title: Predictive performance for population models using stochastic differential equations applied on data from an oral glucose tolerance test.
abstract: Several articles have investigated stochastic differential equations (SDEs) in PK/PD models, but few have quantitatively investigated the benefits to predictive performance of models based on real data. Estimation of first phase insulin secretion which reflects beta-cell function using models of the OGTT is a difficult problem in need of further investigation. The present work aimed at investigating the power of SDEs to predict the first phase insulin secretion (AIR (0-8)) in the IVGTT based on parameters obtained from the minimal model of the OGTT, published by Breda et al. (Diabetes 50(1):150-158, 2001). In total 174 subjects underwent both an OGTT and a tolbutamide modified IVGTT. Estimation of parameters in the oral minimal model (OMM) was performed using the FOCE-method in NONMEM VI on insulin and C-peptide measurements. The suggested SDE models were based on a continuous AR(1) process, i.e. the Ornstein-Uhlenbeck process, and the extended Kalman filter was implemented in order to estimate the parameters of the models. Inclusion of the Ornstein-Uhlenbeck (OU) process caused improved description of the variation in the data as measured by the autocorrelation function (ACF) of one-step prediction errors. A main result was that application of SDE models improved the correlation between the individual first phase indexes obtained from OGTT and AIR (0-8) (r = 0.36 to r = 0.49 and r = 0.32 to r = 0.47 with C-peptide and insulin measurements, respectively). In addition to the increased correlation also the properties of the indexes obtained using the SDE models more correctly assessed the properties of the first phase indexes obtained from the IVGTT. In general it is concluded that the presented SDE approach not only caused autocorrelation of errors to decrease but also improved estimation of clinical measures obtained from the glucose tolerance tests. Since, the estimation time of extended models was not heavily increased compared to basic models, the applied method is concluded to have high relevance not only in theory but also in practice.
authors.affiliation: Technical University of Denmark, Lyngby, Denmark. jbem@novonordisk.com
authors.forename: Jonas B
authors.initials: JB
authors.lastname: Møller
authors.forename: Rune V
authors.initials: RV
authors.lastname: Overgaard
authors.forename: Henrik
authors.initials: H
authors.lastname: Madsen
authors.forename: Torben
authors.initials: T
authors.lastname: Hansen
authors.forename: Oluf
authors.initials: O
authors.lastname: Pedersen
authors.forename: Steen H
authors.initials: SH
authors.lastname: Ingwersen
mesh_terms: D000328:Adult; D002096:C-Peptide / Q000097:blood; D003198:Computer Simulation; D005951:Glucose Tolerance Test / Q000706:statistics & numerical data*; D006801:Humans; D007328:Insulin / Q000097:blood; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D013269:Stochastic Processes
chemical_list: D002096:C-Peptide; D007328:Insulin