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Yang JJ, Goff A, Wild DJ, Ding Y, Annis A, Kerber R, Foote B, Passi A, Duerksen JL, London S, Puhl AC, Lane TR, Braunstein M, Waddell SJ, Ekins S. Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus. Tuberculosis (Edinb) 2024; 146:102500. [PMID: 38432118 PMCID: PMC10978224 DOI: 10.1016/j.tube.2024.102500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/20/2024] [Accepted: 02/24/2024] [Indexed: 03/05/2024]
Abstract
Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 μM; M. abscessus IC90 59.1 μM) and tribromsalan (M. tuberculosis IC90 76.92 μM; M. abscessus IC90 147.4 μM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.
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Affiliation(s)
- Jeremy J Yang
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; Department of Internal Medicine Translational Informatics Division, University of New Mexico, Albuquerque, NM, USA
| | - Aaron Goff
- Department of Global Health and Infection, Brighton & Sussex Medical School, University of Sussex, UK
| | - David J Wild
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA
| | - Ying Ding
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; School of Information, Dell Medical School, University of Texas, Austin, TX, USA
| | - Ayano Annis
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, NC, 27599, USA
| | | | | | - Anurag Passi
- Department of Pediatrics, UC San Diego, San Diego, CA, USA
| | | | | | - Ana C Puhl
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Thomas R Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Miriam Braunstein
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Simon J Waddell
- Department of Global Health and Infection, Brighton & Sussex Medical School, University of Sussex, UK
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
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2
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Ma C, Liu S, Koslicki D. MetagenomicKG: a knowledge graph for metagenomic applications. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.14.585056. [PMID: 38559251 PMCID: PMC10980061 DOI: 10.1101/2024.03.14.585056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Motivation The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities and their interrelations into a cohesive network. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. Results We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information from widely used databases, and further links these with established biomedical knowledge graphs to expand biological connections. Through several use cases, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction. Availability and Implementation The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available at Github: https://github.com/KoslickiLab/MetagenomicKG. We also host a Neo4j instance: http://mkg.cse.psu.edu:7474 for accessing and querying this graph.
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Affiliation(s)
- Chunyu Ma
- Huck Institutes of the Life Sciences, Pennsylvania State University, State College, Pennsylvania, USA
| | - Shaopeng Liu
- Huck Institutes of the Life Sciences, Pennsylvania State University, State College, Pennsylvania, USA
| | - David Koslicki
- Huck Institutes of the Life Sciences, Pennsylvania State University, State College, Pennsylvania, USA
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania, USA
- Department of Biology, Pennsylvania State University, State College, Pennsylvania, USA
- The One Health Microbiome Center, Huck Institutes of the Life Sciences, Pennsylvania State University, State College, Pennsylvania, USA
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3
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Beasley JMT, Korn DR, Tucker NN, Alves ETM, Muratov EN, Bizon C, Tropsha A. ExEmPLAR (Extracting, Exploring, and Embedding Pathways Leading to Actionable Research): a user-friendly interface for knowledge graph mining. Bioinformatics 2024; 40:btad779. [PMID: 38175789 PMCID: PMC10812875 DOI: 10.1093/bioinformatics/btad779] [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: 03/24/2023] [Revised: 12/05/2023] [Accepted: 01/02/2024] [Indexed: 01/06/2024] Open
Abstract
SUMMARY Knowledge graphs are being increasingly used in biomedical research to link large amounts of heterogenous data and facilitate reasoning across diverse knowledge sources. Wider adoption and exploration of knowledge graphs in the biomedical research community is limited by requirements to understand the underlying graph structure in terms of entity types and relationships, represented as nodes and edges, respectively, and learn specialized query languages for graph mining and exploration. We have developed a user-friendly interface dubbed ExEmPLAR (Extracting, Exploring, and Embedding Pathways Leading to Actionable Research) to aid reasoning over biomedical knowledge graphs and assist with data-driven research and hypothesis generation. We explain the key functionalities of ExEmPLAR and demonstrate its use with a case study considering the relationship of Trypanosoma cruzi, the etiological agent of Chagas disease, to frequently associated cardiovascular conditions. AVAILABILITY AND IMPLEMENTATION ExEmPLAR is freely accessible at https://www.exemplar.mml.unc.edu/. For code and instructions for the using the application, see: https://github.com/beasleyjonm/AOP-COP-Path-Extractor.
