Evidence map›Paper›PMID 38399296›Full record

ArticlePharmaceutics2024

Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling.

Junjie Cao, Haoran Shen, Shuying Zhao, Xiao Ma, Liping Chen, Shengyun Dai, Bing Xu, Yanjiang Qiao

Open access · goldAbstract read
In one paragraph

Article in Pharmaceutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 47% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

8 authors at 2 institutions in 1 country.

Junjie CaoDepartment of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China.
Haoran ShenDepartment of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China.
Shuying ZhaoDepartment of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China.
Xiao MaDepartment of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China.
Liping ChenDepartment of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China.
Shengyun DaiNational Institutes for Food and Drug Control, Beijing 100050, China.
Bing XuDepartment of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China.
Yanjiang QiaoDepartment of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11, North Third Ring East Road, Beijing 100029, China.
Beijing University of Chinese Medicine · CNNational Institutes for Food and Drug Control · CN

Funding

Lianyungang Municipal Science and Technology Major Projects CGJBGS2101National Natural Science Foundation of China 82074033Scientific Research Project of Beijing University of Chinese Medicine 2023-JYB-JBZD-060
6 · The paper itself

Abstract

The material library is an emerging, new data-driven approach for developing pharmaceutical process models. How many materials or samples should be involved in a particular application scenario is unclear, and the impact of sample size on process modeling is worth discussing. In this work, the direct compression process was taken as the research object, and the effects of different sample sizes of material libraries on partial least squares (PLS) modeling in the prediction of tablet tensile strength were investigated. A primary material library comprising 45 materials was built. Then, material subsets containing 5 ×

Indexed as

compression behavior classification system (CBCS)few-shot modelingformulation designhierarchical samplinglatent variable modelingmaterial librarysample sizetabletabilitytablet tensile strength

Identifiers

PMID38399296
PMCPMC10893091
OpenAlexW4391612875

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.