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 0API , S 0cof ), 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 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).
RESULTS: 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.
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.