Microfluidic assembly of siRNA-loaded micelleplexes for tumor targeting in an orthotopic model of ovarian cancer

Abstract

The use of cationic polymers to interact with negatively charged siRNA via charge complexation to form polyelectrolyte complexes has been widely studied ever since the 1998 report on RNA interference. These polyelectrolyte complex formulations aim to overcome the many pitfalls associated with the use of RNA interference as a potential cancer therapy. The triblock copolymer polyethylenimine-polycaprolactone-polyethylene glycol (PEI-PCL-PEG) contains the cation PEI and has been shown to be an efficient carrier capable of complexing with nucleic acids for gene delivery. This copolymer system also allows for targeting moieties to be linked to the micelleplex, thereby exploiting overexpressed receptors (such as the folate receptor) located within tumors. Additionally, we demonstrated recently that microfluidic mixing of PEI-PCL-PEG nanoparticles allows for the rapid, scaled-up production of micelleplexes while maintaining small and uniform particle distributions. The preparation of small and reproducible particles is imperative for clinical translation of nanomedicine and for tumor targeting via systemic administration. Furthermore, to enable tracing of its deposition in vivo after its administration, micelleplexes can be radiolabeled. To assess tumor targeting over time, the noninvasive imaging technique single-photon emission computed tomography (SPECT) offers the ability to examine the same subject at multiple time points and generate biodistribution profiles. Since the biodistribution and tumor targeting of the therapeutic load of micelleplexes is of foremost interest, we recently described an approach to modify siRNA with a DTPA (diethylenetriaminepentaacetic acid) chelator. Herein, we explain the details of encapsulating indium-labeled siRNA via microfluidic mixing in PEI-PCL-PEG nanoparticles with a folic acid targeting ligand for assessment of their in vivo tumor targeting in an orthotopic ovarian cancer model.