Evidence map›Paper›PMID 22638844›Full record

Trial reportEuropean journal of drug metabolism and pharmacokinetics2013

Limited sampling strategy models for estimating the AUC of gliclazide in Chinese healthy volunteers.

Ji-Han Huang, Kun Wang, Xiao-Hui Huang, Ying-Chun He, Lu-Jin Li, Yu-Cheng Sheng, Juan Yang, Qing-Shan Zheng

Abstract readClinical Trial
PubMed Publisher
In one paragraph

Trial report in European journal of drug metabolism and pharmacokinetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 34% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 2 institutions in 1 country.

Ji-Han HuangDepartment of Pharmacometrics, Center for Drug Clinical Research, Shanghai University of Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China.
Kun Wang
Xiao-Hui Huang
Ying-Chun He
Lu-Jin Li
Yu-Cheng Sheng
Juan Yang
Qing-Shan Zheng
Shanghai University of Traditional Chinese Medicine · CNAnhui Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this work is to reduce the cost of required sampling for the estimation of the area under the gliclazide plasma concentration versus time curve within 60 h (AUC0-60t ). The limited sampling strategy (LSS) models were established and validated by the multiple regression model within 4 or fewer gliclazide concentration values. Absolute prediction error (APE), root of mean square error (RMSE) and visual prediction check were used as criterion. The results of Jack-Knife validation showed that 10 (25.0 %) of the 40 LSS based on the regression analysis were not within an APE of 15 % using one concentration-time point. 90.2, 91.5 and 92.4 % of the 40 LSS models were capable of prediction using 2, 3 and 4 points, respectively. Limited sampling strategies were developed and validated for estimating AUC0-60t of gliclazide. This study indicates that the implementation of an 80 mg dosage regimen enabled accurate predictions of AUC0-60t by the LSS model. This study shows that 12, 6, 4, 2 h after administration are the key sampling times. The combination of (12, 2 h), (12, 8, 2 h) or (12, 8, 4, 2 h) can be chosen as sampling hours for predicting AUC0-60t in practical application according to requirement.

Indexed as

AdolescentAdultArea Under CurveGliclazideHumansHypoglycemic AgentsMaleRegression AnalysisTherapeutic EquivalencyYoung AdultGliclazideHypoglycemic Agents

Identifiers

PMID22638844
OpenAlexW2065153746

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.