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Model-Based Optimization of Clinical Trial Designs
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Research Group)
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 [en]
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: urn:nbn:se:uu:diva-233445ISBN: 978-91-554-9063-8 (print)OAI: oai:DiVA.org:uu-233445DiVA: diva2:753438
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
List of papers
1. Rapid Sample Size Calculations for a Defined Likelihood Ratio Test-Based Power in Mixed-Effects Models
Open this publication in new window or tab >>Rapid Sample Size Calculations for a Defined Likelihood Ratio Test-Based Power in Mixed-Effects Models
2012 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 14, no 2, 176-186 p.Article in journal (Refereed) Published
Abstract [en]

Efficient power calculation methods have previously been suggested for Wald test-based inference in mixed-effects models but the only available alternative for Likelihood ratio test-based hypothesis testing has been to perform computer-intensive multiple simulations and re-estimations. The proposed Monte Carlo Mapped Power (MCMP) method is based on the use of the difference in individual objective function values (Delta iOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The Delta iOFV is sampled and summed (a Delta iOFVs) for each study at each sample size of interest to study, and the percentage of a Delta iOFVs greater than the significance criterion is taken as the power. The power versus sample size relationship established via the MCMP method was compared to traditional assessment of model-based power for six different pharmacokinetic and pharmacodynamic models and designs. In each case, 1,000 simulated datasets were analysed with the full and reduced models. There was concordance in power between the traditional and MCMP methods such that for 90% power, the difference in required sample size was in most investigated cases less than 10%. The MCMP method was able to provide relevant power information for a representative pharmacometric model at less than 1% of the run-time of an SSE. The suggested MCMP method provides a fast and accurate prediction of the power and sample size relationship.

Keyword
likelihood ratio test, NONMEM, pharmacometrics, power, sample size
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-174344 (URN)10.1208/s12248-012-9327-8 (DOI)000302814900004 ()
Available from: 2012-05-24 Created: 2012-05-15 Last updated: 2017-12-07Bibliographically approved
2. Comparisons of Analysis Methods for Proof-of-Concept Trials
Open this publication in new window or tab >>Comparisons of Analysis Methods for Proof-of-Concept Trials
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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.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-132777 (URN)10.1038/psp.2012.24 (DOI)
Available from: 2010-11-01 Created: 2010-10-26 Last updated: 2015-01-23Bibliographically approved
3. Handling Below Limit of Quantification Data in Optimal Trial Design
Open this publication in new window or tab >>Handling Below Limit of Quantification Data in Optimal Trial Design
2014 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744Article in journal (Other academic) Accepted
Abstract [en]

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.

Keyword
Population Optimal Design, LOQ, BQL data, NLME, Pharmacometrics, Population modeling
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-233441 (URN)
Available from: 2014-10-05 Created: 2014-10-05 Last updated: 2017-12-05Bibliographically approved
4. Optimal Design Applied to Hematological Toxicity-Induced Anticancer Treatment
Open this publication in new window or tab >>Optimal Design Applied to Hematological Toxicity-Induced Anticancer Treatment
Show others...
(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
Optimal design, Neutropenia, Nonlinear mixed-effects models, Docetaxel, Myelosuppression
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-233442 (URN)
Available from: 2014-10-05 Created: 2014-10-05 Last updated: 2015-02-12
5. 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
Open this publication in new window or tab >>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
Show others...
(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
population PKPD, thrombocytopenia, NONMEM, MTD, Bayesian methods, 3+3 algorithm, dose escalation study
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-233443 (URN)
Available from: 2014-10-05 Created: 2014-10-05 Last updated: 2015-01-23

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