The Padova Type 2 Diabetes Simulator from Triple-Tracer Single-Meal Studies: <i>In Silico</i> Trials Also Possible in Rare but Not-So-Rare Individuals

Visentin, R.; Cobelli, Claudio; Man, Chiara Dalla

doi:10.1089/dia.2020.0110

Cited by 23 publications

(40 citation statements)

References 34 publications

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“…Actually, being able to account for subject variability and to detect useful covariates will pave the way for precision medicine in the field of diabetes, providing a customized approach to the disease for each patient. Furthermore, this model will be useful in the development of algorithms for subcutaneous insulin delivery implemented in insulin pump devices [20,21] and will be an important component of in silico platforms, like the UVa/Padova T1D Simulator [14][15][16][17][18][19]. The incorporation into the simulator of models that account for subject variability would allow for more realistic simulations, providing great benefits on the way to the development and approval of new insulin compounds.…”

Section: Discussionmentioning

confidence: 99%

“…Such a model will become an important component of the University of Virginia (UVa)/Padova T1D Simulator [14], an in silico platform accepted by the Food and Drug Administration (FDA) as a substitute for preclinical trials for certain insulin treatments, including closed-loop algorithms for artificial pancreas [15], recently used as an ideal test bench for the development and evaluation of glucose sensors [16] and novel insulin analogues [17,18]. Furthermore, the presented model could be incorporated also in the recently proposed Padova T2D simulator [19], allowing optimizing insulin therapy in this population. The incorporation into the simulators of a model that explicitly accounts for BSV would decrease the gap between the simulation environment and the real subject behavior, improving the in silico optimization of personalized insulin treatment.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Between-Subject Variability in Subcutaneous Absorption of a Fast-Acting Insulin Analogue by a Nonlinear Mixed Effects Approach

2021

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Despite the great progress made in insulin preparation and titration, many patients with diabetes are still experiencing dangerous fluctuations in their blood glucose levels. This is mainly due to the large between- and within-subject variability, which considerably hampers insulin therapy, leading to defective dosing and timing of the administration process. In this work, we present a nonlinear mixed effects model describing the between-subject variability observed in the subcutaneous absorption of fast-acting insulin. A set of 14 different models was identified on a large and frequently-sampled database of lispro pharmacokinetic data, collected from 116 subjects with type 1 diabetes. The tested models were compared, and the best one was selected on the basis of the ability to fit the data, the precision of the estimated parameters, and parsimony criteria. The selected model was able to accurately describe the typical trend of plasma insulin kinetics, as well as the between-subject variability present in the absorption process, which was found to be related to the subject’s body mass index. The model provided a deeper understanding of the insulin absorption process and can be incorporated into simulation platforms to test and develop new open- and closed-loop treatment strategies, allowing a step forward toward personalized insulin therapy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Between-Subject Variability in Subcutaneous Absorption of a Fast-Acting Insulin Analogue by a Nonlinear Mixed Effects Approach

2021

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“…This paper is a step towards model-free algorithms for insulin titration in T2D, and data-driven closed-loop glycemic regulation. Future work will involve testing the proposed algorithm on a more detailed physiological model that have recently been developed for T2D such as [17], [18]. The proposed method may also be used for model-free basal insulin titration for patients in T1D.…”

Section: Discussionmentioning

confidence: 99%

A Model-Free Approach to Automatic Dose Guidance in Long Acting Insulin Treatment of Type 2 Diabetes

Krishnamoorthy

Boiroux

Aradóttir

et al. 2021

IEEE Control Syst. Lett.

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“…Insulin secretion is described by the model of Breda et al (2001), discussed above. This model was successively expanded to describe other aspects of glucose homeostasis, including: a more detailed representation of the dependence of glucose utilization on glucose concentration, glucagon secretion and kinetics and its effects on glucose production, in type 1 diabetic patients (Dalla Man et al, 2014); circadian variations in insulin sensitivity [derived from Visentin et al (2015)]; the "dawn" phenomenon (early-morning increase in blood glucose concentration) (Visentin et al, 2018); and a three-compartment model of insulin kinetics (Visentin et al, 2020). The incretin effect was not modeled.…”

Section: Glucose Homeostasismentioning

confidence: 99%

Mathematical Modeling for the Physiological and Clinical Investigation of Glucose Homeostasis and Diabetes

et al. 2020

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Mathematical modeling in the field of glucose metabolism has a longstanding tradition. The use of models is motivated by several reasons. Models have been used for calculating parameters of physiological interest from experimental data indirectly, to provide an unambiguous quantitative representation of pathophysiological mechanisms, to determine indices of clinical usefulness from simple experimental tests. With the growing societal impact of type 2 diabetes, which involves the disturbance of the glucose homeostasis system, development and use of models in this area have increased. Following the approaches of physiological and clinical investigation, the focus of the models has spanned from representations of whole body processes to those of cells, i.e., from in vivo to in vitro research. Model-based approaches for linking in vivo to in vitro research have been proposed, as well as multiscale models merging the two areas. The success and impact of models has been variable. Two kinds of models have received remarkable interest: those widely used in clinical applications, e.g., for the assessment of insulin sensitivity and β-cell function and some models representing specific aspects of the glucose homeostasis system, which have become iconic for their efficacy in describing clearly and compactly key physiological processes, such as insulin secretion from the pancreatic β cells. Models are inevitably simplified and approximate representations of a physiological system. Key to their success is an appropriate balance between adherence to reality, comprehensibility, interpretative value and practical usefulness. This has been achieved with a variety of approaches. Although many models concerning the glucose homeostasis system have been proposed, research in this area still needs to address numerous issues and tackle new opportunities. The mathematical representation of the glucose homeostasis processes is only partial, also because some mechanisms are still only partially understood. For in vitro research, mathematical models still need to develop their potential. This review illustrates the problems, approaches and contribution of mathematical modeling to the physiological and clinical investigation of glucose homeostasis and diabetes, focusing on the most relevant and stimulating models.

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The Padova Type 2 Diabetes Simulator from Triple-Tracer Single-Meal Studies: In Silico Trials Also Possible in Rare but Not-So-Rare Individuals

Cited by 23 publications

References 34 publications

Modeling Between-Subject Variability in Subcutaneous Absorption of a Fast-Acting Insulin Analogue by a Nonlinear Mixed Effects Approach

Modeling Between-Subject Variability in Subcutaneous Absorption of a Fast-Acting Insulin Analogue by a Nonlinear Mixed Effects Approach

A Model-Free Approach to Automatic Dose Guidance in Long Acting Insulin Treatment of Type 2 Diabetes

Mathematical Modeling for the Physiological and Clinical Investigation of Glucose Homeostasis and Diabetes

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