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A Stepwise Framework for the Systematic Development of Lipid Nanoparticles.

João Basso1,2, Maria Mendes1,2, Tânia Cova2

  • 1Faculty of Pharmacy, University of Coimbra, 3000-548 Coimbra, Portugal.

Biomolecules
|February 25, 2022
PubMed
Summary

This study presents a systematic framework for developing stable lipid nanoparticles for drug delivery. It details methods for selecting lipids and surfactants to ensure optimal nanoparticle formulation and performance.

Keywords:
NLCsSLNsdrug formulationlipid nanoparticlesmultivariate analysisscreening

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Area of Science:

  • Pharmaceutical Nanotechnology
  • Materials Science
  • Drug Delivery Systems

Background:

  • Designing effective nanosystems requires optimizing active pharmaceutical ingredient (API) concentration at the target site for therapeutic efficacy and reduced side effects.
  • The wide array of lipids and surfactants offers formulation versatility but complicates the development of stable lipid dispersions due to complex material interactions, often necessitating extensive pre-formulation studies.

Purpose of the Study:

  • To present a stepwise framework for the rational development of nanoparticles for disulfiram delivery.
  • To establish premises for a "right at first time" nanoparticle formulation approach.
  • To guide the selection of optimal excipients for solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs).

Main Methods:

  • Drug solubility analysis in liquid lipids to identify suitable candidates.
  • Drug partitioning studies in solid lipids to assess solubilization and entrapment capacity.
  • Microscopical evaluation of liquid-solid lipid compatibility for core composition selection.
  • Machine learning algorithms (hierarchical clustering, PCA, PLS regression) to analyze surfactant impact on colloidal properties.

Main Results:

  • Identification of optimal liquid and solid lipids for disulfiram incorporation based on solubility and partitioning.
  • Determination of promising core compositions through physical compatibility assessments.
  • Quantification of the influence of the surfactant layer on nanosystem colloidal characteristics using machine learning.

Conclusions:

  • The presented systematic approach facilitates the rational selection of excipients for nanoparticle formulation.
  • This framework streamlines the development of stable and effective lipid-based nanosystems for drug delivery.
  • The study provides a basis for the efficient design of SLNs and NLCs, minimizing laborious pre-formulation efforts.