Physiologically based pharmacokinetic (PBPK) brain models were recently constructed to try to predict brain concentrations and previous work often fit in vivo data to extract required parameters retrospectively. Using in vitro - in vivo extrapolation to generate input parameters could provide additional mechanistic understanding and allow for predictions at an early stage for clinical candidate selection, but this has not yet been extensively evaluated. This work assessed the translatability of in vitro data from gMDCK and PAMPA-BBB or using QSAR predictions for capturing BBB passive permeability, and its impact on predicting human CSF/brain penetration using PBPK modeling. This work provides the first step of a promising approach using bottom-up PBPK modeling for CSF/brain penetration prediction.
Learning Objectives:
understand the challenges and progress made thus far in using PBPK to predict human brain / CSF concentrations.
understand required data for PBPK models of the brain.
understand case examples on the translatability of different passive permeability inputs for prediction human CSF/brain penetration.
