Stochastic nonlinear mixed effects: a metformin case study

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• 作者：Brett Matzuka ; Jason Chittenden…
• 关键词：Population pharmacokinetics ; Kalman filter ; Stochastic differential equations ; Model development ; Parameter estimation ; Nonlinear mixed effects
• 刊名：Journal of Pharmacokinetics and Pharmacodynamics
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：43
• 期：1
• 页码：85-98
• 全文大小：1,043 KB
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• 作者单位：Brett Matzuka (1) (2)
  Jason Chittenden (3)
  Jonathan Monteleone (4)
  Hien Tran (1)
  
  1. Department of Mathematics, North Carolina State University, Raleigh, NC, USA
  2. Department of Pharmacology and Therapeutic innovation, Children’s Mercy Hospital, Kansas City, MO, USA
  3. qPharmetra, LLC, Raleigh, NC, USA
  4. Alexion Pharmaceuticals Inc., New Haven, CT, USA
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Pharmacology and Toxicology
  Pharmacy
  Veterinary medicine
  Biomedical Engineering
  Biochemistry
  
• 出版者：Springer Netherlands
• ISSN：1573-8744

文摘

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. Keywords Population pharmacokinetics Kalman filter Stochastic differential equations Model development Parameter estimation Nonlinear mixed effects