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Chittavanich P, Saengwimol D, Roytrakul S, Rojanaporn D, Chaitankar V, Srimongkol A, Anurathapan U, Hongeng S, Kaewkhaw R. Ceftriaxone exerts antitumor effects in MYCN-driven retinoblastoma and neuroblastoma by targeting DDX3X for translation repression. Mol Oncol 2024; 18:918-938. [PMID: 37975412 PMCID: PMC10994227 DOI: 10.1002/1878-0261.13553] [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: 05/16/2023] [Revised: 10/13/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023] Open
Abstract
MYCN proto-oncogene, bHLH transcription factor (MYCN) amplification is associated with aggressive retinoblastoma (RB) and neuroblastoma (NB) cancer recurrence that is resistant to chemotherapies. Therefore, there is an urgent need to identify new therapeutic tools. This study aimed to evaluate the potential repurposing of ceftriaxone for the treatment of MYCN-amplified RB and NB, based on the clinical observations that the drug was serendipitously found to decrease the volume of the MYCN-driven RB subtype. Using patient-derived tumor organoids and tumor cell lines, we demonstrated that ceftriaxone is a potent and selective growth inhibitor targeting MYCN-driven RB and NB cells. Profiling of drug-induced transcriptomic changes, cell-cycle progression, and apoptotic death indicated cell-cycle arrest and death of drug-treated MYCN-amplified tumor cells. Drug target identification, using an affinity-based proteomic and molecular docking approach, and functional studies of the target proteins revealed that ceftriaxone targeted DEAD-box helicase 3 X-linked (DDX3X), thereby inhibiting translation in MYCN-amplified tumors but not in MYCN-nonamplified cells. The data suggest the feasibility of repurposing ceftriaxone as an anticancer drug and provide insights into the mechanism of drug action, highlighting DDX3X as a potential target for treating MYCN-driven tumors.
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Affiliation(s)
- Pamorn Chittavanich
- Program in Translational Medicine, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Duangporn Saengwimol
- Research Center, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Sittiruk Roytrakul
- Functional Proteomics Technology Laboratory, National Center for Genetic Engineering and BiotechnologyNational Science and Technology Development AgencyPathum ThaniThailand
| | - Duangnate Rojanaporn
- Department of Ophthalmology, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Vijender Chaitankar
- Biodata Mining and Discovery Section, National Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthBethesdaMDUSA
| | - Atthapol Srimongkol
- Research Center, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Usanarat Anurathapan
- Department of Pediatrics, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Suradej Hongeng
- Department of Pediatrics, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Rossukon Kaewkhaw
- Program in Translational Medicine, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi HospitalMahidol UniversitySamut PrakanThailand
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2
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Parvez A, Lee JS, Alam W, Tayara H, Chong KT. Integrated Computational Approaches for Drug Design Targeting Cruzipain. Int J Mol Sci 2024; 25:3747. [PMID: 38612558 PMCID: PMC11011879 DOI: 10.3390/ijms25073747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/15/2024] [Accepted: 03/24/2024] [Indexed: 04/14/2024] Open
Abstract
Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi.
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Affiliation(s)
- Aiman Parvez
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; (A.P.); (W.A.)
| | - Jeong-Sang Lee
- Department of Functional Food and Biotechnology, College of Medical Sciences, Jeonju University, Jeonju 55069, Republic of Korea;
| | - Waleed Alam
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; (A.P.); (W.A.)
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; (A.P.); (W.A.)
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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3
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Potenza RL, Armida M, Popoli P. Can Some Anticancer Drugs Be Repurposed to Treat Amyotrophic Lateral Sclerosis? A Brief Narrative Review. Int J Mol Sci 2024; 25:1751. [PMID: 38339026 PMCID: PMC10855887 DOI: 10.3390/ijms25031751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/25/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a rare progressive motor neuron disease that, due to its high complexity, still lacks effective treatments. Development of a new drug is a highly costly and time-consuming process, and the repositioning of approved drugs can represent an efficient strategy to provide therapeutic opportunities. This is particularly true for rare diseases, which are characterised by small patient populations and therefore attract little commercial interest. Based on the overlap between the biological background of cancer and neurodegeneration, the repurposing of antineoplastic drugs for ALS has been suggested. The objective of this narrative review was to summarise the current experimental evidence on the use of approved anticancer drugs in ALS. Specifically, anticancer drugs belonging to different classes were found to act on mechanisms involved in the ALS pathogenesis, and some of them proved to exert beneficial effects in ALS models. However, additional studies are necessary to confirm the real therapeutic potential of anticancer drugs for repositioning in ALS treatment.
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Affiliation(s)
- Rosa Luisa Potenza
- National Centre for Drug Research and Evaluation, Istituto Superiore di Sanità, 00161 Rome, Italy; (M.A.); (P.P.)
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Amiri R, Razmara J, Parvizpour S, Izadkhah H. A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks. BMC Bioinformatics 2023; 24:442. [PMID: 37993777 PMCID: PMC10664633 DOI: 10.1186/s12859-023-05572-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.
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Affiliation(s)
- Ramin Amiri
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
| | - Jafar Razmara
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran.
| | - Sepideh Parvizpour
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Habib Izadkhah
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
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Memantine derived compounds as potent in vitro inhibitors of urease: Repurposing of memantine, sonication assisted derivatization and in vitro enzyme inhibition, kinetics and molecular docking studies. Med Chem Res 2023. [DOI: 10.1007/s00044-023-03020-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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Lang X, Liu J, Zhang G, Feng X, Dan W. Knowledge Mapping of Drug Repositioning's Theme and Development. Drug Des Devel Ther 2023; 17:1157-1174. [PMID: 37096060 PMCID: PMC10122475 DOI: 10.2147/dddt.s405906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/11/2023] [Indexed: 04/26/2023] Open
Abstract
Background In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends. Methodology The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field. Results The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning. Conclusion The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.
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Affiliation(s)
- Xiaona Lang
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Jinlei Liu
- Cardiology Department, Guang ‘anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, People’s Republic of China
| | - Guangzhong Zhang
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
| | - Xin Feng
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Wenchao Dan
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
- Correspondence: Wenchao Dan, Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13652001152, Email
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Wang MN, Xie XJ, You ZH, Ding DW, Wong L. A weighted non-negative matrix factorization approach to predict potential associations between drug and disease. J Transl Med 2022; 20:552. [PMID: 36463215 PMCID: PMC9719187 DOI: 10.1186/s12967-022-03757-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 11/06/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Associations of drugs with diseases provide important information for expediting drug development. Due to the number of known drug-disease associations is still insufficient, and considering that inferring associations between them through traditional in vitro experiments is time-consuming and costly. Therefore, more accurate and reliable computational methods urgent need to be developed to predict potential associations of drugs with diseases. METHODS In this study, we present the model called weighted graph regularized collaborative non-negative matrix factorization for drug-disease association prediction (WNMFDDA). More specifically, we first calculated the drug similarity and disease similarity based on the chemical structures of drugs and medical description information of diseases, respectively. Then, to extend the model to work for new drugs and diseases, weighted [Formula: see text] nearest neighbor was used as a preprocessing step to reconstruct the interaction score profiles of drugs with diseases. Finally, a graph regularized non-negative matrix factorization model was used to identify potential associations between drug and disease. RESULTS During the cross-validation process, WNMFDDA achieved the AUC values of 0.939 and 0.952 on Fdataset and Cdataset under ten-fold cross validation, respectively, which outperforms other competing prediction methods. Moreover, case studies for several drugs and diseases were carried out to further verify the predictive performance of WNMFDDA. As a result, 13(Doxorubicin), 13(Amiodarone), 12(Obesity) and 12(Asthma) of the top 15 corresponding candidate diseases or drugs were confirmed by existing databases. CONCLUSIONS The experimental results adequately demonstrated that WNMFDDA is a very effective method for drug-disease association prediction. We believe that WNMFDDA is helpful for relevant biomedical researchers in follow-up studies.
