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Picard M, Scott-Boyer MP, Bodein A, Leclercq M, Prunier J, Périn O, Droit A. Target repositioning using multi-layer networks and machine learning: The case of prostate cancer. Comput Struct Biotechnol J 2024; 24:464-475. [PMID: 38983753 PMCID: PMC11231507 DOI: 10.1016/j.csbj.2024.06.012] [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: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024] Open
Abstract
The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Julien Prunier
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Transformation and Innovation Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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2
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Gómez-Gaviria M, Contreras-López LM, Aguilera-Domínguez JI, Mora-Montes HM. Strategies of Pharmacological Repositioning for the Treatment of Medically Relevant Mycoses. Infect Drug Resist 2024; 17:2641-2658. [PMID: 38947372 PMCID: PMC11214559 DOI: 10.2147/idr.s466336] [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: 02/28/2024] [Accepted: 06/14/2024] [Indexed: 07/02/2024] Open
Abstract
Fungal infections represent a worldwide concern for public health, due to their prevalence and significant increase in cases each year. Among the most frequent mycoses are those caused by members of the genera Candida, Cryptococcus, Aspergillus, Histoplasma, Pneumocystis, Mucor, and Sporothrix, which have been treated for years with conventional antifungal drugs, such as flucytosine, azoles, polyenes, and echinocandins. However, these microorganisms have acquired the ability to evade the mechanisms of action of these drugs, thus hindering their treatment. Among the most common evasion mechanisms are alterations in sterol biosynthesis, modifications of drug transport through the cell wall and membrane, alterations of drug targets, phenotypic plasticity, horizontal gene transfer, and chromosomal aneuploidies. Taking into account these problems, some research groups have sought new therapeutic alternatives based on drug repositioning. Through repositioning, it is possible to use existing pharmacological compounds for which their mechanism of action is already established for other diseases, and thus exploit their potential antifungal activity. The advantage offered by these drugs is that they may be less prone to resistance. In this article, a comprehensive review was carried out to highlight the most relevant repositioning drugs to treat fungal infections. These include antibiotics, antivirals, anthelmintics, statins, and anti-inflammatory drugs.
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Affiliation(s)
- Manuela Gómez-Gaviria
- Departamento de Biología, División de Ciencias Naturales y Exactas, Universidad de Guanajuato, Guanajuato, Gto, México
| | - Luisa M Contreras-López
- Departamento de Biología, División de Ciencias Naturales y Exactas, Universidad de Guanajuato, Guanajuato, Gto, México
| | - Julieta I Aguilera-Domínguez
- Departamento de Biología, División de Ciencias Naturales y Exactas, Universidad de Guanajuato, Guanajuato, Gto, México
| | - Héctor M Mora-Montes
- Departamento de Biología, División de Ciencias Naturales y Exactas, Universidad de Guanajuato, Guanajuato, Gto, México
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3
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Sikirzhytskaya A, Tyagin I, Sutton SS, Wyatt MD, Safro I, Shtutman M. AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597745. [PMID: 38895485 PMCID: PMC11185689 DOI: 10.1101/2024.06.06.597745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing. Author Summary This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.
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4
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Jardim C, de Waal A, Fabris-Rotelli I, Rad NN, Mazarura J, Sherry D. Feature engineered embeddings for classification of molecular data. Comput Biol Chem 2024; 110:108056. [PMID: 38796282 DOI: 10.1016/j.compbiolchem.2024.108056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 05/28/2024]
Abstract
The classification of molecules is of particular importance to the drug discovery process and several other use cases. Data in this domain can be partitioned into structural and sequence/text data. Several techniques such as deep learning are able to classify molecules and predict their functions using both types of data. Molecular structure and encoded chemical information are sufficient to classify a characteristic of a molecule. However, the use of a molecule's structural information typically requires large amounts of computational power with deep learning models that take a long time to train. In this study, we present an alternative approach to molecule classification that addresses the limitations of other techniques. This approach uses natural language processing techniques in the form of count vectorisation, term frequency-inverse document frequency, word2vec and Latent Dirichlet Allocation to feature engineer molecular text data. Through this approach, we aim to make a robust and easily reproducible embedding that is fast to implement and solely dependent on chemical (text) data such as the sequence of a protein. Further, we investigate the usefulness of these embeddings for machine learning models. We apply the techniques to two different types of molecular text data: FASTA sequence data and Simplified Molecular Input Line Entry Specification data. We show that these embeddings provide excellent performance for classification.
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García Sánchez N, Ugarte Carro E, Prieto-Santamaría L, Rodríguez-González A. Protein sequence analysis in the context of drug repurposing. BMC Med Inform Decis Mak 2024; 24:122. [PMID: 38741115 DOI: 10.1186/s12911-024-02531-1] [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: 12/01/2023] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
MOTIVATION Drug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs. METHODS In this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases. RESULTS We found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future.
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Affiliation(s)
- Natalia García Sánchez
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
| | - Esther Ugarte Carro
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
| | - Lucía Prieto-Santamaría
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
- ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, 28660, Spain
| | - Alejandro Rodríguez-González
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain.
- ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, 28660, Spain.
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6
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Xu M, Li W, He J, Wang Y, Lv J, He W, Chen L, Zhi H. DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm. Int J Mol Sci 2024; 25:5267. [PMID: 38791306 PMCID: PMC11121335 DOI: 10.3390/ijms25105267] [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: 02/29/2024] [Revised: 04/25/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.
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Affiliation(s)
- Manyi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Jiaheng He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin 150000, China;
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Hui Zhi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
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7
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Crestani B. Repositioning compounds for idiopathic pulmonary fibrosis treatment: seeking the future in the past. Eur Respir J 2024; 63:2400678. [PMID: 38754951 DOI: 10.1183/13993003.00678-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 04/19/2024] [Indexed: 05/18/2024]
Affiliation(s)
- Bruno Crestani
- Université Paris Cité, INSERM, PHERE, Paris, France
- APHP, Hôpital Bichat, Service de Pneumologie A, Centre de Référence des Maladies Pulmonaires Rares, Paris, France
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8
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Kim JB, Kim SJ, So M, Kim DK, Noh HR, Kim BJ, Choi YR, Kim D, Koo H, Kim T, Woo HG, Park SM. Artificial intelligence-driven drug repositioning uncovers efavirenz as a modulator of α-synuclein propagation: Implications in Parkinson's disease. Biomed Pharmacother 2024; 174:116442. [PMID: 38513596 DOI: 10.1016/j.biopha.2024.116442] [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: 12/21/2023] [Revised: 03/09/2024] [Accepted: 03/15/2024] [Indexed: 03/23/2024] Open
Abstract
Parkinson's disease (PD) is a complex neurodegenerative disorder with an unclear etiology. Despite significant research efforts, developing disease-modifying treatments for PD remains a major unmet medical need. Notably, drug repositioning is becoming an increasingly attractive direction in drug discovery, and computational approaches offer a relatively quick and resource-saving method for identifying testable hypotheses that promote drug repositioning. We used an artificial intelligence (AI)-based drug repositioning strategy to screen an extensive compound library and identify potential therapeutic agents for PD. Our AI-driven analysis revealed that efavirenz and nevirapine, approved for treating human immunodeficiency virus infection, had distinct profiles, suggesting their potential effects on PD pathophysiology. Among these, efavirenz attenuated α-synuclein (α-syn) propagation and associated neuroinflammation in the brain of preformed α-syn fibrils-injected A53T α-syn Tg mice and α-syn propagation and associated behavioral changes in the C. elegans BiFC model. Through in-depth molecular investigations, we found that efavirenz can modulate cholesterol metabolism and mitigate α-syn propagation, a key pathological feature implicated in PD progression by regulating CYP46A1. This study opens new avenues for further investigation into the mechanisms underlying PD pathology and the exploration of additional drug candidates using advanced computational methodologies.
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Affiliation(s)
- Jae-Bong Kim
- Department of Pharmacology, Ajou University School of Medicine, Suwon, Korea; Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea
| | - Soo-Jeong Kim
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
| | | | - Dong-Kyu Kim
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
| | - Hye Rin Noh
- Department of Pharmacology, Ajou University School of Medicine, Suwon, Korea; Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea
| | - Beom Jin Kim
- Department of Pharmacology, Ajou University School of Medicine, Suwon, Korea; Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea
| | - Yu Ree Choi
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
| | - Doyoon Kim
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Department of Physiology, Ajou University School of Medicine, Suwon, Korea
| | | | | | - Hyun Goo Woo
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Department of Physiology, Ajou University School of Medicine, Suwon, Korea
| | - Sang Myun Park
- Department of Pharmacology, Ajou University School of Medicine, Suwon, Korea; Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea.
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9
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Zabihian A, Asghari J, Hooshmand M, Gharaghani S. A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method. Mol Divers 2024:10.1007/s11030-024-10851-7. [PMID: 38683487 DOI: 10.1007/s11030-024-10851-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/18/2024] [Indexed: 05/01/2024]
Abstract
Efficient drug discovery relies on drug repurposing, an important and open research field. This work presents a novel factorization method and a practical comparison of different approaches for drug repurposing. First, we propose a novel tensor-matrix-tensor (TMT) formulation as a new data array method with a gradient-based factorization procedure. Additionally, this paper examines and contrasts four computational drug repurposing approaches-factorization-based methods, machine learning methods, deep learning methods, and graph neural networks-to fulfill the second purpose. We test the strategies on two datasets and assess each approach's performance, drawbacks, problems, and benefits based on results. The results demonstrate that deep learning techniques work better than other strategies and that their results might be more reliable. Ultimately, graph neural methods need to be in an inductive manner to have a reliable prediction.
