Many thousands of pounds are spent every year by pharmaceutical companies on understanding the mechanisms and kinetics of chemical reactions involved in drug discovery and production. NMR spectroscopy is often at the core of these studies as it is a powerful, non-destructive method for structure elucidation. As such investigations can be time-consuming and cost-inefficient, AstraZeneca, the project sponsor, is interested in more efficient methods for studying the kinetics of pharmaceutical reactions. In this work a number of different techniques have been devised, studied, and implemented to study the kinetics of chemical reactions by time-resolved NMR spectroscopy, in which every species in a reaction can be monitored simultaneously. These novel techniques allow the study of reactions which are difficult or impossible to study by conventional NMR methods (such as heterogeneous reactions), or which are complicated by having overlapping signals.It is possible to monitor the kinetics of a reaction very simply by acquiring a series of 1H spectra, and obtaining the integrals of the signals by least squares fitting. This technique has been used for kinetic studies of static and on-flow reactions. In the static systems the reaction mixture was placed in the normal NMR tube in the magnet, while in the flow system the reaction mixture was placed outside of the magnet, and the solution flowed through an NMR tube placed in the magnet. The novel flow system designed, constructed and tested here has been used for kinetic studies of illustrative homogeneous and heterogeneous reactions, and is suitable for use in a wide range of NMR instrumentation. Kinetic studies have also been carried out by acquiring a series of DOSY datasets, analysing the results using the multi-way method PARAFAC (PARAllel FACtor analysis). A series of DOSY datasets contains multivariate information on spectrum, time evolution and diffusion. Without providing any predetermined model, the data can be decomposed by PARAFAC to yield the spectrum, kinetics, and diffusion profiles for each of the components. It has also been shown that PARAFAC is remarkably robust to low signal-to-noise ratio data, significantly below the level at which conventional methods would fail.
|Date of Award||1 Aug 2010|
- The University of Manchester
|Supervisor||Gareth Morris (Supervisor)|