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Bellavite P, Ferraresi A, Isidoro C. Immune Response and Molecular Mechanisms of Cardiovascular Adverse Effects of Spike Proteins from SARS-CoV-2 and mRNA Vaccines. Biomedicines 2023; 11:451. [PMID: 36830987 PMCID: PMC9953067 DOI: 10.3390/biomedicines11020451] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/25/2023] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
The SARS-CoV-2 (severe acute respiratory syndrome coronavirus responsible for the COVID-19 disease) uses the Spike proteins of its envelope for infecting target cells expressing on the membrane the angiotensin converting enzyme 2 (ACE2) enzyme that acts as a receptor. To control the pandemic, genetically engineered vaccines have been designed for inducing neutralizing antibodies against the Spike proteins. These vaccines do not act like traditional protein-based vaccines, as they deliver the message in the form of mRNA or DNA to host cells that then produce and expose the Spike protein on the membrane (from which it can be shed in soluble form) to alert the immune system. Mass vaccination has brought to light various adverse effects associated with these genetically based vaccines, mainly affecting the circulatory and cardiovascular system. ACE2 is present as membrane-bound on several cell types, including the mucosa of the upper respiratory and of the gastrointestinal tracts, the endothelium, the platelets, and in soluble form in the plasma. The ACE2 enzyme converts the vasoconstrictor angiotensin II into peptides with vasodilator properties. Here we review the pathways for immunization and the molecular mechanisms through which the Spike protein, either from SARS-CoV-2 or encoded by the mRNA-based vaccines, interferes with the Renin-Angiotensin-System governed by ACE2, thus altering the homeostasis of the circulation and of the cardiovascular system. Understanding the molecular interactions of the Spike protein with ACE2 and the consequent impact on cardiovascular system homeostasis will direct the diagnosis and therapy of the vaccine-related adverse effects and provide information for development of a personalized vaccination that considers pathophysiological conditions predisposing to such adverse events.
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Affiliation(s)
| | - Alessandra Ferraresi
- Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, 28100 Novara, Italy
| | - Ciro Isidoro
- Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, 28100 Novara, Italy
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2
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Charzewska A, Terczyńska I, Lipiec A, Mazurczak T, Górka-Skoczylas P, Szlendak R, Kanabus K, Tataj R, Dawidziuk M, Wojtaś B, Gielniewski B, Bal J, Stawicka E, Hoffman-Zacharska D. Genetic Risk Factors for Neurological Disorders in Children with Adverse Events Following Immunization: A Descriptive Study of a Polish Case Series. Int J Mol Sci 2023; 24:ijms24021117. [PMID: 36674629 PMCID: PMC9864286 DOI: 10.3390/ijms24021117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/30/2022] [Accepted: 01/04/2023] [Indexed: 01/09/2023] Open
Abstract
Studies conducted on large populations show a lack of connection between vaccination and serious neurological symptoms. However, there are isolated cases that indicate such a relationship. These reports on adverse effects following immunization (AEFI) reduce social confidence in vaccination; however, their background may be rare genetic defects. The aim of the presented study was to examine if neurological AEFI in children may be associated with variants in genes related to neurodevelopment. To identify such possible associations, a descriptive study of the Polish case series was conducted. We performed next-generation sequencing in patients who, up to 4 weeks of injection of any vaccine, manifested neurological AEFI. We included 23 previously normally developing children with first seizures that occurred after vaccination. We identified pathogenic/likely pathogenic variants in genes engaged in neurodevelopment in nine patients and variants of uncertain significance in another nine patients. The mutated genes belonged to the group of genes related to epilepsy syndromes/epileptic encephalopathy. We showed that AEFI might have a genetic background. We hypothesized that in some AEFI patients, the vaccine might only trigger neurological symptoms that would have been manifested anyway as a result of a pathogenic variant in a gene engaged in neurodevelopment.
