2015 - Hersonissos, Crete - Greece

PAGE 2015: Methodology - Other topics
Moustafa M. A. Ibrahim

Design of Phase I Studies based on Mechanism of Action of Anti-Diabetic Drugs; Assessing power, precision and accuracy in a simulation study of glucose tolerance tests

Moustafa M. A. Ibrahim, Siti M. Sheikh Ghadzi, , Maria C. Kjellsson, Mats O. Karlsson

Department of Pharmaceutical Bioscience, Uppsala university

Background and Objectives: In anti-diabetic drug development, phase I studies usually involve short-term glucose provocations, e.g. meal tolerance test (MTT) and graded glucose infusion (GGI). With a highly nonlinear, complex system as the glucose homeostasis, the various provocations will contribute with somewhat different information. The aim with this project was to investigate the most appropriate study design in phase I, for several hypothetical mechanisms of action (MoA) of a study drug. Power to detect drug effect and accuracy of quantification of drug effect was assessed using simulations.

Methods: Five drug effects in diabetes therapeutic area were investigated using six study design models. The MoA of drug effects were stimulation of basal insulin (BINS), glucose clearance, both insulin dependent (CLGI) and independent (CLG), as well as inhibition of endogenous glucose production (EGP) and absorption of oral glucose (GABS). The study design were oral glucose tolerance test (OGTT), intravenous glucose tolerance test (IVGTT) single meal tolerance test (sMTT), 24-hours meal tolerance test (MTT-24), graded glucose infusion (GGI) and repeated fasting glucose sampling, i.e. no provocation (NO). The models used for simulations and estimations were different versions of the integrated glucose-insulin model (IGI) [1-3]. Monte carlo mapped power (MCMP) [4] was used to calculate, for each study design, the power to detect each of the above mentioned drug effects. Stochastic simulation and estimation (SSE) was used for determination of the most precise study design model in computing the size of a particular drug effect The power and precision of models, were determined by using the MCMP and SSE tools implemented in PsN version 3.5 (PsN, Uppsala University, Uppsala, Sweden) [5] and NONMEM version 7.3 (ICON Development Solutions, Ellicott City, MD) [6]. Graphs and Data set creation for NONMEM were performed using R [7].

Results: The power of IVGTT and GGI was similar for all MoAs, always higher than sMTT if the drug effect was not on GABS. The 24-MTT was more powerful than sMTT for all MoAs except CLG. Using repeated fasting measurements were surprisingly powerful, and was for many MoAs similar to sMTT. The study design with the highest drug parameter precision was not always the most powerful to detect the drug effect.

Conclusion: Pharmacometric model-based simulations can be a valuable tool in the design of phase I studies in phase I anti-diabetic drug studies as the power and precision of various study designs is highly dependent on MoA of study drug.

[1] Silber HE, Jauslin PM, Frey N, Gieschke R, Simonsson US, Karlsson MO. An integrated model for glucose and insulin regulation in healthy volunteers and type 2 diabetic patients following intravenous glucose provocations. Journal of clinical pharmacology 2007, 47(9): 1159-1171.
[2] Jauslin PM, Silber HE, Frey N, Gieschke R, Simonsson US, Jorga K, et al. An integrated glucose-insulin model to describe oral glucose tolerance test data in type 2 diabetics. Journal of clinical pharmacology 2007, 47(10): 1244-1255.261-81.
[3] Jauslin PM, Karlsson MO, Frey N. 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. Journal of clinical pharmacology 2012, 52(12): 1861-1871.
[4] Vong C, Bergstrand M, Nyberg J, Karlsson MO. Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models. The AAPS journal 2012, 14(2): 176-186.
[5] Keizer RJ, Karlsson MO, Hooker A. Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose. CPT: pharmacometrics & systems pharmacology 2013, 2: e50.
[6] Beal, S., Sheiner, L.B., Boeckmann, A., & Bauer, R.J., NONMEM User's Guides. (1989-2009), Icon Development Solutions, Ellicott City, MD, USA, 2009.
[7] Team RC. R: A Language and Environment for Statistical Computing. Vienna, Austria: 2014. Available from: http://www.R-project.org.

This work was supported by the DDMoRe (www.ddmore.eu) project.

Reference: PAGE 24 (2015) Abstr 3532 [www.page-meeting.org/?abstract=3532]
Poster: Methodology - Other topics
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