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In Silico Comparison of Maximum Tolerated Dose Determination in a Phase I Dose-Finding Framework: Application to Hematological Toxicity for a Histone Deacetylase Inhibitor Abexinostat, Co-Administered with Free or Liposomal Doxorubicin in Solid Tumors
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group)
Institut de Recherches Internationales Servier, Suresnes, France. (Clinical Pharmacokinetic and Pharmacometrics Department)
Université Paul-Sabatier and Institut Claudius Regaud, Toulouse, France. (EA4553)
Institut de Recherches Internationales Servier, Suresnes, France. (Oncology business Unit)
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Determination of a maximum tolerated dose (MTD) continues to mainly rely on dose escalation studies using the algorithm-based 3+3 design although it has repeatedly been shown to result in a biased and imprecise MTD. Alternative Bayesian methods, i.e. the Continuous Reassessment Method (CRM), the Escalation with Overdose Control (EWOC), the Bayesian Logistic Regression Model (BLRM), and the modified Toxicity Probability Intervals (mTPI) are increasingly gaining interest for Phase I studies. Here we propose to develop an in silico Clinical Trial Simulation (CTS) framework for multiple comparisons of MTD determination and to highlight the potential benefits for model-based methods. This groundwork was exemplified for a combination therapy in which thrombocytopenia was the most frequent Dose Limiting Toxicity (DLT) in two 3+3 dose escalation trials in solid tumors and in ovarian cancer. The recommended Phase II dose (RP2D) was assessed through simulations from a thrombocytopenic toxicity PKPD model developed using the data of these two trials. Dose finding designs (3+3, CRM, EWOC, BLRM and mTPI) were evaluated for accuracy and precision of the predicted RP2D, percentage of DLTs, proportion of under- and over- dosing patients and dose escalation trajectory. Using this framework, the Bayesian methods were shown to be in better agreement with the reference model-based RP2D and provided an increase of 2 dose levels compared to the 3+3 design approach. Furthermore, they provided a better precision of the RP2D and yielded to more ethical trials. This work is in line with the methodology shift advocated by regulators and academics in phase I oncology studies.

Keyword [en]
population PKPD, thrombocytopenia, NONMEM, MTD, Bayesian methods, 3+3 algorithm, dose escalation study
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-233443OAI: oai:DiVA.org:uu-233443DiVA: diva2:752560
Available from: 2014-10-05 Created: 2014-10-05 Last updated: 2015-01-23
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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