OAE-based data mining and modeling analysis of adverse events associated with three licensed HPV vaccines.

Purpose: Three licensed human papillomavirus (HPV) vaccines (Cervarix, Gardasil, and Gardasil 9) have been effectively used to prevent infection with oncogenic HPV types; however, many adverse events (AEs) have also been reported following their vaccinations. We assessed AE profiles after receiving the HPV vaccines based on the reported data from Vaccine Adverse Event Reporting System (VAERS). Methods: The AE data associated with Cervarix, Gardasil, and Gardasil 9 were retrieved from VAERS database respectively. The combinatorial biomedical statistical methods were used to identify the statistically significant AEs. The Gamma-Poisson Shrinker (GPS) model with gender/age stratification was applied to ascertain the serious adverse events (SAEs) related to the three licensed HPV vaccines. The AE profiles were classified and represented by the Ontology of Adverse Events (OAE) for further analysis. Results: As of July 31, 2020, VAERS recorded 3,112, 31,606, and 6,872 AE case reports for Cervarix, Gardasil, and Gardasil 9, respectively. Our Frequentist statistical methods identified 135 Cervarix-enriched AEs, 55 Gardasil-enriched AEs, and 17 Gardasil 9-enriched AEs. Based on the OAE hierarchical classification, these AEs were clustered in the AEs related to behavioral and neurological conditions, immune system, nervous system, and reproductive system. Combined with GPS modeling, 46 unique statistically significant SAEs were founded to be associated with at least one of the three vaccines. Conclusions: Our study led to the better understanding of the AEs associated with the licensed HPV vaccines. The hypotheses on the cause and effect relationships between the HPV vaccination and specific AEs deserve further epidemiological investigations as well as clinical trial studies.

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.

BACKGROUND: The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020. RESULTS: As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CONCLUSION: CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

Profiling COVID-19 Vaccine Adverse Events by Statistical and Ontological Analysis of VAERS Case Reports.

Since the beginning of the COVID-19 pandemic, vaccines have been developed to mitigate the spread of SARS-CoV-2, the virus that causes COVID-19. These vaccines have been effective in reducing the rate and severity of COVID-19 infection but also have been associated with various adverse events (AEs). In this study, data from the Vaccine Adverse Event Reporting System (VAERS) was queried and analyzed via the Cov19VaxKB vaccine safety statistical analysis tool to identify statistically significant (i.e., enriched) AEs for the three currently FDA-authorized or approved COVID-19 vaccines. An ontology-based classification and literature review were conducted for these enriched AEs. Using VAERS data as of 31 December 2021, 96 AEs were found to be statistically significantly associated with the Pfizer-BioNTech, Moderna, and/or Janssen COVID-19 vaccines. The Janssen COVID-19 vaccine had a higher crude reporting rate of AEs compared to the Moderna and Pfizer COVID-19 vaccines. Females appeared to have a higher case report frequency for top adverse events compared to males. Using the Ontology of Adverse Event (OAE), these 96 adverse events were classified to different categories such as behavioral and neurological AEs, cardiovascular AEs, female reproductive system AEs, and immune system AEs. Further statistical comparison between different ages, doses, and sexes was also performed for three notable AEs: myocarditis, GBS, and thrombosis. The Pfizer vaccine was found to have a closer association with myocarditis than the other two COVID-19 vaccines in VAERS, while the Janssen vaccine was more likely to be associated with thrombosis and GBS AEs. To support standard AE representation and study, we have also modeled and classified the newly identified thrombosis with thrombocytopenia syndrome (TTS) AE and its subclasses in the OAE by incorporating the Brighton Collaboration definition. Notably, severe COVID-19 vaccine AEs (including myocarditis, GBS, and TTS) rarely occur in comparison to the large number of COVID-19 vaccinations administered in the United States, affirming the overall safety of these COVID-19 vaccines.

Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection.

