Assay Error for Improved Pharmacokinetic Modeling and Simulation of Vancomycin

반코마이신의 약물동태학적 모델링과 시뮬레이션의 향상을 위한 분석오차

  • Received : 2013.01.11
  • Accepted : 2013.02.20
  • Published : 2013.02.28

Abstract

The purpose of this study was to determine the influence of assay error for improved pharmacokinetic modeling and simulation of vancomycin on the Bayesian and nonlinear least squares regression analysis in 24 Korean gastric cancer patients. Vancomycin 1.0 g was administered intravenously over 1 hr every 12 hr. Three specimens were collected at 72 hr after the first dose from all patients at the following times, at 0.5 hr before regularly scheduled infusion, at 0.5 hr and 2 hr after the end of 1 hr infusion. Serum vancomycin levels were analyzed by fluorescence polarization immunoassay technique with TDX-FLX. The standard deviation (SD) of the assay over its working range had been determined at the serum vancomycin concentrations of 0, 20, 40, 60, 80 and $120{\mu}g/ml$ in quadruplicate. The polynomial equation of vancomycin assay error was found to be SD $({\mu}g/ml)=0.0224+0.0540C+0.00173C^2$ ($R^2=0.935$). There were differences in the influence of weight with vancomycin assay error on pharmacokinetic parameters of vancomycin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynomial equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result suggests the improvement of dosage regimens for the better and safer care of patients receiving vancomycin.

