Issue 2, 2019

Genetically-engineered protein prodrug-like nanoconjugates for tumor-targeting biomimetic delivery via a SHEATH strategy

Abstract

The delivery issue is a major hurdle against drug development and the clinical application of the cytoplasmic active proteins (e.g., ribosome-inactivating proteins, RIPs). As a case in point, trichosanthin (TCS) has a very high cytoplasmic activity of killing cancer cells, but the translation is hampered by its unfavorable nature, such as the short half-life, poor tumor targeting and cell permeation. To address this issue, a novel delivery method called a smart hitchhike via endogenous albumin-trichosanthin hinge (SHEATH) system was developed by the genetic fusion of an albumin-binding domain (ABD) and a legumain-substrate peptide to TCS. The SHEATH system is characterized by the feature of smart hitchhike by binding to serum albumin via its ABD domain, and the two proteins (i.e., TCS and albumin) thus form a prodrug-like noncovalent nanoconjugate. The TCS could detach from the albumin carrier by responding to the protease legumain cleavage of the substrate peptide at the tumor site. Such a system can take advantage of the albumin-mediated biomimetic delivery to the tumor via the nutrient transporter pathway of albumin-binding proteins (e.g., SPARC). The antitumor effects were evaluated in orthotopic breast cancer animal models and showed remarkably improved antitumor effects. Our work provides a useful protocol for improving the druggability of such a class of protein toxins for targeted cancer therapy by an endogenous albumin-hitchhike strategy.

Graphical abstract: Genetically-engineered protein prodrug-like nanoconjugates for tumor-targeting biomimetic delivery via a SHEATH strategy

Supplementary files

Article information

Article type
Paper
Submitted
06 Nov 2018
Accepted
25 Nov 2018
First published
17 Dec 2018

Nanoscale, 2019,11, 611-621

Genetically-engineered protein prodrug-like nanoconjugates for tumor-targeting biomimetic delivery via a SHEATH strategy

Y. Chang, S. Yao, Y. Chen, J. Huang, A. Wu, M. Zhang, F. Xu, F. Li and Y. Huang, Nanoscale, 2019, 11, 611 DOI: 10.1039/C8NR08951E

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