Our systematic literature collection and annotation identified 106 chemical drugs and 31 antibodies effective against the infection of at least one human coronavirus (including SARS-CoV, SAR-CoV-2, and MERS-CoV) in vitro or in vivo in an experimental or clinical setting. A total of 163 drug protein targets were identified, and 125 biological processes involving the drug targets were significantly enriched based on a Gene Ontology (GO) enrichment analysis. The Coronavirus Infectious Disease Ontology (CIDO) was used as an ontological platform to represent the anti-coronaviral drugs, chemical compounds, drug targets, biological processes, viruses, and the relations among these entities. In addition to new term generation, CIDO also adopted various terms from existing ontologies and developed new relations and axioms to semantically represent our annotated knowledge. The CIDO knowledgebase was systematically analyzed for scientific insights. To support rational drug design, a "Host-coronavirus interaction (HCI) checkpoint cocktail" strategy was proposed to interrupt the important checkpoints in the dynamic HCI network, and ontologies would greatly support the design process with interoperable knowledge representation and reasoning.

Ontology-based Precision Vaccinology for Deep Mechanism Understanding and Precision Vaccine Development.

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.

Ontology-Based Meta-Analysis of Animal and Human Adverse Events Associated With Licensed Brucellosis Vaccines.

Brucella abortus strain 19 (S19), Brucella melitensis Rev 1 (Rev1), and B. abortus strain RB51 (RB51) are the three licensed animal brucellosis vaccines, and they have been most commonly and successfully used in prevent brucellosis in animals. However, many adverse events (AEs) have been associated with these three vaccines after their administering to animals or being accidentally exposed to humans. In this study, 27 peer-reviewed publications containing animal and human AE reports associated with these three brucellosis vaccines were manually annotated from the PubMed database. Our meta-analysis identified 20 animal AEs and 46 human AEs associated with the three vaccines. Based on the Ontology of Adverse Events (OAE) hierarchical classification, these animal AEs were enriched in the immune and reproductive systems that might eventually result in the occurrence of abortion or infertility. The human AEs were concentrated in the behavioral and neurological conditions, and these AEs showed flu-like symptoms that are consistent with human brucellosis. Furthermore, an analysis of variance (ANOVA) statistics analysis with linear model fits was used to determine the major variables that might affect the occurrence of abortion AE in animals. The ANOVA results indicated that three variables (P-value < 0.05) are significantly associated with the occurrence of abortion AE: animal species, vaccination dose, and vaccination route. The other two variables (i.e., vaccine type and animal age at vaccination) did not significantly (P-value > 0.05) associated with the occurrence of abortion AE. Overall, this study represents the first ontology-based meta-analysis of adverse events associated with animal vaccines. The results of such a study led to the better understanding of brucellosis vaccine AEs, facilitating rational design of more secure and effective vaccines.

Ontology-Based Vaccine Adverse Event Representation and Analysis.