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Affiliation(s)
- Jon-Michael T Beasley
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Daniel R Korn
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Nyssa N Tucker
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Erick T M Alves
- Department of Pharmacy, University of São Paulo, São Paulo, SP 05508, Brazil
| | - Eugene N Muratov
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Chris Bizon
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Alexander Tropsha
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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4
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Sessions Z, Bobrowski T, Martin HJ, Beasley JMT, Kothari A, Phares T, Li M, Alves VM, Scotti MT, Moorman NJ, Baric R, Tropsha A, Muratov EN. Praemonitus praemunitus: can we forecast and prepare for future viral disease outbreaks? FEMS Microbiol Rev 2023; 47:fuad048. [PMID: 37596064 PMCID: PMC10532129 DOI: 10.1093/femsre/fuad048] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 07/04/2023] [Accepted: 08/17/2023] [Indexed: 08/20/2023] Open
Abstract
Understanding the origins of past and present viral epidemics is critical in preparing for future outbreaks. Many viruses, including SARS-CoV-2, have led to significant consequences not only due to their virulence, but also because we were unprepared for their emergence. We need to learn from large amounts of data accumulated from well-studied, past pandemics and employ modern informatics and therapeutic development technologies to forecast future pandemics and help minimize their potential impacts. While acknowledging the complexity and difficulties associated with establishing reliable outbreak predictions, herein we provide a perspective on the regions of the world that are most likely to be impacted by future outbreaks. We specifically focus on viruses with epidemic potential, namely SARS-CoV-2, MERS-CoV, DENV, ZIKV, MAYV, LASV, noroviruses, influenza, Nipah virus, hantaviruses, Oropouche virus, MARV, and Ebola virus, which all require attention from both the public and scientific community to avoid societal catastrophes like COVID-19. Based on our literature review, data analysis, and outbreak simulations, we posit that these future viral epidemics are unavoidable, but that their societal impacts can be minimized by strategic investment into basic virology research, epidemiological studies of neglected viral diseases, and antiviral drug discovery.
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Affiliation(s)
- Zoe Sessions
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
| | - Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
| | - Holli-Joi Martin
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
| | - Jon-Michael T Beasley
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
| | - Aneri Kothari
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
| | - Trevor Phares
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
- School of Chemistry, University of Louisville, 2320 S Brook St, Louisville, KY 40208, United States
| | - Michael Li
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
| | - Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
| | - Marcus T Scotti
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Campus I Lot. Cidade Universitaria, PB, 58051-900, Brazil
| | - Nathaniel J Moorman
- Department of Microbiology and Immunology, University of North Carolina, 116 Manning Drive, Chapel Hill, NC 27599, United States
| | - Ralph Baric
- Department of Epidemiology, University of North Carolina, 401 Pittsboro St, Chapel Hill, NC 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, Chapel Hill, NC 27599, United States
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5
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Veljković AN, Orlov YL, Mitić NS. BioGraph: Data Model for Linking and Querying Diverse Biological Metadata. Int J Mol Sci 2023; 24:ijms24086954. [PMID: 37108117 PMCID: PMC10138499 DOI: 10.3390/ijms24086954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/30/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Studying the association of gene function, diseases, and regulatory gene network reconstruction demands data compatibility. Data from different databases follow distinct schemas and are accessible in heterogenic ways. Although the experiments differ, data may still be related to the same biological entities. Some entities may not be strictly biological, such as geolocations of habitats or paper references, but they provide a broader context for other entities. The same entities from different datasets can share similar properties, which may or may not be found within other datasets. Joint, simultaneous data fetching from multiple data sources is complicated for the end-user or, in many cases, unsupported and inefficient due to differences in data structures and ways of accessing the data. We propose BioGraph-a new model that enables connecting and retrieving information from the linked biological data that originated from diverse datasets. We have tested the model on metadata collected from five diverse public datasets and successfully constructed a knowledge graph containing more than 17 million model objects, of which 2.5 million are individual biological entity objects. The model enables the selection of complex patterns and retrieval of matched results that can be discovered only by joining the data from multiple sources.
