Dataset Information

Design and Clinical Evaluation of a Novel Low-Glucose Prediction Algorithm with Mini-Dose Stable Glucagon Delivery in Post-Bariatric Hypoglycemia.

ABSTRACT: BACKGROUND:Postbariatric hypoglycemia (PBH) is a complication of bariatric surgery with limited therapeutic options. We developed an event-based system to predict and detect hypoglycemia based on continuous glucose monitor (CGM) data and recommend delivery of minidose liquid glucagon. METHODS:We performed an iterative development clinical study employing a novel glucagon delivery system: a Dexcom CGM connected to a Windows tablet running a hypoglycemia prediction algorithm and an Omnipod pump filled with an investigational stable liquid glucagon formulation. Meal tolerance testing was performed in seven participants with PBH and history of neuroglycopenia. Glucagon was administered when hypoglycemia was predicted. Primary outcome measures included the safety and feasibility of this system to predict and prevent severe hypoglycemia. Secondary outcomes included hypoglycemia prediction by the prediction algorithm, minimization of time below hypoglycemia threshold using glucagon, and prevention of rebound hyperglycemia. RESULTS:The hypoglycemia prediction algorithm alerted for impending hypoglycemia in the postmeal state, prompting delivery of glucagon (150??g). After observations of initial incomplete efficacy to prevent hypoglycemia in the first two participants, system modifications were implemented: addition of PBH-specific detection algorithm, increased glucagon dose (300??g), and a second glucagon dose if needed. These modifications, together with rescue carbohydrates provided to some participants, contributed to progressive improvements in glucose time above the hypoglycemia threshold (75?mg/dL). CONCLUSIONS:Preliminary results indicate that our event-based automatic monitoring algorithm successfully predicted likely hypoglycemia. Minidose glucagon therapy was well tolerated, without prolonged or severe hypoglycemia, and without rebound hyperglycemia.

SUBMITTER: Laguna Sanz AJ

PROVIDER: S-EPMC5771550 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Design and Clinical Evaluation of a Novel Low-Glucose Prediction Algorithm with Mini-Dose Stable Glucagon Delivery in Post-Bariatric Hypoglycemia.

Laguna Sanz Alejandro J AJ Mulla Christopher M CM Fowler Kristen M KM Cloutier Emilie E Goldfine Allison B AB Newswanger Brett B Cummins Martin M Deshpande Sunil S Prestrelski Steven J SJ Strange Poul P Zisser Howard H Doyle Francis J FJ Dassau Eyal E Patti Mary-Elizabeth ME

Diabetes technology & therapeutics 20180201 2

<h4>Background</h4>Postbariatric hypoglycemia (PBH) is a complication of bariatric surgery with limited therapeutic options. We developed an event-based system to predict and detect hypoglycemia based on continuous glucose monitor (CGM) data and recommend delivery of minidose liquid glucagon.<h4>Methods</h4>We performed an iterative development clinical study employing a novel glucagon delivery system: a Dexcom CGM connected to a Windows tablet running a hypoglycemia prediction algorithm and an ...[more]

PMID: 29355439

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Project description:ObjectiveWhile the majority of current diabetes treatments focus on reducing blood glucose levels, hypoglycemia represents a significant risk associated with insulin treatment. Glucagon plays a major regulatory role in controlling hypoglycemia in vivo, but its short half-life and hyperglycemic effects prevent its therapeutic use for non-acute applications. The goal of this study was to identify a modified form of glucagon suitable for prophylactic treatment of hypoglycemia without increasing baseline blood glucose levels.Methodology/principal findingsThrough application of the XTEN technology, we report the construction of a glucagon fusion protein with an extended exposure profile (Gcg-XTEN). The in vivo half-life of the construct was tuned to support nightly dosing through design and testing in cynomolgus monkeys. Efficacy of the construct was assessed in beagle dogs using an insulin challenge to induce hypoglycemia. Dose ranging of Gcg-XTEN in fasted beagle dogs demonstrated that the compound was biologically active with a pharmacodynamic profile consistent with the designed half-life. Prophylactic administration of 0.6 nmol/kg Gcg-XTEN to dogs conferred resistance to a hypoglycemic challenge at 6 hours post-dose without affecting baseline blood glucose levels. Consistent with the designed pharmacokinetic profile, hypoglycemia resistance was not observed at 12 hours post-dose. Importantly, the solubility and stability of the glucagon peptide were also significantly improved by fusion to XTEN.Conclusions/significanceThe data show that Gcg-XTEN is effective in preventing hypoglycemia without the associated hyperglycemia expected for unmodified glucagon. While the plasma clearance of this Gcg-XTEN has been optimized for overnight dosing, specifically for the treatment of nocturnal hypoglycemia, constructs with significantly longer exposure profiles are feasible. Such constructs may have multiple applications such as allowing for more aggressive insulin treatment regimens, treating hypoglycemia due to insulin-secreting tumors, providing synergistic efficacy in combination therapies with long-acting GLP1 analogs, and as an appetite suppressant for treatment of obesity. The improved physical properties of the Gcg-XTEN molecule may also allow for novel delivery systems not currently possible with native glucagon.

Dataset Information

Design and Clinical Evaluation of a Novel Low-Glucose Prediction Algorithm with Mini-Dose Stable Glucagon Delivery in Post-Bariatric Hypoglycemia.

Publications

Design and Clinical Evaluation of a Novel Low-Glucose Prediction Algorithm with Mini-Dose Stable Glucagon Delivery in Post-Bariatric Hypoglycemia.

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