Development of a Quantitative Structure–Activity Relationship (QSAR) Model relating Solvent Structure to Ibuprofen Crystal Morphology using 2D and 3D Molecular Descriptors

Abstract

The objective of this thesis is to develop a quantitative structure-activity relationship (QSAR) that relates solvent structure to the morphology of ibuprofen crystals grown within that solvent. Morphology can be quantified by aspect ratio, and ibuprofen aspect ratio data was obtained for crystals grown in 16 different organic solvents. Developing this QSAR requires accurate geometry optimization using empirical force fields to estimate the three-dimensional structure of the solvent molecules. Three different force fields are implemented and their effect on the developed models is analyzed. Next, a combination of 2D and 3D molecular descriptors are calculated using those structures to provide a quantitative representation of the geometry optimized solvent molecules. The descriptor data matrix is then reduced in size for regression into linear models. This stage is executed using Bayesian Information Criterion (BIC) methods and also Principal Component Analysis (PCA) with Principal Component Regression. The final step in the development is to evaluate the predictive capabilities of the resulting models. The QSAR models developed with either technique were all able to fit the training set data and PCA models generally had better predictive capabilities than the models developed using BIC. However, it was also shown that the applicability domain for the models is very small and the predictive capabilities were less than expected. The principal conclusion from this work is that both methods produce models that can fit the training set data, but that additional experimental data should be obtained to produce better predictive models that could be used for crystallization solvent design for pharmaceutical or other industrial applications.