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Related Experiment Videos

Prospective allometric scaling: does the emperor have clothes?

P L Bonate1, D Howard

  • 1Quintiles, Kansas City, Missouri 64134, USA.

Journal of Clinical Pharmacology
|June 27, 2000
PubMed
Summary

Prospective allometric scaling (AS) for animal-to-human pharmacokinetic predictions may offer a false sense of security. Wide prediction intervals and publication bias limit its utility, highlighting the need for more transparent reporting of study failures.

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Area of Science:

  • Pharmacokinetics and Drug Development
  • Translational Medicine
  • Biostatistics

Background:

  • Allometric scaling (AS) is frequently used to predict human pharmacokinetic (PK) parameters from animal data.
  • However, the reliability and limitations of prospective AS in early-phase clinical trials require careful consideration.

Purpose of the Study:

  • To evaluate the potential pitfalls and limitations of using prospective allometric scaling for dose selection in first-in-human (FTIM) studies.
  • To highlight the impact of publication bias on the perceived success of AS.

Main Methods:

  • The study critically examines the application of prospective AS in FTIM studies.
  • It discusses common challenges including wide prediction intervals and prediction errors comparable to arbitrary constants.

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  • The authors address the difficulty in a priori identification of drugs likely to fail.

    • Main Results:

    • Prospective AS may provide a misleading sense of security due to significant limitations.
    • Prediction intervals are often too wide for practical application.
    • Prediction error using AS is frequently not superior to using arbitrary constants.
    • Identifying drugs likely to fail before FTIM studies remains challenging.

    Conclusions:

    • While an allometric relationship between animal and human PK exists, prospective AS for dose selection in FTIM studies has unrecognized limitations.
    • Publication bias obscures the true failure rate of AS.
    • Encouraging publication of failed AS studies is crucial for improving future prediction models and understanding drug-specific differences.