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Modelling Response Time Profiles in the Absence of Drug Concentrations: Definition and Performance Evaluation of the K–PD Model

Overview of attention for article published in Journal of Pharmacokinetics and Pharmacodynamics, October 2006
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  • Good Attention Score compared to outputs of the same age (66th percentile)

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Citations

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116 Mendeley
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Title
Modelling Response Time Profiles in the Absence of Drug Concentrations: Definition and Performance Evaluation of the K–PD Model
Published in
Journal of Pharmacokinetics and Pharmacodynamics, October 2006
DOI 10.1007/s10928-006-9035-z
Pubmed ID
Authors

P. Jacqmin, E. Snoeck, E.A. van Schaick, R. Gieschke, P. Pillai, J.-L. Steimer, P. Girard

Abstract

The plasma concentration-time profile of a drug is essential to explain the relationship between the administered dose and the kinetics of drug action. However, in some cases such as in pre-clinical pharmacology or phase-III clinical studies where it is not always possible to collect all the required PK information, this relationship can be difficult to establish. In these circumstances several authors have proposed simple models that can analyse and simulate the kinetics of the drug action in the absence of PK data. The present work further develops and evaluates the performance of such an approach. A virtual compartment representing the biophase in which the concentration is in equilibrium with the observed effect is used to extract the (pharmaco)kinetic component from the pharmacodynamic data alone. Parameters of this model are the elimination rate constant from the virtual compartment (KDE), which describes the equilibrium between the rate of dose administration and the observed effect, and the second parameter, named EDK(50) which is the apparent in vivo potency of the drug at steady state, analogous to the product of EC(50), the pharmacodynamic potency, and clearance, the PK "potency" at steady state. Using population simulation and subsequent (blinded) analysis to evaluate this approach, it is demonstrated that the proposed model usually performs well and can be used for predictive simulations in drug development. However, there are several important limitations to this approach. For example, the investigated doses should extend from those producing responses well below the EC(50) to those producing ones close to the maximum response, optimally reach steady state response and followed until the response returns to baseline. It is shown that large inter-individual variability on PK-PD parameters will produce biases as well as large imprecision on parameter estimates. It is also clear that extrapolations to dosage routes or schedules other than those used to estimate the parameters should be undertaken with great caution (e.g., in case of non-linearity or complex drug distribution). Consequently, it is advised to apply this approach only when the underlying structural PD and PK are well understood. In any case, K-PD model should definitively not be substituted for the gold standard PK-PD model when correct full model can and should be identified.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 2 2%
Australia 1 <1%
Germany 1 <1%
Netherlands 1 <1%
South Africa 1 <1%
Unknown 108 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 30%
Student > Ph. D. Student 32 28%
Student > Bachelor 9 8%
Other 8 7%
Professor > Associate Professor 5 4%
Other 10 9%
Unknown 17 15%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 36 31%
Medicine and Dentistry 34 29%
Agricultural and Biological Sciences 12 10%
Mathematics 5 4%
Engineering 4 3%
Other 7 6%
Unknown 18 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 May 2022.
All research outputs
#7,355,930
of 25,374,647 outputs
Outputs from Journal of Pharmacokinetics and Pharmacodynamics
#106
of 477 outputs
Outputs of similar age
#26,492
of 84,642 outputs
Outputs of similar age from Journal of Pharmacokinetics and Pharmacodynamics
#1
of 2 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 477 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 84,642 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them