Evidence map›Paper›PMID 35423324›Full record

ReviewRSC advances2021

Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets.

Guolin Shi, Longfei Lin, Yuling Liu, Gongsen Chen, Yuting Luo, Yanqiu Wu, Hui Li

Abstract readReview
In one paragraph

Review in RSC advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
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  7. Article
  8. 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

7 authors.

Guolin ShiInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China lihuizys@126.com.ORCID https://orcid.org/0000-0003-4404-725X
Longfei LinInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China lihuizys@126.com.ORCID https://orcid.org/0000-0001-6690-1600
Yuling LiuInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China lihuizys@126.com.
Gongsen ChenInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China lihuizys@126.com.
Yuting LuoInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China lihuizys@126.com.
Yanqiu WuInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China lihuizys@126.com.
Hui LiInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China lihuizys@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

Identifiers

PMID35423324
PMCPMC8695199

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Registered trials

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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.