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

PURPOSE

To predict the aqueous solubility product (K ) and the solubility enhancement of cocrystals (CCs), using an approach based on measured drug and coformer intrinsic solubility (S , S ), 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 ) and that the concentration of the uncharged coformer can be estimated from a linear combination of the log of the coformer intrinsic solubility, S , plus in silico H-bond descriptors (Abraham acidities, α, and basicities, β).

RESULTS

The optimal model found for n:1 CCs (-log form) is pK  = 1.12 n pS  + 1.07 pS  + 1.01 + 0.74 α·β - 0.61 β; r  = 0.95, SD = 0.62, N = 38. In illustrative CC systems with unknown K , predicted K 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 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.