Can Pure Predictions of Activity Coefficients from PC-SAFT Assist Drug–Polymer Compatibility Screening?

Abstract

The bioavailability of poorly water-soluble active pharmaceutical ingredients (APIs) can be improved via the formulation of an amorphous solid dispersion (ASD), where the API is incorporated into a suitable polymeric carrier. Optimal carriers that exhibit good compatibility (i.e., solubility and miscibility) with given APIs are typically identified through experimental means, which are routinely labor- and cost-inefficient. Therefore, the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state, a popular thermodynamic model in pharmaceutical applications, is examined in terms of its performance regarding the computational pure prediction of API–polymer compatibility based on activity coefficients (API fusion properties were taken from experiments) without any binary interaction parameters fitted to API–polymer experimental data (that is, k ij = 0 in all cases). This kind of prediction does not need any experimental binary information and has been underreported in the literature so far, as the routine modeling strategy used in the majority of the existing PC-SAFT applications to ASDs comprised the use of nonzero k ij values. The predictive performance of PC-SAFT was systematically and thoroughly evaluated against reliable experimental data for almost 40 API–polymer combinations. We also examined the effect of different sets of PC-SAFT parameters for APIs on compatibility predictions. Quantitatively, the total average error calculated over all systems was approximately 50% in the weight fraction solubility of APIs in polymers, regardless of the specific API parametrization. The magnitude of the error for individual systems was found to vary significantly from one system to another. Interestingly, the poorest results were obtained for systems with self-associating polymers such as poly(vinyl alcohol). Such polymers can form intramolecular hydrogen bonds, which are not accounted for in the PC-SAFT variant routinely applied to ASDs (i.e., that used in this work). However, the qualitative ranking of polymers with respect to their compatibility with a given API was reasonably predicted in many cases. It was also predicted correctly that some polymers always have better compatibility with the APIs than others. Finally, possible future routes to improve the cost–performance ratio of PC-SAFT in terms of parametrization are discussed.

COSMOPharm: Drug–Polymer Compatibility of Pharmaceutical Amorphous Solid Dispersions from COSMO-SAC

“…40,112 However, as will be discussed in Section 3.2.1, CS dsp FV does qualitatively predict AAPS and the overall limited compatibility of these systems. Interestingly, similar challenges regarding the numerical accuracy were recently encountered with PC-SAFT, 41 indicating that the systems of IBP with EUD and PLGA pose a broader challenge for thermodynamic models. Conversely, most other API exhibited AAD values ranging from 8% to 13%.…”

“…To contextualize these findings and the observed predictive performance, the total AAD of 14% obtained from the fully QM-aided CS dsp SG model was about 10% lower than that of other available models for ASD phase behavior. 41 It requires no experimental data for parametrization because it only relies on first-principles calculations, reflecting its practical efficacy.…”

“…It is remarkable that a similar AAPS behavior in these two PCM-based systems was entirely independently predicted with PC-SAFT. 41 The only systems with an experimentally observed AAPS that CS dsp FV found to be homogeneous are NPX−PVA and NPX−PLGA75. However, while CS dsp FV predicts AAPS for PLGA50, but not for PLGA75, the experimental results indicate the opposite.…”

“…They typically require experimental data for parametrization of the involved molecular species (i.e., pure component parameters) and, for better accuracy, also for mixtures (binary interaction parameters, k ij ). It was recently shown that predictions of the API solubility in polymers obtained from PC-SAFT without any mixture-specific parameters (i.e., with all k ij set to zero) achieve an overall average absolute relative deviation of approximately 50% 41 in terms of weight fraction solubility (statistical parameters are explained in Section 2.4). Regardless of the quantitative performance, the qualitative ranking of polymers with respect to predicted compatibility with API showed good results.…”

“…Another important group of semiempirical tools is constituted by equations of state (EOS). One of the most popular EOS for modeling the phase behavior of polymer, pharmaceutical, and ASD systems is PC-SAFT. ,− Unlike the Flory–Huggins and UNIFAC models, EOS like PC-SAFT are capable of explicitly describing HB. They typically require experimental data for parametrization of the involved molecular species (i.e., pure component parameters) and, for better accuracy, also for mixtures (binary interaction parameters, k ij ).…”