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Fluorescent Leakage Assay to Investigate Membrane Destabilization by Cell-Penetrating Peptide

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Published on: December 19, 2020

Brain shuttle peptides: From permeability assay toolbox to data-driven discovery.

Nikolina Mohović¹, Marko Njirjak², Ena Dražić²

¹Faculty of Biotechnology and Drug Development, University of Rijeka, Rijeka, Croatia.

Advances in Pharmacology (San Diego, Calif.)

|June 7, 2026

Summary

Peptides show promise for brain drug delivery by crossing the blood-brain barrier (BBB). Researchers can use various in vitro and in vivo models, alongside machine learning, to study peptide transport and distribution for central nervous system (CNS) therapies.

Keywords:

BBB assays Blood-brain barrier Brain shuttle peptides Datasets Machine learning

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

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Published on: December 1, 2020

Area of Science:

Neuroscience
Pharmacology
Biotechnology

Background:

The blood-brain barrier (BBB) protects the central nervous system (CNS) but hinders drug delivery.
Peptides offer potential as brain shuttles due to their transport capabilities.

Purpose of the Study:

To review models for assessing peptide transport across the BBB.
To guide researchers in selecting tools for studying peptide shuttles and brain distribution.

Main Methods:

Overview of in vitro models (artificial membranes, BBB-on-a-chip) and in vivo models (brain perfusion, molecular imaging).
Discussion of mass-based quantification techniques.
Highlighting the role of machine learning in identifying BBB-crossing peptides.

Main Results:

Various models can assess BBB permeability, transport mechanisms, and peptide distribution.
Machine learning is increasingly used to predict peptide BBB potential.

Conclusions:

Appropriate selection and combination of models are crucial for studying peptide BBB transport.
This chapter provides methodological guidance for researchers in the field.