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Hollow-layered nanoparticles for therapeutic delivery of peptide prepared using electrospraying

  • Manoochehr RasekhEmail author
  • Christopher Young
  • Marta Roldo
  • Frédéric Lancien
  • Jean-Claude Le Mével
  • Sassan Hafizi
  • Zeeshan Ahmad
  • Eugen Barbu
  • Darek Gorecki
Delivery Systems Original Research
Part of the following topical collections:
  1. Delivery Systems

Abstract

The viability of single and coaxial electrospray techniques to encapsulate model peptide—angiotensin II into near mono-dispersed spherical, nanocarriers comprising N-octyl-O-sulphate chitosan and tristearin, respectively, was explored. The stability of peptide under controlled electric fields (during particle generation) was evaluated. Resulting nanocarriers were analysed using dynamic light scattering and electron microscopy. Cell toxicity assays were used to determine optimal peptide loading concentration (~1 mg/ml). A trout model was used to assess particle behaviour in vivo. A processing limit of 20 kV was determined. A range of electrosprayed nanoparticles were formed (between 100 and 300 nm) and these demonstrated encapsulation efficiencies of ~92 ± 1.8 %. For the single needle process, particles were in matrix form and for the coaxial format particles demonstrated a clear core–shell encapsulation of peptide. The outcomes of in vitro experiments demonstrated triphasic activity. This included an initial slow activity period, followed by a rapid and finally a conventional diffusive phase. This was in contrast to results from in vivo cardiovascular activity in the trout model. The results are indicative of the substantial potential for single/coaxial electrospray techniques. The results also clearly indicate the need to investigate both in vitro and in vivo models for emerging drug delivery systems.

Keywords

Encapsulation Dynamic Light Scattering Tristearin Outer Needle Base Drug Delivery System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to acknowledge PeReNE (EU-INTERREG) for supporting this study. The authors also thank Professor Simon Cragg for assistance with the SEM and TEM and Dr Simone Elgass for developing the HPLC method.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Manoochehr Rasekh
    • 1
    Email author
  • Christopher Young
    • 1
  • Marta Roldo
    • 1
  • Frédéric Lancien
    • 2
  • Jean-Claude Le Mével
    • 2
  • Sassan Hafizi
    • 1
  • Zeeshan Ahmad
    • 3
  • Eugen Barbu
    • 1
  • Darek Gorecki
    • 1
  1. 1.School of Pharmacy and Biomedical SciencesUniversity of PortsmouthPortsmouthUK
  2. 2.Neurophysiology Laboratory, LaTIM UMR 1101University of BrestBrest Cedex 3France
  3. 3.Leicester School of PharmacyDe Montfort UniversityLeicesterUK

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