This has been an area of intensive research and development in recent years (e.g. for reviews, see references 1, 2 and 3 below). Now, Scale-up Systems has independently developed a new, user friendly solubility prediction method and calculation utility, denoted 'RU' (for Regressed UNIFAC) below; when applied to predict solubility for a variety of common solutes, the method gives results to date of equivalent and sometimes better accuracy when compared to published results using other techniques.
RU builds on the well established UNIFAC and UNIQUAC group contribution approaches, similar to the 'local UNIFAC' method described in reference 1, and requires a small number of experiments to regress the interaction parameters between the solute (e.g. new chemical entity) and the functional groups of solvents in the liquid phase. These results are then leveraged in the utility to select a shortlist of solvent and / or antisolvent candidates.
For reference and illustration purposes, predictions using RU are compared with those of NRTL-SAC below for the solute Cimetidine. The NRTL-SAC predictions are those published in reference 3.
Figure 1: Results of regression to 6 solvents. Both RU and NRTL-SAC have apparent outliers. Because of the solvents (functional groups) excluded from the regression, RU is not applicable to two of the solvents.
Figure 2: Results of regression of RU to 12 solvents compared to NRTL-SAC regression to 6 solvents. RU no longer has significant outliers as all relevant solvent functional groups are covered by the regression.
Figure 3: Results of regression of both RU and NRTL-SAC to 6 solvents, applied to predictions for ethanol/water mixture. Both RU and NRTL-SAC capture the overall shape, with a maximum solubility around 70% ethanol. NRTL-SAC under-estimates and RU over-estimates the maximum solubility.
The new DynoChem utility containing RU will be published shortly and full details will be available to members of DynoChem Resources. (Our new utility for detailed design of crystallizations in late-phase development has been available since December.)
1. Peter A. Crafts, The role of crystallization and solubility modeling in the design of active pharmaceutical ingredients, Chapter 2 in Chemical product design : toward a perspective through case studies, By Ka M. Ng, Rafiqul Gani, Kim Dam-Johansen (editors), preview at: http://bit.ly/bPQ4D1
2. Peter A. Crafts, Pharmaceutical PSE An Industrial Perspective, PSE 2009, Salvador, Brazil: http://www.bit.ly/92nsER
3. Chau-Chyun Chen & Peter A. Crafts, Correlation and Prediction of Drug Molecule Solubility in Mixed Solvent Systems with the Nonrandom Two-Liquid Segment Activity Coefficient (NRTL-SAC) Model, Ind. Eng. Chem. Res. 2006, 45, 4816-4824
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