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Towards systems pharmacology models of druggable targets and disease mechanisms

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cam.restrictionthesis_access_open
cam.supervisorLe Novère, Nicolas
cam.supervisor.orcidLe Novère, Nicolas [0000-0002-6309-7327]
cam.thesis.fundingtrue
dc.contributor.authorKnight-Schrijver, Vincent
dc.contributor.orcidKnight-Schrijver, Vincent [0000-0002-7916-3827]
dc.date.accessioned2019-02-20T15:33:24Z
dc.date.available2019-02-20T15:33:24Z
dc.date.issued2019-07-20
dc.date.submitted2018-03-31
dc.date.updated2019-02-20T05:48:22Z
dc.description.abstractThe development of essential medicines is being slowed by a lack of efficiency in drug development as ninety per cent of drugs fail at some stage during clinical evaluation. This attrition in drug development is seen not because of a reduction in pharmaceutical research expenditure nor is it caused by a declining understanding of biology, if anything, these are both increasing. Instead, drugs are failing because we are unable to effectively predict how they will work before they are given to patients. This is due to limitations of the current methods used to evaluate a drug’s toxicity and efficacy prior to its development. Quite simply, these methods do not account for the full complexity of biology in humans. Systems pharmacology models are a likely candidate for increasing the efficiency of drug discovery as they seek to comprehensively model the fundamental biology of disease mechanisms in a quantit- ative manner. They are computational models, designed and hailed as a strategy for making well-informed and cost effective decisions on drug viability and target druggability and therefore attempt to reduce this time-consuming and costly attrition. Using text mining and text classification I present a growing landscape of systems pharmacology models in literature growing from humble roots because of step-wise increases in our understanding of biology. Furthermore, I develop a case for the capability of systems pharmacology models in making predictions by constructing a model of interleukin-6 signalling for rheumatoid arthritis. This model shows that druggable target selection is not necessarily an intuitive task as it results in an emergent but unanswered hypothesis for safety concerns in a monoclonal antibody. Finally, I show that predictive classification models can also be used to explore gene expression data in a novel work flow by attempting to predict patient response classes to an influenza vaccine.
dc.description.sponsorshipFunded by the BBSRC and GlaxoSmithKline as part of an industrial CASE studentship.
dc.identifier.doi10.17863/CAM.36977
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/289731
dc.language.isoen
dc.publisher.collegeSt Edmund's College
dc.publisher.departmentBabraham Institute
dc.publisher.institutionUniversity of Cambridge
dc.rightsAttribution-ShareAlike 4.0 International (CC BY-SA 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/
dc.subjectSystems Pharmacology
dc.subjectText Mining
dc.subjectQuantitative Systems Pharmacology
dc.subjectDrug Discovery
dc.subjectComputational Modelling
dc.subjectInterleukin-6
dc.subjectRheumatoid Arthritis
dc.subjectDrug Development
dc.subjectBioinformatics
dc.subjectClassification
dc.subjectSystems Biology
dc.titleTowards systems pharmacology models of druggable targets and disease mechanisms
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.type.qualificationtitlePhD in Biological Science

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