2013 - Glasgow - Scotland

PAGE 2013: Clinical Applications
Jonas Bech Møller

Optimizing clinical diabetes drug development – what is the recipe?

Jonas B Møller (1), Rune V Overgaard (1), Maria C Kjellsson (2), Niels R Kristensen (1), Søren Klim (1), Steen H Ingwersen (1), Mats Karlsson (2)

(1) Quantitative Clinical Pharmacology, Novo Nordisk A/S, Søborg, Denmark (2) The Pharmacometrics Group, Uppsala University, Uppsala, Sweden;

Objectives: A key challenge in diabetes drug development is to extrapolate the results from early clinical efficacy assessments (clamp studies or glucose provocation tests) to late phase efficacy outcomes such as HbA1c. With the increasing need for investigating anti-diabetic medicine in special populations (e.g. paediatrics), another challenge is to use available data to extrapolate from one population to another. These challenges call for a library of quantitative models to link and predict key endpoints in diabetes trials during the different phases of drug development. The objective of this presentation is to show how specific pharmacometric diabetes models, alone or in combination, can be applied to optimize clinical diabetes drug development.

Methods: By combining a PK model with a model for glucose homeostasis [1], a link between drug concentration and drug effect on plasma glucose can be established, as previously shown for an oral anti-diabetic (OAD) or insulin treatment  [2,3]. By subsequently applying a model linking glucose to HbA1c [4], the predicted plasma glucose response can be used for prediction of late phase efficacy outcome. Clearly, assessment of treatment dependence on the link between glucose and HbA1c is crucial, and thus we applied individual data from 4 clinical trials covering 12 treatment arms (OADs, GLP-1 agonist, and insulins) to test our approach.

Results: The performance of the proposed framework is illustrated through a case study where trial outcome wrt. HbA1c for each treatment arm was predicted. The HbA1c predictions were successful with a mean absolute error ranging from 0.0% to 0.24% across treatment arms. Calculations of the mean ∆HbA1c vs. comparator and the corresponding confidence intervals were shown to provide identical conclusions based on predictions and observations at end-of-trial.

Conclusions: In this presentation we outlined and applied models for linking early phase assessments and late phase treatment outcomes within clinical diabetes drug development. Implementation and validation of these models were driven by a consistent focus on the ability to predict future trial outcomes and link data from different stages of clinical development. We find this a key ingredient in the recipe for optimizing diabetes drug development.

Acknowledgements: This work was part of the DDMoRe project.

[1] Jauslin PM et. al: An integrated glucose-insulin model to describe oral glucose tolerance test data in type 2 diabetics, 2007
[2] Jauslin PM et. al: Identification of the mechanism of action of a glucokinase activator from oral glucose tolerance test data in type 2 diabetic patients based on an integrated glucose-insulin model, 2011
[3] Roege RM et. al.: PAGE poster n. l-61, 2012: Integrated model of glucose homeostasis including the effect of exogenous insulin
[4] Moeller et. al.: ADOPT (A Dynamic HbA1c EndpOint Prediction Tool) - A framework for predicting primary endpoint in Phase 3 diabetes trials, abstract accepted for ACOP 2013

Reference: PAGE 22 (2013) Abstr 2884 [www.page-meeting.org/?abstract=2884]
Oral: Clinical Applications
Click to open PDF poster/presentation (click to open)