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Use of a Pharmacokinetic/Pharmacodynamic Model to Design an Optimal Dose Input Profile

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Abstract

A model for the pharmacodynamic effect of a drug (designated only X), and the use of the model to explore optimal input is described. The data analyzed here are from a crossover comparison study of the effect of 4 active treatments, yielding distinct concentration vs. time curves, plus placebo in 32 subjects. The model expresses total effect as the sum of placebo effect and (pure) drug effect. The latter allows for possible tolerance (found) and time effects (not found). Random effects allow interindividual differences to be expressed. Conditioning on the fitted model, a population optimal input profile is designed that obeys certain protocol constraints. The profile minimizes the expectation of a sum of squared differences between target effect and the resulting response over a given time interval. The target is a fixed constant, chosen to be either the individuals' maximum effect level in response to a baseline input regimen used in the study or the maximum effect level for the typical individual in the population in response to this regimen, as predicted from the model. The expectation is taken over the estimated nonparametric distribution of the 32 subjects' random effects. As one goal of early clinical studies of drugs may be to provide a basis for designing an optimal delivery profile (with respect to a specified loss function), we suggest this report as an example of a reasonable way to go about finding such a profile.

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REFERENCES

  1. D. Verotta. Longitudinal splines. Technical report 36. Department of Biostatistics. University of California San Francisco, San Francisco, 1993.

    Google Scholar 

  2. D. Verotta. New approaches to self-modeling nonlinear regression. In D. Z. D'Argenio (ed.), Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis, Vol. 2, Plenum Press, New York, 1995, pp. 121–143.

    Google Scholar 

  3. K. E. Fattinger and D. Verotta. A nonparametric subject-specific population method for deconvolution: I. Description, internal validation, and real data examples. J. Pharmacokin. Biopharm. 23:581–610 (1995).

    Article  CAS  Google Scholar 

  4. K. E. Fattinger and D. Verotta. A nonparametric subject-specific population method for deconvolution: II. External validation. J. Pharmacokin. Biopharm. 23:611–634 (1995).

    Article  CAS  Google Scholar 

  5. J. M. Gries, I. F. Troconiz, D. Verotta, M. Jacobson, and L. B. Sheiner. A pooled analysis of CD4 response to zidovudine and zalcitabine treatment in patients with AIDs and AIDS-related complex. Clin. Pharmacol. Ther. 61:70–82 (1997).

    Article  CAS  PubMed  Google Scholar 

  6. T. J. Hastie and R. J. Tibshirani. Generalized Additive Models. Monographs on Statistics and Applied Probability 43, Chapman & Hall, New York, 1990.

    Google Scholar 

  7. D. Verotta. Estimation and model selection using splines in constrained deconvolution. Ann. Bioeng. 21:605–620 (1993).

    CAS  Google Scholar 

  8. H. Akaike. A new look at the statistical model identification problem. IEEE Trans. Automat. Contr. 19:716–723 (1974).

    Article  Google Scholar 

  9. H. C. Porchet, N. L. Benowitz, and L. B. Sheiner. Pharmacodynamic model of tolerance: application to nicotine. J. Pharmacol. Exp. Ther. 244:231–236 (1988).

    CAS  PubMed  Google Scholar 

  10. S. L. Beal and L. B. Sheiner. NONMEM Users Guides. Version 5, NONMEM Project Group, University of California at San Francisco, San Francisco, 1996.

    Google Scholar 

  11. D. R. Cox and D. V. Hinkley. Theoretical Statistics, Chapman & Hall, London, 1974, chapt. 9.

    Book  Google Scholar 

  12. A. Schumitzky. Nonparametric EM algorithms for estimating prior distributions. Appl. Math. Comput. 45:143–157 (1991).

    Article  Google Scholar 

  13. D. Z. D'Argenio and J. H. Rodman. Targeting the systemic exposure of teniposide in the population and the individual using a stochastic therapeutic objective. J. Pharmacokin. Biopharm. 21:223–251 (1993).

    Article  Google Scholar 

  14. K. Park and D. Z. D'Argenio. Stochastic control of pharmacodynamic processes with application to ergotamine. In D. Z. D'Argenio (ed.), Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis, Vol, 2, Plenum Press, New York, 1995, pp. 189–204.

    Google Scholar 

  15. J. Gaillot, J. L. Steimer, A. J. Mallet, J. Thebault, and A. Beider. A priori lithium dosage regimen using population characteristics of pharmacokinetic parameters. J. Pharmacokin. Biopharm. 7:579–628 (1979).

    Article  CAS  Google Scholar 

  16. O. Richter and D. Reinhardt. Methods for evaluating optimal dosage regimens and their application to theophylline. Int. J. Clin. Pharm. 20:564–575 (1982).

    CAS  Google Scholar 

  17. A. Mallet, F. Mentre, J. Gilles, A. W. Kelman, A. H. Thomson, S. M. Bryson, and B. Whiting. Handling covariates in population pharmacokinetics, with an application to gentamicin. Biomed. Meas. Inform. Contr. 2:138–146 (1988).

    Google Scholar 

  18. A. Amrani, E. Walter, U. Lecourtier, and R. Gomeni. Robust control of uncertain pharmacokinetic systems. IFAC 9th Triennial World Congress, Budapest, 1984, pp. 3079–3083.

  19. P. J. A. Lago. A first order approximation to the open-loop stochastic control of pharmacokinetic systems. IFAC Workshop on Decision Support for Patient Management, London, 1989, pp. 161–167.

  20. D. Katz and D. Z. D'Argenio. Implementation and evaluation of control strategies for individualizing dosage regimens, with application to the aminoglycoside antibiotics. J. Pharmacokin. Biopharm. 14:523–537 (1986).

    Article  CAS  Google Scholar 

  21. D. Z. D'Argenio and D. Katz. Application of stochastic control methods to the problem of individualizing intravenous theophylline therapy. Biomed. Meas. Inform. Contr. 2:115–122 (1988).

    Google Scholar 

  22. C. Hu, W. S. Lovejoy, and S. L. Shafer. An efficient control strategy for dosage regimens. J. Pharmacokin. Biopharm. 22:73–94 (1994).

    Article  CAS  Google Scholar 

  23. J. Wakefield. An expected loss approach to the design of dosage regimens via sampling-based methods. Statistician 43:13–29 (1994).

    Article  Google Scholar 

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Park, K., Verotta, D., Gupta, S.K. et al. Use of a Pharmacokinetic/Pharmacodynamic Model to Design an Optimal Dose Input Profile. J Pharmacokinet Pharmacodyn 26, 471–492 (1998). https://doi.org/10.1023/A:1021068202606

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