Predicting tablet properties using In-Line measurements and evolutionary equation Discovery: A high shear wet granulation study

Issa Munu*, Andrei L Nicusan, Jason Crooks, Kendal Pitt, Christopher Windows-Yule, Andrew Ingram

*Corresponding author for this work

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Abstract

High shear wet granulation (HSWG) is widely used in tablet manufacturing mainly because of its advantages in improving flowability, powder handling, process run time, size distribution, and preventing segregation. In line process analytical technology measurements are essential in capturing detailed particle dynamics and presenting real-time data to uncover the complexity of the HSWG process and ultimately for process control. This study presents an opportunity to predict the properties of the granules and tablets through torque measurement of the granulation bowl and the force exerted on a novel force probe within the powder bed. Inline force measurements are found to be more sensitive than torque measurements to the granulation process. The characteristic force profiles present the overall fingerprint of the high shear wet granulation, in which the evolution of the granule formation can improve our understanding of the granulation process. This provides rich information relating to the properties of the granules, identification of the even distribution of the binder liquid, and potential granulation end point. Data were obtained from an experimental high shear mixer across a range of key process parameters using a face-centred surface response design of experiment (DoE). A closed-form analytical model was developed from the DOE matrix using the discovery of evolutionary equations. The model is able to provide a strong predictive indication of the expected tablet tensile strength based only on the data in-line. The use of a closed form mathematical equation carries notable advantages over other AI methodologies such as artificial neural networks, notably improved interpretability/interrogability, and minimal inference costs, thus allowing the model to be used for real-time decision making and process control. The capability of accurately predicting, in real time, the required compaction force required to achieve the desired tablet tensile strength from upstream data carries the potential to ensure compression machine settings rapidly reach and are maintained at optimal values, thus maximising efficiency and minimising waste.

Original languageEnglish
Article number124405
JournalInternational Journal of Pharmaceutics
Volume661
Early online date29 Jun 2024
DOIs
Publication statusPublished - 15 Aug 2024

Bibliographical note

Acknowledgement
We would like to acknowledge Lenterra Inc, New Jersey, USA, for providing the Lenterra in-line rheometer. Experimental work was carried out as part of an Engineering Doctorate programme funded by Engineering and Physical Sciences Research Council (EPSRC), United Kingdom, through the Centre for Doctoral Training in Formulation Engineering (grant no. EP/L015153/1), and from GlaxoSmithKline plc, Ware, United Kingdom. The multiphase materials exploration via evolutionary equation discovery computations described in this paper was performed using the University of Birmingham’s BEAR Cloud service, which provides a flexible resource for intensive computational work for the University’s research community. See http://www.birmingham.ac.uk/bear for more details.
Copyright © 2024 The Authors. Published by Elsevier B.V.

Keywords

  • Tablets/chemistry
  • Tensile Strength
  • Powders/chemistry
  • Excipients/chemistry
  • Drug Compounding/methods
  • Technology, Pharmaceutical/methods
  • Particle Size
  • Chemistry, Pharmaceutical/methods

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