Proof-of-concept for incorporating mechanistic insights from multi-omics analyses of polymyxin B in combination with chloramphenicol against Klebsiella pneumoniae.

Carbapenemase-resistant Klebsiella pneumoniae (KP) resistant to multiple antibiotic classes necessitates optimized combination therapy. Our objective is to build a workflow leveraging omics and bacterial count data to identify antibiotic mechanisms that can be used to design and optimize combination regimens. For pharmacodynamic analysis, previously published static time-kill studies (Abdul Rahim N et al. 2015) were employed with polymyxin B and chloramphenicol mono and combination therapy against three KP clinical isolates over 24 hours. A mechanism-based model (MBM) was developed using time-kill data in S-ADAPT describing polymyxin B-chloramphenicol pharmacodynamic activity against each isolate. Previously published results of polymyxin B (1 mg/L continuous infusion) and chloramphenicol (C max :8 mg/L; bolus q6h) mono and combination regimens were evaluated employing an in vitro one-compartment dynamic infection model against a KP clinical isolate (10 8 CFU/mL inoculum) over 24 hours to obtain bacterial samples for multi-omics analyses. The differentially expressed genes and metabolites in these bacterial samples served as input to develop a partial least squares regression (PLSR) in R that links pharmacodynamic responses with the multi-omics responses via a multi-omics pathway analysis. Polymyxin B efficacy was increased when combined with chloramphenicol, and the MBM described the observed pharmacodynamic well for all strains. The PLSR consisted of 29 omics inputs and predicted MBM pharmacodynamic response (R 2 =0.946). Our analysis found that chloramphenicol downregulated metabolites and genes pertinent to lipid A, hence limiting the emergence of polymyxin B resistance. Our workflow linked insights from analysis of multi-omics data with MBM to identify biological mechanisms explaining observed pharmacodynamic activity in combination therapy.