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Affiliation(s)
- Aleksandar N Veljković
- Faculty of Mathematics, University of Belgrade, Studentski trg 16, 11158 Belgrade, Serbia
| | - Yuriy L Orlov
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
- Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples' Friendship University of Russia, 117198 Moscow, Russia
| | - Nenad S Mitić
- Faculty of Mathematics, University of Belgrade, Studentski trg 16, 11158 Belgrade, Serbia
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6
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Zhao C, Tan T, Zhang E, Wang T, Gong H, Jia Q, Liu T, Yang X, Zhao J, Wu Z, Wei H, Xiao J, Yang C. A chronicle review of new techniques that facilitate the understanding and development of optimal individualized therapeutic strategies for chordoma. Front Oncol 2022; 12:1029670. [PMID: 36465398 PMCID: PMC9708744 DOI: 10.3389/fonc.2022.1029670] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 10/19/2022] [Indexed: 09/01/2023] Open
Abstract
Chordoma is a rare malignant bone tumor that mainly occurs in the sacrum and the clivus/skull base. Surgical resection is the treatment of choice for chordoma, but the local recurrence rate is high with unsatisfactory prognosis. Compared with other common tumors, there is not much research and individualized treatment for chordoma, partly due to the rarity of the disease and the lack of appropriate disease models, which delay the discovery of therapeutic strategies. Recent advances in modern techniques have enabled gaining a better understanding of a number of rare diseases, including chordoma. Since the beginning of the 21st century, various chordoma cell lines and animal models have been reported, which have partially revealed the intrinsic mechanisms of tumor initiation and progression with the use of next-generation sequencing (NGS) techniques. In this study, we performed a systematic overview of the chordoma models and related sequencing studies in a chronological manner, from the first patient-derived chordoma cell line (U-CH1) to diverse preclinical models such as the patient-derived organoid-based xenograft (PDX) and patient-derived organoid (PDO) models. The use of modern sequencing techniques has discovered mutations and expression signatures that are considered potential treatment targets, such as the expression of Brachyury and overactivated receptor tyrosine kinases (RTKs). Moreover, computational and bioinformatics techniques have made drug repositioning/repurposing and individualized high-throughput drug screening available. These advantages facilitate the research and development of comprehensive and personalized treatment strategies for indicated patients and will dramatically improve their prognoses in the near feature.
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Affiliation(s)
- Chenglong Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tao Tan
- Department of Orthopedics, 905 Hospital of People’s Liberation Army Navy, Shanghai, China
| | - E. Zhang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Ting Wang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haiyi Gong
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Qi Jia
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tielong Liu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Xinghai Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jian Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Zhipeng Wu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haifeng Wei
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jianru Xiao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Cheng Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
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7
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Waldrop AM, Cheadle JB, Bradford K, Preiss A, Chew R, Holt JR, Kebede Y, Braswell N, Watson M, Hench V, Crerar A, Ball CM, Schreep C, Linebaugh PJ, Hiles H, Boyles R, Bizon C, Krishnamurthy A, Cox S. Dug: a semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories. Bioinformatics 2022; 38:3252-3258. [PMID: 35441678 PMCID: PMC9991886 DOI: 10.1093/bioinformatics/btac284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 03/04/2022] [Accepted: 04/15/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. RESULTS Developed through the National Heart, Lung and Blood Institute's (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets. On a manually curated search dataset, Dug's total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch's total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results. AVAILABILITY AND IMPLEMENTATION Dug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexander M Waldrop
- Center for Genomics, Bioinformatics, and Translational Research, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - John B Cheadle
- Research Computing Division, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Kira Bradford
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Alexander Preiss
- Center for Data Science, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Robert Chew
- Center for Data Science, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Jonathan R Holt
- Center for Data Science, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Yaphet Kebede
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Nathan Braswell
- Research Computing Division, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Matt Watson
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Virginia Hench
- Center for Genomics, Bioinformatics, and Translational Research, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Andrew Crerar
- Center for Genomics, Bioinformatics, and Translational Research, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Chris M Ball
- Research Computing Division, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Carl Schreep
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - P J Linebaugh
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Hannah Hiles
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Rebecca Boyles
- Research Computing Division, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Chris Bizon
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Ashok Krishnamurthy
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7548, USA
| | - Steve Cox
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
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8
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Korn D, Thieme AJ, Alves VM, Yeakey M, V V B Borba J, Capuzzi SJ, Fecho K, Bizon C, Edwards SW, Chirkova R, Colvis CM, Southall NT, Austin CP, Muratov EN, Tropsha A. Defining clinical outcome pathways. Drug Discov Today 2022; 27:1671-1678. [PMID: 35182735 DOI: 10.1016/j.drudis.2022.02.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/07/2022] [Accepted: 02/14/2022] [Indexed: 12/23/2022]
Abstract
Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) → intermediate event(s) → clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.e., drug repurposing) therapeutic applications. We also describe the elucidation of COPs for several drugs of interest using the publicly accessible Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP) biomedical knowledge graph-mining tool. We propose that broader use of COP uncovered with the help of biomedical knowledge graph mining will likely accelerate drug discovery and repurposing efforts.
