A structurally biased combinatorial approach for discovering new anti-picornaviral compounds

Abstract

Background: Picornaviruses comprise a family of small, non-enveloped RNA viruses. A common feature amongst many picornaviruses is a hydrophobic pocket in the core of VP1, one of the viral capsid proteins. The pocket is normally occupied by a mixture of unidentified, fatty acid-like moieties, which can be competed out by a family of capsid-binding, antiviral compounds. Many members of the Picornaviridae family are pathogenic to both humans and livestock, yet no adequate therapeutics exist despite over a decade’s worth of research in the field. To address this challenge, we developed a strategy for rapid identification of capsid-binding anti-picornaviral ligands. The approach we took involved synthesizing structurally biased combinatorial libraries that had been targeted to the VP1 pocket of poliovirus and rhinovirus. The libraries are screened for candidate ligands with a high throughput mass spectrometry assay. Results: Using the mass spectrometry assay, we were able to identify eight compounds from a targeted library of 75 compounds. The antiviral activity of these candidates was assessed by (i) measuring the effect on the kinetics of viral uncoating and (ii) the protective effect of each drug in traditional cell-based assays. All eight of the candidates exhibited antiviral activity, but three of them were particularly effective against poliovirus and rhinovirus. Conclusions: The results illustrate the utility of combining structure-based design with combinatorial chemistry. The success of our approach suggests that assessment of small, targeted libraries, which query specific chemical properties, may be the best strategy for surveying all of chemical space for ideal anti-picornaviral compounds.