Design and Evaluation of a Novel Clinical Decision Support Algorithm for Weekly Insulin Efsitora Alfa

Abstract

Background:

Digital health interventions can improve diabetes management and optimize insulin dosing. We describe the design and verification of a novel clinical decision support (CDS) algorithm for titration and management of insulin efsitora alfa (efsitora), an insulin receptor agonist designed for once-weekly administration. The CDS algorithm uses feedback control paired with efsitora pharmacokinetics (PK) and pharmacodynamics.

Methods:

The CDS algorithm incorporates population PK and patient-specific data on fasting blood glucose (FBG) trends, prior insulin doses, hypoglycemia, and demographics. It applies proportional-derivative control, adaptive gain, and insulin activity estimations to guide long-acting basal insulin titration and reduce hypoglycemia. The CDS was assessed in a 52-week simulation using integrated glucose-insulin physiological models that evaluated treatment with efsitora and degludec in insulin-naïve and basal-switch virtual participants with type 2 diabetes (N = 10 000 each). The CDS (FBG target: 80-120 mg/dL) and conventional (Riddle; FBG target: ≤120 mg/dL) algorithms guided efsitora and degludec treatments, respectively. Glycated hemoglobin (HbA1c), FBG, and hypoglycemia outcomes were summarized.

Results:

Glycated hemoglobin and FBG reductions from baseline to week 52 were observed with the efsitora CDS (HbA1c: −1.1% to −1.3%; FBG: −50 to −71 mg/dL) and degludec conventional (HbA1c: −0.7% to −0.8%; FBG: −31 to −49 mg/dL) algorithms. Clinical decision support algorithm-guided dosing led to lower rates (events/participant/30 days) of nocturnal (0.19 vs 0.33) and 24-hour (1.21 vs 2.25) level 1 hypoglycemia compared with the conventional dosing algorithm.

Conclusion:

The investigational CDS system may achieve comparable FBG and HbA1c reductions, and lower hypoglycemia rates, compared with conventional algorithms for daily basal insulin titration.