ArticleEuropean journal of drug metabolism and pharmacokinetics2020
Investigating Stochastic Differential Equations Modelling for Levodopa Infusion in Patients with Parkinson's Disease.
Article in European journal of drug metabolism and pharmacokinetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Stochastic Differential Equations (SDEs) in NONMEM for Probing Population Pharmacokinetic Model Misspecification: Diagnostic Utility, Practical Considerations, and Future Directions.The AAPS journal · 2026Article
- Theory and simulations of delayed stochastic and deterministic models of prion diseases.Journal of mathematical biology · 2026Article
- Statistical analysis of one-compartment pharmacokinetic models with drug adherence.Journal of pharmacokinetics and pharmacodynamics · 2022Article
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Authors and funding
4 authors.
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Abstract
BACKGROUND AND
objectivesLevodopa concentration in patients with Parkinson's disease is frequently modelled with ordinary differential equations (ODEs). Here, we investigate a pharmacokinetic model of plasma levodopa concentration in patients with Parkinson's disease by introducing stochasticity to separate the intra-individual variability into measurement and system noise, and to account for auto-correlated errors. We also investigate whether the induced stochasticity provides a better fit than the ODE approach.
methodsIn this study, a system noise variable is added to the pharmacokinetic model for duodenal levodopa/carbidopa gel (LCIG) infusion described by three ODEs through a standard Wiener process, leading to a stochastic differential equations (SDE) model. The R package population stochastic modelling (PSM) was used for model fitting with data from previous studies for modelling plasma levodopa concentration and parameter estimation. First, the diffusion scale parameter (σ
resultsBoth the ODE and the SDE models estimated bioavailability to be approximately 75%. The SDE model converged at different values of σ
conclusionsThe SDE model provided a better fit for LCIG plasma levodopa concentration by approximately 5.5% in terms of mean percentage change of RMSE.
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