title: Evaluation of Bayesian estimation in comparison to NONMEM for population pharmacokinetic data analysis: application to pefloxacin in intensive care unit patients.
abstract: The pharmacokinetics of pefloxacin (PF) were investigated in a population of 74 intensive care unit patients receiving 400 mg bid as 1-hr infusion using (i) Bayesian estimation (BE) of individual patient parameters followed by multiple linear regression (MLR) analysis and (ii) NONMEM analysis. The data consisted of 3 to 9 PF plasma levels per patient measured over 1 to 3 dosage intervals (total 113) according to four different limited (suboptimal) sampling 3-point protocols. Twenty-nine covariates (including 15 comedications) were considered to explain the interpatient variability. Predicted PF CL for a patient with median covariates values was similar in both BE/MLR and NONMEM analysis (4.02 and 3.92 L/hr, respectively). Bilirubin level and age were identified as the major determinants of PF CL by both approaches with similar predicted magnitude of effects (about 40 and 30% decrease of median CL, respectively). Confounding effects were observed between creatinine clearance (26% decrease of PF CL in the BE/MLR model), simplified acute physiology score (a global score based on 14 biological and clinical variables) (18% decrease of median CL in the NONMEM model) and age (entered in both models) which were highly correlated in our data base. However, both models predicted similar PF CL for actual subpopulations by using actual covariate values. Finally, the NONMEM analysis allowed identification of an effect of weight on CL (decrease of CL for weight < 65 kg) whereas the BE/MLR analysis predicted an increase of CL in patients treated with phenobarbital. In conclusion, both approaches allowed identification of the major risk factors of PF pharmacokinetics in ICU patients. Their potential use at different stages of drug development is discussed.
authors.affiliation: Rhône-Poulenc Rorer Recherche-Développement, Antony, France.
authors.forename: R
authors.initials: R
authors.lastname: Bruno
authors.forename: M C
authors.initials: MC
authors.lastname: Iliadis
authors.forename: B
authors.initials: B
authors.lastname: Lacarelle
authors.forename: V
authors.initials: V
authors.lastname: Cosson
authors.forename: J W
authors.initials: JW
authors.lastname: Mandema
authors.forename: Y
authors.initials: Y
authors.lastname: Le Roux
authors.forename: G
authors.initials: G
authors.lastname: Montay
authors.forename: A
authors.initials: A
authors.lastname: Durand
authors.forename: M
authors.initials: M
authors.lastname: Ballereau
authors.forename: M
authors.initials: M
authors.lastname: Alasia
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D001499:Bayes Theorem; D001663:Bilirubin / Q000097:blood; D003422:Critical Care; D005069:Evaluation Studies as Topic; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015366:Pefloxacin / Q000493:pharmacokinetics*; D010599:Pharmacokinetics; D012044:Regression Analysis; D012984:Software
chemical_list: D015366:Pefloxacin; D001663:Bilirubin