Amaury O'Jeanson1,2, Romaric Larcher3, Cosette Le Souder4, Nassim Djebli5, Sonia Khier6,7. 1. Pharmacokinetic Modeling Department, UFR Pharmacie, Montpellier University (School of Pharmacy), 15 Avenue Charles Flahault, 34000, Montpellier, France. 2. Probabilities and Statistics Department, Institut Montpelliérain Alexander Grothendieck (IMAG), CNRS UMR 5149, Montpellier University, Montpellier, France. 3. Intensive Care Unit Department, Montpellier University Hospital (CHU Lapeyronie), Montpellier, France. 4. Toxicology and Target Drug Monitoring Department, Montpellier University Hospital (CHU Lapeyronie), Montpellier, France. 5. Roche Innovation Center Basel, Roche Pharma Research and Early Development, Basel, Switzerland. 6. Pharmacokinetic Modeling Department, UFR Pharmacie, Montpellier University (School of Pharmacy), 15 Avenue Charles Flahault, 34000, Montpellier, France. sonia.khier@umontpellier.fr. 7. Probabilities and Statistics Department, Institut Montpelliérain Alexander Grothendieck (IMAG), CNRS UMR 5149, Montpellier University, Montpellier, France. sonia.khier@umontpellier.fr.
Abstract
BACKGROUND AND OBJECTIVES: Meropenem is frequently used for the treatment of severe bacterial infections in critically ill patients. Because critically ill patients are more prone to pharmacokinetic variability than other patients, ensuring an effective blood concentration can be complex. Therefore, describing this variability to ensure a proper use of this antibiotic drug limits the rise and dissemination of antimicrobial resistance, and helps preserve the current antibiotic arsenal. The aims of this study were to describe the pharmacokinetics of meropenem in critically ill patients, to identify and quantify the patients' characteristics responsible for the observed pharmacokinetic variability, and to perform different dosing simulations in order to determine optimal individually adapted dosing regimens. METHODS: A total of 58 patients hospitalized in the medical intensive care unit and receiving meropenem were enrolled, including 26 patients with renal replacement therapy. A population pharmacokinetic model was developed (using NONMEM software) and Monte Carlo simulations were performed with different dosing scenarios (bolus-like, extended, and continuous infusion) exploring the impact of clinical categories of residual diuresis (anuria, oliguria, and preserved diuresis) on the probability of target attainment (MIC: 1-45 mg/L). RESULTS: The population pharmacokinetic model included five covariates with a significant impact on clearance: glomerular filtration rate, dialysis (continuous and semi-continuous), renal function status, and volume of residual diuresis. The clearance for a typical patient in our population is 4.20 L/h and volume of distribution approximately 44 L. Performed dosing regimen simulations suggested that, for equivalent doses, the continuous infusion mode (with loading dose) allowed the obtaining of the pharmacokinetic/pharmacodynamic target for a larger number of patients (100% for MIC ≤ 20 mg/L). Nevertheless, for the treatment of susceptible bacteria (MIC ≤ 2 mg/L), differences in the probability of target attainment between bolus-like, extended, and continuous infusions were negligible. CONCLUSIONS: Identified covariates in the model are easily accessible information in patient health records. The model highlighted the importance of considering the patient's overall condition (renal function and dialysis) and the pathogen's characteristics (MIC target) during the establishment of a patient's dosing regimen.
BACKGROUND AND OBJECTIVES: Meropenem is frequently used for the treatment of severe bacterial infections in critically ill patients. Because critically ill patients are more prone to pharmacokinetic variability than other patients, ensuring an effective blood concentration can be complex. Therefore, describing this variability to ensure a proper use of this antibiotic drug limits the rise and dissemination of antimicrobial resistance, and helps preserve the current antibiotic arsenal. The aims of this study were to describe the pharmacokinetics of meropenem in critically ill patients, to identify and quantify the patients' characteristics responsible for the observed pharmacokinetic variability, and to perform different dosing simulations in order to determine optimal individually adapted dosing regimens. METHODS: A total of 58 patients hospitalized in the medical intensive care unit and receiving meropenem were enrolled, including 26 patients with renal replacement therapy. A population pharmacokinetic model was developed (using NONMEM software) and Monte Carlo simulations were performed with different dosing scenarios (bolus-like, extended, and continuous infusion) exploring the impact of clinical categories of residual diuresis (anuria, oliguria, and preserved diuresis) on the probability of target attainment (MIC: 1-45 mg/L). RESULTS: The population pharmacokinetic model included five covariates with a significant impact on clearance: glomerular filtration rate, dialysis (continuous and semi-continuous), renal function status, and volume of residual diuresis. The clearance for a typical patient in our population is 4.20 L/h and volume of distribution approximately 44 L. Performed dosing regimen simulations suggested that, for equivalent doses, the continuous infusion mode (with loading dose) allowed the obtaining of the pharmacokinetic/pharmacodynamic target for a larger number of patients (100% for MIC ≤ 20 mg/L). Nevertheless, for the treatment of susceptible bacteria (MIC ≤ 2 mg/L), differences in the probability of target attainment between bolus-like, extended, and continuous infusions were negligible. CONCLUSIONS: Identified covariates in the model are easily accessible information in patient health records. The model highlighted the importance of considering the patient's overall condition (renal function and dialysis) and the pathogen's characteristics (MIC target) during the establishment of a patient's dosing regimen.