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Benefits of Pharmacometric Model-Based Design and Analysis of Clinical Trials
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
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. , p. 71
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 133
Keywords [en]
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: urn:nbn:se:uu:diva-133104OAI: oai:DiVA.org:uu-133104DiVA, id: diva2:360473
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: 2018-01-12Bibliographically approved
List of papers
1. Modeling Disease Progression in Acute Stroke Using Clinical Assessment Scales
Open this publication in new window or tab >>Modeling Disease Progression in Acute Stroke Using Clinical Assessment Scales
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2010 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 12, no 4, p. 683-691Article in journal (Refereed) Published
Abstract [en]

This article demonstrates techniques for describing and predicting disease progression in acute stroke by modeling scores measured using clinical assessment scales, accommodating dropout as an additional source of information. Scores assessed using the National Institutes of Health Stroke Scale and the Barthel Index in acute stroke patients were used to model the time course of disease progression. Simultaneous continuous and probabilistic models for describing the nature and magnitude of score changes were developed, and used to model the trajectory of disease progression using scale scores. The models described the observed data well, and exhibited good simulation properties. Applications include longitudinal analysis of stroke scale data, clinical trial simulation, and prognostic forecasting. Based upon experience in other areas, it is likely that application of this modeling methodology will enable reductions in the number of patients needed to carry out clinical studies of treatments for acute stroke.

Keywords
Barthel index, disease progression, NIH stroke scale, NONMEM, stroke
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-132772 (URN)10.1208/s12248-010-9230-0 (DOI)000288426100022 ()20857252 (PubMedID)
Available from: 2010-10-26 Created: 2010-10-26 Last updated: 2018-01-12Bibliographically approved
2. Approaches to simultaneous analysis of frequency and severity of symptoms
Open this publication in new window or tab >>Approaches to simultaneous analysis of frequency and severity of symptoms
2010 (English)In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 88, no 2, p. 255-259Article in journal (Refereed) Published
Abstract [en]

Mechanistic models that synthesize pharmacological and (patho) physiological process information provide a rich basis for the characterization of drug action. However, the underlying clinical data are often simplified in a manner that does not allow models to fully elucidate the structure of the drug effect. In this article, we describe data-simplification strategies that are in routine use to describe disease symptoms and compare them with a model developed for handling the true complexities of the data.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-132773 (URN)10.1038/clpt.2010.118 (DOI)000280147500025 ()20613722 (PubMedID)
Available from: 2010-10-26 Created: 2010-10-26 Last updated: 2018-01-12Bibliographically approved
3. Performance of three estimation methods in repeated time-to-event modeling
Open this publication in new window or tab >>Performance of three estimation methods in repeated time-to-event modeling
2011 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 13, no 1, p. 83-91Article in journal (Refereed) Published
Abstract [en]

It is not uncommon that the outcome measurements, symptoms or side effects, of a clinical trial belong to the family of event type data, e.g., bleeding episodes or emesis events. Event data is often low in information content and the mixed-effects modeling software NONMEM has previously been shown to perform poorly with low information ordered categorical data. The aim of this investigation was to assess the performance of the Laplace method, the stochastic approximation expectation-maximization (SAEM) method, and the importance sampling method when modeling repeated time-to-event data. The Laplace method already existed, whereas the two latter methods have recently become available in NONMEM 7. A stochastic simulation and estimation study was performed to assess the performance of the three estimation methods when applied to a repeated time-to-event model with a constant hazard associated with an exponential interindividual variability. Various conditions were investigated, ranging from rare to frequent events and from low to high interindividual variability. The method performance was assessed by parameter bias and precision. Due to the lack of information content under conditions where very few events were observed, all three methods exhibit parameter bias and imprecision, however most pronounced by the Laplace method. The performance of the SAEM and importance sampling were generally higher than Laplace when the frequency of individuals with events was less than 43%, while at frequencies above that all methods were equal in performance.

