Identification of the primary mechanism of action of an insulin secretagogue from meal test data in healthy volunteers based on an integrated glucose-insulin model

et al. 2016

Brit J Clinical Pharma

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AIMThe integrated glucose-insulin (IGI) model is a semi-mechanistic physiological model which can describe the glucose-insulin homeostasis system following various glucose challenge settings. The aim of the present work was to apply the model to a large and diverse population of metformin-only-treated type 2 diabetes mellitus (T2DM) patients and identify patient-specific covariates. METHODSData from four clinical studies were pooled, including glucose and insulin concentration-time profiles from T2DM patients on stable treatment with metformin alone following mixed-meal tolerance tests. The data were collected from a wide range of patients with respect to the duration of diabetes and level of glycaemic control. RESULTSThe IGI model was expanded by four patient-specific covariates. The level of glycaemic control, represented by baseline glycosylated haemoglobin was identified as a significant covariate for steady-state glucose, insulin-dependent glucose clearance and the magnitude of the incretin effect, while baseline body mass index was a significant covariate for steady-state insulin levels. In addition, glucose dose was found to have an impact on glucose absorption rate. The developed model was used to simulate glucose and insulin profiles in different groups of T2DM patients, across a range of glycaemic control, and it was found accurately to characterize their response to the standard oral glucose challenge. CONCLUSIONSThe IGI model was successfully applied to characterize differences between T2DM patients across a wide range of glycaemic control. The addition of patient-specific covariates in the IGI model might be valuable for the future development of antidiabetic treatment and for the design and simulation of clinical studies. British Journal of Clinical Pharmacology WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• The semi-mechanistic integrated glucose-insulin (IGI) model can quantitatively describe glucose-insulin homeostasis following various glucose challenge settings. WHAT THIS STUDY ADDS• Four patient-specific covariates have been indentified and included in the IGI model.• The expanded IGI model allows characterization of different patient groups across a range of glycaemic control.• The model can be applied for the design and simulation of future clinical studies and optimization of antidiabetic drug treatments.

Section: Discussionsupporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Application of the integrated glucose–insulin model for cross‐study characterization of T2DM patients on metformin background treatment

Parkinson

Hamrén

et al. 2016

Brit J Clinical Pharma

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“…(listed in Table ). The model, developed by Jauslin et al ., was chosen because it is a closed‐loop system, enabling integrated simulations of glucose and insulin, and it has shown good estimation and prediction performance as well as the ability to characterize drug effects …”

Section: Methodsmentioning

confidence: 99%

“…The model, developed by Jauslin et al, 2 was chosen because it is a closed-loop system, enabling integrated simulations of glucose and insulin, and it has shown good estimation and prediction performance as well as the ability to characterize drug effects. 3,[13][14][15][16] The pharmacokinetics (PKs) of the study compound was described using an oral one-compartment model with first order elimination, parameterized in terms of absorption rate constant, clearance (CL D ), and volume of distribution (V D ). The PK parameters were chosen such that the time of maximum plasma concentration (T max ) was observed after 1 hour and the 4-hour postdose concentration was half of peak plasma concentration (C max ), ensuring a compound eligible for twice daily dosing (b.i.d.…”

Section: Simulation Of Datamentioning

confidence: 99%

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

Ghadzi

Karlsson

CPT Pharmacom & Syst Pharma

2017

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.

“…and orally administered glucose . The IGI model has been used in drug development to quantify drug effects and to optimize the design of clinical trials by making the trials richer in information . The model consists of a glucose submodel with distribution and with elimination by two routes, an insulin‐dependent route and an insulin‐independent route.…”

mentioning

confidence: 99%

Model‐Based Interspecies Scaling of Glucose Homeostasis

Alskär

Karlsson

CPT Pharmacom & Syst Pharma

2017

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Being able to scale preclinical pharmacodynamic response to clinical would be beneficial in drug development. In this work, the integrated glucose insulin (IGI) model, developed on clinical intravenous glucose tolerance test (IVGTT) data, describing dynamic glucose and insulin concentrations during glucose tolerance tests, was scaled to describe data from similar tests performed in healthy rats, mice, dogs, pigs, and humans. Several approaches to scaling the dynamic glucose and insulin were investigated. The theoretical allometric exponents of 0.75 and 1, for clearances and volumes, respectively, could describe the data well with some species‐specific adaptations: dogs and pigs showed slower first phase insulin secretion than expected from the scaling, pigs also showed more rapid insulin dependent glucose elimination, and rodents showed differences in glucose effectiveness. The resulting scaled IGI model was shown to accurately predict external preclinical IVGTT data and may be useful in facilitating translations of preclinical research into the clinic.