Evidence map›Paper›PMID 21272054›Full record

ReviewBritish journal of clinical pharmacology2011

Understanding the time course of pharmacological effect: a PKPD approach.

Daniel F B Wright, Helen R Winter, Stephen B Duffull

Abstract readReview
In one paragraph

Review in British journal of clinical pharmacology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Trial
  4. Pharmacokinetic-pharmacodynamic modeling of the effect of varenicline on nicotine craving in adult smokers.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2015
    Trial
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. The therapeutic effects ofGut microbiome (Cambridge, England) · 2025
    Review
  14. Article
  15. Efficacy ofJournal of rheumatic diseases · 2025
    Article
  16. Article
  17. Review
  18. Article
  19. Review
  20. Review
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

3 authors.

Daniel F B WrightSchool of Pharmacy, University of Otago, PO Box 56, Dunedin, New Zealand. dan.wright@otago.ac.nz
Helen R Winter
Stephen B Duffull

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The key concepts that underpin the choice of drug and dosing regimen are an understanding of the drugs' effectiveness, the potential for adverse effects, and the expected time course over which both desired and adverse effects are likely to occur. Research in clinical pharmacology should therefore address three fundamental questions: (1) What is the magnitude of drug effects (beneficial or adverse) from a given dose? (2) How quickly will any given effects occur? (3) How long will these effects last? Under steady-state conditions, only the magnitude of drug effects can be examined. This requires researchers to consider non-steady-state conditions, which require more complex models and an understanding of the mechanisms that drive the time course of drug effect. The aim of this review is to provide a conceptual framework for understanding the time course of drug effects using pharmacokinetic-pharmacodynamic models. Key examples will illustrate how this can inform the optimal use of drugs in the clinic.

Indexed as

Models, BiologicalModels, StatisticalPharmacokineticsPharmacology, ClinicalDose-Response Relationship, DrugHumansPharmaceutical PreparationsPopulationResearchTime FactorsPharmaceutical Preparations

Identifiers

PMID21272054
PMCPMC3099368

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.