Journal of Pharmacokinetics and Biopharmaceutics

, Volume 25, Issue 6, pp 695–712 | Cite as

Implementation and Evaluation of a Stochastic Control Strategy for Individualizing Teicoplanin Dosage Regimen

  • Michel Tod
  • Patrice Alet
  • Olivier Lortholary
  • Olivier Petitjean


A stochastic control strategy for individualizing teicoplanin dosing schedule in neutropenic patients is proposed and compared to the usual Bayesian approach based on the mode of the posterior density of the model parameters. Teicoplanin disposition is described by a bicompurlmental model. Age, body weight, serum creatinine, white blood cell count, and sex can be included as covariates. Posterior density of model parameters is obtained by Bayes theorem under a discrete form from which the posterior density of leicoplanin trough concentrations are computed for any dosing schedule. Optimal maintenance dose is determined by minimizing the cost associated, through a logarithmic risk function, to the concentrations being outside the therapeutic range. In Monte Carlo simulation studies on 300 individuals, stochastic control was more accurate than, and equally precise as the usual Bayesian approach. Two-sample based predictions were not better than one-sample based ones. Inclusion of covariates in the model improved dramatically the performances of both strategies. A small retrospective study based on real data (n = 16 patients) shows that reasonable accuracy (bias of 0.7 mg/L) and precision (3 mg/L) in teicoplanin trough concentration prediction is obtained with both strategies provided that covariates are taken into account.

therapeutic drug monitoring stochastic control teicoplanin 


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  1. 1.
    A. P. R. Wilson, R. N. Grüneberg, and H. Neu. A critical review of the dosage of teicoplanin in Europe and the USA. Int. J. Antimicrob. Agents 4(Suppl. 1):S1–S30 (1994).CrossRefGoogle Scholar
  2. 2.
    F. Menichetti, P. Martino, G. Bucaneve, et al. Effects of teicoplanin and those of vancomycin in initial empirical antibiotic regimen for febrile neutropenic patients with hematologic malignancies. Antimicrob. Agents Chemother. 38:2041–2046 (1994).PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    O. Lortholary, M. Tod, N. Rizzo, C. Padoin, O. Biard, P. Casassus, L. Guillevin, and O. Petitjean. Population pharmacokinetic study of teicoplanin in severely neutropenic patients. Antimicrob. Agents Chemother. 40:1242–1247 (1996).PubMedCentralPubMedGoogle Scholar
  4. 4.
    D. Greenwood, K. Bigdood, and M. Turner. A comparison of the responses of staphylococci and streptococci to teicoplanin and vancomycin. J. Antimicrob. Agents 20:155–164 (1987).CrossRefGoogle Scholar
  5. 5.
    B. Vogelman, S. Gudmunsson, J. Legett, J. Tumidge, S. Ebert, and W. A. Craig. Correlation of antimicrobial pharmacokinetic parameters with therapeutic efficacy in an animal model. J. Infect. Dis. 158:831–847 (1988).PubMedCrossRefGoogle Scholar
  6. 6.
    H. F. Chambers and S. Kennedy. Effects of dosage, peak and trough concentrations in serum, protein binding and bactericidal rate on efficacy of teicoplanin in a rabbit model of endocarditis. Antimicrob. Agents Chemother. 34:510–514 (1990).PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    A. E. Zimmerman, B. G. Katona, and K. I. Plaisance. Association of vancomycin serum concentrations with outcomes in patients with Gram-positive bacteremia. Pharmacotherapy 15:85–91 (1995).Google Scholar
  8. 8.
    P. L. Carver, C. H. Nightingale, R. Quintiliani, K. Sweeney, R. C. Stevens, and E. Madezo. Pharmacokinetics of single-and multiple-dose teicoplanin in healthy volunteers. Antimicrob. Agents Chemother. 33:82–86 (1989).PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    J. A. Smithers, H. K. Kulmala, G. A. Thompson, K. K. Anthony, E. W. Lewis, S. J. Ruberg, M. T. Kenny, J. K. Dulworth, and M. A. Brackman. Pharmacokinetics of teicoplanin upon multiple-dose intravenous administration to healthy volunteers. Antimicrob. Agents Chemother. 36:115–120 (1992).PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    K. Katz and D. Z. D'Argenio. Implementation and evaluation of control strategies for individualizing dosage regimens, with application to the aminoglycosides antibiotics. J. Pharmacokin. Biopharm. 14:523–537 (1986).CrossRefGoogle Scholar
  11. 11.
    N. Taright, F. Mentré, A. Mallet, and R. Jouvent. Nonparametric estimation of population characteristics of the kinetics of lithium from observational and experimental data: Individualization of chronic dosing regimen using a new Bayesian approach. Ther. Drug Monit. 16:258–269 (1994).PubMedCrossRefGoogle Scholar
  12. 12.
    J. C. Wakefield. An expected loss approach to the design of dosage regimens via sampling based methods. Statistician 43:13–29 (1994).CrossRefGoogle Scholar
  13. 13.
    J. C. Wakefield. Bayesian individualisation via sampling-based methods. J. Pharmacokin. Biopharm. 24:103–131 (1996).CrossRefGoogle Scholar
  14. 14.
    J. G. Wagner. Pharmacokinetics for the Pharmaceutical Scientist, Technomic, Lancaster, PA, 1993.Google Scholar
  15. 15.
    D. W. Cockroft and M. H. Gault. Prediction of creatinine clearance from serum creatinine. Nephron 16:31–41 (1976).CrossRefGoogle Scholar
  16. 16.
    F. Mentre, A. Mallet and D. Baccart. Optimal design in random effects models. Biometrika 84:429–442 (1997).CrossRefGoogle Scholar
  17. 17.
    W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C: The Art of Scientific Computing, Cambridge University Press, Cambridge, U.K., 1992.Google Scholar
  18. 18.
    L. B. Sheiner and S. Beal. Some suggestions for measuring predictive performances. J. Pharmacokin. Biopharm. 9:503–513 (1981).CrossRefGoogle Scholar
  19. 19.
    Y. Tanigawara, I. Yano, K. Kawakatsu, K. Nishimura, M. Yasuhara, and R. Hori. Predictive performance of the Bayesian analysis: Effect of blood sampling time, population parameters and pharmacostatistical model. J. Pharmacokin. Biopharm. 22:59–71 (1994).CrossRefGoogle Scholar
  20. 20.
    S. Vozeh and C. Steiner. Estimates of the population pharmacokinetic parameters and performance of Bayesian feed back: A sensitivity analysis. J. Pharmacokin. Biopharm. 15:511–528 (1987).CrossRefGoogle Scholar

Copyright information

© Plenum Publishing Corporation 1997

Authors and Affiliations

  • Michel Tod
    • 1
    • 2
  • Patrice Alet
    • 1
  • Olivier Lortholary
    • 2
    • 3
  • Olivier Petitjean
    • 1
    • 2
  1. 1.Departement de PharmacotoxicologieHôpital AvicenneBobignyFrance
  2. 2.Faculte de Medecine Paris-NordCentre de Recherche en Pathologie Infectieuse et TropicaleBobignyFrance
  3. 3.Service de Medecine InterneHôpital AvicenneBobignyFrance

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