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Affiliation(s)
- Daniel Korn
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA; UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Andrew J Thieme
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Vinicius M Alves
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Michael Yeakey
- Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
| | - Joyce V V B Borba
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Stephen J Capuzzi
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Karamarie Fecho
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA
| | - Chris Bizon
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA
| | | | - Rada Chirkova
- Department of Computer Science, North Carolina State University, Raleigh, NC, USA
| | - Christine M Colvis
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Noel T Southall
- Department of Computer Science, North Carolina State University, Raleigh, NC, USA
| | - Christopher P Austin
- Department of Computer Science, North Carolina State University, Raleigh, NC, USA
| | - Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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9
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Alves VM, Korn D, Pervitsky V, Thieme A, Capuzzi S, Baker N, Chirkova R, Ekins S, Muratov EN, Hickey A, Tropsha A. Knowledge-based approaches to drug discovery for rare diseases. Drug Discov Today 2022; 27:490-502. [PMID: 34718207 PMCID: PMC9124594 DOI: 10.1016/j.drudis.2021.10.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/13/2021] [Accepted: 10/21/2021] [Indexed: 02/03/2023]
Abstract
The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.
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Affiliation(s)
- Vinicius M. Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,UNC Catalyst for Rare Diseases, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Daniel Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Vera Pervitsky
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Andrew Thieme
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Stephen Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Nancy Baker
- ParlezChem, 123 W Union Street, Hillsborough, NC, 27278, USA
| | - Rada Chirkova
- Department of Computer Science, North Carolina State University, Raleigh, NC, 27695-8206, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510 Raleigh, North Carolina 27606, USA
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB, Brazil
| | - Anthony Hickey
- UNC Catalyst for Rare Diseases, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Corresponding Authors: Addresses for correspondence: Room 1079, 120 Mason Farm Rd, Genetics Medicine Building, University of North Carolina, Chapel Hill, NC 27514; Telephone: (919) 966-2955; FAX: (919) 966-0204; . 100K Beard Hall, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA; Telephone: (919) 966-2955; FAX: (919) 966-0204;
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Corresponding Authors: Addresses for correspondence: Room 1079, 120 Mason Farm Rd, Genetics Medicine Building, University of North Carolina, Chapel Hill, NC 27514; Telephone: (919) 966-2955; FAX: (919) 966-0204; . 100K Beard Hall, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA; Telephone: (919) 966-2955; FAX: (919) 966-0204;
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10
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Korn D, Pervitsky V, Bobrowski T, Alves VM, Schmitt C, Bizon C, Baker N, Chirkova R, Cherkasov A, Muratov E, Tropsha A. COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19. J Chem Inf Model 2021; 61:5734-5741. [PMID: 34783553 PMCID: PMC8610010 DOI: 10.1021/acs.jcim.1c01285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Indexed: 12/31/2022]
Abstract
The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE.
