Using the LeiCNS-PK3.0 Physiologically-Based Pharmacokinetic Model to Predict Brain Extracellular Fluid Pharmacokinetics in Mice

Introduction: The unbound brain extracellular fluid (brainECF) to plasma steady-state partition coefficient (Kp,uu,BBB) provides key insights into the extent of blood-brain barrier (BBB) transport equilibration. However, it does not directly inform on the pharmacokinetic (PK) profiles seen at brain targets. While mouse models are commonly used to study brain PK, differences in central nervous system (CNS) physiology between mice and humans make direct translation of these findings to human brain PK challenging. Physiologically-based pharmacokinetic (PBPK) models are valuable tools for bridging species-specific differences in PK data.
Aim: To utilize the LeiCNS-PK3.0 PBPK model to predict brain extracellular fluid (brainECF) PK in mice.
Methods: Data on mouse brain physiology was compiled from existing literature. PK data for 10 drugs (cyclophosphamide, quinidine, erlotinib, phenobarbital, colchicine, ribociclib, topotecan, cefradroxil, prexasertib, and methotrexate) were gathered from different mouse strains, focusing on unbound plasma and brainECF PK. A dosing regimen-dependent plasma PK model LY2606368 was developed, Kp,uu,BBB values were estimated, and these were input into the LeiCNS-PK3.0 model to predict the brainECF PK profiles.
Results: The model adequately predicted the brainECF PK profiles for 7 out of the 10 drugs. For these 7 drugs, the predicted brainECF data were within a two-fold error limit compared to observed data. The remaining 3 drugs fell within a five-fold error limit.
Conclusion: The current version of the mouse LeiCNS-PK3.0 model provides reasonable predictions of brainECF PK profiles for most drugs in healthy mice, marking progress toward translating mouse brain PK data to human PK.