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Honap S, Jairath V, Danese S, Peyrin-Biroulet L. Navigating the complexities of drug development for inflammatory bowel disease. Nat Rev Drug Discov 2024:10.1038/s41573-024-00953-0. [PMID: 38778181 DOI: 10.1038/s41573-024-00953-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2024] [Indexed: 05/25/2024]
Abstract
Inflammatory bowel disease (IBD) - consisting of ulcerative colitis and Crohn's disease - is a complex, heterogeneous, immune-mediated inflammatory condition with a multifactorial aetiopathogenesis. Despite therapeutic advances in this arena, a ceiling effect has been reached with both single-agent monoclonal antibodies and advanced small molecules. Therefore, there is a need to identify novel targets, and the development of companion biomarkers to select responders is vital. In this Perspective, we examine how advances in machine learning and tissue engineering could be used at the preclinical stage where attrition rates are high. For novel agents reaching clinical trials, we explore factors decelerating progression, particularly the decline in IBD trial recruitment, and assess how innovative approaches such as reconfiguring trial designs, harmonizing end points and incorporating digital technologies into clinical trials can address this. Harnessing opportunities at each stage of the drug development process may allow for incremental gains towards more effective therapies.
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Affiliation(s)
- Sailish Honap
- Department of Gastroenterology, St George's University Hospitals NHS Foundation Trust, London, UK.
- School of Immunology and Microbial Sciences, King's College London, London, UK.
- INFINY Institute, Nancy University Hospital, Vandœuvre-lès-Nancy, France.
| | - Vipul Jairath
- Division of Gastroenterology, Department of Medicine, Schulich School of Medicine, Western University, London, Ontario, Canada
- Lawson Health Research Institute, Western University, London, Ontario, Canada
- Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada
| | - Silvio Danese
- Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, Vita-Salute San Raffaele University, Milan, Italy
| | - Laurent Peyrin-Biroulet
- INFINY Institute, Nancy University Hospital, Vandœuvre-lès-Nancy, France.
- Department of Gastroenterology, Nancy University Hospital, Vandœuvre-lès-Nancy, France.
- INSERM, NGERE, University of Lorraine, Nancy, France.
- FHU-CURE, Nancy University Hospital, Vandœuvre-lès-Nancy, France.
- Groupe Hospitalier privé Ambroise Paré - Hartmann, Paris IBD Center, Neuilly sur Seine, France.
- Division of Gastroenterology and Hepatology, McGill University Health Centre, Montreal, Quebec, Canada.
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2
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Wang L, Lu Y, Li D, Zhou Y, Yu L, Mesa Eguiagaray I, Campbell H, Li X, Theodoratou E. The landscape of the methodology in drug repurposing using human genomic data: a systematic review. Brief Bioinform 2024; 25:bbad527. [PMID: 38279645 PMCID: PMC10818097 DOI: 10.1093/bib/bbad527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/24/2023] [Accepted: 12/19/2023] [Indexed: 01/28/2024] Open
Abstract
The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.
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Affiliation(s)
- Lijuan Wang
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Lu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Doudou Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yajing Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lili Yu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ines Mesa Eguiagaray
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Xue Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, The University of Edinburgh MRC Institute of Genetics and Cancer, Edinburgh, UK
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3
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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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4
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Lee CW, Kim SM, Sa S, Hong M, Nam SM, Han HW. Relationship between drug targets and drug-signature networks: a network-based genome-wide landscape. BMC Med Genomics 2023; 16:17. [PMID: 36717817 PMCID: PMC9885570 DOI: 10.1186/s12920-023-01444-8] [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: 08/10/2022] [Accepted: 01/24/2023] [Indexed: 01/31/2023] Open
Abstract
Drugs produce pharmaceutical and adverse effects that arise from the complex relationship between drug targets and signatures; by considering such relationships, we can begin to understand the cellular mechanisms of drugs. In this study, we selected 463 genes from the DSigDB database corresponding to targets and signatures for 382 FDA-approved drugs with both protein binding information for a drug-target score (KDTN, i.e., the degree to which the protein encoded by the gene binds to a number of drugs) and microarray signature information for a drug-sensitive score (KDSN, i.e., the degree to which gene expression is stimulated by the drug). Accordingly, we constructed two drug-gene bipartite network models, a drug-target network and drug-signature network, which were merged into a multidimensional model. Analysis revealed that the KDTN and KDSN were in mutually exclusive and reciprocal relationships in terms of their biological network structure and gene function. A symmetric balance between the KDTN and KDSN of genes facilitates the possibility of therapeutic drug effects in whole genome. These results provide new insights into the relationship between drugs and genes, specifically drug targets and drug signatures.