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Affiliation(s)
- Mei-Neng Wang
- grid.449868.f0000 0000 9798 3808School of Mathematics and Computer Science, Yichun University, Yichun, 336000 Jiangxi China
| | - Xue-Jun Xie
- grid.449868.f0000 0000 9798 3808School of Mathematics and Computer Science, Yichun University, Yichun, 336000 Jiangxi China
| | - Zhu-Hong You
- grid.440588.50000 0001 0307 1240School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072 China
| | - De-Wu Ding
- grid.449868.f0000 0000 9798 3808School of Mathematics and Computer Science, Yichun University, Yichun, 336000 Jiangxi China
| | - Leon Wong
- grid.9227.e0000000119573309Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
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Robinson JC. An Innovation Surcharge to Fund the Repurposing of Generic Drugs. JAMA 2022; 328:2798669. [PMID: 36355357 DOI: 10.1001/jama.2022.21250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This Viewpoint discusses the market for generic drugs and an innovation surcharge that would be awarded to public entities, such as the National Institutes of Health, and its newly formed Advanced Research Projects Agency for Health, to fund research for the repurposing of generic drugs.
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Gnilopyat S, DePietro PJ, Parry TK, McLaughlin WA. The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases. Biomolecules 2022; 12:1559. [PMID: 36358909 PMCID: PMC9687941 DOI: 10.3390/biom12111559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 08/13/2023] Open
Abstract
We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.
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Affiliation(s)
| | | | | | - William A. McLaughlin
- Department of Medical Education, Geisinger Commonwealth School of Medicine, 525 Pine Street, Scranton, PA 18509, 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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Repositioning Drugs for Rare Diseases Based on Biological Features and Computational Approaches. Healthcare (Basel) 2022; 10:healthcare10091784. [PMID: 36141396 PMCID: PMC9498751 DOI: 10.3390/healthcare10091784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/12/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
Rare diseases are a group of uncommon diseases in the world population. To date, about 7000 rare diseases have been documented. However, most of them do not have a known treatment. As a result of the relatively low demand for their treatments caused by their scarce prevalence, the pharmaceutical industry has not sufficiently encouraged the research to develop drugs to treat them. This work aims to analyse potential drug-repositioning strategies for this kind of disease. Drug repositioning seeks to find new uses for existing drugs. In this context, it seeks to discover if rare diseases could be treated with medicines previously indicated to heal other diseases. Our approaches tackle the problem by employing computational methods that calculate similarities between rare and non-rare diseases, considering biological features such as genes, proteins, and symptoms. Drug candidates for repositioning will be checked against clinical trials found in the scientific literature. In this study, 13 different rare diseases have been selected for which potential drugs could be repositioned. By verifying these drugs in the scientific literature, successful cases were found for 75% of the rare diseases studied. The genetic associations and phenotypical features of the rare diseases were examined. In addition, the verified drugs were classified according to the anatomical therapeutic chemical (ATC) code to highlight the types with a higher predisposition to be repositioned. These promising results open the door for further research in this field of study.
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RSDB: A rare skin disease database to link drugs with potential drug targets for rare skin diseases. Sci Data 2022; 9:521. [PMID: 36028515 PMCID: PMC9418253 DOI: 10.1038/s41597-022-01654-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 08/19/2022] [Indexed: 11/09/2022] Open
Abstract
Rare skin diseases include more than 800 diseases affecting more than 6.8 million patients worldwide. However, only 100 drugs have been developed for treating rare skin diseases in the past 38 years. To investigate potential treatments through drug repurposing for rare skin diseases, it is necessary to have a well-organized database to link all known disease causes, mechanisms, and related information to accelerate the process. Drug repurposing provides less expensive and faster potential options to develop treatments for known diseases. In this work, we designed and constructed a rare skin disease database (RSDB) as a disease-centered information depository to facilitate repurposing drug candidates for rare skin diseases. We collected and integrated associated genes, chemicals, and phenotypes into a network connected by pairwise relationships between different components for rare skin diseases. The RSDB covers 891 rare skin diseases defined by the Orphanet and GARD databases. The organized network for each rare skin disease comprises associated genes, phenotypes, and chemicals with the corresponding connections. The RSDB is available at https://rsdb.cmdm.tw .
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Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders. Pharmaceutics 2022; 14:pharmaceutics14071464. [PMID: 35890359 PMCID: PMC9319329 DOI: 10.3390/pharmaceutics14071464] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.
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Monticelli M, Liguori L, Allocca M, Bosso A, Andreotti G, Lukas J, Monti MC, Morretta E, Cubellis MV, Hay Mele B. Drug Repositioning for Fabry Disease: Acetylsalicylic Acid Potentiates the Stabilization of Lysosomal Alpha-Galactosidase by Pharmacological Chaperones. Int J Mol Sci 2022; 23:ijms23095105. [PMID: 35563496 PMCID: PMC9105905 DOI: 10.3390/ijms23095105] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/01/2022] [Accepted: 05/02/2022] [Indexed: 12/10/2022] Open
Abstract
Fabry disease is caused by a deficiency of lysosomal alpha galactosidase and has a very large genotypic and phenotypic spectrum. Some patients who carry hypomorphic mutations can benefit from oral therapy with a pharmacological chaperone. The drug requires a very precise regimen because it is a reversible inhibitor of alpha-galactosidase. We looked for molecules that can potentiate this pharmacological chaperone, among drugs that have already been approved for other diseases. We tested candidate molecules in fibroblasts derived from a patient carrying a large deletion in the gene GLA, which were stably transfected with a plasmid expressing hypomorphic mutants. In our cell model, three drugs were able to potentiate the action of the pharmacological chaperone. We focused our attention on one of them, acetylsalicylic acid. We expect that acetylsalicylic acid can be used in synergy with the Fabry disease pharmacological chaperone and prolong its stabilizing effect on alpha-galactosidase.
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Affiliation(s)
- Maria Monticelli
- Department Biology, University of Napoli « Federico II », Complesso Universitario Monte Sant’Angelo, Via Cinthia, 80126 Napoli, Italy; (M.M.); (A.B.); (B.H.M.)
- Department Environmental, Biological and Pharmaceutical Sciences and Technologies (DiSTABiF), University of Campania “Luigi Vanvitelli”, Via Vivaldi 43, 81100 Caserta, Italy; (L.L.); (M.A.)
- Institute of Biomolecular Chemistry ICB, CNR, Via Campi Flegrei 34, 80078 Pozzuoli, Italy;
| | - Ludovica Liguori
- Department Environmental, Biological and Pharmaceutical Sciences and Technologies (DiSTABiF), University of Campania “Luigi Vanvitelli”, Via Vivaldi 43, 81100 Caserta, Italy; (L.L.); (M.A.)
| | - Mariateresa Allocca
- Department Environmental, Biological and Pharmaceutical Sciences and Technologies (DiSTABiF), University of Campania “Luigi Vanvitelli”, Via Vivaldi 43, 81100 Caserta, Italy; (L.L.); (M.A.)
- Institute of Biomolecular Chemistry ICB, CNR, Via Campi Flegrei 34, 80078 Pozzuoli, Italy;
| | - Andrea Bosso
- Department Biology, University of Napoli « Federico II », Complesso Universitario Monte Sant’Angelo, Via Cinthia, 80126 Napoli, Italy; (M.M.); (A.B.); (B.H.M.)
- Institute of Biochemistry and Cellular Biology, National Research Council, Via Pietro Castellino 111, 80131 Napoli, Italy
| | - Giuseppina Andreotti
- Institute of Biomolecular Chemistry ICB, CNR, Via Campi Flegrei 34, 80078 Pozzuoli, Italy;
| | - Jan Lukas
- Translational Neurodegeneration Section “Albrecht-Kossel”, Department of Neurology, University Medical Center Rostock, 18147 Rostock, Germany;
- Center for Transdisciplinary Neurosciences Rostock (CTNR), University Medical Center Rostock, 18147 Rostock, Germany
| | - Maria Chiara Monti
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy; (M.C.M.); (E.M.)
| | - Elva Morretta
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy; (M.C.M.); (E.M.)
| | - Maria Vittoria Cubellis
- Department Biology, University of Napoli « Federico II », Complesso Universitario Monte Sant’Angelo, Via Cinthia, 80126 Napoli, Italy; (M.M.); (A.B.); (B.H.M.)
