Evidence map›Paper›PMID 17051439›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2007

Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K-PD model.

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

Registry-linked trialAbstract readEvaluation Study
PubMed Publisher
In one paragraph

Article in Journal of pharmacokinetics and pharmacodynamics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03413150 (Modeling of Amiodarone Effect on Heart Rate Control in Critically Ill Patients With Atrial Tachyarrhythmias), which is not on this map. Cited by 87 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
87citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03413150 completednot on this map

Modeling of Amiodarone Effect on Heart Rate Control in Critically Ill Patients With Atrial Tachyarrhythmias

TypeobservationalSponsorGroupe Hospitalier Pitie-SalpetriereRan2007 to 2012Enrolled80ConditionsAtrial Fibrillation, Atrial TachycardiaArmsAmiodarone
3 · Its place in the literature

Who cites it

87 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  19. Tissue Pharmacokinetics of Antisense Oligonucleotides.Methods in molecular biology (Clifton, N.J.) · 2026
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27 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

P JacqminExprimo NV, Berenlaan, 4, Beerse, B-2340, Belgium. philippe.jacqmin@exprimo.com
E Snoeck
E A van Schaick
R Gieschke
P Pillai
J-L Steimer
P Girard

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Adenosine A1 Receptor AgonistsModels, BiologicalModels, StatisticalAdenosineAnimalsComputer SimulationData Interpretation, StatisticalDose-Response Relationship, DrugDrug Administration ScheduleHumansLipolysisMaleRatsRats, WistarReproducibility of ResultsTime FactorsAdenosineAdenosine A1 Receptor AgonistsN(6)-(4-sulfophenyl)adenosine

Identifiers

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.