Population Pharmacokinetic Analysis of Linezolid in Patients With Infectious Disease: Application to Lower Body Weight and Elderly Patients

Abstract

Linezolid (Zyvox), belonging to oxazolidinone antibiotics, is commonly used for the treatment of patients infected with methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci. Although linezolid has been approved worldwide, the Japanese pharmacokinetic (PK) profile has not been characterized in detail. The objective of this study is to develop a population PK model for linezolid that can be applied to a Japanese population. This population PK model was established based on the 1 Japanese phase III and 4 Caucasian phase II/III studies. A total of 2539 linezolid plasma concentration measurements from 455 patients, aged 18 to 98 years and body weight 30 to 190.5 kg, were used for the analysis. The data were analyzed using nonlinear mixed effects modeling. Body weight (BW), age, ethnicity, and gender were investigated as covariates. The final model was validated by the bootstrap technique. The PK profiles of linezolid were described with a 1-compartment PK model with first-order absorption and first-order elimination. In the final population PK model, BW and age were influential covariates on clearance, and the distribution volume was affected by BW. The present population PK model of linezolid described well the PK profiles in Japanese patients who have lower BW and are relatively older compared with those in the United States/European Union.

Predictors of Inadequate Linezolid Concentrations after Standard Dosing in Critically Ill Patients

“…In contrast to the presence of ARDS and lactate, the cofactors weight, creatinine clearance, and impairment of liver function were described previously (15,(18)(19)(20)(21)(22)(23)(24)(25) (Fig. 4).…”

“…Several predictors, such as glomerular filtration rate (14,(18)(19)(20)(21)(22), body weight (18,(21)(22)(23)(24)(25), parameters of liver function (15,18,20), renal replacement therapy (15), and comedication such as rifampin (a potent P-glycoprotein inducer) (7), have already been described. However, the effects of other possible covariates, such as the presence of acute respiratory distress syndrome (ARDS), peritonitis, and single nucleotide polymorphisms of the P glycoprotein, remain unclear.…”

Nonclinical and Pharmacokinetic Assessments To Evaluate the Potential of Tedizolid and Linezolid To Affect Mitochondrial Function

“…Human free plasma concentration-time data for tedizolid and linezolid in relation to the MPS IC 50 of each drug were evaluated to compare the potential for mitochondrial recovery between tedizolid and linezolid given at therapeutic doses; pharmacokinetic (PK) data were derived from two sources: (i) extensively sampled data from healthy subjects (25,41) and (ii) Monte Carlo simulations. Simulations were conducted based on previously presented population PK models for tedizolid and linezolid (42,43). Two sets of 2,500 virtual patients were generated by resampling the characteristics of patients in the combined population from phase 3 studies comparing tedizolid and linezolid against acute bacterial skin and skin structure infections.…”

“…Simulated drug concentrations at 10-min increments over the dosing interval were obtained based on the respective final population PK models, including covariate effects. Ideal body weight and total bilirubin were significant covariates in the tedizolid population PK model, and weight and age were significant covariates in the linezolid population PK model (42,43). Thus, these covariates were randomly resampled from the vectors of patient characteristics from the combined population of phase 3 patients in order to maintain the covariance structure inherent between covariates.…”

The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis

“…The pharmacokinetic parameters for standard first-line drugs and for drugs used in both MDR and XDR tuberculosis are shown in table 9, mainly based on publications from South Africa, India, and the USA. [404][405][406][407][408][409][410][411][412][413][414][415][416] Predicting what concentration a patient will achieve is difficult, given the multiple determinants of pharmacokinetic variability. 191 Therefore, to identify the specific concentration-time profile, the drug concentrations should be measured in the patient directly.…”