Structured Data Storage for Data-Driven Process Optimisation in Bioprinting

  • Autor:

    Schmieg, B. / Brandt, N. / Schnepp, V.J. / Radosevic, L. / Gretzinger, S. / Selzer, M. / Hubbuch, J. (2022)

  • Quelle:

    Appl. Sci., 2022, 12, 7728,

  • Datum: August 2022
  • Abstract: 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.