- Institute of Biomolecular Chemistry ICB, CNR, Via Campi Flegrei 34, 80078 Pozzuoli, Italy;
- Correspondence: ; Tel.: +39-081-679152
| | - Bruno Hay Mele
- Department Biology, University of Napoli « Federico II », Complesso Universitario Monte Sant’Angelo, Via Cinthia, 80126 Napoli, Italy; (M.M.); (A.B.); (B.H.M.)
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15
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Amaral MD. Precision medicine for rare diseases: The times they are A-Changin'. Curr Opin Pharmacol 2022; 63:102201. [DOI: 10.1016/j.coph.2022.102201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/26/2022] [Accepted: 01/31/2022] [Indexed: 12/30/2022]
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16
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Qin L, Wang J, Wu Z, Li W, Liu G, Tang Y. Drug Repurposing for Newly Emerged Diseases via Network-Based Inference on A Gene-Disease-Drug Network. Mol Inform 2022; 41:e2200001. [PMID: 35338586 DOI: 10.1002/minf.202200001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/25/2022] [Indexed: 11/06/2022]
Abstract
Identification of disease-drug associations is an effective strategy for drug repurposing, especially in searching old drugs for newly emerged diseases like COVID-19. In this study, we put forward a network-based method named NEDNBI to predict disease-drug associations based on a gene-disease-drug tripartite network, which could be applied in drug repurposing. The novelty of our method lies in the fact that no negative data are required, and new disease could be added into the disease-drug network with gene as the bridge. The comprehensive evaluation results showed that the proposed method had good performance, with AUC value 0.948 ± 0.009 for 10-fold cross validation. In a case study, 8 of the 20 predicted old drugs have been tested clinically for the treatment of COVID-19, which illustrated the usefulness of our method in drug repurposing. The source code and data of the method are available at https://github.com/Qli97/NEDNBI.
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Affiliation(s)
- Li Qin
- East China University of Science and Technology School of Pharmacy, CHINA
| | - Jiye Wang
- East China University of Science and Technology School of Pharmacy, CHINA
| | - Zengrui Wu
- East China University of Science and Technology, CHINA
| | | | - Guixia Liu
- East China University of Science and Technology, CHINA
| | - Yun Tang
- East China University of Science and Technology, CHINA
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17
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Sever B, Ciftci H, DeMirci H, Sever H, Ocak F, Yulug B, Tateishi H, Tateishi T, Otsuka M, Fujita M, Başak AN. Comprehensive Research on Past and Future Therapeutic Strategies Devoted to Treatment of Amyotrophic Lateral Sclerosis. Int J Mol Sci 2022; 23:ijms23052400. [PMID: 35269543 PMCID: PMC8910198 DOI: 10.3390/ijms23052400] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 02/08/2022] [Accepted: 02/08/2022] [Indexed: 02/01/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a rapidly debilitating fatal neurodegenerative disorder, causing muscle atrophy and weakness, which leads to paralysis and eventual death. ALS has a multifaceted nature affected by many pathological mechanisms, including oxidative stress (also via protein aggregation), mitochondrial dysfunction, glutamate-induced excitotoxicity, apoptosis, neuroinflammation, axonal degeneration, skeletal muscle deterioration and viruses. This complexity is a major obstacle in defeating ALS. At present, riluzole and edaravone are the only drugs that have passed clinical trials for the treatment of ALS, notwithstanding that they showed modest benefits in a limited population of ALS. A dextromethorphan hydrobromide and quinidine sulfate combination was also approved to treat pseudobulbar affect (PBA) in the course of ALS. Globally, there is a struggle to prevent or alleviate the symptoms of this neurodegenerative disease, including implementation of antisense oligonucleotides (ASOs), induced pluripotent stem cells (iPSCs), CRISPR-9/Cas technique, non-invasive brain stimulation (NIBS) or ALS-on-a-chip technology. Additionally, researchers have synthesized and screened new compounds to be effective in ALS beyond the drug repurposing strategy. Despite all these efforts, ALS treatment is largely limited to palliative care, and there is a strong need for new therapeutics to be developed. This review focuses on and discusses which therapeutic strategies have been followed so far and what can be done in the future for the treatment of ALS.
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Affiliation(s)
- Belgin Sever
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Anadolu University, Eskisehir 26470, Turkey;
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.C.); (H.T.); (M.O.)
| | - Halilibrahim Ciftci
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.C.); (H.T.); (M.O.)
- Department of Drug Discovery, Science Farm Ltd., Kumamoto 862-0976, Japan
- Department of Molecular Biology and Genetics, Koc University, Istanbul 34450, Turkey;
| | - Hasan DeMirci
- Department of Molecular Biology and Genetics, Koc University, Istanbul 34450, Turkey;
| | - Hilal Sever
- Ministry of Health, Istanbul Training and Research Hospital, Physical Medicine and Rehabilitation Clinic, Istanbul 34098, Turkey;
| | - Firdevs Ocak
- Faculty of Medicine, Kocaeli University, Kocaeli 41001, Turkey;
| | - Burak Yulug
- Department of Neurology and Neuroscience, Faculty of Medicine, Alaaddin Keykubat University, Alanya 07425, Turkey;
| | - Hiroshi Tateishi
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.C.); (H.T.); (M.O.)
| | - Takahisa Tateishi
- Division of Respirology, Neurology and Rheumatology, Department of Medicine, Kurume University School of Medicine, Fukuoka 830-0011, Japan;
| | - Masami Otsuka
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.C.); (H.T.); (M.O.)
- Department of Drug Discovery, Science Farm Ltd., Kumamoto 862-0976, Japan
| | - Mikako Fujita
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.C.); (H.T.); (M.O.)
- Correspondence: (M.F.); (A.N.B.); Tel.: +81-96-371-4622 (M.F.); +90-850-250-8250 (A.N.B.)
| | - Ayşe Nazlı Başak
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (KUTTAM-NDAL), Koc University, Istanbul 34450, Turkey
- Correspondence: (M.F.); (A.N.B.); Tel.: +81-96-371-4622 (M.F.); +90-850-250-8250 (A.N.B.)
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18
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Rufini A, Malisan F, Condò I, Testi R. Drug Repositioning in Friedreich Ataxia. Front Neurosci 2022; 16:814445. [PMID: 35221903 PMCID: PMC8863941 DOI: 10.3389/fnins.2022.814445] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 01/07/2022] [Indexed: 12/14/2022] Open
Abstract
Friedreich ataxia is a rare neurodegenerative disorder caused by insufficient levels of the essential mitochondrial protein frataxin. It is a severely debilitating disease that significantly impacts the quality of life of affected patients and reduces their life expectancy, however, an adequate cure is not yet available for patients. Frataxin function, although not thoroughly elucidated, is associated with assembly of iron-sulfur cluster and iron metabolism, therefore insufficient frataxin levels lead to reduced activity of many mitochondrial enzymes involved in the electron transport chain, impaired mitochondrial metabolism, reduced ATP production and inefficient anti-oxidant response. As a consequence, neurons progressively die and patients progressively lose their ability to coordinate movement and perform daily activities. Therapeutic strategies aim at restoring sufficient frataxin levels or at correcting some of the downstream consequences of frataxin deficiency. However, the classical pathways of drug discovery are challenging, require a significant amount of resources and time to reach the final approval, and present a high failure rate. Drug repositioning represents a viable alternative to boost the identification of a therapy, particularly for rare diseases where resources are often limited. In this review we will describe recent efforts aimed at the identification of a therapy for Friedreich ataxia through drug repositioning, and discuss the limitation of such strategies.
