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Datta C, Das P, Dutta S, Prasad T, Banerjee A, Gehlot S, Ghosal A, Dhabal S, Biswas P, De D, Chaudhuri S, Bhattacharjee A. AMPK activation reduces cancer cell aggressiveness via inhibition of monoamine oxidase A (MAO-A) expression/activity. Life Sci 2024; 352:122857. [PMID: 38914305 DOI: 10.1016/j.lfs.2024.122857] [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: 03/01/2024] [Revised: 06/14/2024] [Accepted: 06/16/2024] [Indexed: 06/26/2024]
Abstract
AIM AMPK can be considered as an important target molecule for cancer for its unique ability to directly recognize cellular energy status. The main aim of this study is to explore the role of different AMPK activators in managing cancer cell aggressiveness and to understand the mechanistic details behind the process. MAIN METHODS First, we explored the AMPK expression pattern and its significance in different subtypes of lung cancer by accessing the TCGA data sets for LUNG, LUAD and LUSC patients and then established the correlation between AMPK expression pattern and overall survival of lung cancer patients using Kaplan-Meire plot. We further carried out several cell-based assays by employing different wet lab techniques including RT-PCR, Western Blot, proliferation, migration and invasion assays to fulfil the aim of the study. KEY FINDINGS SIGNIFICANCE: This study identifies the importance of AMPK activators as a repurposing agent for combating lung and colon cancer cell aggressiveness. It also suggests SRT-1720 as a potent repurposing agent for cancer treatment especially in NSCLC patients where a point mutation is present in LKB1.
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Affiliation(s)
- Chandreyee Datta
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Payel Das
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Subhajit Dutta
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Tuhina Prasad
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Abhineet Banerjee
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Sameep Gehlot
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Arpa Ghosal
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Sukhamoy Dhabal
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Pritam Biswas
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Debojyoti De
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Surabhi Chaudhuri
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Ashish Bhattacharjee
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India.
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2
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Li X, Zan X, Liu T, Dong X, Zhang H, Li Q, Bao Z, Lin J. Integrated edge information and pathway topology for drug-disease associations. iScience 2024; 27:110025. [PMID: 38974972 PMCID: PMC11226970 DOI: 10.1016/j.isci.2024.110025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/06/2024] [Accepted: 05/15/2024] [Indexed: 07/09/2024] Open
Abstract
Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.
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Affiliation(s)
- Xianbin Li
- School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
| | - Xiangzhen Zan
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, Guangdong 520000, China
| | - Tao Liu
- School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
| | - Xiwei Dong
- School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
| | - Haqi Zhang
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
| | - Qizhang Li
- Innovative Drug R&D Center, School of Life Sciences, Huaibei Normal University, Huaibei, Anhui 235000, China
| | - Zhenshen Bao
- College of Information Engineering, Taizhou University, Taizhou 225300, Jiangsu, China
| | - Jie Lin
- Department of Pharmacy, the Third Affiliated Hospital of Wenzhou Medical University, Wenzhou 325200, Zhejiang Province, China
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Li MM, Huang Y, Sumathipala M, Liang MQ, Valdeolivas A, Ananthakrishnan AN, Liao K, Marbach D, Zitnik M. Contextual AI models for single-cell protein biology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.18.549602. [PMID: 37503080 PMCID: PMC10370131 DOI: 10.1101/2023.07.18.549602] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnacle learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. Pinnacle outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle's ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.
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Affiliation(s)
- Michelle M. Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yepeng Huang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Marissa Sumathipala
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Man Qing Liang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alberto Valdeolivas
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Ashwin N. Ananthakrishnan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | - Katherine Liao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, MA, USA
| | - Daniel Marbach
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
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4
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Liu CH, Xue QQ, Zhang YQ, Zhang DY, Li Y. Anti-hypertensive effect and potential mechanism of gastrodia-uncaria granules based on network pharmacology and experimental validation. J Clin Hypertens (Greenwich) 2024. [PMID: 38990083 DOI: 10.1111/jch.14833] [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: 03/04/2024] [Revised: 04/18/2024] [Accepted: 05/05/2024] [Indexed: 07/12/2024]
Abstract
Hypertension has become a major contributor to the morbidity and mortality of cardiovascular diseases worldwide. Despite the evidence of the anti-hypertensive effect of gastrodia-uncaria granules (GUG) in hypertensive patients, little is known about its potential therapeutic targets as well as the underlying mechanism. GUG components were sourced from TCMSP and HERB, with bioactive ingredients screened. Hypertension-related targets were gathered from DisGeNET, OMIM, GeneCards, CTD, and GEO. The STRING database constructed a protein-protein interaction network, visualized by Cytoscape 3.7.1. Core targets were analyzed via GO and KEGG using R package ClusterProfiler. Molecular docking with AutodockVina 1.2.2 revealed favorable binding affinities. In vivo studies on hypertensive mice and rats validated network pharmacology findings. GUG yielded 228 active ingredients and 1190 targets, intersecting with 373 hypertension-related genes. PPI network analysis identified five core genes: AKT1, TNF-α, GAPDH, IL-6, and ALB. Top enriched GO terms and KEGG pathways associated with the anti-hypertensive properties of GUG were documented. Molecular docking indicated stable binding of core components to targets. In vivo study showed that GUG could improve vascular relaxation, alleviate vascular remodeling, and lower blood pressure in hypertensive animal models possibly through inhibiting inflammatory factors such as AKT1, mTOR, and CCND1. Integrated network pharmacology and in vivo experiment showed that GUG may exert anti-hypertensive effects by inhibiting inflammation response, which provides some clues for understanding the effect and mechanisms of GUG in the treatment of hypertension.
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Affiliation(s)
- Chu-Hao Liu
- Department of Cardiovascular Medicine, Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, National Research Centre for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qi-Qi Xue
- Department of Cardiovascular Medicine, Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, National Research Centre for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yi-Qing Zhang
- Department of Cardiovascular Medicine, Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, National Research Centre for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Dong-Yan Zhang
- Department of Cardiovascular Medicine, Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, National Research Centre for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yan Li
- Department of Cardiovascular Medicine, Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, National Research Centre for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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5
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Ganapathiraju MK, Triptish B, Deshpande S, Wesesky M, Wood J, Nimgaonkar VL. Schizophrenia interactome derived repurposable drugs and randomized control trials of two candidates. Biol Psychiatry 2024:S0006-3223(24)01426-4. [PMID: 38950808 DOI: 10.1016/j.biopsych.2024.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/29/2024] [Accepted: 06/09/2024] [Indexed: 07/03/2024]
Abstract
There is a substantial unmet need for effective and patient-acceptable drugs to treat severe mental illnesses like schizophrenia. Computational analysis of genomic, transcriptomic, and pharmacologic data generated in the past two decades enables repurposing of drugs or compounds with acceptable safety profiles, namely those that are FDA-approved or reached late stages in clinical trials. We developed a rational approach to achieve this computationally for schizophrenia by studying drugs that target the proteins in its protein interaction network ('interactome'). This involved contrasting the transcriptomic modulations observed in the disorder and the drug; our analyses resulted in 12 candidate drugs, 9 of which had additional supportive evidence: their target networks were enriched for pathways relevant to schizophrenia etiology or for genes that had an association with diseases pathogenically similar to schizophrenia. To translate these computational results to the clinic, these shortlisted drugs must be tested empirically through randomized controlled trials (RCT), where their prior safety approvals obviate the need for time-consuming phase I and II studies. We selected two among the shortlisted candidates based on likely adherence and side effect profiles. We are testing them through adjunctive RCTs for patients with schizophrenia or schizoaffective disorder who experienced incomplete resolution of psychotic features with conventional treatment. The integrated computational analysis for identifying and ranking drugs for clinical trials can be iterated as additional data are obtained. Our approach could be expanded to enable disease subtype-specific drug discovery in future and should also be exploited for other psychiatric disorders.
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Affiliation(s)
- Madhavi K Ganapathiraju
- Department of Biomedical Informatics and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA; Carnegie Mellon University in Qatar, Doha, Qatar.
| | - Bhatia Triptish
- Dept. of Psychiatry, Centre of Excellence in Mental Health, ABVIMS- Dr Ram Manohar Lohia Hospital, New Delhi 110001, India
| | - Smita Deshpande
- Department of Psychiatry, St John's Medical College Hospital, Koramangala, Bengaluru, Karnataka 560034, India
| | - Maribeth Wesesky
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joel Wood
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vishwajit L Nimgaonkar
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA; Veterans Administration Pittsburgh Healthcare System, Pittsburgh, PA, USA.
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Al-Odat OS, Nelson E, Budak-Alpdogan T, Jonnalagadda SC, Desai D, Pandey MK. Discovering Potential in Non-Cancer Medications: A Promising Breakthrough for Multiple Myeloma Patients. Cancers (Basel) 2024; 16:2381. [PMID: 39001443 PMCID: PMC11240591 DOI: 10.3390/cancers16132381] [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: 05/24/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
MM is a common type of cancer that unfortunately leads to a significant number of deaths each year. The majority of the reported MM cases are detected in the advanced stages, posing significant challenges for treatment. Additionally, all MM patients eventually develop resistance or experience relapse; therefore, advances in treatment are needed. However, developing new anti-cancer drugs, especially for MM, requires significant financial investment and a lengthy development process. The study of drug repurposing involves exploring the potential of existing drugs for new therapeutic uses. This can significantly reduce both time and costs, which are typically a major concern for MM patients. The utilization of pre-existing non-cancer drugs for various myeloma treatments presents a highly efficient and cost-effective strategy, considering their prior preclinical and clinical development. The drugs have shown promising potential in targeting key pathways associated with MM progression and resistance. Thalidomide exemplifies the success that can be achieved through this strategy. This review delves into the current trends, the challenges faced by conventional therapies for MM, and the importance of repurposing drugs for MM. This review highlights a noncomprehensive list of conventional therapies that have potentially significant anti-myeloma properties and anti-neoplastic effects. Additionally, we offer valuable insights into the resources that can help streamline and accelerate drug repurposing efforts in the field of MM.
