Using transduction models to describe the time-course of extracellular metabolites in a hollow-fibre infection model


Extracellular bacterial metabolites have potential as markers of bacterial growth and resistance emergence, but have not been evaluated in dynamic in vitro studies.


To mathematically describe the relationship between bacterial response and extracellular metabolites in a HFIM.


An MDR hypermutable Pseudomonas aeruginosa CF clinical isolate was challenged with ceftolozane-tazobactam (C/T) simulating continuous infusions of standard (4.5 g/day) and high (9 g/day) daily doses in the HFIM for 7-9 days (n=5) (1, 2). Ceftolozane concentrations were confirmed by LC-MS/MS. Spent supernatant from HFIM were analysed with untargeted LCMS-based metabolomics, and correlation analysis with bacterial data. Selected metabolites were co-modelled with their respective correlating bacterial population with a PK/PD-based transduction model.


Both C/T regimens provided some killing, then failed with amplified resistance from 48-72h onwards (Figure). The bacterial model included two subpopulations. One replicate with no quantifiable baseline resistant subpopulation required an adaptation resistance sub-model for late-emerging resistant regrowth. L-ornithine (Figure) and L-arginine were highly correlated with the total bacterial population (0.82 and -0.79 respectively, p<0.0001) and described with a transduction model. Ribose-5-phosphate, sedoheptulose-7-phosphate and trehalose-6-phosphate correlated with the resistant subpopulation (0.64, 0.64 and 0.67, respectively, p<0.0001) and were modelled with this subpopulation. The latter three metabolites were likely secreted by resistant growth overcoming oxidative and osmotic stress induced by C/T.


These proof-of-concept studies suggest further exploration is warranted to determine the generalizability of these findings. The metabolites modeled here are not exclusive to bacteria. Future studies may use this approach to identify bacteria-specific metabolites correlating with resistance, which would ultimately be extremely useful for clinical translation.


  1. Tait JR, Harper M, Cortes-Lara S, Rogers KE, Lopez-Causape C, Smallman TR, et al. Ceftolozane-Tazobactam against Pseudomonas aeruginosa Cystic Fibrosis Clinical Isolates in the Hollow-Fiber Infection Model: Challenges Imposed by Hypermutability and Heteroresistance. Antimicrob Agents Chemother. 2023;67(8):e0041423.
  2. Tait JR, Anderson D, Nation RL, Creek DJ, Landersdorfer CB. Identifying and mathematically modeling the time-course of extracellular metabolic markers associated with resistance to ceftolozane/tazobactam in Pseudomonas aeruginosa. Antimicrobial Agents and Chemotherapy. 2024 (in press);AAC01081-23R1.