hierarchical clustering method. Materials & methods: In the present study, we have employed MeSH data from MEDLINE (2019), 1669 US FDA approved drugs from Open FDA and a refined set of MeSH terms. Refinement of MeSH terms was performed to include terms related to mechanistic information of drugs and diseases. Results and Conclusions: In-depth analysis of the results obtained, demonstrated greater efficiency of the proposed approach, based on refined MeSH terms and hierarchical clustering, in terms of number of selected drug candidates for repurposing. Further, analysis of misclustering and size of noise clusters suggest that the proposed approach is reliable and can be employed in drug repurposing.
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8
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Zhong S, Shengyu Liu, Xin Shi, Zhang X, Li K, Liu G, Li L, Tao S, Zheng B, Sheng W, Ye Z, Xing Q, Zhai Q, Ren L, Wu Y, Bao Y. Disulfiram in glioma: Literature review of drug repurposing. Front Pharmacol 2022; 13:933655. [PMID: 36091753 PMCID: PMC9448899 DOI: 10.3389/fphar.2022.933655] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Gliomas are the most common malignant brain tumors. High-grade gliomas, represented by glioblastoma multiforme (GBM), have a poor prognosis and are prone to recurrence. The standard treatment strategy is tumor removal combined with radiotherapy and chemotherapy, such as temozolomide (TMZ). However, even after conventional treatment, they still have a high recurrence rate, resulting in an increasing demand for effective anti-glioma drugs. Drug repurposing is a method of reusing drugs that have already been widely approved for new indication. It has the advantages of reduced research cost, safety, and increased efficiency. Disulfiram (DSF), originally approved for alcohol dependence, has been repurposed for adjuvant chemotherapy in glioma. This article reviews the drug repurposing method and the progress of research on disulfiram reuse for glioma treatment.
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9
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Jamali AA, Tan Y, Kusalik A, Wu FX. NTD-DR: Nonnegative tensor decomposition for drug repositioning. PLoS One 2022; 17:e0270852. [PMID: 35862409 PMCID: PMC9302855 DOI: 10.1371/journal.pone.0270852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 06/20/2022] [Indexed: 12/12/2022] Open
Abstract
Computational drug repositioning aims to identify potential applications of existing drugs for the treatment of diseases for which they were not designed. This approach can considerably accelerate the traditional drug discovery process by decreasing the required time and costs of drug development. Tensor decomposition enables us to integrate multiple drug- and disease-related data to boost the performance of prediction. In this study, a nonnegative tensor decomposition for drug repositioning, NTD-DR, is proposed. In order to capture the hidden information in drug-target, drug-disease, and target-disease networks, NTD-DR uses these pairwise associations to construct a three-dimensional tensor representing drug-target-disease triplet associations and integrates them with similarity information of drugs, targets, and disease to make a prediction. We compare NTD-DR with recent state-of-the-art methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR) and find that our method outperforms competing methods. Moreover, case studies with five diseases also confirm the reliability of predictions made by NTD-DR. Our proposed method identifies more known associations among the top 50 predictions than other methods. In addition, novel associations identified by NTD-DR are validated by literature analyses.
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Affiliation(s)
- Ali Akbar Jamali
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Yuting Tan
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- School of Mathematics and Statistics, Huazhong Normal University, Wuhan, China
| | - Anthony Kusalik
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
- * E-mail: (AK); (FXW)
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- * E-mail: (AK); (FXW)
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10
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Computational Methods for Drug Repurposing. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:119-141. [PMID: 35230686 DOI: 10.1007/978-3-030-91836-1_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.
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11
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Jin S, Niu Z, Jiang C, Huang W, Xia F, Jin X, Liu X, Zeng X. HeTDR: Drug repositioning based on heterogeneous networks and text mining. PATTERNS (NEW YORK, N.Y.) 2021; 2:100307. [PMID: 34430926 PMCID: PMC8369234 DOI: 10.1016/j.patter.2021.100307] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 05/11/2021] [Accepted: 06/14/2021] [Indexed: 12/14/2022]
Abstract
Using existing knowledge to carry out drug-disease associations prediction is a vital method for drug repositioning. However, effectively fusing the biomedical text and biological network information is one of the great challenges for most current drug repositioning methods. In this study, we propose a drug repositioning method based on heterogeneous networks and text mining (HeTDR). This model can combine drug features from multiple drug-related networks, disease features from biomedical corpora with the known drug-disease associations network to predict the correlation scores between drug and disease. Experiments demonstrate that HeTDR has excellent performance that is superior to that of state-of-the-art models. We present the top 10 novel HeTDR-predicted approved drugs for five diseases and prove our model is capable of discovering potential candidate drugs for disease indications.
