Defining Exposure Predictors of Meropenem That Are Associated with Improved Survival for Severe Bacterial Infection: A Preclinical PK/PD Study in Sepsis Rat Model

Simulated drug disposition in critically ill patients to evaluate effective PK/PD targets for combating Pseudomonas aeruginosa resistance to meropenem

Bacterial infections, including those caused by Pseudomonas aeruginosa, often lead to sepsis, necessitating effective antibiotic treatment like carbapenems. The key pharmacokinetic/pharmacodynamic (PK/PD) index correlated to carbapenem efficacy is the fraction time of unbound plasma concentration above the minimum inhibitory concentration (MIC) of the pathogen (%fT > MIC). While multiple targets exist, determining the most effective one for critically ill patients remains a matter of debate. This study evaluated meropenem's bactericidal potency and its ability to combat drug resistance in Pseudomonas aeruginosa under three representative PK/PD targets: 40% fT > MIC, 100% fT > MIC, and 100% fT > 4× MIC. The hollow fiber infection model (HFIM) was constructed, validated, and subsequently inoculated with a substantial Pseudomonas aeruginosa load (1 × 108 CFU/mL). Different meropenem regimens were administered to achieve the specified PK/PD targets. At specified intervals, samples were collected from the HFIM system and subjected to centrifugation. The resulting supernatant was utilized to determine drug concentrations, while the precipitates were used to track changes in both total and drug-resistant bacterial populations over time by the spread plate method. The HFIM accurately reproduced meropenem's pharmacokinetics in critically ill patients. All three PK/PD target groups exhibited a rapid bactericidal response within 6 h of the initial treatment. However, the 40% fT > MIC and 100% fT > MIC groups subsequently showed bacterial resurgence and resistance, whereas the 100% fT > 4× MIC group displayed sustained bactericidal activity with no evidence of drug resistance. The HFIM system revealed that maintaining 100% fT > 4× MIC offers a desirable microbiological response for critically ill patients, demonstrating strong bactericidal capacity and effective prevention of drug resistance.

Multistate modeling for survival analysis in critically ill patients treated with meropenem

Appropriate antibiotic dosing to ensure early and sufficient target attainment is crucial for improving clinical outcome in critically ill patients. Parametric survival analysis is a preferred modeling method to quantify time-varying antibiotic exposure - response effects, whereas bias may be introduced in hazard functions and survival functions when competing events occur. This study investigated predictors of in-hospital mortality in critically ill patients treated with meropenem by pharmacometric multistate modeling. A multistate model comprising five states (ongoing meropenem treatment, other antibiotic treatment, antibiotic treatment termination, discharge, and death) was developed to capture the transitions in a cohort of 577 critically ill patients treated with meropenem. Various factors were investigated as potential predictors of the transitions, including patient demographics, creatinine clearance calculated by Cockcroft-Gault equation (CLCRCG ), time that unbound concentrations exceed the minimum inhibitory concentration (fT>MIC ), and microbiology-related measures. The probabilities to transit to other states from ongoing meropenem treatment increased over time. A 10 mL/min decrease in CLCRCG was found to elevate the hazard of transitioning from states of ongoing meropenem treatment and antibiotic treatment termination to the death state by 18%. The attainment of 100% fT>MIC significantly increased the transition rate from ongoing meropenem treatment to antibiotic treatment termination (by 9.7%), and was associated with improved survival outcome. The multistate model prospectively assessed predictors of death and can serve as a useful tool for survival analysis in different infection scenarios, particularly when competing risks are present.