AbstractA polymorphic substance is capable of forming a number of different crystalline phases that are referred to as its polymorphs. The critical process that determines the outcome of a crystallization process in a polymorphic system is thought to be the nucleation state, which is the self-assembled stage just prior to the formation of crystals with long-range order. While nucleation is well known to be influenced by macroscopically measurable parameters such as temperature, supersaturation and solvent choice our understanding of the underlying molecular self-assembly processes is very limited. The research described in this thesis explores a new approach to extending our knowledge in this area by the use of a combination of medium throughput crystallisation experiments together with the computation of a range of molecular and solute/solvent descriptors of the system under study.The main objective of the work was to develop a protocol for relating experimental and computational data via artificial neural network (ANN) analysis, to identify significant links between experimental polymorphic outcomes and molecular properties. By creating a model that can predict the polymorphic form in a given experiment it is anticipated that our understanding of links between nucleation and crystallisation will be enhanced through the determining the pivotal properties of a molecule that cause it to form one polymorph over another. The ANN method was developed in the context of the carbamazepine system, applying several statistical techniques to the results of 88 crystallisation experiments, featuring 13 solvents, 3 evaporation rates and 4 temperatures. The results show that this approach allows the formulation of further research hypotheses through examination of the physical meaning of the set of descriptors identified by the ANN approach. Crucially, principal component analysis (PCA) was found to be able to efficiently narrow down large sets of computationally derived descriptors to a manageable set by removing redundancy through strongly cross-correlated parameters. The best ANN model generated in this research was capable of predicting the major polymorphic form in 89 % of cross-validation experiments.The optimised set of descriptors included both solute and solvent properties, which predominantly described the intermolecular interactions in solution. The physical meanings of the descriptors and their impact on the molecular processes during nucleation has been considered and their cross correlation has been examined. Initial results from further experimentation with the tolbutamide and ROY systems indicate that the methodology is also transferable to other polymorphic systems.
|Date of Award||1 Aug 2011|
|Supervisor||Sven Schroeder (Supervisor) & Roger Davey (Supervisor)|
- Artificial Neural Networks
- Molecular Descriptor