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Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0003-1258-8297
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0003-3531-9452
2018 (English)In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 7, no 5, p. 331-341Article in journal (Refereed) Published
Abstract [en]

Reusing published models saves time; time to be used for informing decisions in drug development. In antihyperglycemic drug development, several published HbA1c models are available but selecting the appropriate model for a particular purpose is challenging. This study aims at helping selection by investigating four HbA1c models, specifically the ability to identify drug effects (shape, site of action, and power) and simulation properties. All models could identify glucose effect nonlinearities, although for detecting the site of action, a mechanistic glucose model was needed. Power was highest for models using mean plasma glucose to drive HbA1c formation. Insulin contribution to power varied greatly depending on the drug target; it was beneficial only if the drug target was insulin secretion. All investigated models showed good simulation properties. However, extrapolation with the mechanistic model beyond 12 weeks resulted in drug effect overprediction. This investigation aids drug development in decisions regarding model choice if reusing published HbA1c models.

Place, publisher, year, edition, pages
WILEY , 2018. Vol. 7, no 5, p. 331-341
National Category
Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:uu:diva-357755DOI: 10.1002/psp4.12290ISI: 000434085800006PubMedID: 29575656OAI: oai:DiVA.org:uu-357755DiVA, id: diva2:1241161
Funder
EU, FP7, Seventh Framework ProgrammeAvailable from: 2018-08-22 Created: 2018-08-22 Last updated: 2018-08-22Bibliographically approved

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Wellhagen, GustafKarlsson, Mats OKjellsson, Maria C.

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