TPLS as predictive platform for twin-screw wet granulation process and formulation development.

Continuous manufacturing Formulation development Process understanding Twin-screw granulation

Journal

International journal of pharmaceutics
ISSN: 1873-3476
Titre abrégé: Int J Pharm
Pays: Netherlands
ID NLM: 7804127

Informations de publication

Date de publication:
10 Aug 2021
Historique:
received: 04 05 2021
revised: 03 06 2021
accepted: 04 06 2021
pubmed: 11 6 2021
medline: 4 8 2021
entrez: 10 6 2021
Statut: ppublish

Résumé

In recent years, the interest in continuous manufacturing techniques, such as twin-screw wet granulation, has increased. However, the understanding of the influence of the combination of raw material properties and process settings upon the granule quality attributes is still limited. In this study, a T-shaped partial least squares (TPLS) model was developed to link raw material properties, the ratios in which these raw materials were combined and the applied process parameters for the twin-screw wet granulation process with the granule quality attributes. In addition, the predictive ability of the TPLS model was used to find a suitable combination of formulation composition and twin-screw granulation process settings for a new API leading to desired granule quality attributes. Overall, this study helped to better understand the link between raw material properties, formulation composition and process settings on granule quality attributes. Moreover, as TPLS can provide a reasonable starting point for formulation and process development for new APIs, it can reduce the experimental development efforts and consequently the consumption of expensive (and often limited available) new API.

Identifiants

pubmed: 34111548
pii: S0378-5173(21)00590-1
doi: 10.1016/j.ijpharm.2021.120785
pii:
doi:

Substances chimiques

Tablets 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

120785

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

Auteurs

A Ryckaert (A)

Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical analysis, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium. Electronic address: AlexanderJ.Ryckaert@UGent.be.

D Van Hauwermeiren (D)

Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical analysis, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium; BIOMATH, Department of Mathematical Modelling, Statistics and Bio-informatics, Ghent University, Coupure Links 653, 9000 Ghent, Belgium. Electronic address: Daan.VanHauwermeiren@UGent.be.

J Dhondt (J)

Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical analysis, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium. Electronic address: JensJ.Dhondt@UGent.be.

A De Man (A)

Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical analysis, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium. Electronic address: Alexander.DeMan@UGent.be.

A Funke (A)

Chemical & Pharmaceutical Development, Pharma R&D, Bayer AG, Friedrich-Ebert-Straße 475, 42369 Wuppertal, Germany. Electronic address: Adrian.Funke@bayer.com.

D Djuric (D)

Chemical & Pharmaceutical Development, Pharma R&D, Bayer AG, Friedrich-Ebert-Straße 475, 42369 Wuppertal, Germany. Electronic address: Dejan.Djuric@bayer.com.

C Vervaet (C)

Laboratory of Pharmaceutical Technology, Department of Pharmaceutics Ghent University, Ottergemsesteenweg 460, Ghent, Belgium. Electronic address: Chris.Vervaet@UGent.be.

I Nopens (I)

BIOMATH, Department of Mathematical Modelling, Statistics and Bio-informatics, Ghent University, Coupure Links 653, 9000 Ghent, Belgium. Electronic address: Ingmar.Nopens@ugent.be.

T De Beer (T)

Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical analysis, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium. Electronic address: Thomas.DeBeer@UGent.be.

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Classifications MeSH