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Affiliation(s)
- Daniel Korn
- Department of Computer Science, The
University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
27599, United States
- Laboratory for Molecular Modeling, Division of
Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy,
The University of North Carolina at Chapel Hill, Chapel Hill,
North Carolina 27599, United States
| | - Vera Pervitsky
- Laboratory for Molecular Modeling, Division of
Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy,
The University of North Carolina at Chapel Hill, Chapel Hill,
North Carolina 27599, United States
| | - Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of
Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy,
The University of North Carolina at Chapel Hill, Chapel Hill,
North Carolina 27599, United States
| | - Vinicius M. Alves
- Office of Data Science, National Toxicology Program,
NIEHS, Morrisville, North Carolina 27560, United
States
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program,
NIEHS, Morrisville, North Carolina 27560, United
States
| | - Chris Bizon
- Renaissance Computing Institute, The
University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
27599, United States
| | - Nancy Baker
- ParlezChem, 123 W. Union
Street, Hillsborough, North Carolina 27278, United States
| | - Rada Chirkova
- Department of Computer Science, North Carolina
State University, Raleigh, North Carolina 27606-5550, United
States
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of
British Columbia, Vancouver, BC V6H 3Z6, Canada
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of
Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy,
The University of North Carolina at Chapel Hill, Chapel Hill,
North Carolina 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of
Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy,
The University of North Carolina at Chapel Hill, Chapel Hill,
North Carolina 27599, United States
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11
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Muratov EN, Amaro R, Andrade CH, Brown N, Ekins S, Fourches D, Isayev O, Kozakov D, Medina-Franco JL, Merz KM, Oprea TI, Poroikov V, Schneider G, Todd MH, Varnek A, Winkler DA, Zakharov AV, Cherkasov A, Tropsha A. A critical overview of computational approaches employed for COVID-19 drug discovery. Chem Soc Rev 2021; 50:9121-9151. [PMID: 34212944 PMCID: PMC8371861 DOI: 10.1039/d0cs01065k] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Indexed: 01/18/2023]
Abstract
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.
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Affiliation(s)
- Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
| | - Rommie Amaro
- University of California in San DiegoSan DiegoCAUSA
| | | | | | - Sean Ekins
- Collaborations PharmaceuticalsRaleighNCUSA
| | - Denis Fourches
- Department of Chemistry, North Carolina State UniversityRaleighNCUSA
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Melon UniversityPittsburghPAUSA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookNYUSA
| | | | - Kenneth M. Merz
- Department of Chemistry, Michigan State UniversityEast LansingMIUSA
| | - Tudor I. Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, University of New Mexico, AlbuquerqueNMUSA
- Department of Rheumatology and Inflammation Research, Gothenburg UniversitySweden
- Novo Nordisk Foundation Center for Protein Research, University of CopenhagenDenmark
| | | | - Gisbert Schneider
- Institute of Pharmaceutical Sciences, Swiss Federal Institute of TechnologyZurichSwitzerland
| | | | - Alexandre Varnek
- Department of Chemistry, University of StrasbourgStrasbourgFrance
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido UniversitySapporoJapan
| | - David A. Winkler
- Monash Institute of Pharmaceutical Sciences, Monash UniversityMelbourneVICAustralia
- School of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe UniversityBundooraAustralia
- School of Pharmacy, University of NottinghamNottinghamUK
| | | | - Artem Cherkasov
- Vancouver Prostate Centre, University of British ColumbiaVancouverBCCanada
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
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12
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Fecho K, Bizon C, Miller F, Schurman S, Schmitt C, Xue W, Morton K, Wang P, Tropsha A. A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-World Environmental Health Observations: Cohort Study and Informatics Application. JMIR Med Inform 2021; 9:e26714. [PMID: 34283031 PMCID: PMC8335603 DOI: 10.2196/26714] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Knowledge graphs are a common form of knowledge representation in biomedicine and many other fields. We developed an open biomedical knowledge graph-based system termed Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ROBOKOP consists of both a front-end user interface and a back-end knowledge graph. The ROBOKOP user interface allows users to posit questions and explore answer subgraphs. Users can also posit questions through direct Cypher query of the underlying knowledge graph, which currently contains roughly 6 million nodes or biomedical entities and 140 million edges or predicates describing the relationship between nodes, drawn from over 30 curated data sources. OBJECTIVE We aimed to apply ROBOKOP to survey data on workplace exposures and immune-mediated diseases from the Environmental Polymorphisms Registry (EPR) within the National