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Affiliation(s)
- Agnieszka Charzewska
- Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17A, 01-211 Warsaw, Poland
- Correspondence: ; Tel.: +48-22-32-77-313; Fax: +48-22-32-77-200
| | - Iwona Terczyńska
- Institute of Mother and Child, Clinic of Paediatric Neurology, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Agata Lipiec
- Institute of Mother and Child, Clinic of Paediatric Neurology, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Tomasz Mazurczak
- Institute of Mother and Child, Clinic of Paediatric Neurology, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Paulina Górka-Skoczylas
- Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Róża Szlendak
- Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Karolina Kanabus
- Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Renata Tataj
- Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Mateusz Dawidziuk
- Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Bartosz Wojtaś
- Laboratory of Molecular Neurobiology, Nencki Institute of Experimental Biology PAS, Pasteura 3, 02-093 Warsaw, Poland
| | - Bartłomiej Gielniewski
- Laboratory of Molecular Neurobiology, Nencki Institute of Experimental Biology PAS, Pasteura 3, 02-093 Warsaw, Poland
| | - Jerzy Bal
- Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Elżbieta Stawicka
- Institute of Mother and Child, Clinic of Paediatric Neurology, Kasprzaka 17A, 01-211 Warsaw, Poland
| | - Dorota Hoffman-Zacharska
- Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17A, 01-211 Warsaw, Poland
- Institute of Genetics and Biotechnology, University of Warsaw, Pawińskiego 5a, 02-106 Warsaw, Poland
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3
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Huffman A, Ong E, Hur J, D’Mello A, Tettelin H, He Y. COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning. Brief Bioinform 2022; 23:bbac190. [PMID: 35649389 PMCID: PMC9294427 DOI: 10.1093/bib/bbac190] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 12/11/2022] Open
Abstract
Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.
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Affiliation(s)
- Anthony Huffman
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, North Dakota 58202, USA
| | - Adonis D’Mello
- Department of Microbiology and Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Hervé Tettelin
- Department of Microbiology and Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
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Xie J, Zi W, Li Z, He Y. Ontology-based Precision Vaccinology for Deep Mechanism Understanding and Precision Vaccine Development. Curr Pharm Des 2021; 27:900-910. [PMID: 33238868 DOI: 10.2174/1381612826666201125112131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 10/08/2020] [Indexed: 11/22/2022]
Abstract
Vaccination is one of the most important innovations in human history. It has also become a hot research area in a new application - the development of new vaccines against non-infectious diseases such as cancers. However, effective and safe vaccines still do not exist for many diseases, and where vaccines exist, their protective immune mechanisms are often unclear. Although licensed vaccines are generally safe, various adverse events, and sometimes severe adverse events, still exist for a small population. Precision medicine tailors medical intervention to the personal characteristics of individual patients or sub-populations of individuals with similar immunity-related characteristics. Precision vaccinology is a new strategy that applies precision medicine to the development, administration, and post-administration analysis of vaccines. Several conditions contribute to make this the right time to embark on the development of precision vaccinology. First, the increased level of research in vaccinology has generated voluminous "big data" repositories of vaccinology data. Secondly, new technologies such as multi-omics and immunoinformatics bring new methods for investigating vaccines and immunology. Finally, the advent of AI and machine learning software now makes possible the marriage of Big Data to the development of new vaccines in ways not possible before. However, something is missing in this marriage, and that is a common language that facilitates the correlation, analysis, and reporting nomenclature for the field of vaccinology. Solving this bioinformatics problem is the domain of applied biomedical ontology. Ontology in the informatics field is human- and machine-interpretable representation of entities and the relations among entities in a specific domain. The Vaccine Ontology (VO) and Ontology of Vaccine Adverse Events (OVAE) have been developed to support the standard representation of vaccines, vaccine components, vaccinations, host responses, and vaccine adverse events. Many other biomedical ontologies have also been developed and can be applied in vaccine research. Here, we review the current status of precision vaccinology and how ontological development will enhance this field, and propose an ontology-based precision vaccinology strategy to support precision vaccine research and development.
