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Metabolic Models, in Silico Trials, and Algorithms.

Ali Cinar1, Ananda Basu2, B Wayne Bequette3

  • 1Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA.

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|July 1, 2025
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Summary
This summary is machine-generated.

Artificial pancreas (AP) systems, or automated insulin delivery systems, enhance glucose control and quality of life. Future systems aim for full automation, reducing manual inputs and addressing unique challenges for women with diabetes.

Keywords:
artificial pancreasdigital twinsglucose control algorithmsmathematical modelssimulators for in silico clinical trialswomen with diabetes

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Area of Science:

  • Biomedical Engineering
  • Endocrinology
  • Diabetes Technology

Background:

  • Artificial pancreas (AP) systems, also known as automated insulin delivery systems, have significantly improved glycemic management.
  • Current systems often operate in a hybrid closed-loop mode, requiring user input for meals and exercise.

Purpose of the Study:

  • To review advancements in AP systems, including mathematical models, continuous glucose monitoring, and insulin pumps.
  • To discuss the transition to in silico trials and the development of next-generation, fully automated AP systems.
  • To address the specific glycemic management challenges faced by women with diabetes throughout their lifespan.

Main Methods:

  • Review of mathematical models for glucose-insulin dynamics.
  • Analysis of continuous glucose monitoring and insulin pump technologies.
  • Evaluation of in silico clinical trials and their impact on AP development.
  • Examination of strategies for fully automated AP systems.
  • Discussion of lifelong glycemic management in women with diabetes.

Main Results:

  • AP systems have improved time in range and quality of life for users.
  • In silico trials have accelerated AP technology development.
  • Next-generation AP systems aim for full automation, mitigating disturbances like meals and exercise.
  • Specific challenges in glycemic control for women (menstrual cycle, pregnancy, menopause) are being addressed.

Conclusions:

  • Significant progress has been made in AP technology, moving towards fully automated systems.
  • Future AP systems promise to further reduce the burden of diabetes management.
  • Addressing the unique physiological changes in women is crucial for equitable diabetes care.