Institute of Environmental Health Sciences. METHODS We analyzed EPR survey data and identified 45 associations between workplace chemical exposures and immune-mediated diseases, as self-reported by study participants (n= 4574), with 20 associations significant at P<.05 after false discovery rate correction. We then used ROBOKOP to (1) validate the associations by determining whether plausible connections exist within the ROBOKOP knowledge graph and (2) propose biological mechanisms that might explain them and serve as hypotheses for subsequent testing. We highlight the following three exemplar associations: carbon monoxide-multiple sclerosis, ammonia-asthma, and isopropanol-allergic disease. RESULTS ROBOKOP successfully returned answer sets for three queries that were posed in the context of the driving examples. The answer sets included potential intermediary genes, as well as supporting evidence that might explain the observed associations. CONCLUSIONS We demonstrate real-world application of ROBOKOP to generate mechanistic hypotheses for associations between workplace chemical exposures and immune-mediated diseases. We expect that ROBOKOP will find broad application across many biomedical fields and other scientific disciplines due to its generalizability, speed to discovery and generation of mechanistic hypotheses, and open nature.
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Affiliation(s)
- Karamarie Fecho
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Copperline Professional Solutions, Pittsboro, NC, United States
| | - Chris Bizon
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Frederick Miller
- National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Shepherd Schurman
- National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Charles Schmitt
- National Institute of Environmental Health Sciences, Durham, NC, United States
| | - William Xue
- National Institute of Environmental Health Sciences, Durham, NC, United States
| | | | - Patrick Wang
- CoVar Applied Technologies, Durham, NC, United States
| | - Alexander Tropsha
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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13
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Moon C, Jin C, Dong X, Abrar S, Zheng W, Chirkova RY, Tropsha A. Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses. J Biomed Inform 2021; 119:103838. [PMID: 34119691 DOI: 10.1016/j.jbi.2021.103838] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 05/10/2021] [Accepted: 06/08/2021] [Indexed: 10/21/2022]
Abstract
We aimed to develop and validate a new graph embedding algorithm for embedding drug-disease-target networks to generate novel drug repurposing hypotheses. Our model denotes drugs, diseases and targets as subjects, predicates and objects, respectively. Each entity is represented by a multidimensional vector and the predicate is regarded as a translation vector from a subject to an object vectors. These vectors are optimized so that when a subject-predicate-object triple represents a known drug-disease-target relationship, the summed vector between the subject and the predicate is to be close to that of the object; otherwise, the summed vector is distant from the object. The DTINet dataset was utilized to test this algorithm and discover unknown links between drugs and diseases. In cross-validation experiments, this new algorithm outperformed the original DTINet model. The MRR (Mean Reciprocal Rank) values of our models were around 0.80 while those of the original model were about 0.70. In addition, we have identified and verified several pairs of new therapeutic relations as well as adverse effect relations that were not recorded in the original DTINet dataset. This approach showed excellent performance, and the predicted drug-disease and drug-side-effect relationships were found to be consistent with literature reports. This novel method can be used to analyze diverse types of emerging biomedical and healthcare-related knowledge graphs (KG).
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Affiliation(s)
- Changsung Moon
- Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
| | - Chunming Jin
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC 27707, USA
| | - Xialan Dong
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC 27707, USA
| | - Saad Abrar
- Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
| | - Weifan Zheng
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC 27707, USA; UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Rada Y Chirkova
- Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA.
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599, USA.
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14
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Korn D, Bobrowski T, Li M, Kebede Y, Wang P, Owen P, Vaidya G, Muratov E, Chirkova R, Bizon C, Tropsha A. COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database. Bioinformatics 2021; 37:586-587. [PMID: 33175089 PMCID: PMC7890668 DOI: 10.1093/bioinformatics/btaa718] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 07/30/2020] [Accepted: 11/09/2020] [Indexed: 11/12/2022] Open
Abstract
SUMMARY In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP) biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to generate new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19 by establishing respective confirmatory pathways of drug action. AVAILABILITY AND IMPLEMENTATION COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.