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Affiliation(s)
- Arash Zabihian
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish, Iran
| | - Javad Asghari
- Department of Computer Science and Information Technology, Institute of Advanced Studies in Basic Sciences, Zanjan, Iran
| | - Mohsen Hooshmand
- Department of Computer Science and Information Technology, Institute of Advanced Studies in Basic Sciences, Zanjan, Iran.
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design, University of Tehran, Tehran, Iran
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10
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Xia Y, Sun M, Huang H, Jin WL. Drug repurposing for cancer therapy. Signal Transduct Target Ther 2024; 9:92. [PMID: 38637540 PMCID: PMC11026526 DOI: 10.1038/s41392-024-01808-1] [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: 02/06/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.
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Affiliation(s)
- Ying Xia
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China
- The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, 550001, PR China
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China
- Division of Gastroenterology and Hepatology, Department of Medicine and, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Ming Sun
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China
| | - Hai Huang
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China.
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China.
| | - Wei-Lin Jin
- Institute of Cancer Neuroscience, Medical Frontier Innovation Research Center, The First Hospital of Lanzhou University, The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, PR China.
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11
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Xiao Y, Hou Y, Zhou H, Diallo G, Fiszman M, Wolfson J, Zhou L, Kilicoglu H, Chen Y, Su C, Xu H, Mantyh WG, Zhang R. Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature. Sci Rep 2024; 14:8693. [PMID: 38622164 PMCID: PMC11018822 DOI: 10.1038/s41598-024-58604-8] [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: 12/23/2023] [Accepted: 04/01/2024] [Indexed: 04/17/2024] Open
Abstract
Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.
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Affiliation(s)
- Yongkang Xiao
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Yu Hou
- Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Huixue Zhou
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Gayo Diallo
- INRIA SISTM, Team AHeaD - INSERM 1219 Bordeaux Population Health, University of Bordeaux, 33000, Bordeaux, France
| | - Marcelo Fiszman
- NITES - Núcleo de Inovação e Tecnologia Em Saúde, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
- Semedy Inc, Needham, MA, USA
| | - Julian Wolfson
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Li Zhou
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Halil Kilicoglu
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - You Chen
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Hua Xu
- Section of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT, USA
| | - William G Mantyh
- Department of Neurology, University of Minnesota, Minneapolis, MN, USA
| | - Rui Zhang
- Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
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12
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Israr J, Alam S, Kumar A. System biology approaches for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:221-245. [PMID: 38789180 DOI: 10.1016/bs.pmbts.2024.03.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.
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Affiliation(s)
- Juveriya Israr
- Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Lucknow-Deva Road, Barabanki, Uttar Pradesh, India; Department of Biotechnology Era University, Lucknow, Uttar Pradesh, India
| | - Shabroz Alam
- Department of Biotechnology Era University, Lucknow, Uttar Pradesh, India
| | - Ajay Kumar
- Department of Biotechnology, Faculty of Engineering and Technology, Rama University, Mandhana, Kanpur, Uttar Pradesh, India.
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Sahu M, Vashishth S, Kukreti N, Gulia A, Russell A, Ambasta RK, Kumar P. Synergizing drug repurposing and target identification for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:111-169. [PMID: 38789177 DOI: 10.1016/bs.pmbts.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shrutikirti Vashishth
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Neha Kukreti
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashima Gulia
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashish Russell
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Rashmi K Ambasta
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India.
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Yang R, Fu Y, Zhang Q, Zhang L. GCNGAT: Drug-disease association prediction based on graph convolution neural network and graph attention network. Artif Intell Med 2024; 150:102805. [PMID: 38553169 DOI: 10.1016/j.artmed.2024.102805] [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: 03/31/2023] [Revised: 01/22/2024] [Accepted: 02/08/2024] [Indexed: 04/02/2024]
Abstract
Predicting drug-disease associations can contribute to discovering new therapeutic potentials of drugs, and providing important association information for new drug research and development. Many existing drug-disease association prediction methods have not distinguished relevant background information for the same drug targeted to different diseases. Therefore, this paper proposes a drug-disease association prediction model based on graph convolutional network and graph attention network (GCNGAT) to reposition marketed drugs under the distinguishment of background information. Firstly, in order to obtain initial drug-disease information, a drug-disease heterogeneous graph structure is constructed based on all known drug-disease associations. Secondly, based on the heterogeneous graph structure, the corresponding subgraphs of each group of drug-disease association pairs are extracted to distinguish different background information for the same drug from different diseases. Finally, a model combining Graph neural network with global Average pooling (GnnAp) is designed to predict potential drug-disease associations by learning drug-disease interaction feature representations. The experimental results show that adding subgraph extraction can effectively improve the prediction performance of the model, and the graph representation learning module can fully extract the deep features of drug-disease. Using the 5-fold cross-validation, the proposed model (GCNGAT) achieves AUC (Area Under the receiver operating characteristic Curve) values of 0.9182 and 0.9417 on the PREDICT dataset and CDataset dataset, respectively. Compared with other predictors on the same dataset (PREDICT dataset), GCNGAT outperforms the existing best-performing model (PSGCN), with a 1.58% increase in the AUC value. It is anticipated that this model can provide experimental reference for drug repositioning and further promote the drug research and development process.
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Affiliation(s)
- Runtao Yang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
| | - Yao Fu
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
| | - Qian Zhang
- Heze Institute of Science and Technology Information, Heze, 274000, China.
| | - Lina Zhang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
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Meiners F, Hinz B, Boeckmann L, Secci R, Sueto S, Kuepfer L, Fuellen G, Barrantes I. Computational identification of natural senotherapeutic compounds that mimic dasatinib based on gene expression data. Sci Rep 2024; 14:6286. [PMID: 38491064 PMCID: PMC10943199 DOI: 10.1038/s41598-024-55870-4] [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: 03/05/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
The major risk factor for chronic disease is chronological age, and age-related chronic diseases account for the majority of deaths worldwide. Targeting senescent cells that accumulate in disease-related tissues presents a strategy to reduce disease burden and to increase healthspan. The senolytic combination of the tyrosine-kinase inhibitor dasatinib and the flavonol quercetin is frequently used in clinical trials aiming to eliminate senescent cells. Here, our goal was to computationally identify natural senotherapeutic repurposing candidates that may substitute dasatinib based on their similarity in gene expression effects. The natural senolytic piperlongumine (a compound found in long pepper), and the natural senomorphics parthenolide, phloretin and curcumin (found in various edible plants) were identified as potential substitutes of dasatinib. The gene expression changes underlying the repositioning highlight apoptosis-related genes and pathways. The four compounds, and in particular the top-runner piperlongumine, may be combined with quercetin to obtain natural formulas emulating the dasatinib + quercetin formula.
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Affiliation(s)
- Franziska Meiners
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Burkhard Hinz
- Institute of Pharmacology and Toxicology, Rostock University Medical Center, Rostock, Germany
| | - Lars Boeckmann
- Clinic and Policlinic for Dermatology and Venerology, University Medical Center Rostock, Strempelstr. 13, 18057, Rostock, Germany
| | - Riccardo Secci
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Salem Sueto
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany.
| | - Israel Barrantes
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
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He S, Yun L, Yi H. Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction. BMC Bioinformatics 2024; 25:79. [PMID: 38378479 PMCID: PMC10877759 DOI: 10.1186/s12859-024-05705-w] [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: 12/19/2023] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Identification of potential drug-disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data. RESULTS In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug-disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug-drug, drug-disease, and disease-disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation. CONCLUSIONS Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug-disease association prediction, which is beneficial for drug repositioning and drug safety research.
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Affiliation(s)
- Shihui He
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education, Kunming, 650500, China
| | - Lijun Yun
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education, Kunming, 650500, China.
| | - Haicheng Yi
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
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Barazorda-Ccahuana HL, Cárcamo-Rodriguez EG, Centeno-Lopez AE, Galdino AS, Machado-de-Ávila RA, Giunchetti RC, Coelho EAF, Chávez-Fumagalli MA. Targeting with Structural Analogs of Natural Products the Purine Salvage Pathway in Leishmania (Leishmania) infantum by Computer-Aided Drug-Design Approaches. Trop Med Infect Dis 2024; 9:41. [PMID: 38393130 PMCID: PMC10891554 DOI: 10.3390/tropicalmed9020041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024] Open
Abstract
Visceral Leishmaniasis (VL) has a high death rate, with 500,000 new cases and 50,000 deaths occurring annually. Despite the development of novel strategies and technologies, there is no adequate treatment for the disease. Therefore, the purpose of this study is to find structural analogs of natural products as potential novel drugs to treat VL. We selected structural analogs from natural products that have shown antileishmanial activities, and that may impede the purine salvage pathway using computer-aided drug-design (CADD) approaches. For these, we started with the vastly studied target in the pathway, the adenine phosphoribosyl transferase (APRT) protein, which alone is non-essential for the survival of the parasite. Keeping this in mind, we search for a substance that can bind to multiple targets throughout the pathway. Computational techniques were used to study the purine salvage pathway from Leishmania infantum, and molecular dynamic simulations were used to gather information on the interactions between ligands and proteins. Because of its low homology to human proteins and its essential role in the purine salvage pathway proteins network interaction, the findings further highlight the significance of adenylosuccinate lyase protein (ADL) as a therapeutic target. An analog of the alkaloid Skimmianine, N,N-diethyl-4-methoxy-1-benzofuran-6-carboxamide, demonstrated a good binding affinity to APRT and ADL targets, no expected toxicity, and potential for oral route administration. This study indicates that the compound may have antileishmanial activity, which was granted in vitro and in vivo experiments to settle this finding in the future.