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Affiliation(s)
- Jiangan Xie
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Wenrui Zi
- Chongqing engineering research center of medical electronics and information technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zhangyong Li
- Chongqing engineering research center of medical electronics and information technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yongqun He
- Unit of Laboratory Animal Medicine, Development of Microbiology and Immunology, Center of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, United States
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5
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Bellavite P. Causality assessment of adverse events following immunization: the problem of multifactorial pathology. F1000Res 2020; 9:170. [PMID: 32269767 PMCID: PMC7111503 DOI: 10.12688/f1000research.22600.1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/04/2020] [Indexed: 07/22/2023] Open
Abstract
The analysis of Adverse Events Following Immunization (AEFI) is important in a balanced epidemiological evaluation of vaccines and in the issues related to national vaccine injury compensation programs. If manufacturing defects or vaccine storage and delivering errors are excluded, the majority of adverse reactions to vaccines occur as excessive or biased inflammatory and immune responses. These unwanted phenomena, occasionally severe, are associated with many different endogenous and exogenous factors, which often interact in complex ways. The confirmation or denial of the causal link between an AEFI and vaccination is determined pursuant to WHO guidelines, which propose a four-step analysis and algorithmic diagramming. The evaluation process from the onset considers all possible "other causes" that can explain the AEFI and thus exclude the role of the vaccine. Subsequently, even if there was biological plausibility and temporal compatibility for a causal association between the vaccine and the AEFI, the guidelines ask to look for any possible evidence that the vaccine could not have caused that event. Such an algorithmic method presents some concerns that are discussed here, in the light of the multifactorial nature of the inflammatory and immune pathologies induced by vaccines, including emerging knowledge of genetic susceptibility to adverse effects. It is proposed that the causality assessment could exclude a consistent association of the adverse event with the vaccine only when the presumed "other cause" is independent of an interaction with the vaccine. Furthermore, the scientific literature should be viewed not as an exclusion criterion but as a comprehensive analysis of all the evidence for or against the role of the vaccine in causing an adverse reaction. These issues are discussed in relation to the laws that, in some countries, regulate the mandatory vaccinations and the compensation for those who have suffered serious adverse effects.
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Affiliation(s)
- Paolo Bellavite
- Department of Medicine, Section of General Pathology, University of Verona Medical School, Verona, 37134, Italy
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6
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Abstract
The analysis of Adverse Events Following Immunization (AEFI) is important in a balanced epidemiological evaluation of vaccines and in the issues related to vaccine injury compensation programs. The majority of adverse reactions to vaccines occur as excessive or biased inflammatory and immune responses. These unwanted phenomena, occasionally severe, are associated with many different endogenous and exogenous factors, which often interact in complex ways. The confirmation or denial of the causal link between an AEFI and vaccination is determined pursuant to WHO guidelines, which propose a four-step analysis and algorithmic diagramming. The evaluation process from the onset considers all possible "other causes" that might explain the AEFI and thus exclude the role of the vaccine. Subsequently, even if there was biological plausibility and temporal compatibility for a causal association between the vaccine and the AEFI, the guidelines ask to look for any possible evidence that the vaccine could not have caused that event. Such an algorithmic method presents several concerns that are discussed here, in the light of the multifactorial nature of the inflammatory and immune pathologies induced by vaccines, including emerging knowledge of genetic susceptibility to adverse effects. It is proposed that the causality assessment could exclude a consistent association of the adverse event with the vaccine only when the presumed "other cause" is independent of an interaction with the vaccine. Furthermore, the scientific literature should be viewed not as an exclusion criterion but as a comprehensive analysis of all the evidence for or against the role of the vaccine in causing an adverse reaction. Given these inadequacies in the evaluation of multifactorial diseases, the WHO guidelines need to be reevaluated and revised. These issues are discussed in relation to the laws that, in some countries, regulate the mandatory vaccinations and the compensation for those who have suffered serious adverse effects.
