Joint project QC4CM

 

Model-based quality control in continuous manufacturing of pharmaceutical granules

 

Motivation

Copyright: © DFG

The continuous process chain of wet granulation and drying is an important step in pharmaceutical production. For the production of pharmaceutical granules, the active ingredients and excipients in powder form are homogenized with the addition of water using an extruder. In a subsequent process step, the granules are dried in a controlled manner and can then be pressed into tablets.

The classical production of pharmaceutical granules is usually done in a batch production. Due to the disadvantages of this discontinuous production process, the interest in continuous manufacturing (CM) of the granules has resulted. However, CM brings new challenges in terms of both, monitoring product quality and controlling the particle manufacturing process. Classically, pharmaceutical product quality is assured by statistically studying the range of critical process parameters (CPPs) of the processing units and determining the resulting product qualities in a comprehensive design of experiments. With the help of the CPPs determined in this way, the desired critical quality attributes (CQAs) of the pharmaceutical granules can be achieved. However, a quantitative prediction and control of the CQAs has not been realized so far. Therefore, the aim of this research project is to determine optimal CPPs with the help of an AI-supported model-based control and thus to ensure the continuous quality of the CQAs.

 

Project Goals and Methods

Within the scope of this research project, an AI-supported model-based control concept for the quality control of the CQAs of pharmaceutical products, which are manufactured using a continuous process chain consisting of dosing, twin-screw wet granulation and drying, is being developed in cooperation with the HHU Düsseldorf. For the implementation of the quality control, an active adjustment of the CPPs is considered at each processing unit, whereby occurring fluctuations within the manufacturing process can be taken into account.

In order to represent the essential dynamics of the entire process, the physical and data-based models are first combined using a quality-by-design approach. With the help of the quantitative models created, the relationships between the temporal course of the process states and the quality of the dried granules are determined. The resulting grey-box models are used in a cascade control for explicit control of the CQAs, so that the machine parameters are manipulated according to the quality requirements. Using process analytical technologies, the CQAs can be measured and stored throughout the process chain. Based on the stored data, the optimal target values of the CQAs are determined using a static optimization problem. In this way, an optimal active ingredient content of the pharmaceutical granulate is achieved. The approaches developed are then validated at a laboratory facility at the HHU Düsseldorf.

 

Innovations and Perspectives

While the field of continuous manufacturing has become the focus of interest in the pharmaceutical industry and science in recent years, advanced control strategies and a corresponding degree of automation for their implementation are still lacking. The existing research gaps, such as the lack of models to describe the entire process chain, the lack of process analytical technologies, and the lack of quality control of CQAs, can be closed with the help of the control concept developed in this research project. Successful fulfilment of the project goals promises decisive progress in the area of process analytical technologies and strategies for process monitoring as well as in the realization of quality control for the continuous production of pharmaceutical products.

 

Project partner
Heinrich Heine Universität Düsseldolf