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Affiliation(s)
- Shuting Jin
- Department of Computer Science, Xiamen University, Xiamen 361005, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
- Shenzhen Research Institute of Xiamen University, Shenzhen 518000, China
| | | | - Changzhi Jiang
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Wei Huang
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Feng Xia
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Xurui Jin
- MindRank AI Ltd., Hangzhou, Zhejiang 311113, China
| | - Xiangrong Liu
- Department of Computer Science, Xiamen University, Xiamen 361005, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Hunan University, Changsha 410082, China
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12
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Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25:1517-1539. [PMID: 34282519 DOI: 10.1007/s11030-021-10274-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.
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Affiliation(s)
- Kushagra Kashyap
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India
| | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India. .,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
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13
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Rodriguez S, Hug C, Todorov P, Moret N, Boswell SA, Evans K, Zhou G, Johnson NT, Hyman BT, Sorger PK, Albers MW, Sokolov A. Machine learning identifies candidates for drug repurposing in Alzheimer's disease. Nat Commun 2021; 12:1033. [PMID: 33589615 PMCID: PMC7884393 DOI: 10.1038/s41467-021-21330-0] [Citation(s) in RCA: 105] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 01/21/2021] [Indexed: 01/31/2023] Open
Abstract
Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.
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Affiliation(s)
- Steve Rodriguez
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Clemens Hug
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Petar Todorov
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Nienke Moret
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Sarah A Boswell
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Kyle Evans
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - George Zhou
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Nathan T Johnson
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Mark W Albers
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA.
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
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14
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Huangfu C, Zeng Y, Wang Y. Creating Neuroscientific Knowledge Organization System Based on Word Representation and Agglomerative Clustering Algorithm. Front Neuroinform 2020; 14:38. [PMID: 33013345 PMCID: PMC7461893 DOI: 10.3389/fninf.2020.00038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 07/17/2020] [Indexed: 11/24/2022] Open
Abstract
The literature on neuroscience has grown rapidly in recent years with the emergence of new domains of research. In the context of this progress, creating a knowledge organization system (KOS) that can quickly incorporate terms of a given domain is an important aim in the area. In this article, we develop a systematic method based on word representation and the agglomerative clustering algorithm to semi-automatically build a hierarchical KOS. We collected 35,832 research keywords and 11,497 research methods from PubMed Central database, and organized them in a hierarchical structure according to semantic distance. We show that the proposed KOS can help find terms related to the given topics, analyze articles related to specific domains of research, and characterize the features of article clusters. The proposed method can significantly reduce the manual work required by experts to organize the KOS.
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Affiliation(s)
- Cunqing Huangfu
- Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yi Zeng
- Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yuwei Wang
- Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
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15
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A multimodal deep learning-based drug repurposing approach for treatment of COVID-19. Mol Divers 2020; 25:1717-1730. [PMID: 32997257 PMCID: PMC7525234 DOI: 10.1007/s11030-020-10144-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 09/12/2020] [Indexed: 12/12/2022]
Abstract
Abstract Recently, various computational methods have been proposed to find new therapeutic applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach (MM-RBM), which has the capability to connect the information about the multiple modalities, can be applied to the problem of drug repurposing. The present study utilized MM-RBM to combine two types of data, including the chemical structures data of small molecules and differentially expressed genes as well as small molecules perturbations. In the proposed method, two separate RBMs were applied to find out the features and the specific probability distribution of each datum (modality). Besides, RBM was used to integrate the discovered features, resulting in the identification of the probability distribution of the combined data. The results demonstrated the significance of the clusters acquired by our model. These clusters were used to discover the medicines which were remarkably similar to the proposed medications to treat COVID-19. Moreover, the chemical structures of some small molecules as well as dysregulated genes’ effect led us to suggest using these molecules to treat COVID-19. The results also showed that the proposed method might prove useful in detecting the highly promising remedies for COVID-19 with minimum side effects. All the source codes are accessible using https://github.com/LBBSoft/Multimodal-Drug-Repurposing.git Graphic abstract ![]()
Electronic supplementary material The online version of this article (10.1007/s11030-020-10144-9) contains supplementary material, which is available to authorized users.
