Computational Screening of Drug Solvates.

ABSTRACT

PURPOSE: Solvates are mainly undesired by-products during the pharmaceutical development of new drugs. In addition, solvate formation may also distort solubility measurements. The presented study introduces a simple computational approach that allows for the identification of drug solvent pairs which most likely form crystalline solid phases. METHODS: The mixing enthalpy as a measure for drug-solvent complementarity is obtained by computational liquid phase thermodynamics (COSMO-RS theory). In addition a few other simple descriptors were taking into account describing the shape and topology of the drug and the solvent. Using an extensive dataset of drug solvent pairs a simple and statistically robust model is developed which allows for a rough assessment of a solvent's ability to form a solvate. RESULTS: Similar to the related issue of cocrystal screening, the mixing (or excess) enthalpy of the subcooled liquid mixture of the drug-solvent pair proves to be an important quantity controlling solvate formation. Due to the fact that many solvates form inclusion compounds, the solvent shape is another important factor influencing solvate formation. Solvates forming channel-like voids in the solid state are predicted less well. CONCLUSION: The approach ranks any drug-solvent pair that forms a solvate before any non-solvate by a probability of about 81% (AUC = 0.81), giving a significant advantage over any trial and error approach. Hence it can help to identify suitable solvent candidates early in the drug development process.