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Comparisons of Analysis Methods for Proof-of-Concept Trials
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
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2013 (English)In: CPT: pharmacometrics and systems pharmacology, ISSN 2163-8306, Vol. 2, e23- p.Article in journal (Refereed) Published
Abstract [en]

Drug development struggles with high costs and time consuming processes. Hence, a need for new strategies has been accentuated by many stakeholders in drug development. This study proposes the use of pharmacometric models to rationalize drug development. Two simulated examples, within the therapeutic areas of acute stroke and type 2 diabetes, are utilized to compare a pharmacometric model–based analysis to a t-test with respect to study power of proof-of-concept (POC) trials. In all investigated examples and scenarios, the conventional statistical analysis resulted in several fold larger study sizes to achieve 80% power. For a scenario with a parallel design of one placebo group and one active dose arm, the difference between the conventional and pharmacometric approach was 4.3- and 8.4-fold, for the stroke and diabetes example, respectively. Although the model-based power depend on the model assumptions, in these scenarios, the pharmacometric model–based approach was demonstrated to permit drastic streamlining of POC trials.

Place, publisher, year, edition, pages
2013. Vol. 2, e23- p.
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-132777DOI: 10.1038/psp.2012.24OAI: oai:DiVA.org:uu-132777DiVA: diva2:359904
Available from: 2010-11-01 Created: 2010-10-26 Last updated: 2015-01-23Bibliographically approved
In thesis
1. Benefits of Pharmacometric Model-Based Design and Analysis of Clinical Trials
Open this publication in new window or tab >>Benefits of Pharmacometric Model-Based Design and Analysis of Clinical Trials
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Quantitative pharmacokinetic-pharmacodynamic and disease progression models are the core of the science of pharmacometrics which has been identified as one of the strategies that can make drug development more effective. To adequately develop and utilize these models one needs to carefully consider the nature of the data, choice of appropriate estimation methods, model evaluation strategies, and, most importantly, the intended use of the model.

The general aim of this thesis was to investigate how the use of pharmacometric models can improve the design and analysis of clinical trials within drug development. The development of pharmacometric models for clinical assessment scales in stroke and graded severity events, in this thesis, show the benefit of describing data as close to its true nature as possible, as it increases the predictive abilities and allows for mechanistic interpretations of the models. Performance of three estimation methods implemented in the mixed-effects modeling software NONMEM; 1) Laplace, 2) SAEM, and 3) Importance sampling, applied when modeling repeated time-to-event data, was investigated. The two latter methods are to be preferred if less than approximately half of the individuals experience events. In addition, predictive performance of two validation procedures, internal and external validation, was explored, with internal validation being preferred in most cases. Model-based analysis was compared to conventional methods by the use of clinical trial simulations and the power to detect a drug effect was improved with a pharmacometric design and analysis.

Throughout this thesis several examples have shown the possibility of significantly reducing sample sizes in clinical trials with a pharmacometric model-based analysis. This approach will reduce time and costs spent in the development of new drug therapies, but foremost reduce the number of healthy volunteers and patients exposed to experimental drugs.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2010. 71 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 133
Keyword
model-based analysis, pharmacometrics, modeling, disease progression, NONMEM, SAEM, Importance sampling, repeated time-to-event, RTTCE, RCEpT, NIH stroke scale, Barthel index, internal validation, external validation, study power, study design
National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
urn:nbn:se:uu:diva-133104 (URN)
Public defence
2010-12-17, B41, Biomedicinskt Centrum, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2010-11-24 Created: 2010-11-02 Last updated: 2011-01-13Bibliographically approved
2. 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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Karlsson, Kristin EVong, CamilleBergstrand, MartinKarlsson, Mats O

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