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Affiliation(s)
- Chae Won Lee
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
| | - Sung Min Kim
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
| | - Soonok Sa
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
| | - Myunghee Hong
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
| | - Sang-Min Nam
- grid.452398.10000 0004 0570 1076Department of Ophthalmology, CHA Bundang Medical Center, CHA University, Seongnam, South Korea
| | - Hyun Wook Han
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
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5
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Garza-Hernandez D, Sepulveda-Villegas M, Garcia-Pelaez J, Aguirre-Gamboa R, Lakatos PL, Estrada K, Martinez-Vazquez M, Trevino V. A systematic review and functional bioinformatics analysis of genes associated with Crohn's disease identify more than 120 related genes. BMC Genomics 2022; 23:302. [PMID: 35418025 PMCID: PMC9008988 DOI: 10.1186/s12864-022-08491-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/28/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Crohn's disease is one of the two categories of inflammatory bowel diseases that affect the gastrointestinal tract. The heritability estimate has been reported to be 0.75. Several genes linked to Crohn's disease risk have been identified using a plethora of strategies such as linkage-based studies, candidate gene association studies, and lately through genome-wide association studies (GWAS). Nevertheless, to our knowledge, a compendium of all the genes that have been associated with CD is lacking. METHODS We conducted functional analyses of a gene set generated from a systematic review where genes potentially related to CD found in the literature were analyzed and classified depending on the genetic evidence reported and putative biological function. For this, we retrieved and analyzed 2496 abstracts comprising 1067 human genes plus 22 publications regarding 133 genes from GWAS Catalog. Then, each gene was curated and categorized according to the type of evidence associated with Crohn's disease. RESULTS We identified 126 genes associated with Crohn's disease risk by specific experiments. Additionally, 71 genes were recognized associated through GWAS alone, 18 to treatment response, 41 to disease complications, and 81 to related diseases. Bioinformatic analysis of the 126 genes supports their importance in Crohn's disease and highlights genes associated with specific aspects such as symptoms, drugs, and comorbidities. Importantly, most genes were not included in commercial genetic panels suggesting that Crohn's disease is genetically underdiagnosed. CONCLUSIONS We identified a total of 126 genes from PubMed and 71 from GWAS that showed evidence of association to diagnosis, 18 to treatment response, and 41 to disease complications in Crohn's disease. This prioritized gene catalog can be explored at http://victortrevino.bioinformatics.mx/CrohnDisease .