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Affiliation(s)
- Haruna Luz Barazorda-Ccahuana
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa 04000, Peru
| | - Eymi Gladys Cárcamo-Rodriguez
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa 04000, Peru
- Facultad de Ciencias Farmacéuticas, Bioquímicas y Biotecnológicas, Universidad Católica de Santa María, Arequipa 04000, Peru
| | - Angela Emperatriz Centeno-Lopez
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa 04000, Peru
- Facultad de Ciencias Farmacéuticas, Bioquímicas y Biotecnológicas, Universidad Católica de Santa María, Arequipa 04000, Peru
| | - Alexsandro Sobreira Galdino
- Laboratório de Biotecnologia de Microrganismos, Universidade Federal São João Del-Rei, Divinópolis 35501-296, MG, Brazil
| | | | - Rodolfo Cordeiro Giunchetti
- Laboratório de Biologia das Interações Celulares, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
- Instituto Nacional de Ciência e Tecnologia em Doenças Tropicais, INCT-DT, Salvador 40015-970, BA, Brazil
| | - Eduardo Antonio Ferraz Coelho
- Programa de Pós-Graduação em Ciências da Saúde: Infectologia e Medicina Tropical, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
- Departamento de Patologia Clínica, COLTEC, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | - Miguel Angel Chávez-Fumagalli
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa 04000, Peru
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Kumar P, Sheokand D, Grewal A, Saini V, Kumar A. Clinical side-effects based drug repositioning for anti-epileptic activity. J Biomol Struct Dyn 2024; 42:1443-1454. [PMID: 37042987 DOI: 10.1080/07391102.2023.2199874] [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: 12/06/2022] [Accepted: 04/01/2023] [Indexed: 04/13/2023]
Abstract
Several generations of anti-epileptic drugs (AEDs) are available but have several associated side effects apart from a limited success rate. Drug repositioning strategies have gained importance in the last two decades owing to lower failure rates and economic burden. Drugs with similar side effect profiles may share a common mechanism of action and thus can be linked to other disease treatments. The present study was carried out to identify the newly approved drug candidate(s) as AEDs using clinical side-effects drug repositioning strategy. The clinical side effect similarity of drugs available in the SIDER v4.1 database was estimated against common side effects of 5 major marketed AEDs, using the 'dplyr' package library in the R. Further drugs were filtered based on Blood Brain Barrier permeability prediction and FDA-approval status. Molecular docking studies were performed for selected 26 hits (drugs) against previously identified epilepsy target receptors: Voltage-gated sodium channel α2 (Nav1.2), GABA receptor α1-β1 (GABAr α1-β1), and Voltage-gated calcium channel α-1 G (Cav3.1). Only 2 drugs (Ziprasidone and Paroxetine) showed better binding affinities against studied epilepsy receptors Nav1.2, GABAr α1-β1, and Cav3.1, than their corresponding standard AEDs, i.e. Carbamazepine, Clonazepam, and Pregabalin, respectively. Ziprasidone reportedly showed seizure-like symptoms in ∼3% of patients and was hence omitted from further study. The MDS study of docked complexes of Paroxetine with selected epilepsy target receptors showed stable RMSD values and better interaction energies. The study reveals Paroxetine as a potential candidate to be repurposed for 1st line epileptic seizure medication.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Pawan Kumar
- Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Deepak Sheokand
- Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Annu Grewal
- Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Vandana Saini
- Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Ajit Kumar
- Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, India
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da Silva CR, Rebouças JDDO, Cabral VPDF, Rodrigues DS, Barbosa AD, Moreira LEA, Barroso FDD, Coutinho TDNP, de Lima EA, de Andrade CR, de Andrade Neto JB, Lima ISP, Nobre Júnior HV, Gurgel do Amaral Valente Sá L. Promising activity of etomidate against mixed biofilms of fluconazole-resistant Candida albicans and methicillin-resistant Staphylococcus aureus. J Med Microbiol 2024; 73. [PMID: 38385528 DOI: 10.1099/jmm.0.001810] [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] [Indexed: 02/23/2024] Open
Abstract
Introduction. Candida albicans and Staphylococcus aureus are recognized for their development of resistance and biofilm formation. New therapeutic alternatives are necessary in this context.Hypothesis. Etomidate shows potential application in catheters against mixed biofilms of fluconazole-resistant C. albicans and methicillin-resistant S. aureus (MRSA).Aim. The present study aimed to evaluate the activity of etomidate against mixed biofilms of fluconazole-resistant C. albicans and MRSA.Methodology. The action of etomidate against mature biofilms was verified through the evaluation of biomass and cell viability, and its ability to prevent biofilm formation in peripheral venous catheters was determined based on counts of colony forming units (c.f.u.) and confirmed by morphological analysis through scanning electron microscopy (SEM).Results. Etomidate generated a reduction (P<0.05) in biomass and cell viability starting from a concentration of 250 µg ml-1. In addition, it showed significant ability to prevent the formation of mixed biofilms in a peripheral venous catheter, as shown by a reduction in c.f.u. SEM revealed that treatment with etomidate caused substantial damage to the fungal cells.Conclusion. The results showed the potential of etomidate against polymicrobial biofilms of fluconazole-resistant C. albicans and MRSA.
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Affiliation(s)
- Cecília Rocha da Silva
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
| | | | - Vitória Pessoa de Farias Cabral
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
| | - Daniel Sampaio Rodrigues
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
| | - Amanda Dias Barbosa
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
| | - Lara Elloyse Almeida Moreira
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
| | - Fátima Daiana Dias Barroso
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
- Christus University Center (UNICHRISTUS), Fortaleza, CE, Brazil
| | | | - Elaine Aires de Lima
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
| | | | - João Batista de Andrade Neto
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
- Christus University Center (UNICHRISTUS), Fortaleza, CE, Brazil
| | - Iri Sandro Pampolha Lima
- Department of Pharmacology, School of Medicine, Federal University of Ceará, Barbalha, CE, Brazil
| | - Hélio Vitoriano Nobre Júnior
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
| | - Lívia Gurgel do Amaral Valente Sá
- School of Pharmacy, Laboratory of Bioprospection of Antimicrobial Molecules (LABIMAN), Federal University of Ceará, Fortaleza, CE, Brazil
- Christus University Center (UNICHRISTUS), Fortaleza, CE, Brazil
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Otero-Carrasco B, Ugarte Carro E, Prieto-Santamaría L, Diaz Uzquiano M, Caraça-Valente Hernández JP, Rodríguez-González A. Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenarios. BMC Genomics 2024; 25:43. [PMID: 38191292 PMCID: PMC10775474 DOI: 10.1186/s12864-023-09913-1] [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: 07/19/2023] [Accepted: 12/15/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Drug repurposing plays a significant role in providing effective treatments for certain diseases faster and more cost-effectively. Successful repurposing cases are mostly supported by a classical paradigm that stems from de novo drug development. This paradigm is based on the "one-drug-one-target-one-disease" idea. It consists of designing drugs specifically for a single disease and its drug's gene target. In this article, we investigated the use of biological pathways as potential elements to achieve effective drug repurposing. METHODS Considering a total of 4214 successful cases of drug repurposing, we identified cases in which biological pathways serve as the underlying basis for successful repurposing, referred to as DREBIOP. Once the repurposing cases based on pathways were identified, we studied their inherent patterns by considering the different biological elements associated with this dataset, as well as the pathways involved in these cases. Furthermore, we obtained gene-disease association values to demonstrate the diminished significance of the drug's gene target in these repurposing cases. To achieve this, we compared the values obtained for the DREBIOP set with the overall association values found in DISNET, as well as with the drug's target gene (DREGE) based repurposing cases using the Mann-Whitney U Test. RESULTS A collection of drug repurposing cases, known as DREBIOP, was identified as a result. DREBIOP cases exhibit distinct characteristics compared with DREGE cases. Notably, DREBIOP cases are associated with a higher number of biological pathways, with Vitamin D Metabolism and ACE inhibitors being the most prominent pathways. Additionally, it was observed that the association values of GDAs in DREBIOP cases were significantly lower than those in DREGE cases (p-value < 0.05). CONCLUSIONS Biological pathways assume a pivotal role in drug repurposing cases. This investigation successfully revealed patterns that distinguish drug repurposing instances associated with biological pathways. These identified patterns can be applied to any known repurposing case, enabling the detection of pathway-based repurposing scenarios or the classical paradigm.
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Affiliation(s)
- Belén Otero-Carrasco
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain
| | - Esther Ugarte Carro
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
| | - Lucía Prieto-Santamaría
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain
| | - Marina Diaz Uzquiano
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
| | | | - Alejandro Rodríguez-González
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain.
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain.
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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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Verburgt J, Jain A, Kihara D. Recent Deep Learning Applications to Structure-Based Drug Design. Methods Mol Biol 2024; 2714:215-234. [PMID: 37676602 PMCID: PMC10578466 DOI: 10.1007/978-1-0716-3441-7_13] [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] [Indexed: 09/08/2023]
Abstract
Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning-based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.
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Affiliation(s)
- Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Anika Jain
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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Gao Z, Ding P, Xu R. IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder. Pharmacol Res 2024; 199:106960. [PMID: 37832859 DOI: 10.1016/j.phrs.2023.106960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023]
Abstract
Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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Li D, Xiao Z, Sun H, Jiang X, Zhao W, Shen X. Prediction of Drug-Disease Associations Based on Multi-Kernel Deep Learning Method in Heterogeneous Graph Embedding. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:120-128. [PMID: 38051617 DOI: 10.1109/tcbb.2023.3339189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of research for this task, existing methods are deficient in utilizing complex relationships among biological entities, which may not be conducive to subsequent simulation of drug treatment processes. In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first construct a heterogeneous information network by combining drug-disease, drug-protein and disease-protein biological networks. Then, a multi-layer graph attention model is utilized to capture the complex associations in the network to derive representations for drugs and diseases. Finally, to maintain the relationship of nodes in different feature spaces, we propose a multi-kernel learning method to transform and combine the representations. Experimental results demonstrate that HMLKGAT outperforms six state-of-the-art methods in drug-related disease prediction, and case studies of five classical drugs further demonstrate the effectiveness of HMLKGAT.
