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Simultaneous Modeling of Pharmacokinetics and Pharmacodynamics with a Nonparametric Pharmacodynamic Model

Authors: Eliane Fuseau; Lewis B. Sheiner;

Simultaneous Modeling of Pharmacokinetics and Pharmacodynamics with a Nonparametric Pharmacodynamic Model

Abstract

We describe a variation on an approach to simultaneous modeling of pharmacokinetics (PK) and pharmacodynamics (PD). Both approaches model the often-observed time lag between plasma drug concentration (Cp) and drug effect (E) in non-steady-state experiments by postulating an E site whose concentration (Ce) is kinetically linked to Cp by a first-order process. With the linking model, the time lag can be removed from the data and the underlying concentration-response (Ce-E) relationship can be estimated. The original method requires the analyst to postulate a particular parametric form for the Ce-E model, whereas ours does not. It estimates the rate constant of the linking model as the value that causes the hysteresis curve (Ce vs E points connected in time order) to collapse to a single curve that represents the (empirical) Ce-E relationship. The method is presented as an algorithm and is tested by means of simulation and a real-world example. The results suggest that the method can faithfully estimate the Ce-E curve for a variety of PD models and degrees of experimental error when its basic assumption of time-invariant PD holds.

Keywords

Pharmacology, Kinetics, Dose-Response Relationship, Drug, Pharmaceutical Preparations, Heart Rate, Drug Resistance, Humans, Dronabinol, Euphoria, Models, Biological, Mathematics

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    173
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Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
173
Top 10%
Top 1%
Top 10%
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