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Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaci.ORCID-id: 0000-0003-2249-7911
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaci.ORCID-id: 0009-0002-0663-0532
Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
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(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Farmaceutiska vetenskaper
Identifikatorer
URN: urn:nbn:se:uu:diva-517823OAI: oai:DiVA.org:uu-517823DiVA, id: diva2:1819150
Tillgänglig från: 2023-12-13 Skapad: 2023-12-13 Senast uppdaterad: 2023-12-17
Ingår i avhandling
1. Garnishing the smorgasbord of pharmacometric methods
Öppna denna publikation i ny flik eller fönster >>Garnishing the smorgasbord of pharmacometric methods
2024 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Uppsala: Acta Universitatis Upsaliensis, 2024. s. 55
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 345
Nyckelord
pharmacometrics, pharmacokinetics, saddle points, nonidentifiability, modelling and simulation, tuberculosis, ethionamide, bioequivalence, model-integrated evidence
Nationell ämneskategori
Farmaceutiska vetenskaper
Forskningsämne
Farmaceutisk vetenskap
Identifikatorer
urn:nbn:se:uu:diva-518178 (URN)978-91-513-1999-5 (ISBN)
Disputation
2024-02-16, BMC A1:107, Husargatan 3, Uppsala, 09:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2024-01-25 Skapad: 2023-12-17 Senast uppdaterad: 2024-01-25

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Bjugård Nyberg, HenrikChen, XiaomeiKarlsson, MatsHooker, Andrew

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