Clinical Validation of a New Control-Oriented Model of Insulin and Glucose Dynamics in Subjects with Type 1 Diabetes

Fabietti, Pier Giorgio; Canonico, Valentina; Orsini-Federici, M.; Sarti, E.; Massi‐Benedetti, Massimo

doi:10.1089/dia.2006.0030

Cited by 21 publications

(23 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A common feature of most drug‐disease models in diabetes is that they have been developed for descriptive rather than predictive purposes. However, models for predictive purposes do exist, such as closed loop models for the glucose–insulin system in T1DM under continuous insulin infusion therapy . Models that describe the effects of drugs on the glucose–insulin system can be used to characterize the mechanism of action of a drug, guide dose selection, and contribute to the assessment of efficacy and safety.…”

Section: Discussionmentioning

confidence: 99%

Modeling of 24‐hour glucose and insulin profiles in patients with type 2 diabetes mellitus treated with biphasic insulin aspart

Røge

Klim

Kristensen

et al. 2014

The Journal of Clinical Pharma

View full text Add to dashboard Cite

Insulin therapy for diabetes patients is designed to mimic the endogenous insulin response of healthy subjects and thereby generate normal blood glucose levels. In order to control the blood glucose in insulin-treated diabetes patients, it is important to be able to predict the effect of exogenous insulin on blood glucose. A pharmacokinetic/pharmacodynamic model for glucose homoeostasis describing the effect of exogenous insulin would facilitate such prediction. Thus the aim of this work was to extend the previously developed integrated glucose-insulin (IGI) model to predict 24-hour glucose profiles for patients with Type 2 diabetes following exogenous insulin administration. Clinical data from two trials were included in the analysis. In both trials, 24-hour meal tolerance tests were used as the experimental setup, where exogenous insulin (biphasic insulin aspart) was administered in relation to meals. The IGI model was successfully extended to include the effect of exogenous insulin. Circadian variations in glucose homeostasis were assessed on relevant parameters, and a significant improvement was achieved by including a circadian rhythm on the endogenous glucose production in the model. The extended model is a useful tool for clinical trial simulation and for elucidating the effect profile of new insulin products.

show abstract

Section: Discussionmentioning

confidence: 99%

Modeling of 24‐hour glucose and insulin profiles in patients with type 2 diabetes mellitus treated with biphasic insulin aspart

Røge

Klim

Kristensen

et al. 2014

The Journal of Clinical Pharma

View full text Add to dashboard Cite

show abstract

“…With the exception of models for insulin therapy, 7 , 19 , 20 drug‐disease models in the diabetes area have mainly been developed for descriptive analysis of summary parameters rather than for predictive purposes. Agersø and Vicini 10 presented a glucose‐insulin model with components of the minimal model that could reproduce a dose‐dependent effect of glucagon‐like peptide‐1 on insulin secretion.…”

Section: Discussionmentioning

confidence: 99%

“…Nonlinear mixed effects modeling was applied using the first‐order conditional estimation method 19 of NONMEM 6 and the differential equation solver ADVAN6. Simulations were performed with the same software.…”

Section: Methodsmentioning

confidence: 99%

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

Jauslin

Karlsson

Frey

2012

The Journal of Clinical Pharma

View full text Add to dashboard Cite

A mechanistic drug‐disease model was developed on the basis of a previously published integrated glucose‐insulin model by Jauslin et al. A glucokinase activator was used as a test compound to evaluate the model's ability to identify a drug's mechanism of action and estimate its effects on glucose and insulin profiles following oral glucose tolerance tests. A kinetic‐pharmacodynamic approach was chosen to describe the drug's pharmacodynamic effects in a dose‐response‐time model. Four possible mechanisms of action of antidiabetic drugs were evaluated, and the corresponding affected model parameters were identified: insulin secretion, glucose production, insulin effect on glucose elimination, and insulin‐independent glucose elimination. Inclusion of drug effects in the model at these sites of action was first tested one‐by‐one and then in combination. The results demonstrate the ability of this model to identify the dual mechanism of action of a glucokinase activator and describe and predict its effects: Estimating a stimulating drug effect on insulin secretion and an inhibiting effect on glucose output resulted in a significantly better model fit than any other combination of effect sites. The model may be used for dose finding in early clinical drug development and for gaining more insight into a drug candidate's mechanism of action.

show abstract

“…For instance, one could design a controller to regulate glucose in an ICU setting (cf. Sedigh-Sarvestan et al [38] where the an unscented Kalman filter is applied to the model in this paper), one could use the controller to design optimal treatment strategies over long periods of time for outpatient diabetics, or one could design artificial organs such as the artificial pancreas project [39] [40] [41]. But these possibilities are only possible in practice when we have a constructive model available.…”

Section: Discussionmentioning

confidence: 99%

“…Such a situation is referred to as in silico experimentation, and it has already begun in some contexts. For example, recently an endocrine model of the type 1 diabetes, being used in the context of developing an artificial pancreas [36], has been approved by the FDA as a substitute for animal trials for preclinical trials [41] [39] [40]. In this case, artificial data are created (based on real data, but not a DA analysis), and then different treatment strategies are tested.…”

Section: Discussionmentioning

confidence: 99%

Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

2012

View full text Add to dashboard Cite

Studying physiology and pathophysiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data can be intrusive, dangerous, and expensive. One solution is to use data that have been collected for a different purpose. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse populations and allow correlation with clinical outcomes, but limitations in the data have thus far thwarted such use. For example, using uncontrolled population-scale EHR data to verify the outcome of time dependent behavior of mechanistic, constructive models can be difficult because: (i) aggregation of the population can obscure or generate a signal, (ii) there is often no control population with a well understood health state, and (iii) diversity in how the population is measured can make the data difficult to fit into conventional analysis techniques. This paper shows that it is possible to use EHR data to test a physiological model for a population and over long time scales. Specifically, a methodology is developed and demonstrated for testing a mechanistic, time-dependent, physiological model of serum glucose dynamics with uncontrolled, population-scale, physiological patient data extracted from an EHR repository. It is shown that there is no observable daily variation the normalized mean glucose for any EHR subpopulations. In contrast, a derived value, daily variation in nonlinear correlation quantified by the time-delayed mutual information (TDMI), did reveal the intuitively expected diurnal variation in glucose levels amongst a random population of humans. Moreover, in a population of continuously (tube) fed patients, there was no observable TDMI-based diurnal signal. These TDMI-based signals, via a glucose insulin model, were then connected with human feeding patterns. In particular, a constructive physiological model was shown to correctly predict the difference between the general uncontrolled population and a subpopulation whose feeding was controlled.

show abstract

Clinical Validation of a New Control-Oriented Model of Insulin and Glucose Dynamics in Subjects with Type 1 Diabetes

Abstract: Simulation results confirmed the capacity of the model to faithfully represent the glucose-insulin relationship in type 1 diabetes in different circumstances.

Cited by 21 publications

References 31 publications

Modeling of 24‐hour glucose and insulin profiles in patients with type 2 diabetes mellitus treated with biphasic insulin aspart

Modeling of 24‐hour glucose and insulin profiles in patients with type 2 diabetes mellitus treated with biphasic insulin aspart

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

Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

Contact Info

Product

Resources

About