Clinical Pharmacokinetics

, Volume 40, Issue 12, pp 947–953 | Cite as

Renal Elimination of Amikacin and the Aging Process

  • Michel Ducher
  • Pascal Maire
  • Catherine Cerutti
  • Yann Bourhis
  • Fréderic Foltz
  • Pernille Sorensen
  • Roger Jelliffe
  • Jean-Pierre Fauvel
Original Research Article



Although amikacin is primarily eliminated via glomerular filtration, drug concentrations are not consistently predicted in all patients. To better describe the relationship between amikacin clearance and both age and renal function, we used a new heuristic approach involving statistical analysis of dependence.

Design and setting

Retrospective pharmacokinetic study using data from seven centres in France.


634 patients with sepsis aged between 18 and 98 years of age who received intravenous amikacin.


Clearance of amikacin was modelled using the NonParametric EM algorithm for a two-compartment model (NPEM2) with intravenous infusion.


A total of 2499 serum amikacin determinations was available for analysis. The relationship between the clearance of amikacin and age was weak. Interestingly, the Z method, which filters data based on dependence criteria, selected data that were best fitted by a polynomial function (r = 0.90; p < 0.001). This representation of the polynomial function was similar to a previously proposed theoretical model describing covariations between the clearance of amikacin and age. However, the polynomial function applied to only 33% of the patients that were selected by the Z method. The correlation between the clearance of amikacin and renal function was also relatively low (r = 0.39). The Z method exhibited a continuous and strong dependence pattern between the clearance of amikacin and age for 49% of the patients.


The Z methodology, which filters data using dependence criteria, confirms that age, renal function and amikacin clearance are strongly related, but only in less than half of a large sample of patients with sepsis without renal pathology. These results suggest that other variables should be taken into account in order to improve the description of the behaviour of amikacin. The Z methodology improved the classical description of relationships between variables, and should be applied to better select pertinent variables in pharmacokinetic studies.


Renal Function Amikacin Significant Linear Correlation Dependence Criterion Gault Formula 
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.



The authors received no funding, and had no conflict of interest, in the writing of this article.

The authors appreciated the provision of part of the data from Dr G. Chapelle (Hospital of Poitiers, France), Dr C. Pobel (Hospital of Saintes, France), Dr C. Pivot (Hospital E. Herriot of Lyon, France), Dr B. Debord (Hospital of Limoges, France), Dr B. Lacarelle (Hospital of Marseille, France) and Dr T. Berod (Hospital of Le Mans, France).


