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Related Concept Videos

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Related Experiment Video

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Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
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Population pharmacodynamic parameter estimation from sparse sampling: effect of sigmoidicity on parameter estimates.

Sudhakar M Pai1, Suzette Girgis, Vijay K Batra

  • 1Clinical Pharmacology, Akros Pharma Inc, Princeton, NJ 08540, USA.

The AAPS Journal
|July 25, 2009
PubMed
Summary

Population parameter estimation from sparse sampling is more efficient with sampling designs tailored to the concentration-effect (C-E) relationship

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Area of Science:

  • Pharmacometrics
  • Pharmacokinetics and Pharmacodynamics
  • Statistical Modeling

Background:

  • Sparse sampling in drug development poses challenges for accurate population parameter estimation.
  • Previous work focused on high sigmoidicity (gamma > 5) concentration-effect (C-E) relationships.
  • Understanding the impact of varying sigmoidicity is crucial for optimizing sampling strategies.

Purpose of the Study:

  • To evaluate how the sigmoidicity of the concentration-effect (C-E) relationship influences population parameter estimation efficiency from sparse sampling.
  • To compare sampling strategies for drugs with different C-E curve shapes.

Main Methods:

  • Simulated concentration-effect (C-E) profiles for octreotide (simple Emax) and remifentanil (sigmoidal Emax) models.
  • Employed non-linear mixed-effects modeling (NMEM) with first-order conditional estimation (FOCE).
  • Evaluated sampling designs with 4-5 samples per individual, focusing on EC50 and Emax regions.

Main Results:

  • For sigmoidal Emax models, 4-5 samples reliably estimated parameters; 5 samples improved inter-individual variability estimates.
  • For simple Emax models, enriching the EC50 region was critical due to shallower C-E profiles.
  • Adequate delineation of the Emax region is essential for both model types.

Conclusions:

  • Sampling design optimization for sparse data depends on the degree of C-E relationship sigmoidicity.
  • Steeper C-E curves require less data enrichment in the EC50 region compared to shallower curves.
  • Provides a framework for designing efficient sparse sampling strategies in clinical trials.