Pharmacokinetics of insulin lispro in type 2 diabetes during closed-loop insulin delivery

Ruan, Yue; Thabit, Hood; Kumareswaran, Kavita; Hovorka, Roman

doi:10.1016/j.cmpb.2014.07.004

Cited by 5 publications

(5 citation statements)

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“…All models adopted the following assumptions: a linear relationship exists between plasma glucose concentration and posthepatic insulin secretion rate; MCR I is identical between study visits; the equilibration is instantaneous between posthepatic insulin appearance in plasma and plasma insulin, reflecting a short plasma insulin half-life relative to the frequency of plasma insulin measurements. The latter assumption was adopted in our previous study during which individual MCR I values were estimated [ 13 ].…”

Section: Discussionmentioning

confidence: 99%

“…I ENDO ( t ) (mU/l), the measured endogenous plasma insulin concentration and model output, is assumed proportional to I s (t), through the inverse of the product of insulin metabolic clearance rate MCR I (l/kg/min) and subject’s body weight W (kg). This assumption is based on the relatively short plasma insulin half-life (~5 min) compared to the sampling frequency in our study (15 to 60 min), allowing us to assume an instantaneous equilibration between posthepatic insulin appearance in plasma and plasma insulin [ 13 ]. The individual parameter values of MCR I are taken from our previous study [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

“…This assumption is based on the relatively short plasma insulin half-life (~5 min) compared to the sampling frequency in our study (15 to 60 min), allowing us to assume an instantaneous equilibration between posthepatic insulin appearance in plasma and plasma insulin [ 13 ]. The individual parameter values of MCR I are taken from our previous study [ 13 ]. In Eq.…”

Section: Methodsmentioning

confidence: 99%

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Modelling endogenous insulin concentration in type 2 diabetes during closed-loop insulin delivery

et al. 2015

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BackgroundClosed-loop insulin delivery is an emerging treatment for type 1 diabetes (T1D) evaluated clinically and using computer simulations during pre-clinical testing. Efforts to make closed-loop systems available to people with type 2 diabetes (T2D) calls for the development of a new type of simulators to accommodate differences between T1D and T2D. Presented here is the development of a model of posthepatic endogenous insulin concentration, a component omitted in T1D simulators but key for simulating T2D physiology.MethodsWe evaluated six competing models to describe the time course of endogenous insulin concentration as a function of the plasma glucose concentration and time. The models were fitted to data collected in insulin-naive subjects with T2D who underwent two 24-h visits and were treated, in a random order, by either closed-loop insulin delivery or glucose-lowering oral agents. The model parameters were estimated using a Bayesian approach, as implemented in the WinBUGS software. Model selection criteria were used to identify the best model describing our clinical data.ResultsThe selected model successfully described endogenous insulin concentration over 24 h in both study periods and provided plausible parameter estimates. Model-derived results were in concordance with a clinical finding which revealed increased posthepatic endogenous insulin concentration during the control study period (P < 0.05). The modelling results indicated that the excess amount of insulin can be attributed to the glucose-independent effect as the glucose-dependent effect was similar between visits (P > 0.05).ConclusionsA model to describe endogenous insulin concentration in T2D including components of posthepatic glucose-dependent and glucose-independent insulin secretion was identified and validated. The model is suitable to be incorporated in a simulation environment for evaluating closed-loop insulin delivery in T2D.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-015-0009-5) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Modelling endogenous insulin concentration in type 2 diabetes during closed-loop insulin delivery

et al. 2015

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“…A model describing glucose-dependent and glucose-independent insulin secretion was identified and validated by one research team, although their model now needs to be tested in clinical trials (5). The same researchers analyzed the pharmacokinetics of exogenous insulin in patients with type 2 during a closed-loop trial (6). Outpatient studies geared toward statistical significance are needed to validate their results.…”

Section: Closing the Loop S-41mentioning

confidence: 99%

Closing the Loop

Dassau

Hennings

Fazio

et al. 2016

Diabetes Technology & Therapeutics

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T ype 1 diabetes mellitus presents a significant challenge to patients, as it involves integrating treatment into daily life to compensate for the inability to produce insulin to meet regular metabolic needs. The risks of hyperglycemia and hypoglycemia are obstacles to patients trying to achieve and maintain healthy blood glucose levels. Type 1 patients must be attuned to their treatment needs in order to avoid such risks, which can prove very difficult with inevitably varied lifestyles. Certain technological advances, such as the continuous glucose monitor and subcutaneous insulin infusion pumps, have aided to ease the burden of blood glucose control, but patients are still expected to intervene and ultimately are still susceptible to lapses in effective treatment. The development of the artificial pancreas (AP) is being pursued to combat this burden. Significant advancements in the development of closed-loop AP have made its use in the treatment of type 1 diabetes mellitus in real-world settings more feasible. A closed-loop AP system consists of a continuous glucose monitor, subcutaneous insulin infusion pump, and an automated control algorithm to bridge the communication gap between the two devices. To date, a number of promising AP clinical trials have taken place to test the safety and efficacy of closed-loop systems. Current studies are now beginning to transition from more inpatient to outpatient settings of larger sample sizes and longer durations, and initial studies of nonsupervised use of AP systems at home in real-life environments have already been performed. As technology and positive evidence for closed-loop therapy progresses, attitudes of acceptance toward the use of AP by patients in the real world are growing as well (1). Single-hormone, closed-loop systems are close to becoming a reality in the world of treatment for patients with type 1 diabetes mellitus, signaling AP research to transition its focus to the development of closed-loop, bihormonal systems. Our aim in this article is to present a few of the most important studies related to closed-loop AP published within the previous year.

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“…Non-parametric models are true data models suitable to work with erratic glucose-insulin dynamics (Daskalaki et al, 2013(Daskalaki et al, , 2012, which makes them a powerful tool for adaptive control by reducing the problem of model bias, which frequently occurs when deterministic models are used. In (Ruan, Thabit, Kumareswaran, & Hovorka, 2014), the proposal is to estimate the parameters of the model developed to describe the insulin pharmaco-kinetics; a Bayesian inference approach is used based on the collected data from patients. More specifically, a Gaussian process is a novel modeling tool within the nonparametric framework which when combined with tractable Bayesian inference (Rasmussen & Williams, 2006) is useful to cope with modeling the glucose-insulin dynamics under uncertainty (Markakis, Mitsis, Papavassilopoulos, & Marmarelis, 2010).…”

Section: Introductionmentioning

confidence: 99%

On-line policy learning and adaptation for real-time personalization of an artificial pancreas

Paula

Acosta

Martínez

2015

Expert Systems with Applications

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Pharmacokinetics of insulin lispro in type 2 diabetes during closed-loop insulin delivery

Cited by 5 publications

References 29 publications

Modelling endogenous insulin concentration in type 2 diabetes during closed-loop insulin delivery

Modelling endogenous insulin concentration in type 2 diabetes during closed-loop insulin delivery

Closing the Loop

On-line policy learning and adaptation for real-time personalization of an artificial pancreas

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