Stochastic control methods to individualise drug therapy by incorporating pharmacokinetic, pharmacodynamic and adverse event data

Francis, Ben ORCID: 0000-0002-2130-5976
Stochastic control methods to individualise drug therapy by incorporating pharmacokinetic, pharmacodynamic and adverse event data. Doctor of Philosophy thesis, University of Liverpool.

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There are a number of methods available to clinicians for determining an individualised dosage regimen for a patient. However, often these methods are non-adaptive to the patient’s requirements and do not allow for changing clinical targets throughout the course of therapy. The drug dose algorithm constructed in this thesis, using stochastic control methods, harnesses information on the variability of the patient’s response to the drug thus ensuring the algorithm is adapting to the needs of the patient. Novel research is undertaken to include process noise in the Pharmacokinetic/Pharmacodynamic (PK/PD) response prediction to better simulate the patient response to the dose by allowing values sampled from the individual PK/PD parameter distributions to vary over time. The Kalman filter is then adapted to use these predictions alongside measurements, feeding information back into the algorithm in order to better ascertain the current PK/PD response of the patient. From this a dosage regimen is estimated to induce desired future PK/PD response via an appropriately formulated cost function. Further novel work explores different formulations of this cost function by considering probabilities from a Markov model. In applied examples, previous methodology is adapted to allow control of patients that have missing covariate information to be appropriately dosed in warfarin therapy. Then using the introduced methodology in the thesis, the drug dose algorithm is shown to be adaptive to patient needs for imatinib and simvastatin therapy. The differences, between standard dosing and estimated dosage regimens using the methodologies developed, are wide ranging as some patients require no dose alterations whereas other required a substantial change in dosing to meet the PK/PD targets. The outdated paradigm of ‘one size fits all’ dosing is subject to debate and the research in this thesis adds to the evidence and also provides an algorithm for a better approach to the challenge of individualising drug therapy to treat the patient more effectively. The drug dose algorithm developed is applicable to many different drug therapy scenarios due to the enhancements made to the formulation of the cost functions. With this in mind, application of the drug dose algorithm in a wide range of clinical dosing decisions is possible.

Item Type: Thesis (Doctor of Philosophy)
Additional Information: Date: 2013-06 (completed)
Uncontrolled Keywords: Individualised Medicine, Stochastic Control, Pharmacokinetics, Pharmacodynamics, Adverse Event, Markov Model
Divisions: Faculty of Health and Life Sciences
Depositing User: Symplectic Admin
Date Deposited: 08 Aug 2013 10:10
Last Modified: 17 Dec 2022 01:28
DOI: 10.17638/00011855
  • Lane, Steven
  • Jorgensen, Andrea