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dc.contributor.advisorNeagu, Daniel
dc.contributor.advisorTrundle, Paul R.
dc.contributor.advisorVessey, J.D.
dc.contributor.authorMistry, Pritesh*
dc.date.accessioned2018-01-10T16:49:41Z
dc.date.available2018-01-10T16:49:41Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10454/14440
dc.description.abstractThis multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicity reduction is provided. Examples of studies reported in the literature that have used these technologies to reduce drug toxicity are also reported. The thesis also provides an overview of the computational approaches currently employed in the field of in silico predictive toxicology. This overview focuses on the machine learning approaches used to build predictive QSAR classification models, with examples discovered from the literature provided. Two methodologies have been developed as part of the main work of this thesis. The first is focused on use of directed bipartite graphs and Venn diagrams for the visualisation and extraction of drug-vehicle relationships from large un-curated datasets which show changes in the patterns of toxicity. These relationships can be rapidly extracted and visualised using the methodology proposed in chapter 4. The second methodology proposed, involves mining large datasets for the extraction of drug-vehicle toxicity data. The methodology uses an area-under-the-curve principle to make pairwise comparisons of vehicles which are classified according to the toxicity protection they offer, from which predictive classification models based on random forests and decisions trees are built. The results of this methodology are reported in chapter 6.en_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectPredictive toxicology; Drug formulation; Random forest; Decision trees; Big data; Data mining; Area-under-curve (AUC)en_US
dc.titleA Knowledge Based Approach of Toxicity Prediction for Drug Formulation. Modelling Drug Vehicle Relationships Using Soft Computing Techniquesen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentFaculty of Engineering and Informaticsen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2015
refterms.dateFOA2018-07-29T03:38:59Z


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