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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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The power of modelling pulsatile profiles.

Michiel J van Esdonk1, Jasper Stevens2

  • 1Centre for Human Drug Research, Leiden, The Netherlands. mvesdonk@chdr.nl.

Journal of Pharmacokinetics and Pharmacodynamics
|March 4, 2021
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Standard models struggle with pulsatile biomarkers like growth hormone. A deconvolution-analysis-informed approach significantly improves statistical power and individual-level description for complex pharmacodynamic modeling.

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ChronopharmacometricsDeconvolutionEndocrinologyPopulation modelsStatistical power

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Area of Science:

  • Pharmacometrics
  • Biomarker Analysis
  • Statistical Modeling

Background:

  • Non-linear mixed effects models face challenges quantifying pulsatile biomarker release (e.g., growth hormone).
  • Standard pharmacodynamic models lack the ability to accurately capture complex secretion profiles over time.
  • High intra- and inter-individual variability in biomarkers complicates quantitative descriptions.

Purpose of the Study:

  • To compare the statistical power of standard methods versus a deconvolution-analysis-informed approach for growth hormone pharmacodynamics.
  • To evaluate the ability of different modeling strategies to quantify drug effects in the presence of pulsatile biomarker release.
  • To assess the performance of a novel modeling approach in simulated dense concentration-time profiles.

Main Methods:

  • Simulated dense concentration-time profiles of growth hormone were used.
  • Statistical power was compared between standard methods (e.g., post-dose measurements, area under the curve) and a deconvolution-analysis-informed model.
  • Monte-Carlo Mapped Power analysis was employed to determine the statistical power of the deconvolution approach.

Main Results:

  • The deconvolution-analysis-informed approach achieved >80% statistical power with <200 subjects per cohort.
  • Standard statistical methods failed to reach adequate statistical power, even with larger sample sizes.
  • The deconvolution-informed approach enhanced individual-level observation descriptions and enabled drug effect quantification.

Conclusions:

  • A deconvolution-analysis-informed modeling approach is superior for analyzing pulsatile biomarkers in non-linear mixed effects models.
  • This method provides robust statistical power essential for clinical trial simulations involving complex biomarkers.
  • Improved individual-level quantification facilitates more accurate drug effect assessment and trial design.