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.

Study Design Selection in Early Clinical Anti‐Hyperglycemic Drug Development: A Simulation Study of Glucose Tolerance Tests

“…This method does not require assessment of the type I error rate or repeated sampling and is thus faster than the above alternatives. This method has been shown to produce similar results to the ratio of individuals at a certain power using the Monte‐Carlo Mapped Power Method . Yet, another way to compare these designs would be to compare the SE of θ x estimates, in a similar manner to efficiency calculations in optimal design.…”

“…been shown to produce similar results to the ratio of individuals at a certain power using the Monte-Carlo Mapped Power Method. 32 Yet, another way to compare these designs would be to compare the SE of θ x estimates, in a similar manner to efficiency calculations in optimal design. With optimal design, it is possible also to change the standard design variables to increase the power for a certain drug effect.…”