Therapeutic Gene Silencing Using Targeted Lipid Nanoparticles in Metastatic Ovarian Cancer

Manu Smriti Singh, Srinivas Ramishetti, Dalit Landesman-Milo, Meir Goldsmith, Sushmita Chatterjee, Ramesh Palakuri, Dan Peer

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Ovarian cancer is an aggressive tumor owing to its ability to metastasize from stage II onward. Herein, lipid nanoparticles (LNPs) that encapsulate combination of small interfering RNAs (siRNAs), polo-like kinase-1 (PLK1), and eukaryotic translation-initiation factor 3c (eIF3c), to target different cellular pathways essential for ovarian cancer progression are generated. The LNPs are further modified with hyaluronan (tNPs) to target cluster of differentiation 44 (CD44) expressing cells. Interestingly, hyaluronan-coated LNPs (tNPs) prolong functional activity and reduce growth kinetics of spheroids in in vitro assay as compared to uncoated LNPs (uNPs) due to ≈1500-fold higher expression of CD44. Treatment of 2D and 3D cultured ovarian cancer cells with LNPs encapsulating both siRNAs result in 85% cell death and robust target gene silencing. In advanced orthotopic ovarian cancer model, intraperitoneal administration of LNPs demonstrates CD44 specific tumor targeting of tNPs compared to uNPs and robust gene silencing in tissues involved in ovarian cancer pathophysiology. At very low siRNA dose, enhanced overall survival of 60% for tNPs treated mice is observed compared to 10% and 20% for single siRNA-, eIF3c-tNP, and PLK1-tNP treatment groups, respectively. Overall, LNPs represent promising platform in the treatment of advanced ovarian cancer by improving median- and overall-survival.

Original languageEnglish (US)
Article number2100287
JournalSmall
Volume17
Issue number19
DOIs
StatePublished - May 12 2021

Keywords

  • gene delivery
  • lipid nanoparticle
  • ovarian cancer
  • small interfering RNA
  • targeting

ASJC Scopus subject areas

  • Biotechnology
  • Biomaterials
  • Chemistry(all)
  • Materials Science(all)

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