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Affiliation(s)
- Daniel Korn
- Department of Computer Science, University of North Carolina at Chapel Hill, USA
| | - Tesia Bobrowski
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, USA
| | - Michael Li
- Department of Computer Science, University of North Carolina at Chapel Hill, USA
| | - Yaphet Kebede
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7568, USA
| | - Patrick Wang
- CoVar Applied Technologies, Durham, NC 27701, USA
| | - Phillips Owen
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7568, USA
| | - Gaurav Vaidya
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7568, USA
| | - Eugene Muratov
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, USA
| | - Rada Chirkova
- Department of Computer Science, North Carolina State University, Raleigh, NC 27606-5550, USA
| | - Chris Bizon
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7568, USA
| | - Alexander Tropsha
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, USA
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15
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Fecho K, Balhoff J, Bizon C, Byrd WE, Hang S, Koslicki D, Rensi SE, Schmitt PL, Wawer MJ, Williams M, Ahalt SC. Application of MCAT questions as a testing tool and evaluation metric for knowledge graph-based reasoning systems. Clin Transl Sci 2021; 14:1719-1724. [PMID: 33742785 PMCID: PMC8504839 DOI: 10.1111/cts.13021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/14/2021] [Accepted: 02/02/2021] [Indexed: 11/26/2022] Open
Abstract
“Knowledge graphs” (KGs) have become a common approach for representing biomedical knowledge. In a KG, multiple biomedical data sets can be linked together as a graph representation, with nodes representing entities, such as “chemical substance” or “genes,” and edges representing predicates, such as “causes” or “treats.” Reasoning and inference algorithms can then be applied to the KG and used to generate new knowledge. We developed three KG‐based question‐answering systems as part of the Biomedical Data Translator program. These systems are typically tested and evaluated using traditional software engineering tools and approaches. In this study, we explored a team‐based approach to test and evaluate the prototype “Translator Reasoners” through the application of Medical College Admission Test (MCAT) questions. Specifically, we describe three “hackathons,” in which the developers of each of the three systems worked together with a moderator to determine whether the applications could be used to solve MCAT questions. The results demonstrate progressive improvement in system performance, with 0% (0/5) correct answers during the first hackathon, 75% (3/4) correct during the second hackathon, and 100% (5/5) correct during the final hackathon. We discuss the technical and sociologic lessons learned and conclude that MCAT questions can be applied successfully in the context of moderated hackathons to test and evaluate prototype KG‐based question‐answering systems, identify gaps in current capabilities, and improve performance. Finally, we highlight several published clinical and translational science applications of the Translator Reasoners.
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Affiliation(s)
- Karamarie Fecho
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - James Balhoff
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Chris Bizon
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - William E Byrd
- University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Sui Hang
- Institute for Systems Biology, Seattle, Washington, USA
| | - David Koslicki
- Penn State University, University Park, Pennsylvania, USA
| | | | - Patrick L Schmitt
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Mark Williams
- National Center for Advancing Translational Sciences, Bethesda, Maryland, USA
| | - Stanley C Ahalt
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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16
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Bobrowski T, Chen L, Eastman RT, Itkin Z, Shinn P, Chen CZ, Guo H, Zheng W, Michael S, Simeonov A, Hall MD, Zakharov AV, Muratov EN. Synergistic and Antagonistic Drug Combinations against SARS-CoV-2. Mol Ther 2021; 29:873-885. [PMID: 33333292 PMCID: PMC7834738 DOI: 10.1016/j.ymthe.2020.12.016] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/15/2020] [Accepted: 12/09/2020] [Indexed: 01/15/2023] Open
Abstract
Antiviral drug development for coronavirus disease 2019 (COVID-19) is occurring at an unprecedented pace, yet there are still limited therapeutic options for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Sixteen synergistic and eight antagonistic combinations were identified; among 16 synergistic cases, combinations of the US Food and Drug Administration (FDA)-approved drug nitazoxanide with remdesivir, amodiaquine, or umifenovir were most notable, all exhibiting significant synergy against SARS-CoV-2 in a cell model. However, the combination of remdesivir and lysosomotropic drugs, such as hydroxychloroquine, demonstrated strong antagonism. Overall, these results highlight the utility of drug repurposing and preclinical testing of drug combinations for discovering potential therapies to treat COVID-19.
