Development of Novel Anti-influenza Thiazolides with Relatively Broad-Spectrum Antiviral Potentials

ABSTRACT Seasonal and pandemic influenza causes 650,000 deaths annually in the world. The emergence of drug resistance to specific anti-influenza virus drugs such as oseltamivir and baloxavir marboxil highlights the urgency of novel anti-influenza chemical entity discovery. In this study, we report a series of novel thiazolides derived from an FDA-approved drug, nitazoxanide, with antiviral activity against influenza and a broad range of viruses. The preferred candidates 4a and 4d showed significantly enhanced anti-influenza virus potentials, with 10-fold improvement compared to results with nitazoxanide, and were effective against a variety of influenza virus subtypes including oseltamivir-resistant strains. Notably, the combination using compounds 4a/4d and oseltamivir carboxylate or zanamivir displayed synergistic antiviral effects against oseltamivir-resistant strains. Mode-of-action analysis demonstrated that compounds 4a/4d acted at the late phase of the viral infection cycle through inhibiting viral RNA transcription and replication. Further experiments showed that treatment with compounds 4a/4d significantly inhibited influenza virus infection in human lung organoids, suggesting the druggability of the novel thiazolides. In-depth transcriptome analysis revealed a series of upregulated cellular genes that may contribute to the antiviral activities of 4a/4d. Together, the results of our study indicated the direction to optimize nitazoxanide as an anti-influenza drug and discovered two candidates with novel structures, compounds 4a/4d, that have relatively broad-spectrum antiviral potentials.

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

MotivationSemantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommend the use of the Resource Description Framework (RDF). The RDF implementation provides the flexibility of model entity searching (e.g. flux of sodium across apical plasma membrane) by utilising SPARQL. However, the rigidity and complexity of SPARQL syntax and the nature of semantic annotation which is not merely as a simple triple yet forming a tree-like structure may cause a difficulty. Therefore, the availability of an interface to convert a natural language query to SPARQL is beneficial.ResultsWe propose NLIMED, a natural language query to SPARQL interface to retrieve model entities from biosimulation models. Our interface can be applied to various repositories utilising RDF such as the PMR and Biomodels. We evaluate our interface by collecting RDF in the biosimulation models coded using CellML in PMR. First, we extract RDF as a tree structure and then store each subtree of a model entity as a modified triple of a model entity name, path, and class ontology into the RDF Graph Index. We also extract class ontology’s textual metadata from the BioPortal and CellML and manage it in the Text Feature Index. With the Text Feature Index, we annotate phrases resulted by the NLQ Parser (Stanford parser or NLTK parser) into class ontologies. Finally, the detected class ontologies then are composed as SPARQL by incorporating the RDF Graph Index. Our annotator performance is far more powerful compared to the available service provided by BioPortal with F-measure of 0.756 and our SPARQL composer can find all possible SPARQL in the collection based on the annotation results. Currently, we already implement our interface in Epithelial Modelling Platform tool.Availabilityhttps://github.com/napakalas/NLIMED