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Affiliation(s)
- Omar S. Al-Odat
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA; (O.S.A.-O.); (E.N.)
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA;
| | - Emily Nelson
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA; (O.S.A.-O.); (E.N.)
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA;
| | | | | | - Dhimant Desai
- Department of Pharmacology, Penn State Neuroscience Institute, Penn State College of Medicine, Hershey, PA 17033, USA;
| | - Manoj K. Pandey
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA; (O.S.A.-O.); (E.N.)
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Carnivali GS, Borges CC. Method to link medicines to diseases using multiplex networks. Comput Methods Biomech Biomed Engin 2024:1-14. [PMID: 38907637 DOI: 10.1080/10255842.2024.2362860] [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: 01/25/2024] [Accepted: 05/28/2024] [Indexed: 06/24/2024]
Abstract
The reuse of well-established medicines using computational modeling has gained a lot of attention due to its tremendous benefits. Based on this perspective, a new method for linking known medicines to diseases is proposed. The creation of a new treatment or medicine can be financially and temporally costly and the reuse of medicines is one possibility to accelerate this process efficiently. The main purpose of the reuse of medicines is to reduce some stages of the development of new medicines, motivating the proposition of several methods nowadays. In this work, a new method is developed aiming to connect known medicines to diseases based on available networks of protein interactions and available lists of medicines that affect protein action. The concepts of multiplex networks are used to connect subgraphs of vertices that represent medicines and proteins. The core of the procedure is determined by a weighting strategy constructed to define precisely the more relevant connections. The method was compared to other network link methods in the literature and a case study was presented and evaluated by the proposed method.
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8
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Leighton J, Jones DEJ, Dyson JK, Cordell HJ. Network proximity analysis as a theoretical model for identifying potential novel therapies in primary sclerosing cholangitis. BMC Med Genomics 2024; 17:157. [PMID: 38862968 PMCID: PMC11165726 DOI: 10.1186/s12920-024-01927-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 06/05/2024] [Indexed: 06/13/2024] Open
Abstract
Primary Sclerosing Cholangitis (PSC) is a progressive cholestatic liver disease with no licensed therapies. Previous Genome Wide Association Studies (GWAS) have identified genes that correlate significantly with PSC, and these were identified by systematic review. Here we use novel Network Proximity Analysis (NPA) methods to identify already licensed candidate drugs that may have an effect on the genetically coded aspects of PSC pathophysiology.Over 2000 agents were identified as significantly linked to genes implicated in PSC by this method. The most significant results include previously researched agents such as metronidazole, as well as biological agents such as basiliximab, abatacept and belatacept. This in silico analysis could potentially serve as a basis for developing novel clinical trials in this rare disease.
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Affiliation(s)
- Jessica Leighton
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
| | - David E J Jones
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Liver Unit, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Jessica K Dyson
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Liver Unit, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Heather J Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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Sha Z, Freda PJ, Bhandary P, Ghosh A, Matsumoto N, Moore JH, Hu T. Distinct Network Patterns Emerge from Cartesian and XOR Epistasis Models: A Comparative Network Science Analysis. RESEARCH SQUARE 2024:rs.3.rs-4392123. [PMID: 38826481 PMCID: PMC11142370 DOI: 10.21203/rs.3.rs-4392123/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, can significantly contribute to the observed phenotypic variance of complex traits. To date, it has been generally assumed that genetic interactions can be detected using a Cartesian, or multiplicative, interaction model commonly utilized in standard regression approaches. However, a recent study investigating epistasis in obesity-related traits in rats and mice has identified potential limitations of the Cartesian model, revealing that it only detects some of the genetic interactions occurring in these systems. By applying an alternative approach, the exclusive-or (XOR) model, the researchers detected a greater number of epistatic interactions and identified more biologically relevant ontological terms associated with the interacting loci. This suggests that the XOR model may provide a more comprehensive understanding of epistasis in these species and phenotypes. To further explore these findings and determine if different interaction models also make up distinct epistatic networks, we leverage network science to provide a more comprehensive view into the genetic interactions underlying BMI in this system. Results Our comparative analysis of networks derived from Cartesian and XOR interaction models in rats (Rattus norvegicus) uncovers distinct topological characteristics for each model-derived network. Notably, we discover that networks based on the XOR model exhibit an enhanced sensitivity to epistatic interactions. This sensitivity enables the identification of network communities, revealing novel trait-related biological functions through enrichment analysis. Furthermore, we identify triangle network motifs in the XOR epistatic network, suggestive of higher-order epistasis, based on the topology of lower-order epistasis. Conclusions These findings highlight the XOR model's ability to uncover meaningful biological associations as well as higher-order epistasis from lower-order epistatic networks. Additionally, our results demonstrate that network approaches not only enhance epistasis detection capabilities but also provide more nuanced understandings of genetic architectures underlying complex traits. The identification of community structures and motifs within these distinct networks, especially in XOR, points to the potential for network science to aid in the discovery of novel genetic pathways and regulatory networks. Such insights are important for advancing our understanding of phenotype-genotype relationships.
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Affiliation(s)
- Zhendong Sha
- School of Computing, Queen’s University, 557 Goodwin Hall, 21-25 Union St, Kingston, Ontario, K7L 2N8, Canada
| | - Philip J. Freda
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Priyanka Bhandary
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Attri Ghosh
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Nicholas Matsumoto
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Jason H. Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Ting Hu
- School of Computing, Queen’s University, 557 Goodwin Hall, 21-25 Union St, Kingston, Ontario, K7L 2N8, Canada
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Khan MRUZ, Trivedi V. Molecular modelling, docking and network analysis of phytochemicals from Haritaki churna: role of protein cross-talks for their action. J Biomol Struct Dyn 2024; 42:4297-4312. [PMID: 37288779 DOI: 10.1080/07391102.2023.2220036] [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: 04/09/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023]
Abstract
Phytochemicals are bioactive agents present in medicinal plants with therapeutic values. Phytochemicals isolated from plants target multiple cellular processes. In the current work, we have used fractionation techniques to identify 13 bioactive polyphenols in ayurvedic medicine Haritaki Churna. Employing the advanced spectroscopic and fractionation, structure of bioactive polyphenols was determined. Blasting the phytochemical structure allow us to identify a total of 469 protein targets from Drug bank and Binding DB. Phytochemicals with their protein targets from Drug bank was used to create a phytochemical-protein network comprising of 394 nodes and 1023 edges. It highlights the extensive cross-talk between protein target corresponding to different phytochemicals. Analysis of protein targets from Binding data bank gives a network comprised of 143 nodes and 275 edges. Taking the data together from Drug bank and binding data, seven most prominent drug targets (HSP90AA1, c-Src kinase, EGFR, Akt1, EGFR, AR, and ESR-α) were found to be target of the phytochemicals. Molecular modelling and docking experiment indicate that phytochemicals are fitting nicely into active site of the target proteins. The binding energy of the phytochemicals were better than the inhibitors of these protein targets. The strength and stability of the protein ligand complexes were further confirmed using molecular dynamic simulation studies. Further, the ADMET profiles of phytochemicals extracted from HCAE suggests that they can be potential drug targets. The phytochemical cross-talk was further proven by choosing c-Src as a model. HCAE down regulated c-Src and its downstream protein targets such as Akt1, cyclin D1 and vimentin. Hence, network analysis followed by molecular docking, molecular dynamics simulation and in-vitro studies clearly highlight the role of protein network and subsequent selection of drug candidate based on network pharmacology.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Md Rafi Uz Zama Khan
- Malaria Research Group, Department of Biosciences and Bioengineering, Indian Institute of Technology-Guwahati, Guwahati, Assam, India
| | - Vishal Trivedi
- Malaria Research Group, Department of Biosciences and Bioengineering, Indian Institute of Technology-Guwahati, Guwahati, Assam, India
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Lalagkas PN, Melamed RD. Shared etiology of Mendelian and complex disease supports drug discovery. RESEARCH SQUARE 2024:rs.3.rs-4250176. [PMID: 38699347 PMCID: PMC11065072 DOI: 10.21203/rs.3.rs-4250176/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Background Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease. Methods In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations. Results Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated. Conclusions Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.
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Mishra A, Vasanthan M, Malliappan SP. Drug Repurposing: A Leading Strategy for New Threats and Targets. ACS Pharmacol Transl Sci 2024; 7:915-932. [PMID: 38633585 PMCID: PMC11019736 DOI: 10.1021/acsptsci.3c00361] [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: 12/13/2023] [Revised: 03/01/2024] [Accepted: 03/06/2024] [Indexed: 04/19/2024]
Abstract
Less than 6% of rare illnesses have an appropriate treatment option. Repurposed medications for new indications are a cost-effective and time-saving strategy that results in excellent success rates, which may significantly lower the risk associated with therapeutic development for rare illnesses. It is becoming a realistic alternative to repurposing "conventional" medications to treat joint and rare diseases considering the significant failure rates, high expenses, and sluggish stride of innovative medication advancement. This is due to delisted compounds, cheaper research fees, and faster development time frames. Repurposed drug competitors have been developed using strategic decisions based on data analysis, interpretation, and investigational approaches, but technical and regulatory restrictions must also be considered. Combining experimental and computational methodologies generates innovative new medicinal applications. It is a one-of-a-kind strategy for repurposing human-safe pharmaceuticals to treat uncommon and difficult-to-treat ailments. It is a very effective method for discovering and creating novel medications. Several pharmaceutical firms have developed novel therapies by repositioning old medications. Repurposing drugs is practical, cost-effective, and speedy and generally involves lower risks when compared to developing a new drug from the beginning.
