Driving Technology: Peptide Blood–Brain Barrier Shuttles for Therapeutic Transport

Diseases involving the central nervous system impact approximately 1.5 billion people across the globe. Therapeutic intervention for these conditions requires not only effective therapeutics but also delivery systems that can cross the blood–brain barrier (BBB) without producing secondary effects.

To this end, researchers have developed a Trojan Horse strategy: employing BBB shuttles to escort therapeutic molecules into the brain parenchyma. One previously excluded option is peptide shuttles, which bear a particular suitability to mimic the active sites of receptors and transporters. As a first instance of applying a structure-based approach to peptide BBB shuttle design, Guixer et al. (2016) recently explored water-soluble, protease-resistant shuttle options via mass spectrometry (MS) and in vitro modeling.¹

First, the team used a mix-and-split method with seven amino acids (D-alanine, D-arginine, D-glutamic acid, D-isoleucine, D-proline, D-serine and D-tryptophan) to construct a peptide library using the following construct: Ac-D-Arg-XXXXX-NH2 (where X is an amino acid). They then subjected this Ac-D-Arg library to two in vitro assays: parallel artificial membrane permeability assay (PAMPA) and an in vitro cell-based BBB assay using layers of bovine brain endothelial cells and rat astrocytes. The first modeled only passive diffusion, while the second modeled both active and passive transport.Chemically synthesized peptide libraries as a new source of BBB shuttles. Use of mass spectrometry for peptide identification

 

The research team combined the data resulting from both assays and MS₁ to compile two lists of peptide families that met selection criteria: candidates expected to cross the BBB via passive diffusion and candidates expected to cross using other mechanisms (i.e., active transport, paracellular flux). From these, they selected seven candidate families for further evaluation via targeted SRM. The seven candidate families included three candidate families from the “passive diffusion transport” list (i3-p2-r1, p3-s2-r1 and a3-r2-p1) and four candidate families from the “other mechanisms of transport” list (i3-a1-p1-r1, e2-p2-a1-r1, p2-w2-i1-r1 and i4-p1-r1).

The researchers reported highly consistent results for most sequences associated with the families validated from both the “passive diffusion” list and the “other mechanisms” list. They highlighted these individual candidate peptides from the evaluated families:

Passive diffusion transport	Other mechanisms of transport
i3-p2-r1: ripipi, rpiiip and rpipii	i3-a1-p1-r1: rapiii, riiaip, riiiap, riiipa, riipai, riipia, ripiai, rpaiii and rpiiai
p3-s2-r1: rsppsp, rspspp and rssppp	e2-p2-a1-r1: no significant data
a3-r2-p1: raaarp, rraaap and rrapaa	p2-w2-i1-r1: no significant data
	i4-p1-r1: riiiip, riiipi, riipii, ripiii and rpiiii

The team indicated that they intend to further validate these peptides individually using the in vitro cell-based BBB assay as a component of continued investigation of the implicated transport mechanisms.

Overall, Guixer et al. offered this novel high-throughput, two-level MS (MS₁ and targeted SRM) screening method to identify the most promising candidate peptides for crossing in vitro models of the BBB. They noted the accessibility of the technology employed as well as its promise for identifying peptide BBB shuttles capable of escorting therapeutics as a component of treatment for diseases involving the central nervous system.

Reference
1. Guixer, B., et al. (2016) “Chemically synthesized peptide libraries as a new source of BBB shuttles. Use of mass spectrometry for peptide identification,” Journal of Peptide Science 22(9) (pp. 577–591), doi: 10.1002/psc.2900.