Sumith K Mathew1, Binu S Mathew, Michael N Neely, Girish S Naik, Ratna Prabha, Gijoe G Jacob, Subramani K, Denise H Fleming. 1. *Clinical Pharmacology Unit, Department of Pharmacology and Clinical Pharmacology, Christian Medical College, Vellore, India; †Laboratory of Applied Pharmacokinetics and Bioinformatics (LAPKB), Children's Hospital of Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California; ‡Biocon Research Limited, Bangalore, India; and §Surgical Intensive Care Unit, Division of Critical Care, Christian Medical College, Vellore, India.
Abstract
BACKGROUND: Pharmacokinetics of meropenem differ widely in the critically ill population. It is imperative to maintain meropenem concentrations above the inhibitory concentrations for most of the interdose interval. A population pharmacokinetic/pharmacodynamic model was developed to determine the probability of target attainment for 3-hour and 30-minute infusion regimens in this population. METHODS: This study was performed in an intensive care setting among adult patients who were initiated on meropenem at a dose of 1000 mg. Multiple blood specimens were collected at predetermined time points during the interdose period, and meropenem concentrations were measured using high performance liquid chromatography. Using Pmetrics, a pharmacokinetic/pharmacodynamic model was developed and validated. Monte Carlo simulation was performed, and probability of target attainment (100% T > minimum inhibitory concentration (MIC), with a probability >0.9) for doubling MICs was determined for different regimens of meropenem. RESULTS: A 2-compartment multiplicative gamma error model best described the population parameters from 34 patients. The pharmacokinetic parameters used in the final model were Ke (elimination rate constant from the central compartment), Vc (volume of distribution of central compartment), KCP and KPC (intercompartmental rate constants), and IC2 (the fitted amount of meropenem in the peripheral compartment). Inclusion of creatinine clearance (CLcreat) and body weight as covariates improved the model prediction (Ke = Ke0 × (Equation is included in full-text article.), Vc = Vc0 × Weight). The Ke and Vc [geometric mean (range)] of the individuals were 0.54 (0.01-2.61)/h and 9.36 (4.35-21.62) L, respectively. The probability of attaining the target, T > MIC of 100%, was higher for 3-hour infusion regimens compared with 30-minute infusion regimens for all ranges of CLcreat. CONCLUSIONS: This study emphasizes that extended regimens of meropenem are preferable for treating infections caused by bacteria with higher MICs. The nonparametric analysis using body weight and CLcreat as covariate adequately predicted the pharmacokinetics of meropenem in critically ill patients with a wide range of renal function.
BACKGROUND: Pharmacokinetics of meropenem differ widely in the critically ill population. It is imperative to maintain meropenem concentrations above the inhibitory concentrations for most of the interdose interval. A population pharmacokinetic/pharmacodynamic model was developed to determine the probability of target attainment for 3-hour and 30-minute infusion regimens in this population. METHODS: This study was performed in an intensive care setting among adult patients who were initiated on meropenem at a dose of 1000 mg. Multiple blood specimens were collected at predetermined time points during the interdose period, and meropenem concentrations were measured using high performance liquid chromatography. Using Pmetrics, a pharmacokinetic/pharmacodynamic model was developed and validated. Monte Carlo simulation was performed, and probability of target attainment (100% T > minimum inhibitory concentration (MIC), with a probability >0.9) for doubling MICs was determined for different regimens of meropenem. RESULTS: A 2-compartment multiplicative gamma error model best described the population parameters from 34 patients. The pharmacokinetic parameters used in the final model were Ke (elimination rate constant from the central compartment), Vc (volume of distribution of central compartment), KCP and KPC (intercompartmental rate constants), and IC2 (the fitted amount of meropenem in the peripheral compartment). Inclusion of creatinine clearance (CLcreat) and body weight as covariates improved the model prediction (Ke = Ke0 × (Equation is included in full-text article.), Vc = Vc0 × Weight). The Ke and Vc [geometric mean (range)] of the individuals were 0.54 (0.01-2.61)/h and 9.36 (4.35-21.62) L, respectively. The probability of attaining the target, T > MIC of 100%, was higher for 3-hour infusion regimens compared with 30-minute infusion regimens for all ranges of CLcreat. CONCLUSIONS: This study emphasizes that extended regimens of meropenem are preferable for treating infections caused by bacteria with higher MICs. The nonparametric analysis using body weight and CLcreat as covariate adequately predicted the pharmacokinetics of meropenem in critically illpatients with a wide range of renal function.
Authors: Mohammad H Alshaer; Nicole Maranchick; Chen Bai; Kelly L Maguigan; Bethany Shoulders; Timothy W Felton; Sumith K Mathew; Mamoun T Mardini; Charles A Peloquin Journal: Antimicrob Agents Chemother Date: 2022-06-14 Impact factor: 5.938
Authors: Uwe Liebchen; Ferdinand Weinelt; Jette Jung; Robin Michelet; Christina Scharf; Ines Schroeder; Michael Paal; Michael Zoller; Charlotte Kloft Journal: Antimicrob Agents Chemother Date: 2021-12-06 Impact factor: 5.938