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Affiliation(s)
- Ashish
Sriram Mishra
- Department
of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, 603202, Tamil Nadu, India
| | - Manimaran Vasanthan
- Department
of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, 603202, Tamil Nadu, India
| | - Sivakumar Ponnurengam Malliappan
- School
of Medicine and Pharmacy, Duy Tan University, Da Nang Vietnam, Institute
of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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13
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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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14
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Yu M, Xu J, Dutta R, Trapp B, Pieper AA, Cheng F. Network medicine informed multi-omics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.586949. [PMID: 38585774 PMCID: PMC10996626 DOI: 10.1101/2024.03.27.586949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. To date, the challenge to establishing effective treatment for ALS remains formidable, partly due to inadequate translation of existing human genetic findings into actionable ALS-specific pathobiology for subsequent therapeutic development. This study evaluates the feasibility of network medicine methodology via integrating human brain-specific multi-omics data to prioritize drug targets and repurposable treatments for ALS. Using human brain-specific genome-wide quantitative trait loci (x-QTLs) under a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in various known ALS pathobiological pathways, including regulation of T cell activation, monocyte differentiation, and lymphocyte proliferation. Specifically, we leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on brain-specific expression quantitative trait loci (QTL) (eQTL), protein QTLs (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Applying network proximity analysis of predicted ALS-associated gene-coding targets and existing drug-target networks under the human protein-protein interactome (PPI) model, we identified a set of potential repurposable drugs (including Diazoxide, Gefitinib, Paliperidone, and Dimethyltryptamine) for ALS. Subsequent validation established preclinical and clinical evidence for top-prioritized repurposable drugs. In summary, we presented a network-based multi-omics framework to identify potential drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.
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Affiliation(s)
- Mucen Yu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- College of Arts and Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Ranjan Dutta
- Department of Neuroscience, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Bruce Trapp
- Department of Neuroscience, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Andrew A. Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center; Cleveland, OH 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland 44106, OH, USA
- Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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15
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Schäfer S, Smelik M, Sysoev O, Zhao Y, Eklund D, Lilja S, Gustafsson M, Heyn H, Julia A, Kovács IA, Loscalzo J, Marsal S, Zhang H, Li X, Gawel D, Wang H, Benson M. scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases. Genome Med 2024; 16:42. [PMID: 38509600 PMCID: PMC10956347 DOI: 10.1186/s13073-024-01314-7] [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: 03/02/2023] [Accepted: 03/12/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. METHODS Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. RESULTS scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. CONCLUSIONS We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).
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Affiliation(s)
- Samuel Schäfer
- Centre for Personalised Medicine, Linköping University, Linköping, Sweden
- Department of Gastroenterology and Hepatology, University Hospital, Linköping, Sweden
| | - Martin Smelik
- Postal Address: LIME/Medical Digital Twin Research Group, Division of ENT, CLINTEC, Karolinska Institute, Tomtebodavägen 18A. 171 65 Solna, Stockholm, Sweden
| | - Oleg Sysoev
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linkoping University, Linköping, Sweden
| | - Yelin Zhao
- Postal Address: LIME/Medical Digital Twin Research Group, Division of ENT, CLINTEC, Karolinska Institute, Tomtebodavägen 18A. 171 65 Solna, Stockholm, Sweden
| | - Desiré Eklund
- Centre for Personalised Medicine, Linköping University, Linköping, Sweden
| | - Sandra Lilja
- Centre for Personalised Medicine, Linköping University, Linköping, Sweden
- Mavatar, Inc, Stockholm, Sweden
| | - Mika Gustafsson
- Division for Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08002, Barcelona, Spain
| | - Antonio Julia
- Grup de Recerca de Reumatologia, Institut de Recerca Vall d'Hebron, Barcelona, Spain
| | - István A Kovács
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA
- Northwestern Institute On Complex Systems, Northwestern University, Evanston, IL, 60208, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sara Marsal
- Grup de Recerca de Reumatologia, Institut de Recerca Vall d'Hebron, Barcelona, Spain
| | - Huan Zhang
- Centre for Personalised Medicine, Linköping University, Linköping, Sweden
| | - Xinxiu Li
- Postal Address: LIME/Medical Digital Twin Research Group, Division of ENT, CLINTEC, Karolinska Institute, Tomtebodavägen 18A. 171 65 Solna, Stockholm, Sweden
| | | | - Hui Wang
- Postal Address: LIME/Medical Digital Twin Research Group, Division of ENT, CLINTEC, Karolinska Institute, Tomtebodavägen 18A. 171 65 Solna, Stockholm, Sweden
- Jiangsu Key Laboratory of Immunity and Metabolism, Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu, China
| | - Mikael Benson
- Postal Address: LIME/Medical Digital Twin Research Group, Division of ENT, CLINTEC, Karolinska Institute, Tomtebodavägen 18A. 171 65 Solna, Stockholm, Sweden.
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16
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Wang Y, Zou Z, Wang S, Ren A, Ding Z, Li Y, Wang Y, Qian Z, Bian B, Huang B, Xu G, Cui G. Golden bile powder prevents drunkenness and alcohol-induced liver injury in mice via the gut microbiota and metabolic modulation. Chin Med 2024; 19:39. [PMID: 38431607 PMCID: PMC10908100 DOI: 10.1186/s13020-024-00912-2] [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: 12/31/2023] [Accepted: 02/23/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Drunkenness and alcoholic liver disease (ALD) are critical public health issues associated with significant morbidity and mortality due to chronic overconsumption of alcohol. Traditional remedies, such as bear bile powder, have been historically acclaimed for their hepatoprotective properties. This study assessed the efficacy of a biotransformed bear bile powder known as golden bile powder (GBP) in alleviating alcohol-induced drunkenness and ALD. METHODS A murine model was engineered to simulate alcohol drunkenness and acute hepatic injury through the administration of a 50% ethanol solution. Intervention with GBP and its effects on alcohol-related symptoms were scrutinized, by employing an integrative approach that encompasses serum metabolomics, network medicine, and gut microbiota profiling to elucidate the protective mechanisms of GBP. RESULTS GBP administration significantly delayed the onset of drunkenness and decreased the duration of ethanol-induced inebriation in mice. Enhanced liver cell recovery was indicated by increased hepatic aldehyde dehydrogenase levels and superoxide dismutase activity, along with significant decreases in the serum alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, triglyceride, and total cholesterol levels (P < 0.05). These biochemical alterations suggest diminished hepatic damage and enhanced lipid homeostasis. Microbiota analysis via 16S rDNA sequencing revealed significant changes in gut microbial diversity and composition following alcohol exposure, and these changes were effectively reversed by GBP treatment. Metabolomic analyses demonstrated that GBP normalized the alcohol-induced perturbations in phospholipids, fatty acids, and bile acids. Correlation assessments linked distinct microbial genera to serum bile acid profiles, indicating that the protective efficacy of GBP may be attributable to modulatory effects on metabolism and the gut microbiota composition. Network medicine insights suggest the prominence of two active agents in GBP as critical for addressing drunkenness and ALD. CONCLUSION GBP is a potent intervention for alcohol-induced pathology and offers hepatoprotective benefits, at least in part, through the modulation of the gut microbiota and related metabolic cascades.
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Affiliation(s)
- Yarong Wang
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Zhenzhuang Zou
- Department of Pediatrics, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Sihua Wang
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Airong Ren
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Zhaolin Ding
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Yingying Li
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Yifang Wang
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Zhengming Qian
- College of Medical Imaging Laboratory and Rehabilitation, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Baolin Bian
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Bo Huang
- Department of Pediatrics, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Guiwei Xu
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Guozhen Cui
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China.
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17
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Cheng F, Wang F, Tang J, Zhou Y, Fu Z, Zhang P, Haines JL, Leverenz JB, Gan L, Hu J, Rosen-Zvi M, Pieper AA, Cummings J. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell Rep Med 2024; 5:101379. [PMID: 38382465 PMCID: PMC10897520 DOI: 10.1016/j.xcrm.2023.101379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 08/15/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.
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Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec Institute for Learning Algorithms and CIFAR AI Research Chair, HEC Montreal, Montréal, QC H3T 2A7, Canada
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zhimin Fu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; College of Pharmacy, Northeast Ohio Medical University, Rootstown, OH 44272, USA
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46037, USA
| | - Jonathan L Haines
- Cleveland Institute for Computational Biology, and Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Jianying Hu
- IBM Research, Yorktown Heights, New York, NY 10598, USA
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare and Life Sciences Discovery, IBM Research Labs, Haifa 3498825, Israel; Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9190500, Israel
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland OH 44106, USA; Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA; Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
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18
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Manoj M, Sowmyanarayan S, Kowshik AV, Chatterjee J. Identification of Potentially Repurposable Drugs for Lewy Body Dementia Using a Network-Based Approach. J Mol Neurosci 2024; 74:21. [PMID: 38363395 DOI: 10.1007/s12031-024-02199-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 02/06/2024] [Indexed: 02/17/2024]
Abstract
The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.
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Affiliation(s)
- Megha Manoj
- Department of Biotechnology, PES University, Bangalore, 560085, India
| | | | - Arjun V Kowshik
- Department of Biotechnology, PES University, Bangalore, 560085, India
| | - Jhinuk Chatterjee
- Department of Biotechnology, PES University, Bangalore, 560085, India.
