Evidence map›Paper›PMID 34765271›Full record

ArticleJournal of pharmaceutical analysis2021

A strategy for population pharmaceutical quality assessment based on quality by design.

Yu Zhao, Changqin Hu, Shangchen Yao, Lihui Yin, Xiaomei Ling

Open access · goldAbstract read
In one paragraph

Article in Journal of pharmaceutical analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 27% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

5 authors at 2 institutions in 1 country.

Yu ZhaoNMPA Key Laboratory for Quality Research and Evaluation of Chemical Drugs, National Institutes for Food and Drug Control, Beijing, 102629, China.
Changqin HuNMPA Key Laboratory for Quality Research and Evaluation of Chemical Drugs, National Institutes for Food and Drug Control, Beijing, 102629, China.
Shangchen YaoNMPA Key Laboratory for Quality Research and Evaluation of Chemical Drugs, National Institutes for Food and Drug Control, Beijing, 102629, China.
Lihui YinNMPA Key Laboratory for Quality Research and Evaluation of Chemical Drugs, National Institutes for Food and Drug Control, Beijing, 102629, China.
Xiaomei LingPeking University Health Science Center, Peking University, Beijing, 100191, China.
National Institutes for Food and Drug Control · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

From a regulatory perspective, drug quality consistency evaluation must concern different processes used for the same drug. In this study, an assessment strategy based on quality by design (QbD) was developed for population pharmaceutical quality evaluation. A descriptive analysis method based on QbD concept was first established to characterize the process by critical evaluation attributes (CEAs). Then quantitative analysis method based on an improved statistical process control (SPC) method was established to investigate the process indicators (PIs) in the process population, such as mean distribution, batch-to-batch difference and abnormal quality probability. After that rules for risk assessment were established based on the SPC limitations and parameters. Both the SPC parameters of the CEAs and the risk of PIs were visualized according to the interaction test results to obtain a better understanding of the population pharmaceutical quality. Finally, an assessment strategy was built and applied to generic drug consistency assessment, process risk assessment and quality trend tracking. The strategy demonstrated in this study could help reveal quality consistency from the perspective of process control and process risk, and further show the recent development status of domestic pharmaceutical production processes. In addition, a process risk assessment and population quality trend tracking provide data-based information for approval. Not only can this information serve as a further basis for decision-making by the regulatory authority regarding early warnings, but it can also reduce some avoidable adverse reactions. With continuous addition of data, dynamic population pharmaceutical quality is meaningful for emergencies and decision-making regarding drug regulation.

Indexed as

Crucial evaluation attributes (CEAs)Improved statistical process control (SPC)Population pharmaceutical qualityProcess indicators (PIs)Quality by design (QbD)Risk assessment

Identifiers

PMID34765271
PMCPMC8572710
OpenAlexW3096978406

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.