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Ali FEM, Abdel-Reheim MA, Hassanein EHM, Abd El-Aziz MK, Althagafy HS, Badran KSA. Exploring the potential of drug repurposing for liver diseases: A comprehensive study. Life Sci 2024; 347:122642. [PMID: 38641047 DOI: 10.1016/j.lfs.2024.122642] [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: 02/09/2024] [Revised: 03/24/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024]
Abstract
Drug repurposing involves the investigation of existing drugs for new indications. It offers a great opportunity to quickly identify a new drug candidate at a lower cost than novel discovery and development. Despite the importance and potential role of drug repurposing, there is no specific definition that healthcare providers and the World Health Organization credit. Unfortunately, many similar and interchangeable concepts are being used in the literature, making it difficult to collect and analyze uniform data on repurposed drugs. This research was conducted based on understanding general criteria for drug repurposing, concentrating on liver diseases. Many drugs have been investigated for their effect on liver diseases even though they were originally approved (or on their way to being approved) for other diseases. Some of the hypotheses for drug repurposing were first captured from the literature and then processed further to test the hypothesis. Recently, with the revolution in bioinformatics techniques, scientists have started to use drug libraries and computer systems that can analyze hundreds of drugs to give a short list of candidates to be analyzed pharmacologically. However, this study revealed that drug repurposing is a potential aid that may help deal with liver diseases. It provides available or under-investigated drugs that could help treat hepatitis, liver cirrhosis, Wilson disease, liver cancer, and fatty liver. However, many further studies are needed to ensure the efficacy of these drugs on a large scale.
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Affiliation(s)
- Fares E M Ali
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Al-Azhar University, Assiut 71524, Egypt; Michael Sayegh, Faculty of Pharmacy, Aqaba University of Technology, Aqaba 77110, Jordan
| | - Mustafa Ahmed Abdel-Reheim
- Department of Pharmaceutical Sciences, College of Pharmacy, Shaqra University, Shaqra 11961, Saudi Arabia; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Beni-Suef University, Beni Suef 62521, Egypt.
| | - Emad H M Hassanein
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Al-Azhar University, Assiut 71524, Egypt.
| | - Mostafa K Abd El-Aziz
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Al-Azhar University, Assiut 71524, Egypt
| | - Hanan S Althagafy
- Department of Biochemistry, Faculty of Science, University of Jeddah, Jeddah, Saudi Arabia
| | - Khalid S A Badran
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Al-Azhar University, Assiut 71524, Egypt
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Zheng W, Zeng Z, Lin S, Hou P. Revisiting potential value of antitumor drugs in the treatment of COVID-19. Cell Biosci 2022; 12:165. [PMID: 36182930 PMCID: PMC9526459 DOI: 10.1186/s13578-022-00899-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 09/12/2022] [Indexed: 01/08/2023] Open
Abstract
Since an outbreak started in China in 2019, coronavirus disease 2019 (COVID-19) has rapidly become a worldwide epidemic with high contagiousness and caused mass mortalities of infected cases around the world. Currently, available treatments for COVID-19, including supportive care, respiratory support and antiviral therapy, have shown limited efficacy. Thus, more effective therapeutic modalities are highly warranted. Drug repurposing, as an efficient strategy to explore a potential broader scope of the application of approved drugs beyond their original indications, accelerates the process of discovering safe and effective agents for a given disease. Since the outbreak of COVID-19 pandemic, drug repurposing strategy has been widely used to discover potential antiviral agents, and some of these drugs have advanced into clinical trials. Antitumor drugs compromise a vast variety of compounds and exhibit extensive mechanism of action, showing promising properties in drug repurposing. In this review, we revisit the potential value of antitumor drugs in the treatment of COVID-19 and systematically discuss their possible underlying mechanisms of the antiviral actions.
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Affiliation(s)
- Wenfang Zheng
- grid.452438.c0000 0004 1760 8119Department of Endocrinology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 People’s Republic of China
| | - Zekun Zeng
- grid.452438.c0000 0004 1760 8119Department of Endocrinology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 People’s Republic of China
| | - Shumei Lin
- grid.452438.c0000 0004 1760 8119Department of Infectious Diseases, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 People’s Republic of China
| | - Peng Hou
- grid.452438.c0000 0004 1760 8119Department of Endocrinology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 People’s Republic of China ,grid.452438.c0000 0004 1760 8119Key Laboratory for Tumor Precision Medicine of Shaanxi Province, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 People’s Republic of China
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Travi BL. Current status of antihistamine drugs repurposing for infectious diseases. MEDICINE IN DRUG DISCOVERY 2022. [DOI: 10.1016/j.medidd.2022.100140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Churkin A, Kriss S, Uziel A, Goyal A, Zakh R, Cotler SJ, Etzion O, Shlomai A, Rotstein HG, Dahari H, Barash D. Machine learning for mathematical models of HCV kinetics during antiviral therapy. Math Biosci 2022; 343:108756. [PMID: 34883104 PMCID: PMC8792269 DOI: 10.1016/j.mbs.2021.108756] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 11/04/2021] [Accepted: 11/04/2021] [Indexed: 01/03/2023]
Abstract
Mathematical models for hepatitis C virus (HCV) dynamics have provided a means for evaluating the antiviral effectiveness of therapy and estimating treatment outcomes such as the time to cure. Recently, a mathematical modeling approach was used in the first proof-of-concept clinical trial assessing in real-time the utility of response-guided therapy with direct-acting antivirals (DAAs) in chronic HCV-infected patients. Several retrospective studies have shown that mathematical modeling of viral kinetics predicts time to cure of less than 12 weeks in the majority of individuals treated with sofosbuvir-based as well as other DAA regimens. A database of these studies was built, and machine learning methods were evaluated for their ability to estimate the time to cure for each patient to facilitate real-time modeling studies. Data from these studies exploring mathematical modeling of HCV kinetics under DAAs in 266 chronic HCV-infected patients were gathered. Different learning methods were applied and trained on part of the dataset ('train' set), to predict time to cure on the untrained part ('test' set). Our results show that this machine learning approach provides a means for establishing an accurate time to cure prediction that will support the implementation of individualized treatment.