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19
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Junus A, Yip PSF. Evaluating potential effects of distress symptoms' interventions on suicidality: Analyses of in silico scenarios. J Affect Disord 2024; 347:352-363. [PMID: 37992776 DOI: 10.1016/j.jad.2023.11.060] [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: 07/28/2023] [Revised: 10/23/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023]
Abstract
BACKGROUND Complexity science perspectives like the network approach to psychopathology have emerged as a prominent methodological toolkit to generate novel hypotheses on complex etiologies surrounding various mental health problems and inform intervention targets. Such approach may be pivotal in advancing early intervention of suicidality among the younger generation (10-35 year-olds), the increasing burden of which needs to be reversed within a limited window of opportunity to avoid massive long-term repercussions. However, the network approach currently lends limited insight into the potential extent of proposed intervention targets' effectiveness, particularly for target outcomes in comorbid conditions. METHODS This paper proposes an in silico (i.e., computer-simulated) intervention approach that maps symptoms' complex interactions onto dynamic processes and analyzes their evolution. The proposed methodology is applied to investigate potential effects of changes in 1968 community-dwelling individuals' distress symptoms on their suicidal ideation. Analyses on specific subgroups were conducted. Results were also compared with centrality indices employed in typical network analyses. RESULTS Findings concur with symptom networks' centrality indices in suggesting that timely deactivating hopelessness among distressed individuals may be instrumental in preventing distress to develop into suicidal ideation. Additionally, however, they depict nuances beyond those provided by centrality indices, e.g., among young adults, reducing nervousness and tension may have similar effectiveness as deactivating hopeless in reducing suicidal ideation. LIMITATIONS Caution is warranted when generalizing findings here to the general population. CONCLUSION The proposed methodology may help facilitate timely agenda-setting in population mental health measures, and may also be augmented for future co-creation projects.
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Affiliation(s)
- Alvin Junus
- Centre for Urban Mental Health, University of Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam UMC location AMC, University of Amsterdam, The Netherlands
| | - Paul S F Yip
- Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong; The Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong.
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20
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Gajić M, Schröder-Heurich B, Mayer-Pickel K. Deciphering the immunological interactions: targeting preeclampsia with Hydroxychloroquine's biological mechanisms. Front Pharmacol 2024; 15:1298928. [PMID: 38375029 PMCID: PMC10875033 DOI: 10.3389/fphar.2024.1298928] [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: 09/22/2023] [Accepted: 01/23/2024] [Indexed: 02/21/2024] Open
Abstract
Preeclampsia (PE) is a complex pregnancy-related disorder characterized by hypertension, followed by organ dysfunction and uteroplacental abnormalities. It remains a major cause of maternal and neonatal morbidity and mortality worldwide. Although the pathophysiology of PE has not been fully elucidated, a two-stage model has been proposed. In this model, a poorly perfused placenta releases various factors into the maternal circulation during the first stage, including pro-inflammatory cytokines, anti-angiogenic factors, and damage-associated molecular patterns into the maternal circulation. In the second stage, these factors lead to a systemic vascular dysfunction with consecutive clinical maternal and/or fetal manifestations. Despite advances in feto-maternal management, effective prophylactic and therapeutic options for PE are still lacking. Since termination of pregnancy is the only curative therapy, regardless of gestational age, new treatment/prophylactic options are urgently needed. Hydroxychloroquine (HCQ) is mainly used to treat malaria as well as certain autoimmune conditions such as systemic lupus and rheumatoid arthritis. The exact mechanism of action of HCQ is not fully understood, but several mechanisms of action have been proposed based on its pharmacological properties. Interestingly, many of them might counteract the proposed processes involved in the development of PE. Therefore, based on a literature review, we aimed to investigate the interrelated biological processes of HCQ and PE and to identify potential molecular targets in these processes.
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Affiliation(s)
- Maja Gajić
- Department of Obstetrics and Gynecology, Medical University of Graz, Graz, Austria
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21
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Kowshik AV, Manoj M, Sowmyanarayan S, Chatterjee J. Drug repurposing: databases and pipelines. CNS Spectr 2024; 29:6-9. [PMID: 37489503 DOI: 10.1017/s1092852923002365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
The concept of drug repurposing is focused on the repositioning of drug molecules that have already undergone safety trials. There are different strategies for drug repurposing. Network-based strategy focuses on the evaluation of drug combinations in a molecular environment with multi-target hits and analysis of drug interactions. Implementation of any in silico strategy requires several databases and pipelines for executing the process of shortlisting appropriate drugs.
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Affiliation(s)
- Arjun V Kowshik
- Department of Biotechnology, PES University, Bengaluru, India
| | - Megha Manoj
- Department of Biotechnology, PES University, Bengaluru, India
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22
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Späth J, Wang R, Humphrey M, Baumbach J, Loscalzo J. Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis. CPT Pharmacometrics Syst Pharmacol 2024; 13:257-269. [PMID: 37950385 PMCID: PMC10864927 DOI: 10.1002/psp4.13076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/12/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.
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Affiliation(s)
- Julian Späth
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Institute of Computational Systems BiologyUniversity of HamburgHamburgGermany
| | - Rui‐Sheng Wang
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Maeve Humphrey
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jan Baumbach
- Institute of Computational Systems BiologyUniversity of HamburgHamburgGermany
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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23
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Moll M, Silverman EK. Precision Approaches to Chronic Obstructive Pulmonary Disease Management. Annu Rev Med 2024; 75:247-262. [PMID: 37827193 DOI: 10.1146/annurev-med-060622-101239] [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: 10/14/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD heterogeneity has hampered progress in developing pharmacotherapies that affect disease progression. This issue can be addressed by precision medicine approaches, which focus on understanding an individual's disease risk, and tailoring management based on pathobiology, environmental exposures, and psychosocial issues. There is an urgent need to identify COPD patients at high risk for poor outcomes and to understand at a mechanistic level why certain individuals are at high risk. Genetics, omics, and network analytic techniques have started to dissect COPD heterogeneity and identify patients with specific pathobiology. Drug repurposing approaches based on biomarkers of specific inflammatory processes (i.e., type 2 inflammation) are promising. As larger data sets, additional omics, and new analytical approaches become available, there will be enormous opportunities to identify high-risk individuals and treat COPD patients based on their specific pathophysiological derangements. These approaches show great promise for risk stratification, early intervention, drug repurposing, and developing novel therapeutic approaches for COPD.
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Affiliation(s)
- Matthew Moll
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Division of Pulmonary, Critical Care, Sleep and Allergy, Veterans Affairs Boston Healthcare System, West Roxbury, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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24
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Ahmed F, Samantasinghar A, Ali W, Choi KH. Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer. Mol Divers 2024:10.1007/s11030-023-10784-7. [PMID: 38227161 DOI: 10.1007/s11030-023-10784-7] [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: 05/31/2023] [Accepted: 11/25/2023] [Indexed: 01/17/2024]
Abstract
Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Jeju, Republic of Korea
| | - Anupama Samantasinghar
- Department of Mechatronics Engineering, Jeju National University, Jeju, Republic of Korea
| | - Wajid Ali
- Department of Mechatronics Engineering, Jeju National University, Jeju, Republic of Korea
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Jeju, Republic of Korea.
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25
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Pillai M, Wu D. Validation approaches for computational drug repurposing: a review. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:559-568. [PMID: 38222367 PMCID: PMC10785886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Affiliation(s)
- Malvika Pillai
- Stanford University, Stanford, CA
- University of North Carolina, Chapel Hill, NC
| | - Di Wu
- University of North Carolina, Chapel Hill, NC
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26
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Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Xiang S, Lawrence PJ, Peng B, Chiang C, Kim D, Shen L, Ning X. Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:306-321. [PMID: 38160288 PMCID: PMC11056095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.
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Affiliation(s)
- Shunian Xiang
- Biomedical Informatics Department, The Ohio State University, Columbus, OH 43210, USA
| | - Patrick J. Lawrence
- Biomedical Informatics Department, The Ohio State University, Columbus, OH 43210, USA
| | - Bo Peng
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH 43210, USA
| | - ChienWei Chiang
- Biomedical Informatics Department, The Ohio State University, Columbus, OH 43210, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Xia Ning
- Biomedical Informatics Department, The Ohio State University, Columbus, OH 43210, USA
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
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28
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Lv S, Wang Q, Zhang X, Ning F, Liu W, Cui M, Xu Y. Mechanisms of multi-omics and network pharmacology to explain traditional chinese medicine for vascular cognitive impairment: A narrative review. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 123:155231. [PMID: 38007992 DOI: 10.1016/j.phymed.2023.155231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 11/07/2023] [Accepted: 11/18/2023] [Indexed: 11/28/2023]
Abstract
BACKGROUND The term "vascular cognitive impairment" (VCI) describes various cognitive conditions that include vascular elements. It increases the risk of morbidity and mortality in the elderly population and is the most common cognitive impairment associated with cerebrovascular disease. Understanding the etiology of VCI may aid in identifying approaches to target its possible therapy for the condition. Treatment of VCI has focused on vascular risk factors. There are no authorized conventional therapies available right now. The medications used to treat VCI are solely approved for symptomatic relief and are not intended to prevent or slow the development of VCI. PURPOSE The function of Chinese medicine in treating VCI has not yet been thoroughly examined. This review evaluates the preclinical and limited clinical evidence to comprehend the "multi-component, multi-target, multi-pathway" mechanism of Traditional Chinese medicine (TCM). It investigates the various multi-omics approaches in the search for the pathological mechanisms of VCI, as well as the new research strategies, in the hopes of supplying supportive evidence for the clinical treatment of VCI. METHODS This review used the Preferred Reporting Items for Preferred reporting items for systematic reviews and meta-analyses (PRISMA) statements. Using integrated bioinformatics and network pharmacology approaches, a thorough evaluation and analysis of 25 preclinical studies published up to July 1, 2023, were conducted to shed light on the mechanisms of TCM for vascular cognitive impairment. The studies for the systematic review were located using the following databases: PubMed, Web of Science, Scopus, Cochrane, and ScienceDirect. RESULTS We discovered that the multi-omics analysis approach would hasten the discovery of the role of TCM in the treatment of VCI. It will explore components, compounds, targets, and pathways, slowing the progression of VCI from the perspective of inhibiting oxidative stress, stifling neuroinflammation, increasing cerebral blood flow, and inhibiting iron deposition by a variety of molecular mechanisms, which have significant implications for the treatment of VCI. CONCLUSION TCM is a valuable tool for developing dementia therapies, and further research is needed to determine how TCM components may affect the operation of the neurovascular unit. There are still some limitations, although several research have offered invaluable resources for searching for possible anti-dementia medicines and treatments. To gain new insights into the molecular mechanisms that precisely modulate the key molecules at different levels during pharmacological interventions-a prerequisite for comprehending the mechanism of action and determining the potential therapeutic value of the drugs-further research should employ more standardized experimental methods as well as more sophisticated science and technology. Given the results of this review, we advocate integrating chemical and biological component analysis approaches in future research on VCI to provide a more full and objective assessment of the standard of TCM. With the help of bioinformatics, a multi-omics analysis approach will hasten the discovery of the role of TCM in the treatment of VCI, which has significant implications for the treatment of VCI.
