Evaluation of Predictive Solubility Models in Pharmaceutical Process Development─an Enabling Technologies Consortium Collaboration
Michael A. Lovette, Jacob Albrecht, Ravi S. Ananthula, Francesco Ricci, Rahul P. Sangodkar, Mansi Shah, Simone Tomasi
Abstract
Solubility is an important property in the design of many unit operations within the development of synthetic pharmaceutical processes. However, the direct measurement of solubility in potential solvents and solvent mixtures is not always ideal or practical. Several models have been developed and gained usage within the pharmaceutical industry to predict the solubility of compounds within organic solvents. As part of an Enabling Technologies Consortium (ETC) collaboration, the need for pre-competitive benchmarking was identified to guide the use of solubility models. In this paper, a set of predictive solubility models are evaluated against an extensive set of measured solubilities (24 solutes over 80 solvent and solvent mixtures). Acetazolamide, caffeine, cimetidine, cinnarizine, glyburide, omeprazole, paracetamol, piroxicam, risperidone, and saccharin were measured as part of a joint effort within ETC. Additional in-house solubility data, spanning a more limited range of solvents, were contributed by ETC collaboration participants. The solubility models vary in their approach from first-principles models (COSMO-RS and COSMO-SAC) to regression-based models (R-UNIFAC and the “Lovette–Amgen” model). The performance of each model was extensively evaluated using different fitness measures. This benchmarking study supports the continued application of solubility modeling as a tool to augment experimentation within chemical process development.