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Tettevi EJ, Kuevi DNO, Sumabe BK, Simpong DL, Maina MB, Dongdem JT, Osei-Atweneboana MY, Ocloo A. In Silico Identification of a Potential TNF-Alpha Binder Using a Structural Similarity: A Potential Drug Repurposing Approach to the Management of Alzheimer's Disease. BIOMED RESEARCH INTERNATIONAL 2024; 2024:9985719. [PMID: 38221912 PMCID: PMC10787656 DOI: 10.1155/2024/9985719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 11/25/2023] [Accepted: 12/13/2023] [Indexed: 01/16/2024]
Abstract
Introduction Alzheimer's disease (AD) is a neurodegenerative disorder with no conclusive remedy. Yohimbine, found in Rauwolfia vomitoria, may reduce brain inflammation by targeting tumour necrosis factor-alpha (TNFα), implicated in AD pathogenesis. Metoserpate, a synthetic compound, may inhibit TNFα. The study is aimed at assessing the potential utility of repurposing metoserpate for TNFα inhibition to reduce neuronal damage and inflammation in AD. The development of safe and effective treatments for AD is crucial to address the growing burden of the disease, which is projected to double over the next two decades. Methods Our study repurposed an FDA-approved drug as TNFα inhibitor for AD management using structural similarity studies, molecular docking, and molecular dynamics simulations. Yohimbine was used as a reference compound. Molecular docking used SeeSAR, and molecular dynamics simulation used GROMACS. Results Metoserpate was selected from 10 compounds similar to yohimbine based on pharmacokinetic properties and FDA approval status. Molecular docking and simulation studies showed a stable interaction between metoserpate and TNFα over 100 ns (100000 ps). This suggests a reliable and robust interaction between the protein and ligand, supporting the potential utility of repurposing metoserpate for TNFα inhibition in AD treatment. Conclusion Our study has identified metoserpate, a previously FDA-approved antihypertensive agent, as a promising candidate for inhibiting TNFα in the management of AD.
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Affiliation(s)
- Edward Jenner Tettevi
- Department of Biochemistry, Cell and Molecular Biology, School of Biological Science, University of Ghana, Legon, Accra, P.O. Box LG 25, Ghana
- West African Centre for Cell Biology of Infectious Pathogens, School of Biological Science, University of Ghana, Legon, Accra, P.O. Box LG 25, Ghana
- Biomedical and Public Health Research Unit, Council for Scientific and Industrial Research-Water Research Institute, Accra, P.O. Box M 32, Ghana
| | - Deryl Nii Okantey Kuevi
- Biomedical and Public Health Research Unit, Council for Scientific and Industrial Research-Water Research Institute, Accra, P.O. Box M 32, Ghana
| | - Balagra Kasim Sumabe
- Biomedical and Public Health Research Unit, Council for Scientific and Industrial Research-Water Research Institute, Accra, P.O. Box M 32, Ghana
| | - David Larbi Simpong
- Department of Medical Laboratory Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Mahmoud B. Maina
- Serpell Laboratory, Sussex Neuroscience, School of Life Sciences, University of Sussex, UK
- Biomedical Science Research and Training Centre, College of Medical Sciences, Yobe State University, Damaturu, Nigeria
| | - Julius T. Dongdem
- Department of Biochemistry and Molecular Medicine, School of Medicine, University for Development Studies, Tamale Campus, Ghana
| | - Mike Y. Osei-Atweneboana
- Biomedical and Public Health Research Unit, Council for Scientific and Industrial Research-Water Research Institute, Accra, P.O. Box M 32, Ghana
- CSIR-College of Science and Technology, 2nd CSIR Close, Airport Residential Area, Behind Golden Tulip Hotel, Greater Accra Region, Ghana
| | - Augustine Ocloo
- Department of Biochemistry, Cell and Molecular Biology, School of Biological Science, University of Ghana, Legon, Accra, P.O. Box LG 25, Ghana
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Ghorbanali Z, Zare-Mirakabad F, Salehi N, Akbari M, Masoudi-Nejad A. DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing. BMC Bioinformatics 2023; 24:374. [PMID: 37789314 PMCID: PMC10548718 DOI: 10.1186/s12859-023-05479-7] [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: 07/04/2023] [Accepted: 09/12/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Drug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs. RESULTS This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%. CONCLUSION We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph .
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Affiliation(s)
- Zahra Ghorbanali
- Computational Biology Research Center (CBRC), Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
| | - Fatemeh Zare-Mirakabad
- Computational Biology Research Center (CBRC), Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.
| | - Najmeh Salehi
- School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mohammad Akbari
- Computational Biology Research Center (CBRC), Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Torab-Miandoab A, Poursheikh Asghari M, Hashemzadeh N, Ferdousi R. Analysis and identification of drug similarity through drug side effects and indications data. BMC Med Inform Decis Mak 2023; 23:35. [PMID: 36788528 PMCID: PMC9926629 DOI: 10.1186/s12911-023-02133-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND The measurement of drug similarity has many potential applications for assessing drug therapy similarity, patient similarity, and the success of treatment modalities. To date, a family of computational methods has been employed to predict drug-drug similarity. Here, we announce a computational method for measuring drug-drug similarity based on drug indications and side effects. METHODS The model was applied for 2997 drugs in the side effects category and 1437 drugs in the indications category. The corresponding binary vectors were built to determine the Drug-drug similarity for each drug. Various similarity measures were conducted to discover drug-drug similarity. RESULTS Among the examined similarity methods, the Jaccard similarity measure was the best in overall performance results. In total, 5,521,272 potential drug pair's similarities were studied in this research. The offered model was able to predict 3,948,378 potential similarities. CONCLUSION Based on these results, we propose the current method as a robust, simple, and quick approach to identifying drug similarity.
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Affiliation(s)
- Amir Torab-Miandoab
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Mehdi Poursheikh Asghari
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Nastaran Hashemzadeh
- grid.412888.f0000 0001 2174 8913Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran ,grid.412888.f0000 0001 2174 8913Research Center for Pharmaceutical Nanotechnology, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Ferdousi
- Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711, Iran.
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Qin S, Li W, Yu H, Xu M, Li C, Fu L, Sun S, He Y, Lv J, He W, Chen L. Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks. Int J Mol Sci 2023; 24:ijms24032244. [PMID: 36768566 PMCID: PMC9917231 DOI: 10.3390/ijms24032244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/05/2023] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.
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Affiliation(s)
- Shimei Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hongzheng Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Manyi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Chao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shibin Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin 150001, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Correspondence: ; Tel.: +86-451-8667-4768
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Literature-based drug-drug similarity for drug repurposing: impact of Medical Subject Headings term refinement and hierarchical clustering. Future Med Chem 2022; 14:1309-1323. [PMID: 36017692 DOI: 10.4155/fmc-2022-0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background: We describe herein, an improved procedure for drug repurposing based on refined Medical Subject Headings (MeSH) terms and hierarchical clustering method. Materials & methods: In the present study, we have employed MeSH data from MEDLINE (2019), 1669 US FDA approved drugs from Open FDA and a refined set of MeSH terms. Refinement of MeSH terms was performed to include terms related to mechanistic information of drugs and diseases. Results and Conclusions: In-depth analysis of the results obtained, demonstrated greater efficiency of the proposed approach, based on refined MeSH terms and hierarchical clustering, in terms of number of selected drug candidates for repurposing. Further, analysis of misclustering and size of noise clusters suggest that the proposed approach is reliable and can be employed in drug repurposing.
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