Ontology-based collection, representation and analysis of drug-associated neuropathy adverse events

An ontology-based tool for modeling and documenting events in neurosurgery

BACKGROUND

Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool.

METHODS

We structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology's creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool's functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool's productive utilization.

RESULTS

The core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool's evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise.

CONCLUSIONS

Through the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it's crucial to address potential modeling issues that are associated with the Ontology of Adverse Events.

Patient safety classifications, taxonomies and ontologies, part 2: A systematic review on content coverage

BACKGROUND

Content coverage of patient safety ontology and classification systems should be evaluated to provide a guide for users to select appropriate ones for specific applications. In this review, we identified and compare content coverage of patient safety classifications and ontologies.

METHODS

We searched different databases and ontology/classification repositories to identify these classifications and ontologies. We included patient safety-related taxonomies, ontologies, classifications, and terminologies. We identified and extracted different concepts covered by these systems and mapped these concepts to international classification for patient safety (ICPS) and finally compared the content of these systems.

RESULTS

Finally, 89 papers (77 classifications or ontologies) were analyzed. Thirteen classifications have been developed to cover all medical domains. Among specific domain systems, most systems cover medication (16), surgery (8), medical devices (3), general practice (3), and primary care (3). The most common patient safety-related concepts covered in these systems include incident types (41), contributing factors/hazards (31), patient outcomes (29), degree of harm (25), and action (18). However, stage/phase (6), incident characteristics (5), detection (5), people involved (5), organizational outcomes (4), error type (4), and care setting (3) are some of the less covered concepts in these classifications/ontologies.

CONCLUSION

Among general systems, ICPS, World Health Organization's Adverse Reaction Terminology (WHO-ART), and Ontology of Adverse Events (OAE) cover most patient safety concepts and can be used as a gold standard for all medical domains. As a result, reporting systems could make use of these broad classifications, but the majority of their covered concepts are related to patient outcomes, with the exception of ICPS, which covers other patient safety concepts. However, the ICPS does not cover specialized domain concepts. For specific medical domains, MedDRA, NCC MERP, OPAE, ADRO, PPST, OCCME, TRTE, TSAHI, and PSIC-PC provide the broadest coverage of concepts. Many of the patient safety classifications and ontologies are not formally registered or available as formal classification/ontology in ontology repositories such as BioPortal. This study may be used as a guide for choosing appropriate classifications for various applications or expanding less developed patient safety classifications/ontologies. Furthermore, the same concepts are not represented by the same terms; therefore, the current study could be used to guide a harmonization process for existing or future patient safety classifications/ontologies.

Patient safety classification, taxonomy and ontology systems: A systematic review on development and evaluation methodologies

INTRODUCTION

Patient safety classifications/ontologies enable patient safety information systems to receive and analyze patient safety data to improve patient safety. Patient safety classifications/ontologies have been developed and evaluated using a variety of methods. The purpose of this review was to discuss and analyze the methodologies for developing and evaluating patient safety classifications/ontologies.

METHODS

Studies that developed or evaluated patient safety classifications, terminologies, taxonomies, or ontologies were searched through Google Scholar, Google search engines, National Center for Biomedical Ontology (NCBO) BioPortal, Open Biological and Biomedical Ontology (OBO) Foundry and World Health Organization (WHO) websites and Scopus, Web of Science, PubMed, and Science Direct. We updated our search on 30 February 2021 and included all studies published until the end of 2020. Studies that developed or evaluated classifications only for patient safety and provided information on how they were developed or evaluated were included. Systems with covered patient safety terms (such as ICD-10) but are not specifically developed for patient safety were excluded. The quality and the risk of bias of studies were not assessed because all methodologies and criteria were intended to be covered. In addition, we analyzed the data through descriptive narrative synthesis and compared and classified the development and evaluation methods and evaluation criteria according to available development and evaluation approaches for biomedical ontologies.

RESULTS

We identified 84 articles that met all of the inclusion criteria, resulting in 70 classifications/ontologies, nine of which were for the general medical domain. The most papers were published in 2010 and 2011, with 8 and 7 papers, respectively. The United States (50) and Australia (23) have the most studies. The most commonly used methods for developing classifications/ontologies included the use of existing systems (for expanding or mapping) (44) and qualitative analysis of event reports (39). The most common evaluation methods were coding or classifying some safety report samples (25), quantitative analysis of incidents based on the developed classification (24), and consensus among physicians (16). The most commonly applied evaluation criteria were reliability (27), content and face validity (9), comprehensiveness (6), usability (5), linguistic clarity (5), and impact (4), respectively.

CONCLUSIONS

Because of the weaknesses and strengths of the development/evaluation methods, it is advised that more than one method for development or evaluation, as well as evaluation criteria, should be used. To organize the processes of developing classification/ontologies, well-established approaches such as Methontology are recommended. The most prevalent evaluation methods applied in this domain are well fitted to the biomedical ontology evaluation methods, but it is also advised to apply some evaluation approaches such as logic, rules, and Natural language processing (NLP) based in combination with other evaluation approaches. This research can assist domain researchers in developing or evaluating domain ontologies using more complete methodologies. There is also a lack of reporting consistency in the literature and same methods or criteria were reported with different terminologies.

