Modeling of Large Pharmacokinetic Data Using Nonlinear Mixed-Effects: A Paradigm Shift in Veterinary Pharmacology. A Case Study With Robenacoxib in Cats

Population pharmacokinetic (PPK) models play a pivotal role in quantitative pharmacology study, which are classically analyzed by nonlinear mixed-effects models based on ordinary differential equations. This paper describes the implementation of SDEs in population pharmacokinetic models, where parameters are estimated by a novel approximation of likelihood function. This approximation is constructed by combining the MCMC method used in nonlinear mixed-effects modeling with the extended Kalman filter used in SDE models. The analysis and simulation results show that the performance of the approximation of likelihood function for mixed-effects SDEs model and analysis of population pharmacokinetic data is reliable. The results suggest that the proposed method is feasible for the analysis of population pharmacokinetic data.

Download Full-text

Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution

Journal of Pharmacokinetics and Pharmacodynamics ◽

10.1007/s10928-017-9510-8 ◽

2017 ◽

Vol 44 (3) ◽

pp. 223-232 ◽

Cited By ~ 7

Author(s):

Reza Drikvandi

Keyword(s):

Data Analysis ◽

Random Effects ◽

Mixed Effects ◽

Mixed Effects Models ◽

Pharmacokinetic Data ◽

Nonlinear Mixed Effects ◽

Nonlinear Mixed Effects Models

Download Full-text

Modeling Between-Subject Variability in Subcutaneous Absorption of a Fast-Acting Insulin Analogue by a Nonlinear Mixed Effects Approach

Metabolites ◽

10.3390/metabo11040235 ◽

2021 ◽

Vol 11 (4) ◽

pp. 235

Author(s):

Edoardo Faggionato ◽

Michele Schiavon ◽

Chiara Dalla Man

Keyword(s):

Insulin Therapy ◽

Mixed Effects ◽

Pharmacokinetic Data ◽

Nonlinear Mixed Effects ◽

Absorption Process ◽

Nonlinear Mixed Effects Model ◽

Acting Insulin ◽

Subject Variability ◽

Subcutaneous Absorption ◽

Fast Acting

Despite the great progress made in insulin preparation and titration, many patients with diabetes are still experiencing dangerous fluctuations in their blood glucose levels. This is mainly due to the large between- and within-subject variability, which considerably hampers insulin therapy, leading to defective dosing and timing of the administration process. In this work, we present a nonlinear mixed effects model describing the between-subject variability observed in the subcutaneous absorption of fast-acting insulin. A set of 14 different models was identified on a large and frequently-sampled database of lispro pharmacokinetic data, collected from 116 subjects with type 1 diabetes. The tested models were compared, and the best one was selected on the basis of the ability to fit the data, the precision of the estimated parameters, and parsimony criteria. The selected model was able to accurately describe the typical trend of plasma insulin kinetics, as well as the between-subject variability present in the absorption process, which was found to be related to the subject’s body mass index. The model provided a deeper understanding of the insulin absorption process and can be incorporated into simulation platforms to test and develop new open- and closed-loop treatment strategies, allowing a step forward toward personalized insulin therapy.

Download Full-text