Evaluation of Cell-Penetrating Peptides Using Microfluidic In Vitro 3D Brain Endothelial Barrier.


Journal

Macromolecular bioscience
ISSN: 1616-5195
Titre abrégé: Macromol Biosci
Pays: Germany
ID NLM: 101135941

Informations de publication

Date de publication:
06 2020
Historique:
received: 17 12 2019
revised: 27 02 2020
pubmed: 25 4 2020
medline: 3 6 2021
entrez: 25 4 2020
Statut: ppublish

Résumé

In drug delivery to the human brain, blood vessels are a significant hurdle because they restrict the entry of most solutes to protect brain. To overcome this hurdle, an in vitro 3D model for brain endothelial barrier is developed using a microfluidic device with hydrogel providing a 3D extracellular matrix scaffold. Using the model, peptides known to utilize receptor-mediated transcytosis are verified, which has been one of the most promising mechanisms for brain-specific penetration. The cytotoxicity and cellular damage to the peptide are investigated and the receptor-mediated transcytosis and brain endothelial specific penetrating abilities of the peptides in a quantitative manner are demonstrated. As a preclinical test, applying the quantification assays conducted in this study are suggested, including the penetrating ability, cytotoxicity, endothelial damage, and receptor specificity. Using this microfluidic device as an in vitro platform for evaluating various brain targeting drugs and drug carrier candidates is also proposed.

Identifiants

pubmed: 32329170
doi: 10.1002/mabi.201900425
doi:

Substances chimiques

Cell-Penetrating Peptides 0

Types de publication

Evaluation Study Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1900425

Subventions

Organisme : National Research Foundation of Korea
Pays : International
Organisme : Korea government
Pays : International

Informations de copyright

© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Auteurs

Bohye Chung (B)

KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, Republic of Korea.

Jaehoon Kim (J)

School of Mechanical Engineering, Korea University, Seoul, Republic of Korea.

Jiyoung Nam (J)

Sungkyunkwan University, Suwon, Republic of Korea.

Hyunho Kim (H)

School of Mechanical Engineering, Korea University, Seoul, Republic of Korea.

Yeju Jeong (Y)

School of Mechanical Engineering, Korea University, Seoul, Republic of Korea.

Hui-Wen Liu (HW)

School of Mechanical Engineering, Korea University, Seoul, Republic of Korea.

Youngkyu Cho (Y)

Department of IT Convergence, Korea University, Seoul, Republic of Korea.

Yong Ho Kim (YH)

Sungkyunkwan University, Suwon, Republic of Korea.

Hyun Jeong Oh (HJ)

School of Mechanical Engineering, Korea University, Seoul, Republic of Korea.

Seok Chung (S)

KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, Republic of Korea.
School of Mechanical Engineering, Korea University, Seoul, Republic of Korea.
Department of IT Convergence, Korea University, Seoul, Republic of Korea.

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