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Summary

This study shows that the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) can accurately model drug-polymer solubility by directly using experimental data. Optimizing interaction parameters, rather than pure-component values, is key for reliable solubility predictions.

Keywords:
PC-SAFTamorphous solid dispersiondrug solubilityphase diagrampoly(2-oxazoline)

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Area of Science:

  • Thermodynamics
  • Pharmaceutical Science
  • Materials Science

Background:

  • Accurate drug-polymer solubility modeling is crucial for amorphous solid dispersions and advanced pharmaceutical formulations.
  • The Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) is a robust framework for thermodynamic interactions but requires validated parameters.
  • Parameter availability and binary interaction optimization often limit PC-SAFT's predictive accuracy.

Purpose of the Study:

  • To present a novel data-driven application of PC-SAFT as an extrapolation tool for drug-polymer solubility.
  • To enable solubility estimation without relying on pretabulated parameters or speculative binary interaction adjustments.
  • To investigate the impact of pure-component parameter values versus binary interaction optimization on predictive performance.

Main Methods:

  • Developed a data-driven PC-SAFT approach by directly regressing model parameters to experimental solubility data for specific drug-polymer pairs.
  • Applied PC-SAFT as an extrapolative framework, bypassing the need for literature-derived parameters.
  • Conducted a separate analysis optimizing the binary interaction parameter (k_ij) with arbitrary pure-component values.

Main Results:

  • The data-driven PC-SAFT approach successfully enabled solubility estimation without pretabulated parameters or k_ij adjustments.
  • Optimizing k_ij with arbitrary pure-component values yielded predictive performance comparable to literature-derived parameters.
  • Both strategies reliably reproduced experimental solubility trends in case studies.

Conclusions:

  • PC-SAFT can be effectively used as a data-driven extrapolation tool for pharmaceutical solubility modeling.
  • The binary interaction parameter plays a dominant role, suggesting detailed pure-component calibration may not be essential.
  • These strategies provide practical methods for bridging data gaps in drug-polymer thermodynamic modeling.