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Affiliation(s)
- Debora Garza-Hernandez
- Tecnologico de Monterrey, Escuela de Medicina, Cátedra de Bioinformática, Av. Morones Prieto No. 3000, Colonia Los Doctores, 64710, Monterrey, Nuevo León, Mexico
| | - Maricruz Sepulveda-Villegas
- Tecnologico de Monterrey, Escuela de Medicina, Cátedra de Bioinformática, Av. Morones Prieto No. 3000, Colonia Los Doctores, 64710, Monterrey, Nuevo León, Mexico
| | - Jose Garcia-Pelaez
- Instituto de Investigação e Inovação em Saude-i3S, Universidade do Porto, Porto, Portugal.,Ipatimup, Institute of Molecular Pathology and Immunology at the University of Porto, Porto, Portugal
| | | | - Peter L Lakatos
- McGill University Health Centre, Division of Gastroenterology, IBD Centre, Montreal General Hospital, 1650 Ave. Cedar, D16.173.1, Montreal, QC, H3G 1A4, Canada
| | - Karol Estrada
- Graduate Professional Studies, Brandeis University, Waltham, MA, 02453, USA
| | - Manuel Martinez-Vazquez
- Tecnologico de Monterrey, Instituto de Medicina Interna, Centro Médico Zambrano Hellion, Av. Batallón de San Patricio No. 112, Colonia Real San Agustín, 66278, San Pedro Garza García, Nuevo León, Mexico
| | - Victor Trevino
- Tecnologico de Monterrey, Escuela de Medicina, Cátedra de Bioinformática, Av. Morones Prieto No. 3000, Colonia Los Doctores, 64710, Monterrey, Nuevo León, Mexico. .,Tecnologico de Monterrey, The Institute for Obesity Research, Integrative Biology Unit, Eugenio Garza Sada 2501 Avenue, 64849, Monterrey, Nuevo Leon, Mexico.
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6
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Halder D, Das S, Joseph A, Jeyaprakash RS. Molecular docking and dynamics approach to in silico drug repurposing for inflammatory bowels disease by targeting TNF alpha. J Biomol Struct Dyn 2022; 41:3462-3475. [PMID: 35285757 DOI: 10.1080/07391102.2022.2050948] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Inflammatory bowel disease is a chronic disorder of the large intestine with the prevalence of approximately 400 cases in 100000, and it is rising day by day. However, several drugs like sulfasalazine (composed of sulfapyridine and 5-aminosalicylic acid or 5-ASA), corticosteroids, and immunosuppressants manage the disease. But there are no absolute treatments for the pain and inflammation of the disease. TNFα is an important target, and drugs like infliximab and adalimumab have pharmacological potency but with pronounced toxicity. So, we choose this major target TNFα for the virtual screening of US-FDA-approved drugs for its repurposing using the in silico method. The protein TNFα (PDB ID: 2AZ5) with small molecule inhibitor and the US-FDA-approved drug molecules (from Zinc database) were first imported and prepared using Protein Preparation Wizard and LigPrep, respectively, followed by molecular docking, ADMET analysis and prime MMGBSA. After that, the drugs were shortlisted according to dock score, ADMET parameters and MM GBSA dG binding score. After that, the shortlisted drug molecules were subjected to an induced-fit docking analysis. Two of the most promising molecules, ZINC000003830957 (Iopromide) and ZINC000003830635 (Deferoxamine), were chosen for molecular dynamics simulation. Finally, the bioisosteric replacement was used to improve the ADMET properties of these molecules. This research provides an idea for drug exploration and computational tools for drug discovery in treating inflammatory bowel disease.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Debojyoti Halder
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Subham Das
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Alex Joseph
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - R S Jeyaprakash
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
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7
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Schuler J, Falls Z, Mangione W, Hudson ML, Bruggemann L, Samudrala R. Evaluating the performance of drug-repurposing technologies. Drug Discov Today 2022; 27:49-64. [PMID: 34400352 PMCID: PMC10014214 DOI: 10.1016/j.drudis.2021.08.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 06/20/2021] [Accepted: 08/08/2021] [Indexed: 01/22/2023]
Abstract
Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.
