Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based
Nan-Hung Hsieh1, Brad Reisfeld2, Frederic Y Bois3
1Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States.
Frontiers in Pharmacology
|June 26, 2018
Summary
Global sensitivity analysis (GSA) objectively identifies influential parameters in physiologically-based pharmacokinetic (PBPK) models. This approach improves model efficiency and accuracy by distinguishing parameters for estimation versus fixing, reducing bias from expert judgment.
Area of Science:
- Pharmacokinetics and Pharmacodynamics
- Computational Biology
- Systems Biology
Background:
- Physiologically-based pharmacokinetic (PBPK) models reduce parameter dimensionality via expert judgment.
- This traditional method risks bias in parameter estimates and predictions due to uncertain fixed parameters.
Purpose of the Study:
- To apply global sensitivity analysis (GSA) for identifying non-influential PBPK model parameters.
- To enable fixing non-influential parameters in Bayesian estimation, minimizing bias.
- To compare Morris and Sobol methods for distinguishing influential from non-influential parameters.
Main Methods:
- Employed GSA, specifically Morris method and variance-based Sobol indices.
- Utilized a human PBPK model for acetaminophen and its metabolites.
- Compared Bayesian calibration using original vs. GSA-identified influential parameters (OMP vs. OIP) and full parameter sets (FMP vs. FIP).
Main Results:
- Sobol indices via eFAST offered the best balance of reliability and computational efficiency.
- Identified originally calibrated parameters that were non-influential, allowing them to be fixed.
- Discovered six previously fixed parameters that were influential, improving model performance when included.
Conclusions:
- GSA provides an objective, transparent, and reproducible method for PBPK model optimization.
- GSA enhances both the performance and computational efficiency of PBPK models.
- This approach reduces bias associated with traditional expert judgment in parameter reduction.
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