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Predicting and validating surfactant performance in nanocrystal stabilization using multi-criteria decision analysis

Tanaka Ndongwe1, Pedzisai A Makoni1, Gauta G Matlou2

  • 1Department of Pharmaceutical Sciences, School of Pharmacy, Sefako Makgatho Health Sciences University, Pretoria, South Africa.

European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences
|February 21, 2026
PubMed
Summary

Selecting the right stabilizer is key for making curcumin nanocrystals. This study found that partial miscibility, predicted by Hansen Solubility Parameters and confirmed experimentally, is crucial for optimal quality and size.

Keywords:
CurcuminExcipient compatibilityHansen solubility theoryNanocrystalsTOPSIS

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Area of Science:

  • Pharmaceutical Nanotechnology
  • Materials Science
  • Computational Chemistry

Background:

  • Curcumin (CUR) is a poorly water-soluble drug (BCS Class IV).
  • Developing stable nanocrystals (NCs) is essential for improving CUR bioavailability.
  • Rational selection of stabilizers is challenging but critical for nanocrystal manufacturing.

Purpose of the Study:

  • To develop an integrated framework for rational stabilizer selection in curcumin nanocrystal (CUR-NC) production.
  • To evaluate the efficacy of seven surfactants using Hansen Solubility Parameter (HSP) theory and experimental validation.
  • To identify optimal conditions for producing high-quality CUR-NCs with desired particle size and low polydispersity.

Main Methods:

  • Application of Hansen Solubility Parameter (HSP) theory to predict drug-surfactant miscibility.
  • General Factorial Design (DoE) for systematic experimental evaluation of surfactants.
  • Wet milling technique to produce CUR-NCs at varying surfactant concentrations.
  • Assessment of particle size (PS) and polydispersity index (PDI) as critical quality attributes (CQAs).
  • Multi-criteria decision analysis using Cumulative Weighted TOPSIS Score (CWTS).

Main Results:

  • HSP and Relative Energy Difference (RED) values effectively predicted stabilizer-drug interactions.
  • An intermediate miscibility/solubility window (ΔδT ≈ 3-6 MPa½, RED ≈ 0.7-1.0) was identified for optimal CUR-NC quality.
  • TPGS 1000 and Tween 20 were identified as the most effective stabilizers for CUR-NC manufacture.
  • Partial miscibility was confirmed as a critical factor for successful nanocrystal stabilization.

Conclusions:

  • The integration of in silico HSP modeling with experimental Design of Experiments (DoE) provides a resource-efficient strategy for pharmaceutical nanocrystal development.
  • This framework enables rational stabilizer selection, optimizing the production of poorly water-soluble drug nanocrystals.
  • The findings support the use of partial miscibility as a key parameter for guiding stabilizer selection in top-down nanocrystallization.