Patient-specific in vitro drug release testing coupled with in silico PBPK modeling to forecast the in vivo performance of oral extended-release levodopa formulations in Parkinson’s disease patients

• Published in: European journal of pharmaceutics and biopharmaceutics 2022-11, Vol.180, p.101-118
• Main Authors: Wollmer, Erik, Klein, Sandra
• Format: Article
• Language: English
• Subjects: Biorelevant in vitro release models; Gastrointestinal motility; Gastrointestinal passage; Individual motility profiles; Reciprocating Cylinder apparatus; Special populations
• ISSN: 0939-6411; 1873-3441
• DOI: 10.1016/j.ejpb.2022.09.015

[Display omitted] Biorelevant in vitro release models are valuable analytical tools for oral drug development but often tailored to gastrointestinal conditions in ‘average’ healthy adults. However, predicting in vivo performance in individual patients whose gastrointestinal conditions do not match those of healthy adults would be of great value for optimizing oral drug therapy for such patients. This study focused on establishing patient-specific in vitro and in silico models to predict the in vivo performance of levodopa extended-release products in Parkinson’s disease patients. Current knowledge on gastrointestinal conditions in these patients was incorporated into model development. Relevant in vivo pharmacokinetic data and patient-specific in vitro release data from a novel in vitro test setup were integrated into patient-specific physiologically-based pharmacokinetic models. AUC, cmax and tmax of the computed plasma profiles were calculated using PK-Sim®. For the products studied, levodopa plasma concentration–time profiles modeled using this novel approach compared far better with published average plasma profiles in Parkinson’s disease patients than those derived from in vitro release data obtained from the ‘average’ healthy adult setup. Although further work is needed, results of this study highlight the importance of addressing patient-specific gastrointestinal conditions when aiming to predict drug release in such specific patient groups.