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Affiliation(s)
- Alessandra Rufini
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Fratagene Therapeutics, Rome, Italy
- Saint Camillus International University of Health and Medical Sciences, Rome, Italy
- *Correspondence: Alessandra Rufini,
| | - Florence Malisan
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
| | - Ivano Condò
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
| | - Roberto Testi
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Fratagene Therapeutics, Rome, Italy
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19
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Repurposing approved therapeutics for new indication: Addressing unmet needs in psoriasis treatment. CURRENT RESEARCH IN PHARMACOLOGY AND DRUG DISCOVERY 2021; 2:100041. [PMID: 34909670 PMCID: PMC8663928 DOI: 10.1016/j.crphar.2021.100041] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/04/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Psoriasis is a chronic inflammatory autoimmune condition manifested by the hyperproliferation of keratinocytes with buildup of inflammatory red patches and scales on skin surfaces. The available treatment options for the management of psoriasis have various drawbacks, and the clinical need for effective therapeutics for this disease remain unmet; therefore, the approaches of drug repurposing or drug repositioning could potentially be used for treating indications of psoriasis. The undiscovered potential of drug repurposing or repositioning compensates for the limitations and hurdles in drug discovery and drug development processes. Drugs initially approved for other indications, including anticancer, antidiabetic, antihypertensive, and anti-arthritic activities, are being investigated for their potential in psoriasis management as a new therapeutic indication by using repurposing strategies. This article envisages the potential of various therapeutics for the management of psoriasis. Psoriasis is an autoimmune inflammatory skin disorder with complex physiology. Conventional treatments for psoriasis cause severe adverse effects; therefore an unmet need remains for safer and more effective therapies for psoriasis. Various drugs that effectively decrease the inflammation and proliferation of skin cells can be repurposed for the management of psoriasis. Repurposed drugs provide various incentives to the pharmaceutical industry.
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20
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Martínez-deMiguel C, Segura-Bedmar I, Chacón-Solano E, Guerrero-Aspizua S. The RareDis corpus: A corpus annotated with rare diseases, their signs and symptoms. J Biomed Inform 2021; 125:103961. [PMID: 34879250 DOI: 10.1016/j.jbi.2021.103961] [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: 08/02/2021] [Revised: 11/08/2021] [Accepted: 11/22/2021] [Indexed: 11/26/2022]
Abstract
Rare diseases affect a small number of people compared to the general population. However, more than 6,000 different rare diseases exist and, in total, they affect more than 300 million people worldwide. Rare diseases share as part of their main problem, the delay in diagnosis and the sparse information available for researchers, clinicians, and patients. Finding a diagnostic can be a very long and frustrating experience for patients and their families. The average diagnostic delay is between 6-8 years. Many of these diseases result in different manifestations among patients, which hampers even more their detection and the correct treatment choice. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Processing (NLP) can help to extract relevant information about rare diseases to facilitate their diagnosis and treatments, but most NLP techniques require manually annotated corpora. Therefore, our goal is to create a gold standard corpus annotated with rare diseases and their clinical manifestations. It could be used to train and test NLP approaches and the information extracted through NLP could enrich the knowledge of rare diseases, and thereby, help to reduce the diagnostic delay and improve the treatment of rare diseases. The paper describes the selection of 1,041 texts to be included in the corpus, the annotation process and the annotation guidelines. The entities (disease, rare disease, symptom, sign and anaphor) and the relationships (produces, is a, is acron, is synon, increases risk of, anaphora) were annotated. The RareDis corpus contains more than 5,000 rare diseases and almost 6,000 clinical manifestations are annotated. Moreover, the Inter Annotator Agreement evaluation shows a relatively high agreement (F1-measure equal to 83.5% under exact match criteria for the entities and equal to 81.3% for the relations). Based on these results, this corpus is of high quality, supposing a significant step for the field since there is a scarcity of available corpus annotated with rare diseases. This could open the door to further NLP applications, which would facilitate the diagnosis and treatment of these rare diseases and, therefore, would improve dramatically the quality of life of these patients.
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Affiliation(s)
- Claudia Martínez-deMiguel
- Tissue Engineering and Regenerative Medicine group, Department of Bioengineering, Universidad Carlos III de Madrid, Avenidad de la Universidad, 30, Leganés 28911, Madrid, Spain
| | - Isabel Segura-Bedmar
- Human Language and Accesibility Technologies, Computer Science Department, Avenidad de la Universidad 30, Leganés 28911, Madrid, Spain.
| | - Esteban Chacón-Solano
- Tissue Engineering and Regenerative Medicine group, Department of Bioengineering, Universidad Carlos III de Madrid, Avenidad de la Universidad, 30, Leganés 28911, Madrid, Spain; Hospital Fundación Jiménez Díaz e, Instituto de Investigación, FJD, Av. de los Reyes Católicos 2, Madrid 28040, Madrid, Spain; Epithelial Biomedicine Division, CIEMAT, Madrid 28040, Madrid, Spain
| | - Sara Guerrero-Aspizua
- Tissue Engineering and Regenerative Medicine group, Department of Bioengineering, Universidad Carlos III de Madrid, Avenidad de la Universidad, 30, Leganés 28911, Madrid, Spain; Hospital Fundación Jiménez Díaz e, Instituto de Investigación, FJD, Av. de los Reyes Católicos 2, Madrid 28040, Madrid, Spain; Epithelial Biomedicine Division, CIEMAT, Madrid 28040, Madrid, Spain; Centre for Biomedical Network Research on Rare Diseases (CIBERER), C/Monforte de Lemos 3-5, Madrid 28029, Madrid, Spain
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21
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van den Berg S, de Visser S, Leufkens HGM, Hollak CEM. Drug Repurposing for Rare Diseases: A Role for Academia. Front Pharmacol 2021; 12:746987. [PMID: 34744726 PMCID: PMC8564285 DOI: 10.3389/fphar.2021.746987] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 09/15/2021] [Indexed: 11/19/2022] Open
Abstract
Background: The European Commission highlights in its Pharmaceutical Strategy the role of academic researchers in drug repurposing, especially in the development of orphan medicinal products (OMPs). This study summarizes the contribution of academia over the last 5 years to registered repurposed OMPs and describes barriers to success, based upon three real world cases. Methods: OMPs granted marketing authorization between January 2016 and December 2020 were reviewed for repurposing and whether the idea originated from academia or industry. Three cases of drug repurposing were selected from different therapeutic areas and stages of development to identify obstacles to success. Results: Thirteen of the 68 OMPs were the result of drug repurposing. In three OMPs, there were two developments such as both a new indication and a modified application. In total, twelve developments originated from academia and four from industry. The three cases showed as barriers to success: lack of outlook for sufficient return of investments (abatacept), lack of regulatory alignment and timing of interaction between healthcare professionals and regulators (etidronate), failure to register an old drug for a fair price, resulting in commercialization as a high priced orphan drug (mexiletine). Conclusion: While the majority of repurposed OMPs originates in academia, a gap exists between healthcare professionals, regulators and industry. Future strategies should aim to overcome these hurdles leading to more patient benefit through sustainable access of repurposed drugs. Potential solutions include improved regulatory and reimbursement knowledge by academia and the right for regulators to integrate new effectiveness data into product labels.
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Affiliation(s)
- Sibren van den Berg
- Medicine for Society, Platform at Amsterdam UMC-University of Amsterdam, Amsterdam, Netherlands.,Department of Endocrinology and Metabolism, Amsterdam UMC-University of Amsterdam, Amsterdam, Netherlands
| | - Saco de Visser
- Medicine for Society, Platform at Amsterdam UMC-University of Amsterdam, Amsterdam, Netherlands
| | - Hubert G M Leufkens
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, Netherlands
| | - Carla E M Hollak
- Medicine for Society, Platform at Amsterdam UMC-University of Amsterdam, Amsterdam, Netherlands.,Department of Endocrinology and Metabolism, Amsterdam UMC-University of Amsterdam, Amsterdam, Netherlands
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22
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Chen P, Bao T, Yu X, Liu Z. A drug repositioning algorithm based on a deep autoencoder and adaptive fusion. BMC Bioinformatics 2021; 22:532. [PMID: 34717542 PMCID: PMC8556784 DOI: 10.1186/s12859-021-04406-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 09/27/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods; thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning. RESULTS In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset. CONCLUSION The proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning.