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Affiliation(s)
- James Schuler
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Matthew L Hudson
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Liana Bruggemann
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
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Fiocchi C, Dragoni G, Iliopoulos D, Katsanos K, Ramirez VH, Suzuki K, Torres J, Scharl M. Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How. J Crohns Colitis 2021; 15:1410-1430. [PMID: 33733656 DOI: 10.1093/ecco-jcc/jjab051] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Many diseases that affect modern humans fall in the category of complex diseases, thus called because they result from a combination of multiple aetiological and pathogenic factors. Regardless of the organ or system affected, complex diseases present major challenges in diagnosis, classification, and management. Current forms of therapy are usually applied in an indiscriminate fashion based on clinical information, but even the most advanced drugs only benefit a limited number of patients and to a variable and unpredictable degree. This 'one measure does not fit all' situation has spurred the notion that therapy for complex disease should be tailored to individual patients or groups of patients, giving rise to the notion of 'precision medicine' [PM]. Inflammatory bowel disease [IBD] is a prototypical complex disease where the need for PM has become increasingly clear. This prompted the European Crohn's and Colitis Organisation to focus the Seventh Scientific Workshop on this emerging theme. The articles in this special issue of the Journal address the various complementary aspects of PM in IBD, including what PM is; why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM in IBD. This first article of this series is structured on three simple concepts [what, why, and how] and addresses the definition of PM, discusses the rationale for the need of PM in IBD, and outlines the methodology required to implement PM in IBD in a correct and clinically meaningful way.
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Affiliation(s)
- Claudio Fiocchi
- Department of Inflammation & Immunity, Lerner Research Institute, and Department of Gastroenterology, Hepatology & Nutrition, Digestive Disease Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Gabriele Dragoni
- Gastroenterology Research Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence,Italy.,IBD Referral Center, Gastroenterology Department, Careggi University Hospital, Florence,Italy
| | | | - Konstantinos Katsanos
- Division of Gastroenterology, Department of Internal Medicine, University of Ioannina School of Health Sciences, Ioannina,Greece
| | - Vicent Hernandez Ramirez
- Department of Gastroenterology, Xerencia Xestión Integrada de Vigo, and Research Group in Digestive Diseases, Galicia Sur Health Research Institute [IIS Galicia Sur], SERGAS-UVIGO, Vigo, Spain
| | - Kohei Suzuki
- Division of Digestive and Liver Diseases, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX,USA
| | | | - Joana Torres
- Division of Gastroenterology, Hospital Beatriz Ângelo, Loures, Portugal
| | - Michael Scharl
- Department of Gastroenterology and Hepatology, University Hospital Zürich, Zürich, Switzerland
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Madugula SS, John L, Nagamani S, Gaur AS, Poroikov VV, Sastry GN. Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing. Comput Biol Med 2021; 138:104856. [PMID: 34555571 DOI: 10.1016/j.compbiomed.2021.104856] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/24/2021] [Accepted: 09/06/2021] [Indexed: 12/27/2022]
Abstract
Machine learning and data-driven approaches are currently being widely used in drug discovery and development due to their potential advantages in decision-making based on the data leveraged from existing sources. Applying these approaches to drug repurposing (DR) studies can identify new relationships between drug molecules, therapeutic targets and diseases that will eventually help in generating new insights for developing novel therapeutics. In the current study, a dataset of 1671 approved drugs is analyzed using a combined approach involving unsupervised Machine Learning (ML) techniques (Principal Component Analysis (PCA) followed by k-means clustering) and Structure-Activity Relationships (SAR) predictions for DR. PCA is applied on all the two dimensional (2D) molecular descriptors of the dataset and the first five Principal Components (PC) were subsequently used to cluster the drugs into nine well separated clusters using k-means algorithm. We further predicted the biological activities for the drug-dataset using the PASS (Predicted Activities Spectra of Substances) tool. These predicted activity values are analyzed systematically to identify repurposable drugs for various diseases. Clustering patterns obtained from k-means showed that every cluster contains subgroups of structurally similar drugs that may or may not have similar therapeutic indications. We hypothesized that such structurally similar but therapeutically different drugs can be repurposed for the native indications of other drugs of the same cluster based on their high predicted biological activities obtained from PASS analysis. In line with this, we identified 66 drugs from the nine clusters which are structurally similar but have different therapeutic uses and can therefore be repurposed for one or more native indications of other drugs of the same cluster. Some of these drugs not only share a common substructure but also bind to the same target and may have a similar mechanism of action, further supporting our hypothesis. Furthermore, based on the analysis of predicted biological activities, we identified 1423 drugs that can be repurposed for 366 new indications against several diseases. In this study, an integrated approach of unsupervised ML and SAR analysis have been used to identify new indications for approved drugs and the study provides novel insights into clustering patterns generated through descriptor level analysis of approved drugs.
