Abstract
INTRODUCTION: The unbound brain extracelullar fluid (brain(ECF)) to plasma steady state partition coefficient, K(p,uu,BBB), values provide steady-state information on the extent of blood-brain barrier (BBB) transport equilibration, but not on pharmacokinetic (PK) profiles seen by the brain targets. Mouse models are frequently used to study brain PK, but this information cannot directly be used to inform on human brain PK, given the different CNS physiology of mouse and human. Physiologically based PK (PBPK) models are useful to translate PK information across species. AIM: Use the LeiCNS-PK3.0 PBPK model, to predict brain extracellular fluid PK in mice. METHODS: Information on mouse brain physiology was collected from literature. All available connected data on unbound plasma, brain(ECF) PK of 10 drugs (cyclophosphamide, quinidine, erlotonib, phenobarbital, colchicine, ribociclib, topotecan, cefradroxil, prexasertib, and methotrexate) from different mouse strains were used. Dosing regimen dependent plasma PK was modelled, and Kpuu,BBB values were estimated, and provided as input into the LeiCNS-PK3.0 model to result in prediction of PK profiles in brain(ECF). RESULTS: Overall, the model gave an adequate prediction of the brain(ECF) PK profile for 7 out of the 10 drugs. For 7 drugs, the predicted versus observed brain(ECF) data was within two-fold error limit and the other 2 drugs were within five-fold error limit. CONCLUSION: The current version of the mouse LeiCNS-PK3.0 model seems to reasonably predict available information on brain(ECF) from healthy mice for most drugs. This brings the translation between mouse and human brain PK one step further.