Published on 23 February 2021
PLGA nanoparticles generation (Poly(lactic-co-glycolic acid)) is of great interest for biomedical applications and especially for drug delivery. These polymeric nanoparticles (NPs) are valued for their biocompatibility and biodegradability. For most biomedical applications implying the use of PLGA nanoparticles synthesis, it is important to have a monodisperse solution of NPs, i.e., a narrow size distribution. Hence, the preparation of PLGA nanoparticles in microfluidics is more suitable for that particular purpose than common batch methods. For more complete information, the reader is advised to have a look at the review here. Other nanoparticles, like lipid nanoparticles, can also be synthesized with the same instruments and minor modifications to this protocole, contact our experts for more information.
Advantages of this setup for PLGA nanoparticle preparation
This application note describes how to fabricate PLGA nanoparticles with an hydrodynamic flow focusing microfluidic chip, use the OB1 flow controller and digital flow sensors to maintain a precise flow rate ratio and produce highly monodisperse nanoparticles.
In this setup, the nanoparticles are produced by a process called nanoprecipitation. For more general information, read also our review on microfluidic nanoparticle synthesis. Using this microfluidic setup to manufacture PLGA NPs allows you to easily tune their sizes and concentration by varying the flow rates of the respective reagents (aqueous and polymeric). The NPs produced have less batch-to-batch variability and a narrow size distribution with a polydispersity index (PDI) typically below 0.2.
Some of the main applications of PLGA NPs include (Danhier et al., 2012):
Flow controller OB1 Mk3+
Flow sensor (MFS3 and MFS4)
Fluidic 386 chip from Microfluidic ChipShop Gmbh
Microfluidic T-junction
Tubings, fittings and reservoirs
The selected design of the microfluidic chip (Fluidic 386) is suitable to produce highly monodisperse PLGA nanoparticles ranging from 160 to 230 nm.
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Application note written by Audrey NSAMELA – Acknowledgement: This work was done thanks to the funding of European Union’s Horizon 2020 research and innovation programme (ActiveMatter H2020-MSCA-ITN-2018-Action “Innovative Training Networks”, Grant agreement number: 812780).
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