Lifecycle DoE—The Companion for a Holistic Development Process

• Published in: Bioengineering (Basel) Vol. 11; no. 11; p. 1089
• Main Authors: Kunzelmann, Marco, Wittmann, Anja, Presser, Beate, Brosig, Philipp, Marhoffer, Pia Kristin, Haider, Marlene Antje, Martin, Julia, Berger, Martina, Wucherpfennig, Thomas
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.11.2024; MDPI
• Subjects: Adaptation; Design; design augmentation; design of experiments; Design optimization; Engineering research; Experiments; Flexibility; holistic experimental design; Impurities; Knowledge; Life cycle assessment; Methods; optimization and robustness analysis; Parameter identification; Pharmaceutical industry; process characterization study; Process parameters; Product development; Product life cycle; quality by design; Germany; holistic experimental design; quality by design; optimization and robustness analysis; bioprocess development; process characterization study; design augmentation; design of experiments; CMC development
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11111089

Within process development, numerous experimental studies are undertaken to establish, optimize and characterize individual bioprocess unit operations. These studies pursue diverse objectives such as enhancing titer or minimizing impurities. Consequently, Design of Experiment (DoE) studies are planned and analyzed independently from each other, making it challenging to interlink individual data sets to form a comprehensive overview at the conclusion of the development process. This paper elucidates the methodology for constructing a Life-Cycle-DoE (LDoE), which integrates data-driven process knowledge through design augmentations. It delves into the strategy, highlights the challenges encountered and provides solutions for overcoming them. The LDoE approach facilitates the augmentation of an existing model with new experiments in a unified design. It allows for flexible design adaptations as per the requirements of subject matter experts (SME) during process development, concurrently enhancing model predictions by utilizing all available data. The LDoE boasts a broad application spectrum as it consolidates all data generated within bioprocess development into a single file and model. The study demonstrates that the LDoE approach enables a process characterization study (PCS) to be performed solely with development data. Furthermore, it identifies potentially critical process parameters (pCPPs) early, allowing for timely adaptations in process development to address these challenges.