Development of in Vitro-in Vivo Correlations Using Various Artificial Neural Network Configurations

  • James A. Dowell
  • Ajaz S. Hussain
  • Paul Stark
  • John Devane
  • David Young
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 423)


It is desirable to have a predictive tool to determine the in vivo pharmacokinetics based on the in vitro dissolution and other important variables. We can see the in vitro — in vivo correlation (IVIVC) as an input-output relationship, and often are not interested in the internal structure of this model as long as we have a good, validated, predictive tool. This may be important, for example, in product development or to establish dissolution specifications. Many of the previous examples in this book use parametric models to define an IVIVC. For example, simple linear models are often used to relate a parameter or a time point descriptive of the dissolution to a parameter or a time point descriptive of the pharmacokinetic absorption1–3. These models, however, can be unsuccessful in completely describing the IVIVC, and sometimes no correlation can be determined. The number of possible variables, the model unable to account for some physiological rate determining process, and the possible amount of variability intrinsic to the parameters of these modeled relationships are some examples of these difficulties 4–6.


Artificial Neural Network Dissolution Profile Network Configuration Logistic Linear General Regression Neural Network 
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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Copyright information

© Plenum Press, New York 1997

Authors and Affiliations

  • James A. Dowell
    • 1
  • Ajaz S. Hussain
    • 2
  • Paul Stark
    • 3
  • John Devane
    • 3
  • David Young
    • 1
  1. 1.University of Maryland at BaltimoreBaltimoreUSA
  2. 2.Food and Drug Administration/CDERRockvilleUSA
  3. 3.Elan Corporation, PLCAthloneIreland

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