[Thesis]. Manchester, UK: The University of Manchester; 2010.
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.