In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies

Viceconti, Marco; Cobelli, Claudio; Haddad, Tarek; Himes, Adam; Kovatchev, Boris; Palmer, Mark

doi:10.1177/0954411917702931

Cited by 51 publications

(43 citation statements)

References 100 publications

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“…Since then, no animal experiments have been conducted for the purpose of designing AP algorithms. It can be envisioned that virtual environments will increasingly allow thorough testing of myriad control cycles in extreme physiological situations, or during low-probability system failures, that cannot be reproduced in real life (Viceconti et al 2017). Modular architecture allows AP systems to be assembled from independent (but compatible) modules, each performing a specific function, e.g.…”

Section: Discussionmentioning

confidence: 99%

Automated closed-loop control of diabetes: the artificial pancreas

Kovatchev

2018

Bioelectron Med

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The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closedloop AP is well on its way to become the digital-age treatment of diabetes.

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Section: Discussionmentioning

confidence: 99%

Automated closed-loop control of diabetes: the artificial pancreas

Kovatchev

2018

Bioelectron Med

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“…Models are indeed simplified versions of the real world but, if properly developed and used, they can be of help in reducing costs and time both in the design and in product development. 40…”

Section: Discussionmentioning

confidence: 99%

In SilicoHead-to-Head Comparison of Insulin Glargine 300 U/mL and Insulin Degludec 100 U/mL in Type 1 Diabetes

Schiavon

Visentin

Giegerich

et al. 2020

Diabetes Technology & Therapeutics

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Background: Second-generation long-acting insulin glargine 300 U/mL (Gla-300) and degludec 100 U/mL (Deg-100) provide novel basal insulin therapies for the treatment of type 1 diabetes (T1D). Both offer a flatter pharmacokinetic (PK) profile than the previous generation of long-acting insulins, thus improving glycemic control while reducing hypoglycemic events. This work describes an in silico head-to-head comparison of the two basal insulins on 24-h glucose profiles and was used to guide the design of a clinical trial. Materials and Methods: The Universities of Virginia (UVA)/Padova T1D simulator describes the intra-/ interday variability of glucose-insulin dynamics and thus provides a robust bench-test for assessing glucose control for basal insulin therapies. A PK model describing subcutaneous absorption of Deg-100, in addition to the one already available for Gla-300, has been developed based on T1D clinical data and incorporated into the simulator. One hundred in silico T1D subjects received a basal insulin dose (Gla-300 or Deg-100) for 12 weeks (8 weeks uptitration, 4 weeks stable dosing) by morning or evening administration in a basal/bolus regimen. The virtual patients were uptitrated to their individual doses with two different titration rules. Results: The last 2-week simulated continuous glucose monitoring data were used to calculate various outcome metrics for both basal insulin treatments, with primary outcome being the percent time in glucose target (70-140 mg/dL). The simulations show no statistically significant difference for Gla-300 versus Deg-100 in the main endpoints. Conclusions: This work suggests comparable glucose control using either Gla-300 or Deg-100 and was used to guide the design of a clinical trial intended to compare second-generation long-acting insulin analogues.

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“…There are several metabolic models of human metabolism particularly related to studies in diabetes and general physiology [82,91,96,[121][122][123][124][125][126]. They are primarily deterministic compartment models with specific terms for all relevant physiology and dynamics [91].…”

Section: Dynamic System Models Of the Metabolic Systemmentioning

confidence: 99%

Glycemic control in the intensive care unit: A control systems perspective

Chase

Benyó

Desaive

2019

Annual Reviews in Control

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Computers and automation have revolutionised quality and productivity in many industries, but not in medicine. Healthcare costs are thus growing beyond the ability of society to pay for them, multiplied by the impact of increasingly aging populations, and specifically in the demand placed on intensive care unit (ICU) services. Glycemic control is a core ICU therapy with the potential to reduce both mortality and cost, which makes it an area where greater personalisation and automation could play a leading role in improving productivity and care. This review presents the background to the problem and the main issues arising in this area of care from a control systems technology perspective. It then presents a vision of a more automated future with specific goals in the areas of dynamic systems modeling, system identification and control. These areas are then given a state of the art review, mixing both medicine and control systems perspectives. It is concluded by specific recommendations for the field, where control systems expertise can be leveraged to best advantage.

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In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies

Cited by 51 publications

References 100 publications

Automated closed-loop control of diabetes: the artificial pancreas

Automated closed-loop control of diabetes: the artificial pancreas

In SilicoHead-to-Head Comparison of Insulin Glargine 300 U/mL and Insulin Degludec 100 U/mL in Type 1 Diabetes

Glycemic control in the intensive care unit: A control systems perspective

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