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Title: Optimisation of study design in the pharmacokinetics of anticancer drugs
Author: Carmichael, S. J.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2001
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Abstract:
"Optimal sampling strategies" are based upon the concept of 'information-rich' times within a concentration-time profile. In this thesis, the selection of optimal sampling times was based on sensitivity analysis and applied to the one and two-compartment PK models. Simulation studies were used to show that parameter estimates obtained using an optimal design method with a reduced number of samples were as good as, if not better than, those obtained from PK studies in which the sampling times were selected empirically. In addition, the effect of adding sampling windows around the "optimal" times offered improved estimation of the inter-subject variability parameters, when compared to designs with fixed sampling times. This result has particular relevance in a clinical setting where a sample may not be collected at the stipulated time, but is still useful in the analysis if the "actual" sampling time is recorded accurately. Further simulations were based on published sampling designs for the anticancer drug carboplatin, and these were used for comparison with the results when an "optimal design" was used. Finally, a population analysis was carried out on data from a phase I clinical trial of the broad-spectrum neuropeptide antagonist, Antagonist G. The parameter values were used to design on "optimal" sampling strategy. As the sample times of the optimal strategy were different to those used in the clinical study, further simulations were used to compare the design. Using sensitivity analysis to design sampling strategies for population PK studies allowed the selection of a minimum number of sampling times, but still resulted in accurate estimation of the parameters of the one and two-compartment PK models. These sampling times provide a basis for PK study design, around which further samples could be added to improve the identification of the model and also the estimation of parameters and their inter and intra-subject of variability.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.642604  DOI: Not available
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