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Related Experiment Videos

Prospective allometric scaling: does the emperor have clothes?

P L Bonate1, D Howard

  • 1Quintiles, Clinical Pharmacokinetics, Kansas City, MO 64134, USA.

Journal of Clinical Pharmacology
|April 13, 2000
PubMed
Summary

Prospective allometric scaling (AS) for animal-to-human pharmacokinetic predictions may offer false security. Wide prediction intervals and significant publication bias hinder reliable dose selection in first-in-human (FTIM) studies.

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Area of Science:

  • Pharmacokinetics
  • Drug Development
  • Translational Science

Background:

  • Allometric scaling (AS) is frequently used to predict human pharmacokinetics from animal data.
  • However, the reliability of AS in selecting doses for first-in-human (FTIM) studies is debated.
  • Publication bias in the literature may skew the perceived success of AS.

Purpose of the Study:

  • To evaluate the potential pitfalls of using prospective AS for dose selection in FTIM studies.
  • To highlight limitations that may lead to a false sense of security.
  • To encourage the publication of studies where prospective AS has failed.

Main Methods:

  • The study critically reviews the application and limitations of prospective allometric scaling.
  • It examines prediction intervals, prediction error, and a priori identification of drug failures.

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  • Analysis considers the impact of publication bias on the interpretation of AS results.

    • Main Results:

    • Prospective AS may yield prediction intervals that are too wide for practical use.
    • Prediction error using AS is often comparable to arbitrarily chosen constants.
    • It is currently not possible to predict a priori which drugs will fail in human trials based on AS.

    Conclusions:

    • The utility of prospective AS for FTIM dose selection may be overestimated due to inherent limitations.
    • Wider publication of AS failures is crucial for improving predictive accuracy.
    • Further research is needed to identify factors differentiating successful and unsuccessful AS predictions for human pharmacokinetics.