Vaccine is the one of the greatest inventions of modern medicine that has contributed most to the relief of human misery and the exciting increase in life expectancy. In 1796, an English country physician, Edward Jenner, discovered that inoculating mankind with cowpox can protect them from smallpox (Riedel S, Edward Jenner and the history of smallpox and vaccination. Proceedings (Baylor University. Medical Center) 18(1):21, 2005). Based on the vaccination worldwide, we finally succeeded in the eradication of smallpox in 1977 (Henderson, Vaccine 29:D7-D9, 2011). Other disabling and lethal diseases, like poliomyelitis and measles, are targeted for eradication (Bonanni, Vaccine 17:S120-S125, 1999).Although vaccine development and administration are tremendously successful and cost-effective practices to human health, no vaccine is 100% safe for everyone because each person reacts to vaccinations differently given different genetic background and health conditions. Although all licensed vaccines are generally safe for the majority of people, vaccinees may still suffer adverse events (AEs) in reaction to various vaccines, some of which can be serious or even fatal (Haber et al., Drug Saf 32(4):309-323, 2009). Hence, the double-edged sword of vaccination remains a concern.To support integrative AE data collection and analysis, it is critical to adopt an AE normalization strategy. In the past decades, different controlled terminologies, including the Medical Dictionary for Regulatory Activities (MedDRA) (Brown EG, Wood L, Wood S, et al., Drug Saf 20(2):109-117, 1999), the Common Terminology Criteria for Adverse Events (CTCAE) (NCI, The Common Terminology Criteria for Adverse Events (CTCAE). Available from: http://evs.nci.nih.gov/ftp1/CTCAE/About.html . Access on 7 Oct 2015), and the World Health Organization (WHO) Adverse Reactions Terminology (WHO-ART) (WHO, The WHO Adverse Reaction Terminology - WHO-ART. Available from: https://www.umc-products.com/graphics/28010.pdf ), have been developed with a specific aim to standardize AE categorization. However, these controlled terminologies have many drawbacks, such as lack of textual definitions, poorly defined hierarchies, and lack of semantic axioms that provide logical relations among terms. A biomedical ontology is a set of consensus-based and computer and human interpretable terms and relations that represent entities in a specific biomedical domain and how they relate each other. To represent and analyze vaccine adverse events (VAEs), our research group has initiated and led the development of a community-based ontology: the Ontology of Adverse Events (OAE) (He et al., J Biomed Semant 5:29, 2014). The OAE has been found to have advantages to overcome the drawbacks of those controlled terminologies (He et al., Curr Pharmacol Rep :1-16. doi:10.1007/s40495-016-0055-0, 2014). By expanding the OAE and the community-based Vaccine Ontology (VO) (He et al., VO: vaccine ontology. In The 1st International Conference on Biomedical Ontology (ICBO-2009). Nature Precedings, Buffalo. http://precedings.nature.com/documents/3552/version/1 ; J Biomed Semant 2(Suppl 2):S8; J Biomed Semant 3(1):17, 2009; Ozgur et al., J Biomed Semant 2(2):S8, 2011; Lin Y, He Y, J Biomed Semant 3(1):17, 2012), we have also developed the Ontology of Vaccine Adverse Events (OVAE) to represent known VAEs associated with licensed vaccines (Marcos E, Zhao B, He Y, J Biomed Semant 4:40, 2013).In this book chapter, we will first introduce the basic information of VAEs, VAE safety surveillance systems, and how to specifically query and analyze VAEs using the US VAE database VAERS (Chen et al., Vaccine 12(10):960-960, 1994). In the second half of the chapter, we will introduce the development and applications of the OAE and OVAE. Throughout this chapter, we will use the influenza vaccine Flublok as the vaccine example to launch the corresponding elaboration (Huber VC, McCullers JA, Curr Opin Mol Ther 10(1):75-85, 2008). Flublok is a recombinant hemagglutinin influenza vaccine indicated for active immunization against disease caused by influenza virus subtypes A and type B. On January 16, 2013, Flublok was approved by the FDA for the prevention of seasonal influenza in people 18 years and older in the USA. Now, more than 3 years later, an exploration of the reported AEs associated with this vaccine is urgently needed.

Ontology-based systematical representation and drug class effect analysis of package insert-reported adverse events associated with cardiovascular drugs used in China.

With increased usage of cardiovascular drugs (CVDs) for treating cardiovascular diseases, it is important to analyze CVD-associated adverse events (AEs). In this study, we systematically collected package insert-reported AEs associated with CVDs used in China, and developed and analyzed an Ontology of Cardiovascular Drug AEs (OCVDAE). Extending the Ontology of AEs (OAE) and NDF-RT, OCVDAE includes 194 CVDs, CVD ingredients, mechanisms of actions (MoAs), and CVD-associated 736 AEs. An AE-specific drug class effect is defined to exist when all the drugs (drug chemical ingredients or drug products) in a drug class are associated with an AE, which is formulated as a new proportional class level ratio ("PCR") = 1. Our PCR-based heatmap analysis identified many class level drug effects on different AE classes such as behavioral and neurological AE and digestive system AE. Additional drug-AE correlation tests (i.e., class-level PRR, Chi-squared, and minimal case reports) were also modified and applied to further detect statistically significant drug class effects. Two drug ingredient classes and three CVD MoA classes were found to have statistically significant class effects on 13 AEs. For example, the CVD Active Transporter Interactions class (including reserpine, indapamide, digoxin, and deslanoside) has statistically significant class effect on anorexia and diarrhea AEs.

Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses.

BACKGROUND: Aiming to understand cellular responses to different perturbations, the NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many institutes and laboratories working on over a thousand cell lines. The community-based Cell Line Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integration. RESULTS: CLO has consistently represented all 1097 LINCS cell lines and included information extracted from the LINCS Data Portal and ChEMBL. Using MCF 10A cell line cells as an example, we demonstrated how to ontologically model LINCS cellular signatures such as their non-tumorigenic epithelial cell type, three-dimensional growth, latrunculin-A-induced actin depolymerization and apoptosis, and cell line transfection. A CLO subset view of LINCS cell lines, named LINCS-CLOview, was generated to support systematic LINCS cell line analysis and queries. In summary, LINCS cell lines are currently associated with 43 cell types, 131 tissues and organs, and 121 cancer types. The LINCS-CLO view information can be queried using SPARQL scripts. CONCLUSIONS: CLO was used to support ontological representation, integration, and analysis of over a thousand LINCS cell line cells and their cellular responses.

Differential Adverse Event Profiles Associated with BCG as a Preventive Tuberculosis Vaccine or Therapeutic Bladder Cancer Vaccine Identified by Comparative Ontology-Based VAERS and Literature Meta-Analysis.

M. bovis strain Bacillus Calmette-Guérin (BCG) has been the only licensed live attenuated vaccine against tuberculosis (TB) for nearly one century and has also been approved as a therapeutic vaccine for bladder cancer treatment since 1990. During its long time usage, different adverse events (AEs) have been reported. However, the AEs associated with the BCG preventive TB vaccine and therapeutic cancer vaccine have not been systematically compared. In this study, we systematically collected various BCG AE data mined from the US VAERS database and PubMed literature reports, identified statistically significant BCG-associated AEs, and ontologically classified and compared these AEs related to these two types of BCG vaccine. From 397 VAERS BCG AE case reports, we identified 64 AEs statistically significantly associated with the BCG TB vaccine and 14 AEs with the BCG cancer vaccine. Our meta-analysis of 41 peer-reviewed journal reports identified 48 AEs associated with the BCG TB vaccine and 43 AEs associated with the BCG cancer vaccine. Among all identified AEs from VAERS and literature reports, 25 AEs belong to serious AEs. The Ontology of Adverse Events (OAE)-based ontological hierarchical analysis indicated that the AEs associated with the BCG TB vaccine were enriched in immune system (e.g., lymphadenopathy and lymphadenitis), skin (e.g., skin ulceration and cyanosis), and respiratory system (e.g., cough and pneumonia); in contrast, the AEs associated with the BCG cancer vaccine mainly occurred in the urinary system (e.g., dysuria, pollakiuria, and hematuria). With these distinct AE profiles detected, this study also discovered three AEs (i.e., chills, pneumonia, and C-reactive protein increased) shared by the BCG TB vaccine and bladder cancer vaccine. Furthermore, our deep investigation of 24 BCG-associated death cases from VAERS identified the important effects of age, vaccine co-administration, and immunosuppressive status on the final BCG-associated death outcome.

Statistical and Ontological Analysis of Adverse Events Associated with Monovalent and Combination Vaccines against Hepatitis A and B Diseases.

