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Clinical Pharmacokinetics

, Volume 47, Issue 1, pp 35–45 | Cite as

Predicting Oral Clearance in Humans

How Close Can We Get with Allometry?
  • Vikash K. Sinha
  • Stefan S. De Buck
  • Luca A. Fenu
  • Johan W. Smit
  • Marjoleen Nijsen
  • Ron A. H. J. Gilissen
  • Achiel Van Peer
  • Karel Lavrijsen
  • Claire E. Mackie
Original Research Article

Abstract

Background

Oral clearance (CL/F) is an important pharmacokinetic parameter and plays an important role in the selection of a safe and tolerable dose for first-in-human studies. Throughout the pharmaceutical industry, many drugs are administered via the oral route; however, there are only a handful of published scaling studies for the prediction of oral pharmacokinetic parameters.

Methods

We evaluated the predictive performances of four different allometric approaches–simple allometry (SA), the rule of exponents, the unbound CL/F approach, and the unbound fraction corrected intercept method (FCIM)–for the prediction of human CL/F and the oral area under the plasma concentration-time curve (AUC). Twenty-four compounds developed at Johnson and Johnson Pharmaceutical Research and Development, covering a wide range of physicochemical and pharmacokinetic properties, were selected. The CL/F was predicted using these approaches, and the oral AUC was then estimated using the predicted CL/F.

Results

The results of this study indicated that the most successful predictions of CL/F and the oral AUC were obtained using the unbound CL/F approach in combination with the maximum lifespan potential or the brain weight as correction factors based on the rule of exponents. We also observed that the unbound CL/F approach gave better predictions when the exponent of SA was between 0.5 and 1.2. However, the FCIM seemed to be the method of choice when the exponent of SA was <0.50 or >1.2.

Conclusions

Overall, we were able to predict CL/F and the oral AUC within 2-fold of the observed value for 79% and 83% of the compounds, respectively, by selecting the allometric approaches based on the exponents of SA.

Keywords

Root Mean Square Error Plasma Protein Binding Tamsulosin Allometric Equation Brain Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to acknowledge the many Johnson and Johnson Research and Development ADME-TOX, Bioanalysis Department and Clinical Pharmacokinetics colleagues who generated data used in these analyses, and Drs Jim Dow, Eef Hoeben and Kelly Van Uytsel for their support and critical input. The authors have no conflict of interest relevant to the content of the study.

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Copyright information

© Adis Data Information BV 2008

Authors and Affiliations

  • Vikash K. Sinha
    • 1
  • Stefan S. De Buck
    • 1
  • Luca A. Fenu
    • 1
  • Johan W. Smit
    • 2
  • Marjoleen Nijsen
    • 1
  • Ron A. H. J. Gilissen
    • 1
  • Achiel Van Peer
    • 2
  • Karel Lavrijsen
    • 3
  • Claire E. Mackie
    • 1
  1. 1.ADME-TOX DepartmentJohnson and Johnson Pharmaceutical Research and DevelopmentBeerseBelgium
  2. 2.Clinical PharmacologyJohnson and Johnson Pharmaceutical Research and DevelopmentBeerseBelgium
  3. 3.Drug Metabolism and PharmacokineticsJohnson and Johnson Pharmaceutical Research and DevelopmentBeerseBelgium

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