  1. 1.
    Vozeh S. Therapeutic drug monitoring. Clin Pharmacol Ther 1988; 44: 713–4PubMedCrossRefGoogle Scholar
  2. 2.
    Moore RD, Smith CR, Lipsky JJ, et al. Risk factors for nephrotoxicity in patients treated with aminoglycosides. Ann Intern Med 1984; 100: 352–7PubMedGoogle Scholar
  3. 3.
    Lacarelle B, Granthil C, Manelli JC, et al. Evaluation of a Bayesian method of amikacin dosing in intensive care unit patients with normal or impaired renal function. Ther Drug Monit 1987; 9: 154–60PubMedCrossRefGoogle Scholar
  4. 4.
    Maire P, Barbaut X, Girard P, et al. Preliminary results of three methods for population pharmacokinetic analysis (NON-MEM, NPML, NPEM) of amikacin in geriatric and general medicine patients. Int J Biomed Comput 1994; 36: 139–41PubMedCrossRefGoogle Scholar
  5. 5.
    Lindeman RD, Tobin J, Shock NW. Longitudinal studies on the rate of decline in renal function with age. J Am Geriatr Soc 1985; 33: 278–85PubMedGoogle Scholar
  6. 6.
    Hadj-Aissa A, Dumarest C, Maire P, et al. Renal function in the elderly. Nephron 1990; 54(4): 364PubMedCrossRefGoogle Scholar
  7. 7.
    Ducher M, Cerutti C, Gustin MP, et al. Statistical relationships between systolic blood pressure and heart rate and their functional significance in conscious rats. Med Biol Eng Comput 1994; 32: 649–55PubMedCrossRefGoogle Scholar
  8. 8.
    Ducher M, Fauvel JP, Gustin MP, et al. A new non-invasive statistical method to assess the spontaneous cardiac baroreflex in humans. Clin Sci 1995; 88: 651–5PubMedGoogle Scholar
  9. 9.
    Ducher M, Ceratti C, Gustin MP, et al. Noninvasive exploration of cardiac autonomic neuropathy. Four reliable method for diabetes? Diabetes Care 1999; 22: 388–93PubMedCrossRefGoogle Scholar
  10. 10.
    Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976; 16: 31–41PubMedCrossRefGoogle Scholar
  11. 11.
    Jelliffe RW. The USC*PACK PC programs for population pharmacokinetic modelling of large kinetic/dynamic systems, and adaptive control of drug dosage regimens. Proc Annu Symp Comput Appl Med Care 1991: 922–44Google Scholar
  12. 12.
    Jelliffe RW, Maire P, Sattler F, et al. Adaptive control of drug dosage regimens: basic foundations, relevant issues, and clinical examples. Int J Biomed Comput 1994; 36: 1–23PubMedCrossRefGoogle Scholar
  13. 13.
    Joubert P, Bressolle F, Gouby A, et al. A population approach to the forecasting of amikacin plasma and urinary levels using a prescribed dosage regimen. Eur J Drug Metab Pharmacokinet 1999; 24: 39–46PubMedCrossRefGoogle Scholar
  14. 14.
    Sheiner LB, Melmeon K. Modelling of individual pharmacokinetics for computer-aided drug dosage. Comput Biomed Res 1972; 5: 441–59CrossRefGoogle Scholar
  15. 15.
    Lanao JM, Dominguez-Gil A, Tabernero JM, et al. Pharmacokinetics of amikacin (BB-K8) in patients with normal or impaired renal function. Int J Clin Pharmacol Biopharm 1979; 17(4): 171–5PubMedGoogle Scholar
  16. 16.
    Kirby WM, Clarke JT, Libke RD, et al. Clinical pharmacology of amikacin and kanamycin. J Infect Dis 1976; 134 Suppl.: S312–5PubMedCrossRefGoogle Scholar
  17. 17.
    Debord J, Pessis C, Voultoury JC, et al. Population pharmacokinetics of amikacin in intensive care unit patients studied by NPEM algorithm. Fundam Clin Pharmacol 1995; 9: 57–61PubMedCrossRefGoogle Scholar
  18. 18.
    Talbert RL. Drug dosing in renal insufficiency. J Clin Pharmacol 1994; 34: 99–110PubMedGoogle Scholar
  19. 19.
    Zaske DE, Cipolle RJ, Rotschafer JC, et al. Individualizing amikacin regimens: accurate method to achieve therapeutic concentrations. Ther Drug Monit 1991 Nov; 13(6): 502–6PubMedCrossRefGoogle Scholar
  20. 20.
    Sheiner LB. Analysis of pharmacokinetic data using parametric models. J Pharmacokinet Biopharm 1984; 12: 93–117PubMedGoogle Scholar
  21. 21.
    Rowe JW, Andres R, Tobin JD, et al. The effect of age on creatinine clearance in men: a cross sectional and longitudinal study. J Gerontol 1976; 31: 155–63PubMedCrossRefGoogle Scholar
  22. 22.
    Love LJ, Schimpff SC, Hahn DM, et al. Randomized trial of empiric antibiotic therapy with ticarcillin in combination with gentamicin, amikacin or netilmicin in febrile patients with granulocytopenia and cancer. Am J Med 1979 Apr; 66(4): 603–10PubMedCrossRefGoogle Scholar
  23. 23.
    Bjornsson TD, Cocchetto DM, McGowan FX, et al. Nomogram for estimating creatinine clearance. Clin Pharmacokinet 1983; 8: 365–9PubMedCrossRefGoogle Scholar

Copyright information

© Adis International Limited 2001

Authors and Affiliations

  • Michel Ducher
    • 1
    • 2
  • Pascal Maire
    • 1
    • 3
  • Catherine Cerutti
    • 2
  • Yann Bourhis
    • 1
  • Fréderic Foltz
    • 1
  • Pernille Sorensen
    • 1
  • Roger Jelliffe
    • 3
  • Jean-Pierre Fauvel
    • 3
  1. 1.Hôpital A. CharialFranchevilleFrance
  2. 2.Equipe d’Accueil 645Université Claude BernardLyonFrance
  3. 3.Laboratory of Applied Pharmacokinetics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA

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