Cocrystal Solubility Product Prediction Using an in combo Model and Simulations to Improve Design of Experiments. 
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 0API , S 0cof ), combined with in silico H-bond descriptors.  A regression model was constructed, assuming that the concentration of the uncharged drug (API) can be nearly equated to drug intrinsic solubility (S 0API ) and that the concentration of the uncharged coformer can be estimated from a linear combination of the log of the coformer intrinsic solubility, S 0cof , plus in silico H-bond descriptors (Abraham acidities, α, and basicities, β).  The optimal model found for n:1 CCs (-log10 form) is pK sp = 1.12 n pS 0API + 1.07 pS 0cof + 1.01 + 0.74 αAPI·βcof - 0.61 βAPI; 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.  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.