Predicting and validating surfactant performance in nanocrystal stabilization using multi-criteria decision analysis and solubility parameter modeling.

This study presents an integrative experimental and computational framework for the rational stabilizer selection in the top-down nanocrystallization of curcumin (CUR), a poorly water-soluble biopharmaceutical classification system (BCS) Class IV compound. Seven surfactants (TPGS 1000, P407, Tween 20, Tween 80, Span 80, SLS, and PEG 4000) were systematically evaluated through combinatorial application of Hansen Solubility Parameter (HSP) theory and a General Factorial Design. Theoretical solubility parameters (δD, δP, δH) and derived Relative Energy Difference (RED) values predicted stabilizer-drug miscibility and solubility, while experimental validation utilized wet milling media to generate curcumin nanocrystals (CUR-NCs) at varying surfactant concentrations (0.125- 0.5% w/v). Particle size (PS) and polydispersity index (PDI) were assessed as critical quality attributes (CQAs). A non-linear relationship between solubility parameters and stabilizer efficiency was observed, revealing that optimal nanocrystal quality typically occurs within an intermediate miscibility/solubility window (ΔδT ≈ 3-6 MPa½, RED ≈ 0.7-1.0). Multi-criteria decision analysis using a Cumulative Weighted TOPSIS Score (CWTS) confirmed the effectiveness of the performance of TPGS 1000 and Tween 20 as the most ideal stabilizers for curcumin nanocrystal manufacture. These findings demonstrate that partial miscibility is critical for nanocrystal stabilization and validate the integration of in silico HSP modelling with Design of Experiments (DoE) as a resource-efficient strategy for pharmaceutical nanocrystal development.