In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes

Abstract

Arguably, a minimally invasive system using subcutaneous (s.c.) continuous glucose monitoring (CGM) and s.c. insulin delivery via insulin pump would be a most feasible step to closed-loop control in type 1 diabetes mellitus (T1DM). Consequently, diabetes technology is focusing on developing an artificial pancreas using control algorithms to link CGM with s.c. insulin delivery. The future development of the artificial pancreas will be greatly accelerated by employing mathematical modeling and computer simulation. Realistic computer simulation is capable of providing invaluable information about the safety and the limitations of closed-loop control algorithms, guiding clinical studies, and out-ruling ineffective control scenarios in a cost-effective manner. Thus computer simulation testing of closed-loop control algorithms is regarded as a prerequisite to clinical trials of the artificial pancreas. In this paper, we present a system for in silico testing of control algorithms that has three principal components: (1) a large cohort of n=300 simulated "subjects" (n=100 adults, 100 adolescents, and 100 children) based on real individuals' data and spanning the observed variability of key metabolic parameters in the general population of people with T1DM; (2) a simulator of CGM sensor errors representative of Freestyle Navigator™, Guardian RT, or Dexcom™ STS™, 7-day sensor; and (3) a simulator of discrete s.c. insulin delivery via OmniPod Insulin Management System or Deltec Cozmo(®) insulin pump. The system has been shown to represent adequate glucose fluctuations in T1DM observed during meal challenges, and has been accepted by the Food and Drug Administration as a substitute to animal trials in the preclinical testing of closed-loop control strategies.

Keywords: computer simulation; diabetes control; modeling.

Figure 1.

Principal components of computer simulation environment: a model of the glucose–insulin system; a model of sensor error; the controller to be tested; and a model of insulin pump and s.c. insulin kinetics.

Figure 2.

Main user interface window of the simulation software.

Figure 3.

Simulated glucose and insulin traces of three different subjects that received the same amounts of CHO (75 g) and insulin (5 U) at the same time.

Figure 4.

Simulation of CGM errors: example of ideal plasma glucose (red), ideal interstitial glucose (blue), and noisy interstitial glucose (green).

Figure 5.

Simulation of extreme insulin pump failure locking the pump at maximum insulin infusion rate in subjects with high (top), intermediate (middle), and low (bottom) insulin sensitivity. SI, International System of Units.

Figure 6.

In silico comparison of plasma glucose concentration during 72 h in an healthy subject (green), a type 1 diabetes subjects controlled with a PID (red), and MPC (blue) closed-loop control algorithms.