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Optimal Design Applied to Hematological Toxicity-Induced Anticancer Treatment
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group)
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

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.

Keyword [en]
Optimal design, Neutropenia, Nonlinear mixed-effects models, Docetaxel, Myelosuppression
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-233442OAI: oai:DiVA.org:uu-233442DiVA: diva2:752559
Available from: 2014-10-05 Created: 2014-10-05 Last updated: 2015-02-12
In thesis
1. Model-Based Optimization of Clinical Trial Designs
Open this publication in new window or tab >>Model-Based Optimization of Clinical Trial Designs
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 124 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 192
Keyword
nonlinear mixed-effects models, pharmacometrics, likelihood ratio test, NONMEM, power, sample size, study design, proof-of-concept, dose-finding, population optimal design, LOQ, BQL data, neutropenia, docetaxel, myelosuppression, thrombocytopenia, MTD, Bayesian methods, 3+3 algorithm, dose escalation study
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-233445 (URN)978-91-554-9063-8 (ISBN)
Public defence
2014-11-21, B41, BMC, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2014-10-31 Created: 2014-10-05 Last updated: 2015-01-23

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Vong, CamilleNyberg, JoakimKarlsson, Mats O.Hooker, Andrew C.

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