Bioprinting is gaining importance for the manufacturing of tailor‐made hydrogel scaffolds in tissue engineering, pharmaceutical research and cell therapy. However, structure fidelity and geometric deviations of printed objects heavily influence mass transport and process reproducibility. Fast, three‐dimensional and nondestructive quality control methods will be decisive for the approval in larger studies or industry. Magnetic resonance imaging (MRI) meets these requirements for characterizing heterogeneous soft materials with different properties. Complementary to the idea of decentralized 3D printing, magnetic resonance tomography is common in medicine, and image data processing tools can be transferred system‐independently. In this study, a MRI measurement and image analysis protocol was evaluated to jointly assess the reproducibility of three different hydrogels and a reference material. Critical parameters for object quality, namely porosity, hole areas and deviations along the height of the scaffolds are discussed. Geometric deviations could be correlated to specific process parameters, anomalies of the ink or changes of ambient conditions. This strategy allows the systematic investigation of complex 3D objects as well as an implementation as a process control tool. Combined with the monitoring of metadata this approach might pave the way for future industrial applications of 3D printing in the field of biopharmaceutics.
Bioprinting is a method to fabricate 3D models that mimic tissue. Future fields of application might be in pharmaceutical or medical context. As the number of applicants might vary between only one patient to manufacturing tissue for high-throughput drug screening, designing a process will necessitate a high degree of flexibility, robustness, as well as comprehensive monitoring. To enable quality by design process optimisation for future application, establishing systematic data storage routines suitable for automated analytical tools is highly desirable as a first step. This manuscript introduces a workflow for process design, documentation within an electronic lab notebook and monitoring to supervise the product quality over time or at different locations. Lab notes, analytical data and corresponding metadata are stored in a systematic hierarchy within the research data infrastructure Kadi4Mat, which allows for continuous, flexible data structuring and access management. To support the experimental and analytical workflow, additional features were implemented to enhance and build upon the functionality provided by Kadi4Mat, including browser-based file previews and a Python tool for the combined filtering and extraction of data. The structured research data management with Kadi4Mat enables retrospective data grouping and usage by process analytical technology tools connecting individual analysis software to machine-readable data exchange formats.
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