New analyses of MIC90 data to aid antibacterial drug discovery

Abstract

In this work we present a number of statistical and visualization methods derived from MIC₉₀ data designed to aid decision-making in antibacterial drug discovery research. A statistical method known as bootstrapping was applied to MIC₉₀ raw data to uncover data trends and a metric termed Net Percent Superior (NPS) was developed to capture a strain-by-strain analysis of analogs to enable rank-ordering of similar compounds. We also present novel methods of reporting the data using a variety of visualization techniques. Furthermore, the work was cross-validated using experimental results generated with siderophore-conjugated monocarbam analogs to demonstrate the effectiveness of the various parameters and visualization techniques. The methods reported herein have been incorporated in a Scitegic Pipeline Pilot protocol to enable facile, automated generation of MIC₉₀ analyses from experimental raw data to aid prospective medicinal chemistry design as well as retrospective analyses.