Literature DB >> 30107220

Impact of blend properties on die filling during tableting.

B Van Snick1, W Grymonpré2, J Dhondt3, K Pandelaere2, G Di Pretoro4, J P Remon2, T De Beer3, C Vervaet2, V Vanhoorne2.   

Abstract

Based on characterization of a wide range of fillers and APIs, thirty divergent blends were composed and subsequently compressed on a rotary tablet press, varying paddle speed and turret speed. The tablet weight variability was determined of 20 grab samples consisting of each 20 tablets. Additionally, the bulk residence time, ejection force, pre-compression displacement, main compression force, die fill fraction and feed frame fill fraction were determined during each run. Multivariate data analysis was applied to investigate the relation between the process parameters, blend characteristics, product and process responses. Blends with metoprolol tartrate as API showed high ejection forces. This behavior could be linked to the high wall friction value of metoprolol tartrate. The main responses related to the die filling could be predicted via a PLS model based on blend characteristics. Tablet weight variability was highly correlated with the variability on pre-compression displacement and main compression force. A good predictive model for tablet weight variability was obtained taking the porosity, wall friction angle, flowability, density, compressibility and permeability into account. Additionally, turret speed and paddle speed were included in the calibration of the model. The applied approach can save resources (material, time) during early drug product development.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Blend properties; Continuous direct compression; Continuous manufacturing; Die filling; PLS model; Tableting

Mesh:

Substances:

Year:  2018        PMID: 30107220     DOI: 10.1016/j.ijpharm.2018.08.015

Source DB:  PubMed          Journal:  Int J Pharm        ISSN: 0378-5173            Impact factor:   5.875


  2 in total

1.  Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT implementation.

Authors:  B Bekaert; B Van Snick; K Pandelaere; J Dhondt; G Di Pretoro; T De Beer; C Vervaet; V Vanhoorne
Journal:  Int J Pharm X       Date:  2021-12-25

Review 2.  Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets.

Authors:  Guolin Shi; Longfei Lin; Yuling Liu; Gongsen Chen; Yuting Luo; Yanqiu Wu; Hui Li
Journal:  RSC Adv       Date:  2021-02-23       Impact factor: 3.361

  2 in total

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