BACKGROUND: Doripenem often is used in the intensive care unit (ICU) to treat serious infections. However, pharmacokinetics in this population often are altered by various physiologic changes. Current pharmacokinetic data in critically ill patients receiving doripenem are limited. OBJECTIVE: To determine the pharmacokinetics of doripenem in patients treated in the ICU versus outside the ICU. METHODS: A total of 3-4 serum samples were collected from 25 infected patients receiving doripenem. A 2-compartment model was fit to serum pharmacokinetic data with nonparametric adaptive grid with adaptive γ. In the structural pharmacokinetic model, clearance (Cl) was made proportional to creatinine clearance (CrCl) and an intercept term. Bayesian pharmacokinetic parameters were compared between the 2 populations. A 5000-patient Monte Carlo simulation was performed for various CrCl ranges. The probability of pharmacodynamic target attainment was calculated over a range of minimum inhibitory concentrations (MICs), assuming a target of 35% of the dosing interval that unbound drug concentrations remain above the MIC. RESULTS: Mean (range) age, body mass index, and CrCl were 61 (31-90) years, 31.2 (15.1-55.5) kg/m(2), and 86 (15-221) mL/min, respectively. After the Bayesian step, r(2), bias, and precision were 0.97, 0.04, and 1.44 μg/mL, respectively. Mean (SD) parameters for ICU (n = 13) and non-ICU (n = 12) patients were not significantly different (p > 0.05): volume of central compartment (17.3 [11.2] vs 18.5 [11.7] L), Cl (10.1 [10.2] vs 15.5 [16.9] L/h), k12 (4.7 [4.7] vs 4.7 [4.8] h(-1)), and k21 (7.1 [5.5] vs 5.7 [5.3] h(-1)), respectively. Optimal target attainments were obtained for patients with normal renal function up to MICs of 2 μg/mL with a dose of 500 mg every 8 hours as 1-hour and 4-hour infusions. CONCLUSIONS: Doripenem pharmacokinetics were similar between ICU and non-ICU patients in this population. Optimal dosing regimens should be selected based on underlying renal function and suspected MIC of the infecting pathogen.
BACKGROUND:Doripenem often is used in the intensive care unit (ICU) to treat serious infections. However, pharmacokinetics in this population often are altered by various physiologic changes. Current pharmacokinetic data in critically illpatients receiving doripenem are limited. OBJECTIVE: To determine the pharmacokinetics of doripenem in patients treated in the ICU versus outside the ICU. METHODS: A total of 3-4 serum samples were collected from 25 infectedpatients receiving doripenem. A 2-compartment model was fit to serum pharmacokinetic data with nonparametric adaptive grid with adaptive γ. In the structural pharmacokinetic model, clearance (Cl) was made proportional to creatinine clearance (CrCl) and an intercept term. Bayesian pharmacokinetic parameters were compared between the 2 populations. A 5000-patient Monte Carlo simulation was performed for various CrCl ranges. The probability of pharmacodynamic target attainment was calculated over a range of minimum inhibitory concentrations (MICs), assuming a target of 35% of the dosing interval that unbound drug concentrations remain above the MIC. RESULTS: Mean (range) age, body mass index, and CrCl were 61 (31-90) years, 31.2 (15.1-55.5) kg/m(2), and 86 (15-221) mL/min, respectively. After the Bayesian step, r(2), bias, and precision were 0.97, 0.04, and 1.44 μg/mL, respectively. Mean (SD) parameters for ICU (n = 13) and non-ICU (n = 12) patients were not significantly different (p > 0.05): volume of central compartment (17.3 [11.2] vs 18.5 [11.7] L), Cl (10.1 [10.2] vs 15.5 [16.9] L/h), k12 (4.7 [4.7] vs 4.7 [4.8] h(-1)), and k21 (7.1 [5.5] vs 5.7 [5.3] h(-1)), respectively. Optimal target attainments were obtained for patients with normal renal function up to MICs of 2 μg/mL with a dose of 500 mg every 8 hours as 1-hour and 4-hour infusions. CONCLUSIONS:Doripenem pharmacokinetics were similar between ICU and non-ICU patients in this population. Optimal dosing regimens should be selected based on underlying renal function and suspected MIC of the infecting pathogen.
Authors: Mohd H Abdul-Aziz; Azrin N Abd Rahman; Mohd-Basri Mat-Nor; Helmi Sulaiman; Steven C Wallis; Jeffrey Lipman; Jason A Roberts; Christine E Staatz Journal: Antimicrob Agents Chemother Date: 2015-10-19 Impact factor: 5.191
Authors: P Voirol; Y-A Que; A Fournier; P Eggimann; O Pantet; J L Pagani; E Dupuis-Lozeron; A Pannatier; F Sadeghipour Journal: Antimicrob Agents Chemother Date: 2018-02-23 Impact factor: 5.191