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

Abstract

The quantum mechanics-aided COSMO-SAC activity coefficient model is applied and systematically examined for predicting the thermodynamic compatibility of drugs and polymers. The drug-polymer compatibility is a key aspect in the rational selection of optimal polymeric carriers for pharmaceutical amorphous solid dispersions (ASD) that enhance drug bioavailability. The drug-polymer compatibility is evaluated in terms of both solubility and miscibility, calculated using standard thermodynamic equilibrium relations based on the activity coefficients predicted by COSMO-SAC. As inherent to COSMO-SAC, our approach relies only on quantum-mechanically derived σ-profiles of the considered molecular species and involves no parameter fitting to experimental data. All σ-profiles used were determined in this work, with those of the polymers being derived from their shorter oligomers by replicating the properties of their central monomer unit(s). Quantitatively, COSMO-SAC achieved an overall average absolute deviation of 13% in weight fraction drug solubility predictions compared to experimental data. Qualitatively, COSMO-SAC correctly categorized different polymer types in terms of their compatibility with drugs and provided meaningful estimations of the amorphous-amorphous phase separation. Furthermore, we analyzed the sensitivity of the COSMO-SAC results for ASD to different model configurations and σ-profiles of polymers. In general, while the free volume and dispersion terms exerted a limited effect on predictions, the structures of oligomers used to produce σ-profiles of polymers appeared to be more important, especially in the case of strongly interacting polymers. Explanations for these observations are provided. COSMO-SAC proved to be an efficient method for compatibility prediction and polymer screening in ASD, particularly in terms of its performance-cost ratio, as it relies only on first-principles calculations for the considered molecular species. The open-source nature of both COSMO-SAC and the Python-based tool COSMOPharm, developed in this work for predicting the API-polymer thermodynamic compatibility, invites interested readers to explore and utilize this method for further research or assistance in the design of pharmaceutical formulations.

Keywords: COSMO-SAC; amorphous solid dispersions (ASD); drug−polymer thermodynamic compatibility; miscibility; prediction; quantum mechanics; solubility.

Figure 1

Molecular structures of representative API: (a) naproxen (abbreviated NPX) and (b) indomethacin (IMC); along with trimers of representative polymers: (c) poly(vinylpyrrolidone) (PVP) and (d) poly(vinyl alcohol) (PVA).

Figure 2

Schematic illustration of how oligomers were used to produce the σ-profile of polymers: (a) a trimer of the homopolymer PVA (32,000 g mol^–1, N_units = 726) and (b) a tetramer of the copolymer EUD (212,000 g mol^–1, N_units,A = N_units,B = 1140). The structures were drawn by GaussView and post-processed in Inkscape.

Figure 3

Graphical overview of AAD(w_API) values derived from CS_dsp^FV and CS_dsp^SG.

Figure 4

Solubility curves (solid lines) and AAPS curves (dashed lines) predicted by CS_dsp^FV in comparison with experimental solubility data (symbols).

Figure 5

Δw_API values of the individual data points vs (a) experimental w_API values, (b) predicted w_API values, and (c) T/T_m,API values, all obtained from CS_dsp^FV. Adjacent to the figures, an integrated density chart, complemented by a histogram, visually represents the distribution of Δw_API values along the shared vertical axis. The shaded area denotes a Δw_API range of ±10% in which about 60% of all data points reside.

Figure 6

Phase diagrams over a broader temperature range for the systems predicted by CS_dsp^FV and CS_dsp^SG to exhibit AAPS (dashed lines). Note: All SLE curves (solid lines) obtained from CS_dsp^FV are depicted in Figure 4 and are repeated here for completeness.

Figure 7

Ranking of the polymers based on their compatibility with the API as predicted by CS_dsp^FV and CS_dsp^SG compared to the experiment-based polymer ranking. For each API, the axes depict the experiment-based and predicted rankings, respectively, arranged from the most to the least compatible polymer in sequential order. The compatibility thus increases from right to left and from top to bottom, as illustrated in figure (a). The lines serve as a guide to the eye.

Figure 8

Sensitivity analysis regarding the combinatorial contributions in terms of the calculated API solubility values: (a) CS_dsp^SG vs CS_dsp^FV by means of a parity plot and (b) difference between CS_dsp^SG and CS_dsp^FV as a function of w_API (CS_dsp^FV). The data points were calculated at T_exp.

Figure 9

Ratio of FV to total bulk volume at 298 K of the API and polymers studied in this work, and a selection of ordinary low-M solvents considered in refs (60, 61).

Figure 10

Sensitivity analysis regarding the polymer σ-profiles: total AAD and AD values for (top) PVP and (bottom) PVA calculated with different σ-profiles based on the different molecules. The asterisk (*) denotes the oligomer considered in the reference approach.