Type 2 diabetes is a complex chronic metabolic disorder characterized by hyperglycemia associated with a relative deficiency of insulin secretion and a reduced response of target tissues to insulin. Considerable efforts have been put into the development of models describing the glucose-insulin system. The best known is Bergman’s “minimal” model for glucose, which is estimating glucose concentrations using fixed insulin concentrations as input. However, due to the involved feedback mechanisms, simultaneous modeling of both entities would be advantageous. This is particularly relevant if the model is intended to be used as a predictive tool. The mechanism-based glucose-insulin model presented in this thesis is able to simultaneously describe glucose and insulin profiles following a wide variety of clinical provocation experiments, such as intravenous and oral glucose tolerance tests, clamp studies and sequential meal tests over 24 hours. It consists of sub-models for glucose, labeled glucose and insulin kinetics. It also incorporates control mechanisms for the regulation of glucose production, insulin secretion, and glucose uptake. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed in NONMEM. Even if this model is a crude representation of a complex physiological system, its ability to represent the main processes of this system was established by identifying: 1) the difference in insulin secretion and insulin sensitivity between healthy volunteers and type 2 diabetics, 2) the action of incretin hormones after oral administration of glucose, 3) the circadian variation of insulin secretion and 4) the correct mechanism of action of a glucokinase activator, a new oral antidiabetic compound acting on both the pancreas and the liver. These promising results represent a proof of concept of a mechanistic drug-disease model that could play an important role in the clinical development of anti-diabetic drugs.

In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.

Mål: Syftet med studien är att beskriva patienternas inställningar till användningen av en QPL, huruvida det är ett möjligt redskap att använda under farmaceutmötet på svenska öppenvårdsapotek. Dessutom att undersöka den rapporterade skillnaden i kunskapsnivå efter användningen av QPL mellan patienterna som valde att använda QPL:en under farmaceutmötet och patienterna som valde att inte göra det. Samt att undersöka om olika bakgrundsfaktorer påverkar användningen av QPL.      Metod: En kvantitativ analys av redan besvarade enkäter genomfördes för att besvara syftet och frågeställningen. Enkäterna analyserades deskriptivt med hjälp av beskrivande mått och genom inferentiell statistik i Microsoft Excel. Delstudien bestod av 179 enkäter.    Resultat: Totalt kodades 179 enkäter utifrån inklusionskriterierna. Patienterna rapporterade att QPL introducerades i 96% (n=172) och användes i 46% (n=83) av farmaceutmöten. Majoriteten av patienterna (n=101) upplevde att QPL inte tog mycket tid att läsa och (n=121) tyckte att frågorna var enkla att förstå. Å andra sidan rapporterade patienterna som hämtade minst ett nytt läkemedel och patienterna som hade svenska som modersmål att de använde QPL mer än andra. Samtidigt upplevde patienterna som använde QPL:en under farmaceutmötet en ökad kunskapsnivå om läkemedel jämfört med patienterna som inte använde QPL:en under mötet.   Slutsats: Patienterna upplevde att QPL:en var ett funktionellt verktyg som fick de att ställa fler frågor och förstå sitt sjukdomstillstånd bättre. QPL:en kan effektiviseras ytterligare genom att förkortas ned och finnas i fler språk. Samtidigt bör farmaceut-patientkommunikation på öppenvårdsapotek effektiviseras ytterligare.

<p>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.</p><p>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.</p><p>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.</p>

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.