An integrated model for the regulation of glucose and insulin concentrations following intravenous glucose provocations in healthy volunteers and type 2 diabetic patients was developed. Data from 72 individuals were included. Total glucose, labeled glucose, and insulin concentrations were determined. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed in NONMEM. Integrated models for glucose, labeled glucose, and insulin were developed. Control mechanisms for regulation of glucose production, insulin secretion, and glucose uptake were incorporated. Physiologically relevant differences between healthy volunteers and patients were identified in the regulation of glucose production, elimination rate of glucose, and secretion of insulin. The model was able to describe the insulin and glucose profiles well and also showed a good ability to simulate data. The features of the present model are likely to be of interest for analysis of data collected in antidiabetic drug development and for optimization of study design.

An integrated model for the glucose-insulin system describing oral glucose tolerance test data was developed, extending on a previously introduced model for intravenous glucose provocations. Model extensions comprised the description of glucose absorption by a chain of transit compartments with a mean transit time of 35 minutes, a bioavailability of 80%, and a representation of the incretin effect, expressed as a direct effect of the glucose absorption rate on insulin secretion. The ability of the model to predict the incretin effect was assessed by simulating the observed difference in insulin response following an oral glucose tolerance test compared with an isoglycemic glucose infusion mimicking an oral glucose tolerance test profile. The extension of the integrated glucose-insulin model to gain information from oral glucose tolerance test data considerably expands its range of applications because the oral glucose tolerance test is one of the most common glucose challenge experiments for assessing the efficacy of hypoglycemic agents in clinical drug development.

A few approaches for handling baseline responses are available for use in pharmacokinetic (PK)-pharmacodynamic (PD) analysis. They include: (method 1-B1) estimation of the typical value and interindividual variability (IIV) of baseline in the population, (B2) inclusion of the observed baseline response as a covariate acknowledging the residual variability, (B3) a more general version of B2 as it also takes the IIV of the baseline in the population into account, and (B4) normalization of all observations by the baseline value. The aim of this study was to investigate the relative performance of B1-B4. PD responses over a single dosing interval were simulated from an indirect response model in which a drug acts through stimulation or inhibition of the response according to an Emax model. The performance of B1-B4 was investigated under 22 designs, each containing 100 datasets. NONMEM VI beta was used to estimate model parameters with the FO and the FOCE method. The mean error (ME, %) and root mean squared error (RMSE, %) of the population parameter estimates were computed and used as an indicator of bias and imprecision. Absolute ME (|ME|) and RMSE from all methods were ranked within the same design, the lower the rank value the better method performance. Average rank of each method from all designs was reported. The results showed that with B1 and FOCE, the average of |ME| and RMSE across all typical individual parameters and all conditions was 5.9 and 31.8%. The average rank of |ME| for B1, B2, B3, and B4 was 3.7, 3.8, 3.3, and 5.2 for the FOCE method, and 4.6, 4.3, 4.7, and 6.4 for the FO method. The smallest imprecision was noted with the use of B1 (rank of 3.1 for FO, and 2.9 for FOCE) and increased, in order, with B3 (3.9-FO and 3.6-FOCE), B2 (4.8-FO; 4.7-FOCE), and B4 (6.4-FO; 6.5-FOCE). We conclude that when considering both bias and imprecision B1 was slightly better than B3 which in turn was better than B2. Differences between these methods were small. B4 was clearly inferior. The FOCE method led to a smaller bias, but no marked reduction in imprecision of parameter estimates compared to the FO method.

The extension of the previously developed integrated models for glucose and insulin (IGI) 1,2 to include the oral glucose tolerance test (OGTT) in healthy volunteers could be valuable to better understand the differences between healthy individuals and type 2 diabetes mellitus (T2DM). Data from and OGTT in 23 healthy volunteers was used. Analysis was based on the previously developed intravenous model with extensions for glucose absorption and incretin effect on insulin secretion. The need for additional structural components was evaluated. The model was evaluated by simulation and a bootstrap. Multiple glucose and insulin concentration peaks were observed in most individuals as well as hypoglycemic episodes in the second half of the experiment. The OGTT data was successfully described by the extended basic model. An additional control mechanism of insulin on glucose production improved the description of the data. The model showed good predictive properties and parameters were estimated with good precision.In conclusion, a previously presented integrated model has been extended to describe glucose and insulin concentrations in healthy volunteers following an OGTT. The characterization of the differences between the healthy and diabetic stages in the IGI model could potentially be used to extrapolate drug effect from healthy volunteers to T2DM.3

The integrated glucose-insulin model was originally developed on a variety of intravenous glucose provocation experiments in healthy volunteers and type 2 diabetic patients. The model, which simultaneously describes time-courses of glucose and insulin based on mechanism-based components for production, elimination and homeostatic feedback, has been further extended to oral glucose provocations, meal tests and insulin administration. The model has been used to describe experiments ranging from 4 to 24 hr. Applications of the integrated glucose-insulin model include the clinical assessment of the mechanism of action of anti-diabetic drugs and the magnitude of their effects. Finally, the model was used for optimizing the design of provocation experiments.