Single-tablet-scale direct-compression: An on-demand manufacturing route for personalized tablets

July 26, 2023

Andreas Kottlan (1), Andrea Zirkl (1), Jakob Geistlinger (1), Eduardo Machado Charry (2), Benjamin J. Glasser (3), Johannes G. Khinast (1) (4)
International Journal of Pharmaceutics. Volume 643 (26 July 2023). DOI: https://doi.org/10.1016/j.ijpharm.2023.123274


Keywords

Personalized medicine, supply chain security, on-demand tableting, direct compression, vibratory mixing, single-tablet-scale


Abstract

Today’s pharmaceutical industry is facing various challenges. Two of them are issues with supply chain security and the increasing demand for personalized medicine. Both can be addressed by increasing flexibility and a more decentralized approach to pharmaceutical manufacturing. In this study, we present a setup that provides flexibility in terms of supplied raw materials and the product, i.e., a direct-compression setup for personalized tablets operating at a single-tablet-scale. The performance of the implemented single-tablet-scale technology for dosing and mixing was investigated. In addition, an analysis of the critical quality attributes (CQAs) of immediate release ibuprofen and loratadine tablets was performed. The developed dosing device achieved acceptance rates of > 90 % for doses ≥ 20 mg for various pharmaceutical powders. Regarding the vibratory mixing process, a dependency of the performance on the applied frequencies and acceleration was observed, with 100 Hz and ∼ 90 G performing best, yet still exhibiting varying mixing efficacies depending on the granular system. The tablets produced met U.S. Pharmacopeia requirements regarding mechanical stability and dissolution characteristics. Given these results, we consider the developed setup a proof of concept of a tool to provide personalized tablets to patients while minimizing the dependency on complex supply chains.


How Our Software Was Used

Segmentation of the images to separate the tablet’s components was performed via Dragonfly’s Segmentation Wizard and an iterative supervised machine learning approach.


Author Affiliation

(1) Institute for Process- and Particle Engineering, Graz University of Technology, Inffeldgasse 13, A-8010 Graz, Austria
(2) Institute of Solid State Physics and NAWI Graz, Graz University of Technology, Petersgasse 16/III, 8010 Graz, Austria
(3) Rutgers, The State University of New Jersey, Department of Chemical and Biochemical Engineering, 98 Brett Road, Piscataway, NJ 08854, USA
(4) Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13, A-8010 Graz, Austria