Keywords

References

  1. Kirby, W. M. : Vancomycin therapy of severe staphylococcal infections. J. Antimacrob. Chemother. 14, 73 (1984). https://doi.org/10.1093/jac/14.suppl_D.73
  2. Watanakunakorn, C. : Treatment of infections due to methicillin-resistant staphylococcus aureus. Ann. Intern. Med. 97, 376 (1982). https://doi.org/10.7326/0003-4819-97-3-376
  3. Sorrell, T. C., Packham, D. R., Shanker, S., Foldes, M. and Munro, R. : Vancomycin therapy for methcillin-resistant staphylococcus aureus. Ann. Intern. Med. 97, 344 (1982).
  4. Harris, C. M. and Kopecka, H. : Vancomycin structure and trasformation. J. Am. Chem. Soc. 105, 6915 (1983). https://doi.org/10.1021/ja00361a029
  5. Watanakunakorn, C. : The antibacterial action of vancomycin. Rev. Infect. Dis. 3, 210 (1981). https://doi.org/10.1093/clinids/3.Supplement.S210
  6. Cook, F. V. and Farrar, W. W. : Vancomycin revisited. Ann. Intern. Med. 88, 813 (1978). https://doi.org/10.7326/0003-4819-88-6-813
  7. Beringer, P. M., Wong-Beringer, A. and Rho, J. P. : Predictive performance of a vancomycin-aminoglycoside population model. Ann. Pharmacother. 32, 176 (1998). https://doi.org/10.1345/aph.17129
  8. Yasuhara, M., Iga, T., Zenda, H., Okumura, K., Oguma, T., Yano, Y. and Hori. R. : Population pharmacokinetics of vancomycin in Japanese adult patients. Ther. Drug. Monit. 20, 139 (1998). https://doi.org/10.1097/00007691-199804000-00003
  9. Choi, J. S., Min, Y. D. and Burm, J. P. : Population pharmacokinetic modeling of vancomycin in patients with cancer. Yakhak Hoeji 43, 160 (1999).
  10. Kim, Y. W., Choi, J. S., Lee, J. W., Park, J. Y., Choi, B. C. and Burm, J. P. : Clinical pharmacokinetics of vancomycin in ovarian cancer patients. Kor. J. Clin. Pharm. 8, 13 (1998).
  11. Ohnishi, A., Yano, Y., Ishibashi, T., Katsube, T. and Oguma, T. : Evaluation of Bayesian predictability of vancomycin concentration using population pharmacokinetic parameters in pediatric patients. Drug Metab. Pharmacokinet. 20, 415 (2005). https://doi.org/10.2133/dmpk.20.415
  12. Thomson, A. H. and Whiting, B. : Bayesian parameter estimation and population pharmacokinetics. Clin. Pharmacokinet. 22, 447 (1992). https://doi.org/10.2165/00003088-199222060-00004
  13. Jelliffe, R. W., Iglesiias, T., Hurst, A., Foo, K. and Rodriguez, J. : Individualizing gentamicin dosage regimens. A comparative review of selected models, data fitting methods and monitoring stategies. Clin. Pharmacokinet. 21, 461 (1991). https://doi.org/10.2165/00003088-199121060-00006
  14. Erdmann, S. M., Rodvold, K. and Pryka, R. D. : An updated comparison of drug dosing methods. Part : aminoglycoside antibiotics. Clin. Pharmacokinet. 20, 374 (1991). https://doi.org/10.2165/00003088-199120050-00003
  15. Jelliffe, R. W., D'Argenio, D. Z., Schumitzky, A., Hu, L. and Liu, M. : The USC-PACK PC programs for planning, monitoring and adjusting drug dosage regimens. Proceedings of the twenty-third annual meeting of the Association for the Advancement of Medical Instrumentation. Washington DC May. 13 (1988).
  16. Sheiner, L. B., Beal, S. L., Rosenberg, B. and Marathe, B. : Forecasting individual pharmacokinetics. Clin. Pharmacol. Ther. 26, 294 (1979). https://doi.org/10.1002/cpt1979263294
  17. Kim, J. K., Yoo, D. S., Shin, H. T., Kim, N. D., Kim, J. K., Kim, G. Y. and Kim, Y. K. : Theophylline clearance of Korean population in comparison with American. J. Kor. Soc. Hosp. Pharm. 4, 36 (1987).
  18. Burton, M. E., Brater, D. C. and Chen, P. S. : A Bayesian method of aminoglycoside dosing. Clin. Pharmacol. Ther. 37, 349 (1985). https://doi.org/10.1038/clpt.1985.51
  19. Chrystn, H. : Validation of use of Bayesian analysis in the optimization of gentamicin therpy from the commencement of dosing. Drug. Intell. Clin. Pharm. 22, 49 (1988). https://doi.org/10.1177/106002808802200112
  20. Hurst, A., Yoshinaga, M., Mitani, G., Foo, K., Jelliffe, R. and Harrison, E. : Application of a bayesian method to monitor and adjust vancomycin dosage regimens. Antimicrob. Agents Chemother. 34, 1165 (1990). https://doi.org/10.1128/AAC.34.6.1165
  21. Steimer, J. L., Mallet, A. and Mentre, F. : Estimating interindividual pharmacokinetic variability. In variability if drug therapy: description, estimation, and control. Raven Press, New York, 65 (1985).
  22. Racine-Poon, A. and Smith, A. F. M. : Population models. In statistical methodology in the pharmaceutical sciences. Marcel Dekker, 139 (1990).
  23. Beal, S. : Population pharmacokinc data and parameter estimation based on their first two statistical moments. Drug Metab. Rev. 16, 173 (1984).
  24. Beal, S. and Sheiner, L. : NONMEM users guide-part 1:users basic guide, Technical report of the division of Clinical Pharmaology. University of Califonia, San Francisco (1980).
  25. Mallet, A. : A maximum likelihood estimation method for random coefficient regression models. Biometrika. 73, 645 (1986). https://doi.org/10.1093/biomet/73.3.645
  26. Mallet, A., Mentre, F., Steimer, J. L. and Lookiec, F. : Nonparametric maxmum likelihood estimation for population pharmacokinetics, with application to cyclosporine. J. Pharmacokinet. Biopharm. 16, 311 (1988). https://doi.org/10.1007/BF01062140
  27. Kisor, D. F., Watling, S. M., Zarowitz, B. J. and Jelliffe, R. W. : Population pharmacokinetics of gentamicin: use of the nonparametric expectation maximisation (NPEM) algorithm. Clin. Pharmacokinet. 23, 62 (1992). https://doi.org/10.2165/00003088-199223010-00005
  28. Dodge, W. F., Jellife, R. W., Richardson, C. J., McCleery, R. A. and Hokanson, J. A. : Gentamicin population pharmacokinetic models for low birth weight infants; using a new nonparametric algorithm. Clin. Pharmacol. Ther. 50, 25 (1991). https://doi.org/10.1038/clpt.1991.100
  29. Gill, M. A., Okamoto, M. P., Nakahiro, R. K., Chin, A., Inagaki, K. and Sclar, D. : Pharmacokinetic population parameters for aminoglycosides in cholecystitis patients. Ther. Drug Monit. 14, 107 (1992). https://doi.org/10.1097/00007691-199204000-00005
  30. Gilman, T. M., Brunnemann, S. R. and Segal, J. L. : Comparison of population pharmacokinetic models for gentamicin in spinal cord-injured and able-bodies patients. Antimicrob. Agents Chemother. 37, 93 (1993). https://doi.org/10.1128/AAC.37.1.93
  31. DeGroot, M. H. : Probability and statistics. Reading, Mass. Addison-Wesley Company. 357 (1975).