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A Signal Processing Approach to Pharmacokinetic Data Analysis

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Abstract

The connection between pharmacokinetic models and system theory has been established for a long time. In this approach, the drug concentration is seen as the output of a system whose input is the drug administered at different times. In this article we further explore this connection. We show that system theory can be used to easily accommodate any therapeutic regime, no matter its complexity, allowing the identification of the pharmacokinetic parameters by means of a non-linear regression analysis. We illustrate how to exploit the properties of linear systems to identify non-linearities in the pharmacokinetic data. We also explore the use of bootstrapping as a way to compare populations of pharmacokinetic parameters and how to handle the common situation of using multiple hypothesis tests as a way to distinguish two different populations. Finally, we demonstrate how the bootstrap values can be used to estimate the distribution of derived parameters, as can be the allometric scale factors.

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Acknowledgments

The authors wish to thank Justesa Imagen SAU to grant us the use of ICJ3393 experimental data. C.O.S. Sorzano is recipient of a Ramón y Cajal fellowship from the Spanish Ministry of Economy and Competitiveness.

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Authors and Affiliations

  1. National Center of Biotechnology, CSIC, Madrid, Spain

    C. O. S. Sorzano

  2. University CEU San Pablo, Madrid, Spain

    C. O. S. Sorzano, F. R. Martín, C. Montejo & A. Aguilar-Ros

  3. Actual: Chemo Group, Madrid, Spain

    M. A. Pérez-de-la-Cruz Moreno

Authors
  1. C. O. S. Sorzano
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  2. M. A. Pérez-de-la-Cruz Moreno
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  3. F. R. Martín
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  4. C. Montejo
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  5. A. Aguilar-Ros
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Correspondence to C. O. S. Sorzano.

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Keypoints

• We provide a discrete system approach to the analysis of pharmacokinetic data that considers any therapeutic regimen.

• The main advantage of this technique is that it does not require the analytical solution of a differential equation. In this way, it can be used in situations in which this explicit solution does not exist.

• This approach allows identifying non-linearities in pharmacokinetic data.

• This approach can easily be integrated with bootstrapping so that population-wide comparisons can be easily done. In this article we illustrate the use of the method to estimate the statistical distribution of derived parameters (like the parameters of an allometric scaling, which are based on the estimation of the primary pharmacokinetic parameters).

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Sorzano, C.O.S., Moreno, M.A.PdlC., Martín, F.R. et al. A Signal Processing Approach to Pharmacokinetic Data Analysis. Pharm Res 38, 625–635 (2021). https://doi.org/10.1007/s11095-021-03000-4

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  • DOI: https://doi.org/10.1007/s11095-021-03000-4

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