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Dosne, Anne-Gaëlle
Publications (6 of 6) Show all publications
Dosne, A.-G., Bergstrand, M. & Karlsson, M. O. (2017). An Automated Sampling Importance Resampling Procedure For Estimating Parameter Uncertainty. Paper presented at Annual Meeting of the American-Society-for-Clinical-Pharmacology-and-Therapeutics (ASCPT), MAR 15-18, 2017, Washington, DC. Clinical Pharmacology and Therapeutics, 101(S1), S57-S57
Open this publication in new window or tab >>An Automated Sampling Importance Resampling Procedure For Estimating Parameter Uncertainty
2017 (English)In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 101, no S1, p. S57-S57Article in journal, Meeting abstract (Other academic) Published
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-316204 (URN)000391935700197 ()
Conference
Annual Meeting of the American-Society-for-Clinical-Pharmacology-and-Therapeutics (ASCPT), MAR 15-18, 2017, Washington, DC
Available from: 2017-02-27 Created: 2017-02-27 Last updated: 2018-01-13Bibliographically approved
Dosne, A.-G., Bergstrand, M. & Karlsson, M. O. (2017). An Automated Sampling Importance Resampling Procedure For Estimating Parameter Uncertainty. Journal of Pharmacokinetics and Pharmacodynamics, 44(6), 509-520
Open this publication in new window or tab >>An Automated Sampling Importance Resampling Procedure For Estimating Parameter Uncertainty
2017 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, no 6, p. 509-520Article in journal (Refereed) Published
Abstract [en]

Quantifying the uncertainty around endpoints used for decision-making in drug development is essential. In nonlinear mixed-effects models (NLMEM) analysis, this uncertainty is derived from the uncertainty around model parameters. Different methods to assess parameter uncertainty exist, but scrutiny towards their adequacy is low. In a previous publication, sampling importance resampling (SIR) was proposed as a fast and assumption-light method for the estimation of parameter uncertainty. A non-iterative implementation of SIR proved adequate for a set of simple NLMEM, but the choice of SIR settings remained an issue. This issue was alleviated in the present work through the development of an automated, iterative SIR procedure. The new procedure was tested on 25 real data examples covering a wide range of pharmacokinetic and pharmacodynamic NLMEM featuring continuous and categorical endpoints, with up to 39 estimated parameters and varying data richness. SIR led to appropriate results after 3 iterations on average. SIR was also compared with the covariance matrix, bootstrap and stochastic simulations and estimations (SSE). SIR was about 10 times faster than the bootstrap. SIR led to relative standard errors similar to the covariance matrix and SSE. SIR parameter 95% confidence intervals also displayed similar asymmetry to SSE. In conclusion, the automated SIR procedure was successfully applied over a large variety of cases, and its user-friendly implementation in the PsN program enables an efficient estimation of parameter uncertainty in NLMEM.

Keywords
Sampling importance resampling; Parameter uncertainty; Confidence intervals; Asymptotic covariance matrix; Bootstrap; Nonlinear mixed-effects models
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-303630 (URN)10.1007/s10928-017-9542-0 (DOI)000415375800001 ()28887735 (PubMedID)
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2018-02-20Bibliographically approved
Dosne, A.-G., Bergstrand, M. & Karlsson, M. O. (2016). A strategy for residual error modeling incorporating scedasticity of variance and distribution shape. Journal of Pharmacokinetics and Pharmacodynamics, 43(2), 137-151
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, p. 137-151Article 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.

Keywords
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: 2018-01-10Bibliographically approved
Dosne, A.-G., Niebecker, R. & Karlsson, M. O. (2016). dOFV distributions: A New Diagnostic For The Adequacy Of Parameter Uncertainty In Nonlinear Mixed-Effects Models Applied To The Bootstrap. Journal Of Pharmacokinetics And Pharmacodynamics, 43(6), 597-608
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
2016 (English)In: Journal Of Pharmacokinetics And Pharmacodynamics, ISSN 1567-567X, Vol. 43, no 6, p. 597-608Article in journal (Refereed) Published
Abstract [en]