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Cabral-Pacheco GA, Flores-Morales V, Garza-Veloz I, Damián-Sandoval M, Martínez-Flores RB, Martínez-Vázquez MC, Delgado-Enciso I, Rodriguez-Sanchez IP, Martinez-Fierro ML. Evaluation of dapsone and its synthetic derivative DDS‑13 in cancer in vitro. Exp Ther Med 2024; 27:47. [PMID: 38144918 PMCID: PMC10739155 DOI: 10.3892/etm.2023.12335] [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: 07/04/2023] [Accepted: 11/02/2023] [Indexed: 12/26/2023] Open
Abstract
The present study highlighted the repositioning of the drug dapsone (DDS) for cancer therapy. Due to its mechanism of action, DDS has a dual effect as an antibiotic and as an anti-inflammatory/immunomodulator; however, at high doses, it has important adverse effects. The derivative DDS-13 [N,N'-(sulfonyl bis (4,1-phenylene)) dioctanamide] was synthesized through an N-acylation reaction to compare it with DDS. Its cytotoxic effects in cancer cells (DU145 and HeLa) and non-cancer cells (HDFa) were observed at concentrations ranging 0.01-100 µM and its physicochemical/pharmacokinetic properties were analyzed using the SwissADME tool. The objectives of the present study were to evaluate the anticancer activity of both DDS and DDS-13 and to identify the physicochemical and pharmacokinetic properties of DDS-13. The results showed that DDS-13 presented a cytotoxic effect in the DU145 cell line (IC50=19.06 µM), while DDS showed a cytotoxic effect on both the DU145 (IC50=11.11 µM) and HeLa (IC50=13.07 µM) cell lines. DDS-13 appears to be a good cytotoxic candidate for the treatment of prostate cancer, while DDS appears to be a good candidate for both cervical and prostate cancer. Neither candidate showed a cytotoxic effect in non-cancerous cells. The different pharmacokinetic properties of DDS-13 make it a new candidate for evaluation in preclinical models for the treatment of cancer.
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Affiliation(s)
- Griselda A. Cabral-Pacheco
- Molecular Medicine Laboratory, Academic Unit of Human Medicine and Health Sciences, Autonomous University of Zacatecas, Zacatecas 98160, Mexico
| | - Virginia Flores-Morales
- Laboratory of Asymmetric Synthesis and Bio-Chemoinformatics, Chemical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico
| | - Idalia Garza-Veloz
- Molecular Medicine Laboratory, Academic Unit of Human Medicine and Health Sciences, Autonomous University of Zacatecas, Zacatecas 98160, Mexico
| | - Miriam Damián-Sandoval
- Molecular Medicine Laboratory, Academic Unit of Human Medicine and Health Sciences, Autonomous University of Zacatecas, Zacatecas 98160, Mexico
| | - Rosa B. Martínez-Flores
- Molecular Medicine Laboratory, Academic Unit of Human Medicine and Health Sciences, Autonomous University of Zacatecas, Zacatecas 98160, Mexico
| | - María C. Martínez-Vázquez
- Molecular Medicine Laboratory, Academic Unit of Human Medicine and Health Sciences, Autonomous University of Zacatecas, Zacatecas 98160, Mexico
| | - Iván Delgado-Enciso
- School of Medicine, University of Colima, Colima 28040, Mexico
- Cancerology State Institute, Colima State Health Services, Colima 28085, Mexico
| | - Iram P. Rodriguez-Sanchez
- Molecular and Structural Physiology Laboratory, School of Biological Sciences, Autonomous University of Nuevo Leon, Nuevo León 66455, Mexico
| | - Margarita L. Martinez-Fierro
- Molecular Medicine Laboratory, Academic Unit of Human Medicine and Health Sciences, Autonomous University of Zacatecas, Zacatecas 98160, Mexico
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Sanjak J, Binder J, Yadaw AS, Zhu Q, Mathé EA. Clustering rare diseases within an ontology-enriched knowledge graph. J Am Med Inform Assoc 2023; 31:154-164. [PMID: 37759342 PMCID: PMC10746319 DOI: 10.1093/jamia/ocad186] [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: 03/03/2023] [Revised: 08/01/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
OBJECTIVE Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing. Toward that aim, we utilized an integrative knowledge graph to construct clusters of rare diseases. MATERIALS AND METHODS Data on 3242 rare diseases were extracted from the National Center for Advancing Translational Science Genetic and Rare Diseases Information center internal data resources. The rare disease data enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data, and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were trained and clustered. We validated the disease clusters through semantic similarity and feature enrichment analysis. RESULTS Thirty-seven disease clusters were created with a mean size of 87 diseases. We validate the clusters quantitatively via semantic similarity based on the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters are highly related. DISCUSSION We demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and drugs are enumerated for follow-up efforts. CONCLUSION We lay out a method for clustering rare diseases using graph node embeddings. We develop an easy-to-maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems.
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Affiliation(s)
- Jaleal Sanjak
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
- Chief Technology Office, Booz Allen Hamilton, Bethesda, MD, United States
| | - Jessica Binder
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
| | - Arjun Singh Yadaw
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
| | - Qian Zhu
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
| | - Ewy A Mathé
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
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Anderson C, Bucholc M, McClean PL, Zhang SD. The Potential of a Stratified Approach to Drug Repurposing in Alzheimer's Disease. Biomolecules 2023; 14:11. [PMID: 38275752 PMCID: PMC10813465 DOI: 10.3390/biom14010011] [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: 11/10/2023] [Revised: 12/13/2023] [Accepted: 12/15/2023] [Indexed: 01/27/2024] Open
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative condition that is characterized by the build-up of amyloid-beta plaques and neurofibrillary tangles. While multiple theories explaining the aetiology of the disease have been suggested, the underlying cause of the disease is still unknown. Despite this, several modifiable and non-modifiable factors that increase the risk of developing AD have been identified. To date, only eight AD drugs have ever gained regulatory approval, including six symptomatic and two disease-modifying drugs. However, not all are available in all countries and high costs associated with new disease-modifying biologics prevent large proportions of the patient population from accessing them. With the current patient population expected to triple by 2050, it is imperative that new, effective, and affordable drugs become available to patients. Traditional drug development strategies have a 99% failure rate in AD, which is far higher than in other disease areas. Even when a drug does reach the market, additional barriers such as high cost and lack of accessibility prevent patients from benefiting from them. In this review, we discuss how a stratified medicine drug repurposing approach may address some of the limitations and barriers that traditional strategies face in relation to drug development in AD. We believe that novel, stratified drug repurposing studies may expedite the discovery of alternative, effective, and more affordable treatment options for a rapidly expanding patient population in comparison with traditional drug development methods.
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Affiliation(s)
- Chloe Anderson
- Personalised Medicine Centre, School of Medicine, Altnagelvin Hospital Campus, Ulster University, Glenshane Road, Derry/Londonderry BT47 6SB, UK;
| | - Magda Bucholc
- School of Computing, Engineering and Intelligent Systems, Magee Campus, Ulster University, Northland Road, Derry/Londonderry BT48 7JL, UK
| | - Paula L. McClean
- Personalised Medicine Centre, School of Medicine, Altnagelvin Hospital Campus, Ulster University, Glenshane Road, Derry/Londonderry BT47 6SB, UK;
| | - Shu-Dong Zhang
- Personalised Medicine Centre, School of Medicine, Altnagelvin Hospital Campus, Ulster University, Glenshane Road, Derry/Londonderry BT47 6SB, UK;
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Boudin M, Diallo G, Drancé M, Mougin F. The OREGANO knowledge graph for computational drug repurposing. Sci Data 2023; 10:871. [PMID: 38057380 PMCID: PMC10700660 DOI: 10.1038/s41597-023-02757-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 11/16/2023] [Indexed: 12/08/2023] Open
Abstract
Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).
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Affiliation(s)
- Marina Boudin
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France.
| | - Gayo Diallo
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
| | - Martin Drancé
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
| | - Fleur Mougin
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
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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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30
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Pinzi L, Rastelli G. Trends and Applications in Computationally Driven Drug Repurposing. Int J Mol Sci 2023; 24:16511. [PMID: 38003701 PMCID: PMC10671888 DOI: 10.3390/ijms242216511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Drug repurposing is a widely used approach originally developed to aid in the identification of new uses of already existing drugs outside the scope of the original medical indication [...].
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Affiliation(s)
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy;
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31
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Bourdakou MM, Fernández-Ginés R, Cuadrado A, Spyrou GM. Drug repurposing on Alzheimer's disease through modulation of NRF2 neighborhood. Redox Biol 2023; 67:102881. [PMID: 37696195 PMCID: PMC10500459 DOI: 10.1016/j.redox.2023.102881] [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: 08/03/2023] [Revised: 08/30/2023] [Accepted: 09/06/2023] [Indexed: 09/13/2023] Open
Abstract
Alzheimer's disease (AD) is an age-dependent neurodegenerative disorder and the most common cause of cognitive decline. The alarming epidemiological features of Alzheimer's disease, combined with the high failure rate of candidate drugs tested in the preclinical phase, impose more intense investigations for new curative treatments. NRF2 (Nuclear factor-erythroid factor 2-related factor 2) plays a critical role in the inflammatory response and in the cellular redox homeostasis and provides cytoprotection in several diseases including those in the neurodegeneration spectrum. These roles suggest that NRF2 and its directly associated proteins may be novel attractive therapeutic targets in the fight against AD. In this study, through a systemics perspective, we propose an in silico drug repurposing approach for AD, based on the NRF2 interactome and regulome, with the aim of highlighting possible repurposed drugs for AD. Using publicly available information based on differential expressions of the NRF2-neighborhood in AD and through a computational drug repurposing pipeline, we derived to a short list of candidate repurposed drugs and small molecules that affect the expression levels of the majority of NRF2-partners. The relevance of these findings was assessed in a four-step computational meta-analysis including i) structural similarity comparisons with currently ongoing NRF2-related drugs in clinical trials ii) evaluation based on the NRF2-diseasome iii) comparison of relevance between targeted pathways of shortlisted drugs and NRF2-related drugs in clinical trials and iv) further comparison with existing knowledge on AD and NRF2-related drugs in clinical trials based on their known modes of action. Overall, our analysis yielded in 5 candidate repurposed drugs for AD. In cell culture, these 5 candidates activated a luciferase reporter for NRF2 activity and in hippocampus derived TH22 cells they increased NRF2 protein levels and the NRF2 transcriptional signatures as determined by increased expression of its downstream target heme oxygenase 1. We expect that our proposed candidate repurposed drugs will be useful for further research and clinical translation for AD.
