COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug–Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19

Korn, Daniel; Pervitsky, Vera; Bobrowski, Tesia; Alves, Vinícius M.; Schmitt, Charles; Bizon, Chris; Baker, Nancy C.; Chirkova, Rada; Cherkasov, Artem; Muratov, Eugene; Tropsha, Alexander

doi:10.1021/acs.jcim.1c01285

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…Where such data exist, the current framework could potentially benefit from concurrent use and validation against empirical drug data-driven approaches such as the Drug Atlas 58 with the proviso that predictions made here include maximally synergistic drugs in addition to those that are maximally complementary in their actions. Similarly, recent digital repositories focused specifically on consolidating clinical experience and putative targets of COVID-19 therapeutics 59 , 60 might also be leveraged to further inform the family of candidate drugs to be assessed in the context of host immune response dynamics captured here by our regulatory network model. Finally, in the current analysis, the selection of drugs useful in disrupting cytokine storm was limited to compounds having received FDA approval.…”

Section: Discussionmentioning

confidence: 99%

Old drugs, new tricks: leveraging known compounds to disrupt coronavirus-induced cytokine storm

et al. 2022

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A major complication in COVID-19 infection consists in the onset of acute respiratory distress fueled by a dysregulation of the host immune network that leads to a run-away cytokine storm. Here, we present an in silico approach that captures the host immune system’s complex regulatory dynamics, allowing us to identify and rank candidate drugs and drug pairs that engage with minimal subsets of immune mediators such that their downstream interactions effectively disrupt the signaling cascades driving cytokine storm. Drug–target regulatory interactions are extracted from peer-reviewed literature using automated text-mining for over 5000 compounds associated with COVID-induced cytokine storm and elements of the underlying biology. The targets and mode of action of each compound, as well as combinations of compounds, were scored against their functional alignment with sets of competing model-predicted optimal intervention strategies, as well as the availability of like-acting compounds and known off-target effects. Top-ranking individual compounds identified included a number of known immune suppressors such as calcineurin and mTOR inhibitors as well as compounds less frequently associated for their immune-modulatory effects, including antimicrobials, statins, and cholinergic agonists. Pairwise combinations of drugs targeting distinct biological pathways tended to perform significantly better than single drugs with dexamethasone emerging as a frequent high-ranking companion. While these predicted drug combinations aim to disrupt COVID-induced acute respiratory distress syndrome, the approach itself can be applied more broadly to other diseases and may provide a standard tool for drug discovery initiatives in evaluating alternative targets and repurposing approved drugs.

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Section: Discussionmentioning

confidence: 99%

Old drugs, new tricks: leveraging known compounds to disrupt coronavirus-induced cytokine storm

et al. 2022

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“…In recent years, literature-based discovery (LBD) has been added to the arsenal of tools employed specially to identify novel or poorly known uses of existing drugs (also known as ‘ drug repurposing ’ [4]) or identify potential side effects of the existing drugs [5]. Commonly, LBD approaches have been used to analyze textual sources such as abstracts of full texts of the scientific papers [6,7]) although in some cases social media mining has been explored as well [8]. In this study, we proposed an uncommon LBD approach based on text mining of transcribed audio podcast recordings.…”

Section: Background and Significancementioning

confidence: 99%

Text Mining of the People’s Pharmacy Radio Show Transcripts Can Identify Novel Drug Repurposing Hypotheses

Yedida¹,

Beasley

Korn

et al. 2022

Preprint

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Objective: Social media mining may provide surprising information about unknown effects of drugs. We endeavored to uncover such unknown drug-disease relationships by text mining of audio record transcripts from the popular NPR show, The People's Pharmacy. Materials and Methods: We used Google Cloud to transcribe episodes of the NPR podcast into textual documents. We then built a pipeline for systematically pre-processing the text to ensure quality input to the core classification model. Finally, results of the model were filtered by a series of post-processing steps. Our classification model itself uses the FLAIR language model pre-trained on PubMed abstracts. The modular nature of our pipeline allows for ease of future developments in this area by substituting higher quality components at each stage of the pipeline. To validate the drug-disease relating assertions extracted from the podcast, we utilized the DrugCentral database and ROBOKOP biomedical knowledge graph, which capture drug-disease relationships for FDA approved medications. Results: Our model identified 128 drug-disease pairs that were found in DrugCentral and 112 novel candidate pairs requiring expert review. To demonstrate the expert review process, we found literature evidence supporting the assertions for novel drug-disease pairs. Discussion and Conclusion: Text mining of social media is increasingly used to uncover novel relationships between semantic concepts corresponding to biomedical concepts. However, mining audio transcripts of specialized podcast shows has not been explored previously for this purpose. Using this approach, we have identified several unknown drug-disease relationships with support in biomedical literature. The proposed approach can extend beyond radio podcasts and could be applied to any source of audio and textual data.

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“…Data linked in this manner can be organically connected through a context-awareness system without being confined to a specific field or domain, aiding in the identification of various causes of urban problems. Specifically, the development of technology that can infer potential risks by considering various urban situations comprehensively is necessary in order to provide specialized cause analysis and intelligent predictive services for urban problems in smart cities [7,8].…”

Section: Introductionmentioning

confidence: 99%

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems

Lee,

Song

2023

Sensors

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Smart cities provide integrated management and operation of urban data emerging within a city, supplying the infrastructure for smart city services and resolving various urban challenges. Nevertheless, cities continue to grapple with substantial issues, such as contagious diseases and terrorism, that pose severe financial and human risks. These problems sporadically arise in various locales, and current smart city frameworks lack the capability to autonomously identify and address these issues. The challenge intensifies especially when trying to recognize and respond to unprecedented problems. The primary objective of this research is to predict potential urban issues and support their resolution proactively. To achieve this, our system makes use of semantic reasoning to understand the ongoing situations within the city. In this process, the 5W1H principles serve as inference rules, guiding the extraction and consolidation of context. Firstly, utilizing domain-specific annotation templates, we craft a semantic graph by amalgamating information from various sources available in the city, such as municipal public data and IoT platforms. Subsequently, the system autonomously infers and accumulates contexts of situations occurring in the city using 5W1H-based reasoning. As a result, the accumulated contexts allow for inferring potential urban problems by identifying repeated disruptions in city services at specific times or locations and establishing connections among them. The main contribution of this paper lies in proposing a comprehensive conceptual model for the suggested system and presenting actual implementation cases and applicable use cases. These contributions facilitate awareness among city administrators and citizens within a smart city regarding potential problem-prone areas or times, thereby aiding in the preemptive identification and mitigation of urban challenges.

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COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug–Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19

Cited by 5 publications

References 43 publications

Old drugs, new tricks: leveraging known compounds to disrupt coronavirus-induced cytokine storm

Old drugs, new tricks: leveraging known compounds to disrupt coronavirus-induced cytokine storm

Text Mining of the People’s Pharmacy Radio Show Transcripts Can Identify Novel Drug Repurposing Hypotheses

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems

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