Skip to main content
3.144.35.187

Development of a high-fidelity digital twin using the discrete element method for a continuous direct compression process. Part 2. Validation of calibration workflow

By Peter Toson; Marko Matić; Theresa Hörmann-Kincses; Michela Beretta; Jakob Rehrl; Johannes Poms; Peter Boehling; Thomas O’Connor; Abdollah Koolivand; Geng Tian; Scott M. Krull; Johannes G. Khinast; Johan Remmelgas; Dalibor Jajcevic

Published on CMKC

Abstract

This paper is the second in a series of two that describes the application of discrete element method (DEM) and reduced order modeling to predict the effect of disturbances in the concentration of drug substance at the inlet of a continuous powder mixer on the concentration of the drug substance at the outlet of the mixer. In the companion publication, small-scale material characterization tests, a careful DEM parameter calibration and DEM simulations of the manufacturing process were used to develop a reliable RTD models. In the current work, the same calibration workflow was employed to evaluate the predictive ability of the resulting reduced-order model for an extended design space. DEM simulations were extrapolated using a relay race method and the cumulative RTD was accurately parameterized using the n-CSTR model. By performing experiments and simulations, a calibrated DEM model predicted the response of a continuous powder mixer to step changes in the inlet concentration of an API. Thus, carefully calibrated DEM models was used to guide and reduce experimental work and to establish an adequate control strategy. In addition, a further reduction in the computational effort was obtained by using the relay race method to extrapolate results. The predicted RTD curves were then parameterized to develop reduced order models and used to simulate the process in a matter of seconds. Overall, a control strategy evaluation tool based on high-fidelity DEM simulations was developed using material-sparing small-scale characterization tests.

Journal

International Journal of Pharmaceutics

DOI

10.1016/j.ijpharm.2024.124797

Type of publication

Peer-reviewed journal

Affiliations

  • Research Center Pharmaceutical Engineering GmbH, Graz, Austria
  • Office of Pharmaceutical Quality, US Food and Drug Administration, USA
  • Institute of Process and Particle Engineering, Graz University of Technology, Graz, Austria

Article Classification

Review article

Classification Areas

  • Process Modeling

Tags