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Translation Between Two Models; Application with Integrated Glucose Homeostasis Models.

Moustafa M A Ibrahim1,2, Anna Largajolli1, Maria C Kjellsson1

  • 1Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 23, Uppsala, Sweden.

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Summary

This study introduces a novel method to evaluate nonlinear mixed-effects (NLME) models by comparing real and simulated data using different models. Key parameters from the Integrated Minimal Model (IMM) were successfully mapped to the Integrated Glucose Insulin (IGI) model.

Keywords:
NLMEPCglucose effectivenessinsulin sensitivityintegrated glucose insulin modelintegrated minimal model

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Area of Science:

  • Pharmacometrics
  • Systems Biology
  • Computational Biology

Background:

  • Multiple nonlinear mixed-effects (NLME) models often exist for biological systems, each with unique features.
  • Evaluating and comparing these models is crucial for understanding their strengths and limitations.

Purpose of the Study:

  • To propose and demonstrate an evaluation method for NLME models using a structurally different, yet similar, NLME model.
  • To compare the Integrated Glucose Insulin (IGI) model and the Integrated Minimal Model (IMM) using real and simulated data.
  • To map parameters between the IGI and IMM that carry similar biological information.

Main Methods:

  • Analysis of real and simulated datasets from both the IMM and IGI models using both models.
  • Bootstrap analysis of data.
  • Parameter mapping between IMM and IGI models using a large simulated IMM dataset analyzed with the IGI model.

Main Results:

  • Differences were observed when comparing parameters estimated from real data versus data simulated by the IMM and analyzed by the IGI model.
  • Similar, but less pronounced, differences were found when analyzing real data versus data simulated by the IGI model and analyzed by the IMM.
  • Strongest parameter correlations identified: insulin-dependent glucose clearance (IGI) with insulin sensitivity (IMM); insulin-independent glucose clearance (IGI) with glucose effectiveness (IMM); and insulin effect parameter (IGI) with insulin action (IMM).

Conclusions:

  • A novel approach was demonstrated for assessing NLME models' ability to simulate realistic data.
  • The information captured by each model was evaluated in comparison to real-world data.
  • Clinically relevant parameters from the IMM were successfully mapped to their corresponding parameters in the IGI model.