Knowledge of the uncertainty in model parameters is essential for decision-making in drug development. Contrarily to other aspects of nonlinear mixed effects models (NLMEM), scrutiny towards assumptions around parameter uncertainty is low, and no diagnostic exists to judge whether the estimated uncertainty is appropriate. This work aims at introducing a diagnostic capable of assessing the appropriateness of a given parameter uncertainty distribution. The new diagnostic was applied to case bootstrap examples in order to investigate for which dataset sizes case bootstrap is appropriate for NLMEM. The proposed diagnostic is a plot comparing the distribution of differences in objective function values (dOFV) of the proposed uncertainty distribution to a theoretical Chi square distribution with degrees of freedom equal to the number of estimated model parameters. The uncertainty distribution was deemed appropriate if its dOFV distribution was overlaid with or below the theoretical distribution. The diagnostic was applied to the bootstrap of two real data and two simulated data examples, featuring pharmacokinetic and pharmacodynamic models and datasets of 20-200 individuals with between 2 and 5 observations on average per individual. In the real data examples, the diagnostic indicated that case bootstrap was unsuitable for NLMEM analyses with around 70 individuals. A measure of parameter-specific "effective" sample size was proposed as a potentially better indicator of bootstrap adequacy than overall sample size. In the simulation examples, bootstrap confidence intervals were shown to underestimate inter-individual variability at low sample sizes. The proposed diagnostic proved a relevant tool for assessing the appropriateness of a given parameter uncertainty distribution and as such it should be routinely used.

Keywords
Parameter uncertainty distributions, Bootstrap, Model diagnostics, Nonlinear mixed-effects models
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-303628 (URN)10.1007/s10928-016-9496-7 (DOI)000388634800004 ()27730481 (PubMedID)
Funder
EU, FP7, Seventh Framework Programme, 115156
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2018-01-14Bibliographically approved
Dosne, A.-G., Bergstrand, M., Harling, K. & Karlsson, M. O. (2016). Improving The Estimation Of Parameter Uncertainty Distributions In Nonlinear Mixed Effects Models Using Sampling Importance Resampling. Journal of Pharmacokinetics and Pharmacodynamics, 43(6), 583-596
Open this publication in new window or tab >>Improving The Estimation Of Parameter Uncertainty Distributions In Nonlinear Mixed Effects Models Using Sampling Importance Resampling
2016 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, no 6, p. 583-596Article in journal (Refereed) Published
Abstract [en]

Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis.

Keywords
Sampling importance resampling, Parameter uncertainty, Confidence intervals, Asymptotic covariance matrix, Nonlinear mixed-effects models, Bootstrap
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-303629 (URN)10.1007/s10928-016-9487-8 (DOI)000388634800003 ()27730482 (PubMedID)
Funder
EU, European Research Council, 602552 115156
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2018-01-14Bibliographically approved
Svensson, E. M., Dosne, A.-G. & Karlsson, M. O. (2016). Population Pharmacokinetics of Bedaquiline and Metabolite M2 in Patients With Drug-Resistant Tuberculosis: The Effect of Time-Varying Weight and Albumin. CPT: Pharmacometrics and Systems Pharmacology (PSP), 5(12), 682-691
Open this publication in new window or tab >>Population Pharmacokinetics of Bedaquiline and Metabolite M2 in Patients With Drug-Resistant Tuberculosis: The Effect of Time-Varying Weight and Albumin
2016 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 5, no 12, p. 682-691Article in journal (Refereed) Published
Abstract [en]

Albumin concentration and body weight are altered in patients with multidrug-resistant tuberculosis (MDR-TB) and change during the long treatment period, potentially affecting drug disposition. We here describe the pharmacokinetics (PKs) of the novel anti-TB drug bedaquiline and its metabolite M2 in 335 patients with MDR-TB receiving 24 weeks of bedaquiline on top of a longer individualized background regimen. Semiphysiological models were developed to characterize the changes in weight and albumin over time. Bedaquiline and M2 disposition were well described by three and one-compartment models, respectively. Weight and albumin were correlated, typically increasing after the start of treatment, and significantly affected bedaquiline and M2 plasma disposition. Additionally, age and race were significant covariates, whereas concomitant human immunodeficiency virus (HIV) infection, sex, or having extensively drug-resistant TB was not. This is the first population model simultaneously characterizing bedaquiline and M2 PKs in its intended use population. The developed model will be used for efficacy and safety exposure-response analyses.

National Category
Medical and Health Sciences
Research subject
Pharmaceutical Pharmacology
Identifiers
urn:nbn:se:uu:diva-281724 (URN)10.1002/psp4.12147 (DOI)000390923300005 ()
Funder
Swedish Research Council, 521-2011-3442EU, FP7, Seventh Framework Programme, FP7/2007-2013
Note

Title in Thesis list of papers: Population pharmacokinetics of bedaquiline and metabolite M2 in drug-resistant tuberculosis patients – the effect of time-varying weight and albumin

Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2020-12-17Bibliographically approved
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