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Affiliation(s)
- Marilena M Bourdakou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Raquel Fernández-Ginés
- Instituto de Investigaciones Biomédicas "Alberto Sols" UAM-CSIC, Instituto de Investigación Sanitaria La Paz (IdiPaz), Department of Biochemistry, Faculty of Medicine, Autonomous University of Madrid, Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid, Spain
| | - Antonio Cuadrado
- Instituto de Investigaciones Biomédicas "Alberto Sols" UAM-CSIC, Instituto de Investigación Sanitaria La Paz (IdiPaz), Department of Biochemistry, Faculty of Medicine, Autonomous University of Madrid, Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid, Spain
| | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.
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Muniyappan S, Rayan AXA, Varrieth GT. EGeRepDR: An enhanced genetic-based representation learning for drug repurposing using multiple biomedical sources. J Biomed Inform 2023; 147:104528. [PMID: 37858852 DOI: 10.1016/j.jbi.2023.104528] [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: 07/13/2023] [Revised: 09/11/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023]
Abstract
MOTIVATION Drug repurposing (DR) is an imminent approach for identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical industry due to the high cost and time of launching new drugs to the market through traditional drug development. DR task majorly depends on genetic information since the drugs revert the modified Gene Expression (GE) of diseases to normal. Many of the existing studies have not considered the genetic importance of predicting the potential candidates. METHOD We proposed a novel multimodal framework that utilizes genetic aspects of drugs and diseases such as genes, pathways, gene signatures, or expression to enhance the performance of DR using various data sources. Firstly, the heterogeneous biological network (HBN) is constructed with three types of nodes namely drug, disease, and gene, and 4 types of edges similarities (drug, gene, and disease), drug-gene, gene-disease, and drug-disease. Next, a modified graph auto-encoder (GAE*) model is applied to learn the representation of drug and disease nodes using the topological structure and edge information. Secondly, the HBN is enhanced with the information extracted from biomedical literature and ontology using a novel semi-supervised pattern embedding-based bootstrapping model and novel DR perspective representation learning respectively to improve the prediction performance. Finally, our proposed system uses a neural network model to generate the probability score of drug-disease pairs. RESULTS We demonstrate the efficiency of the proposed model on various datasets and achieved outstanding performance in 5-fold cross-validation (AUC = 0.99, AUPR = 0.98). Further, we validated the top-ranked potential candidates using pathway analysis and proved that the known and predicted candidates share common genes in the pathways.
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Affiliation(s)
- Saranya Muniyappan
- Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India.
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33
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Ji X, Williams KP, Zheng W. Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer. Cancer Inform 2023; 22:11769351231202588. [PMID: 37846218 PMCID: PMC10576937 DOI: 10.1177/11769351231202588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/01/2023] [Indexed: 10/18/2023] Open
Abstract
The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.
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Affiliation(s)
- Xiaojia Ji
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Kevin P Williams
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Weifan Zheng
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
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Richardson PJ, Smith DP, de Giorgio A, Snetkov X, Almond-Thynne J, Cronin S, Mead RJ, McDermott CJ, Shaw PJ. Janus kinase inhibitors are potential therapeutics for amyotrophic lateral sclerosis. Transl Neurodegener 2023; 12:47. [PMID: 37828541 PMCID: PMC10568794 DOI: 10.1186/s40035-023-00380-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a poorly treated multifactorial neurodegenerative disease associated with multiple cell types and subcellular organelles. As with other multifactorial diseases, it is likely that drugs will need to target multiple disease processes and cell types to be effective. We review here the role of Janus kinase (JAK)/Signal transducer and activator of transcription (STAT) signalling in ALS, confirm the association of this signalling with fundamental ALS disease processes using the BenevolentAI Knowledge Graph, and demonstrate that inhibitors of this pathway could reduce the ALS pathophysiology in neurons, glia, muscle fibres, and blood cells. Specifically, we suggest that inhibition of the JAK enzymes by approved inhibitors known as Jakinibs could reduce STAT3 activation and modify the progress of this disease. Analysis of the Jakinibs highlights baricitinib as a suitable candidate due to its ability to penetrate the central nervous system and exert beneficial effects on the immune system. Therefore, we recommend that this drug be tested in appropriately designed clinical trials for ALS.
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Affiliation(s)
| | | | | | | | | | - Sara Cronin
- BenevolentAI, 15 MetroTech Centre, 8th FL, Brooklyn, NY, 11201, USA
| | - Richard J Mead
- Sheffield Institute for Translational Neuroscience, Faculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield, UK
| | - Christopher J McDermott
- Sheffield Institute for Translational Neuroscience, Faculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield, UK
- NIHR Sheffield Biomedical Research Centre, University of Sheffield and Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience, Faculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield, UK
- NIHR Sheffield Biomedical Research Centre, University of Sheffield and Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
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Ye S, Zhao W, Shen X, Jiang X, He T. An effective multi-task learning framework for drug repurposing based on graph representation learning. Methods 2023; 218:48-56. [PMID: 37516260 DOI: 10.1016/j.ymeth.2023.07.008] [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: 02/20/2023] [Revised: 07/04/2023] [Accepted: 07/20/2023] [Indexed: 07/31/2023] Open
Abstract
Drug repurposing, which typically applies the procedure of drug-disease associations (DDAs) prediction, is a feasible solution to drug discovery. Compared with traditional methods, drug repurposing can reduce the cost and time for drug development and advance the success rate of drug discovery. Although many methods for drug repurposing have been proposed and the obtained results are relatively acceptable, there is still some room for improving the predictive performance, since those methods fail to consider fully the issue of sparseness in known drug-disease associations. In this paper, we propose a novel multi-task learning framework based on graph representation learning to identify DDAs for drug repurposing. In our proposed framework, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a module consisting of multiple layers of graph convolutional networks is utilized to learn low-dimensional representations of nodes in the constructed heterogeneous information network. Finally, two types of auxiliary tasks are designed to help to train the target task of DDAs prediction in the multi-task learning framework. Comprehensive experiments are conducted on real data and the results demonstrate the effectiveness of the proposed method for drug repurposing.
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Affiliation(s)
- Shengwei Ye
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
| | - Weizhong Zhao
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China.
| | - Xianjun Shen
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
| | - Xingpeng Jiang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
| | - Tingting He
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
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McGowan E, Sanjak J, Mathé EA, Zhu Q. Integrative rare disease biomedical profile based network supporting drug repurposing or repositioning, a case study of glioblastoma. Orphanet J Rare Dis 2023; 18:301. [PMID: 37749605 PMCID: PMC10519087 DOI: 10.1186/s13023-023-02876-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/24/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing or repositioning candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data. METHODS We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repurposing or repositioning candidates for GBM. RESULTS We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM. CONCLUSION Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing or repositioning. Further validation will be conducted by using other different types of biomedical and clinical data and biological experiments. The findings could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.
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Affiliation(s)
- Erin McGowan
- Division of Pre-Clinical Innovation National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD, 20850, USA
| | - Jaleal Sanjak
- Division of Pre-Clinical Innovation National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD, 20850, USA
| | - Ewy A Mathé
- Division of Pre-Clinical Innovation National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD, 20850, USA
| | - Qian Zhu
- Division of Pre-Clinical Innovation National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD, 20850, USA.
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Wang Y, Wang J, Yan Z, Liu S, Xu W. Potential drug targets for asthma identified in the plasma and brain through Mendelian randomization analysis. Front Immunol 2023; 14:1240517. [PMID: 37809092 PMCID: PMC10551444 DOI: 10.3389/fimmu.2023.1240517] [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: 06/15/2023] [Accepted: 08/31/2023] [Indexed: 10/10/2023] Open
Abstract
Background Asthma is a heterogeneous disease, and the involvement of neurogenic inflammation is crucial in its development. The standardized treatments focus on alleviating symptoms. Despite the availability of medications for asthma, they have proven to be inadequate in controlling relapses and halting the progression of the disease. Therefore, there is a need for novel drug targets to prevent asthma. Methods We utilized Mendelian randomization to investigate potential drug targets for asthma. We analyzed summary statistics from the UK Biobank and then replicated our findings in GWAS data by Demenais et al. and the FinnGen cohort. We obtained genetic instruments for 734 plasma and 73 brain proteins from recently reported GWAS. Next, we utilized reverse causal relationship analysis, Bayesian co-localization, and phenotype scanning as part of our sensitivity analysis. Furthermore, we performed a comparison and protein-protein interaction analysis to identify causal proteins. We also analyzed the possible consequences of our discoveries by the given existing asthma drugs and their targets. Results Using Mendelian randomization analysis, we identified five protein-asthma pairs that were significant at the Bonferroni level (P < 6.35 × 10-5). Specifically, in plasma, we found that an increase of one standard deviation in IL1R1 and ECM1 was associated with an increased risk of asthma, while an increase in ADAM19 was found to be protective. The corresponding odds ratios were 1.03 (95% CI, 1.02-1.04), 1.00 (95% CI, 1.00-1.01), and 0.99 (95% CI, 0.98-0.99), respectively. In the brain, per 10-fold increase in ECM1 (OR, 1.05; 95% CI, 1.03-1.08) and PDLIM4 (OR, 1.05; 95% CI, 1.04-1.07) increased the risk of asthma. Bayesian co-localization found that ECM1 in the plasma (coloc.abf-PPH4 = 0.965) and in the brain (coloc.abf-PPH4 = 0.931) shared the same mutation with asthma. The target proteins of current asthma medications were found to interact with IL1R1. IL1R1 and PDLIM4 were validated in two replication cohorts. Conclusion Our integrative analysis revealed that asthma risk is causally affected by the levels of IL1R1, ECM1, and PDLIM4. The results suggest that these three proteins have the potential to be used as drug targets for asthma, and further investigation through clinical trials is needed.
