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Mediating Passive Tumor Accumulation through Particle Size, Tumor Type, and Location.

Jillian L PerryKevin G ReuterJ Christopher LuftChad V PecotWilliam ZamboniJoseph M DeSimone
Published in: Nano letters (2017)
As the enhanced permeation and retention (EPR) effect continues to be a controversial topic in nanomedicine, we sought to examine EPR as a function of nanoparticle size, tumor model, and tumor location, while also evaluating tumors for EPR mediating factors such as microvessel density, vascular permeability, lymphatics, stromal content, and tumor-associated immune cells. Tumor accumulation was evaluated for 55 × 60, 80 × 180, and 80 × 320 nm PRINT particles in four subcutaneous flank tumor models (SKOV3 human ovarian, 344SQ murine nonsmall cell lung, A549 human nonsmall cell lung, and A431 human epidermoid cancer). Each tumor model revealed specific particle accumulation trends with evident particle size dependence. Immuno-histochemistry staining revealed differences in tumor microvessel densities that correlated with overall tumor accumulation. Immunofluorescence images displayed size-mediated tumor penetration with signal from the larger particles concentrated close to the blood vessels, while signal from the smaller particle was observed throughout the tissue. Differences were also observed for the 55 × 60 nm particle tumor penetration across flank tumor models as a function of stromal content. The 55 × 60 nm particles were further evaluated in three orthotopic, metastatic tumor models (344SQ, A549, and SKOV3), revealing preferential accumulation in primary tumors and metastases over healthy tissue. Moreover, we observed higher tumor accumulation in the orthotopic lung cancer models than in the flank lung cancer models, whereas tumor accumulation was constant for both orthotopic and flank ovarian cancer models, further demonstrating the variability in the EPR effect as a function of tumor model and location.
Keyphrases
  • squamous cell carcinoma
  • stem cells
  • photodynamic therapy
  • deep learning
  • machine learning
  • mesenchymal stem cells
  • single cell
  • drug delivery
  • lymph node metastasis