Evaluation of the effects of nanoprecipitation process parameters on the size and morphology of poly(ethylene oxide)-block-polycaprolactone nanostructures

Voitto Känkänen, Jani Seitsonen, Henri Tuovinen, Janne Ruokolainen, Jouni Hirvonen*, Vimalkumar Balasubramanian, Hélder A. Santos

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Nanoprecipitation is a straightforward method for the production of block copolymer nanoparticles for drug delivery applications. However, the effects of process parameters need to be understood to optimize and control the particle size distribution (PSD). To this end, we investigated the effects of material and process factors on PSD and morphology of nanoparticles prepared from an amphiphilic diblock copolymer, poly(ethylene oxide)-block-polycaprolactone. Using a Design of Experiments approach, we explored the joint effects of molecular weight, block length ratios, water volume fraction, stirring rate, polymer concentration and organic phase addition rate on hydrodynamic size and polydispersity index of the nanostructures and created statistical models explaining up to 94% of the variance in hydrodynamic diameter. In addition, we performed morphological characterization by cryogenic transmission electron microscopy and showed that increasing the process temperature may favor the formation of vesicles from these polymers. We showed that the effects of process parameters are dependent on the polymer configuration and we found that the most useful parameters to fine-tune the PSD are the initial polymer concentration and the stirring rate. Overall, this study provides evidence on the joint effects of material and process parameters on PSD and morphology, which will be useful for rational design of formulation-specific optimization studies, scale-up and process controls.

Original languageEnglish
Article number119900
Number of pages11
JournalInternational Journal of Pharmaceutics
Volume590
DOIs
Publication statusPublished - 30 Nov 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Block copolymers
  • Nanoparticles
  • Nanoprecipitation
  • Process optimization
  • Self-assembly
  • Statistical models

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