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Tags
HERO ID
3223482
Reference Type
Journal Article
Title
Combining the 'bottom up' and 'top down' approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data
Author(s)
Tsamandouras, N; Rostami-Hodjegan, A; Aarons, L
Year
2015
Is Peer Reviewed?
Yes
Journal
British Journal of Clinical Pharmacology
ISSN:
0306-5251
EISSN:
1365-2125
Volume
79
Issue
1
Page Numbers
48-55
DOI
10.1111/bcp.12234
Web of Science Id
WOS:000346659100007
Abstract
Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.
Keywords
Bayesian analysis; identifiability; middle-out approach; parameter estimation; PBPK; population variability
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