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Mag P, Nemes-Terényi M, Jerzsele Á, Mátyus P. Some Aspects and Convergence of Human and Veterinary Drug Repositioning. Molecules 2024; 29:4475. [PMID: 39339469 PMCID: PMC11433938 DOI: 10.3390/molecules29184475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Revised: 09/11/2024] [Accepted: 09/18/2024] [Indexed: 09/30/2024] Open
Abstract
Drug innovation traditionally follows a de novo approach with new molecules through a complex preclinical and clinical pathway. In addition to this strategy, drug repositioning has also become an important complementary approach, which can be shorter, cheaper, and less risky. This review provides an overview of drug innovation in both human and veterinary medicine, with a focus on drug repositioning. The evolution of drug repositioning and the effectiveness of this approach are presented, including the growing role of data science and computational modeling methods in identifying drugs with potential for repositioning. Certain business aspects of drug innovation, especially the relevant factors of market exclusivity, are also discussed. Despite the promising potential of drug repositioning for innovation, it remains underutilized, especially in veterinary applications. To change this landscape for mutual benefits of human and veterinary drug innovation, further exploitation of the potency of drug repositioning is necessary through closer cooperation between all stakeholders, academia, industry, pharmaceutical authorities, and innovation policy makers, and the integration of human and veterinary repositioning into a unified innovation space. For this purpose, the establishment of the conceptually new "One Health Drug Repositioning Platform" is proposed. Oncology is one of the disease areas where this platform can significantly support the development of new drugs for human and dog (or other companion animals) anticancer therapies. As an example of the utilization of human and veterinary drugs for veterinary repositioning, the use of COX inhibitors to treat dog cancers is reviewed.
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Affiliation(s)
- Patrik Mag
- Department of Pharmacology and Toxicology, University of Veterinary Medicine, István Street 2, 1078 Budapest, Hungary
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine, István Street 2, 1078 Budapest, Hungary
| | - Melinda Nemes-Terényi
- Department of Pharmacology and Toxicology, University of Veterinary Medicine, István Street 2, 1078 Budapest, Hungary
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine, István Street 2, 1078 Budapest, Hungary
| | - Ákos Jerzsele
- Department of Pharmacology and Toxicology, University of Veterinary Medicine, István Street 2, 1078 Budapest, Hungary
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine, István Street 2, 1078 Budapest, Hungary
| | - Péter Mátyus
- National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine, István Street 2, 1078 Budapest, Hungary
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Azevedo PHRDA, Peçanha BRDB, Flores-Junior LAP, Alves TF, Dias LRS, Muri EMF, Lima CHDS. In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor. J Biomol Struct Dyn 2024; 42:1417-1428. [PMID: 37054524 DOI: 10.1080/07391102.2023.2199868] [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: 10/20/2022] [Accepted: 04/01/2023] [Indexed: 04/15/2023]
Abstract
O-linked N-acetylglucosamine (O-GlcNAc) is a unique intracellular post-translational glycosylation at the hydroxyl group of serine or threonine residues in nuclear, cytoplasmic and mitochondrial proteins. The enzyme O-GlcNAc transferase (OGT) is responsible for adding GlcNAc, and anomalies in this process can lead to the development of diseases associated with metabolic imbalance, such as diabetes and cancer. Repurposing approved drugs can be an attractive tool to discover new targets reducing time and costs in the drug design. This work focuses on drug repurposing to OGT targets by virtual screening of FDA-approved drugs through consensus machine learning (ML) models from an imbalanced dataset. We developed a classification model using docking scores and ligand descriptors. The SMOTE approach to resampling the dataset showed excellent statistical values in five of the seven ML algorithms to create models from the training set, with sensitivity, specificity and accuracy over 90% and Matthew's correlation coefficient greater than 0.8. The pose analysis obtained by molecular docking showed only H-bond interaction with the OGT C-Cat domain. The molecular dynamics simulation showed the lack of H-bond interactions with the C- and N-catalytic domains allowed the drug to exit the binding site. Our results showed that the non-steroidal anti-inflammatory celecoxib could be a potentially OGT inhibitor.
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Affiliation(s)
| | | | | | - Tatiana Fialho Alves
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
| | - Luiza Rosaria Sousa Dias
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
| | - Estela Maris Freitas Muri
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
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DTIP-TC2A: An analytical framework for drug-target interactions prediction methods. Comput Biol Chem 2022; 99:107707. [DOI: 10.1016/j.compbiolchem.2022.107707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 05/01/2022] [Accepted: 05/26/2022] [Indexed: 11/18/2022]
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Sadeghi S, Lu J, Ngom A. A network-based drug repurposing method via non-negative matrix factorization. Bioinformatics 2021; 38:1369-1377. [PMID: 34875000 PMCID: PMC8825773 DOI: 10.1093/bioinformatics/btab826] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/05/2021] [Accepted: 12/01/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This article presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug-disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs. RESULTS The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug-disease association prediction. AVAILABILITY AND IMPLEMENTATION The program is available at https://github.com/sshaghayeghs/NMF-DR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shaghayegh Sadeghi
- School of Computer Science, University of Windsor, 401 Sunset Avenue, N9B 3P4, Windsor, Ontario, Canada,To whom correspondence should be addressed.
| | - Jianguo Lu
- School of Computer Science, University of Windsor, 401 Sunset Avenue, N9B 3P4, Windsor, Ontario, Canada
| | - Alioune Ngom
- School of Computer Science, University of Windsor, 401 Sunset Avenue, N9B 3P4, Windsor, Ontario, Canada
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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: 13] [Impact Index Per Article: 4.3] [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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Mayburd AL, Baranova A. Increased lifespan, decreased mortality, and delayed cognitive decline in osteoarthritis. Sci Rep 2019; 9:18639. [PMID: 31819096 PMCID: PMC6901554 DOI: 10.1038/s41598-019-54867-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 11/13/2019] [Indexed: 12/19/2022] Open
Abstract
In absence of therapies targeting symptomatic dementia, better understanding of the biology underlying a cognitive decline is warranted. Here we present the results of a meta-analysis of the impact of osteoarthritis (OA) on cognitive decline and overall mortality. Across 7 independent datasets obtained in studies of populations in the USA, EU and Australia (NBER, NSHAP, TILDA, NACC, Kaiser Permanente, GRIM BOOKS, OAI, with a total of >7 × 107 profiles), OA cohorts demonstrated higher cognitive scores, later dementia onset as well as longer lifespan and lower age-specific all-cause mortality. Moreover, generalized OA with multiple localizations is associated with more significant reduction of mortality and dementia than a singly localized OA or no arthritis. In OA patients with younger ages, all-cause mortality was disproportionally reduced as compared to that in controls, while exponential term of Gompert'z hazard function was increased, accelerating mortality accrual at later ages. Up to 8-10% of poly-osteoarthritic patients are predicted and observed to reach centenarian lifespan, while in matched non-OA population the same benchmark is reached by less than 1% of patients. These results point at a possibility of life-extending and cognition preserving impacts of OA-conditioned immune system.
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Affiliation(s)
- Anatoly L. Mayburd
- George Mason University, School of Systems Biology, Manassas, VA 22030 USA
- Neurocombinatorix, 5902 Mount Eagle Dr, Suite 1103, Alexandria, VA 22303 USA
| | - Ancha Baranova
- George Mason University, School of Systems Biology, Manassas, VA 22030 USA
- Research Centre for Medical Genetics, Moskvorechie str., 1, Moscow, Russia
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