In Silico Examination of Initiation of Long-Acting Insulin Analogs Toujeo Compared to Lantus Under 3 Dosing Titration Rules in Virtual Type 2 Diabetes Subjects.

BACKGROUND: Despite the benefits and clinical necessity of insulin treatment in type 2 diabetes (T2D), healthcare providers are reluctant to initiate insulin, and patients are reluctant to start it for several reasons, one of these being the complexity of insulin treatment. Patients and their healthcare providers can benefit from titration algorithms (TAs) or rules that assist with the initiation and titration of insulin, performing the calculations that are needed to safely initiate and conservatively adjust. METHODS: The primary objective for this in silico study was to examine the effectiveness of 3 dose TAs (1-3) for optimization of basal insulin glargine (Gla-100 and Gla-300). In the simulations, 100 virtual subjects with T2D were included (50% men, age 62 ± 3 years, HbA1c 8.1% ± 2.9%, body weight 94 ± 16 kg). Subjects were studied under each TA (TA1 and TA2 fasting blood glucose [FBG] targets 90-130 mg/dL, TA3 FBG target 110-150 mg/dL). Initial dose of both insulins was based on 0.2 U/kg body weight. During 3 months, subjects reported their FBG to the LTHome web-based dose guidance system with a rules engine to safely guide long-acting insulin titration and maintenance. Subjects followed dose recommendations to reach designated FBG target ranges. RESULTS: All subjects reached stable doses under all TAs with both Gla-100 and Gla-300 insulin, and 93 or more of the 100 subjects, depending on the assigned TA, achieved the target FBG range within the 3-month simulation for all TAs. Mean FBG was lowered (Gla-100: 155 ± 40 to 118 ± 11 mg/dL with TA1 and TA2 and 132 ± 12 mg/dL for TA3; Gla-300: 125 ± 14 with TA1 and TA2 and 134 ± 15 mg/dL with TA3). Calculated HbA1c improved from 8.1% ± 2.9% to 7.1% ± 2.5% for TA1 and TA2 and 7.5% ± 2.5% for TA3, a reduction of 0.9% and 0.6% over 3 months for both insulins. Three subjects on Gla-100 and one subject on Gla-300 experienced mild hypoglycemia. CONCLUSION: All TAs delivered safe dose recommendations with minimal hypoglycemia, leading to a stable glucose control in the majority of subjects.

Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator.

BACKGROUND: Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia. METHOD: We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events. RESULTS: The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min). CONCLUSION: The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.