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A General Framework for Non-Parametric Subject-Specific and Population Deconvolution Methods for in Vivo-in Vitro Correlation

  • Davide Verotta
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 423)

Abstract

Suppose that a drug is given in some formulation to a system resulting in an (unknown) input A(t), and n observations are collected at different times following the input. The i-th observation takes the form:
$$ {y_i} = \mathop \smallint \limits_0^{{t_i}} A\left( \tau \right)K\left( {t - \tau } \right)d\tau + {\varepsilon _i} $$
(1)
where εi indicates a random measurement error.

Keywords

True Model Input Function Model Selection Criterion Random Measurement Error Order Absorption 
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.

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References

  1. 1.
    Akaike, H., “A New Look at the Statistical Model Identification Problem,” IEEE Trans Automat Contr, 19: 716–723, 1974.CrossRefGoogle Scholar
  2. 2.
    Boeckmann, A.J., S.L. Beal, and L.B. Sheiner, NONMEM Users Guides, 1989.Google Scholar
  3. 3.
    Fattinger, K.E. and D. Verotta, “A Non-Parametric Subject-Specific Population Methods for Deconvolution. I. Description, Internal Validation and Real Data Examples,” J Pharmacokin Biopharm, 23:1996.Google Scholar
  4. 4.
    Fattinger, K.E. and D. Verotta, “A Non-Parametric Subject-Specific Population Methods for Deconvolution. II. External Validation,” J harmacokin Biopharm, 23:1996.Google Scholar
  5. 5.
    Verotta, D., “Concepts, Properties, and Applications of Linear Systems to Describe the Distribution, Identify Input, And Control Endogenous Substances and Drugs in Biological Systems,” Critical Review Bioengineers, 1996. In Press.Google Scholar

Copyright information

© Plenum Press, New York 1997

Authors and Affiliations

  • Davide Verotta
    • 1
    • 2
  1. 1.Department of Biopharmaceutical Sciences and Pharmaceutical ChemistryUniversity of California San FranciscoSan FranciscoUSA
  2. 2.Department of Epidemiology and BiostatisticsUniversity of California San FranciscoSan FranciscoUSA

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