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Affiliation(s)
- Yuting Wang
- Department of Otorhinolaryngology, Dongfang Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Jiaxi Wang
- Department of Otorhinolaryngology, Dongfang Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Zhanfeng Yan
- Department of Otorhinolaryngology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Siming Liu
- Department of Otorhinolaryngology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Wenlong Xu
- Department of Otorhinolaryngology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
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Guthrie J, Ko¨stel Bal S, Lombardo SD, Mu¨ller F, Sin C, Hu¨tter CV, Menche J, Boztug K. AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation. SCIENCE ADVANCES 2023; 9:eadg6375. [PMID: 37656781 PMCID: PMC10848965 DOI: 10.1126/sciadv.adg6375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/01/2023] [Indexed: 09/03/2023]
Abstract
Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.
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Affiliation(s)
- Julia Guthrie
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Sevgi Ko¨stel Bal
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
| | - Salvo Danilo Lombardo
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Felix Mu¨ller
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Celine Sin
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Christiane V. R. Hu¨tter
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna BioCenter, A-1030 Vienna, Austria
| | - Jo¨rg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria
| | - Kaan Boztug
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
- St. Anna Children’s Hospital, Kinderspitalgasse 6, A-1090, Vienna, Austria
- Medical University of Vienna, Department of Pediatrics and Adolescent Medicine, Währinger Gürtel 18-20, A-1090 Vienna, Austria
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Abu-Mahfouz A, Ali M, Elfiky A. Anti-breast cancer drugs targeting cell-surface glucose-regulated protein 78: a drug repositioning in silico study. J Biomol Struct Dyn 2023; 41:7794-7808. [PMID: 36129131 DOI: 10.1080/07391102.2022.2125076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 09/10/2022] [Indexed: 10/14/2022]
Abstract
Breast cancer (BC) is prevalent worldwide and is a leading cause of death among women. However, cell-surface glucose-regulated protein 78 (cs-GRP78) is overexpressed in several types of cancer and during pathogen infections. This study examines two well-known BC drugs approved by the FDA as BC treatments to GRP78. The first type consists of inhibitors of cyclin-based kinases 4/6, including abemaciclib, palbociclib, ribociclib, and dinaciclib. In addition, tunicamycin, and doxorubicin, which are among the most effective anticancer drugs for early and late-stage BC, are tested against GRP78. As (-)-epiGallocatechin gallate inhibits GRP78, it is also being evaluated (used as positive control). Thus, using molecular dynamics simulation approaches, this study aims to examine the advantages of targeting GRP78, which represents a promising cancer therapy regime. In light of recent advances in computational drug response prediction models, this study aimed to examine the benefits of GRP78 targeting, which represents a promising cancer therapy regime, by utilizing combined molecular docking and molecular dynamics simulation approaches. The simulated protein (50 ns) was docked with the drugs, then a second round of dynamics simulation was performed for 100 ns. After that, the binding free energies were calculated from 30 to 100 ns for each complex during the simulation period. These findings demonstrate the efficacy of abemaciclib, ribociclib, and tunicamycin in binding to the nucleotide-binding domain of the GRP78, paving the way for elucidating the mode of interactions between these drugs and cancer (and other stressed) cells that overexpress GRP78.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Alaa Abu-Mahfouz
- Biophysics Department, Faculty of Sciences, Cairo University, Giza, Egypt
| | - Maha Ali
- Biophysics Department, Faculty of Sciences, Cairo University, Giza, Egypt
| | - Abdo Elfiky
- Biophysics Department, Faculty of Sciences, Cairo University, Giza, Egypt
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40
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Flanary VL, Fisher JL, Wilk EJ, Howton TC, Lasseigne BN. Computational Advancements in Cancer Combination Therapy Prediction. JCO Precis Oncol 2023; 7:e2300261. [PMID: 37824797 DOI: 10.1200/po.23.00261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/20/2023] [Accepted: 08/15/2023] [Indexed: 10/14/2023] Open
Abstract
Given the high attrition rate of de novo drug discovery and limited efficacy of single-agent therapies in cancer treatment, combination therapy prediction through in silico drug repurposing has risen as a time- and cost-effective alternative for identifying novel and potentially efficacious therapies for cancer. The purpose of this review is to provide an introduction to computational methods for cancer combination therapy prediction and to summarize recent studies that implement each of these methods. A systematic search of the PubMed database was performed, focusing on studies published within the past 10 years. Our search included reviews and articles of ongoing and retrospective studies. We prioritized articles with findings that suggest considerations for improving combination therapy prediction methods over providing a meta-analysis of all currently available cancer combination therapy prediction methods. Computational methods used for drug combination therapy prediction in cancer research include networks, regression-based machine learning, classifier machine learning models, and deep learning approaches. Each method class has its own advantages and disadvantages, so careful consideration is needed to determine the most suitable class when designing a combination therapy prediction method. Future directions to improve current combination therapy prediction technology include incorporation of disease pathobiology, drug characteristics, patient multiomics data, and drug-drug interactions to determine maximally efficacious and tolerable drug regimens for cancer. As computational methods improve in their capability to integrate patient, drug, and disease data, more comprehensive models can be developed to more accurately predict safe and efficacious combination drug therapies for cancer and other complex diseases.
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Affiliation(s)
- Victoria L Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
| | - Jennifer L Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
| | - Elizabeth J Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
| | - Timothy C Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
| | - Brittany N Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
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Suviriyapaisal N, Wichadakul D. iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction. RSC Adv 2023; 13:25218-25228. [PMID: 37636509 PMCID: PMC10448119 DOI: 10.1039/d3ra03796g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 08/14/2023] [Indexed: 08/29/2023] Open
Abstract
Artificial intelligence has become more prevalent in broad fields, including drug discovery, in which the process is costly and time-consuming when conducted through wet experiments. As a result, drug repurposing, which tries to utilize approved and low-risk drugs for a new purpose, becomes more attractive. However, screening candidates from many drugs for specific protein targets is still expensive and tedious. This study aims to leverage computational resources to aid drug discovery by utilizing drug-protein interaction data and estimating their interaction strength, so-called binding affinity. Our estimation approach addresses multiple challenges encountered in the field. First, we employed a graph-based deep learning technique to overcome the limitations of drug compounds represented in string format by incorporating background knowledge of node and edge information as separate multi-dimensional features. Second, we tackled the complexities associated with extracting the representation and structure of proteins by utilizing a pre-trained model for feature extraction. Also, we employed graph operations over the 1D representation of a protein sequence to overcome the fixed-length problem typically encountered in language model tasks. In addition, we conducted a comparative analysis with a baseline model that creates a protein graph from a contact map prediction model, giving valuable insights into the performance and effectiveness of our proposed method. We evaluated the performance of our model using the same benchmark datasets with a variety of matrices as other previous work, and the results show that our model achieved the best prediction results while requiring no contact map information compared to other graph-based methods.
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Affiliation(s)
- Natchanon Suviriyapaisal
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand
| | - Duangdao Wichadakul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand
- Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University Bangkok 10330 Thailand
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Gao Z, Winhusen TJ, Gorenflo M, Ghitza UE, Davis PB, Kaelber DC, Xu R. Repurposing ketamine to treat cocaine use disorder: integration of artificial intelligence-based prediction, expert evaluation, clinical corroboration and mechanism of action analyses. Addiction 2023; 118:1307-1319. [PMID: 36792381 PMCID: PMC10631254 DOI: 10.1111/add.16168] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 01/25/2023] [Indexed: 02/17/2023]
Abstract
BACKGROUND AND AIMS Cocaine use disorder (CUD) is a significant public health issue for which there is no Food and Drug Administration (FDA) approved medication. Drug repurposing looks for new cost-effective uses of approved drugs. This study presents an integrated strategy to identify repurposed FDA-approved drugs for CUD treatment. DESIGN Our drug repurposing strategy combines artificial intelligence (AI)-based drug prediction, expert panel review, clinical corroboration and mechanisms of action analysis being implemented in the National Drug Abuse Treatment Clinical Trials Network (CTN). Based on AI-based prediction and expert knowledge, ketamine was ranked as the top candidate for clinical corroboration via electronic health record (EHR) evaluation of CUD patient cohorts prescribed ketamine for anesthesia or depression compared with matched controls who received non-ketamine anesthesia or antidepressants/midazolam. Genetic and pathway enrichment analyses were performed to understand ketamine's potential mechanisms of action in the context of CUD. SETTING The study utilized TriNetX to access EHRs from more than 90 million patients world-wide. Genetic- and functional-level analyses used DisGeNet, Search Tool for Interactions of Chemicals and Kyoto Encyclopedia of Genes and Genomes databases. PARTICIPANTS A total of 7742 CUD patients who received anesthesia (3871 ketamine-exposed and 3871 anesthetic-controlled) and 7910 CUD patients with depression (3955 ketamine-exposed and 3955 antidepressant-controlled) were identified after propensity score-matching. MEASUREMENTS EHR analysis outcome was a CUD remission diagnosis within 1 year of drug prescription. FINDINGS Patients with CUD prescribed ketamine for anesthesia displayed a significantly higher rate of CUD remission compared with matched individuals prescribed other anesthetics [hazard ratio (HR) = 1.98, 95% confidence interval (CI) = 1.42-2.78]. Similarly, CUD patients prescribed ketamine for depression evidenced a significantly higher CUD remission ratio compared with matched patients prescribed antidepressants or midazolam (HR = 4.39, 95% CI = 2.89-6.68). The mechanism of action analysis revealed that ketamine directly targets multiple CUD-associated genes (BDNF, CNR1, DRD2, GABRA2, GABRB3, GAD1, OPRK1, OPRM1, SLC6A3, SLC6A4) and pathways implicated in neuroactive ligand-receptor interaction, cAMP signaling and cocaine abuse/dependence. CONCLUSIONS Ketamine appears to be a potential repurposed drug for treatment of cocaine use disorder.