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Affiliation(s)
- Peng Chen
- College of Computer and Information Technology, China Three Gorges University, Hubei, China
| | - Tianjiazhi Bao
- College of Computer and Information Technology, China Three Gorges University, Hubei, China
| | - Xiaosheng Yu
- College of Computer and Information Technology, China Three Gorges University, Hubei, China
| | - Zhongtu Liu
- College of Computer and Information Technology, China Three Gorges University, Hubei, China
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23
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Luo L, Qiu Q, Huang F, Liu K, Lan Y, Li X, Huang Y, Cui L, Luo H. Drug repurposing against coronavirus disease 2019 (COVID-19): A review. J Pharm Anal 2021; 11:683-690. [PMID: 34513115 PMCID: PMC8416689 DOI: 10.1016/j.jpha.2021.09.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 08/16/2021] [Accepted: 09/03/2021] [Indexed: 02/07/2023] Open
Abstract
Since December 2019, severe acute respiratory syndrome coronavirus 2 has been found to be the culprit in the coronavirus disease 2019 (COVID-19), causing a global pandemic. Despite the existence of many vaccine programs, the number of confirmed cases and fatalities due to COVID-19 is still increasing. Furthermore, a number of variants have been reported. Because of the absence of approved anti-coronavirus drugs, the treatment and management of COVID-19 has become a global challenge. Under these circumstances, drug repurposing is an effective method to identify candidate drugs with a shorter cycle of clinical trials. Here, we summarize the current status of the application of drug repurposing in COVID-19, including drug repurposing based on virtual computer screening, network pharmacology, and bioactivity, which may be a beneficial COVID-19 treatment. Mechanism of SARS-CoV-2 infection and drug targets were reviewed. Drug repurposing against COVID-19 based on computer virtual screening, network pharmacology, bioactivity were summarized. The use of drug repurposing in COVID-19 was addressed.
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Affiliation(s)
- Lianxiang Luo
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China.,Marine Medical Research Institute of Zhanjiang, Zhanjiang, 524023, Guangdong, China
| | - Qin Qiu
- Graduate School, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China
| | - Fangfang Huang
- Graduate School, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China
| | - Kaifeng Liu
- The First Clinical College, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China
| | - Yongqi Lan
- The First Clinical College, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China
| | - Xiaoling Li
- Animal Experiment Center, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China
| | - Yuge Huang
- Department of Pediatrics, the Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524023, Guangdong, China
| | - Liao Cui
- Guangdong Key Laboratory for Research and Development of Natural Drugs, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Hui Luo
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China
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24
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Spahr A, Rosli Z, Legault M, Tran LT, Fournier S, Toutounchi H, Darbelli L, Madjar C, Lucia C, St-Jean ML, Das S, Evans AC, Bernard G. The LORIS MyeliNeuroGene rare disease database for natural history studies and clinical trial readiness. Orphanet J Rare Dis 2021; 16:328. [PMID: 34301277 PMCID: PMC8299589 DOI: 10.1186/s13023-021-01953-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/11/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Rare diseases are estimated to affect 150-350 million people worldwide. With advances in next generation sequencing, the number of known disease-causing genes has increased significantly, opening the door for therapy development. Rare disease research has therefore pivoted from gene discovery to the exploration of potential therapies. With impending clinical trials on the horizon, researchers are in urgent need of natural history studies to help them identify surrogate markers, validate outcome measures, define historical control patients, and design therapeutic trials. RESULTS We customized a browser-accessible multi-modal (e.g. genetics, imaging, behavioral, patient-determined outcomes) database to increase cohort sizes, identify surrogate markers, and foster international collaborations. Ninety data entry forms were developed including family, perinatal, developmental history, clinical examinations, diagnostic investigations, neurological evaluations (i.e. spasticity, dystonia, ataxia, etc.), disability measures, parental stress, and quality of life. A customizable clinical letter generator was created to assist in continuity of patient care. CONCLUSIONS Small cohorts and underpowered studies are a major challenge for rare disease research. This online, rare disease database will be accessible from all over the world, making it easier to share and disseminate data. We have outlined the methodology to become Title 21 Code of Federal Regulations Part 11 Compliant, which is a requirement to use electronic records as historical controls in clinical trials in the United States. Food and Drug Administration compliant databases will be life-changing for patients and families when historical control data is used for emerging clinical trials. Future work will leverage these tools to delineate the natural history of several rare diseases and we are confident that this database will be used on a larger scale to improve care for patients affected with rare diseases.
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Affiliation(s)
- Aaron Spahr
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Zaliqa Rosli
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Mélanie Legault
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Luan T Tran
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Simon Fournier
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Helia Toutounchi
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Lama Darbelli
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Cécile Madjar
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Cassandra Lucia
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Marie-Lou St-Jean
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Samir Das
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Geneviève Bernard
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
- Department of Pediatrics, McGill University, Montréal, Québec, Canada.
- Department of Human Genetics, McGill University, Montréal, Québec, Canada.
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada.
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada.
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Sadeghi SS, Keyvanpour MR. Computational Drug Repurposing: Classification of the Research Opportunities and Challenges. Curr Comput Aided Drug Des 2021; 16:354-364. [PMID: 31198115 DOI: 10.2174/1573409915666190613113822] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 02/13/2019] [Accepted: 05/18/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Drug repurposing has grown significantly in recent years. Research and innovation in drug repurposing are extremely popular due to its practical and explicit advantages. However, its adoption into practice is slow because researchers and industries have to face various challenges. OBJECTIVE As this field, there is a lack of a comprehensive platform for systematic identification for removing development limitations. This paper deals with a comprehensive classification of challenges in drug repurposing. METHODS Initially, a classification of various existing repurposing models is propounded. Next, the benefits of drug repurposing are summarized. Further, a categorization for computational drug repurposing shortcomings is presented. Finally, the methods are evaluated based on their strength to addressing the drawbacks. RESULTS This work can offer a desirable platform for comparing the computational repurposing methods by measuring the methods in light of these challenges. CONCLUSION A proper comparison could prepare guidance for a genuine understanding of methods. Accordingly, this comprehension of the methods will help researchers eliminate the barriers thereby developing and improving methods. Furthermore, in this study, we conclude why despite all the benefits of drug repurposing, it is not being done anymore.
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Boyd NK, Teng C, Frei CR. Brief Overview of Approaches and Challenges in New Antibiotic Development: A Focus On Drug Repurposing. Front Cell Infect Microbiol 2021; 11:684515. [PMID: 34079770 PMCID: PMC8165386 DOI: 10.3389/fcimb.2021.684515] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 05/04/2021] [Indexed: 12/19/2022] Open
Abstract
Drug repurposing, or identifying new uses for existing drugs, has emerged as an alternative to traditional drug discovery processes involving de novo synthesis. Drugs that are currently approved or under development for non-antibiotic indications may possess antibiotic properties, and therefore may have repurposing potential, either alone or in combination with an antibiotic. They might also serve as "antibiotic adjuvants" to enhance the activity of certain antibiotics.
