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Information Tools for Exploratory Data Analysis in Population Pharmacokinetics

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Abstract

For a group of individuals, population pharmacokinetic studies describe the interindividual variability through a statistical distribution. These studies conducted during the drug development serve as a useful marker of the safety of the drug, provide information that might be decisive for future experiments and, in a clinical context, help establish guidelines for optimal use in each patient.

As complementary tools to the existing statistical and graphical techniques for population pharmacokinetic data analysis, indexes derived from information theory were used to select the most appropriate model for the statistical distribution, to detect atypical individuals, and to screen influential covariates. The rationale for using these indexes is shown using simulated and real data.

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REFERENCES

  1. L. B. Sheiner. The population approach to pharmacokinetic data analysis: Rationale and standard data analysis methods. Drug Metab. Reû . 15:153–171 (1984).

    Google Scholar 

  2. H. Fhüler, H. Huber, E. Widmer, and S. Brechbüler. Experiences in the application of NONMEM to pharmacokinetic data analysis. Drug Metab. Reυ. 15:317–339 (1984).

    Google Scholar 

  3. J. L. Steimer, S. Vozeh, A. Racine-Poon, N. H. G. Holford, and R. O'Neil. The population approach: Rationale, methods, applications in clinical pharmacology and drug development. In P. G. Welling and L. P. Balant, (eds.), Pharmacokinetics of Drugs, Handbook of Experimental Pharmacology Springer, Verlag, Berlin, 1994, pp. 405–451.

    Google Scholar 

  4. T. H. Grasela and L. B. Sheiner. Pharmacostatistical modeling for observational data. J. Pharmacokin. Biopharm. 19:25–36 (1991).

    Google Scholar 

  5. A. Iliadis. Information tools used in population pharmacokinetics. In J. M. Aiache (ed.), 6th European Congress of Biopharmaceutics and Pharmacokinetics, Athens, Médecine et Hygiéne, 1996, pp. 143.

    Google Scholar 

  6. E. I. Ette and T. M. Ludden. Population pharmacokinetic modeling: The importance of informative graphics. Pharm. Res. 12:1845–1855 (1995).

    Google Scholar 

  7. S. Kapur. Maximum Entropy Models in Science and Engineering. John Wiley, New York, 1989.

    Google Scholar 

  8. D. V. Lindley. On a measure of the information provided by an experiment. Ann. Math. Stat. 27:986–1005 (1956).

    Google Scholar 

  9. M. Stone. Application of a measure of information to the design and comparison of regression experiments. Ann. Math. Stat. 30:55–70 (1959).

    Google Scholar 

  10. M. C. Jones and R. Sibson. What is projection pursuit? (with discussion). J. Roy. Stat. Soc. 150:1–36 (1987).

    Google Scholar 

  11. Y. Bard. Nonlinear Parameter Estimation. Academic Press, New York, 1974.

    Google Scholar 

  12. S. J. Press. Applied Multiυariate Analysis. Holt, Rinehart, and Winston, New York, 1972.

    Google Scholar 

  13. D. W. Scott. Multiυariate Density Estimation. Theory, Practice, and Visualization. John Wiley, New York 1992.

    Google Scholar 

  14. B. W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman and Hall, London 1992.

    Google Scholar 

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

    Google Scholar 

  16. H. Akaike. On entropy maximization principle. In P. R. Krishnaiah (ed.), Applications of Statistics, North Holland, Amsterdam, 1977, pp. 27–41.

    Google Scholar 

  17. V. Barnett and T. Lewis. Outliers in Statistical Data. John Wiley, New York 1994.

    Google Scholar 

  18. S-PLUS. Guide to Statistics. 4.5 ed. MathSoft, Inc., Seattle WA, 1998.

  19. J. P. Richard. Entropy. In D. P. Atherton and P. Borne (eds.), Concise Encyclopedia of Modelling and Simulation, Pergamon Press, Oxford, 1992, pp. 104–105.

    Google Scholar 

  20. H. Joe. Relative entropy measures of multivariate dependence. J. Am. Stat. Assoc. 84:157–164 (1989).

    Google Scholar 

  21. C. Puozzo, C. Filaquier, and M. Briley. Plasma levels of F2207, milnacipran, a novel antidepressant, after single oral administration in volunteers. Br. J. Clin. Pharm. 20:291–292 (1985).

    Google Scholar 

  22. A. Stenger, J. P. Cousinier, and M. Briley. Psychopharmacology of milnacipran, 1-phenyl 1 diethylaminocarbonyl 2 aminomethylcyclopropane hydrochloride (F2207), a new potential antidepressant. Psychopharmacology 91:147–153 (1987).

    Google Scholar 

  23. J. P. Macher, J. P. Sichel, C. Serre, R. von Frenckell, J. C. Huck, and J. P. Demarez. Double blind placebo controlled study of milnacipran in hospitalized patients with major depressive disorders. Neuropsychobiology 22:77–82 (1990).

    Google Scholar 

  24. A. Iliadis, A. C. Brown, and M. L. Huggins. APIS: A software for identification, simulation and dosage regimen calculations in clinical and experimental pharmacokinetics. Comput. Meth. Prog. Biomed. 38:227–239 (1992).

    Google Scholar 

  25. L. Claret and A. Iliadis. Nonparametric density estimation applied to population pharmacokinetics. Math. Biosci. 133:51–68 (1996).

    Google Scholar 

  26. MATLAB. High-performance Numeric Computation and Visualization Software. 5.2 ed. The MathWorks, Natick MA, 1998.

    Google Scholar 

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Petricoul, O., Claret, L., Barbolosi, D. et al. Information Tools for Exploratory Data Analysis in Population Pharmacokinetics. J Pharmacokinet Pharmacodyn 28, 577–599 (2001). https://doi.org/10.1023/A:1014464505261

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