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Ji X, Williams KP, Zheng W. Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer. Cancer Inform 2023; 22:11769351231202588. [PMID: 37846218 PMCID: PMC10576937 DOI: 10.1177/11769351231202588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/01/2023] [Indexed: 10/18/2023] Open
Abstract
The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.
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Affiliation(s)
- Xiaojia Ji
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Kevin P Williams
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Weifan Zheng
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
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Zhou H, Liu H, Yu Y, Yuan X, Xiao L. Informatics on Drug Repurposing for Breast Cancer. Drug Des Devel Ther 2023; 17:1933-1943. [PMID: 37405253 PMCID: PMC10315146 DOI: 10.2147/dddt.s417563] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/17/2023] [Indexed: 07/06/2023] Open
Abstract
Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.
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Affiliation(s)
- Hui Zhou
- Department of Lymphoma and Hematology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China
- Department of Lymphoma and Hematology, Hunan Cancer Hospital, Changsha, Hunan, People’s Republic of China
| | - Hongdou Liu
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
| | - Yan Yu
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
| | - Xiao Yuan
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
- Department of Laboratory Diagnosis, Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, Guangdong, People’s Republic of China
| | - Ling Xiao
- Department of Histology and Embryology of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China
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Xu Z, Eichler B, Klausner EA, Duffy-Matzner J, Zheng W. Lead/Drug Discovery from Natural Resources. Molecules 2022; 27:molecules27238280. [PMID: 36500375 PMCID: PMC9736696 DOI: 10.3390/molecules27238280] [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: 10/03/2022] [Revised: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 11/29/2022] Open
Abstract
Natural products and their derivatives have been shown to be effective drug candidates against various diseases for many years. Over a long period of time, nature has produced an abundant and prosperous source pool for novel therapeutic agents with distinctive structures. Major natural-product-based drugs approved for clinical use include anti-infectives and anticancer agents. This paper will review some natural-product-related potent anticancer, anti-HIV, antibacterial and antimalarial drugs or lead compounds mainly discovered from 2016 to 2022. Structurally typical marine bioactive products are also included. Molecular modeling, machine learning, bioinformatics and other computer-assisted techniques that are very important in narrowing down bioactive core structural scaffolds and helping to design new structures to fight against key disease-associated molecular targets based on available natural products are considered and briefly reviewed.
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Affiliation(s)
- Zhihong Xu
- Department of Chemistry and Biochemistry, Augustana University, 2001 S Summit Ave., Sioux Falls, SD 57197, USA
- Institute of Interventional & Vascular Surgery, Tongji University, Shanghai 200072, China
- Department of Pharmaceutical Sciences, South College School of Pharmacy, 400 Goody’s Lane, Knoxville, TN 37922, USA
- Correspondence: ; Tel.: +1-(605)-274-5008
| | - Barrett Eichler
- Department of Chemistry and Biochemistry, Augustana University, 2001 S Summit Ave., Sioux Falls, SD 57197, USA
| | - Eytan A. Klausner
- Department of Pharmaceutical Sciences, South College School of Pharmacy, 400 Goody’s Lane, Knoxville, TN 37922, USA
| | - Jetty Duffy-Matzner
- Department of Chemistry and Biochemistry, Augustana University, 2001 S Summit Ave., Sioux Falls, SD 57197, USA
| | - Weifan Zheng
- Biomanufacturing Research Institute and Technology Enterprise, North Carolina Central University, 1801 Fayetteville St., Durham, NC 27707, USA
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Sun G, Dong D, Dong Z, Zhang Q, Fang H, Wang C, Zhang S, Wu S, Dong Y, Wan Y. Drug repositioning: A bibliometric analysis. Front Pharmacol 2022; 13:974849. [PMID: 36225586 PMCID: PMC9549161 DOI: 10.3389/fphar.2022.974849] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/12/2022] [Indexed: 11/14/2022] Open
Abstract
Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.
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Affiliation(s)
- Guojun Sun
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Dashun Dong
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Zuojun Dong
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Qian Zhang
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Hui Fang
- Institute of Information Resource, Zhejiang University of Technology, Hangzhou, China
| | - Chaojun Wang
- Hangzhou Aeronautical Sanatorium for Special Service of Chinese Air Force, Hangzhou, China
| | - Shaoya Zhang
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Shuaijun Wu
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Yichen Dong
- Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - Yuehua Wan
- Institute of Information Resource, Zhejiang University of Technology, Hangzhou, China
- *Correspondence: Yuehua Wan,
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Cheng N, Chen Y, Gao W, Liu J, Huang Q, Yan C, Huang X, Ding C. An Improved Deep Learning Model: S-TextBLCNN for Traditional Chinese Medicine Formula Classification. Front Genet 2022; 12:807825. [PMID: 35003231 PMCID: PMC8727750 DOI: 10.3389/fgene.2021.807825] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/15/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose: This study proposes an S-TextBLCNN model for the efficacy of traditional Chinese medicine (TCM) formula classification. This model uses deep learning to analyze the relationship between herb efficacy and formula efficacy, which is helpful in further exploring the internal rules of formula combination. Methods: First, for the TCM herbs extracted from Chinese Pharmacopoeia, natural language processing (NLP) is used to learn and realize the quantitative expression of different TCM herbs. Three features of herb name, herb properties, and herb efficacy are selected to encode herbs and to construct formula-vector and herb-vector. Then, based on 2,664 formulae for stroke collected in TCM literature and 19 formula efficacy categories extracted from Yifang Jijie, an improved deep learning model TextBLCNN consists of a bidirectional long short-term memory (Bi-LSTM) neural network and a convolutional neural network (CNN) is proposed. Based on 19 formula efficacy categories, binary classifiers are established to classify the TCM formulae. Finally, aiming at the imbalance problem of formula data, the over-sampling method SMOTE is used to solve it and the S-TextBLCNN model is proposed. Results: The formula-vector composed of herb efficacy has the best effect on the classification model, so it can be inferred that there is a strong relationship between herb efficacy and formula efficacy. The TextBLCNN model has an accuracy of 0.858 and an F1-score of 0.762, both higher than the logistic regression (acc = 0.561, F1-score = 0.567), SVM (acc = 0.703, F1-score = 0.591), LSTM (acc = 0.723, F1-score = 0.621), and TextCNN (acc = 0.745, F1-score = 0.644) models. In addition, the over-sampling method SMOTE is used in our model to tackle data imbalance, and the F1-score is greatly improved by an average of 47.1% in 19 models. Conclusion: The combination of formula feature representation and the S-TextBLCNN model improve the accuracy in formula efficacy classification. It provides a new research idea for the study of TCM formula compatibility.
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Affiliation(s)
- Ning Cheng
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Yue Chen
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Wanqing Gao
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Jiajun Liu
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Qunfu Huang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Cheng Yan
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China.,Big Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Xindi Huang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Changsong Ding
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China.,Big Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
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