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Biopharmaceuticals have transformed modern medicine over recent decades due to their efficacy in treating various severe diseases such as cancers, autoimmune disorders, and genetic conditions. While representing a wide class of different treatments, biopharmaceuticals are commonly produced through genetically engineered living organisms. In practice, the therapeutics of interest are typically manufactured through cell culture in bioreactor systems. However, this process is complex as it relies on fragile biological systems that need to be monitored and tightly controlled. To achieve such monitoring and control, process analytical technology (PAT) is needed. Spectroscopic methods, such as Raman and bio-capacitance spectroscopy, have been presented as potential PAT candidates due to their real-time, non-invasive measurement capabilities of various critical cell culture parameters (e.g., glucose and cell concentration). However, the use of spectroscopic sensors as PAT tools greatly depends on robust multivariate calibration models. These models are required to translate spectral data into actual process parameter values.

This thesis addresses fundamental challenges in calibration modeling for PAT implementation in biopharmaceutical manufacturing. Specifically, the use of Raman and bio-capacitance spectroscopy as PAT tools in upstream cell culture is investigated. We explore how biological variation impacts the transferability and robustness of Raman-based monitoring models in Paper I. In Paper II, we extend beyond monitoring by demonstrating how the combination of classical chemometric calibration models and simplified mechanistic models can yield accurate forecasts during cell culture, effectively developing a predictive decision support system. Paper III explores calibration data generation by presenting an automated workflow using a miniature-scale high-throughput bioreactor system. Its usefulness is further demonstrated by developing and deploying calibration models for monitoring and control in perfusion culture. Finally, Paper IV explores a novel validation framework for calibration models that tests the specificity and robustness of developed models.