Head-to-Head Comparison of the Penetration Efficiency of Lipid-Based Nanoparticles into Tumor Spheroids

Maria Niora1, Dennis Pedersbæk2, Rasmus Münter2

  • 1The Niels Bohr Institute, University of Copenhagen, 2100 København, Denmark.

ACS Omega
|September 3, 2020
PubMed

Insights

Reconstituted high-density lipoproteins (rHDL) show superior tumor penetration compared to other lipid nanoparticles. This study highlights rHDL

Area of Science:

  • Nanomedicine
  • Biotechnology
  • Cancer Research

Background:

  • Tumor-targeted drug delivery faces challenges with physiological barriers and tumor cell penetration.
  • Optimizing nanoparticle accumulation at tumor sites often neglects penetration efficiency.
  • Effective drug delivery requires overcoming tumor surface adherence and cellular uptake.

Purpose of the Study:

  • To compare the penetration efficiency of various lipid-based nanoparticles in a 3D tumor model.
  • To evaluate the impact of nanoparticle properties on tumor penetration.
  • To introduce a versatile platform for assessing nanoparticle penetration in vitro.

Main Methods:

  • Utilized three-dimensional (3D) tumor spheroids as an in vitro tumor model.
  • Employed light-sheet fluorescence microscopy for high-resolution imaging.
  • Conducted a head-to-head comparison of liposomes, PEGylated liposomes, lipoplexes, and reconstituted high-density lipoproteins (rHDL).

Main Results:

  • PEGylation of liposomes had minimal impact on penetration efficiency.
  • Lipoplexes showed peripheral association with spheroids, likely due to surface charge and aggregation.
  • Reconstituted high-density lipoproteins (rHDL) demonstrated significantly higher penetration efficiency and accumulation within spheroids.

Conclusions:

  • Reconstituted high-density lipoproteins (rHDL) are promising candidates for enhanced tumor drug delivery due to superior penetration.
  • Nanoparticle surface properties, like charge, significantly influence tumor penetration.
  • The 3D spheroid model with light-sheet microscopy is a valuable tool for evaluating nanoparticle penetration efficiency in cancer research.

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