Targeting Ovarian Cancer with Porphysome Nanotechnology
Abstract
Purpose: The Porphysome is a first-in-class porphyrin-based nanotheransotic with inherent biophotonic, photo-physical, and metal chelation properties that can be exploited for multi-modal imaging in Vivo. Herein we report preliminary preclinical data of the use of systemically administered non-targeted Porphysomes for the detection of orthotopic ovarian lesions. Methods: Two ovarian tumour xenograft models are established with human SK-OV-3 and OV-90 cell lines installed into one ovary (orthotopic) of athymic (Nu/Nu) mice. Upon reaching 3.5 mm tumour size, mice are administered non-targeted Porphysome nanovesicles (intravenous, bolus, ~10 mg kg-1 porphyrin dose). Mice are divided into two treatment groups receiving either unlabelled Porphysomes or positron-emitting Copper-64 (64Cu)-labelled Porphysomes (64Cu-Porphysomes). Sham surgical controls and vehicle controls are included. 24-hours post-injection the mice receiving Porphysomes are sacrificed and tissue biodistribution performed using ex Vivo tissue homogenate fluorescence. The mice receiving 64Cu-Porphysomes are sacrificed and tissue biodistribution performed using ex Vivo tissue gamma ()-counting. Results: Quantification of 64Cu signal (-counting) revealed insignificant differences in the accumulation of non-targeted 64Cu-Porphysomes in ovarian lesions versus healthy ovaries. Quantification of porphyrin fluorescence (tissue homogenate fluorescence) also revealed insignificant differences in Porphysome accumulation. Taken together, these preliminary data suggest that non-targeted Porphysomes relying solely on passive mechanisms for accumulation (e.g., enhanced permeability and retention (EPR)-effect phenomena) is an inadequate strategy for yielding significant accumulation and retention of Porphysomes in malignant ovarian tissues compared with healthy ovarian tissues.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2016
- Accession Number
- AD1035134
Entities
People
- Chris Zhang
- Gang Zheng
- Juan Chen
- Lili Ding
- Marcus Q. Bernardini
- Michael S. Valic
- Wenlei Jiang
Organizations
- University Health Network