Clinical Pharmacokinetics

, Volume 58, Issue 1, pp 89–100 | Cite as

Choosing the Allometric Exponent in Covariate Model Building

  • Jaydeep SinhaEmail author
  • Hesham S. Al-Sallami
  • Stephen B. Duffull
Original Research Article



Allometric scaling is often used to describe the covariate model linking total body weight (WT) to clearance (CL); however, there is no consensus on how to select its value.


The aims of this study were to assess the influence of between-subject variability (BSV) and study design on (1) the power to correctly select the exponent from a priori choices, and (2) the power to obtain unbiased exponent estimates.


The influence of WT distribution range (randomly sampled from the Third National Health and Nutrition Examination Survey, 1988–1994 [NHANES III] database), sample size (N = 10, 20, 50, 100, 200, 500, 1000 subjects), and BSV on CL (low 20%, normal 40%, high 60%) were assessed using stochastic simulation estimation. A priori exponent values used for the simulations were 0.67, 0.75, and 1, respectively.


For normal to high BSV drugs, it is almost impossible to correctly select the exponent from an a priori set of exponents, i.e. 1 vs. 0.75, 1 vs. 0.67, or 0.75 vs. 0.67 in regular studies involving < 200 adult participants. On the other hand, such regular study designs are sufficient to appropriately estimate the exponent. However, regular studies with < 100 patients risk potential bias in estimating the exponent.


Those study designs with limited sample size and narrow range of WT (e.g. < 100 adult participants) potentially risk either selection of a false value or yielding a biased estimate of the allometric exponent; however, such bias is only relevant in cases of extrapolating the value of CL outside the studied population, e.g. analysis of a study of adults that is used to extrapolate to children.


Compliance with Ethical Standards

Conflict of interest

Jaydeep Sinha, Hesham S. Al-Sallami and Stephen B. Duffull declare no conflicts of interest.

Ethics approval

No ethical approval was required for this simulation-based work.


This work received no specific funding. Jaydeep Sinha received a doctoral scholarship from the School of Pharmacy, University of Otago, New Zealand, during the course of this work.

Supplementary material

40262_2018_667_MOESM1_ESM.docx (34 kb)
Supplementary material 1 (DOCX 33 kb)
40262_2018_667_MOESM2_ESM.pdf (67 kb)
Supplementary material 2 (PDF 67 kb)
40262_2018_667_MOESM3_ESM.pdf (67 kb)
Supplementary material 3 (PDF 67 kb)
40262_2018_667_MOESM4_ESM.pdf (201 kb)
Supplementary material 4 (PDF 201 kb)


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of PharmacyUniversity of OtagoDunedinNew Zealand

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