Keywords
importance sampling, Laplace, mixed-effects modeling, NONMEM, repeated time-toevent, SAEM
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-132775 (URN)10.1208/s12248-010-9248-3 (DOI)000290204800009 ()21229340 (PubMedID)
Available from: 2010-11-01 Created: 2010-10-26 Last updated: 2018-01-12Bibliographically approved
4. Predictive performance of internal and external validation procedures
Open this publication in new window or tab >>Predictive performance of internal and external validation procedures
(English)Article in journal (Other academic) Submitted
Abstract [en]

Purpose: To compare estimates of predictive performance between internal (IV) and external data-splitting (EV) validation procedures. Methods: Datasets of different study size (n=6, 12, 24, 48, 96, 192, or 384 individuals) were simulated from a one compartment, first-order absorption, pharmacokinetic model and both parametric (FOCE), and nonparametric (NONP) parameter estimates were obtained in NONMEM. From these, three different validation procedures (IV, EV, and a population validation (PV)) were undertaken by means of numerical predictive checks (NPCs) to provide estimates of predictive performance, the PV procedure serving as a reference to assess performance of IV and EV. The predictive performance of NONP versus FOCE estimates was further assessed. Results: Estimates of predictive performance for predicting the median of the population distribution had in general significantly lower imprecision for IV than EV, with little bias for both procedures. For small study sizes, n=6-12 (FOCE) or n=6-24 (NONP), the tails of the population distribution were significantly more biased with IV than EV, but similar imprecision was obtained. The predictive performance for FOCE was similar or superior to that of NONP. Conclusions: Data-splitting is inferior to IV when evaluating predictive models to retain sufficient precision both in predictions and in estimates of predictive performance.

Identifiers
urn:nbn:se:uu:diva-132776 (URN)
Available from: 2010-11-02 Created: 2010-10-26 Last updated: 2011-04-11Bibliographically approved
5. Randomized exposure-controlled trials: Impact of randomization and analysis strategies
Open this publication in new window or tab >>Randomized exposure-controlled trials: Impact of randomization and analysis strategies
2007 (English)In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 64, no 3, p. 266-277Article in journal (Refereed) Published
Abstract [en]

Aims: In the literature, five potential benefits of randomizing clinical trials on concentration levels, rather than dose, have been proposed: (i) statistical study power will increase; (ii) study power will be less sensitive to high variability in the pharmacokinetics (PK); (iii) the power of establishing an exposure-response relationship will be robust to correlations between PK and pharmacodynamics (PD); (iv) estimates of the exposure-response relationship are likely to be less biased; and (v) studies will provide a better control of exposure in situations with toxicity issues. The main aim of this study was to investigate if these five statements are valid when the trial results are evaluated using a model-based analysis. Methods: Quantitative relationships between drug dose, concentration, biomarker and clinical end-point were defined using pharmacometric models. Three randomization schemes for exposure-controlled trials, dose-controlled (RDCT), concentration-controlled (RCCT) and biomarker-controlled (RBCT), were simulated and analysed according to the models. Results: (i) The RCCT and RBCT had lower statistical power than RDCT in a model-based analysis; (ii) with a model-based analysis the power for an RDCT increased with increasing PK variability; (iii) the statistical power in a model-based analysis was robust to correlations between CL and EC 50 or Emax; (iv) under all conditions the bias was negligible (<3%); and (v) for studies with equal power RCCT could produce either more or fewer adverse events compared with an RDCT. Conclusion: Alternative randomization schemes may not have the proposed advantages if a model-based analysis is employed.

Keywords
Clinical trial simulation, Model-based analysis, Pharmacometrics, Randomized concentration-controlled trial, Randomized dose-controlled trial, Study design
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-13726 (URN)10.1111/j.1365-2125.2007.02887.x (DOI)000248924500005 ()17425629 (PubMedID)
Available from: 2008-01-25 Created: 2008-01-25 Last updated: 2018-01-12
6. 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, p. e23-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: 2018-01-12Bibliographically approved

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