A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models

• Published in: Advanced drug delivery reviews Vol. 65; no. 7; pp. 929 - 939
• Main Authors: Donnet, Sophie, Samson, Adeline
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30.06.2013; Elsevier
• Subjects: Algorithms; Bayesian estimation; EM algorithm; Gauss quadrature; Hermite expansion; Humans; Kalman filter; Mathematics; Maximum likelihood estimation; Models, Biological; Pharmacodynamic; Pharmacokinetic; Pharmacokinetics; Population approach; Statistics; Statistics Theory; Stochastic differential equations; Stochastic Processes; Maximum likelihood estimation; Stochastic differential equations; Gauss quadrature; Bayesian estimation; Kalman filter; Population approach; Pharmacodynamic; Hermite expansion; EM algorithm; Pharmacokinetic
• ISSN: 0169-409X; 1872-8294; 1872-8294
• DOI: 10.1016/j.addr.2013.03.005

This paper is a survey of existing estimation methods for pharmacokinetic/pharmacodynamic (PK/PD) models based on stochastic differential equations (SDEs). Most parametric estimation methods proposed for SDEs require high frequency data and are often poorly suited for PK/PD data which are usually sparse. Moreover, PK/PD experiments generally include not a single individual but a group of subjects, leading to a population estimation approach. This review concentrates on estimation methods which have been applied to PK/PD data, for SDEs observed with and without measurement noise, with a standard or a population approach. Besides, the adopted methodologies highly differ depending on the existence or not of an explicit transition density of the SDE solution. [Display omitted]