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Improved Methods for Pharmacometric Model-Based Decision-Making in Clinical Drug Development
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics)
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Pharmacometric model-based analysis using nonlinear mixed-effects models (NLMEM) has to date mainly been applied to learning activities in drug development. However, such analyses can also serve as the primary analysis in confirmatory studies, which is expected to bring higher power than traditional analysis methods, among other advantages. Because of the high expertise in designing and interpreting confirmatory studies with other types of analyses and because of a number of unresolved uncertainties regarding the magnitude of potential gains and risks, pharmacometric analyses are traditionally not used as primary analysis in confirmatory trials.

The aim of this thesis was to address current hurdles hampering the use of pharmacometric model-based analysis in confirmatory settings by developing strategies to increase model compliance to distributional assumptions regarding the residual error, to improve the quantification of parameter uncertainty and to enable model prespecification.

A dynamic transform-both-sides approach capable of handling skewed and/or heteroscedastic residuals and a t-distribution approach allowing for symmetric heavy tails were developed and proved relevant tools to increase model compliance to distributional assumptions regarding the residual error. A diagnostic capable of assessing the appropriateness of parameter uncertainty distributions was developed, showing that currently used uncertainty methods such as bootstrap have limitations for NLMEM. A method based on sampling importance resampling (SIR) was thus proposed, which could provide parameter uncertainty in many situations where other methods fail such as with small datasets, highly nonlinear models or meta-analysis. SIR was successfully applied to predict the uncertainty in human plasma concentrations for the antibiotic colistin and its prodrug colistin methanesulfonate based on an interspecies whole-body physiologically based pharmacokinetic model. Lastly, strategies based on model-averaging were proposed to enable full model prespecification and proved to be valid alternatives to standard methodologies for studies assessing the QT prolongation potential of a drug and for phase III trials in rheumatoid arthritis.

In conclusion, improved methods for handling residual error, parameter uncertainty and model uncertainty in NLMEM were successfully developed. As confirmatory trials are among the most demanding in terms of patient-participation, cost and time in drug development, allowing (some of) these trials to be analyzed with pharmacometric model-based methods will help improve the safety and efficiency of drug development.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. , 91 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 223
Keyword [en]
pharmacometrics, nonlinear mixed-effects models, confirmatory trials, residual error modeling, parameter uncertainty, sampling importance resampling, model-averaging
National Category
Health Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-305697ISBN: 978-91-554-9734-7OAI: oai:DiVA.org:uu-305697DiVA: diva2:1038983
Public defence
2016-12-09, B/A1:107a, Biomedicinskt Centrum, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2016-11-18 Created: 2016-10-20 Last updated: 2016-11-28
List of papers
1. A strategy for residual error modeling incorporating scedasticity of variance and distribution shape
Open this publication in new window or tab >>A strategy for residual error modeling incorporating scedasticity of variance and distribution shape
2016 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, no 2, 137-151 p.Article in journal (Refereed) Published
Abstract [en]

Nonlinear mixed effects models parameters are commonly estimated using maximum likelihood. The properties of these estimators depend on the assumption that residual errors are independent and normally distributed with mean zero and correctly defined variance. Violations of this assumption can cause bias in parameter estimates, invalidate the likelihood ratio test and preclude simulation of real-life like data. The choice of error model is mostly done on a case-by-case basis from a limited set of commonly used models. In this work, two strategies are proposed to extend and unify residual error modeling: a dynamic transform-both-sides approach combined with a power error model (dTBS) capable of handling skewed and/or heteroscedastic residuals, and a t-distributed residual error model allowing for symmetric heavy tails. Ten published pharmacokinetic and pharmacodynamic models as well as stochastic simulation and estimation were used to evaluate the two approaches. dTBS always led to significant improvements in objective function value, with most examples displaying some degree of right-skewness and variances proportional to predictions raised to powers between 0 and 1. The t-distribution led to significant improvement for 5 out of 10 models with degrees of freedom between 3 and 9. Six models were most improved by the t-distribution while four models benefited more from dTBS. Changes in other model parameter estimates were observed. In conclusion, the use of dTBS and/or t-distribution models provides a flexible and easy-to-use framework capable of characterizing all commonly encountered residual error distributions.