Vaccine adverse event enrichment tests

Vaccination safety is critical for individual and public health. Many existing methods have been used to conduct safety studies with the VAERS (Vaccine Adverse Event Reporting System) database. However, these methods frequently identify many adverse event (AE) signals and they are often hard to interpret in a biological context. The AE ontology introduces biologically meaningful structures to the Vaccine Adverse Event Reporting System (VAERS) database by connecting similar AEs, which provides meaningful interpretation for the underlying safety issues. In this paper, we develop rigorous statistical methods to identify "interesting" AE groups by performing AE enrichment analysis. We extend existing gene enrichment tests to perform AE enrichment analysis, while incorporating the special features of the AE data. The proposed methods were evaluated using simulation studies and were further illustrated on two studies using VAERS data. The proposed methods were implemented in R package AEenrich and can be installed from the Comprehensive R Archive Network, CRAN, and source code are available at https://github.com/umich-biostatistics/AEenrich.

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.

OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Protégé. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts.

A 2018 workshop: vaccine and drug ontology studies (VDOS 2018)

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.

Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels

Background

Use of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying ADR information from drug labels is critical in multiple aspects; however, this task is challenging due to the nature of the natural language of drug labels.

Results

In this paper, we present a machine learning- and rule-based system for the identification of ADR entity mentions in the text of drug labels and their normalization through the Medical Dictionary for Regulatory Activities (MedDRA) dictionary. The machine learning approach is based on a recently proposed deep learning architecture, which integrates bi-directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional Random Fields (CRF) for entity recognition. The rule-based approach, used for normalizing the identified ADR mentions to MedDRA terms, is based on an extension of our in-house text-mining system, SciMiner. We evaluated our system on the Text Analysis Conference (TAC) Adverse Drug Reaction 2017 challenge test data set, consisting of 200 manually curated US FDA drug labels. Our ML-based system achieved 77.0% F1 score on the task of ADR mention recognition and 82.6% micro-averaged F1 score on the task of ADR normalization, while rule-based system achieved 67.4 and 77.6% F1 scores, respectively.

Conclusion

Our study demonstrates that a system composed of a deep learning architecture for entity recognition and a rule-based model for entity normalization is a promising approach for ADR extraction from drug labels.

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties

Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA.

Methods: We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term “hepatitis B” is associated to the SNOMED CT concept “type B viral hepatitis”) to extract term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “blood and lymphatic system disorders” is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “cyclic neutropenia” leads to the property has clinical course cyclic).

Results: The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time.

Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.

Text preprocessing for improving hypoglycemia detection from clinical notes - A case study of patients with diabetes

BACKGROUND AND OBJECTIVE

Hypoglycemia is a common safety event when attempting to optimize glycemic control in diabetes (DM). While electronic medical records provide a natural ground for detecting and analyzing hypoglycemia, ICD codes used in the databases may be invalid, insensitive or non-specific in detecting new hypoglycemic events. We developed text preprocessing methods to improve automatic detection of hypoglycemia from analysis of clinical encounter text notes.

METHODS

We set out to improve hypoglycemia detection from clinical notes by introducing three preprocessing methods: stop word filtering, medication signaling, and ICD narrative enrichment. To test the proposed methods, we selected clinical notes from VA Maryland Healthcare System, based on various combinations of three criteria that are suggestive of hypoglycemia, including ICD-9 code of diabetes and hypoglycemia, laboratory glucose values < 70 md/dL, and text reference to a proximate hypoglycemia event. In addition, we constructed one dataset of 395 clinical notes from year 2009 and another of 460 notes from year 2014 to test the generality of the proposed methods. For each of the datasets, two physician judges manually reviewed individual clinical notes to determine whether hypoglycemia was present or absent. A third physician judge served as a final adjudicator for disagreements.

RESULTS

Each of the proposed preprocessing methods contributed to the performance of hypoglycemia detection by significantly increasing the F1 score in the range of 5.3∼7.4% on one dataset (p < .01). Among the methods, stop word filtering contributed most to the performance improvement (7.4%). Combining all the preprocessing methods led to greater performance gain (p < .001) compared with using each method individually. Similar patterns were observed for the other dataset with the F1 score being increased in the range of 7.7%∼9.4% by individual methods (p < .001). Nevertheless, combining the three methods did not yield additional performance gain.

CONCLUSION

The proposed text preprocessing methods improved the performance of hypoglycemia detection from clinical text notes. Stop word filtering achieved the most performance improvement. ICD narrative enrichment boosted the recall of detection. Combining the three preprocessing methods led to additional performance gains.

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

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.