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Affiliation(s)
- Paolo Bellavite
- Department of Medicine, Section of General Pathology, University of Verona Medical School, Verona, 37134, Italy
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7
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Abstract
This Editorial first introduces the background of the vaccine and drug relations and how biomedical terminologies and ontologies have been used to support their studies. The history of the seven workshops, initially named VDOSME, and then named VDOS, is also summarized and introduced. Then the 7th International Workshop on Vaccine and Drug Ontology Studies (VDOS 2018), held on August 10th, 2018, Corvallis, Oregon, USA, is introduced in detail. These VDOS workshops have greatly supported the development, applications, and discussion of vaccine- and drug-related terminology and drug studies.
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Affiliation(s)
- Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND USA
| | - Cui Tao
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI USA
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Natsiavas P, Malousi A, Bousquet C, Jaulent MC, Koutkias V. Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches. Front Pharmacol 2019; 10:415. [PMID: 31156424 PMCID: PMC6533857 DOI: 10.3389/fphar.2019.00415] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 04/02/2019] [Indexed: 12/12/2022] Open
Abstract
Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.
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Affiliation(s)
- Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.,Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Andigoni Malousi
- Laboratory of Biological Chemistry, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Cédric Bousquet
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France.,Public Health and Medical Information Unit, University Hospital of Saint-Etienne, Saint-Étienne, France
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Vassilis Koutkias
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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Amith M, He Z, Bian J, Lossio-Ventura JA, Tao C. Assessing the practice of biomedical ontology evaluation: Gaps and opportunities. J Biomed Inform 2018; 80:1-13. [PMID: 29462669 PMCID: PMC5882531 DOI: 10.1016/j.jbi.2018.02.010] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 02/12/2018] [Accepted: 02/16/2018] [Indexed: 11/26/2022]
Abstract
With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled terminologies are intended to assure interoperability. Nevertheless, the quality of biomedical ontologies has hindered their applicability and subsequent adoption in real-world applications. Ontology evaluation is an integral part of ontology development and maintenance. In the biomedicine domain, ontology evaluation is often conducted by third parties as a quality assurance (or auditing) effort that focuses on identifying modeling errors and inconsistencies. In this work, we first organized four categorical schemes of ontology evaluation methods in the existing literature to create an integrated taxonomy. Further, to understand the ontology evaluation practice in the biomedicine domain, we reviewed a sample of 200 ontologies from the National Center for Biomedical Ontology (NCBO) BioPortal-the largest repository for biomedical ontologies-and observed that only 15 of these ontologies have documented evaluation in their corresponding inception papers. We then surveyed the recent quality assurance approaches for biomedical ontologies and their use. We also mapped these quality assurance approaches to the ontology evaluation criteria. It is our anticipation that ontology evaluation and quality assurance approaches will be more widely adopted in the development life cycle of biomedical ontologies.
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Affiliation(s)
- Muhammad Amith
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Zhe He
- School of Information, Florida State University, Tallahassee, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | | | - Cui Tao
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
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Ong E, He Y. Community-based Ontology Development, Annotation and Discussion with MediaWiki extension Ontokiwi and Ontokiwi-based Ontobedia. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2016; 2016:65-74. [PMID: 27570653 PMCID: PMC5001762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Hundreds of biological and biomedical ontologies have been developed to support data standardization, integration and analysis. Although ontologies are typically developed for community usage, community efforts in ontology development are limited. To support ontology visualization, distribution, and community-based annotation and development, we have developed Ontokiwi, an ontology extension to the MediaWiki software. Ontokiwi displays hierarchical classes and ontological axioms. Ontology classes and axioms can be edited and added using Ontokiwi form or MediaWiki source editor. Ontokiwi also inherits MediaWiki features such as Wikitext editing and version control. Based on the Ontokiwi/MediaWiki software package, we have developed Ontobedia, which targets to support community-based development and annotations of biological and biomedical ontologies. As demonstrations, we have loaded the Ontology of Adverse Events (OAE) and the Cell Line Ontology (CLO) into Ontobedia. Our studies showed that Ontobedia was able to achieve expected Ontokiwi features.
