Search

In contemporary cancer care, several issues are garnering increasing attention. First, significant inter-individual variability among patients challenges the effectiveness of a uniform dosing approach. Second, the escalating costs of treatments necessitate careful consideration when selecting doses and other clinical modalities, including biomarkers, while balancing economic constraints. The objective of this thesis was to evaluate techniques for tailoring doses and guiding clinical decisions for cancer patients through the development and implementation of various models, with the aim of improving treatment outcomes in terms of both efficacy and safety. Through a model-based framework integrating sunitinib pharmacokinetics (PK), adverse events, biomarkers, tumor dynamics and their correlation with overall survival, different treatment schedules and biomarkers for dose individualization were explored. Based on the proposed threshold values, neutrophil count (ANC) and the biomarker sVEGFR-3 were identified as offering the best balance between safety and efficacy for sunitinib in gastro-intestinal stromal tumors (GIST) and could thus serve as viable guides for dose individualization in clinical practice. Given its routine measurement, dose adjustments guided by ANC may be preferable in clinical settings. The feasibility of utilizing diastolic blood pressure (dBP) for personalized dose optimization of tyrosine-kinase inhibitors in clinical settings is constrained due to its reliance on repeated measurements taken at consistent intervals. For axitinib and sunitinib, model-based predictions using multiple clinical measurements were more accurate than single sample measurements. For drugs with high unexplained inter-individual variability (IIV), low residual variability (RUV), and low inter-occasional variability (IOV), therapeutic drug monitoring (TDM) provided a more accurate measure of exposure. Conversely, for drugs with low IIV and high RUV and IOV, pharmacogenetic

Blood pressure measurements form a critical component of adverse event monitoring for tyrosine kinase inhibitors, but might also serve as a biomarker for dose titrations. This study explored the impact of various sources of within-individual variation on blood pressure readings to improve measurement practices and evaluated the utility for individual- and population-level dose selection. A pharmacokinetic-pharmacodynamic modeling framework was created to describe circadian blood pressure changes, inter- and intra-day variability, changes from dipper to non-dipper profiles, and the relationship between drug exposure and blood pressure changes over time. The framework was used to quantitatively evaluate the influence of physiological and pharmacological aspects on blood pressure measurements, as well as to compare measurement techniques, including office-based, home-based, and ambulatory 24-h blood pressure readings. Circadian changes, as well as random intra-day and inter-day variability, were found to be the largest sources of within-individual variation in blood pressure. Office-based and ambulatory 24-h measurements gave rise to potential bias (>5 mmHg), which was mitigated by model-based estimations. Our findings suggest that 5-8 consecutive, home-based, measurements taken at a consistent time around noon, or alternatively within a limited time frame (e.g., 8.00 a.m. to 12.00 p.m. or 12.00 p.m. to 5.00 p.m.), will give rise to the most consistent blood pressure estimates. Blood pressure measurements likely do not represent a sufficiently accurate method for individual-level dose selection, but may be valuable for population-level dose identification. A user-friendly tool has been made available to allow for interactive blood pressure simulations and estimations for the investigated scenarios.

Purpose: Various biomarkers have been proposed for sunitinib therapy in gastrointestinal stromal tumor (GIST). However, the lack of "real-life" comparative studies hampers the selection of the most appropriate one. We, therefore, set up a pharmacometric simulation framework to compare each proposed biomarker. Experimental Design: Models describing relations between sunitinib exposure, adverse events (hand-foot syndrome, fatigue, hypertension, and neutropenia), soluble VEGFR (sVEGFR)-3, and overall survival (OS) were connected to evaluate the differences in survival and adverse events under different dosing algorithms. Various fixed dosing regimens [4/2 (weeks on/weeks off) or 2/1 (50 mg), and continuous daily dosing (37.5 mg)] and individualization approaches [concentration-adjusted dosing (CAD), toxicity-adjusted dosing (TAD), and sVEGFR-3-adjusted dosing (VAD)] were explored following earlier suggested blood sampling schedules and dose-reduction criteria. Model-based forecasts of biomarker changes were evaluated for predictive accuracy and the advantage of a model-based dosing algorithm was evaluated for clinical implementation. Results: The continuous daily dosing regimen was predicted to result in the longest survival. TAD (24.5 months) and VAD (25.5 months) increased median OS as compared with a fixed dose schedule (19.9 and 21.5 months, respectively) and CAD (19.7 and 21.3 months, respectively), without markedly raising the risk of intolerable toxicities. Changes in neutrophil count and sVEGFR-3 were accurately forecasted in the majority of subjects (> 65%), based on biweekly blood sampling. Conclusions: Dose adjustments based on the pharmacodynamic biomarkers neutrophil count and sVEGFR-3 can increase OS while retaining drug safety. Future efforts could explore the possibility of incorporating a model-based dose approach in clinical practice to increase dosing accuracy for these biomarkers.

Background: Cost-effectiveness analyses (CEAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study evaluates a pharmacometric modeling approach against traditional pharmacoeconomic models for CEAs of sunitinib in gastrointestinal stromal tumors (GIST). Methods: A two-arm trial comparing sunitinib 37.5 mg daily to no treatment was simulated using a pharmacometric model framework. Four existing pharmacoeconomic models (time-to-event (TTE) and Markov models) were applied to the survival data and linked to logistic regression models describing the toxicity data (neutropenia, thrombocytopenia, hypertension, fatigue and hand-foot syndrome (HFS)) to create pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario. Results: The pharmacometric model predicted sunitinib treatment costs an additional 147,065 euro/QALY compared to no treatment, with deviations -23.2% (discrete Markov), -17.8%% (continuous Markov), +3.8% (TTE Weibull) and +27.8% (TTE exponential) from the pharmacoeconomic model frameworks. The pharmacometric models captured the change in toxicity over treatment cycles (e.g. increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic models (e.g. stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic models excessively forecasted the percentage of patients encountering sub-therapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16). Conclusions: Model structure significantly influences CEA predictions. The pharmacometric model more closely represented real-world toxicity trends and drug exposure changes. The relevance of thes

Background: Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability (IOV) and residual unexplained variability (RUV)). Methods: Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose-individualization. The models were used to simulate relevant drug exposures (Ctrough or AUC) under varying degrees of IIV, IOV and RUV. Results: Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure predictions is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%) and 5-FU (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred. Conclusions: For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be use