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

Wang, Liwei; Li, Mei; Xie, Jiehong; Cao, Yongguo; Liu, Hongfang; He, Yongqun

doi:10.1038/s41598-017-12580-4

Cited by 15 publications

(27 citation statements)

References 86 publications

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“…The study design and procedure were similar to the expert user study described below. Participants performed tasks with two different sized ontologies to gauge performance at two distinct levels of difficulty; CVDO [6] (536 classes) and OCVDAE [67] (4,589 classes). Overall, the results showed that Protégé [52] slightly outperformed OntoPlot for most of the Hierarchy-related tasks on both accuracy and completion time.…”

Section: Prototype Evaluationmentioning

confidence: 99%

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

Yang

Wybrow

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: OntoPlot interface. Classes with associations are highlighted in colour based on the key on the right-hand side. A panel on the left (not shown) selects different types of non-hierarchy associations found within the ontology (denoted by the OWL object properties involved). The panel on the right provides search and displays additional information for selected classes (not shown).Abstract-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.

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Section: Prototype Evaluationmentioning

confidence: 99%

“…We use two biomedical ontologies: CVDO [6] and OCVDAE [67]. CVDO (536 classes) was used for the training tasks.…”

Section: Datasetsmentioning

confidence: 99%

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

Yang

Wybrow

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…Once a list of drugs is identified, tools such as Ontofox (Xiang et al, 2010) can be used to extract these drugs and their related characteristics from an ontology and perform specific analyses (Guo et al, 2016;Xie et al, 2016;Wang et al, 2017;Yu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Liu¹,

Chan²,

Wang³

et al. 2020

Preprint

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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.

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“…Specifically, an AE-specific drug class effect is defined to exist when all the drugs in a specific drug class (or drug group) are associated with an AE. In a recent study on cardiovascular drug-associated AEs, a proportional class-level ratio (PCR) value was defined and used to identify drug class effect on different AEs [ 15 ]. Specifically, when the PCR value equals to 1, it means that a class effect of a group of drugs on a specific AE exists.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, when the PCR value equals to 1, it means that a class effect of a group of drugs on a specific AE exists. Previous PCR-based heatmap analyses identified many important drug class effects on different AEs [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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

Hur

Özgür

2018

J Biomed Semant

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BackgroundAdverse 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).ResultsWe 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.ConclusionsOur 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.

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

Cited by 15 publications

References 86 publications

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

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

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

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

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