Prediction of Drug Solubility in Mixture Solvents Based on First Principle Solvation Calculations
Date Issued
2009
Date
2009
Author(s)
Shu, Chun-Chieh
Abstract
Solubility plays an important role in drug research and development especially in the factory with massive manufactures. When there is a cheaper and better solvent for crystalline process, there saves lots of money. In this research, we provide an easy method for solubility prediction in mixture solvents based on COSMO theory. In this study, we have considered the solubility of 33 drug compounds [from 2 atoms (iodine), for the smallest, to 49 atoms (testosterone), for the largest] in (the mixture of) 37 different solvents. We have studied a total of 127 solid-liquid equilibrium systems, including 123 binary solvent mixtures, 3 ternary solvent mixtures, and 1 quaternary solvent mixture. The temperature ranges from 273.15K to 323.15K. [There are 235 systems if we consider a mixture at a different temperature to be a different system.] We compare the model used in this research with Nonrandom Two Liquid Segment Activity Coefficient (NRTL-SAC) model in 57 systems, COnductor-like Screening MOdel Segment Activity (COSMOSAC) model, and Peng-Robinson equation of state with COnductor-like Screening MOdel Segment Activity (PR+COSMOSAC) in 235 systems. The results are calculated based on different model and reported with nature logarithm unit of root mean square error (RMSE). We can obtain the RMSE from NRTL-SAC is 0.58 (77% error) with 57 of 235 systems for the restriction of parameters in molecules; meanwhile, the COSMO-SAC and PR+COSMOSAC are reported the RMSE with 1.60 (395%) and 1.42 (314%) in 235 systems. Also as we apply a correction with pure solvent solubility, the COSMO-SAC+Corr and PR+COSMOSAC+Corr would both reduce the RMSE to 0.59 (80%). It excites us that the method we applied improves the prediction of solubility.
Subjects
solubility
prediction
solvation model
Type
thesis
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