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Affiliation(s)
- Edison Ong
- University of Michigan Medical School, Ann Arbor, MI
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI
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11
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Sarntivijai S, Zhang S, Jagannathan DG, Zaman S, Burkhart KK, Omenn GS, He Y, Athey BD, Abernethy DR. Linking MedDRA(®)-Coded Clinical Phenotypes to Biological Mechanisms by the Ontology of Adverse Events: A Pilot Study on Tyrosine Kinase Inhibitors. Drug Saf 2016; 39:697-707. [PMID: 27003817 PMCID: PMC4933310 DOI: 10.1007/s40264-016-0414-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
INTRODUCTION A translational bioinformatics challenge exists in connecting population and individual clinical phenotypes in various formats to biological mechanisms. The Medical Dictionary for Regulatory Activities (MedDRA(®)) is the default dictionary for adverse event (AE) reporting in the US Food and Drug Administration Adverse Event Reporting System (FAERS). The ontology of adverse events (OAE) represents AEs as pathological processes occurring after drug exposures. OBJECTIVES The aim of this work was to establish a semantic framework to link biological mechanisms to phenotypes of AEs by combining OAE with MedDRA(®) in FAERS data analysis. We investigated the AEs associated with tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs) targeting tyrosine kinases. The five selected TKIs/mAbs (i.e., dasatinib, imatinib, lapatinib, cetuximab, and trastuzumab) are known to induce impaired ventricular function (non-QT) cardiotoxicity. RESULTS Statistical analysis of FAERS data identified 1053 distinct MedDRA(®) terms significantly associated with TKIs/mAbs, where 884 did not have corresponding OAE terms. We manually annotated these terms, added them to OAE by the standard OAE development strategy, and mapped them to MedDRA(®). The data integration to provide insights into molecular mechanisms of drug-associated AEs was performed by including linkages in OAE for all related AE terms to MedDRA(®) and the existing ontologies, including the human phenotype ontology (HP), Uber anatomy ontology (UBERON), and gene ontology (GO). Sixteen AEs were shared by all five TKIs/mAbs, and each of 17 cardiotoxicity AEs was associated with at least one TKI/mAb. As an example, we analyzed "cardiac failure" using the relations established in OAE with other ontologies and demonstrated that one of the biological processes associated with cardiac failure maps to the genes associated with heart contraction. CONCLUSION By expanding the existing OAE ontological design, our TKI use case demonstrated that the combination of OAE and MedDRA(®) provides a semantic framework to link clinical phenotypes of adverse drug events to biological mechanisms.
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Affiliation(s)
- Sirarat Sarntivijai
- Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Shelley Zhang
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA
| | | | - Shadia Zaman
- Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
| | - Keith K Burkhart
- Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
| | - Gilbert S Omenn
- Department of Internal Medicine and Human Genetics and School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Yongqun He
- Unit of Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Brian D Athey
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Psychiatry Department, University of Michigan, Ann Arbor, MI, USA
| | - Darrell R Abernethy
- Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
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12
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He Y. Ontology-based Vaccine and Drug Adverse Event Representation and Theory-guided Systematic Causal Network Analysis toward Integrative Pharmacovigilance Research. ACTA ACUST UNITED AC 2016; 2:113-128. [PMID: 27458549 DOI: 10.1007/s40495-016-0055-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Compared with controlled terminologies (e.g., MedDRA, CTCAE, and WHO-ART), the community-based Ontology of AEs (OAE) has many advantages in adverse event (AE) classifications. The OAE-derived Ontology of Vaccine AEs (OVAE) and Ontology of Drug Neuropathy AEs (ODNAE) serve as AE knowledge bases and support data integration and analysis. The Immune Response Gene Network Theory explains molecular mechanisms of vaccine-related AEs. The OneNet Theory of Life treats the whole process of a life of an organism as a single complex and dynamic network (i.e., OneNet). A new "OneNet effectiveness" tenet is proposed here to expand the OneNet theory. Derived from the OneNet theory, the author hypothesizes that one human uses one single genotype-rooted mechanism to respond to different vaccinations and drug treatments, and experimentally identified mechanisms are manifestations of the OneNet blueprint mechanism under specific conditions. The theories and ontologies interact together as semantic frameworks to support integrative pharmacovigilance research.
