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Multi-omics informed mathematical model for meropenem and tobramycin against hypermutable Pseudomonas aeruginosa

ABSTRACT: Multi-omics informed mathematical model for meropenem and tobramycin against hypermutable Pseudomonas aeruginosa

PROVIDER: PRJEB79131 | ENA |

REPOSITORIES: ENA

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Project description:Hypermutable P. aeruginosa isolates are prevalent in cystic fibrosis and associated with acute exacerbations of chronic lung infections leading to early death and increased resistance emergence. Achievable epithelial lining fluid concentration-time profiles of meropenem and tobramycin in monotherapy and combination regimens were simulated against two clinical hypermutable P. aeruginosa isolates; CW8 (MICmeropenem=8mg/L, MICtobramycin=8mg/L) and CW44 (MICmeropenem=4mg/L, MICtobramycin=2mg/L) in an 8-day hollow fiber infection model (HFIM). Both isolates were previously characterised with genotypes resembling those of carbapenem- and aminoglycoside-resistant strains. Meropenem at 1 or 2g every 8h (3h infusion) and tobramycin at 5 or 10mg/kg body weight every 24h (0.5h infusion) were studied. Total and resistant bacterial counts were determined. Whole genome sequencing was performed on mutants and whole population samples at 191h, and transcriptomics at 1 and 191h. Mechanism-based modelling of total and resistant populations was informed by the multi-omics analysis. While all regimens against both isolates produced regrowth, the high dose combination synergistically suppressed resistant regrowth against CW8 up to ~96h. The high dose combination provided some killing against CW44, however failed to prevent resistant regrowth. In CW8, mutations emerged during treatment in pmrB, ampR, and multiple efflux pump regulators; in CW44, mutations in pmrB and PBP2 were observed. In CW8, resistance genes mexB and oprM were downregulated by the combination at 1h and coincided with synergistic killing, with differential expression of outer membrane norspermidine and lipopolysaccharide genes at 191h. Mechanism-based modelling incorporating subpopulation and mechanistic synergy successfully characterized the bacterial response of CW8, while mechanistic synergy was not required for CW44. Incorporating information from the multi-omics analyses was instrumental in building the mechanism-based model to describe the bacterial response of the hypermutable isolates, whereas MICs and traditional PK/PD indices could not predict the outcomes of the HFIM.

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Multi-omics informed mathematical model for meropenem and tobramycin against hypermutable Pseudomonas aeruginosa

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