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16
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Low ZY, Farouk IA, Lal SK. Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak. Viruses 2020; 12:E1058. [PMID: 32972027 PMCID: PMC7551028 DOI: 10.3390/v12091058] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 08/02/2020] [Accepted: 08/21/2020] [Indexed: 02/06/2023] Open
Abstract
Traditionally, drug discovery utilises a de novo design approach, which requires high cost and many years of drug development before it reaches the market. Novel drug development does not always account for orphan diseases, which have low demand and hence low-profit margins for drug developers. Recently, drug repositioning has gained recognition as an alternative approach that explores new avenues for pre-existing commercially approved or rejected drugs to treat diseases aside from the intended ones. Drug repositioning results in lower overall developmental expenses and risk assessments, as the efficacy and safety of the original drug have already been well accessed and approved by regulatory authorities. The greatest advantage of drug repositioning is that it breathes new life into the novel, rare, orphan, and resistant diseases, such as Cushing's syndrome, HIV infection, and pandemic outbreaks such as COVID-19. Repositioning existing drugs such as Hydroxychloroquine, Remdesivir, Ivermectin and Baricitinib shows good potential for COVID-19 treatment. This can crucially aid in resolving outbreaks in urgent times of need. This review discusses the past success in drug repositioning, the current technological advancement in the field, drug repositioning for personalised medicine and the ongoing research on newly emerging drugs under consideration for the COVID-19 treatment.
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Affiliation(s)
- Zheng Yao Low
- School of Science, Monash University, Bandar Sunway, Subang Jaya 47500, Selangor Darul Ehsan, Malaysia; (Z.Y.L.); (I.A.F.)
| | - Isra Ahmad Farouk
- School of Science, Monash University, Bandar Sunway, Subang Jaya 47500, Selangor Darul Ehsan, Malaysia; (Z.Y.L.); (I.A.F.)
| | - Sunil Kumar Lal
- School of Science, Monash University, Bandar Sunway, Subang Jaya 47500, Selangor Darul Ehsan, Malaysia; (Z.Y.L.); (I.A.F.)
- Tropical Medicine & Biology Platform, Monash University, Subang Jaya 47500, Selangor Darul Ehsan, Malaysia
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17
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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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18
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Azad AKM, Dinarvand M, Nematollahi A, Swift J, Lutze-Mann L, Vafaee F. A comprehensive integrated drug similarity resource for in-silico drug repositioning and beyond. Brief Bioinform 2020; 22:5864589. [PMID: 32597467 DOI: 10.1093/bib/bbaa126] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 05/05/2020] [Accepted: 05/22/2020] [Indexed: 01/20/2023] Open
Abstract
Drug similarity studies are driven by the hypothesis that similar drugs should display similar therapeutic actions and thus can potentially treat a similar constellation of diseases. Drug-drug similarity has been derived by variety of direct and indirect sources of evidence and frequently shown high predictive power in discovering validated repositioning candidates as well as other in-silico drug development applications. Yet, existing resources either have limited coverage or rely on an individual source of evidence, overlooking the wealth and diversity of drug-related data sources. Hence, there has been an unmet need for a comprehensive resource integrating diverse drug-related information to derive multi-evidenced drug-drug similarities. We addressed this resource gap by compiling heterogenous information for an exhaustive set of small-molecule drugs (total of 10 367 in the current version) and systematically integrated multiple sources of evidence to derive a multi-modal drug-drug similarity network. The resulting database, 'DrugSimDB' currently includes 238 635 drug pairs with significant aggregated similarity, complemented with an interactive user-friendly web interface (http://vafaeelab.com/drugSimDB.html), which not only enables database ease of access, search, filtration and export, but also provides a variety of complementary information on queried drugs and interactions. The integration approach can flexibly incorporate further drug information into the similarity network, providing an easily extendable platform. The database compilation and construction source-code has been well-documented and semi-automated for any-time upgrade to account for new drugs and up-to-date drug information.
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Affiliation(s)
- A K M Azad
- bioinformatics and computational biology at UNSW Sydney
| | | | | | - Joshua Swift
- School of BABS at UNSW Sydney and is the founder of ZiggyLabs
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19
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Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford) 2020; 2020:baaa010. [PMID: 32185396 PMCID: PMC7078068 DOI: 10.1093/database/baaa010] [Citation(s) in RCA: 167] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 01/05/2020] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
Abstract
Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.
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Affiliation(s)
- Zeeshan Ahmed
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, 67 North Eagleville Road, Storrs, CT, USA
| | - Khalid Mohamed
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
| | - Saman Zeeshan
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - XinQi Dong
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
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20
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Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases. Nat Rev Rheumatol 2019; 16:32-52. [PMID: 31831878 DOI: 10.1038/s41584-019-0337-0] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2019] [Indexed: 02/08/2023]
Abstract
The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.
