From Quantum Chemistry to Prediction of Drug Solubility in Glycerides.

Abstract

Lipid-based delivery is a key technology for dealing with the challenges of poorly soluble drugs. Therefore, prediction of drug solubility in lipid-based excipients and their mixtures is an important research goal in computational pharmaceutics. This study is based on the conductor-like screening model for real solvents (COSMO-RS), which combines quantum chemical surface calculations with fluid phase thermodynamics. An experimental dataset of 51 drugs was collected with measured thermochemical data and solubility results in medium and long-chain tri- and monoglycerides. For the theoretical model, the excipients were represented by a single structure in a simplified glyceride approach. COSMO-RS was able to capture the solubility trends in the different excipients. Only a few compounds showed rather poor predictions and these outliers were often comparatively larger molecules. The present study also evaluated the effects of individual fatty acid hydrolysis on glycerides' solubilization capability. In conclusion, the application of COSMO-RS modeling for drug solubility prediction in lipid-based formulations is highly promising, in particular for rank-ordering excipients in an early development phase. In future, this in silico approach may also address solubilization effects of minor components in excipients or in excipient mixtures, which is interesting from a product quality perspective so that it can further advance this field of molecular pharmaceutics.