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Bjugård Nyberg, HenrikORCID iD iconorcid.org/0000-0003-2249-7911
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Publications (8 of 8) Show all publications
Bjugård Nyberg, H. (2024). Garnishing the smorgasbord of pharmacometric methods. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Open this publication in new window or tab >>Garnishing the smorgasbord of pharmacometric methods
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The smorgasbord of methods that we use within the field of pharmacometrics has developed steadily over several decades and is now a well-laid-out buffet. This thesis adds some garnish to the table in the form of small improvements to the handling of certain problems.

The first problem tackled by the thesis was the challenge of saddle points and local non-identifiability when estimating pharmacometric model parameters. Substituting the common method of randomly perturbing the initial parameter estimates with one saddle-reset step enhances the accuracy of maximum likelihood estimates by overcoming saddle points parameter values, a common issue in nonlinear mixed-effects models. This algorithm, as implemented in the NONMEM software, was applied to various identifiable and nonidentifiable pharmacometric models, showing improved performance over traditional methods.

Part of the thesis was dedicated to the development of a paediatric pharmacokinetic model for ethionamide, a drug used in treating multidrug-resistant tuberculosis. The resulting model was then used to simulate drug exposure under different dosing regimens, a new dosing regimen for children was proposed. The developed model, and therefore the proposed paediatric dosing regimen, considers factors like maturation of pharmacokinetic pathways and, administration by nasogastric tube, and concurrent rifampicin treatment. The regimen, with some modifications, was adopted in the 2022 update to the World Health Organization operational handbook on tuberculosis.

Finally, the thesis explored novel model-integrated evidence (MIE) approaches for bioequivalence (BE) determination. Such methods could offer more robust alternatives to standard BE approached using non-compartmental analysis (NCA). Model-based methods have been shown to be advantageous in sparse data situations, such as is found in studies of ophthalmic formulations, but have suffered from inflated type I error rates. MIE BE approaches using a single model or using model averaging were presented and shown to control type I error at the nominal level while demonstrating increased power in bioequivalence determination.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2024. p. 55
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 345
Keywords
pharmacometrics, pharmacokinetics, saddle points, nonidentifiability, modelling and simulation, tuberculosis, ethionamide, bioequivalence, model-integrated evidence
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-518178 (URN)978-91-513-1999-5 (ISBN)
Public defence
2024-02-16, BMC A1:107, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2024-01-25 Created: 2023-12-17 Last updated: 2024-01-25
Yngman, G., Nyberg, H. B., Nyberg, J., Jonsson, E. N. & Karlsson, M. (2022). An introduction of the full random effects model. CPT: Pharmacometrics and Systems Pharmacology (PSP), 11(2), 149-160
Open this publication in new window or tab >>An introduction of the full random effects model
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2022 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 11, no 2, p. 149-160Article in journal (Refereed) Published
Abstract [en]

The full random-effects model (FREM) is a method for determining covariate effects in mixed-effects models. Covariates are modeled as random variables, described by mean and variance. The method captures the covariate effects in estimated covariances between individual parameters and covariates. This approach is robust against issues that may cause reduced performance in methods based on estimating fixed effects (e.g., correlated covariates where the effects cannot be simultaneously identified in fixed-effects methods). FREM covariate parameterization and transformation of covariate data records can be used to alter the covariate-parameter relation. Four relations (linear, log-linear, exponential, and power) were implemented and shown to provide estimates equivalent to their fixed-effects counterparts. Comparisons between FREM and mathematically equivalent full fixed-effects models (FFEMs) were performed in original and simulated data, in the presence and absence of non-normally distributed and highly correlated covariates. These comparisons show that both FREM and FFEM perform well in the examined cases, with a slightly better estimation accuracy of parameter interindividual variability (IIV) in FREM. In addition, FREM offers the unique advantage of letting a single estimation simultaneously provide covariate effect coefficient estimates and IIV estimates for any subset of the examined covariates, including the effect of each covariate in isolation. Such subsets can be used to apply the model across data sources with different sets of available covariates, or to communicate covariate effects in a way that is not conditional on other covariates.

Place, publisher, year, edition, pages
John Wiley & SonsWiley, 2022
National Category
Probability Theory and Statistics Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-483590 (URN)10.1002/psp4.12741 (DOI)000738371500001 ()34984855 (PubMedID)
Available from: 2022-08-31 Created: 2022-08-31 Last updated: 2024-01-15Bibliographically approved
Bjugård Nyberg, H., Draper, H. R., Garcia-Prats, A. J., Thee, S., Bekker, A., Zar, H. J., . . . Denti, P. (2020). Population Pharmacokinetics and Dosing of Ethionamide in Children with Tuberculosis. Antimicrobial Agents and Chemotherapy, 64(3), Article ID e01984-19.
Open this publication in new window or tab >>Population Pharmacokinetics and Dosing of Ethionamide in Children with Tuberculosis
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2020 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 64, no 3, article id e01984-19Article in journal (Refereed) Published
Abstract [en]

