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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic

Dan Liu1, Linzhong Li2, Amin Rostami-Hodjegan2

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Summary

Global sensitivity analysis (GSA) methods reveal key drug parameters influencing pharmacokinetics (PK). Ignoring parameter correlations can bias results, emphasizing the need for careful interpretation in minimal physiologically based PK (mPBPK) models.

Keywords:
Global sensitivity analysisMorris methodSobol methodextended Sobol methodphysiologically based pharmacokinetic (PBPK) modelling

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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Computational Biology
  • Systems Pharmacology

Background:

  • Physiologically based pharmacokinetic (PBPK) models are crucial for predicting drug behavior.
  • Global sensitivity analysis (GSA) identifies key model parameters influencing predictions.
  • Understanding parameter influence is vital for accurate pharmacokinetic (PK) assessments.

Purpose of the Study:

  • To compare three GSA methods (Morris, Sobol, extended Sobol) for a minimal PBPK (mPBPK) model.
  • To investigate the impact of input parameter correlations on GSA results.
  • To identify key parameters affecting maximal plasma concentration (Cmax), time to Cmax (Tmax), and area under the curve (AUC) for quinidine, alprazolam, and midazolam.

Main Methods:

  • Application of Morris, Sobol, and extended Sobol GSA methods to an mPBPK model.
  • Analysis of PK outputs: Cmax, Tmax, and AUC24h.
  • Comparison of influential parameters identified by methods that do and do not account for parameter correlations.

Main Results:

  • Morris and Sobol methods showed comparable informativeness for independent parameters.
  • Extended Sobol method, accounting for correlations, identified different influential parameters.
  • Overestimation of volume of distribution at steady state (Vss) on AUC24h and underestimation of liver volume (Vliver) were observed.
  • Specific enzyme clearance and abundance parameters were also misjudged by certain methods.

Conclusions:

  • Interpreting GSA results requires careful consideration of model assumptions and method limitations.
  • Ignoring parameter correlations in GSA can lead to biased identification of key PK parameters.
  • Decisions on parameter influence should integrate knowledge of model structure, GSA limitations, and parameter inter-correlations.