Structural identifiability for mathematical pharmacology: models of myelosuppression

Structural identifiability for mathematical pharmacology: models of myelosuppression Structural identifiability is an often overlooked, but essential, prerequisite to the experiment design stage. The application of structural identifiability analysis to models of myelosuppression is used to demonstrate the importance of its considerations. It is shown that, under certain assumptions, these models are structurally identifiable and so drug and system specific parameters can truly be separated. Further it is shown via a meta-analysis of the literature that because of this the reported system parameter estimates for the “Friberg” or “Uppsala” model are consistent in the literature. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Pharmacokinetics and Pharmacodynamics Springer Journals

Structural identifiability for mathematical pharmacology: models of myelosuppression

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Biomedicine; Pharmacology/Toxicology; Pharmacy; Veterinary Medicine/Veterinary Science; Biomedical Engineering; Biochemistry, general
ISSN
1567-567X
eISSN
1573-8744
D.O.I.
10.1007/s10928-018-9569-x
Publisher site
See Article on Publisher Site

Abstract

Structural identifiability is an often overlooked, but essential, prerequisite to the experiment design stage. The application of structural identifiability analysis to models of myelosuppression is used to demonstrate the importance of its considerations. It is shown that, under certain assumptions, these models are structurally identifiable and so drug and system specific parameters can truly be separated. Further it is shown via a meta-analysis of the literature that because of this the reported system parameter estimates for the “Friberg” or “Uppsala” model are consistent in the literature.

Journal

Journal of Pharmacokinetics and PharmacodynamicsSpringer Journals

Published: Feb 2, 2018

References

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