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - T. John Winhusen
- Center for Addiction Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Maria Gorenflo
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Udi E. Ghitza
- Center for the Clinical Trials Network (CCTN), National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Pamela B. Davis
- Center for Community Health Integration, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - David C. Kaelber
- Center for Clinical Informatics Research and Education, The Metro Health System, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
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Nam Y, Lucas A, Yun JS, Lee SM, Park JW, Chen Z, Lee B, Ning X, Shen L, Verma A, Kim D. Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks. J Transl Med 2023; 21:415. [PMID: 37365631 DOI: 10.1186/s12967-023-04223-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/24/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease. METHODS We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses. RESULTS The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype. CONCLUSION We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.
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Affiliation(s)
- Yonghyun Nam
- Department of Biostatistics, Epidemiology & Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA
| | - Anastasia Lucas
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jae-Seung Yun
- Department of Biostatistics, Epidemiology & Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA
- Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Seung Mi Lee
- Department of Biostatistics, Epidemiology & Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, South Korea
| | - Ji Won Park
- Department of Biostatistics, Epidemiology & Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea
| | - Ziqi Chen
- Computer Science and Engineering Department, College of Engineering, The Ohio State University, Columbus, USA
| | - Brian Lee
- Department of Biostatistics, Epidemiology & Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA
| | - Xia Ning
- Computer Science and Engineering Department, College of Engineering, The Ohio State University, Columbus, USA
- Biomedical Informatics Department, College of Medicine, The Ohio State University, Columbus, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology & Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, USA
| | - Anurag Verma
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology & Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, USA.
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Imakura T, Sato S, Koyama K, Ogawa H, Niimura T, Murakami K, Yamashita Y, Haji K, Naito N, Kagawa K, Kawano H, Zamami Y, Ishizawa K, Nishioka Y. A polo-like kinase inhibitor identified by computational repositioning attenuates pulmonary fibrosis. Respir Res 2023; 24:148. [PMID: 37269004 DOI: 10.1186/s12931-023-02446-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/08/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND Idiopathic pulmonary fibrosis (IPF) is a fatal fibrotic lung disease with few effective therapeutic options. Recently, drug repositioning, which involves identifying novel therapeutic potentials for existing drugs, has been popularized as a new approach for the development of novel therapeutic reagents. However, this approach has not yet been fully utilized in the field of pulmonary fibrosis. METHODS The present study identified novel therapeutic options for pulmonary fibrosis using a systematic computational approach for drug repositioning based on integration of public gene expression signatures of drug and diseases (in silico screening approach). RESULTS Among the top compounds predicted to be therapeutic for IPF by the in silico approach, we selected BI2536, a polo-like kinase (PLK) 1/2 inhibitor, as a candidate for treating pulmonary fibrosis using an in silico analysis. However, BI2536 accelerated mortality and weight loss rate in an experimental mouse model of pulmonary fibrosis. Because immunofluorescence staining revealed that PLK1 expression was dominant in myofibroblasts while PLK2 expression was dominant in lung epithelial cells, we next focused on the anti-fibrotic effect of the selective PLK1 inhibitor GSK461364. Consequently, GSK461364 attenuated pulmonary fibrosis with acceptable mortality and weight loss in mice. CONCLUSIONS These findings suggest that targeting PLK1 may be a novel therapeutic approach for pulmonary fibrosis by inhibiting lung fibroblast proliferation without affecting lung epithelial cells. In addition, while in silico screening is useful, it is essential to fully determine the biological activities of candidates by wet-lab validation studies.
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Affiliation(s)
- Takeshi Imakura
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Seidai Sato
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Kazuya Koyama
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hirohisa Ogawa
- Department of Pathology and Laboratory Medicine, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Takahiro Niimura
- Department of Clinical Pharmacology and Therapeutics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Kojin Murakami
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yuya Yamashita
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Keiko Haji
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Nobuhito Naito
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Kozo Kagawa
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hiroshi Kawano
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yoshito Zamami
- Department of Clinical Pharmacology and Therapeutics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
- Department of Pharmacy, Okayama University Hospital, Okayama, Japan
| | - Keisuke Ishizawa
- Department of Clinical Pharmacology and Therapeutics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Yasuhiko Nishioka
- Department of Respiratory Medicine and Rheumatology, Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.
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45
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Barazorda-Ccahuana HL, Goyzueta-Mamani LD, Candia Puma MA, Simões de Freitas C, de Sousa Vieria Tavares G, Pagliara Lage D, Ferraz Coelho EA, Chávez-Fumagalli MA. Computer-aided drug design approaches applied to screen natural product's structural analogs targeting arginase in Leishmania spp. F1000Res 2023; 12:93. [PMID: 37424744 PMCID: PMC10323282 DOI: 10.12688/f1000research.129943.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction: Leishmaniasis is a disease with high mortality rates and approximately 1.5 million new cases each year. Despite the new approaches and advances to fight the disease, there are no effective therapies. Methods: Hence, this study aims to screen for natural products' structural analogs as new drug candidates against leishmaniasis. We applied Computer-aided drug design (CADD) approaches, such as virtual screening, molecular docking, molecular dynamics simulation, molecular mechanics-generalized Born surface area (MM-GBSA) binding free estimation, and free energy perturbation (FEP) aiming to select structural analogs from natural products that have shown anti-leishmanial and anti-arginase activities and that could bind selectively against the Leishmania arginase enzyme. Results: The compounds 2H-1-benzopyran, 3,4-dihydro-2-(2-methylphenyl)-(9CI), echioidinin, and malvidin showed good results against arginase targets from three parasite species and negative results for potential toxicities. The echioidinin and malvidin ligands generated interactions in the active center at pH 2.0 conditions by MM-GBSA and FEP methods. Conclusions: This work suggests the potential anti-leishmanial activity of the compounds and thus can be further in vitro and in vivo experimentally validated.
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Affiliation(s)
- Haruna Luz Barazorda-Ccahuana
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Catolica de Santa Maria de Arequipa, Arequipa, Peru
| | - Luis Daniel Goyzueta-Mamani
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Catolica de Santa Maria de Arequipa, Arequipa, Peru
- Sustainable Innovative Biomaterials Department, Le Qara Research Center, Arequipa, Peru
| | - Mayron Antonio Candia Puma
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Catolica de Santa Maria de Arequipa, Arequipa, Peru
- Universidad Católica de Santa María, Facultad de Ciencias Farmacéuticas, Bioquímicas y Biotecnológicas, Arequipa, Peru
| | - Camila Simões de Freitas
- Universidade Federal de Minas Gerais, Programa de Pós-Graduação em Ciências da Saúde: Infectologia e Medicina Tropical, Faculdade de Medicina, Belo Horizonte, Minas Gerais, Brazil
| | - Grasiele de Sousa Vieria Tavares
- Universidade Federal de Minas Gerais, Programa de Pós-Graduação em Ciências da Saúde: Infectologia e Medicina Tropical, Faculdade de Medicina, Belo Horizonte, Minas Gerais, Brazil
| | - Daniela Pagliara Lage
- Universidade Federal de Minas Gerais, Programa de Pós-Graduação em Ciências da Saúde: Infectologia e Medicina Tropical, Faculdade de Medicina, Belo Horizonte, Minas Gerais, Brazil
| | - Eduardo Antonio Ferraz Coelho
- Universidade Federal de Minas Gerais, Programa de Pós-Graduação em Ciências da Saúde: Infectologia e Medicina Tropical, Faculdade de Medicina, Belo Horizonte, Minas Gerais, Brazil
- Universidade Federal de Minas Gerais, Departamento de Patologia Clínica, COLTEC, Belo Horizonte, Minas Gerais, Brazil
| | - Miguel Angel Chávez-Fumagalli
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Catolica de Santa Maria de Arequipa, Arequipa, Peru
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46
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Li J, Wang Y, Li Z, Lin H, Wu B. LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods. Front Genet 2023; 14:1181592. [PMID: 37229202 PMCID: PMC10203599 DOI: 10.3389/fgene.2023.1181592] [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: 03/07/2023] [Accepted: 04/13/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction: Drug-target interaction (DTI) prediction is a key step in drug function discovery and repositioning. The emergence of large-scale heterogeneous biological networks provides an opportunity to identify drug-related target genes, which led to the development of several computational methods for DTI prediction. Methods: Considering the limitations of conventional computational methods, a novel tool named LM-DTI based on integrated information related to lncRNAs and miRNAs was proposed, which adopted the graph embedding (node2vec) and the network path score methods. First, LM-DTI innovatively constructed a heterogeneous information network containing eight networks composed of four types of nodes (drug, target, lncRNA, and miRNA). Next, the node2vec method was used to obtain feature vectors of drug as well as target nodes, and the path score vector of each drug-target pair was calculated using the DASPfind method. Finally, the feature vectors and path score vectors were merged and input into the XGBoost classifier to predict potential drug-target interactions. Results and Discussion: The 10-fold cross validations evaluate the classification accuracies of the LM-DTI. The prediction performance of LM-DTI in AUPR reached 0.96, which showed a significant improvement compared with those of conventional tools. The validity of LM-DTI has also been verified by manually searching literature and various databases. LM-DTI is scalable and computing efficient; thus representing a powerful drug relocation tool that can be accessed for free at http://www.lirmed.com:5038/lm_dti.
