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Design of an Amphiphilic iRGD Peptide and Self-Assembling Nanovesicles for Improving Tumor Accumulation and Penetration and the Photodynamic Efficacy of the Photosensitizer.

Yue JiangXin PangRuiling LiuQicai XiaoPan WangAlbert Wingnang LeungYuxia LuanChuanshan Xu
Published in: ACS applied materials & interfaces (2018)
Photodynamic therapy (PDT) is a minimally invasive treatment for many diseases, including infections and tumors. Nevertheless, clinical utilization of PDT is severely restricted due to the shortcomings of the photosensitizers, especially their low water solubility and poor tumor selectivity. iRGD (internalizing RGD, CRGDKGPDC), a nine-unit cyclic peptide, was applied as an active ligand to realize tumor homing and tissue penetration. Herein, we innovatively fabricated a novel OFF-ON mode iRGD-based peptide amphiphile (PA) to self-assemble into spherical nanovesicles to enhance the tumor-targeting and tumor-penetrating efficacy of PDT. To introduce the self-assembling feature into iRGD, a hydrophilic arginine-rich sequence and hydrophobic alkyl chains were sequentially linked to the iRGD motif. A short proline sequence was selected to control the morphology of the self-assembled aggregates. Next, the photosensitizer hypocrellin B (HB) was encapsulated into PA vesicles with a high loading efficiency. The aggregation-caused quenching effect inactivated HB in the PA vesicles; however, the iRGD-peptide-based material was able to be selectively degraded in tumor cells. Thus, the HB fluorescence was recovered to achieve tumor-targeted imaging. This approach endows HB-loaded PA vesicles (HB-PA) with tumor-targeted activation, preferable tumor accumulation, and deep tumor penetration, thus leading to an excellent fluorescence-imaging-guided photodynamic efficacy both in vitro and in vivo. These amphiphilic iRGD aggregates provide a novel strategy for improving the accumulation, penetration, and imaging-guided photodynamic efficacy of photosensitizers.
Keyphrases
  • photodynamic therapy
  • fluorescence imaging
  • cancer therapy
  • minimally invasive
  • machine learning
  • deep learning
  • energy transfer