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A Model for Glucose, Insulin, and Beta-Cell Dynamics in Subjects With Insulin Resistance and Patients With 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.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2010 (English)In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 50, no 8, 861-872 p.Article in journal (Refereed) Published
Abstract [en]

Type 2 diabetes mellitus (T2DM) is a progressive, metabolic disorder characterized by reduced insulin sensitivity and loss of beta-cell mass (BCM), resulting in hyperglycemia. Population pharmacokinetic-pharmacodynamic (PKPD) modeling is a valuable method to gain insight into disease and drug action. A semi-mechanistic PKPD model incorporating fasting plasma glucose (FPG), fasting insulin, insulin sensitivity, and BCM in patients at various disease stages was developed. Data from 3 clinical trials (phase II/III) with a peroxisome proliferator-activated receptor agonist, tesaglitazar, were used to develop the model. In this, a modeling framework proposed by Topp et al was expanded to incorporate the effects of treatment and impact of disease, as well as variability between subjects. The model accurately described FPG and fasting insulin data over time. The model included a strong relation between insulin clearance and insulin sensitivity, predicted 40% to 60% lower BCM in T2DM patients, and realistic improvements of BCM and insulin sensitivity with treatment. The treatment response on insulin sensitivity occurs within the first weeks, whereas the positive effects on BCM arise over several months. The semi-mechanistic PKPD model well described the heterogeneous populations, ranging from nondiabetic, insulin-resistant subjects to long-term treated T2DM patients. This model also allows incorporation of clinical-experimental studies and actual observations of BCM.

Place, publisher, year, edition, pages
2010. Vol. 50, no 8, 861-872 p.
Keyword [en]
type-2 diabetes, insulin resistance, beta-cell function, NONMEM, peroxisome proliferator-activated receptor agonist
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-95985DOI: 10.1177/0091270009349711ISI: 000280041600002PubMedID: 20484615OAI: oai:DiVA.org:uu-95985DiVA: diva2:170386
Available from: 2007-05-15 Created: 2007-05-15 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Covariate Model Building in Nonlinear Mixed Effects Models
Open this publication in new window or tab >>Covariate Model Building in Nonlinear Mixed Effects Models
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. Identifying covariates which explain differences between patients is important to discover patient subpopulations at risk of sub-therapeutic or toxic effects and for treatment individualization. Stepwise covariate modelling (SCM) is commonly used to this end. The aim of the current thesis work was to evaluate SCM and to develop alternative approaches. A further aim was to develop a mechanistic PK-PD model describing fasting plasma glucose, fasting insulin, insulin sensitivity and beta-cell mass.

The lasso is a penalized estimation method performing covariate selection simultaneously to shrinkage estimation. The lasso was implemented within NONMEM as an alternative to SCM and is discussed in comparison with that method. Further, various ways of incorporating information and propagating knowledge from previous studies into an analysis were investigated. In order to compare the different approaches, investigations were made under varying, replicated conditions. In the course of the investigations, more than one million NONMEM analyses were performed on simulated data. Due to selection bias the use of SCM performed poorly when analysing small datasets or rare subgroups. In these situations, the lasso method in NONMEM performed better, was faster, and additionally validated the covariate model. Alternatively, the performance of SCM can be improved by propagating knowledge or incorporating information from previously analysed studies and by population optimal design.

A model was also developed on a physiological/mechanistic basis to fit data from three phase II/III studies on the investigational drug, tesaglitazar. This model described fasting glucose and insulin levels well, despite heterogeneous patient groups ranging from non-diabetic insulin resistant subjects to patients with advanced diabetes. The model predictions of beta-cell mass and insulin sensitivity were well in agreement with values in the literature.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2007. 77 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 59
Keyword
Pharmacokinetics/Pharmacotherapy, Pharmacokinetics, Pharmacodynamics, Modeling, Covariate selection, Stepwise selection, Covariate analysis, Methodology, Model validation, Model evaluation, Type-2 diabetes, Beta-cell function, Meta analysis, Cross-validation, Least absolute shrinkage and selection operator, Pharmacometrics, ED optimization, Farmakokinetik/Farmakoterapi
Identifiers
urn:nbn:se:uu:diva-7923 (URN)978-91-554-6915-3 (ISBN)
Public defence
2007-06-05, B41, BMC, Husarg. 3, Uppsala, 09:15
Opponent
Supervisors
Available from: 2007-05-15 Created: 2007-05-15 Last updated: 2010-12-09Bibliographically approved
2. Safety and Efficacy Modelling in Anti-Diabetic Drug Development
Open this publication in new window or tab >>Safety and Efficacy Modelling in Anti-Diabetic Drug Development
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A central aim in drug development is to ensure that the new drug is efficacious and safe in the intended patient population.

Mathematical models describing the pharmacokinetic-pharmacodynamic (PK-PD) properties of a drug are valuable to increase the knowledge about drug effects and disease and can be used to inform decisions. The aim of this thesis was to develop mechanism-based PK-PD-disease models for important safety and efficacy biomarkers used in anti-diabetic drug development.

Population PK, PK-PD and disease models were developed, based on data from clinical studies in subjects with varying degrees of renal function, non-diabetic subjects with insulin resistance and patients with type 2 diabetes mellitus (T2DM), receiving a peroxisome proliferator-activated receptor (PPAR) α/γ agonist, tesaglitazar.

The PK model showed that a decreased renal elimination of the metabolite in renally impaired subjects leads to increased levels of metabolite undergoing interconversion and subsequent accumulation of tesaglitazar. Tesaglitazar negatively affects the glomerular filtration rate (GFR), and since renal function affects tesaglitazar exposure, a PK-PD model was developed to simultaneously describe this interrelationship. The model and data showed that all patients had decreases in GFR, which were reversible when discontinuing treatment.

The PK-PD model described the interplay between fasting plasma glucose (FPG), glycosylated haemoglobin (HbA1c) and haemoglobin in T2DM patients. It provided a mechanistically plausible description of the release and aging of red blood cells (RBC), and the glucose dependent glycosylation of RBC to HbA1c. The PK-PD model for FPG and fasting insulin, incorporating components for β-cell mass, insulin sensitivity and impact of disease and drug treatment, realistically described the complex glucose homeostasis in the heterogeneous patient population.

The mechanism-based PK, PK-PD and disease models increase the understanding about T2DM and important biomarkers, and can be used to improve decision making in the development of future anti-diabetic drugs.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 63 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 71
Keyword
Pharmaceutical biosciences, pharmacokinetic, pharmacodynamic, mechanism-based, modelling, type 2 diabetes mellitus, tesaglitazar, PPAR, drug development, NONMEM, Farmaceutisk biovetenskap
Identifiers
urn:nbn:se:uu:diva-8648 (URN)978-91-554-7164-4 (ISBN)
Public defence
2008-05-09, B21, BMC, Husargatan 3, Uppsala, 09:15
Opponent
Supervisors
Available from: 2008-04-17 Created: 2008-04-17 Last updated: 2010-12-09Bibliographically approved

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