Therapeutic indications and other use-case-driven updates in the drug ontology: anti-malarials, anti-hypertensives, opioid analgesics, and a large term request

Hogan, William R.; Hanna, Josh; Hicks, Amanda; Amirova, Samira; Bramblett, Baxter; Diller, Matthew; Enderez, Rodel; Modzelewski, Timothy; Vasconcelos, Mirela; Delcher, Chris

doi:10.1186/s13326-017-0121-5

Cited by 12 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using literature mining we identified 72 chemical drugs and 27 monoclonal or polyclonal antibodies that have anti-coronavirus effects in experimental studies in vivo or in vitro. Many of these drugs were mapped to three ontologies: Chemical Entities of Biological Interest ontology (ChEBI) 10 , National Drug File -Reference Terminology (NDF-RT) 19 , and the Drug Ontology (DrON) 20 . The subbranches of these ontologies that contain the mapped drugs and their related characteristics were extracted using the Ontofox tool 21 .…”

mentioning

confidence: 99%

CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis

Ong

et al. 2020

Sci Data

100

View full text Add to dashboard Cite

the Coronavirus Infectious Disease Ontology (CIDO) is a community-based ontology that supports coronavirus disease knowledge and data standardization, integration, sharing, and analysis. O ntologies, as the term is used in informatics, are structured vocabularies comprised of human-and computer-interpretable terms and relations that represent entities and relationships. Within informatics fields, ontologies play an important role in knowledge and data standardization, representation, integration, sharing and analysis. They have also become a foundation of artificial intelligence (AI) research. In what follows, we outline the Coronavirus Infectious Disease Ontology (CIDO), which covers multiple areas in the domain of coronavirus diseases, including etiology, transmission, epidemiology, pathogenesis, diagnosis, prevention, and treatment. We emphasize CIDO development relevant to COVID-19. Human coronaviruses have given rise to a series of major crises in global public health. Severe acute respiratory syndrome (SARS) emerged in China in November 2002, lasted for eight months and resulted in 8,098 confirmed human cases in 29 countries with 774 deaths (case-fatality rate: 9.6%) 1. Approximately ten years later in June 2012, the Middle East Respiratory Syndrome (MERS), another highly pathogenic coronavirus disease, was identified in Saudi Arabia. The MERS outbreak has caused 2,260 cases in 27 countries and 803 deaths (35.5%) 2. More recently, the World Health Organization (WHO) declared the Coronavirus Disease 2019 (COVID-19) outbreak as a pandemic on March 11, 2020, when there were 118,326 confirmed cases and 4,292 deaths. As of May 13, there have been over 4.4 million confirmed cases and over 295,000 deaths globally. Unfortunately, we still do not have available effective drugs and vaccines against these highly pathological coronaviruses. Extensive studies have been conducted on coronaviruses, the results of many of which exist in publicly available data repositories such as GEO 3. Publications concerning COVID-19 have exploded in recent months, and new clinical trials have been and are being conducted to develop drugs and vaccines against COVID-19, 1,430 of which have been registered in ClinicalTrials.

show abstract

mentioning

confidence: 99%

CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis

Ong

et al. 2020

Sci Data

100

View full text Add to dashboard Cite

show abstract

“…Reference terminology for data integration (Boyce et al, 2014; Iyer et al, 2014; Wang et al, 2014; Jiang et al, 2015; Cai et al, 2017; Hogan et al, 2017; Knowledge Base Workgroup of the Observational Health Data Sciences and Informatics (OHDSI) Collaborative, 2017; Personeni et al, 2017; Voss et al, 2017)…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

“…The list of identified anti-coronavirus drugs was mapped to ontology IDs from ChEBI (Hastings et al, 2016), NDF-RT (Peters et al, 2015) and DrON (Hogan et al, 2017). The Ontobee ontology --repository (Ong et al, 2017) was used for the mapping.…”

Section: Ontology Extraction and Analysismentioning

confidence: 99%

“…Ontology has played a significant role in knowledge and data standardization, integration, and analysis (Ashburner et al, 2000;Bodenreider, 2008;Schulz et al, 2013;Hoehndorf et al, 2015). Three ontologies, including Chemical Entities of Biological Interest ontology (ChEBI) (Hastings et al, 2016), National Drug File -Reference Terminology (NDF-RT) (Peters et al, 2015), and Drug Ontology (DrON) (Hogan et al, 2017), have been frequently used for drug studies (Tao et al, 2016;Liu et al, 2017). ChEBI is a database and ontology of over 56,000 molecular entities with a focus on small chemical compounds.…”

Section: Introductionmentioning

confidence: 99%

“…Over 90 chemical drugs and antibodies against human coronavirus diseases were identified. We mapped the majority of these drugs to the ontologies ChEBI (Hastings et al, 2016), NDF-RT (Peters et al, 2015), and DrON (Hogan et al, 2017). We applied ontology to categorize these drugs and used ontology-based bioinformatics methods to further analyze various features of these drugs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ontological and Bioinformatic Analysis of Anti-Coronavirus Drugs and Their Implication for Drug Repurposing against COVID-19

Liu¹,

Chan²,

Wang³

et al. 2020

Preprint

View full text Add to dashboard Cite

Coronavirus-infected diseases have posed great threats to human health. In past years, highly infectious coronavirus-induced diseases, including COVID-19, SARS, and MERS, have resulted in world-wide severe infections. Our literature annotations identified 72 chemical drugs and 27 antibodies effective against at least one human coronavirus infection in vitro or in vivo. Many of these drugs inhibit viral entry to cells and viral replication inside cells or modulate host immune responses. Many antimicrobial drugs, including antimalarial (e.g., chloroquine and mefloquine) and antifungal (e.g., terconazole and rapamycin) drugs as well as antibiotics (e.g., teicoplanin and azithromycin) were associated with anti-coronavirus activity. A few drugs, including remdesivir, chloroquine phosphate, favipiravir, and tocilizumab, have already been reported to be effective in treating COVID-19. After mapping our identified drugs to three ontologies ChEBI, NDF-RT, and DrON, many features such as roles and mechanisms of action (MoAs) of these drugs were identified and categorized. For example, out of 35 drugs with MoA annotations in NDF-RT, 34 have MoAs of different types of inhibitors and antagonists. Two clustering analyses, one based on ChEBI-based semantic similarity, the other based on drug chemical similarity, were performed to cluster over 60 drugs to new categories. Moreover, PCA analysis of anti-coronavirus drugs found differences in physicochemical properties between those inhibiting viral entry and viral replication. A total of 137 host genes were identified as the targets of 47 anti-coronavirus drugs, resulting in a network of 370 interactions among these drugs and targets. Chlorpromazine, dasatinib, and anisomycin are the hubs of the drug-target network with the highest number of connected target proteins. Many enriched pathways such as calcium signaling and neuroactive ligand-receptor interaction pathways were identified. These findings may be used to facilitate drug repurposing against COVID-19.

show abstract

Therapeutic indications and other use-case-driven updates in the drug ontology: anti-malarials, anti-hypertensives, opioid analgesics, and a large term request

Cited by 12 publications

References 14 publications

CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis

CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis

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

Ontological and Bioinformatic Analysis of Anti-Coronavirus Drugs and Their Implication for Drug Repurposing against COVID-19

Contact Info

Product

Resources

About