Keyword
Residual error, Transform-both-sides, Skewness, Heteroscedasticity, Heavy tails, t-Distribution
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-297131 (URN)10.1007/s10928-015-9460-y (DOI)000374704100002 ()26679003 (PubMedID)
Funder
EU, European Research Council
Available from: 2016-06-22 Created: 2016-06-21 Last updated: 2016-10-20Bibliographically approved
2. dOFV distributions:  a new diagnostic for the adequacy of parameter uncertainty in nonlinear mixed-effects models applied to the bootstrap
Open this publication in new window or tab >>dOFV distributions:  a new diagnostic for the adequacy of parameter uncertainty in nonlinear mixed-effects models applied to the bootstrap
(English)Article in journal (Refereed) Epub ahead of print
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-303628 (URN)10.1007/s10928-016-9496-7 (DOI)
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2016-10-20
3. Improving The Estimation Of Parameter Uncertainty Distributions In Nonlinear Mixed Effects Models Using Sampling Importance Resampling
Open this publication in new window or tab >>Improving The Estimation Of Parameter Uncertainty Distributions In Nonlinear Mixed Effects Models Using Sampling Importance Resampling
(English)Article in journal (Refereed) Epub ahead of print
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-303629 (URN)10.1007/s10928-016-9487-8 (DOI)
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2016-10-20
4. An Automated Sampling Importance Resampling Procedure For Estimating Parameter Uncertainty
Open this publication in new window or tab >>An Automated Sampling Importance Resampling Procedure For Estimating Parameter Uncertainty
(English)Manuscript (preprint) (Other academic)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-303630 (URN)
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2016-10-20
5. Development of an interspecies whole-body physiologically based pharmacokinetic (WBPBPK) model for colistin methanesulfonate (CMS) and colistin in five animal species and evaluation of its predictive ability in human
Open this publication in new window or tab >>Development of an interspecies whole-body physiologically based pharmacokinetic (WBPBPK) model for colistin methanesulfonate (CMS) and colistin in five animal species and evaluation of its predictive ability in human
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background and purpose

Colistin is a last-line antibiotic administered as the prodrug colistin methanesulfonate (CMS) for the treatment of multidrug resistant Gram-negative bacterial infections. Whole-body physiologically based pharmacokinetic (WBPBPK) models are valuable tools to understand and characterize drug disposition, predict tissue distribution and interpret exposure-response relationship. The aim of this work was to develop a WBPBPK model for colistin and CMS in five animal species and evaluate the utility of the model for predicting colistin and CMS disposition in human.

Methods

A nonlinear mixed-effects WBPBPK model previously developed in rats was extended to describe CMS and colistin plasma data of animals from 5 different species (40 mice, 6 rats, 3 rabbits, 3 baboons and 2 pigs) that had received single doses of CMS. CMS renal clearance and hydrolysis to colistin were allometrically scaled based on glomerular filtration rate (GFR) and tissue volumes, respectively. For the non-renal colistin clearance, three scaling models were evaluated: volume based allometric scaling, volume and maximum lifespan potential (MLP) based allometric scaling, and estimation of specie-specific parameters. Tissue concentrations were predicted for all species. The WBPBPK model was then used to predict human plasma concentrations, which were compared to observed human plasma PK data extracted from literature.

Results

The description of the plasma PK of CMS and colistin in mice, rats, rabbits, baboons and pigs was satisfactory. The volume and MLP based allometric scaling of the non-renal clearance of colistin was best at characterizing colistin concentration-time course, even if a misprediction remained in pigs. In human however, allometric scaling without MLP was closest to the observed data, with satisfactory prediction of the CMS plasma profiles and a slight overprediction of colistin plasma PK profiles.

Conclusions

Interspecies WBPBPK models were developed to describe the disposition of CMS and colistin across five animal species and human plasma concentrations of CMS and colistin were predicted in the right ranges.

Keyword
WBPBPK modeling, colistin, CMS, interspecies scaling, predictions in human, population approach
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-279997 (URN)
Funder
Security Link
Available from: 2016-03-07 Created: 2016-03-07 Last updated: 2016-10-20
6. Model averaging for robust assessment of QT prolongation by concentration-response analysis
Open this publication in new window or tab >>Model averaging for robust assessment of QT prolongation by concentration-response analysis
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(English)Article in journal (Refereed) Submitted
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-303632 (URN)
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2016-10-20
7. Longitudinal Data Analysis Using Model Averaging: Benefits For Pivotal Clinical Trials, Applied To Rheumatoid Arthritis
Open this publication in new window or tab >>Longitudinal Data Analysis Using Model Averaging: Benefits For Pivotal Clinical Trials, Applied To Rheumatoid Arthritis
Show others...
(English)Manuscript (preprint) (Other academic)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-303634 (URN)
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2016-10-20

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