Fritsch, Achim: Pharmacometric approaches for linking pharmacokinetic and pharmacodynamic models of sunitinib and pazopanib with clinical outcome. - Bonn, 2019. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
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author = {{Achim Fritsch}},
title = {Pharmacometric approaches for linking pharmacokinetic and pharmacodynamic models of sunitinib and pazopanib with clinical outcome},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2019,
month = jul,

note = {Aim of this thesis was to build a modeling framework for patients with metastatic renal cell carcinoma (mRCC) treated with two common first-line therapies, sunitinib and pazopanib. Therefore, as part of the European-wide EuroTARGET project, which aimed at identifying predictive biomarkers in mRCC patients, a pharmacokinetic (PK) phase IV study was conducted in Germany and the Netherlands.
Based on a center-specific schedule up to 12 blood samples per patient were collected in conjunction with blood pressure measurements. Plasma concentrations of the respective study drug and the soluble VEGF receptors 2 and 3 were quantified for each time-point using previously established analytical methods.
Published pharmacokinetic and pharmacodynamics (PK/PD) models were used as basis for this work. For both sunitinib and pazopanib, reliable individual PK parameters could be obtained and successfully linked to PD models for the potential biomarkers. Covariate analysis of the PK/PD models revealed two single nucleotide polymorphisms with influence on the intrinsic activity of sunitinib on sVEGFR-2 plasma concentrations (VEGFR-3 rs6877011 and ABCB1 rs2032582).
The final PK/PD models were then used to establish a link to clinical outcome parameters including progression-free survival and the two most commonly observed adverse events in the mRCC population. In a model-based time-to-event analysis, a high sVEGFR-2 baseline plasma concentration was associated with a worse prognosis for sunitinib patients. In a combined analysis of sunitinib and pazopanib the absolute sVEGFR-2 plasma concentration over time was a potentially predictive factor. Hence, this model allows the prediction of PFS based on the measured sVEGFR-2 plasma concentration.
Myelosuppression and fatigue as treatment-associated adverse events were analyzed separately using first-order continuous Markov models. Here, active sunitinib plasma concentration proved to be influential as a higher exposition did result in prolonged time frames of myelosuppression. However, a similar effect was not observed for fatigue.
The modeling framework presented in this thesis provides a better understanding of the relationship between the exposure, pharmacological response, and clinical outcome of antiangiogenic drugs and is therefore an important step towards finding optimal dosing schedules and identifying potential predictive biomarkers for both drugs.},

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