Evidence map›Paper›PMID 30951859›Full record

ArticleInternational journal of pharmaceutics2019

Data reconciliation in the Quality-by-Design (QbD) implementation of pharmaceutical continuous tablet manufacturing.

Qinglin Su, Yasasvi Bommireddy, Yash Shah, Sudarshan Ganesh, Mariana Moreno, Jianfeng Liu, Marcial Gonzalez, Nima Yazdanpanah, Thomas O'Connor, Gintaras V Reklaitis and 1 more

Open access · greenAbstract read
In one paragraph

Article in International journal of pharmaceutics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.9field-weighted citation impact, top 6% 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

13 citing papers in PubMed, 46 citations in OpenAlex.

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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

11 authors at 2 institutions in 1 country.

Qinglin SuDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States. Electronic address: ql_su@live.cn.
Yasasvi BommireddySchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, United States.
Yash ShahDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States.
Sudarshan GaneshDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States.
Mariana MorenoDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States.
Jianfeng LiuDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States.
Marcial GonzalezSchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, United States; Ray W. Herrick Laboratories, Purdue University, West Lafayette, IN 47907, USA.
Nima YazdanpanahOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, United States.
Thomas O'ConnorOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, United States.
Gintaras V ReklaitisDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States.
Zoltan K NagyDavidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States. Electronic address: znagy@purdue.edu.
Purdue University West Lafayette · USCenter for Drug Evaluation and Research · US

Funding

FDA HHS U01 FD005535FDA HHS U01 FD006487
6 · The paper itself

Abstract

Data provided by in situ sensors is always affected by some level of impreciseness as well as uncertainty in the measurements due to process operation disturbance or material property variance. In-process data precision and reliability should be considered when implementing active product quality control and real-time process decision making in pharmaceutical continuous manufacturing. Data reconciliation is an important strategy to address such imperfections effectively, and to exploit the data redundancy and data correlation based on process understanding. In this study, a correlation between tablet weight and main compression force in a rotary tablet press was characterized by the classical Kawakita equation. A load cell, situated at the exit of the tablet press chute, was also designed to measure the tablet production rate as well as the tablet weight. A novel data reconciliation strategy was proposed to reconcile the tablet weight measurement subject to the correlation between tablet weight and main compression force, in such, the imperfect tablet weight measurement can be reconciled with the much more precise main compression force measurement. Special features of the Welsch robust estimator to reject the measurement gross errors and the Kawakita model parameter estimation to monitor the material property variance were also discussed. The proposed data reconciliation strategy was first evaluated with process control open-loop and closed-loop experimental data and then integrated into the process control system in a continuous tablet manufacturing line. Specifically, the real-time reconciled tablet weight measurements were independently verified with an at-line Sotax Auto Test 4 tablet weight measurements every five minutes. Promising and reliable performance of the reconciled tablet weight measurement was demonstrated in achieving process automation and quality control of tablet weight in pilot production runs.

Indexed as

Data AccuracyTabletsAutomationPressureQuality ControlTechnology, PharmaceuticalTabletsContinuous manufacturingData reconciliationPharmaceuticalQuality by DesignTablet press

Identifiers

PMID30951859
PMCPMC9976296
OpenAlexW2930435643

What OpenQuestion holds

Textmetadata
LicenceTDM
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.