Modelling the Dynamics of Glucose, Insulin, Insulin Sensitivity and Beta-Cells in Subjects with Insulin Resistance and Patients with Type 2 Diabetes
J. Ribbing (1), B. HamrÚn (1,2), M.K. Svensson (2,3) and M.O. Karlsson (1)
(1)Uppsala University, Uppsala, Sweden; (2)AstraZeneca R&D M÷lndal, Sweden; (3)Sahlgrenska University Hospital, Gothenburg, Sweden
Introduction: Type 2 diabetes mellitus (T2DM) is a progressive, metabolic disorder characterized by reduced insulin sensitivity (insulin resistance) and loss of beta-cell mass (BCM), resulting in hyperglycaemia. Insulin resistance is abundant among obese subjects. In isolation, insulin resistance does not cause hyperglycaemia due to compensatory upward BCM adaptation. This leads to increased insulin secretion and normoglycaemia. With time some insulin resistant subjects experience loss of BCM, meaning that the progression into T2DM has begun. Today, in-vivo quantification of BCM is not possible, but evolving imaging techniques have the potential to soon accomplish this. Fasting plasma glucose (FPG) is a biomarker for glycaemic control.
Treatment with peroxisome proliferator-activated receptor (PPAR) agonists has been suggested to improve both the insulin resistance and the beta-cell mass. Tesaglitazar is a dual PPAR αγ agonist previously in development for treatment of T2DM. Clinical development was discontinued in May 2006 when results from phase III studies indicated that the overall benefit-risk profile was unlikely to give patients an advantage over currently available therapies.
The objective was to develop a mechanistic pharmacokinetic-pharmacodynamic (PK-PD) model that incorporates FPG, fasting insulin, insulin sensitivity and BCM, describing patients at various stages of disease, from non-diabetic, insulin resistant to long-term treated T2DM patients, and incorporating impact of drug treatment on these four variables.
Methods: Fasting biomarker data from 1460 subjects in three clinical trials with tesaglitazar were available: one phase IIb study in insulin resistant, non-diabetic subjects. One phase IIb and one phase III study in T2DM patients, which were either drug naïve or previously treated with oral antidiabetics. All model fitting and simulation were performed in NONMEM version VI.
A mechanistic model which integrates BCM, insulin and glucose dynamics in normal subjects has been proposed by Topp et al. To our knowledge, this model, derived from different sources in the literature, has never been applied to clinical data. The model consists of three linked differential equations. One of these describes how BCM adapts to maintain glucose at a physiological set-point (5.6 mmol/L), but also include glucose toxicity which causes a negative spiral of BCM degeneration at high glucose levels. The modelling framework suggested by Topp et al. was used as a starting point and was further developed to incorporate impact of disease state and drug treatment on BCM and insulin sensitivity. The effects of tesaglitazar were investigated on insulin sensitivity and on the increased FPG set-point that is maintained by the BCM adaptation. In addition, a positive relation between insulin sensitivity and insulin elimination was included, which was necessary to describe the dynamic changes in FPG and FI within the mechanistic framework. The use of a population-modelling approach allowed estimation of inter-individual variability in model parameters. The final model was evaluated using non-parametric bootstrap and visual-predictive check, both stratified on disease stage and dose group.
Results: The mechanistic PK-PD model described a strong relation between insulin-elimination rate and insulin sensitivity and predicted 40-60% lower BCM in diabetic patients. Steady-state in insulin sensitivity required approximately six weeks of treatment (half-life 12 days) whereas the BCM adaptation to attain a new FPG set-point required about six months.
The mechanistic PK-PD model described all available data well in terms of median and 95% confidence interval over time. When tesaglitazar treatment was initiated in drug naïve T2DM patients both FI and FPG dropped sharply during the first few weeks, mainly due to the improved insulin sensitivity. The improvement of FPG continued at a slower rate until about six months, due to the slower adaptation of BCM. Fasting insulin, however, exhibited a small rebound (i.e. increase), due to the increasing BCM that becomes apparent after insulin sensitivity has reached a new steady-state. Insulin resistant, non-diabetic subjects displayed a comparable drop in fasting insulin during the first weeks, due to improved insulin sensitivity. However, FPG decreased only a few percent in these subjects since the FPG set-point was almost normal in the untreated state.
In the investigated dose range the exposure-response relation was nearly linear for insulin sensitivity (EC50 greater than the observed exposure range). Regarding the exposure-response on FPG set-point, EC50 was within the exposure range (but with 100% inter-individual variability around the typical value). The two EC50 parameters were highly correlated on the individual level, indicating a shared mechanistic pathway.
Discussion and Conclusions: The mechanistic PK-PD model described FPG and FI well in a heterogeneous population ranging from non-diabetic, insulin resistant subjects to T2DM patients. Model predictions that can not be evaluated on the available data were in agreement with literature. 40-60% reduction of BCM in T2DM compared to normal individuals is in agreement with published autopsy data. Our model assumes that, in the fasted state, insulin secretion at a fixed glucose level is proportional to BCM, regardless of disease state. Since our prediction of BCM is in line with the literature, this indicates that beta-cells in T2DM patients indeed have similar function as the beta-cells in the normal subject (at fasting).
The positive correlation between insulin sensitivity and insulin elimination rate has been reported several times in the literature and both are affected by the level of free-fatty acids (FFA), which is lowered by PPAR agonists.[1, 7] Possibly, FFA plays a key role in several ways, since lipotoxicity has been suggested leading to beta-cell death (apoptosis). Consequently, FFA may be part of the common mechanistic pathway indicated by the estimated correlation between the two EC50. Patients that greatly improve insulin sensitivity with tesaglitazar will also respond well in BCM, partly as a result of reduced glucotoxicity following the increased insulin sensitivity and possibly also because both variables are affected by FFA.
The mechanistic PK-PD model allows incorporation of heterogeneous study populations and data, e.g. regular phase I-III trials combined with actual observations of BCM plus clinical experimental studies such as glucose and insulin clamp studies. Merging such information into the same quantitative framework enables in depth insight to physiology, disease and drug effects and will likely be valuable for decision making in drug development by more accurate model extrapolations.
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