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BioMed Research International

Publication date: 2014-01-01
Volume: 2014
Publisher: Hindawi Publishing Corporation

Author:

van Hasselt, Coen
Allegaert, Karel ; Van Calsteren, Kristel ; Beijnen, Jos ; Schellens, Jan ; Huitema, Alwin

Keywords:

Science & Technology, Life Sciences & Biomedicine, Biotechnology & Applied Microbiology, Medicine, Research & Experimental, Research & Experimental Medicine, PROTEIN-BINDING, PLASMA, Adult, Cefazolin, Creatinine, Demography, Empirical Research, Female, Humans, Models, Biological, Nonlinear Dynamics, Pregnancy, Time Factors, Young Adult, 06 Biological Sciences, 08 Information and Computing Sciences, 10 Technology

Abstract:

This work describes a first population pharmacokinetic (PK) model for free and total cefazolin during pregnancy, which can be used for dose regimen optimization. Secondly, analysis of PK studies in pregnant patients is challenging due to study design limitations. We therefore developed a semiphysiological modeling approach, which leveraged gestation-induced changes in creatinine clearance (CrCL) into a population PK model. This model was then compared to the conventional empirical covariate model. First, a base two-compartmental PK model with a linear protein binding was developed. The empirical covariate model for gestational changes consisted of a linear relationship between CL and gestational age. The semiphysiological model was based on the base population PK model and a separately developed mixed-effect model for gestation-induced change in CrCL. Estimates for baseline clearance (CL) were 0.119 L/min (RSE 58%) and 0.142 L/min (RSE 44%) for the empirical and semiphysiological models, respectively. Both models described the available PK data comparably well. However, as the semiphysiological model was based on prior knowledge of gestation-induced changes in renal function, this model may have improved predictive performance. This work demonstrates how a hybrid semiphysiological population PK approach may be of relevance in order to derive more informative inferences.