Nonlinear protein binding of phenytoin in clinical practice: Development and validation of a mechanistic prediction model. Issue 10 (7th August 2019)

• Record Type: Journal Article
• Authors: ter Heine, Rob; Kane, Sean P.; Huitema, Alwin D.R.; Krasowski, Matthew D.; van Maarseveen, Erik M.
• Is Part Of: British journal of clinical pharmacology. Volume 85:Issue 10(2019)
• Page Start: 2360
• Page End: 2368
• Publication Date: 2019-08-07
• Subjects: fraction unbound -- free -- in vivo -- phenytoin -- protein binding; Pharmacology -- Periodicals; Drugs -- Periodicals; 615.1
• DOI: 10.1111/bcp.14053
• Languages: English
• ISSNs: 0306-5251

Abstract

Abstract : Aims: To individualize treatment, phenytoin doses are adjusted based on free concentrations, either measured or calculated from total concentrations. As a mechanistic protein binding model may more accurately reflect the protein binding of phenytoin than the empirical Winter–Tozer equation that is routinely used for calculation of free concentrations, we aimed to develop and validate a mechanistic phenytoin protein binding model. Methods: Data were extracted from routine clinical practice. A mechanistic drug protein binding model was developed using nonlinear mixed effects modelling in a development dataset. The predictive performance of the mechanistic model was then compared with the performance of the Winter–Tozer equation in 5 external datasets. Results: We found that in the clinically relevant concentration range, phenytoin protein binding is not only affected by serum albumin concentrations and presence of severe renal dysfunction, but is also concentration dependent. Furthermore, the developed mechanistic model outperformed the Winter–Tozer equation in 4 out of 5 datasets in predicting free concentrations in various populations. Conclusions: Clinicians should be aware that the free fraction changes when phenytoin exposure changes. A mechanistic binding model may facilitate prediction of free phenytoin concentrations from total concentrations, for example for dose individualization in the clinic.