ODAE: Ontology-based systematic representation and analysis of drug adverse events and its usage in study of adverse events given different patient age and disease conditions

Background

Drug adverse events (AEs), or called adverse drug events (ADEs), are ranked one of the leading causes of mortality. The Ontology of Adverse Events (OAE) has been widely used for adverse event AE representation, standardization, and analysis. OAE-based ADE-specific ontologies, including ODNAE for drug-associated neuropathy-inducing AEs and OCVDAE for cardiovascular drug AEs, have also been developed and used. However, these ADE-specific ontologies do not consider the effects of other factors (e.g., age and drug-treated disease) on the outcomes of ADEs. With more ontological studies of ADEs, it is also critical to develop a general purpose ontology for representing ADEs for various types of drugs.

Results

Our survey of FDA drug package insert documents and other resources for 224 neuropathy-inducing drugs discovered that many drugs (e.g., sirolimus and linezolid) cause different AEs given patients’ age or the diseases treated by the drugs. To logically represent the complex relations among drug, drug ingredient and mechanism of action, AE, age, disease, and other related factors, an ontology design pattern was developed and applied to generate a community-driven open-source Ontology of Drug Adverse Events (ODAE). The ODAE development follows the OBO Foundry ontology development principles (e.g., openness and collaboration). Built on a generalizable ODAE design pattern and extending the OAE and NDF-RT ontology, ODAE has represented various AEs associated with the over 200 neuropathy-inducing drugs given different age and disease conditions. ODAE is now deposited in the Ontobee for browsing and queries. As a demonstration of usage, a SPARQL query of the ODAE knowledge base was developed to identify all the drugs having the mechanisms of ion channel interactions, the diseases treated with the drugs, and AEs after the treatment in adult patients. AE-specific drug class effects were also explored using ODAE and SPARQL.

Conclusion

ODAE provides a general representation of ADEs given different conditions and can be used for querying scientific questions. ODAE is also a robust knowledge base and platform for semantic and logic representation and study of ADEs of more drugs in the future.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2729-1) contains supplementary material, which is available to authorized users.

Disease Specific Ontology of Adverse Events: Ontology extension and adaptation for Chronic Kidney Disease

BACKGROUND

Adverse Event (AE) ontology can be used to support interoperability and computer-assisted reasoning of AEs. Despite significant progress in developing biomedical ontologies, they are facing the obstacle of adoption partly because those ontologies are too general to meet the requirements of a specific domain. Understanding and representing of AEs for a specific domain such as Chronic Kidney Disease (CKD) has both theoretical and clinical significance. CKD patients are at a high risk for an array of disease-intervention specific AEs, and these in turn can contribute to disease progression unlike other diseases. This study proposes Disease Specific Ontology of Adverse Events (DSOAE) to address specific requirements of CKD, and applies it to different usage scenarios with real data.

METHODS

We introduce a method for developing DSOAE through the extension and adaption of general ontologies by incorporating domain-specific information and usage requirements. It starts with specifying the goal and scope of a target domain (i.e. selecting seed ontologies), followed by identifying main AE classes and relations, extracting and creating classes and relations, aligning and identifying upper-level classes and lower-level classes, and finally populating the ontology with instances. Any of these steps may be repeated to refine the ontology.

RESULTS

DSOAE contains 22 CKD-specific AE classes, which are grouped into two general categories: patient-reported AEs and biochemical/laboratory-related AEs. In addition, disease history and comorbidity classes as introduced in this study help model patient-related risk factors for AEs. With the support of DSOAE, we build a knowledge base of CKD-specific AEs using data from different sources (e.g. patient cohort data and social media), and apply the knowledge base to data analysis and data integration.

CONCLUSIONS

DSOAE enables the interoperability of AEs across different sources and supports the development of a knowledge base of domain-specific AEs. DSOAE can also meet the needs of different usage scenarios. The approach to constructing DSOAE is generalizable and can be used to develop AE ontology in other domains.

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs

BACKGROUND

Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs).

RESULTS

We developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems.

CONCLUSIONS

Our study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.

The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability

Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies developed, it has become critical to ensure ontology interoperability and the usage of interoperable ontologies for standardized data representation and integration. The suite of web-based Ontoanimal tools (e.g., Ontofox, Ontorat, and Ontobee) support different aspects of extensible ontology development. By summarizing the common features of Ontoanimal and other similar tools, we identified and proposed an “eXtensible Ontology Development” (XOD) strategy and its associated four principles. These XOD principles reuse existing terms and semantic relations from reliable ontologies, develop and apply well-established ontology design patterns (ODPs), and involve community efforts to support new ontology development, promoting standardized and interoperable data and knowledge representation and integration. The adoption of the XOD strategy, together with robust XOD tool development, will greatly support ontology interoperability and robust ontology applications to support data to be Findable, Accessible, Interoperable and Reusable (i.e., FAIR).

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.

Community-based Ontology Development, Annotation and Discussion with MediaWiki extension Ontokiwi and Ontokiwi-based Ontobedia

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.