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AAPS PharmSciTech

, Volume 19, Issue 5, pp 2383–2394 | Cite as

Unstructured Formulation Data Analysis for the Optimization of Lipid Nanoparticle Drug Delivery Vehicles

  • Jessica Silva
  • Maria Mendes
  • Tânia Cova
  • João Sousa
  • Alberto Pais
  • Carla Vitorino
Research Article

Abstract

Designing nanoparticle formulations with features tailored to their therapeutic targets in demanding timelines assumes increased importance. In this context, nanostructured lipid carriers (NLCs) offer an excellent example of a drug delivery nanosystem that has been broadly explored in the treatment of glioblastoma multiforme (GBM). Distinct fundamental NLC quality attributes can be harnessed to fit this purpose, namely particle size, size distribution, and zeta potential. These critical aspects intrinsically depend on the formulation components, influencing drug loading capacity, drug release, and stability of the NLCs. Wide variations in their composition, including the type of lipids and other surface modifier excipients, lead to differences on these parameters. NLC target product profile involves small mean particle sizes, narrow size distributions, and absolute values of zeta potential higher than 30 mV. In this work, a wealth of data previously obtained in experiments on NLC preparation, encompassing, e.g., results of preliminary studies and those of intermediate formulations, is analyzed in order to extract information useful in further optimization studies. Principal component analysis (PCA) and partial least squares (PLS) are performed to evaluate the influence of NLC composition on the respective characteristics. These methods provide a rapid and discriminatory analysis for establishing a preformulation framework, by selecting the most suitable types of lipids, surfactants, surface modifiers, and drugs, within the set of investigated variables. The results have direct implications in the optimization of formulation and processes.

KEY WORDS

glioblastoma multiforme (GBM) nanostructured lipid carriers (NLCs) critical quality attributes (CQAs) multivariate analysis 

Notes

Funding Information

The study was financially supported by FEDER Funds through the Operational Program Competitiveness Factors - COMPETE 2020 and by the Fundação para a Ciência e a Tecnologia (FCT), the Portuguese Agency for Scientific Research, through the projects no. 016648 (Ref. POCI-01-0145-FEDER-016648) and POCI-01-0145-FEDER-007440 - Center for Neurosciences and Cell Biology (CNC). The Coimbra Chemistry Centre is supported by the FCT through the projects PEst-OE/QUI/UI0313/2014 and POCI-01-0145-FEDER-007630. Maria Mendes and Tânia Cova were also supported, respectively, by the PhD research grants SFRH/BD/133996/2017 and SFRH/BD/95459/2013, assigned by FCT.

Supplementary material

12249_2018_1078_MOESM1_ESM.docx (31 kb)
ESM 1 (DOCX 31 kb)

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Copyright information

© American Association of Pharmaceutical Scientists 2018

Authors and Affiliations

  • Jessica Silva
    • 1
    • 2
  • Maria Mendes
    • 1
    • 2
  • Tânia Cova
    • 3
  • João Sousa
    • 1
    • 4
  • Alberto Pais
    • 3
  • Carla Vitorino
    • 1
    • 2
    • 4
  1. 1.Faculty of PharmacyUniversity of CoimbraCoimbraPortugal
  2. 2.Centre for Neurosciences and Cell Biology (CNC), Faculty of MedicineUniversity of CoimbraCoimbraPortugal
  3. 3.Coimbra Chemistry Center, Department of ChemistryUniversity of CoimbraCoimbraPortugal
  4. 4.LAQV. REQUIMTE, Group of Pharmaceutical TechnologyPortoPortugal

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