Cocrystal Solubility Product Prediction Using an in combo Model and Simulations to Improve Design of Experiments

Purpose To predict the aqueous solubility product ( K sp ) and the solubility enhancement of cocrystals (CCs), using an approach based on measured drug and coformer intrinsic solubility ( S 0 API , S 0 cof ), combined with in silico H-bond descriptors. Method A regression model was constructed, assuming that the concentration of the uncharged drug (API) can be nearly equated to drug intrinsic solubility ( S 0 API ) and that the concentration of the uncharged coformer can be estimated from a linear combination of the log of the coformer intrinsic solubility, S 0 cof , plus in silico H-bond descriptors (Abraham acidities, α, and basicities, β). Results The optimal model found for n:1 CCs (−log 10 form) is pK sp = 1.12 n pS 0 API + 1.07 pS 0 cof + 1.01 + 0.74 α API ·β cof − 0.61 β API ; r 2 = 0.95, SD = 0.62, N = 38. In illustrative CC systems with unknown K sp , predicted K sp was used in simulation of speciation- pH profiles. The extent and pH dependence of solubility enhancement due to CC formation were examined. Suggestions to improve assay design were made. Conclusion The predicted CC K sp can be used to simulate pH -dependent solution characteristics of saturated systems containing CCs, with the aim of ranking the selection of coformers, and of optimizing the design of experiments.