Clinical Evaluation of a Personalized Artificial Pancreas

  1. Francis J. Doyle III, PHD1,2,3
  1. 1Department of Chemical Engineering, University of California, Santa Barbara, California
  2. 2Biomolecular Science and Engineering Program, University of California, Santa Barbara, California
  3. 3Sansum Diabetes Research Institute, Santa Barbara, California
  4. 4The Jesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, The National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel, Petah Tikva, Israel
  1. Corresponding author: F. J. Doyle III, frank.doyle{at}


OBJECTIVE An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden of frequent blood glucose measurements and insulin administration.

RESEARCH DESIGN AND METHODS We conducted a pilot clinical trial evaluating an individualized, fully automated AP using commercial devices. Two trials (n = 22, nsubjects = 17) were conducted using a multiparametric formulation of model predictive control and an insulin-on-board algorithm such that the control algorithm, or “brain,” can be embedded on a chip as part of a future mobile device. The protocol evaluated the control algorithm for three main challenges: 1) normalizing glycemia from various initial glucose levels, 2) maintaining euglycemia, and 3) overcoming an unannounced meal of 30 ± 5 g carbohydrates.

RESULTS Initial glucose values ranged from 84–251 mg/dL. Blood glucose was kept in the near-normal range (80–180 mg/dL) for an average of 70% of the trial time. The low and high blood glucose indices were 0.34 and 5.1, respectively.

CONCLUSIONS These encouraging short-term results reveal the ability of a control algorithm tailored to an individual’s glucose characteristics to successfully regulate glycemia, even when faced with unannounced meals or initial hyperglycemia. To our knowledge, this represents the first truly fully automated multiparametric model predictive control algorithm with insulin-on-board that does not rely on user intervention to regulate blood glucose in individuals with type 1 diabetes.

  • Received May 15, 2012.
  • Accepted September 5, 2012.

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