Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.685456
Title: The application of quantitative structure activity relationship models to the method development of countercurrent chromatography
Author: Marsden-Jones, Siân Catherine
ISNI:       0000 0004 5915 0599
Awarding Body: Brunel University London
Current Institution: Brunel University
Date of Award: 2016
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Abstract:
A fundamental challenge for liquid-liquid separation techniques such as countercurrent chromatography (CCC)and centrifugal partition chromatography (CPC), is the swift, efficient selection of the two phase solvent system containing more than two solvents, for the purification of pharmaceuticals and other molecules. A purely computational model that could predict the optimal solvent systems for separation using just molecular structure would be ideal for this task. The experimental value being predicted is the partition coefficient (Kd), which is the concentration of the compound in one phase divided by the concentration in the other. Using this approach, Quantitative Structure Activity Relationship (QSAR) models have been developed to predict the partitioning of compounds in two phase systems from the molecular structure of the compound using molecular descriptors. A Kd value in the range of 0.5 to 2 will give optimal separation. Molecular descriptors are varied, examples include logP values, hydrogen bond donor values and the number of oxygen atoms. This work describes how the QSAR models were developed and tested. A dataset of experimental logKd values for 54 compounds in six different combinations of four solvents (heptane, ethyl acetate, methanol and water) was used to train the QSAR models. A set of 196 possible molecular descriptors was generated for the 54 compounds and a partial least squares regression was used to identify which of these was significant in the relationship between logKd and molecular structure. The resulting models were used to predict the logKd values of four test compounds that had not been used to build the QSAR models. When these predictions were compared to the experimental logKd values, the root mean squared error for four of the six models was less than 0.5 and less than 0.7 for the remaining two. These models were used to successfully separate a range of structurally diverse pharmaceutical compounds by predicting the best solvent systems to carry out the separation on the CCC/CPC using nothing but their molecular structure.
Supervisor: Ignatova, S. ; Garrard, I. Sponsor: AstraZeneca ; EPSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.685456  DOI: Not available
Keywords: Multivariate analysis ; Machine learning ; Liquid-liquid separation
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