Literature DB >> 31115858

A translational platform PBPK model for antibody disposition in the brain.

Hsueh-Yuan Chang1, Shengjia Wu1, Guy Meno-Tetang2, Dhaval K Shah3.   

Abstract

In this manuscript, we have presented the development of a novel platform physiologically-based pharmacokinetic (PBPK) model to characterize brain disposition of mAbs in the mouse, rat, monkey and human. The model accounts for known anatomy and physiology of the brain, including the presence of distinct blood-brain barrier and blood-cerebrospinal fluid (CSF) barrier. CSF and interstitial fluid turnover, and FcRn mediated transport of mAbs are accounted for. The model was first used to characterize published and in-house pharmacokinetic (PK) data on the disposition of mAbs in rat brain, including the data on PK of mAb in different regions of brain determined using microdialysis. Majority of model parameters were fixed based on literature reported values, and only 3 parameters were estimated using rat data. The rat PBPK model was translated to mouse, monkey, and human, simply by changing the values of physiological parameters corresponding to each species. The translated PBPK models were validated by a priori predicting brain PK of mAbs in all three species, and comparing predicted exposures with observed data. The platform PBPK model was able to a priori predict all the validation PK profiles reasonably well (within threefold), without estimating any parameters. As such, the platform PBPK model presented here provides an unprecedented quantitative tool for prediction of mAb PK at the site-of-action in the brain, and preclinical-to-clinical translation of mAbs being developed against central nervous system (CNS) disorders. The proposed model can be further expanded to account for target engagement, disease pathophysiology, and novel mechanisms, to support discovery and development of novel CNS targeting mAbs.

Entities:  

Keywords:  Blood–CSF barrier; Blood–brain barrier; Brain; Interspecies scaling; Monoclonal antibody; PBPK model; Pharmacokinetics

Mesh:

Substances:

Year:  2019        PMID: 31115858     DOI: 10.1007/s10928-019-09641-8

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


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