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Affiliation(s)
- Shi Lv
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, China
| | - Qian Wang
- Department of Central Laboratory, The Affiliated Taian City Central Hospital of Qingdao University, Taian 271000, China
| | - Xinlei Zhang
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, China
| | - Fangli Ning
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, China
| | - Wenxin Liu
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, China
| | - Mengmeng Cui
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, China
| | - Yuzhen Xu
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, China.
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29
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Osmanoglu Ö, Gupta SK, Almasi A, Yagci S, Srivastava M, Araujo GHM, Nagy Z, Balkenhol J, Dandekar T. Signaling network analysis reveals fostamatinib as a potential drug to control platelet hyperactivation during SARS-CoV-2 infection. Front Immunol 2023; 14:1285345. [PMID: 38187394 PMCID: PMC10768010 DOI: 10.3389/fimmu.2023.1285345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/06/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction Pro-thrombotic events are one of the prevalent causes of intensive care unit (ICU) admissions among COVID-19 patients, although the signaling events in the stimulated platelets are still unclear. Methods We conducted a comparative analysis of platelet transcriptome data from healthy donors, ICU, and non-ICU COVID-19 patients to elucidate these mechanisms. To surpass previous analyses, we constructed models of involved networks and control cascades by integrating a global human signaling network with transcriptome data. We investigated the control of platelet hyperactivation and the specific proteins involved. Results Our study revealed that control of the platelet network in ICU patients is significantly higher than in non-ICU patients. Non-ICU patients require control over fewer proteins for managing platelet hyperactivity compared to ICU patients. Identification of indispensable proteins highlighted key subnetworks, that are targetable for system control in COVID-19-related platelet hyperactivity. We scrutinized FDA-approved drugs targeting indispensable proteins and identified fostamatinib as a potent candidate for preventing thrombosis in COVID-19 patients. Discussion Our findings shed light on how SARS-CoV-2 efficiently affects host platelets by targeting indispensable and critical proteins involved in the control of platelet activity. We evaluated several drugs for specific control of platelet hyperactivity in ICU patients suffering from platelet hyperactivation. The focus of our approach is repurposing existing drugs for optimal control over the signaling network responsible for platelet hyperactivity in COVID-19 patients. Our study offers specific pharmacological recommendations, with drug prioritization tailored to the distinct network states observed in each patient condition. Interactive networks and detailed results can be accessed at https://fostamatinib.bioinfo-wuerz.eu/.
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Affiliation(s)
- Özge Osmanoglu
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Shishir K. Gupta
- Evolutionary Genomics Group, Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
- Institute of Botany, Heinrich Heine University, Düsseldorf, Germany
| | - Anna Almasi
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Seray Yagci
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Mugdha Srivastava
- Core Unit Systems Medicine, University of Wuerzburg, Wuerzburg, Germany
- Algorithmic Bioinformatics, Department of Computer Science, Heinrich Heine University, Düsseldorf, Germany
| | - Gabriel H. M. Araujo
- University Hospital Würzburg, Institute of Experimental Biomedicine, Würzburg, Germany
| | - Zoltan Nagy
- University Hospital Würzburg, Institute of Experimental Biomedicine, Würzburg, Germany
| | - Johannes Balkenhol
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
- Chair of Molecular Microscopy, Rudolf Virchow Center for Integrative and Translation Bioimaging, University of Würzburg, Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
- European Molecular Biology Laboratory (EMBL) Heidelberg, BioComputing Unit, Heidelberg, Germany
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30
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Zang C, Zhang H, Xu J, Zhang H, Fouladvand S, Havaldar S, Cheng F, Chen K, Chen Y, Glicksberg BS, Chen J, Bian J, Wang F. High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data. Nat Commun 2023; 14:8180. [PMID: 38081829 PMCID: PMC10713627 DOI: 10.1038/s41467-023-43929-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.
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Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA
| | - Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Hansi Zhang
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Sajjad Fouladvand
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Shreyas Havaldar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kun Chen
- Department of Statistics, University of Connecticut, Storrs, CT, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics (DBEI), the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Chen
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA.
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31
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Wang Y, Zhang Z, Piao C, Huang Y, Zhang Y, Zhang C, Lu YJ, Liu D. LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening. Health Inf Sci Syst 2023; 11:42. [PMID: 37667773 PMCID: PMC10475000 DOI: 10.1007/s13755-023-00243-w] [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: 05/26/2023] [Accepted: 08/14/2023] [Indexed: 09/06/2023] Open
Abstract
Background Drug-target interaction (DTI) is a vital drug design strategy that plays a significant role in many processes of complex diseases and cellular events. In the face of challenges such as extensive protein data and experimental costs, it is suggested to apply bioinformatics approaches to exploit potential interactions to design new targeted medications. Different data and interaction types bring difficulties to study involving incompatible and heterology formats. The analysis of drug-target interactions in a comprehensive and unified model is a significant challenge. Method Here, we propose a general method for predicting interactions between small-molecule drugs and protein targets, Large-scale Drug target Screening Convolutional Neural Network (LDS-CNN), which used unified encoding to achieve the calculation of the different data formats in an integrated model to realize feature abstraction and potential object prediction. Result On 898,412 interaction data involving 1683 small-molecule compounds and 14,350 human proteins from 8.8 billion records, the proposed method achieved an area under the curve (AUC) of 0.96, an area under the precision-recall curve (AUPRC) of 0.95, and an accuracy of 90.13%. The experimental results illustrated that the proposed method attained high accuracy on the test set, indicating its high predictive ability in drug-target interaction prediction. LDS-CNN is effective for the prediction of large-scale datasets and datasets composed of data with different formats. Conclusion In this study, we propose a DTI prediction method to solve the problems of unified encoding of large-scale data in multiple formats. It provides a feasible way to efficiently abstract the features among different types of drug-related data, thus reducing experimental costs and time consumption. The proposed method can be used to identify potential drug targets and candidates for the treatment of complex diseases. This work provides a reference for DTI to process large-scale data and different formats with deep learning methods and provides certain suggestions for future research.
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Affiliation(s)
- Yang Wang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 China
| | - Zuxian Zhang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006 China
| | - Chenghong Piao
- The First Affiliated Hospital of Ningbo University, Ningbo, 315010 China
| | - Ying Huang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006 China
| | - Yihan Zhang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006 China
| | - Chi Zhang
- Shanghai Institute of Biological Products, Shanghai, 201403 China
| | - Yu-Jing Lu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006 China
- Smart Medical Innovation Technology Center, Guangdong University of Technology, Guangzhou, 510006 China
| | - Dongning Liu
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 China
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32
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Bowen ER, DiGiacomo P, Fraser HP, Guttenplan K, Smith BAH, Heberling ML, Vidano L, Shah N, Shamloo M, Wilson JL, Grimes KV. Beta-2 adrenergic receptor agonism alters astrocyte phagocytic activity and has potential applications to psychiatric disease. DISCOVER MENTAL HEALTH 2023; 3:27. [PMID: 38036718 PMCID: PMC10689618 DOI: 10.1007/s44192-023-00050-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023]
Abstract
Schizophrenia is a debilitating condition necessitating more efficacious therapies. Previous studies suggested that schizophrenia development is associated with aberrant synaptic pruning by glial cells. We pursued an interdisciplinary approach to understand whether therapeutic reduction in glial cell-specifically astrocytic-phagocytosis might benefit neuropsychiatric patients. We discovered that beta-2 adrenergic receptor (ADRB2) agonists reduced phagocytosis using a high-throughput, phenotypic screen of over 3200 compounds in primary human fetal astrocytes. We used protein interaction pathways analysis to associate ADRB2, to schizophrenia and endocytosis. We demonstrated that patients with a pediatric exposure to salmeterol, an ADRB2 agonist, had reduced in-patient psychiatry visits using a novel observational study in the electronic health record. We used a mouse model of inflammatory neurodegenerative disease and measured changes in proteins associated with endocytosis and vesicle-mediated transport after ADRB2 agonism. These results provide substantial rationale for clinical consideration of ADRB2 agonists as possible therapies for patients with schizophrenia.
