Skip to main content
Log in

Choosing the Allometric Exponent in Covariate Model Building

  • Original Research Article
  • Published:
Clinical Pharmacokinetics Aims and scope Submit manuscript

Abstract

Background

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.

Objectives

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.

Methods

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.

Results

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.

Conclusion

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Xu XS, Yuan M, Yang H, Feng Y, Xu J, Pinheiro J. Further evaluation of covariate analysis using empirical Bayes estimates in population pharmacokinetics: the perception of shrinkage and likelihood ratio test. AAPS J. 2017;19(1):264–73.

    Article  PubMed  Google Scholar 

  2. McLeay SC, Morrish GA, Kirkpatrick CM, Green B. The relationship between drug clearance and body size. Clin Pharmacokinet. 2012;51(5):319–30.

    Article  CAS  PubMed  Google Scholar 

  3. Mould DR, Upton RN. Basic concepts in population modeling, simulation, and model-based drug development—part 2: introduction to pharmacokinetic modeling methods. CPT Pharmacomet Syst Pharmacol. 2013;2(4):e38.

    Article  CAS  Google Scholar 

  4. Rubner M. Ueber den einfluss der korpergrosse auf stoffund kaftwechsel. Zeitschrift fur Biologie. 1883;19:535–62.

    Google Scholar 

  5. Kleiber M. Body size and metabolism. Hilgardia. 1932;6(11):315–53.

    Article  CAS  Google Scholar 

  6. Brody S. Bioenergetics and growth; with special reference to the efficiency complex in domestic animals. New York: Reinhold Publishing Corporation; 1945.

    Google Scholar 

  7. West GB, Woodruff WH, Brown JH. Allometric scaling of metabolic rate from molecules and mitochondria to cells and mammals. Proc Natl Acad Sci. 2002;99(Suppl 1):2473–8.

    Article  PubMed  Google Scholar 

  8. West GB, Brown JH, Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science. 1997;276(5309):122–6.

    Article  CAS  PubMed  Google Scholar 

  9. West GB, Brown JH, Enquist BJ. The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science. 1999;284(5420):1677–9.

    Article  CAS  PubMed  Google Scholar 

  10. Mahmood I, Balian J. Interspecies scaling: predicting clearance of drugs in humans. Three different approaches. Xenobiotica. 1996;26(9):887–95.

    Article  CAS  PubMed  Google Scholar 

  11. Mahmood I, Green MD, Fisher JE. Selection of the first-time dose in humans: comparison of different approaches based on interspecies scaling of clearance. J Clin Pharmacol. 2003;43(7):692–7.

    Article  CAS  PubMed  Google Scholar 

  12. Mahmood I. Interspecies scaling of protein drugs: prediction of clearance from animals to humans. J Pharm Sci. 2004;93(1):177–85.

    Article  CAS  PubMed  Google Scholar 

  13. Ling J, Zhou H, Jiao Q, Davis HM. Interspecies scaling of therapeutic monoclonal antibodies: initial look. J Clin Pharmacol. 2009;49(12):1382–402.

    Article  CAS  PubMed  Google Scholar 

  14. Anderson BJ, Holford NH. Mechanistic basis of using body size and maturation to predict clearance in humans. Drug Metab Pharmacokinet. 2009;24(1):25–36.

    Article  CAS  PubMed  Google Scholar 

  15. Dodds PS, Rothman DH, Weitz JS. Re-examination of the “3/4-law” of metabolism. J Theor Biol. 2001;209(1):9–27.

    Article  CAS  PubMed  Google Scholar 

  16. White CR, Seymour RS. Mammalian basal metabolic rate is proportional to body mass2/3. Proc Natl Acad Sci. 2003;100(7):4046–9.

    Article  CAS  PubMed  Google Scholar 

  17. He J-H, Zhang J. Fifth dimension of life and the 4/5 allometric scaling law for human brain. Cell Biol Int. 2004;28(11):809–15.

    Article  PubMed  Google Scholar 

  18. Hu T-M, Hayton WL. Allometric scaling of xenobiotic clearance: uncertainty versus universality. AAPS J. 2001;3(4):30–43.

