Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

With the aim of solubility estimation in water, polyethylene glycol 400 (PEG) and their binary mixtures,

quantitative structure–property relationships (QSPRs) were investigated to relate the solubility

of a large number of compounds to the descriptors of the molecular structures. The relationships were

quantified using linear regression analysis (with descriptors selected by stepwise regression) and formal

inference-based recursive modeling (FIRM). The models were compared in terms of the solubility prediction

accuracy for the validation set. The resulting regression and FIRM models employed a diverse set of

molecular descriptors explaining crystal lattice energy, molecular size, and solute–solvent interactions.

Significance of molecular shape in compound’s solubilitywasevident from several shape descriptors being

selected by FIRM and stepwise regression analysis. Some of these influential structural features, e.g. connectivity

indexes and Balaban topological index, were found to be related to the crystal lattice energy. The

results showed that regression models outperformed most FIRM models and produced higher prediction

accuracy. However, the most accurate estimation was achieved by the use of a combination of FIRM and

regression models. The results also showed that the use of melting point in regression models improves

the estimation accuracy especially for solubility in higher concentrations of PEG. Aqueous or PEG/water

solubilities can be estimated by these models with root mean square error of below 0.70.