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Affiliation(s)
- Ellen R Bowen
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Weill Cornell Medicine, New York, NY, USA
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Phillip DiGiacomo
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Hannah P Fraser
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin Guttenplan
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Vollum Institute, Oregon Health & Science University, Portland, OR, USA
| | - Benjamin A H Smith
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Marlene L Heberling
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Laura Vidano
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Nigam Shah
- Center for Biomedical Informatics Research, Stanford School of Medicine, Stanford, CA, USA
| | - Mehrdad Shamloo
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Jennifer L Wilson
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.
| | - Kevin V Grimes
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
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33
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Li B, Zhang J, Dong H, Feng X, Yu L, Zhu J, Zhang J. Systematic analysis of various RNA transcripts and construction of biological regulatory networks at the post-transcriptional level for chronic obstructive pulmonary disease. J Transl Med 2023; 21:790. [PMID: 37936118 PMCID: PMC10631086 DOI: 10.1186/s12967-023-04674-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/29/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Although chronic inflammation, oxidative stress, airway remodeling, and protease-antiprotease imbalance have been implicated in chronic obstructive pulmonary disease (COPD), the exact pathogenesis is still obscure. Gene transcription and post-transcriptional regulation have been taken into account as key regulators of COPD occurrence and development. Identifying the hub genes and constructing biological regulatory networks at the post-transcriptional level will help extend current knowledge on COPD pathogenesis and develop potential drugs. METHODS All lung tissues from non-smokers (n = 6), smokers without COPD (smokers, n = 7), and smokers with COPD (COPD, n = 7) were collected to detect messenger RNA (mRNA), microRNA (miRNA), circular RNA (circRNA), and long non-coding RNA (lncRNA) expression and identify the hub genes. Biological regulatory networks were constructed at the post-transcriptional level, including the RNA-binding protein (RBP)-hub gene interaction network and the competitive endogenous RNA (ceRNA) network. In addition, we assessed the composition and abundance of immune cells in COPD lung tissue and predicted potential therapeutic drugs for COPD. Finally, the hub genes were confirmed at both the RNA and protein levels. RESULTS Among the 20 participants, a total of 121169 mRNA transcripts, 1871 miRNA transcripts, 4244 circRNA transcripts, and 122130 lncRNA transcripts were detected. There were differences in the expression of 1561 mRNAs, 48 miRNAs, 33 circRNAs, and 545 lncRNAs between smokers and non-smokers, as well as 1289 mRNAs, 69 miRNAs, 32 circRNAs, and 433 lncRNAs between smokers and COPD patients. 18 hub genes were identified in COPD. TGF-β signaling and Wnt/β-catenin signaling may be involved in the development of COPD. Furthermore, the circRNA/lncRNA-miRNA-mRNA ceRNA networks and the RBP-hub gene interaction network were also constructed. Analysis of the immune cell infiltration level revealed that M2 macrophages and activated NK cells were increased in COPD lung tissues. Finally, we identified that the ITK inhibitor and oxybutynin chloride may be effective in treating COPD. CONCLUSIONS We identified several novel hub genes involved in COPD pathogenesis. TGF-β signaling and Wnt/β-catenin signaling were the most dysregulated pathways in COPD patients. Our study constructed post-transcriptional biological regulatory networks and predicted small-molecule drugs for the treatment of COPD, which enhanced the existing understanding of COPD pathogenesis and suggested an innovative direction for the therapeutic intervention of the disease.
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Affiliation(s)
- Beibei Li
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, China
| | - Jiajun Zhang
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, China
| | - Hui Dong
- Center of Research Equipment Management, General Hospital of Ningxia Medical University, Yinchuan, 750004, China
| | - Xueyan Feng
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, China
| | - Liang Yu
- Department of Thoracic Surgery, General Hospital of Ningxia Medical University, Yinchuan, 750004, China
| | - Jinyuan Zhu
- Department of Critical Care Medicine, General Hospital of Ningxia Medical University, Yinchuan, 750004, China
| | - Jin Zhang
- Department of Respiratory and Critical Care Medicine, General Hospital of Ningxia Medical University, 804 Shengli South Street, Xingqing District, Yinchuan, 750004, China.
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Petti M, Farina L. Network medicine for patients' stratification: From single-layer to multi-omics. WIREs Mech Dis 2023; 15:e1623. [PMID: 37323106 DOI: 10.1002/wsbm.1623] [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: 10/14/2022] [Revised: 03/08/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Precision medicine research increasingly relies on the integrated analysis of multiple types of omics. In the era of big data, the large availability of different health-related information represents a great, but at the same time untapped, chance with a potentially fundamental role in the prevention, diagnosis and prognosis of diseases. Computational methods are needed to combine this data to create a comprehensive view of a given disease. Network science can model biomedical data in terms of relationships among molecular players of different nature and has been successfully proposed as a new paradigm for studying human diseases. Patient stratification is an open challenge aimed at identifying subtypes with different disease manifestations, severity, and expected survival time. Several stratification approaches based on high-throughput gene expression measurements have been successfully applied. However, few attempts have been proposed to exploit the integration of various genotypic and phenotypic data to discover novel sub-types or improve the detection of known groupings. This article is categorized under: Cancer > Biomedical Engineering Cancer > Computational Models Cancer > Genetics/Genomics/Epigenetics.
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Affiliation(s)
- Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
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Gan X, Shu Z, Wang X, Yan D, Li J, Ofaim S, Albert R, Li X, Liu B, Zhou X, Barabási AL. Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine. SCIENCE ADVANCES 2023; 9:eadh0215. [PMID: 37889962 PMCID: PMC10610911 DOI: 10.1126/sciadv.adh0215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023]
Abstract
Understanding natural and traditional medicine can lead to world-changing drug discoveries. Despite the therapeutic effectiveness of individual herbs, traditional Chinese medicine (TCM) lacks a scientific foundation and is often considered a myth. In this study, we establish a network medicine framework and reveal the general TCM treatment principle as the topological relationship between disease symptoms and TCM herb targets on the human protein interactome. We find that proteins associated with a symptom form a network module, and the network proximity of an herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. These findings are validated using patient data from a hospital. We highlight the translational value of our framework by predicting herb-symptom treatments with therapeutic potential. Our network medicine framework reveals the scientific foundation of TCM and establishes a paradigm for understanding the molecular basis of natural medicine and predicting disease treatments.
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Affiliation(s)
- Xiao Gan
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
- Network Science Institute, Northeastern University, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Physics, Pennsylvania State University, University Park, PA 16802, USA
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Zixin Shu
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100063, China
| | - Xinyan Wang
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100063, China
| | - Dengying Yan
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100063, China
| | - Jun Li
- Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Shany Ofaim
- Network Science Institute, Northeastern University, Boston, MA 02115, USA
| | - Réka Albert
- Department of Physics, Pennsylvania State University, University Park, PA 16802, USA
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Xiaodong Li
- Hubei University of Chinese Medicine, Wuhan 430065, China
- Hubei Provincial Hospital of Traditional Chinese Medicine (Affiliated Hospital of Hubei University of Traditional Chinese Medicine, Hubei Academy of Chinese Medicine, Wuhan 430061, China
| | - Baoyan Liu
- China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Xuezhong Zhou
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100063, China
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA 02115, USA
- Department of Network and Data Science, Central European University, Budapest 1051, Hungary
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36
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Xiang S, Lawrence PJ, Peng B, Chiang C, Kim D, Shen L, Ning X. Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing. ARXIV 2023:arXiv:2310.15211v2. [PMID: 37961739 PMCID: PMC10635281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.
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Affiliation(s)
- Shunian Xiang
- Biomedical Informatics Department, The Ohio State University,
Columbus, OH 43210, USA
| | - Patrick J. Lawrence
- Biomedical Informatics Department, The Ohio State University,
Columbus, OH 43210, USA
| | - Bo Peng
- Computer Science and Engineering Department, The Ohio State
University, Columbus, OH 43210, USA
| | - ChienWei Chiang
- Biomedical Informatics Department, The Ohio State University,
Columbus, OH 43210, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology, and Informatics,
University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics,
University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Xia Ning
- Biomedical Informatics Department, The Ohio State University,
Columbus, OH 43210, USA
- Computer Science and Engineering Department, The Ohio State
University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State
University, Columbus, OH 43210, USA
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Li X, Liao M, Wang B, Zan X, Huo Y, Liu Y, Bao Z, Xu P, Liu W. A drug repurposing method based on inhibition effect on gene regulatory network. Comput Struct Biotechnol J 2023; 21:4446-4455. [PMID: 37731599 PMCID: PMC10507583 DOI: 10.1016/j.csbj.2023.09.007] [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: 03/22/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023] Open
Abstract
Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.
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Affiliation(s)
- Xianbin Li
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Minzhen Liao
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Bing Wang
- School of Medicine, Southeast University, Nanjing, China
| | - Xiangzhen Zan
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Yanhao Huo
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Yue Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Zhenshen Bao
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Peng Xu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Wenbin Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
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Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther 2023; 17:2691-2725. [PMID: 37701048 PMCID: PMC10493153 DOI: 10.2147/dddt.s424991] [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: 06/28/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
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39
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Bernardo L, Lomagno A, Mauri PL, Di Silvestre D. Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing. BIOLOGY 2023; 12:1196. [PMID: 37759595 PMCID: PMC10525644 DOI: 10.3390/biology12091196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/25/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.
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Affiliation(s)
| | | | | | - Dario Di Silvestre
- Institute for Biomedical Technologies—National Research Council (ITB-CNR), 20054 Segrate, Italy; (L.B.); (A.L.); (P.L.M.)
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40
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Ren A, Wu T, Wang Y, Fan Q, Yang Z, Zhang S, Cao Y, Cui G. Integrating animal experiments, mass spectrometry and network-based approach to reveal the sleep-improving effects of Ziziphi Spinosae Semen and γ-aminobutyric acid mixture. Chin Med 2023; 18:99. [PMID: 37573423 PMCID: PMC10422734 DOI: 10.1186/s13020-023-00814-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/30/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND Ziziphi Spinosae Semen (ZSS) is a plant widely used as medicine and food in Asian countries due to its numerous health benefits. γ-aminobutyric acid (GABA), a non-proteinaceous amino acid, is one of the major inhibitory neurotransmitters with a relaxant function. In this study, a system pharmacology approach was employed to assess the effects of a mixture composed of ZSS and GABA (ZSSG) on sleep improvement. METHODS Mice were divided into five groups (n = 10) and received either no treatment, sodium pentobarbital, or sodium barbital with diazepam or ZSSG. The effects of ZSSG on sleep quality were evaluated in mice, and differential metabolites associated with sleep were identified among the control, ZSS, GABA, and ZSSG groups. Additionally, network-based ingredient-insomnia proximity analysis was applied to explore the major ingredients. RESULTS ZSSG significantly improved sleep quality by decreasing sleep latency and prolonging sleep duration in sodium pentobarbital-induced sleeping mouse model (P < 0.05). ZSSG significantly enhanced the brain content of GABA in mice. Furthermore, ZSSG also significantly decreased sleep latency-induced by sodium barbital in mice (P < 0.05). Metabolic analysis revealed significant differences in 10 metabolites between ZSSG group and the groups administering ZSS or GABA. Lastly, using the network-based ingredient screening model, we discovered potential four active ingredients and three pairwise ingredient combinations with synergistic effect on insomnia from ZSSG among 85 ingredients identified by UPLC-Q/TOF-MS. Also, we have constructed an online computation platform. CONCLUSION Our data demonstrated that ZSSG improved the sleeping quality of mice and helped to balance metabolic disorders-associated with sleep disorders. Moreover, based on the network-based prediction method, the four potential active ingredients in ZSSG could serve as quality markers-associated with insomnia. The network-based framework may open up a new avenue for the discovery of active ingredients of herbal medicine for treating complex chronic diseases or symptoms, such as insomnia.