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Affiliation(s)
- Jianwei Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China
| | - Yinfei Wang
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Zhiguang Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Hongxin Lin
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Baoqin Wu
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
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Cabral VP, Rodrigues DS, Barbosa AD, Moreira LE, Sá LG, Silva CR, Neto JB, Silva J, Marinho ES, Santos HS, Cavalcanti BC, Moraes MO, Júnior HV. Antibacterial activity of paroxetine against Staphylococcus aureus and possible mechanisms of action. Future Microbiol 2023; 18:415-426. [PMID: 37213136 DOI: 10.2217/fmb-2022-0232] [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: 10/07/2022] [Accepted: 03/09/2023] [Indexed: 05/23/2023] Open
Abstract
Aim: To evaluate the antibacterial activity of paroxetine alone and associated with oxacillin against isolates of methicillin-sensitive and -resistant Staphylococcus aureus. Materials & methods: The broth microdilution and checkerboard techniques were used, with investigation of possible mechanisms of action through flow cytometry, fluorescence microscopy and molecular docking, in addition to scanning electron microscopy for morphological analysis. Results: Paroxetine showed a MIC of 64 μg/ml and bactericidal activity, mostly additive interactions in combination with oxacillin, evidence of action on genetic material and membrane, morphological changes in microbial cells and influence on virulence factors. Conclusion: Paroxetine has antibacterial potential from the perspective of drug repositioning.
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Affiliation(s)
- Vitória Pf Cabral
- Faculdade de Farmácia, Laboratório de Bioprospecção em Moléculas Antimicrobianas (LABIMAN), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-372, Brasil
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
| | - Daniel S Rodrigues
- Faculdade de Farmácia, Laboratório de Bioprospecção em Moléculas Antimicrobianas (LABIMAN), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-372, Brasil
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
| | - Amanda D Barbosa
- Faculdade de Farmácia, Laboratório de Bioprospecção em Moléculas Antimicrobianas (LABIMAN), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-372, Brasil
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
| | - Lara Ea Moreira
- Faculdade de Farmácia, Laboratório de Bioprospecção em Moléculas Antimicrobianas (LABIMAN), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-372, Brasil
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
| | - Lívia Gav Sá
- Faculdade de Farmácia, Laboratório de Bioprospecção em Moléculas Antimicrobianas (LABIMAN), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-372, Brasil
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
- Centro Universitário Christus (UNICHRISTUS), Fortaleza, CE, Brasil
| | - Cecília R Silva
- Faculdade de Farmácia, Laboratório de Bioprospecção em Moléculas Antimicrobianas (LABIMAN), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-372, Brasil
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
| | - João Ba Neto
- Faculdade de Farmácia, Laboratório de Bioprospecção em Moléculas Antimicrobianas (LABIMAN), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-372, Brasil
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
- Centro Universitário Christus (UNICHRISTUS), Fortaleza, CE, Brasil
| | - Jacilene Silva
- Departamento de Química, Grupo de Química Teórica e Eletroquímica (GQTE), Universidade Estadual do Ceará, Limoeiro do Norte, Ceará, CEP: 62.930-000, Brasil
| | - Emmanuel S Marinho
- Departamento de Química, Grupo de Química Teórica e Eletroquímica (GQTE), Universidade Estadual do Ceará, Limoeiro do Norte, Ceará, CEP: 62.930-000, Brasil
| | - Hélcio S Santos
- Centro de Ciência e Tecnologia, Curso de Química, Universidade Estadual Vale do Acaraú, Sobral, CE, CEP: 62.040-370, Brasil
| | - Bruno C Cavalcanti
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
| | - Manoel O Moraes
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
| | - Hélio Vn Júnior
- Faculdade de Farmácia, Laboratório de Bioprospecção em Moléculas Antimicrobianas (LABIMAN), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-372, Brasil
- Centro de Pesquisa e Desenvolvimento de Fármacos (NPDM), Universidade Federal do Ceará, Fortaleza, CE, CEP: 60.430-275, Brasil
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Ahmed F, Samantasinghar A, Manzoor Soomro A, Kim S, Hyun Choi K. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. J Biomed Inform 2023; 142:104373. [PMID: 37120047 DOI: 10.1016/j.jbi.2023.104373] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea
| | | | | | - Sejong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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49
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Ali R, Sultan A, Ishrat R, Haque S, Khan NJ, Prieto MA. Identification of New Key Genes and Their Association with Breast Cancer Occurrence and Poor Survival Using In Silico and In Vitro Methods. Biomedicines 2023; 11:biomedicines11051271. [PMID: 37238942 DOI: 10.3390/biomedicines11051271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/20/2023] [Accepted: 03/07/2023] [Indexed: 05/28/2023] Open
Abstract
Breast cancer is one of the most prevalent types of cancer diagnosed globally and continues to have a significant impact on the global number of cancer deaths. Despite all efforts of epidemiological and experimental research, therapeutic concepts in cancer are still unsatisfactory. Gene expression datasets are widely used to discover the new biomarkers and molecular therapeutic targets in diseases. In the present study, we analyzed four datasets using R packages with accession number GSE29044, GSE42568, GSE89116, and GSE109169 retrieved from NCBI-GEO and differential expressed genes (DEGs) were identified. Protein-protein interaction (PPI) network was constructed to screen the key genes. Subsequently, the GO function and KEGG pathways were analyzed to determine the biological function of key genes. Expression profile of key genes was validated in MCF-7 and MDA-MB-231 human breast cancer cell lines using qRT-PCR. Overall expression level and stage wise expression pattern of key genes was determined by GEPIA. The bc-GenExMiner was used to compare expression level of genes among groups of patients with respect to age factor. OncoLnc was used to analyze the effect of expression levels of LAMA2, TIMP4, and TMTC1 on the survival of breast cancer patients. We identified nine key genes, of which COL11A1, MMP11, and COL10A1 were found up-regulated and PCOLCE2, LAMA2, TMTC1, ADAMTS5, TIMP4, and RSPO3 were found down-regulated. Similar expression pattern of seven among nine genes (except ADAMTS5 and RSPO3) was observed in MCF-7 and MDA-MB-231 cells. Further, we found that LAMA2, TMTC1, and TIMP4 were significantly expressed among different age groups of patients. LAMA2 and TIMP4 were found significantly associated and TMTC1 was found less correlated with breast cancer occurrence. We found that the expression level of LAMA2, TIMP4, and TMTC1 was abnormal in all TCGA tumors and significantly associated with poor survival.
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Affiliation(s)
- Rafat Ali
- Department of Biosciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India
| | - Armiya Sultan
- Department of Biosciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India
| | - Romana Ishrat
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan 45142, Saudi Arabia
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut P.O. Box 36, Lebanon
- Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman P.O. Box 346, United Arab Emirates
| | - Nida Jamil Khan
- Department of Biosciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India
| | - Miguel Angel Prieto
- Nutrition and Bromatology Group, Department of Analytical Chemistry and Food Science, Faculty of Science, Universidade de Vigo, E32004 Ourense, Spain
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50
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Flori L, Brogi S, Sirous H, Calderone V. Disruption of Irisin Dimerization by FDA-Approved Drugs: A Computational Repurposing Approach for the Potential Treatment of Lipodystrophy Syndromes. Int J Mol Sci 2023; 24:ijms24087578. [PMID: 37108741 PMCID: PMC10145865 DOI: 10.3390/ijms24087578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023] Open
Abstract
In this paper, we present the development of a computer-based repurposing approach to identify FDA-approved drugs that are potentially able to interfere with irisin dimerization. It has been established that altered levels of irisin dimers are a pure hallmark of lipodystrophy (LD) syndromes. Accordingly, the identification of compounds capable of slowing down or precluding the irisin dimers' formation could represent a valuable therapeutic strategy in LD. Combining several computational techniques, we identified five FDA-approved drugs with satisfactory computational scores (iohexol, XP score = -7.70 kcal/mol, SP score = -5.5 kcal/mol, ΔGbind = -61.47 kcal/mol, ΔGbind (average) = -60.71 kcal/mol; paromomycin, XP score = -7.23 kcal/mol, SP score = -6.18 kcal/mol, ΔGbind = -50.14 kcal/mol, ΔGbind (average) = -49.13 kcal/mol; zoledronate, XP score = -6.33 kcal/mol, SP score = -5.53 kcal/mol, ΔGbind = -32.38 kcal/mol, ΔGbind (average) = -29.42 kcal/mol; setmelanotide, XP score = -6.10 kcal/mol, SP score = -7.24 kcal/mol, ΔGbind = -56.87 kcal/mol, ΔGbind (average) = -62.41 kcal/mol; and theophylline, XP score = -5.17 kcal/mol, SP score = -5.55 kcal/mol, ΔGbind = -33.25 kcal/mol, ΔGbind (average) = -35.29 kcal/mol) that are potentially able to disrupt the dimerization of irisin. For this reason, they deserve further investigation to characterize them as irisin disruptors. Remarkably, the identification of drugs targeting this process can offer novel therapeutic opportunities for the treatment of LD. Furthermore, the identified drugs could provide a starting point for a repositioning approach, synthesizing novel analogs with improved efficacy and selectivity against the irisin dimerization process.
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Affiliation(s)
- Lorenzo Flori
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy
| | - Simone Brogi
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy
- Bioinformatics Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Hajar Sirous
- Bioinformatics Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Vincenzo Calderone
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy
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