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Affiliation(s)
- Yongqun He
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Center for Computational Medicine and Biology, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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Whitaker JA, Ovsyannikova IG, Poland GA. Adversomics: a new paradigm for vaccine safety and design. Expert Rev Vaccines 2015; 14:935-47. [PMID: 25937189 DOI: 10.1586/14760584.2015.1038249] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Despite the enormous population benefits of routine vaccination, vaccine adverse events (AEs) and reactions, whether real or perceived, have posed one of the greatest barriers to vaccine acceptance--and thus to infectious disease prevention--worldwide. A truly integrated clinical, translational, and basic science approach is required to understand the mechanisms behind vaccine AEs, predict them, and then apply this knowledge to new vaccine design approaches that decrease, or avoid, these events. The term 'adversomics' was first introduced in 2009 and refers to the study of vaccine adverse reactions using immunogenomics and systems biology approaches. In this review, we present the current state of adversomics research, review known associations and mechanisms of vaccine AEs/reactions, and outline a plan for the further development of this emerging research field.
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He Y, Sarntivijai S, Lin Y, Xiang Z, Guo A, Zhang S, Jagannathan D, Toldo L, Tao C, Smith B. OAE: The Ontology of Adverse Events. J Biomed Semantics 2014; 5:29. [PMID: 25093068 PMCID: PMC4120740 DOI: 10.1186/2041-1480-5-29] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2013] [Accepted: 06/27/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. DESCRIPTION The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term 'adverse event' denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data. For example, using the data extracted from the Vaccine Adverse Event Reporting System (VAERS), OAE was used to analyse vaccine adverse events associated with the administrations of different types of influenza vaccines. OAE has also been used to represent and classify the vaccine adverse events cited in package inserts of FDA-licensed human vaccines in the USA. CONCLUSION OAE is a biomedical ontology that logically defines and classifies various adverse events occurring after medical interventions. OAE has successfully been applied in several adverse event studies. The OAE ontological framework provides a platform for systematic representation and analysis of adverse events and of the factors (e.g., vaccinee age) important for determining their clinical outcomes.
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Affiliation(s)
- Yongqun He
- University of Michigan, Ann Arbor, MI, USA
| | - Sirarat Sarntivijai
- University of Michigan, Ann Arbor, MI, USA
- US Food and Drug Administration, Silver Spring, MD, USA
| | - Yu Lin
- University of Michigan, Ann Arbor, MI, USA
| | | | - Abra Guo
- University of Michigan, Ann Arbor, MI, USA
| | | | | | | | - Cui Tao
- University at Texas Health Science Center at Houston, Houston, TX, USA
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A 2013 workshop: vaccine and drug ontology studies (VDOS 2013). J Biomed Semantics 2014; 5:16. [PMID: 24650607 PMCID: PMC3994568 DOI: 10.1186/2041-1480-5-16] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 03/17/2014] [Indexed: 11/23/2022] Open
Abstract
The 2013 “Vaccine and Drug Ontology Studies” (VDOS 2013) international workshop series focuses on vaccine- and drug-related ontology modeling and applications. Drugs and vaccines have contributed to dramatic improvements in public health worldwide. Over the last decade, tremendous efforts have been made in the biomedical ontology community to ontologically represent various areas associated with vaccines and drugs – extending existing clinical terminology systems such as SNOMED, RxNorm, NDF-RT, and MedDRA, as well as developing new models such as Vaccine Ontology. The VDOS workshop series provides a platform for discussing innovative solutions as well as the challenges in the development and applications of biomedical ontologies for representing and analyzing drugs and vaccines, their administration, host immune responses, adverse events, and other related topics. The six full-length papers included in this thematic issue focuses on three main areas: (i) ontology development and representation, (ii) ontology mapping, maintaining and auditing, and (iii) ontology applications.
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