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Affiliation(s)
- Airong Ren
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Tingbiao Wu
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Yarong Wang
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Qing Fan
- Basic Medical Science Department, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Zhenhao Yang
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Shixun Zhang
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Yongjun Cao
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Guozhen Cui
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China.
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Chandrasekaran J, Elumalai S, Murugesan V, Kunjiappan S, Pavadai P, Theivendren P. Computational design of PD-L1 small molecule inhibitors for cancer therapy. Mol Divers 2023; 27:1633-1644. [PMID: 36006501 DOI: 10.1007/s11030-022-10516-3] [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: 05/07/2022] [Accepted: 08/14/2022] [Indexed: 10/15/2022]
Abstract
Drug repurposing opens new avenues in cancer therapy. Drug repurposing, or finding new uses for existing drugs, can substantially reduce drug discovery time and costs. Cheminformatics, genetics, and systems biology advances enable repositioning drugs. Clinical usage of PD-1/PD-L1 blocking has been approved because of its efficacy in improving prognosis in select groups. The PD-1/PD-L1 axis was considered to represent a mechanism for tumour evasion of host tumour antigen-specific T-cell immunity in early preclinical research. The expression of PD-L1 in cancer cells causes T lymphocytes to become exhausted by transmitting a co-inhibitory signal. A better understanding of how PD-L1 is regulated in cancer cells could lead to new therapeutic options. In this view, the study was aimed to repurpose the existing FDA-approved drugs as a potential PD-L1 inhibitor through e-Pharmacophore modelling, molecular docking and dynamic simulation. e-Pharmacophore screening retrieved 324 FDA-approved medications with the fitness score ≥ 1. The top 10-docked FDA candidates were compared with IN-35 (Clinical trial candidate) for its interaction pattern with critical amino acid residues. Mirabegron and Indacaterol exhibited a greater affinity for PD-L1 with docking scores of - 9.213 kcal mol-1 and - 8.023 kcal mol-1, respectively. Mirabegron retain interactions at all three major hotspots in the PD-L1 dimer interface similar to IN-35. MM-GBSA analyses indicated that Mirabegron uses less energy to create a more stable complex and retains all of the inhibitor's positive interactions found in clinical trial ligand IN-35. Molecular dynamics simulation analysis of the Mirabegron complex showed a similar pattern of deviation in correlation with IN-35, and it retains the interaction with the active key amino acids throughout the simulation time. Our present study has shown Mirabegron as a powerful inhibitor of PD-L1 expression in cancer cells using a drug-repurposing screen.
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Affiliation(s)
- Jaikanth Chandrasekaran
- Department of Pharmacology, School of Pharmacy & Technology Management, SVKM'S NMIMS University, Secunderabad, 500017, India.
| | - Senthilkumar Elumalai
- Department of Pharmacology, PSG College of Pharmacy, Peelamedu, Coimbatore, 641004, India
| | - Vidya Murugesan
- Department of Chemistry and Biochemistry, Science and Commerce, M S Ramaiah College of Arts, Bengaluru, 560054, India
| | - Selvaraj Kunjiappan
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India
| | - Parasuraman Pavadai
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bengaluru, 560054, India
| | - Panneerselvam Theivendren
- Department of Pharmaceutical Chemistry, Swamy Vivekanandha College of Pharmacy, Tiruchengodu, 637205, India
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42
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Panossian A. Challenges in phytotherapy research. Front Pharmacol 2023; 14:1199516. [PMID: 37324491 PMCID: PMC10264668 DOI: 10.3389/fphar.2023.1199516] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/18/2023] [Indexed: 06/17/2023] Open
Affiliation(s)
- Alexander Panossian
- Phytomed AB, Västervik, Sweden
- EuroPharma USA Inc., Green Bay, WI, United States
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43
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Her QL, Kahrs JC, Stürmer T. Comparator choices in pharmacoepidemiology studies of Alzheimer's disease. NATURE AGING 2023:10.1038/s43587-023-00417-x. [PMID: 37217663 DOI: 10.1038/s43587-023-00417-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 03/31/2023] [Indexed: 05/24/2023]
Affiliation(s)
- Qoua L Her
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jacob C Kahrs
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Bayraktar A, Li X, Kim W, Zhang C, Turkez H, Shoaie S, Mardinoglu A. Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer's disease patients. J Transl Med 2023; 21:332. [PMID: 37210557 DOI: 10.1186/s12967-023-04192-6] [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: 02/26/2023] [Accepted: 05/11/2023] [Indexed: 05/22/2023] Open
Abstract
BACKGROUND Despite numerous clinical trials and decades of endeavour, there is still no effective cure for Alzheimer's disease. Computational drug repositioning approaches may be employed for the development of new treatment strategies for Alzheimer's patients since an extensive amount of omics data has been generated during pre-clinical and clinical studies. However, targeting the most critical pathophysiological mechanisms and determining drugs with proper pharmacodynamics and good efficacy are equally crucial in drug repurposing and often imbalanced in Alzheimer's studies. METHODS Here, we investigated central co-expressed genes upregulated in Alzheimer's disease to determine a proper therapeutic target. We backed our reasoning by checking the target gene's estimated non-essentiality for survival in multiple human tissues. We screened transcriptome profiles of various human cell lines perturbed by drug induction (for 6798 compounds) and gene knockout using data available in the Connectivity Map database. Then, we applied a profile-based drug repositioning approach to discover drugs targeting the target gene based on the correlations between these transcriptome profiles. We evaluated the bioavailability, functional enrichment profiles and drug-protein interactions of these repurposed agents and evidenced their cellular viability and efficacy in glial cell culture by experimental assays and Western blotting. Finally, we evaluated their pharmacokinetics to anticipate to which degree their efficacy can be improved. RESULTS We identified glutaminase as a promising drug target. Glutaminase overexpression may fuel the glutamate excitotoxicity in neurons, leading to mitochondrial dysfunction and other neurodegeneration hallmark processes. The computational drug repurposing revealed eight drugs: mitoxantrone, bortezomib, parbendazole, crizotinib, withaferin-a, SA-25547 and two unstudied compounds. We demonstrated that the proposed drugs could effectively suppress glutaminase and reduce glutamate production in the diseased brain through multiple neurodegeneration-associated mechanisms, including cytoskeleton and proteostasis. We also estimated the human blood-brain barrier permeability of parbendazole and SA-25547 using the SwissADME tool. CONCLUSIONS This study method effectively identified an Alzheimer's disease marker and compounds targeting the marker and interconnected biological processes by use of multiple computational approaches. Our results highlight the importance of synaptic glutamate signalling in Alzheimer's disease progression. We suggest repurposable drugs (like parbendazole) with well-evidenced activities that we linked to glutamate synthesis hereby and novel molecules (SA-25547) with estimated mechanisms for the treatment of Alzheimer's patients.
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Affiliation(s)
- Abdulahad Bayraktar
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Xiangyu Li
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, 92101, USA
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-17121, Stockholm, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-17121, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-17121, Stockholm, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK.
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-17121, Stockholm, Sweden.
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Wang RS, Loscalzo J. Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations. Circ Res 2023; 132:1374-1386. [PMID: 37167362 PMCID: PMC10171294 DOI: 10.1161/circresaha.122.321879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.
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Affiliation(s)
- Rui-Sheng Wang
- Channing Division of Network Medicine (R.-S.W., J.L.), Department of Medicine, Brigham and Women's Hospital, Harvard Medical School Boston, MA
| | - Joseph Loscalzo
- Channing Division of Network Medicine (R.-S.W., J.L.), Department of Medicine, Brigham and Women's Hospital, Harvard Medical School Boston, MA
- Division of Cardiovascular Medicine (J.L.), Department of Medicine, Brigham and Women's Hospital, Harvard Medical School Boston, MA
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Watson DC, Bayik D, Storevik S, Moreino SS, Sprowls SA, Han J, Augustsson MT, Lauko A, Sravya P, Røsland GV, Troike K, Tronstad KJ, Wang S, Sarnow K, Kay K, Lunavat TR, Silver DJ, Dayal S, Joseph JV, Mulkearns-Hubert E, Ystaas LAR, Deshpande G, Guyon J, Zhou Y, Magaut CR, Seder J, Neises L, Williford SE, Meiser J, Scott AJ, Sajjakulnukit P, Mears JA, Bjerkvig R, Chakraborty A, Daubon T, Cheng F, Lyssiotis CA, Wahl DR, Hjelmeland AB, Hossain JA, Miletic H, Lathia JD. GAP43-dependent mitochondria transfer from astrocytes enhances glioblastoma tumorigenicity. NATURE CANCER 2023; 4:648-664. [PMID: 37169842 PMCID: PMC10212766 DOI: 10.1038/s43018-023-00556-5] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/06/2023] [Indexed: 05/13/2023]
Abstract
The transfer of intact mitochondria between heterogeneous cell types has been confirmed in various settings, including cancer. However, the functional implications of mitochondria transfer on tumor biology are poorly understood. Here we show that mitochondria transfer is a prevalent phenomenon in glioblastoma (GBM), the most frequent and malignant primary brain tumor. We identified horizontal mitochondria transfer from astrocytes as a mechanism that enhances tumorigenesis in GBM. This transfer is dependent on network-forming intercellular connections between GBM cells and astrocytes, which are facilitated by growth-associated protein 43 (GAP43), a protein involved in neuron axon regeneration and astrocyte reactivity. The acquisition of astrocyte mitochondria drives an increase in mitochondrial respiration and upregulation of metabolic pathways linked to proliferation and tumorigenicity. Functionally, uptake of astrocyte mitochondria promotes cell cycle progression to proliferative G2/M phases and enhances self-renewal and tumorigenicity of GBM. Collectively, our findings reveal a host-tumor interaction that drives proliferation and self-renewal of cancer cells, providing opportunities for therapeutic development.