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Affiliation(s)
- Natalie K Boyd
- College of Pharmacy, The University of Texas at Austin, San Antonio, TX, United States.,Long School of Medicine, University of Texas Health San Antonio, San Antonio, TX, United States
| | - Chengwen Teng
- Department of Clinical Pharmacy and Outcomes Sciences, College of Pharmacy, The University of South Carolina, Columbia, SC, United States
| | - Christopher R Frei
- College of Pharmacy, The University of Texas at Austin, San Antonio, TX, United States.,Long School of Medicine, University of Texas Health San Antonio, San Antonio, TX, United States.,Research Department, South Texas Veterans Health Care System, San Antonio, TX, United States.,Pharmacy Department, University Health System, San Antonio, TX, United States
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27
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Hudson ML, Samudrala R. Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform. Molecules 2021; 26:2581. [PMID: 33925237 PMCID: PMC8125683 DOI: 10.3390/molecules26092581] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 12/02/2022] Open
Abstract
Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.
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Affiliation(s)
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA;
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28
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Noor A, Assiri A. A novel computational drug repurposing approach for Systemic Lupus Erythematosus (SLE) treatment using Semantic Web technologies. Saudi J Biol Sci 2021; 28:3886-3892. [PMID: 34220244 PMCID: PMC8241633 DOI: 10.1016/j.sjbs.2021.03.068] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 03/20/2021] [Accepted: 03/25/2021] [Indexed: 12/11/2022] Open
Affiliation(s)
- Adeeb Noor
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80221, Saudi Arabia
| | - Abdullah Assiri
- Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha 62529, Saudi Arabia
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29
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Sadeghi SS, Keyvanpour MR. An Analytical Review of Computational Drug Repurposing. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:472-488. [PMID: 31403439 DOI: 10.1109/tcbb.2019.2933825] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or failed drugs. It is cost-effective and reliable in contrast to experimental drug discovery, which is a costly, time-consuming, and risky process and limited to a relatively small number of targets. Accordingly, a plethora of computational methodologies have been propounded to repurpose drugs on a large scale by utilizing available high throughput data. The available literature, however, lacks a contemporary and comprehensive analysis of the current computational drug repurposing methodologies. In this paper, we presented a systematic analysis of computational drug repurposing which consists of three main sections: Initially, we categorize the computational drug repurposing methods based on their technical approach and artificial intelligence perspective and discuss the strengths and weaknesses of various methods. Secondly, some general criteria are recommended to analyze our proposed categorization. In the third and final section, a qualitative comparison is made between each approach which is a guide to understanding their preference to one another. Further, this systematic analysis can help in the efficient selection and improvement of drug repurposing techniques based on the nature of computational methods implemented on biological resources.
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30
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Roy S, Dhaneshwar S, Bhasin B. Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery. Curr Drug Res Rev 2021; 13:101-119. [PMID: 33573567 DOI: 10.2174/2589977513666210211163711] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 09/07/2020] [Accepted: 10/26/2020] [Indexed: 11/22/2022]
Abstract
Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.
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Affiliation(s)
- Supriya Roy
- Amity Institute of Pharmacy, Amity University Uttar Pradesh, Lucknow Campus, India
| | - Suneela Dhaneshwar
- Amity Institute of Pharmacy, Amity University Uttar Pradesh, Lucknow Campus, India
| | - Bhavya Bhasin
- Poona College of Pharmacy, Bharati Vidyapeeth University, Pune, India
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31
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Roessler HI, Knoers NVAM, van Haelst MM, van Haaften G. Drug Repurposing for Rare Diseases. Trends Pharmacol Sci 2021; 42:255-267. [PMID: 33563480 DOI: 10.1016/j.tips.2021.01.003] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/13/2021] [Accepted: 01/19/2021] [Indexed: 12/17/2022]
Abstract
Currently, there are about 7000 identified rare diseases, together affecting 10% of the population. However, fewer than 6% of all rare diseases have an approved treatment option, highlighting their tremendous unmet needs in drug development. The process of repurposing drugs for new indications, compared with the development of novel orphan drugs, is a time-saving and cost-efficient method resulting in higher success rates, which can therefore drastically reduce the risk of drug development for rare diseases. Although drug repurposing is not novel, new strategies have been developed in recent years to do it in a systematic and rational way. Here, we review applied methodologies, recent accomplished progress, and the challenges associated in drug repurposing for rare diseases.
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Affiliation(s)
- Helen I Roessler
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Nine V A M Knoers
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - Mieke M van Haelst
- Department of Clinical Genetics, Amsterdam University Medical Center, Location AMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Clinical Genetics, Amsterdam University Medical Center, Location VUMC, VU University Amsterdam, Amsterdam, The Netherlands
| | - Gijs van Haaften
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
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32
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From Homology Modeling to the Hit Identification and Drug Repurposing: A Structure-Based Approach in the Discovery of Novel Potential Anti-Obesity Compounds. Methods Mol Biol 2021; 2266:263-277. [PMID: 33759132 DOI: 10.1007/978-1-0716-1209-5_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Although science and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In this scenario, drug repurposing has appeared as an alternative tool to accelerate the drug development process. Herein, we applied such an approach to the highly popular human Carbonic Anhydrase (hCA) VA drug target, that is involved in ureagenesis, gluconeogenesis, lipogenesis, and in the metabolism regulation. Albeit several hCA inhibitors have been designed and are currently in clinical use, serious drug interactions have been reported due to their poor selectivity. In this perspective, the drug repurposing approach could be a useful tool for investigating the drug promiscuity/polypharmacology profile. In this chapter, we describe a combination of virtual screening techniques and in vitro assays aimed to identify novel selective hCA VA inhibitors and to repurpose drugs known for other clinical indications.
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33
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Wu J, Hu B, Sun X, Wang H, Huang Y, Zhang Y, Liu M, Liu Y, Zhao Y, Wang J, Yu Z. In silico study reveals existing drugs as α-glucosidase inhibitors: Structure-based virtual screening validated by experimental investigation. J Mol Struct 2020. [DOI: 10.1016/j.molstruc.2020.128532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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34
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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: 37] [Impact Index Per Article: 9.3] [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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35
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Computational Drug Repositioning: Current Progress and Challenges. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155076] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.
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36
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A computational drug repositioning method applied to rare diseases: Adrenocortical carcinoma. Sci Rep 2020; 10:8846. [PMID: 32483162 PMCID: PMC7264316 DOI: 10.1038/s41598-020-65658-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 05/08/2020] [Indexed: 01/12/2023] Open
Abstract
Rare or orphan diseases affect only small populations, thereby limiting the economic incentive for the drug development process, often resulting in a lack of progress towards treatment. Drug repositioning is a promising approach in these cases, due to its low cost. In this approach, one attempts to identify new purposes for existing drugs that have already been developed and approved for use. By applying the process of drug repositioning to identify novel treatments for rare diseases, we can overcome the lack of economic incentives and make concrete progress towards new therapies. Adrenocortical Carcinoma (ACC) is a rare disease with no practical and definitive therapeutic approach. We apply Heter-LP, a new method of drug repositioning, to suggest novel therapeutic avenues for ACC. Our analysis identifies innovative putative drug-disease, drug-target, and disease-target relationships for ACC, which include Cosyntropin (drug) and DHCR7, IGF1R, MC1R, MAP3K3, TOP2A (protein targets). When results are analyzed using all available information, a number of novel predicted associations related to ACC appear to be valid according to current knowledge. We expect the predicted relations will be useful for drug repositioning in ACC since the resulting ranked lists of drugs and protein targets can be used to expedite the necessary clinical processes.
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Prakash AV, Park JW, Seong JW, Kang TJ. Repositioned Drugs for Inflammatory Diseases such as Sepsis, Asthma, and Atopic Dermatitis. Biomol Ther (Seoul) 2020; 28:222-229. [PMID: 32133828 PMCID: PMC7216745 DOI: 10.4062/biomolther.2020.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 02/11/2020] [Accepted: 02/17/2020] [Indexed: 12/14/2022] Open
Abstract
The process of drug discovery and drug development consumes billions of dollars to bring a new drug to the market. Drug development is time consuming and sometimes, the failure rates are high. Thus, the pharmaceutical industry is looking for a better option for new drug discovery. Drug repositioning is a good alternative technology that has demonstrated many advantages over de novo drug development, the most important one being shorter drug development timelines. In the last two decades, drug repositioning has made tremendous impact on drug development technologies. In this review, we focus on the recent advances in drug repositioning technologies and discuss the repositioned drugs used for inflammatory diseases such as sepsis, asthma, and atopic dermatitis.
