A Parameter-Fitted PC-SAFT Framework for Solubility Extrapolation in Drug–Polymer Systems

Accurate modeling of drug-polymer solubility is essential for the rational design of amorphous solid dispersions and other advanced pharmaceutical formulations. The perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state has emerged as a robust framework for capturing complex thermodynamic interactions in such systems. However, its predictive accuracy is often constrained by the limited availability of validated pure-component parameters and the frequent need to optimize the binary interaction parameter (<i>k</i>_ij) to match experimental data. In this study, we present a novel application of PC-SAFT as a data-driven extrapolation tool in which model parameters are directly regressed to experimental solubility data for specific drug-polymer pairs. This approach repositions PC-SAFT from a purely predictive model to a pragmatic extrapolative framework, enabling solubility estimation without reliance on pretabulated parameters or speculative <i>k</i>_ij adjustments. In a separate analysis, we further demonstrate that using arbitrary pure-component parameter values─when coupled with <i>k</i>_ij optimization─can achieve predictive performance comparable to that of literature-derived parameters. This finding underscores the dominant role of the binary interaction parameter and suggests that detailed pure-component calibration may not be essential for capturing the solubility behavior. Case studies confirm that both strategies reliably reproduce experimental trends and offer practical paths for bridging data gaps in the thermodynamic modeling of drug-polymer systems.