<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.

Background: African-Americans are a vulnerable population group with disproportionately elevated rates of type 2 Diabetes Mellitus (DM). Resistant starch is a promising food ingredient that has the potential to reduce the risk factors involved in the development of type 2 DM. To date, there is a dearth of published research studies on the effect of resistant starch on African-Americans who are at increased risk for type 2 DM. Objective: The major objective of this study was to determine if daily consumption of approximately twelve grams of high-maize™ 260 resistant starch (RS) added to bread improved glucose homeostasis by monitoring changes in fasting plasma glucose, fructosamine, hemoglobin A1c, insulin, glucagon-like peptide-1, C-reactive protein, homeostasis model assessment insulin resistant (HOMA- IR) and beta-cell function (HOMA-Beta), serum acetate, propionate, and butyrate levels. Design: A fourteen-week, randomized, double-blind, within-subject crossover design feeding study was carried out in African-American males (n=8) and females (n=7) at increased risk for type 2 DM who resided in Southwest Virginia. All participants consumed bread containing added RS or control bread (no added RS) for six-weeks. RS and control bread feedings were separated by a two-week washout period. Results: Fasting Plasma Glucose (FPG) levels were significantly lower (P = 0.0179) after six-week control bread feedings compared to baseline. FPG levels were also significantly lower (P < 0.0001) after two-week washout period than at baseline. FPG levels were significantly higher (P < 0.0001) after six-week resistant starch bread feeding than at washout. FPG levels due to consumption of resistant starch versus control bread approached significance (P = 0.0574). Fructosamine levels were significantly lower (P = 0.0054) after control bread and resistant starch bread (P < 0.0012) consumption compared to baseline. No significant differences were found in fructosamine levels due to resistant bread intake versus control (P = 0.9692). Mean baseline HbA1c levels were 6.9% (n=15). This value was slightly lowered to 6.79% (n=14) at the end of the fourteen-week study, although statistical significance was not found. Mean ± standard errors for HbA1c values were 6.9% ± 0.18% and 6.9% ± 0.14% at baseline for the sequence groups, resistant starch first (n=7) and control treatment first (n=8) groups, respectively. Mean± standard error HbA1c values were 6.7%± 0.27% and 6.9% ± 0.27% at the conclusion of fourteen-week study for sequence groups, resistant starch first group (n=7) and control treatment first group, respectively. Baseline mean and standard errors C-reactive Protein (CRP) levels for male and female combined results were 0.62 ± 0.16 mg/dL (n=15). Mean CRP levels were 0.53 ± 0.12 mg/dL for resistant starch bread and 0.64 ± 0.21 mg/dL for control bread feeding periods. No significant differences were found for treatment, gender, or sequence effects for C-reactive protein levels during the fourteen-week study (P > 0.05). Mean HOMA-IR levels following six-week resistant starch and control bread consumption decreased to normal values (> 2.5), although no significant differences were found for treatment (P = 0.5923). Conclusions: Eighty-seven grams of Hi- maize™ 260 Resistant Starch added to baked loaves of bread consumed by a free-living African-American population at increased risk for type 2 diabetes did not consistently show significance in all clinical indicators and biochemical markers assessed. On the basis of the evidence in this study we do not have evidence that this amount of resistant starch in this population's diet will prevent the onset of diabetes. However, results are suggestive that higher levels of resistant starch in a more controlled experiment could reduce clinical risk factors for type 2 diabetes. / Ph. D.