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Affiliation(s)
- Annamneedi Venkata Prakash
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Jun Woo Park
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Ju-Won Seong
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Tae Jin Kang
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
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38
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Sohraby F, Aryapour H. Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs. Semin Cancer Biol 2020; 68:249-257. [PMID: 32360530 DOI: 10.1016/j.semcancer.2020.04.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 03/07/2020] [Accepted: 04/22/2020] [Indexed: 12/13/2022]
Abstract
Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.
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Affiliation(s)
- Farzin Sohraby
- Department of Biology, Faculty of Science, Golestan University, Gorgan, Iran
| | - Hassan Aryapour
- Department of Biology, Faculty of Science, Golestan University, Gorgan, Iran.
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Bellera CL, Alberca LN, Sbaraglini ML, Talevi A. In Silico Drug Repositioning for Chagas Disease. Curr Med Chem 2020; 27:662-675. [PMID: 31622200 DOI: 10.2174/0929867326666191016114839] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 09/12/2019] [Accepted: 09/23/2019] [Indexed: 12/18/2022]
Abstract
Chagas disease is an infectious tropical disease included within the group of neglected tropical diseases. Though historically endemic to Latin America, it has lately spread to high-income countries due to human migration. At present, there are only two available drugs, nifurtimox and benznidazole, approved for this treatment, both with considerable side-effects (which often result in treatment interruption) and limited efficacy in the chronic stage of the disease in adults. Drug repositioning involves finding novel therapeutic indications for known drugs, including approved, withdrawn, abandoned and investigational drugs. It is today a broadly applied approach to develop innovative medications, since indication shifts are built on existing safety, ADME and manufacturing information, thus greatly shortening development timeframes. Drug repositioning has been signaled as a particularly interesting strategy to search for new therapeutic solutions for neglected and rare conditions, which traditionally present limited commercial interest and are mostly covered by the public sector and not-for-profit initiatives and organizations. Here, we review the applications of computer-aided technologies as systematic approaches to drug repositioning in the field of Chagas disease. In silico screening represents the most explored approach, whereas other rational methods such as network-based and signature-based approximations have still not been applied.
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Affiliation(s)
- Carolina L Bellera
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - Lucas N Alberca
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - María L Sbaraglini
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - Alan Talevi
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
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40
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Zhu Y, Jung W, Wang F, Che C. Drug repurposing against Parkinson's disease by text mining the scientific literature. LIBRARY HI TECH 2020. [DOI: 10.1108/lht-08-2019-0170] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeDrug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.Design/methodology/approachThe literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.FindingsThe proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.Originality/valueThe drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.
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41
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Parisi D, Adasme MF, Sveshnikova A, Bolz SN, Moreau Y, Schroeder M. Drug repositioning or target repositioning: A structural perspective of drug-target-indication relationship for available repurposed drugs. Comput Struct Biotechnol J 2020; 18:1043-1055. [PMID: 32419905 PMCID: PMC7215100 DOI: 10.1016/j.csbj.2020.04.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 03/31/2020] [Accepted: 04/04/2020] [Indexed: 12/18/2022] Open
Abstract
Drug repositioning aims to find new indications for existing drugs in order to reduce drug development cost and time. Currently,there are numerous stories of successful drug repositioning that have been reported and many repurposed drugs are already available on the market. Although drug repositioning is often a product of serendipity, repositioning opportunities can be uncovered systematically. There are three systematic approaches to drug repositioning: disease-centric approach, target-centric and drug-centric. Disease-centric approaches identify close relationships between an old and a new indication. A target-centric approach links a known target and its established drug to a new indication. Lastly, a drug-centric approach connects a known drug to a new target and its associated indication. These three approaches differ in their potential and their limitations, but above all else, in the required start information and computing power. This raises the question of which approach prevails in current drug discovery and what that implies for future developments. To address this question, we systematically evaluated over 100 drugs, 200 target structures and over 300 indications from the Drug Repositioning Database. Each analyzed case was classified as one of the three repositioning approaches. For the majority of cases (more than 60%) the disease-centric definition was assigned. Almost 30% of the cases were classified as target-centric and less than 10% as drug-centric approaches. We concluded that, despite the use of umbrella term “drug” repositioning, disease- and target-centric approaches have dominated the field until now. We propose the use of drug-centric approaches while discussing reasons, such as structure-based repositioning techniques, to exploit the full potential of drug-target-disease connections.
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Affiliation(s)
| | - Melissa F Adasme
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | - Anastasia Sveshnikova
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | | | - Yves Moreau
- ESAT-STADIUS, KU Leuven, B-3001 Heverlee, Belgium
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
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Abstract
AbstractDuring three decades, only about 20 new drugs have been developed for malaria, tuberculosis and all neglected tropical diseases (NTDs). This critical situation was reached because NTDs represent only 10% of health research investments; however, they comprise about 90% of the global disease burden. Computational simulations applied in virtual screening (VS) strategies are very efficient tools to identify pharmacologically active compounds or new indications for drugs already administered for other diseases. One of the advantages of this approach is the low time-consuming and low-budget first stage, which filters for testing experimentally a group of candidate compounds with high chances of binding to the target and present trypanocidal activity. In this work, we review the most common VS strategies that have been used for the identification of new drugs with special emphasis on those applied to trypanosomiasis and leishmaniasis. Computational simulations based on the selected protein targets or their ligands are explained, including the method selection criteria, examples of successful VS campaigns applied to NTDs, a list of validated molecular targets for drug development and repositioned drugs for trypanosomatid-caused diseases. Thereby, here we present the state-of-the-art of VS and drug repurposing to conclude pointing out the future perspectives in the field.
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Adasme MF, Parisi D, Sveshnikova A, Schroeder M. Structure-based drug repositioning: Potential and limits. Semin Cancer Biol 2020; 68:192-198. [PMID: 32032699 DOI: 10.1016/j.semcancer.2020.01.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 01/08/2020] [Accepted: 01/16/2020] [Indexed: 12/28/2022]
Abstract
Drug repositioning, the assignment of new therapeutic purposes to known drugs, is an established strategy with many repurposed drugs on the market and many more at experimental stage. We review three use cases, a herpes drug with benefits in cancer, a cancer drug with potential in autoimmune disease, and a selective and an unspecific drug binding the same target (GPCR). We explore these use cases from a structural point of view focusing on a deep understanding of the underlying drug-target interactions. We review tools and data needed for such a drug-centric structural repositioning approach. Finally, we show that the availability of data on targets is an important limiting factor to realize the full potential of structural drug-repositioning.
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Affiliation(s)
- Melissa F Adasme
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | - Daniele Parisi
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany; ESAT-STADIUS, KU Leuven, B-3001 Heverlee, Belgium
| | - Anastasia Sveshnikova
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany.
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Réda C, Kaufmann E, Delahaye-Duriez A. Machine learning applications in drug development. Comput Struct Biotechnol J 2019; 18:241-252. [PMID: 33489002 PMCID: PMC7790737 DOI: 10.1016/j.csbj.2019.12.006] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 12/10/2019] [Accepted: 12/10/2019] [Indexed: 02/07/2023] Open
Abstract
Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.