    Article  Google Scholar 

  19. Calvier EA, Krekels EH, Välitalo PA, Rostami-Hodjegan A, Tibboel D, Danhof M, et al. Allometric scaling of clearance in paediatric patients: when does the magic of 0.75 fade? Clin Pharmacokinet. 2017;56(3):273–85.

    Article  PubMed  Google Scholar 

  20. Eleveld DJ, Proost JH, Absalom AR, Struys MM. Obesity and allometric scaling of pharmacokinetics. Clin Pharmacokinet. 2011;50(11):751–3.

    Article  CAS  PubMed  Google Scholar 

  21. Fisher DM, Shafer SL. Allometry, shallometry! Anesth Analg. 2016;122(5):1234–8.

    Article  PubMed  Google Scholar 

  22. Anderson B, Holford N. Mechanism-based concepts of size and maturity in pharmacokinetics. Annu Rev Pharmacol Toxicol. 2008;48:303–32.

    Article  CAS  PubMed  Google Scholar 

  23. Ribbing J, Jonsson EN. Power, selection bias and predictive performance of the Population Pharmacokinetic Covariate Model. J Pharmacokinet Pharmacodyn. 2004;31(2):109–34.

    Article  CAS  PubMed  Google Scholar 

  24. La Caze A, Duffull S. Estimating risk from underpowered, but statistically significant, studies: was APPROVe on TARGET? J Clin Pharmacy Ther. 2011;36(6):637–41.

    Article  Google Scholar 

  25. Al-Sallami HS, Cheah SL, Han SY, Liew J, Lim J, Ng MA, et al. Between-subject variability: should high be the new normal? Eur J Clin Pharmacol. 2014;70(11):1403.

    Article  PubMed  Google Scholar 

  26. Third National Health and Nutrition Examination Survey (NHANES III), 1988–1994. https://wwwn.cdc.gov/nchs/nhanes/nhanes3/datafiles.aspx. Accessed 15 July 2017.

  27. CDC Growth Chart. https://www.cdc.gov/growthcharts/html_charts/wtage.htm. Accessed 15 July 2017.

  28. Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;19(6):716–23.

    Article  Google Scholar 

  29. Nguyen L, Leger F, Lennon S, Puozzo C. Intravenous busulfan in adults prior to haematopoietic stem cell transplantation: a population pharmacokinetic study. Cancer Chemother Pharmacol. 2006;57(2):191–8.

    Article  CAS  PubMed  Google Scholar 

  30. Peloquin CA, Hadad DJ, Molino LPD, Palaci M, Boom WH, Dietze R, et al. Population pharmacokinetics of levofloxacin, gatifloxacin, and moxifloxacin in adults with pulmonary tuberculosis. Antimicrob Agents Chemother. 2008;52(3):852–7.

    Article  CAS  PubMed  Google Scholar 

  31. Petain A, Kattygnarath D, Azard J, Chatelut E, Delbaldo C, Geoerger B, et al. Population pharmacokinetics and pharmacogenetics of imatinib in children and adults. Clin Cancer Res. 2008;14(21):7102–9.

    Article  CAS  PubMed  Google Scholar 

  32. Wang DD, Zhang S, Zhao H, Men AY, Parivar K. Fixed dosing versus body size—based dosing of monoclonal antibodies in adult clinical trials. J Clin Pharmacol. 2009;49(9):1012–24.

    Article  CAS  PubMed  Google Scholar 

  33. Friberg LE, Ravva P, Karlsson MO, Liu P. Integrated population pharmacokinetic analysis of voriconazole in children, adolescents and adults. Antimicrob Agents Chemother. 2012;56(6):3032–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jaydeep Sinha.

Ethics declarations

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.

Funding

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.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sinha, J., Al-Sallami, H.S. & Duffull, S.B. Choosing the Allometric Exponent in Covariate Model Building. Clin Pharmacokinet 58, 89–100 (2019). https://doi.org/10.1007/s40262-018-0667-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40262-018-0667-0

Navigation