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Garnishing the smorgasbord of pharmacometric methods
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.ORCID iD: 0000-0003-2249-7911
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Description
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 [en]
pharmacometrics, pharmacokinetics, saddle points, nonidentifiability, modelling and simulation, tuberculosis, ethionamide, bioequivalence, model-integrated evidence
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-518178ISBN: 978-91-513-1999-5 (print)OAI: oai:DiVA.org:uu-518178DiVA, id: diva2:1820258
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
List of papers
1. Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models
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
2. Population Pharmacokinetics and Dosing of Ethionamide in Children with Tuberculosis
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
3. 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
4. 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

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