Lifespan based indirect response models
Journal of Pharmacokinetics and Pharmacodynamics, ISSN: 1567-567X, Vol: 39, Issue: 1, Page: 109-123
2012
- 18Citations
- 27Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations18
- Citation Indexes18
- 18
- CrossRef13
- Captures27
- Readers27
- 27
Article Description
In the field of hematology, several mechanism-based pharmacokinetic- pharmacodynamic models have been developed to understand the dynamics of several blood cell populations under different clinical conditions while accounting for the essential underlying principles of pharmacology, physiology and pathology. In general, a population of blood cells is basically controlled by two processes: the cell production and cell loss. The assumption that each cell exits the population when its lifespan expires implies that the cell loss rate is equal to the cell production rate delayed by the lifespan and justifies the use of delayed differential equations for compartmental modeling. This review is focused on lifespan models based on delayed differential equations and presents the structure and properties of the basic lifespan indirect response (LIDR) models for drugs affecting cell production or cell lifespan distribution. The LIDR models for drugs affecting the precursor cell production or decreasing the precursor cell population are also presented and their properties are discussed. The interpretation of transit compartment models as LIDR models is reviewed as the basis for introducing a new LIDR for drugs affecting the cell lifespan distribution. Finally, the applications and limitations of the LIDR models are discussed. © Springer Science+Business Media, LLC 2012.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84861483410&origin=inward; http://dx.doi.org/10.1007/s10928-011-9236-y; http://www.ncbi.nlm.nih.gov/pubmed/22212685; http://link.springer.com/10.1007/s10928-011-9236-y; https://dx.doi.org/10.1007/s10928-011-9236-y; https://link.springer.com/article/10.1007/s10928-011-9236-y; http://www.springerlink.com/index/10.1007/s10928-011-9236-y; http://www.springerlink.com/index/pdf/10.1007/s10928-011-9236-y
Springer Science and Business Media LLC
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