Glucose-Sensitive Insulin On-Board Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for managing insulin treatment in diabetes, particularly with glucose-sensitive insulin (GSI), fail to accurately estimate insulin on board (IOB) over time, leading to suboptimal treatment due to the lack of consideration for glycaemic influence on pharmacokinetic and pharmacodynamic profiles.
Innovation Solution
A method and system that estimate IOB using glucose-dependent rate constants, based on continuous blood glucose and insulin dose data, employing compartment models or data-driven approaches to calculate the dynamic changes in rate constants and thereby determine the amount of insulin in the body, ensuring optimal GSI usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional insulin management methods are used, then the treatment is simple to implement, but the estimation of insulin on board (IOB) is inaccurate due to lack of consideration for glycaemic influence on pharmacokinetic and pharmacodynamic profiles
Solution Approach 1:
The patent applies parameter changes by making the rate constants (absorption, distribution, clearance) functions of glucose concentration rather than constant values. This allows the PK/PD model to dynamically adjust to glycaemic conditions, significantly improving IOB estimation accuracy for GSI while maintaining a computationally feasible structure through the use of measurable glucose parameters.
Solution Approach 2:
The patent implements feedback mechanisms by continuously incorporating glucose concentration measurements into the PK/PD model to update rate constants and IOB estimates in real-time. This closed-loop approach allows the system to adapt to changing glycaemic conditions and provides accurate, dynamic IOB estimation that reflects actual physiological states.
2Adaptability or versatility
If glucose-sensitive insulin is used, then the treatment adapts to glycaemic levels, but the traditional PK/PD curve method fails to account for glycaemic influence on drug usage and insulin available in the body
Solution Approach 1:
The patent applies dynamics by transforming the static PK/PD model into a dynamic system where rate constants continuously change with glucose concentration. This dynamic approach captures the time-varying nature of GSI pharmacokinetics and pharmacodynamics, enabling reliable IOB calculation that adapts to changing glycaemic conditions throughout the day.
Solution Approach 2:
The patent changes the parameters of the PK/PD model from fixed constants to glucose-dependent functions. By expressing absorption, distribution, and clearance rate constants as functions of glucose concentration, the model accurately reflects the adaptive behavior of GSI while maintaining computational tractability through the use of measurable glucose parameters.
3Productivity
If fixed rate constants are used in PK/PD models, then the calculation is computationally simple, but the model cannot capture the dynamic nature of glucose-dependent insulin action
Solution Approach 1:
The patent changes rate constants from fixed parameters to glucose-dependent functions, allowing the model to capture dynamic insulin action while maintaining computational efficiency. The glucose concentration, being directly measurable, serves as a convenient input that drives parameter changes without requiring complex computational resources.
Solution Approach 2:
The patent substitutes complex physiological modeling with a glucose-dependent parameter approach. Instead of modeling complex metabolic pathways and tissue interactions, the system uses glucose concentration measurements to directly modulate rate constants, achieving accurate IOB estimation with simplified computational mechanics.
Data Source
AI summary
A method of estimating insulin on board (IOB) for a given glucose sensitive insulin (GSI) in a subject is provided. The method comprises the steps (i) for the given GSI, providing at least one rate constant (rc) as function of glucose concentration (rc(G)), (ii) providing for a period of time a continuous blood glucose log G(t) from the subject, (iii) providing for the period of time 5 an insulin dose log I(t) from the subject, (iv) based on rc(G) and G(t), calculating for each rc a rate constant as function of time (rc(t)), and (v) based on the at least one rc(t) and I(t) and using an estimating algorithm, calculating an estimated IOB for the subject.