Ethionamide has proven efficacy against both drug-susceptible and some drug-resistant strains of Mycobacterium tuberculosis. Limited information on its pharmacokinetics in children is available, and current doses are extrapolated from weight-based adult doses. Pediatric doses based on more robust evidence are expected to improve antituberculosis treatment, especially in small children. In this analysis, ethionamide concentrations in children from 2 observational clinical studies conducted in Cape Town, South Africa, were pooled. All children received ethionamide once daily at a weight-based dose of approximately 20 mg/kg of body weight (range, 10.4 to 25.3 mg/kg) in combination with other first- or second-line antituberculosis medications and with antiretroviral therapy in cases of HIV coinfection. Pharmacokinetic parameters were estimated using nonlinear mixed-effects modeling. The MDR-PK1 study contributed data for 110 children on treatment for multidrug-resistant tuberculosis, while the DATiC study contributed data for 9 children treated for drug-susceptible tuberculosis. The median age of the children in the studies combined was 2.6 years (range, 0.23 to 15 years), and the median weight was 12.5 kg (range, 2.5 to 66 kg). A one-compartment, transit absorption model with first-order elimination best described ethionamide pharmacokinetics in children. Allometric scaling of clearance (typical value, 8.88 liters/h), the volume of distribution (typical value, 21.4 liters), and maturation of clearance and absorption improved the model fit. HIV coinfection decreased the ethionamide bioavailability by 22%, rifampin coadministration increased clearance by 16%, and ethionamide administration by use of a nasogastric tube increased the rate, but the not extent, of absorption. The developed model was used to predict pediatric doses achieving the same drug exposure achieved in 50- to 70-kg adults receiving 750-mg once-daily dosing. Based on model predictions, we recommend a weight-banded pediatric dosing scheme using scored 125-mg tablets.

Place, publisher, year, edition, pages
American Society for Microbiology, 2020
Keywords
ethionamide, multidrug resistance, pediatric infectious disease, population pharmacokinetics, tuberculosis
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-407966 (URN)10.1128/AAC.01984-19 (DOI)000516763200049 ()31871093 (PubMedID)
Funder
The Swedish Foundation for International Cooperation in Research and Higher Education (STINT)Wellcome trust, 206379/Z/17/Z
Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2023-12-17Bibliographically approved
Bjugård Nyberg, H., Hooker, A. C., Bauer, R. J. & Aoki, Y. (2020). Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models. AAPS Journal, 22(4), Article ID 90.
Open this publication in new window or tab >>Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models
2020 (English)In: AAPS Journal, E-ISSN 1550-7416, Vol. 22, no 4, article id 90Article in journal (Refereed) Published
Abstract [en]

Parameter estimation of a nonlinear model based on maximizing the likelihood using gradient-based numerical optimization methods can often fail due to premature termination of the optimization algorithm. One reason for such failure is that these numerical optimization methods cannot distinguish between the minimum, maximum, and a saddle point; hence, the parameters found by these optimization algorithms can possibly be in any of these three stationary points on the likelihood surface. We have found that for maximization of the likelihood for nonlinear mixed effects models used in pharmaceutical development, the optimization algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) often terminates in saddle points, and we propose an algorithm, saddle-reset, to avoid the termination at saddle points, based on the second partial derivative test. In this algorithm, we use the approximated Hessian matrix at the point where BFGS terminates, perturb the point in the direction of the eigenvector associated with the lowest eigenvalue, and restart the BFGS algorithm. We have implemented this algorithm in industry standard software for nonlinear mixed effects modeling (NONMEM, version 7.4 and up) and showed that it can be used to avoid termination of parameter estimation at saddle points, as well as unveil practical parameter non-identifiability. We demonstrate this using four published pharmacometric models and two models specifically designed to be practically non-identifiable.

Keywords
NLME, estimation methods, parameter estimation, pharmacometrics, practical identifiability
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-419009 (URN)10.1208/s12248-020-00471-y (DOI)000545830800001 ()32617704 (PubMedID)
Available from: 2020-09-08 Created: 2020-09-08 Last updated: 2023-12-17Bibliographically approved
Smith, M. K., Moodie, S. L., Bizzotto, R., Blaudez, E., Borella, E., Carrara, L., . . . Holford, N. H. (2017). Model Description Language (MDL): A Standard for Modeling and Simulation. CPT: Pharmacometrics and Systems Pharmacology (PSP), 6(10), 647-650
Open this publication in new window or tab >>Model Description Language (MDL): A Standard for Modeling and Simulation
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2017 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 6, no 10, p. 647-650Article in journal (Refereed) Published
Abstract [en]

Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics. 1-3 Currently, models are encoded in a variety of computer languages and are shared through publications that rarely include original code and generally lack reproducibility. The DDMoRe Model Description Language (MDL) has been developed primarily as a language standard to facilitate sharing knowledge and understanding of models.

Place, publisher, year, edition, pages
WILEY, 2017
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-340957 (URN)10.1002/psp4.12222 (DOI)000413899000001 ()28643440 (PubMedID)
Funder
EU, FP7, Seventh Framework Programme, 115156
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2023-12-15Bibliographically approved
Swat, M. J., Moodie, S., Wimalaratne, S. M., Kristensen, N. R., Lavielle, M., Mari, A., . . . Le Novère, N. (2015). Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development. CPT: Pharmacometrics and Systems Pharmacology (PSP), 4(6), 316-319
Open this publication in new window or tab >>Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development
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2015 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 4, no 6, p. 316-319Article in journal (Refereed) Published
Abstract [en]

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Place, publisher, year, edition, pages
American Society for Clinical Pharmacology & Therapeutics, 2015
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-276254 (URN)10.1002/psp4.57 (DOI)000218954600002 ()26225259 (PubMedID)
Funder
EU, FP7, Seventh Framework Programme
Available from: 2016-02-10 Created: 2016-02-10 Last updated: 2023-12-15Bibliographically approved
Chen, X., Bjugård Nyberg, H., Donnelly, M., Zhao, L., Fang, L., Karlsson, M. & Hooker, A.Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic endpoints.
Open this publication in new window or tab >>Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic endpoints
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(English)Manuscript (preprint) (Other academic)
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-517824 (URN)
Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-17
Bjugård Nyberg, H., Chen, X., Donnelly, M., Fang, L., Zhao, L., Karlsson, M. & Hooker, A.Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods.
Open this publication in new window or tab >>Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods
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(English)Manuscript (preprint) (Other academic)
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-517823 (URN)
Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-17
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2249-7911

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