A model-based approach to predict longitudinal HbA1c, using early phase glucose data from type 2 diabetes mellitus patients after anti-diabetic treatment.

Predicting late phase outcomes from early-phase findings can help inform decisions in drug development. If the measurements in early-phase differ from those in late phase, forecasting is more challenging. In this paper, we present a model-based approach for predicting glycosylated hemoglobin (HbA1c) in late phase using glucose and insulin concentrations from an early-phase study, investigating an anti-diabetic treatment. Two previously published models were used; an integrated glucose and insulin (IGI) model for meal tolerance tests and an integrated glucose-red blood cell-HbA1c (IGRH) model predicting the formation of HbA1c from the average glucose concentration (Cg,av ). Output from the IGI model was used as input to the IGRH model. Parameters of the IGI model and drug effects were estimated using data from a phase1 study in 59 diabetic patients receiving various doses of a glucokinase activator. Cg,av values were simulated according to a Phase 2 study design and used in the IGRH model for predictions of HbA1c. The performance of the model-based approach was assessed by comparing the predicted to the actual outcome of the Phase 2 study. We have shown that this approach well predicts the longitudinal HbA1c response in a 12-week study using only information from a 1-week study where glucose and insulin concentrations were measured.