Dissertation

Advancing the LeiCNS-PK3.0 model for prediction of CNS Pharmacokinetics Nonlinear BBB Transport, Inter-species Scaling, and Machine Learning

This thesis focuses on enhancing predictions of central nervous system drug exposure using the LeiCNS-PK3.0, a physiologically based pharmacokinetic model.

Author

B. Gülave

Date

04 September 2025

Links

Thesis in Leiden Repository

The research expands the model's capability to simulate central nervous system drug distribution, with a particular focus on morphine and its metabolites. Key findings include the impact of nonlinear blood-brain barrier transport, μ-opioid receptor binding kinetics, and P-glycoprotein-mediated drug-drug interactions on morphine exposure and effects in the brain. The model was also extended to mouse physiology, improving its translational potential. A machine learning model was developed to predict the blood-brain barrier partition coefficient, reducing the need for animal testing.The integration of these methods enables more accurate central nervous system pharmacokinetic predictions and informs better dosing strategies. The thesis highlights the potential of central nervous system physiologically based pharmacokinetic model models to improve drug development, support personalized medicine, and minimize reliance on animal experiments. Future directions include incorporating disease-specific data, genetic variability, and developing a user-friendly interface to promote clinical and regulatory use. The overall framework holds promise for optimizing central nervous system drug therapies and facilitating drug repurposing.

• central nervous system
• drug-drug interaction
• machine learning
• morphine
• nonlinear pk
• pbpk modelling