Evidence map›Paper›PMID 21487930›Full record

ReviewPharmaceutical research2011

Using partial area for evaluation of bioavailability and bioequivalence.

Mei-Ling Chen, Barbara Davit, Robert Lionberger, Zakaria Wahba, Hae-Young Ahn, Lawrence X Yu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pharmaceutical research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
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

6 authors.

Mei-Ling ChenOffice of Pharmaceutical Science Center for Drug Evaluation and Research Food and Drug Administration, 10903 New Hampshire Avenue, Building 51, Rm. 4108, Silver Spring, Maryland 20993-0002, USA. meiling.chen@fda.hhs.gov
Barbara Davit
Robert Lionberger
Zakaria Wahba
Hae-Young Ahn
Lawrence X Yu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessment of bioavailability/bioequivalence generally relies on the comparison of rate and extent of drug absorption between products. Rate of absorption is commonly expressed by peak concentration (C(max)) and time to peak concentration (T(max)), although these parameters are indirect measures of absorption rate. Recognizing the importance of systemic exposure to drug efficacy and safety, FDA recommended that systemic exposure be better used for bioavailability/bioequivalence assessment. Apart from peak exposure and total exposure, FDA also recommended a new metric for early exposure that is considered necessary when a control of input rate is critical to ascertain drug efficacy and/or safety profile. The early exposure can be measured by truncating the area under the curve at T(max) of the reference product (PAUC(r,tmax)) or some designated early time after dosing. The choice of truncation is most appropriately based on PK/PD relationship or efficacy/safety data for the drug under examination. Compared with C(max), PAUC(r,tmax) has higher sensitivity in detecting formulation differences and may be more variable. If the metric is highly variable, the reference-scaling approach can be employed for bioequivalence evaluation. The partial area metric is useful in PK/PD characterization as well as in the evaluation of bioavailability, bioequivalence and/or comparability.

Indexed as

Area Under CurveBiological AvailabilityTherapeutic EquivalencyHumansUnited StatesUnited States Food and Drug Administration

Identifiers

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.