Yumi Emoto-Yamamoto (1), Meindert Danhof (1), Piet Hein van der Graaf (1), Elizabeth CM de Lange (1)
(1) Division of Pharmacology, LACDR, Leiden University, the Netherlands
Background: Prediction of drug distribution in human brain is of importance for CNS drug development, but also for non-CNS drug development. Previously, PBPK rat brain distribution models have been developed separately for acetaminophen (APAP) [1], quinidine (QUIN) [2] and methotrexate (MTX) [3]. For APAP, human lumbar cerebrospinal fluid (CSF) concentrations were successfully predicted by the PBPK model, indicating the validity of this approach.
Objectives: The purpose of this research is to further develop a generic brain distribution PBPK model enabling the prediction of brain distribution of a drug on the basis of its physico-chemical properties.
Methods: Multilevel brain and plasma rat data on APAP, QUIN and MTX that have been used to develop the separate PBPK models was analyzed simultaneously with NONMEM, to explicitly distinguish between physico-chemical properties and systems physiological characteristics. First, drug brain distribution based on passive processes is addressed (i.e. passive permeability of the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier (BCSFB), brain extracellular fluid (ECF) bulk flow and CSF turnover). To that end, the “passive only” data were used (obtained following co-administration of transporter inhibitors).
Results: The passive transport clearance from plasma to brain ECF, CSF in the lateral ventricle (LV) and CSF in the cisterna magna (CM) is 27, 3.6 and 0.96 µl/min for APAP, 50, 9.0 and 1.1 µl/min for QUIN, 1.9, 0.13 and 0.018 µl/min for MTX. The passive transport clearance from brain ECF, CSF in LV and CSF in CM to plasma is 35, 5.2 and 6.1 µl/min for APAP, 6.3, 0.04 and 4.1 µl/min for QUIN, 17, 5.2 and 4.4 µl/min for MTX. The rank order of the passive transport clearance into the brain of the compounds is in line with rank order of lipophilicities at physiological pH, but not for the passive transport clearance out of the brain.
Conclusions: The currently published PBPK model for the brain drug distribution needs to be refined to a generic PBPK model by further investigation such as pH dependent passive permeability.
Acknowledgement: This work is supported by the members of WP1 of the PKPD Platform 2.0 from Astellas (Walter Krauwinkel), Janssen Research (An Vermeulen, Dymphy Huntjens), University of Groningen (Hans Proost, Amit Taneja), Takeda (Max Tsai, Andy Sykes) and Leiden University (Suruchi Bakshi).
References:
[1] Westerhout J, Ploeger B, Smeets J, Danhof M, De Lange ECM. Physiologically based pharmacokinetic modeling to investigate regional brain distribution kinetics in rats. The AAPS Journal 2012;14:543-53.
[2] Westerhout J, Smeets J, Danhof M, De Lange ECM. The impact of P-gp functionality on non-steady state relationships between CSF and brain extracellular fluid. J Pharmacokinet Pharmacodyn 2013;40:327-42.
[3] Westerhout J, van den Berg DJ, Hartman R, Danhof M, De Lange ECM. Prediction of methotrexate CNS distribution in different species – influence of disease conditions. Eur J Pharm Sci 2014 Jan 22.doi: 10.1016/j.ejps.2013.12.020.
Reference: PAGE 23 (2014) Abstr 3094 [www.page-meeting.org/?abstract=3094]
Poster: Drug/Disease modeling - CNS