Data mining of solubility parameters for computational prediction of drug-excipient miscibility.

Abstract Computational data mining is of interest in the pharmaceutical arena for the analysis of massive amounts of data and to assist in the management and utilization of the data. In this study, a data mining approach was used to predict the miscibility of a drug and several excipients, using Hansen solubility parameters (HSPs) as the data set. The K-means clustering algorithm was applied to predict the miscibility of indomethacin with a set of more than 30 compounds based on their partial solubility parameters [dispersion forces (δd), polar forces (δp) and hydrogen bonding (δh)]. The miscibility of the compounds was determined experimentally, using differential scanning calorimetry (DSC), in a separate study. The results of the K-means algorithm and DSC were compared to evaluate the K-means clustering prediction performance using the HSPs three-dimensional parameters, the two-dimensional parameters such as volume-dependent solubility (δv) and hydrogen bonding (δh) and selected single (one-dimensional) parameters. Using HSPs, the prediction of miscibility by the K-means algorithm correlated well with the DSC results, with an overall accuracy of 94%. The prediction accuracy was the same (94%) when the two-dimensional parameters or the hydrogen-bonding (one-dimensional) parameter were used. The hydrogen-bonding parameter was thus a determining factor in predicting miscibility in such set of compounds, whereas the dispersive and polar parameters had only a weak correlation. The results show that data mining approach is a valuable tool for predicting drug-excipient miscibility because it is easy to use, is time and cost-effective, and is material sparing.

Hansen solubility parameter as a tool to predict cocrystal formation.

The objective of this study was to investigate whether the miscibility of a drug and coformer, as predicted by Hansen solubility parameters (HSPs), can indicate cocrystal formation and guide cocrystal screening. It was also our aim to evaluate various HSPs-based approaches in miscibility prediction. HSPs for indomethacin (the model drug) and over thirty coformers were calculated according to the group contribution method. Differences in the HSPs between indomethacin and each coformer were then calculated using three established approaches, and the miscibility was predicted. Subsequently, differential scanning calorimetry was used to investigate the experimental miscibility and cocrystal formation. The formation of cocrystals was also verified using liquid-assisted grinding. All except one of the drug-coformers that were predicted to be miscible were confirmed experimentally as miscible. All tested theoretical approaches were in agreement in predicting miscibility. All systems that formed cocrystals were miscible. Remarkably, two new cocrystals of indomethacin were discovered in this study. Though it may be necessary to test this approach in a wide range of different coformer and drug compound types for accurate generalizations, the trends with tested systems were clear and suggest that the drug and coformer should be miscible for cocrystal formation. Thus, predicting the miscibility of cocrystal components using solubility parameters can guide the selection of potential coformers prior to exhaustive cocrystal screening work.