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Affiliation(s)
- Sita Sirisha Madugula
- Centre for Molecular Modeling, CSIR-Indian Institute of Chemical Technology, Hyderabad, 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Lijo John
- Centre for Molecular Modeling, CSIR-Indian Institute of Chemical Technology, Hyderabad, 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Selvaraman Nagamani
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India; Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, Assam, 785 006, India
| | - Anamika Singh Gaur
- Centre for Molecular Modeling, CSIR-Indian Institute of Chemical Technology, Hyderabad, 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India; Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, Assam, 785 006, India
| | - Vladimir V Poroikov
- Laboratory for Structure-Function Drug Design, Institute of Biomedical Chemistry, Moscow, 119121, Russia
| | - G Narahari Sastry
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India; Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, Assam, 785 006, India.
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10
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Xu Y, Kong J, Hu P. Computational Drug Repurposing for Alzheimer's Disease Using Risk Genes From GWAS and Single-Cell RNA Sequencing Studies. Front Pharmacol 2021; 12:617537. [PMID: 34276354 PMCID: PMC8277916 DOI: 10.3389/fphar.2021.617537] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 06/15/2021] [Indexed: 01/14/2023] Open
Abstract
Background: Traditional therapeutics targeting Alzheimer's disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.
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Affiliation(s)
- Yun Xu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
| | - Jiming Kong
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
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11
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Abstract
BACKGROUND Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results. METHODS Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed. RESULTS The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies. CONCLUSIONS The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.
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Affiliation(s)
- Claudio Fiocchi
- Department of Inflammation & Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.,Department of Gastroenterology, Hepatology and Nutrition, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, Ohio, USA
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Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease. PLoS Comput Biol 2021; 17:e1008631. [PMID: 33544718 PMCID: PMC7891788 DOI: 10.1371/journal.pcbi.1008631] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 02/18/2021] [Accepted: 12/14/2020] [Indexed: 12/15/2022] Open
Abstract
For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment. Offering personalized treatment results is an important tenant of precision medicine, particularly in complex diseases which have high variability in disease manifestation and treatment response. We have developed a novel framework, NetPTP (Network-based Personalized Treatment Prediction), for making personalized drug ranking lists for patient samples. Our method uses networks to model drug effects from gene expression data and applies these captured effects to individual samples to produce tailored drug treatment rankings. We applied NetPTP to inflammatory bowel disease, yielding insights into the treatment of this particular disease. Our method is modular and generalizable, and thus can be applied to other diseases that could benefit from a personalized treatment approach.
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Mousavi T, Hadizadeh N, Nikfar S, Abdollahi M. Drug discovery strategies for modulating oxidative stress in gastrointestinal disorders. Expert Opin Drug Discov 2020; 15:1309-1341. [DOI: 10.1080/17460441.2020.1791077] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Taraneh Mousavi
- Pharmaceutical Sciences Research Center (PSRC), The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, Iran
- School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Nastaran Hadizadeh
- Pharmaceutical Sciences Research Center (PSRC), The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, Iran
- School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
- Personalized Medicine Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Shekoufeh Nikfar
- Pharmaceutical Sciences Research Center (PSRC), The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, Iran
- School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
- Personalized Medicine Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Abdollahi
- Pharmaceutical Sciences Research Center (PSRC), The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, Iran
- School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
- Personalized Medicine Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
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Ferreira-Duarte M, Sousa JB, Diniz C, Sousa T, Duarte-Araújo M, Morato M. Experimental and Clinical Evidence of Endothelial Dysfunction in Inflammatory Bowel Disease. Curr Pharm Des 2020; 26:3733-3747. [PMID: 32611296 DOI: 10.2174/1381612826666200701212414] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 06/04/2020] [Indexed: 02/07/2023]
Abstract
The endothelium has a crucial role in proper hemodynamics. Inflammatory bowel disease (IBD) is mainly a chronic inflammatory condition of the gastrointestinal tract. However, considerable evidence points to high cardiovascular risk in patients with IBD. This review positions the basic mechanisms of endothelial dysfunction in the IBD setting (both clinical and experimental). Furthermore, we review the main effects of drugs used to treat IBD in endothelial (dys)function. Moreover, we leave challenging points for enlarging the therapeutic arsenal for IBD with new or repurposed drugs that target endothelial dysfunction besides inflammation.
