Distinguishing Cocrystals from Simple Eutectic Mixtures: Phenolic Acids as Potential Pharmaceutical Coformers

The multiparameter model comprising 1D and 2D QSPR/QSAR descriptors was proposed and validated for phenolic acid binary systems. This approach is based on the optimization of regression coefficients for maximization of the percentage of true positives in the pool of systems comprising either simple binary eutectics or cocrystals. The training set consisted of 58 eutectics and 168 cocrystals. The solid dispersions collection used for model generation comprised literature data enriched with our new experimental results. From all 1445 descriptors computable in PaDEL, only 13 orthogonal descriptors with the highest predicting power were taken into account. The analysis revealed the importance of the parameters characterizing atom types (naaN, SHsOH, SsssN, nHeteroRing, maxHBint6, C1SP2), autocorrelation functions (ATSC1i, AATSC1v, MATS8m, GATS1i), and also other molecule structure measures (WTPT-5, MLFER_A, MDEN-22). The proposed approach is very simple and requires only information about the structure encoded in canonical SMILES string. The inversion of the problem of cocrystal screening and focusing on the homogeneous group of coformers for cocrystallization with a variety of drugs rather than seeking coformers for a particular active pharmaceutical ingredient proved to be very efficient. This led to very valuable clues for selection of pairs for cocrystallization with a probability of about 80%.