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

• Published in: CPT: pharmacometrics and systems pharmacology Vol. 12; no. 3; pp. 387 - 400
• Main Authors: Hanafin, Patrick O., Abdul Rahim, Nusaibah, Sharma, Rajnikant, Cess, Colin G., Finley, Stacey D., Bergen, Phillip J., Velkov, Tony, Li, Jian, Rao, Gauri G.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2023; John Wiley and Sons Inc; Wiley
• Subjects: Anti-Bacterial Agents - pharmacology; Antibiotics; Bacteria; Bacterial infections; Chloramphenicol - metabolism; Chloramphenicol - pharmacology; Drug dosages; Gram-negative bacteria; Humans; Infections; Klebsiella pneumoniae - genetics; Klebsiella pneumoniae - metabolism; Lipids; Microbial Sensitivity Tests; Mortality; Multiomics; Pathogens; Pharmacodynamics; Physiology; Polymyxin B - pharmacology
• ISSN: 2163-8306; 2163-8306
• DOI: 10.1002/psp4.12923

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 (PD) analysis, previously published static time‐kill studies (J Antimicrob Chemother 70, 2015, 2589) were used with polymyxin B (PMB) and chloramphenicol (CHL) mono and combination therapy against three KP clinical isolates over 24 h. A mechanism‐based model (MBM) was developed using time‐kill data in S‐ADAPT describing PMB‐CHL PD activity against each isolate. Previously published results of PMB (1 mg/L continuous infusion) and CHL (Cmax: 8 mg/L; bolus q6h) mono and combination regimens were evaluated using an in vitro one‐compartment dynamic infection model against a KP clinical isolate (108 CFU/ml inoculum) over 24 h 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 PD responses with the multi‐omics responses via a multi‐omics pathway analysis. PMB efficacy was increased when combined with CHL, and the MBM described the observed PD well for all strains. The PLSR consisted of 29 omics inputs and predicted MBM PD response (R2 = 0.946). Our analysis found that CHL downregulated metabolites and genes pertinent to lipid A, hence limiting the emergence of PMB resistance. Our workflow linked insights from analysis of multi‐omics data with MBM to identify biological mechanisms explaining observed PD activity in combination therapy.