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Affiliation(s)
- Dionysios C Watson
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Defne Bayik
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Simon Storevik
- Department of Biomedicine, University of Bergen, Bergen, Norway
- Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | | | | | - Jianhua Han
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | | | - Adam Lauko
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Medical Scientist Training Program, Case Western Reserve University, Cleveland, OH, USA
| | - Palavalasa Sravya
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | | | - Katie Troike
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Sabrina Wang
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Kristen Kay
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Taral R Lunavat
- Department of Biomedicine, University of Bergen, Bergen, Norway
- Department of Neurology, Molecular Neurogenetics Unit-West, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel J Silver
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Sahil Dayal
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Erin Mulkearns-Hubert
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | | | | | - Joris Guyon
- University of Bordeaux, INSERM, BRIC, Pessac, France
| | - Yadi Zhou
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Juliana Seder
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Laura Neises
- Cancer Metabolism Group, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | | | - Johannes Meiser
- Cancer Metabolism Group, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Andrew J Scott
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | | | - Jason A Mears
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Rolf Bjerkvig
- Department of Biomedicine, University of Bergen, Bergen, Norway
- NorLux Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Abhishek Chakraborty
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Thomas Daubon
- University of Bordeaux, CNRS, IBGC, Bordeaux, France
| | - Feixiong Cheng
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Costas A Lyssiotis
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, MI, USA
| | - Daniel R Wahl
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Hrvoje Miletic
- Department of Biomedicine, University of Bergen, Bergen, Norway.
- Department of Pathology, Haukeland University Hospital, Bergen, Norway.
| | - Justin D Lathia
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Case Comprehensive Cancer Center, Cleveland, OH, USA.
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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Sperry MM, Oskotsky TT, Marić I, Kaushal S, Takeda T, Horvath V, Powers RK, Rodas M, Furlong B, Soong M, Prabhala P, Goyal G, Carlson KE, Wong RJ, Kosti I, Le BL, Logue J, Hammond H, Frieman M, Stevenson DK, Ingber DE, Sirota M, Novak R. Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients. PLoS Comput Biol 2023; 19:e1011050. [PMID: 37146076 PMCID: PMC10191356 DOI: 10.1371/journal.pcbi.1011050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 05/17/2023] [Accepted: 03/27/2023] [Indexed: 05/07/2023] Open
Abstract
Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.
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Affiliation(s)
- Megan M. Sperry
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
- Department of Biology, Tufts University, Medford, Massachusetts, United States of America
| | - Tomiko T. Oskotsky
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
- Department of Pediatrics, University of California San Francisco, San Francisco, California, United States of America
| | - Ivana Marić
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, California, United States of America
- Center for Academic Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Shruti Kaushal
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Takako Takeda
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Viktor Horvath
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Rani K. Powers
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Melissa Rodas
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Brooke Furlong
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Mercy Soong
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Pranav Prabhala
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Girija Goyal
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Kenneth E. Carlson
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
| | - Ronald J. Wong
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, California, United States of America
- Center for Academic Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Idit Kosti
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
- Department of Pediatrics, University of California San Francisco, San Francisco, California, United States of America
| | - Brian L. Le
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
- Department of Pediatrics, University of California San Francisco, San Francisco, California, United States of America
| | - James Logue
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Holly Hammond
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Matthew Frieman
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - David K. Stevenson
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, California, United States of America
- Center for Academic Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Donald E. Ingber
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts, United States of America
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
- Department of Pediatrics, University of California San Francisco, San Francisco, California, United States of America
| | - Richard Novak
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
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Zhang P, Hou Y, Tu W, Campbell N, Pieper AA, Leverenz JB, Gao S, Cummings J, Cheng F. Population-based discovery and Mendelian randomization analysis identify telmisartan as a candidate medicine for Alzheimer's disease in African Americans. Alzheimers Dement 2023; 19:1876-1887. [PMID: 36331056 PMCID: PMC10156891 DOI: 10.1002/alz.12819] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/11/2022] [Accepted: 09/02/2022] [Indexed: 11/06/2022]
Abstract
INTRODUCTION African Americans (AAs) and European Americans (EAs) differ in Alzheimer's disease (AD) prevalence, risk factors, and symptomatic presentation and AAs are less likely to enroll in AD clinical trials. METHODS We conducted race-conscious pharmacoepidemiologic studies of 5.62 million older individuals (age ≥60) to investigate the association of telmisartan exposure and AD outcome using Cox analysis, Kaplan-Meier analysis, and log-rank test. We performed Mendelian randomization (MR) analysis of large ethnically diverse genetic data to test likely causal relationships between telmisartan's target and AD. RESULTS We identified that moderate/high telmisartan exposure was significantly associated with a reduced incidence of AD in the AAs compared to low/no telmisartan exposure (hazard ratio [HR] = 0.77, 95% CI: 0.65-0.91, p-value = 0.0022), but not in the non-Hispanic EAs (HR = 0.97, 95% CI: 0.89-1.05, p-value = 0.4110). Sensitivity and sex-/age-stratified patient subgroup analyses identified that telmisartan's medication possession ratio (MPR) and average hypertension daily dosage were significantly associated with a stronger reduction in the incidence of both AD and dementia in AAs. Using MR analysis from large genome-wide association studies (GWAS) (over 2 million individuals) across AD, hypertension, and diabetes, we further identified AA-specific beneficial effects of telmisartan for AD. DISCUSSION Randomized controlled trials with ethnically diverse patient cohorts are warranted to establish causality and therapeutic outcomes of telmisartan and AD. HIGHLIGHTS Telmisartan is associated with lower risk of Alzheimer's disease (AD) in African Americans (AAs). Telmisartan is the only angiotensin II receptor blockers having PPAR-γ agonistic properties with beneficial anti-diabetic and renal function effects, which mitigate AD risk in AAs. Mendelian randomization (MR) analysis demonstrates the specificity of telmisartan's protective mechanism to AAs.
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Affiliation(s)
- Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Wanzhu Tu
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA
| | - Noll Campbell
- Department of Pharmacy Practice, Purdue University, West Lafayette, Indiana, USA
| | - Andrew A. Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, Ohio, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, Ohio, USA
| | - James B. Leverenz
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Sujuan Gao
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, Nevada, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
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Boswell Z, Verga JU, Mackle J, Guerrero-Vazquez K, Thomas OP, Cray J, Wolf BJ, Choo YM, Croot P, Hamann MT, Hardiman G. In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses. Infect Drug Resist 2023; 16:2321-2338. [PMID: 37155475 PMCID: PMC10122865 DOI: 10.2147/idr.s395203] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 03/16/2023] [Indexed: 05/10/2023] Open
Abstract
The urgent need for SARS-CoV-2 controls has led to a reassessment of approaches to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. There are yet no clinically approved broad-spectrum antivirals available for beta-coronaviruses. Discovery pipelines for pan-virus medications against a broad range of betacoronaviruses are therefore a priority. A variety of marine natural product (MNP) small molecules have shown inhibitory activity against viral species. Access to large data caches of small molecule structural information is vital to finding new pharmaceuticals. Increasingly, molecular docking simulations are being used to narrow the space of possibilities and generate drug leads. Combining in-silico methods, augmented by metaheuristic optimization and machine learning (ML) allows the generation of hits from within a virtual MNP library to narrow screens for novel targets against coronaviruses. In this review article, we explore current insights and techniques that can be leveraged to generate broad-spectrum antivirals against betacoronaviruses using in-silico optimization and ML. ML approaches are capable of simultaneously evaluating different features for predicting inhibitory activity. Many also provide a semi-quantitative measure of feature relevance and can guide in selecting a subset of features relevant for inhibition of SARS-CoV-2.
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Affiliation(s)
- Zachary Boswell
- School of Biological Sciences and Institute for Global Security, Queen's University, Belfast, Northern Ireland, UK
| | - Jacopo Umberto Verga
- School of Biological Sciences and Institute for Global Security, Queen's University, Belfast, Northern Ireland, UK
- Genomic Data Science, University of Galway, Galway, Ireland
| | - James Mackle
- School of Biological Sciences and Institute for Global Security, Queen's University, Belfast, Northern Ireland, UK
| | | | - Olivier P Thomas
- School of Biological and Chemical Sciences, Ryan Institute, University of Galway, Galway, H91TK33Ireland
| | - James Cray
- Department of Biomedical Education and Anatomy, College of Medicine and Division of Biosciences, College of Dentistry, Ohio State University, Columbus, OH, USA
| | - Bethany J Wolf
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Yeun-Mun Choo
- Department of Chemistry, University of Malaya, Kuala Lumpur, Malaysia
| | - Peter Croot
- Irish Centre for Research in Applied Geoscience, Earth and Ocean Sciences and Ryan Institute, School of Natural Sciences, University of Galway, Galway, Ireland
| | - Mark T Hamann
- Departments of Drug Discovery and Biomedical Sciences and Public Health, Colleges of Pharmacy and Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Gary Hardiman
- School of Biological Sciences and Institute for Global Security, Queen's University, Belfast, Northern Ireland, UK
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
- Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
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50
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Pandey AK, Loscalzo J. Network medicine: an approach to complex kidney disease phenotypes. Nat Rev Nephrol 2023:10.1038/s41581-023-00705-0. [PMID: 37041415 DOI: 10.1038/s41581-023-00705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.
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Affiliation(s)
- Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
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