Hanafin, Patrick O. and Abdul Rahim, Nusaibah and Sharma, Rajnikant and Cess, Colin G. and Finley, Stacey D. and Bergen, Phillip J. and Velkov, Tony and Li, Jian and Rao, Gauri G. (2023) Proof-of-concept for incorporating mechanistic insights from multi-omics analyses of polymyxin B in combination with chloramphenicol against Klebsiella pneumoniae. CPT-Pharmacometrics & Systems Pharmacology, 12 (3). pp. 387-400. ISSN 2163-8306, DOI https://doi.org/10.1002/psp4.12923.
Full text not available from this repository.Abstract
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 (C-max: 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 (10(8) 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 (R-2 = 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.
| Item Type: | Article |
|---|---|
| Funders: | United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Allergy & Infectious Diseases (NIAID) (Grant No: R01 AI111965) |
| Uncontrolled Keywords: | Antibiotic combinations; Resistant bacteria pharmacokinetics; Pharmacodynamics; Infections; PPGPP; Tool |
| Subjects: | R Medicine > RM Therapeutics. Pharmacology |
| Divisions: | Faculty of Pharmacy > Department of Clinical Pharmacy and Pharmacy Practice |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 01 Dec 2023 04:17 |
| Last Modified: | 01 Dec 2023 04:17 |
| URI: | http://eprints.um.edu.my/id/eprint/38694 |
Actions (login required)
 |
View Item |
