Protease inhibitors broadly effective against feline, ferret and mink coronaviruses

Ferret and mink coronaviruses typically cause catarrhal diarrhea in ferrets and minks, respectively. In recent years, however, systemic fatal coronavirus infection has emerged in ferrets, which resembles feline infectious peritonitis (FIP) in cats. FIP is a highly fatal systemic disease caused by a virulent feline coronavirus infection in cats. Despite the importance of coronavirus infections in these animals, there are no effective commercial vaccines or antiviral drugs available for these infections. We have previously reported the efficacy of a protease inhibitor in cats with FIP, demonstrating that a virally encoded 3C-like protease (3CLpro) is a valid target for antiviral drug development for coronavirus infections. In this study, we extended our previous work on coronavirus inhibitors and investigated the structure-activity relationships of a focused library of protease inhibitors for ferret and mink 3CLpro. Using the fluorescence resonance energy transfer assay, we identified potent inhibitors broadly effective against feline, ferret and mink coronavirus 3CLpro. Multiple amino acid sequence analysis and modelling of 3CLpro of ferret and mink coronaviruses were conducted to probe the structural basis for these findings. The results of this study provide support for further research to develop broad-spectrum antiviral agents for multiple coronavirus infections. To the best of our knowledge, this is the first report on small molecule inhibitors of ferret and mink coronaviruses.

Repurposing an inhibitor of ribosomal biogenesis with broad anti-fungal activity

The lack of new antifungal compounds with unique mechanisms of action is a concern for therapeutic management of patients. To identify inhibitors against human pathogenic fungi, we screened ~3000 compounds provided by the Developmental Therapeutics Program of NIH/NCI against a panel of pathogenic fungi including Candida species, Aspergillus fumigatus, and Cryptococcus neoformans. NSC319726 (a thiosemicarbazone) had broad antifungal activity in the range of 0.1–2.0 µg/ml and was also inhibitory to fluconazole-resistant isolates of Candida species. Synergy was demonstrated with NSC319726 and azoles, as well as caspofungin. The inhibitory concentration 50% (IC₅₀) of NSC319726 was 35–800-fold higher than the Minimum Inhibitory Concentration 50% (MIC₅₀ values), which indicates low compound toxicity to human cells in vitro. Transcriptome analysis of treated and untreated C. albicans using Gene Ontology (GO) revealed a large cluster of down regulated genes that encode translational proteins, especially those with ribosome biogenesis functions. As NSC319726 was first shown to have anti-cancer activity, its affects against human pathogenic fungi establish NSC319726 as a repurposed, off-patent compound that has potential antifungal activity. The minimal in vitro toxicity of lead optimized NSC319726 and its reasonable inhibitory activity against pathogens suggest advancing this compound to in vivo toxicity testing and protection studies against candidiasis.

Antifungal drug discovery: the process and outcomes

New data suggest that the global incidence of several types of fungal diseases have traditionally been under-documented. Of these, mortality caused by invasive fungal infections remains disturbingly high, equal to or exceeding deaths caused by drug-resistant tuberculosis and malaria. It is clear that basic research on new antifungal drugs, vaccines and diagnostic tools is needed. In this review, we focus upon antifungal drug discovery including in vitro assays, compound libraries and approaches to target identification. Genome mining has made it possible to identify fungal-specific targets; however, new compounds to these targets are apparently not in the antimicrobial pipeline. We suggest that 'repurposing' compounds (off patent) might be a more immediate starting point. Furthermore, we examine the dogma on antifungal discovery and suggest that a major thrust in technologies such as structural biology, homology modeling and virtual imaging is needed to drive discovery.