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A case study of modeling and exposure-response prediction for count data.

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  • 1a Biostatistics and Programming, Sanofi, Bridgewater , New Jersey , USA.

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|June 11, 2014
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Determining the optimal drug dose can be challenging. Modeling plasma concentration data helps predict treatment effects for unstudied doses, aiding final dose selection in clinical trials.

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Area of Science:

  • Pharmacometrics
  • Clinical Trial Design
  • Biostatistics

Background:

  • Selecting the final commercial drug dose can be difficult even after Phase III studies.
  • Traditional methods may not fully leverage available plasma concentration data for dose optimization.

Purpose of the Study:

  • To illustrate a modeling approach for predicting treatment effects at various plasma concentrations.
  • To demonstrate how this approach can justify final dose selection in Phase III clinical trials.

Main Methods:

  • Utilizing plasma concentration data from Phase III studies.
  • Applying pharmacokinetic-pharmacodynamic (PK/PD) modeling to link concentration and effect.
  • Comparing statistical models including overdispersed Poisson, negative binomial, and recurrent event models.

Main Results:

  • The negative binomial model demonstrated superior data fitting.
  • The chosen model allowed for within-treatment and between-treatment comparisons.
  • The modeling approach successfully predicted treatment effects for unstudied doses.

Conclusions:

  • Modeling plasma concentration data is a valuable tool for dose confirmation.
  • The negative binomial model is recommended for count data in similar scenarios.
  • This method supports evidence-based final dose selection in drug development.