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Affiliation(s)
- Clémence Réda
- NeuroDiderot, UMR 1141, Inserm, Université de Paris, Sorbonne Paris Cité, Hôpital Robert Debré, 48, boulevard Sérurier, Paris 75019, France
- Université Paris Diderot, Université de Paris, Sorbonne Paris Cité, 5, rue Thomas Mann, Paris 75013, France
| | - Emilie Kaufmann
- Univ. Lille, CNRS, Centrale Lille, Inria, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France
| | - Andrée Delahaye-Duriez
- NeuroDiderot, UMR 1141, Inserm, Université de Paris, Sorbonne Paris Cité, Hôpital Robert Debré, 48, boulevard Sérurier, Paris 75019, France
- Université Paris 13, Sorbonne Paris Cité, UFR de santé, médecine et biologie humaine, Bobigny 93000, France
- Service histologie-embryologie-cytogénétique-biologie de la reproduction-CECOS, Hôpital Jean Verdier, AP-HP, Bondy 93140, France
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Wang Y, Guo M, Ren Y, Jia L, Yu G. Drug repositioning based on individual bi-random walks on a heterogeneous network. BMC Bioinformatics 2019; 20:547. [PMID: 31874623 PMCID: PMC6929332 DOI: 10.1186/s12859-019-3117-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 09/27/2019] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Traditional drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing drugs, referred as drug repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods have been proposed for drug repositioning and most of them apply random walk on a heterogeneous network consisted with disease and drug nodes. However, these methods generally adopt the same walk-length for all nodes, and ignore the different contributions of different nodes. RESULTS In this study, we propose a drug repositioning approach based on individual bi-random walks (DR-IBRW) on the heterogeneous network. DR-IBRW firstly quantifies the individual work-length of random walks for each node based on the network topology and knowledge that similar drugs tend to be associated with similar diseases. To account for the inner structural difference of the heterogeneous network, it performs bi-random walks with the quantified walk-lengths, and thus to identify new indications for approved drugs. Empirical study on public datasets shows that DR-IBRW achieves a much better drug repositioning performance than other related competitive methods. CONCLUSIONS Using individual random walk-lengths for different nodes of heterogeneous network indeed boosts the repositioning performance. DR-IBRW can be easily generalized to prioritize links between nodes of a network.
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Affiliation(s)
- Yuehui Wang
- College of Computer and Information Sciences, Southwest University, Beibei, Chongqing, 400715, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China.
| | - Yazhou Ren
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Lianyin Jia
- College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650504, China
| | - Guoxian Yu
- College of Computer and Information Sciences, Southwest University, Beibei, Chongqing, 400715, China.
- Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430074, China.
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Imatinib revives the therapeutic potential of metformin on ewing sarcoma by attenuating tumor hypoxic response and inhibiting convergent signaling pathways. Cancer Lett 2019; 469:195-206. [PMID: 31672491 DOI: 10.1016/j.canlet.2019.10.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 09/30/2019] [Accepted: 10/21/2019] [Indexed: 02/06/2023]
Abstract
Ewing sarcoma (EwS) is an aggressive pediatric tumor treated with intensive cytotoxic chemotherapies. Overall survival for metastatic or relapsed disease is only 20-30%. Metformin has long been an attractive therapeutic option for EwS, but hypoxia limits its efficacy. Through a systematic integration of drug combination screening, bioinformatics analyses, functional and in vivo studies, and correlation with clinical outcome, we identified another known drug, imatinib that could augment the in vivo anti-tumor capacity of metformin by attenuating tumor hypoxic response. This drug combination regimen widely suppressed multiple dominant mechanisms in EwS genesis, growth, and metastasis, including key EWS-FLI1 downstream targets that converge into the PI3K/AKT/mTOR signaling pathway. In addition, the combination significantly enhanced inhibition on tumor cell proliferation by standard EwS chemotherapy drugs, including cyclophosphamide and ifosfamide. This suggests a potential clinical benefit of the metformin/imatinib combination by allowing the reduction in dose intensity of standard chemotherapy without compromising survival outcome and represents a potential faster track application for EwS patients.
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Schuler J, Samudrala R. Fingerprinting CANDO: Increased Accuracy with Structure- and Ligand-Based Shotgun Drug Repurposing. ACS OMEGA 2019; 4:17393-17403. [PMID: 31656912 PMCID: PMC6812124 DOI: 10.1021/acsomega.9b02160] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 08/30/2019] [Indexed: 05/08/2023]
Abstract
We have upgraded our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing by including ligand-based, data fusion, and decision tree pipelines. The goal of shotgun drug repurposing is to screen and rank every existing human use drug or compound for every disease/indication. The first version of CANDO implemented a structure-based pipeline that modeled interactions between compounds and proteins on a large scale, generating compound-proteome interaction signatures used to infer the similarity of drug behavior; the new pipelines accomplish this by incorporating molecular fingerprints and the Tanimoto coefficient. We obtain improved benchmarking performance with the new pipelines across all three evaluation metrics used: average indication accuracy, pairwise accuracy, and coverage. The best performing pipeline achieves an average indication accuracy of 19.0% at the top10 cutoff, compared to 11.7% for v1, and 2.2% for a random control. Our results demonstrate that the CANDO drug recovery accuracy is substantially improved by integrating multiple pipelines, thereby enhancing our ability to generate putative therapeutic repurposing candidates, and increasing drug discovery efficiency.
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Affiliation(s)
- James Schuler
- Department of Biomedical
Informatics, Jacobs School of Medicine and
Biomedical Sciences at the University at Buffalo, Buffalo, New York 14203, United States
| | - Ram Samudrala
- Department of Biomedical
Informatics, Jacobs School of Medicine and
Biomedical Sciences at the University at Buffalo, Buffalo, New York 14203, United States
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Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit. Molecules 2019; 24:molecules24152712. [PMID: 31349692 PMCID: PMC6696443 DOI: 10.3390/molecules24152712] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 07/18/2019] [Accepted: 07/19/2019] [Indexed: 12/15/2022] Open
Abstract
Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications.
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 342] [Impact Index Per Article: 68.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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Suarez A, Reilly C, Fajgenbaum DC. Quantitative analysis of a rare disease network's international contact database and E-repository provides insights into biobanking in the electronic consent era. Orphanet J Rare Dis 2019; 14:173. [PMID: 31296233 PMCID: PMC6625003 DOI: 10.1186/s13023-019-1145-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 06/25/2019] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Castleman disease (CD) describes a group of rare and poorly understood lymphoproliferative disorders that include unicentric CD (UCD), Human Herpes Virus-8 (HHV8)-associated multicentric CD (HHV8 + MCD), and HHV8-negative/idiopathic MCD (iMCD). Efforts to advance research and drug discovery for CD have been slowed by challenges shared by other rare diseases, such as collecting and centralizing data and biospecimens for research. To collect disease characteristic data and identify individuals interested in contributing biospecimens for research, a global research organization - the Castleman Disease Collaborative Network (CDCN) - established an international Contact Database and electronic repository (E-repository). Herein, we performed analyses of these datasets to further characterize CD and gain insights into research biospecimen acquisition. RESULTS Descriptive statistical analyses were performed on 891 participants from the Contact Database and 166 patients in the E-repository. The median age of patients at the time of enrollment in the Contact Database and E-repository was 42 ± 15.7 and 35 ± 14.8, respectively. The E-repository had increased representation from patients with MCD and the iMCD subtype compared to other sub-groups. Though the majority of participants were from the USA, a total of 49 countries on 6 continents were represented. Several patient characteristics in the Contact Database were associated with subsequent enrollment in the E-repository. There were significantly more MCD patients (p < 0.0001) and females (p = 0.002) enrolled in the E-repository compared to the Contact Database. Patient's year of birth, date of registration, preferred method of communication, and relationship to the patient were also significantly associated with enrollment in the e-Repository. CONCLUSIONS This study of the largest- dataset of CD patients worldwide provides insights into disease phenotypes, characteristics of patients interested in contributing data and biospecimens for research, and methods for successfully acquiring data and biospecimens. Generally, the factors associated with enrollment in the E-repository represented severity of disease subtype, proximity to the research, and patient motivation. We hope that these findings and the sample documentation (e.g., electronic consent, recruitment materials) provided with this article will assist future rare disease efforts with overcoming hurdles.
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Affiliation(s)
- Alexander Suarez
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Curran Reilly
- Castleman Disease Collaborative Network, Philadelphia, PA USA
| | - David C. Fajgenbaum
- Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
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