The COVID-19 Ontology.

MOTIVATION: The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. RESULTS: The ontology comprises 2.270 classes of concepts and 38.987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. AVAILABILITY: COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19.

Clinical safety and pharmacokinetic evaluation of aqueous extract of Cocculus hirsutus, an anti-viral phytopharmacetical drug as a potential for the treatment of dengue and COVID-19.

Background and aim: Dengue a worldwide concern for public health has no effective vaccine or drug available for its prevention or treatment. There are billions of people who are at risk of contracting the dengue virus (DENV) infections with only anti-mosquito strategies to combat this disease. Based on the reports, particularly in vitro studies and small animal studies showing anti-viral activity of aqueous extract of Cocculus hirsutus (AQCH), studies were conducted on AQCH tablets as a potential for the treatment of dengue and COVID-19 infections. The current study was part of the research on AQCH tablet formulation and was aimed to evaluate safety and pharmacokinetics in healthy human subjects. Materials and methods: Sixty healthy adult human subjects were divided into 5 groups (cohorts: I to V; n = 12 per cohort) and randomized in the ratio of 3:1 to receive active treatment or placebo in a blinded manner. Five doses 100 mg, 200 mg, 400 mg, 600 mg and 800 mg tablets were administered three times daily at an interval of 8 h for days 01-09 under fasting conditions and a single dose in morning on day 10. Safety assessment was based on monitoring the occurrence, pattern, intensity, and severity of adverse events during study period. Blood samples were collected for measurement of the bio-active marker Sinococuline concentrations by a validated LC-MS/MS method followed by pharmacokinetic evaluation. Results and conclusion: The test formulation was well tolerated in all cohorts. Sinococuline peak plasma concentration (Cmax) and total exposure of plasma concentration (AUC) demonstrated linearity up to 600 mg and saturation kinetics at 800 mg dose. There was no difference observed in elimination half-life for all the cohorts, suggesting absence of saturation in rate of elimination. Dose accumulation was observed and steady state was achieved within 3 days. The information on human pharmacokinetics of AQCH tablets would assist in further dose optimization with defined pharmacokinetic-pharmacodynamic relationship.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings

Summary Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences. Graphical Highlights • Deep learning approach (STEP) predicts virus protein to human host protein interactions• It is based on recent deep protein sequence embeddings and Siamese neural network• Prediction of PPIs of the JCV VP1 protein and of the SARS-CoV-2 spike protein• Identify parts of sequences that most likely contribute to the PPI using explainable AI The bigger picture The development of novel cell and tissue-specific therapies requires a profound knowledge about protein-protein interactions (PPIs). Identifying these PPIs with experimental approaches such as biochemical assays or yeast two-hybrid screens is cumbersome, costly, and at the same time difficult to scale. Computational approaches can help to prioritize huge amounts of possible PPIs by learning from biological sequences plus already known PPIs. In this work, we developed an approach that is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network architecture. We use this approach to train models by using protein sequence information and known PPIs. We apply the models to two use cases to predict virus protein to human host interactions. Altogether our work highlights the potential of deep sequence embedding techniques as well as explainable artificial intelligence methods for the analysis of biological sequence data. Protein-protein interaction (PPI) databases that include already-known PPIs represent an important resource in bioinformatics. A major challenge is to extend our knowledge of PPIs, which are highly relevant for the development of novel virus-like particles that can deliver therapeutics to targeted cells and tissues. Here, we use these PPI databases and the protein sequence information to train deep Siamese neural network architecture while using transfer learning and apply them to predict new virus-host PPIs with high accuracy.

CTO: a Community-Based Clinical Trial Ontology and its Applications in PubChemRDF and SCAIView

Driven by the use cases of PubChemRDF and SCAIView, we have developed a first community-based clinical trial ontology (CTO) by following the OBO Foundry principles. CTO uses the Basic Formal Ontology (BFO) as the top level ontology and reuses many terms from existing ontologies. CTO has also defined many clinical trial-specific terms. The general CTO design pattern is based on the PICO framework together with two applications. First, the PubChemRDF use case demonstrates how a drug Gleevec is linked to multiple clinical trials investigating Gleevec’s related chemical compounds. Second, the SCAIView text mining engine shows how the use of CTO terms in its search algorithm can identify publications referring to COVID-19-related clinical trials. Future opportunities and challenges are discussed.

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.

Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.

COVID-19 preVIEW: Semantic Search to Explore COVID-19 Research Preprints.

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.