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- Vong, Camille, et al.
(författare)
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Handling Below Limit of Quantification Data in Optimal Trial Design
- 2014
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - 1567-567X .- 1573-8744.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Methods that perform well in handling limit of quantification (LOQ) data exist in estimation of parameters for non-linear mixed effect models but are not well developed in experimental design. The aim of this work was to evaluate existing methods and to explore new methods of handling LOQs in Optimal Design (OD). Seven different methods were implemented in PopED 2.13: D1 (Ignore LOQ), D2 (Non-informative Fisher information matrix (FIM) for median response below LOQ), new D3 (Non-informative FOCE linearized FIM for individual response below LOQ), D4 (Addition of a homoscedastic variance), new D5 (Simulation & Rescaling), new D6 (Integration & Rescaling) and new D7 (joint likelihood using the Laplace approximation). Predictive performance of D1-D7 was first assessed and sample time optimization was performed for a number of different LOQ levels. Resulting designs were evaluated for bias and imprecision, robustness and predictability from multiple stochastic simulations and estimations (SSE) in NONMEM using the M3 method. Evaluated determinants of the FIM for all methods, except D1 and D4, were in good agreement with SSE-derived covariance. In optimization, D6 provided the most accurate and precise parameter estimates and the designs with the best predictive performance under the M3 method. Methods D1 and D2 resulted in the least robust designs for estimation. Method D4 was shown to be insensitive to LOQ levels. For the scenarios investigated, method D6 showed the best compromise in terms of speed and accuracy. The use of OD methods anticipating LOQ data in planned designs allows better parameter estimation.
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- Vong, Camille
(författare)
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Model-Based Optimization of Clinical Trial Designs
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- General attrition rates in drug development pipeline have been recognized as a necessity to shift gears towards new methodologies that allow earlier and correct decisions, and the optimal use of all information accrued throughout the process. The quantitative science of pharmacometrics using pharmacokinetic-pharmacodynamic models was identified as one of the strategies core to this renaissance. Coupled with Optimal Design (OD), they constitute together an attractive toolkit to usher more rapidly and successfully new agents to marketing approval.The general aim of this thesis was to investigate how the use of novel pharmacometric methodologies can improve the design and analysis of clinical trials within drug development. The implementation of a Monte-Carlo Mapped power method permitted to rapidly generate multiple hypotheses and to adequately compute the corresponding sample size within 1% of the time usually necessary in more traditional model-based power assessment. Allowing statistical inference across all data available and the integration of mechanistic interpretation of the models, the performance of this new methodology in proof-of-concept and dose-finding trials highlighted the possibility to reduce drastically the number of healthy volunteers and patients exposed to experimental drugs. This thesis furthermore addressed the benefits of OD in planning trials with bio analytical limits and toxicity constraints, through the development of novel optimality criteria that foremost pinpoint information and safety aspects. The use of these methodologies showed better estimation properties and robustness for the ensuing data analysis and reduced the number of patients exposed to severe toxicity by 7-fold. Finally, predictive tools for maximum tolerated dose selection in Phase I oncology trials were explored for a combination therapy characterized by main dose-limiting hematological toxicity. In this example, Bayesian and model-based approaches provided the incentive to a paradigm change away from the traditional rule-based “3+3” design algorithm.Throughout this thesis several examples have shown the possibility of streamlining clinical trials with more model-based design and analysis supports. Ultimately, efficient use of the data can elevate the probability of a successful trial and increase paramount ethical conduct.
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- Vong, Camille, et al.
(författare)
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Optimal Design Applied to Hematological Toxicity-Induced Anticancer Treatment
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Anticancer regimens are often a delicate compromise between dose intensity and acceptable toxicity, for example neutropenia. The aim of the present study was to develop a theoretical framework using optimal design theory to select the optimal dosing and sampling based on several criteria derived from the predicted neutrophil counts. A semi-physiological PK/PD model for docetaxel's hematological toxicity was used to determine the population typical nadir value of absolute neutrophil count and the time of occurrence of the nadir. An optimization on both time and size of dosing was performed in PopED v.2.11.The optimizations maximized the expected nadir value given a set of clinical criteria using a penalty function. Sampling schedules were also optimized to allow for model identification of the nadir value using D- , C-, MAP-optimal criteria and by using a Sample Reuse Simulation approach. Optimized dosing schedules were found to expose fewer patients to grade 4 neutropenia and total dose could be further increased with recommended dosing intervals. Predicted population nadir was more precisely estimated with a D-optimal design while sampling a true nadir value was more frequently done with a design derived from a Sample Reuse Simulation method. Optimal design methodology can be applied for toxicity monitoring within clinical constraints in oncology studies.
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