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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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22
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Yoon S, Lee D. Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks. Sci Rep 2019; 9:5469. [PMID: 30940832 PMCID: PMC6445150 DOI: 10.1038/s41598-019-41814-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 03/14/2019] [Indexed: 11/09/2022] Open
Abstract
Functional drug actions refer to drug-affected GO terms. They aid in the investigation of drug effects that are therapeutic or adverse. Previous studies have utilized the linkage information between drugs and functions in molecular level biological networks. Since the current knowledge of molecular level mechanisms of biological functions is still limited, such previous studies were incomplete. We expected that the multi-level biological networks would allow us to more completely investigate the functional drug actions. We constructed multi-level biological networks with genes, GO terms, and diseases. Meta-paths were utilized to extract the features of each GO term. We trained 39 SVM models to prioritize the functional drug actions of the various 39 drugs. Through the multi-level networks, more functional drug actions were utilized for the 39 models and inferred by the models. Multi-level based features improved the performance of the models, and the average AUROC value in the cross-validation was 0.86. Moreover, 60% of the candidates were true.
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Affiliation(s)
- Seyeol Yoon
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea
- Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea.
- Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea.
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23
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Measure clinical drug–drug similarity using Electronic Medical Records. Int J Med Inform 2019; 124:97-103. [DOI: 10.1016/j.ijmedinf.2019.02.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 12/21/2018] [Accepted: 02/10/2019] [Indexed: 12/22/2022]
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24
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Ahmed Z, Kim M, Liang BT. MAV-clic: management, analysis, and visualization of clinical data. JAMIA Open 2018; 2:23-28. [PMID: 31984341 PMCID: PMC6951942 DOI: 10.1093/jamiaopen/ooy052] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 07/18/2018] [Accepted: 11/22/2018] [Indexed: 11/12/2022] Open
Abstract
Objectives Develop a multifunctional analytics platform for efficient management and analysis of healthcare data. Materials and Methods Management, Analysis, and Visualization of Clinical Data (MAV-clic) is a Health Insurance Portability and Accountability Act of 1996 (HIPAA)-compliant framework based on the Butterfly Model. MAV-clic extracts, cleanses, and encrypts data then restructures and aggregates data in a deidentified format. A graphical user interface allows query, analysis, and visualization of clinical data. Results MAV-clic manages healthcare data for over 800 000 subjects at UConn Health. Three analytic capabilities of MAV-clic include: creating cohorts based on specific criteria; performing measurement analysis of subjects with a specific diagnosis and medication; and calculating measure outcomes of subjects over time. Discussion MAV-clic supports clinicians and healthcare analysts by efficiently stratifying subjects to understand specific scenarios and optimize decision making. Conclusion MAV-clic is founded on the scientific premise that to improve the quality and transition of healthcare, integrative platforms are necessary to analyze heterogeneous clinical, epidemiological, metabolomics, proteomics, and genomics data for precision medicine.
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Affiliation(s)
- Zeeshan Ahmed
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, School of Medicine, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Minjung Kim
- The Pat and Jim Calhoun Cardiology Center, School of Medicine, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Bruce T Liang
- Ray Neag Distinguished Professor of Cardiovascular Biology and Medicine, Director Pat and Jim Calhoun Cardiology Center, Dean UConn School of Medicine, University of Connecticut Health Center, Farmington, Connecticut, USA
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25
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Brown AS, Rasooly D, Patel CJ. Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:124-129. [PMID: 28941007 PMCID: PMC5824113 DOI: 10.1002/psp4.12258] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 09/07/2017] [Accepted: 09/19/2017] [Indexed: 12/13/2022]
Abstract
Computational drug repositioning methods can scalably nominate approved drugs for new diseases, with reduced risk of unforeseen side effects. The majority of methods eschew individual‐level phenotypes despite the promise of biomarker‐driven repositioning. In this study, we propose a framework for discovering serendipitous interactions between drugs and routine clinical phenotypes in cross‐sectional observational studies. Key to our strategy is the use of a healthy and nondiabetic population derived from the National Health and Nutrition Examination Survey, mitigating risk for confounding by indication. We combine complementary diagnostic phenotypes (fasting glucose and glucose response) and associate them with prescription drug usage. We then sought confirmation of phenotype‐drug associations in unidentifiable member claims data from the Aetna Insurance company using a retrospective self‐controlled case analysis approach. We identify bupropion as a plausible glucose lowering agent, suggesting that surveying otherwise healthy individuals in cross‐sectional studies can discover new drug repositioning hypotheses that have applicability to longitudinal clinical practice.
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Affiliation(s)
- Adam S Brown
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Danielle Rasooly
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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26
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Sam E, Athri P. Web-based drug repurposing tools: a survey. Brief Bioinform 2017; 20:299-316. [DOI: 10.1093/bib/bbx125] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Indexed: 12/15/2022] Open
Affiliation(s)
- Elizabeth Sam
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
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