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Published on: July 3, 2018
Parallel microfluidic synthesis of size-tunable polymeric nanoparticles using 3D flow focusing towards in vivo study
Jong-Min Lim1, Nicolas Bertrand2, Pedro M Valencia3
1Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Laboratory of Nanomedicine and Biomaterials, Department of Anesthesiology, Brigham and Women's Hospital - Harvard Medical School, Boston, MA, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Abstract:
Microfluidic synthesis of nanoparticles (NPs) can enhance the controllability and reproducibility in physicochemical properties of NPs compared to bulk synthesis methods. However, applications of microfluidic synthesis are typically limited to in vitro studies due to low production rates. Herein, we report the parallelization of NP synthesis by 3D hydrodynamic flow focusing (HFF) using a multilayer microfluidic system to enhance the production rate without losing the advantages of reproducibility, controllability, and robustness. Using parallel 3D HFF, polymeric poly(lactide-co-glycolide)-b-polyethyleneglycol (PLGA-PEG) NPs with sizes tunable in the range of 13-150 nm could be synthesized reproducibly with high production rate. As a proof of concept, we used this system to perform in vivo pharmacokinetic and biodistribution study of small (20 nm diameter) PLGA-PEG NPs that are otherwise difficult to synthesize. Microfluidic parallelization thus enables synthesis of NPs with tunable properties with production rates suitable for both in vitro and in vivo studies.
From The Clinical Editor:
Applications of nanoparticle synthesis with microfluidic methods are typically limited to in vitro studies due to low production rates. The team of authors of this proof-of-principle study reports on the successful parallelization of NP synthesis by 3D hydrodynamic flow focusing using a multilayer microfluidic system to enhance production rate without losing the advantages of reproducibility, controllability, and robustness.