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Affiliation(s)
- Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Lu Chen
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Richard T Eastman
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Zina Itkin
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Paul Shinn
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Catherine Z Chen
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Hui Guo
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Wei Zheng
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Sam Michael
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Matthew D Hall
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, MD 20850, USA.
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
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17
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Korn D, Pervitsky V, Bobrowski T, Alves VM, Schmitt C, Bizon C, Baker N, Chirkova R, Cherkasov A, Muratov E, Tropsha A. COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:13289222. [PMID: 33269341 PMCID: PMC7709174 DOI: 10.26434/chemrxiv.13289222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Revised: 11/26/2020] [Indexed: 12/02/2022]
Abstract
Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts. Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair. Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period. These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing. Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2. COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19. COKE is freely available at https://coke.mml.unc.edu/ and the code is available at https://github.com/DnlRKorn/CoKE.
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Affiliation(s)
- Daniel Korn
- Department of Computer Science, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Vera Pervitsky
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Vinicius M. Alves
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Chris Bizon
- Renaissance Computing Institute, the University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7568, USA
| | - Nancy Baker
- ParlezChem, 123 W Union Street, Hillsborough, NC, 27278, USA
| | - Rada Chirkova
- Department of Computer Science, North Carolina State University, Raleigh, NC, 27606-5550
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
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18
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Cox S, Ahalt SC, Balhoff J, Bizon C, Fecho K, Kebede Y, Morton K, Tropsha A, Wang P, Xu H. Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface. JMIR Med Inform 2020; 8:e17964. [PMID: 33226347 PMCID: PMC7721550 DOI: 10.2196/17964] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 06/30/2020] [Accepted: 07/17/2020] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Efforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results. OBJECTIVE We aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs. METHODS We developed a biomedical query language and web application interphase-termed as Translator Query Language (TranQL)-to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph. RESULTS We developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as "gene," "chemical substance," and "disease," that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated. CONCLUSIONS TranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.
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Affiliation(s)
- Steven Cox
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Stanley C Ahalt
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - James Balhoff
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Chris Bizon
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Karamarie Fecho
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Yaphet Kebede
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | | | - Alexander Tropsha
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Patrick Wang
- CoVar Applied Technologies, Durham, NC, United States
| | - Hao Xu
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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Bobrowski T, Chen L, Eastman RT, Itkin Z, Shinn P, Chen C, Guo H, Zheng W, Michael S, Simeonov A, Hall MD, Zakharov AV, Muratov EN. Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.06.29.178889. [PMID: 32637956 PMCID: PMC7337386 DOI: 10.1101/2020.06.29.178889] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
COVID-19 is undoubtedly the most impactful viral disease of the current century, afflicting millions worldwide. As yet, there is not an approved vaccine, as well as limited options from existing drugs for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro . Overall, we identified 16 synergistic and 8 antagonistic combinations, 4 of which were both synergistic and antagonistic in a dose-dependent manner. Among the 16 synergistic cases, combinations of nitazoxanide with three other compounds (remdesivir, amodiaquine and umifenovir) were the most notable, all exhibiting significant synergy against SARS-CoV-2. The combination of nitazoxanide, an FDA-approved drug, and remdesivir, FDA emergency use authorization for the treatment of COVID-19, demonstrate a strong synergistic interaction. Notably, the combination of remdesivir and hydroxychloroquine demonstrated strong antagonism. Overall, our results emphasize the importance of both drug repurposing and preclinical testing of drug combinations for potential therapeutic use against SARS-CoV-2 infections.
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Affiliation(s)
- Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Lu Chen
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Richard T. Eastman
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Zina Itkin
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Paul Shinn
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Catherine Chen
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Hui Guo
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Wei Zheng
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Sam Michael
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Matthew D. Hall
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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