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Affiliation(s)
- Alexander Churkin
- Department of Software Engineering, Sami Shamoon College of Engineering, Beer-Sheba, Israel
| | - Stephanie Kriss
- Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA
| | - Asher Uziel
- Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA
| | - Ashish Goyal
- Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA
| | - Rami Zakh
- Department of Computer Science, Ben-Gurion University, Israel
| | - Scott J Cotler
- Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA
| | - Ohad Etzion
- Soroka University Medical Center, Beer-Sheba, Israel
| | - Amir Shlomai
- Department of Medicine D and The Liver Institute, Rabin Medical Center, Beilinson Hospital, Petah-Tikva and the Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Horacio G Rotstein
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, NJ, USA; Institute for Future Technologies, New Jersey Institute of Technology, Newark, NJ, USA
| | - Harel Dahari
- Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA.
| | - Danny Barash
- Department of Computer Science, Ben-Gurion University, Israel.
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Into the Unknown: A Chemical Biology Approach Provides Mechanistic Insight into HCV Entry. Cell Chem Biol 2021; 27:767-769. [PMID: 32679091 DOI: 10.1016/j.chembiol.2020.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
In this issue of Cell Chemical Biology, Hu et al. (2020) demonstrate that chlorcyclizine blocks HCV fusion by targeting the putative fusion peptide on the viral envelope glycoprotein E1. The study provides new insights into the viral fusion machinery, presenting an opportunity to study novel antivirals against HCV.
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Churkin A, Lewkiewicz S, Reinharz V, Dahari H, Barash D. Efficient Methods for Parameter Estimation of Ordinary and Partial Differential Equation Models of Viral Hepatitis Kinetics. MATHEMATICS 2020; 8. [PMID: 33224865 PMCID: PMC7676746 DOI: 10.3390/math8091483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Parameter estimation in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation that addresses all cases of data points available presents a formidable challenge and efficiency considerations need to be employed in order for the method to become practical. In the case of age-structured models of viral hepatitis dynamics under antiviral treatment that deal with partial differential equations, a fully numerical parameter estimation method was developed that does not require an analytical approximation of the solution to the multiscale model equations, avoiding the necessity to derive the long-term approximation for each model. However, the method is considerably slow because of precision problems in estimating derivatives with respect to the parameters near their boundary values, making it almost impractical for general use. In order to overcome this limitation, two steps have been taken that significantly reduce the running time by orders of magnitude and thereby lead to a practical method. First, constrained optimization is used, letting the user add constraints relating to the boundary values of each parameter before the method is executed. Second, optimization is performed by derivative-free methods, eliminating the need to evaluate expensive numerical derivative approximations. The newly efficient methods that were developed as a result of the above approach are described for hepatitis C virus kinetic models during antiviral therapy. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for both the ordinary and partial differential equation models.
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Affiliation(s)
- Alexander Churkin
- Department of Software Engineering, Sami Shamoon College of Engineering, Beer-Sheva 8410501, Israel
- Correspondence: (A.C.); (D.B.); Tel.: +972-8-647-5281 (A.C.); +972-8-647-2714 (D.B.)
| | - Stephanie Lewkiewicz
- Department of Mathematics, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Vladimir Reinharz
- Department of Computer Science, Université du Québec à Montréal, Montreal, QC H3C 3P8, Canada
| | - Harel Dahari
- Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Medical Center, Maywoood, IL 60153, USA
| | - Danny Barash
- Department of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, Israel
- Correspondence: (A.C.); (D.B.); Tel.: +972-8-647-5281 (A.C.); +972-8-647-2714 (D.B.)
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Huang R, Zhu H, Shinn P, Ngan D, Ye L, Thakur A, Grewal G, Zhao T, Southall N, Hall MD, Simeonov A, Austin CP. The NCATS Pharmaceutical Collection: a 10-year update. Drug Discov Today 2019; 24:2341-2349. [PMID: 31585169 DOI: 10.1016/j.drudis.2019.09.019] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 09/16/2019] [Accepted: 09/24/2019] [Indexed: 12/25/2022]
Abstract
The National Center for Advancing Translational Sciences (NCATS) Pharmaceutical Collection (NPC), a comprehensive collection of clinically approved drugs, was made a public resource in 2011. Over the past decade, the NPC has been systematically profiled for activity across an array of pathways and disease models, generating an unparalleled amount of data. These data have not only enabled the identification of new repurposing candidates with several in clinical trials, but also uncovered new biological insights into drug targets and disease mechanisms. This retrospective provides an update on the NPC in terms of both successes and lessons learned. We also report our efforts in bringing the NPC up-to-date with drugs approved in recent years.
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Affiliation(s)
- Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA.
| | - Hu Zhu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Paul Shinn
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Deborah Ngan
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Lin Ye
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Ashish Thakur
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Gurmit Grewal
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Tongan Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Noel Southall
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Mathew D Hall
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Anton Simeonov
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Christopher P Austin
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
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