Vaccinations often induce various adverse events (AEs), and sometimes serious AEs (SAEs). While many vaccines are used in combination, the effects of vaccine-vaccine interactions (VVIs) on vaccine AEs are rarely studied. In this study, AE profiles induced by hepatitis A vaccine (Havrix), hepatitis B vaccine (Engerix-B), and hepatitis A and B combination vaccine (Twinrix) were studied using the VAERS data. From May 2001 to January 2015, VAERS recorded 941, 3,885, and 1,624 AE case reports where patients aged at least 18 years old were vaccinated with only Havrix, Engerix-B, and Twinrix, respectively. Using these data, our statistical analysis identified 46, 69, and 82 AEs significantly associated with Havrix, Engerix-B, and Twinrix, respectively. Based on the Ontology of Adverse Events (OAE) hierarchical classification, these AEs were enriched in the AEs related to behavioral and neurological conditions, immune system, and investigation results. Twenty-nine AEs were classified as SAEs and mainly related to immune conditions. Using a logistic regression model accompanied with MCMC sampling, 13 AEs (e.g., hepatosplenomegaly) were identified to result from VVI synergistic effects. Classifications of these 13 AEs using OAE and MedDRA hierarchies confirmed the advantages of the OAE-based method over MedDRA in AE term hierarchical analysis.

The VHSE-based prediction of proteasomal cleavage sites.

Prediction of proteasomal cleavage sites has been a focus of computational biology. Up to date, the predictive methods are mostly based on nonlinear classifiers and variables with little physicochemical meanings. In this paper, the physicochemical properties of 14 residues both upstream and downstream of a cleavage site are characterized by VHSE (principal component score vector of hydrophobic, steric, and electronic properties) descriptors. Then, the resulting VHSE descriptors are employed to construct prediction models by support vector machine (SVM). For both in vivo and in vitro datasets, the performance of VHSE-based method is comparatively better than that of the well-known PAProC, MAPPP, and NetChop methods. The results reveal that the hydrophobic property of 10 residues both upstream and downstream of the cleavage site is a dominant factor affecting in vivo and in vitro cleavage specificities, followed by residue's electronic and steric properties. Furthermore, the difference in hydrophobic potential between residues flanking the cleavage site is proposed to favor substrate cleavages. Overall, the interpretable VHSE-based method provides a preferable way to predict proteasomal cleavage sites.

Peptide binding specificities of HLA-B*5701 and B*5801.

Recently, genome wide association studies showed that there is a strong association between abacavir-induced serious, idiosyncratic, adverse drug reactions (ADRs) and human leukocyte antigen-B*5701 (HLA-B*5701). Studies also found that abacavir-induced ADRs were seldom observed in patients carrying the HLA-B*5801 subtype. HLA-B*5801 of the same serotype (B17) as B*5701 differs by only 4 amino acids from B*5701. It is believed that because of these sequence differences, HLA-B*5801 cannot bind the specific peptides which are required for HLA-B*5701 to stimulate the T cell immune response. Thus, the difference in peptide binding profiles between HLA-B*5701 and B*5801 is an important clue for exploring the mechanisms of abacavir-induced ADRs. VHSE (principal component score vector of hydrophobic, steric, and electronic properties), a set of amino acid structural descriptors, was employed to establish QSAR models of peptide-binding affinities of HLA-B*5701 and B*5801. Optimal linear SVM (support vector machine) models with high predictive capabilities were obtained for both B*5701 and B*5801. The R(2) (coefficient of determination), Q(2) (cross-validated R(2)), and R(PRE)(2) (R(2) of test set) of two optimal models were 0.7530, 0.7037, 0.6153 (B*5701) and 0.6074, 0.5966, 0.5762 (B*5801), respectively. For B*5701 and B*5801, the mutations in positions 45 (MET-THR) and 46 (ALA-GLU) have little influence on the selection specificity of the P2 position of the bound peptide. However, the mutation in position 97 (VAL-ARG) greatly influences the selection specificity of the P7 position. HLA-B*5701 prefers the bulky and positively charged amino acids at the P7 position. In contrast, HLA-B*5801 prefers the non-polar hydrophobic amino acids at the P7 position while positively charged amino acids are unfavored.