ADA 2022 Scientific Sessions Publication
Jun 5, 2022 8:13:11 GMT -5
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Post by mango on Jun 5, 2022 8:13:11 GMT -5
Augmenting Clinical Trial Data with In-Silico Evidence to Evaluate Initial Conversion Dose Changes for Technosphere Insulin (TI)
Background: TI is an inhaled mealtime insulin with ultra-rapid onset and short duration of action closely mimicking physiologic insulin. Clinical studies demonstrated a higher initial dose could result in faster titration to overcome a perceived lack of effect. The aim was to develop an in silico model to augment results of a study (NCT04849845) evaluating an increased initial conversion of TI from injected insulin vs. the current label in 20 adults with diabetes that showed significantly improved post-prandial control after 45 minutes.
Methods: The in silico model characterizes blood glucose trends and outcomes utilizing published equations and distributions derived from real-world data sources. Inputs with the largest effects on glucose outcomes were combined to match study parameters to generate one million meal boluses. The model was validated using common modeling and simulation recommendations.
Results: Model inputs were constrained to match clinical study parameters; in silico results substantiated and complemented in vivo safety and efficacy results with similar predictive values and acceptable hypoglycemia distributions.
Conclusions: Clinical data augmented by in silico data support that an increased initial dose is not associated with any new safety concerns. Leveraging in silico data generates high quality evidence faster and with less risk to patients.
eppro02.ativ.me/web/page.php?page=IntHtml&project=ADA22&id=752
Background: TI is an inhaled mealtime insulin with ultra-rapid onset and short duration of action closely mimicking physiologic insulin. Clinical studies demonstrated a higher initial dose could result in faster titration to overcome a perceived lack of effect. The aim was to develop an in silico model to augment results of a study (NCT04849845) evaluating an increased initial conversion of TI from injected insulin vs. the current label in 20 adults with diabetes that showed significantly improved post-prandial control after 45 minutes.
Methods: The in silico model characterizes blood glucose trends and outcomes utilizing published equations and distributions derived from real-world data sources. Inputs with the largest effects on glucose outcomes were combined to match study parameters to generate one million meal boluses. The model was validated using common modeling and simulation recommendations.
Results: Model inputs were constrained to match clinical study parameters; in silico results substantiated and complemented in vivo safety and efficacy results with similar predictive values and acceptable hypoglycemia distributions.
Conclusions: Clinical data augmented by in silico data support that an increased initial dose is not associated with any new safety concerns. Leveraging in silico data generates high quality evidence faster and with less risk to patients.
eppro02.ativ.me/web/page.php?page=IntHtml&project=ADA22&id=752
