Proof-of-Concept Study of Drug Brain Permeability Between in Vivo Human Brain and an in Vitro iPSCs-Human Blood-Brain Barrier Model.
Gwenaëlle Le RouxRafika JarrayAnne-Cécile GuyotSerena PavoniNarciso CostaFrédéric ThéodoroFerid NassorAlain PruvostNicolas TournierYulia KiyanOliver LangerFrank YatesJean Philippe DeslysAloïse MabondzoPublished in: Scientific reports (2019)
The development of effective central nervous system (CNS) drugs has been hampered by the lack of robust strategies to mimic the blood-brain barrier (BBB) and cerebrovascular impairments in vitro. Recent technological advancements in BBB modeling using induced pluripotent stem cells (iPSCs) allowed to overcome some of these obstacles, nonetheless the pertinence for their use in drug permeation study remains to be established. This mandatory information requires a cross comparison of in vitro and in vivo pharmacokinetic data in the same species to avoid failure in late clinical drug development. Here, we measured the BBB permeabilities of 8 clinical positron emission tomography (PET) radioligands with known pharmacokinetic parameters in human brain in vivo with a newly developed in vitro iPSC-based human BBB (iPSC-hBBB) model. Our findings showed a good correlation between in vitro and in vivo drug brain permeability (R2 = 0.83; P = 0.008) which contrasted with the limited correlation between in vitro apparent permeability for a set of 18 CNS/non-CNS compounds using the in vitro iPSCs-hBBB model and drug physicochemical properties. Our data suggest that the iPSC-hBBB model can be integrated in a flow scheme of CNS drug screening and potentially used to study species differences in BBB permeation.
Keyphrases
- blood brain barrier
- induced pluripotent stem cells
- cerebral ischemia
- positron emission tomography
- endothelial cells
- computed tomography
- adverse drug
- drug induced
- white matter
- resting state
- healthcare
- pet imaging
- magnetic resonance
- multiple sclerosis
- subarachnoid hemorrhage
- functional connectivity
- magnetic resonance imaging
- artificial intelligence
- brain injury
- deep learning