NDLI: Stochastic nonlinear mixed effects: a metformin case study

Content Provider	Springer Nature Link
Author	Matzuka, Brett Chittenden, Jason Monteleone, Jonathan Tran, Hien
Copyright Year	2015
Abstract	In nonlinear mixed effect (NLME) modeling, the intra-individual variability is a collection of errors due to assay sensitivity, dosing, sampling, as well as model misspecification. Utilizing stochastic differential equations (SDE) within the NLME framework allows the decoupling of the measurement errors from the model misspecification. This leads the SDE approach to be a novel tool for model refinement. Using Metformin clinical pharmacokinetic (PK) data, the process of model development through the use of SDEs in population PK modeling was done to study the dynamics of absorption rate. A base model was constructed and then refined by using the system noise terms of the SDEs to track model parameters and model misspecification. This provides the unique advantage of making no underlying assumptions about the structural model for the absorption process while quantifying insufficiencies in the current model. This article focuses on implementing the extended Kalman filter and unscented Kalman filter in an NLME framework for parameter estimation and model development, comparing the methodologies, and illustrating their challenges and utility. The Kalman filter algorithms were successfully implemented in NLME models using MATLAB with run time differences between the ODE and SDE methods comparable to the differences found by Kakhi [10] for their stochastic deconvolution.
Starting Page	85
Ending Page	98
Page Count	14
File Format	PDF
ISSN	1567567X
Journal	Journal of Pharmacokinetics and Pharmacodynamics
Volume Number	43
Issue Number	1
e-ISSN	15738744
Language	English
Publisher	Springer US
Publisher Date	2015-11-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Population pharmacokinetics Kalman filter Stochastic differential equations Model development Parameter estimation Nonlinear mixed effects Pharmacology/Toxicology Pharmacy Veterinary Medicine Biomedical Engineering Biochemistry
Content Type	Text
Resource Type	Article
Subject	Pharmacology

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