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Affiliation(s)
| | | | - Carmen Diniz
- LAQV@REQUIMTE, University of Porto, Porto, Portugal
| | - Teresa Sousa
- Department of Biomedicine, Unit of Pharmacology and Therapeutics, Faculty of Medicine, University of Porto, Porto, Portugal
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15
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Lau A, So HC. Turning genome-wide association study findings into opportunities for drug repositioning. Comput Struct Biotechnol J 2020; 18:1639-1650. [PMID: 32670504 PMCID: PMC7334463 DOI: 10.1016/j.csbj.2020.06.015] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 06/05/2020] [Accepted: 06/05/2020] [Indexed: 02/02/2023] Open
Abstract
Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored. The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations. Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.
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Affiliation(s)
- Alexandria Lau
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hon-Cheong So
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Zoology Institute of Zoology and The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
- Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong SAR, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Corresponding author at: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
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16
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Hoter A, Naim HY. The Functions and Therapeutic Potential of Heat Shock Proteins in Inflammatory Bowel Disease-An Update. Int J Mol Sci 2019; 20:ijms20215331. [PMID: 31717769 PMCID: PMC6862201 DOI: 10.3390/ijms20215331] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 10/24/2019] [Accepted: 10/25/2019] [Indexed: 02/07/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a multifactorial human intestinal disease that arises from numerous, yet incompletely defined, factors. Two main forms, Crohn's disease (CD) and ulcerative colitis (UC), lead to a chronic pathological form. Heat shock proteins (HSPs) are stress-responsive molecules involved in various pathophysiological processes. Several lines of evidence link the expression of HSPs to the development and prognosis of IBD. HSP90, HSP70 and HSP60 have been reported to contribute to IBD in different aspects. Moreover, induction and/or targeted inhibition of specific HSPs have been suggested to ameliorate the disease consequences. In the present review, we shed the light on the role of HSPs in IBD and their targeting to prevent further disease progression.
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Affiliation(s)
- Abdullah Hoter
- Department of Biochemistry and Chemistry of Nutrition, Faculty of Veterinary Medicine, Cairo University, Giza 12211, Egypt or
- Department of Physiological Chemistry, University of Veterinary Medicine Hannover, 30559 Hannover, Germany
| | - Hassan Y. Naim
- Department of Physiological Chemistry, University of Veterinary Medicine Hannover, 30559 Hannover, Germany
- Correspondence: ; Tel.: +49-511-953-8780; Fax: +49-511-953-8585
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Satish Kumar K, Velayutham R, Roy KK. A systematic computational analysis of human matrix metalloproteinase 13 (MMP-13) crystal structures and structure-based identification of prospective drug candidates as MMP-13 inhibitors repurposable for osteoarthritis. J Biomol Struct Dyn 2019; 38:3074-3086. [PMID: 31378153 DOI: 10.1080/07391102.2019.1651221] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
| | - Ravichandiran Velayutham
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Kolkata, West Bengal, India
| | - Kuldeep K. Roy
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Kolkata, West Bengal, India
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