Multiomics informed mathematical model for meropenem and tobramycin against hypermutable Pseudomonas aeruginosa

Pseudomonas aeruginosa causes serious bacterial infections in people with cystic fibrosis (pwCF), and has numerous resistance mechanisms. Current empirical approaches to informing antibiotic regimen selection have important limitations. This study exposed two P. aeruginosa clinical isolates to concentration-time profiles of meropenem and tobramycin as would be observed in lung fluid of pwCF. The combination elicited different bacterial count profiles between the isolates, despite similar bacterial baseline characteristics. We found differences between the isolates in the expression of a key resistance mechanism against meropenem at 1 h, and expression that implied a loss of cell membrane permeability for tobramycin without the expected DNA mutation. This information enabled mathematical modeling to accurately describe all bacterial profiles over time. For the first time, this multiomics informed modeling approach using DNA and RNA data was applied to a hollow-fiber infection study. Using bacterial molecular insights with mechanism-based mathematical modeling has high potential for ultimately informing personalised antibiotic therapy.