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Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms.

Periklis Tsiros1, Vasileios Minadakis1, Dingsheng Li2

  • 1School of Chemical Engineering, National Technical University of Athens, Attiki 15772, Greece.

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Summary

This study introduces a new automated parameter grouping method for physiologically based kinetic (PBK) models. This approach reduces model complexity and improves parameter estimation accuracy for substance disposition prediction.

Keywords:
PBKPBPKPBTKPFASPFOATiO2genetic algorithmstitanium dioxide

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Area of Science:

  • Pharmacokinetics and Toxicokinetics
  • Computational Modeling
  • Systems Biology

Background:

  • Physiologically based kinetic (PBK) models are crucial for predicting chemical disposition but often suffer from overparameterization and unrealistic estimates when fitted to in vivo data.
  • Complex PBK models require numerous parameters, necessitating robust estimation strategies to ensure model reliability and interpretability.

Purpose of the Study:

  • To develop and validate a novel, automated parameter grouping approach for PBK models to reduce parametric space and improve parameter estimation.
  • To demonstrate the efficacy of this methodology in developing new PBK models and refining existing ones.

Main Methods:

  • A novel parameter grouping approach using genetic algorithms to co-estimate groups of parameters across compartments.
  • Development of a new goodness-of-fit metric to guide automated parameter grouping.
  • Application of the methodology to develop a PBK model for titanium dioxide (TiO2) nanoparticles and refine a PFOA PBK model in rats.
  • Validation of developed models using independent in vivo studies.

Main Results:

  • The proposed parameter grouping methodology resulted in PBK models with improved goodness-of-fit compared to standard estimation approaches.
  • The method effectively reduced the number of parameters, leading to more parsimonious and potentially more realistic model structures.
  • Case studies demonstrated successful application in both de novo model development and model refinement, enhancing the portrayal of substance biodistribution.

Conclusions:

  • Automated parameter grouping offers a powerful strategy to overcome overparameterization challenges in PBK modeling.
  • This approach enhances the accuracy and reliability of PBK models for predicting substance disposition in toxicological and pharmacological applications.
  • The validated methodology provides a robust tool for developing and refining PBK models for diverse chemical exposures.