Implications for Drug Characterization in Glucose Tolerance Tests Without Insulin: Simulation Study of Power and Predictions Using Model-Based Analysis

Abstract

In antihyperglycemic drug development, drug effects are usually characterized using glucose provocations. Analyzing provocation data using pharmacometrics has shown powerful, enabling small studies. In preclinical drug development, high power is attractive due to the experiment sizes; however, insulin is not always available, which potentially impacts power and predictive performance. This simulation study was performed to investigate the implications of performing model-based drug characterization without insulin. The integrated glucose-insulin model was used to simulate and re-estimated oral glucose tolerance tests using a crossover design of placebo and study compound. Drug effects were implemented on seven different mechanisms of action (MOA); one by one or in two-drug combinations. This study showed that exclusion of insulin may severely reduce the power to distinguish the correct from competing drug effect, and to detect a primary or secondary drug effect, however, it did not affect the predictive performance of the model.

Figure 1

Schematic presentation of integrated glucose‐insulin oral glucose tolerance test model in type 2 diabetes mellitus (Jauslin et al.2) with seven drug mechanism of actions. BINS, basal insulin secretion; CLG, insulin‐independent glucose clearance; CLGI, insulin‐dependent glucose clearance; CL_I, insulin clearance; EGP, endogenous glucose production; GABS, glucose absorption; GSEN, glucose sensitivity; INCR, incretin activity.

Figure 2

Part 1: relative estimation error for glucose under the curve (AUCG) ratio in seven drug effects. BINS, basal insulin secretion; CLG, insulin‐independent glucose clearance; CLGI, insulin‐dependent glucose clearance; EGP, endogenous glucose production; GABS, glucose absorption; Glu, glucose only data; Glu + Ins, glucose and insulin data; GSEN, glucose sensitivity; INCR, incretin activity.