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

Abstract

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.

A model‐based approach to investigating the relationship between glucose‐insulin dynamics and dapagliflozin treatment effect in patients with type 2 diabetes

“…Parameters from the final model can be found in Table S3; approximately half of the model parameters were fixed to previously estimated values (indicated in italics in Table S3). 12,13,18,19,25,26 The following parameters were estimated: steady‐state plasma glucose (GSS), steady‐state plasma insulin (ISS), glucose absorption rate (K abs (meal) and K abs (OGTT)), insulin‐dependent glucose clearance, slope of the incretin effect (S INCR ), amplitude of time‐variant glucose production (MA), time of maximal glucose modulation, width of glucose modulation function, SGLT2 maximum renal glucose reabsorption, and dapagliflozin PK parameters (plasma clearance exclusive of renal drug clearance [CL dapa ], volume of distribution for central and peripheral compartments, intercompartmental clearance, and absorption lag time). Glucose absorption rates can vary depending on the type of carbohydrate consumed 27 .…”

“…parameters were fixed to previously estimated values (indicated in italics in Table S3). 12,13,18,19,25,26 The following parameters were esti- These parameters were chosen for estimation because they are known to vary between diabetes cohorts (eg, steady-state plasma glucose and insulin) and are often associated with disease severity (eg, insulin-dependent glucose clearance, which reflects a patientʼs insulin resistance). For example, S INCR is progressively lost during diabetes onset.…”

“…Parameters from the final model can be found in Table S3; approximately half of the model S3). 12,13,18,19,25,26 The following parameters were esti- 27 To capture the difference in OGTT and meal administration, two different absorption rates were estimated: one for meals consisting of complex carbohydrates and one for the OGTT glucose solution.…”

Characterisation of individual ferritin response in patients receiving chelation therapy

“…Whereas significant interindividual differences exist in the time course of serum ferritin concentrations relative to the start of chelation therapy, it is striking that so little has happened to date to establish how baseline characteristics, transfusion blood volume and chelation determines such differences 7,47 . Similar approaches have been developed successfully in other therapeutic areas 48–54 . For instance, compartmental models have been developed for antidiabetic drugs where the delay in response and impact of disease progression were characterised and subsequently used to assess the need to adjust therapy in Type 2 diabetes mellitus (T2DM) 50 .…”

“…7,47 Similar approaches have been developed successfully in other therapeutic areas. [48][49][50][51][52][53][54] For instance, compartmental models have been developed for antidiabetic drugs where the delay in response and impact of disease progression were characterised and subsequently used to assess the need to adjust therapy in Type 2 diabetes mellitus (T2DM). 50 More recently, an integrated glucose-insulin model was successfully applied to evaluate the differences between T2DM patients across a wide range of glycaemic control.…”