Biopharmaceutical manufacturers lack fast, multiplexed, and sensitive methods to monitor low‑abundance nutrients and stress‑related metabolites in complex, high‑cell‑density upstream cultures, making timely process intervention difficult. Existing offline and Raman‑based tools are too slow or insufficiently reproducible, limiting real‑time control and increasing the risk of batch failures.
TRACE‑R integrates digital SERS with deep‑learning spectral analysis to deliver ultrasensitive, reproducible, and multiplexed quantification of key metabolites in complex media. Its revised scope emphasizes rigorous analytical validation and benchmarking under realistic upstream conditions to provide a robust, deployable PAT platform for proactive process control.
TRACE‑R provides high‑frequency, multiplexed insight into complex upstream media, enabling earlier detection of nutrient depletion and metabolic stress, significantly reducing batch‑failure risk and improving bioprocess consistency.
By delivering reproducible, sensitive digital SERS measurements validated under realistic industrial conditions, TRACE‑R strengthens process understanding and accelerates optimization across development, scale‑up, and manufacturing.
By implementing digital SERS–based, multiplexed upstream monitoring with deep‑learning analysis, an organization will reduce batch‑failure risk and offline analytical burden by enabling earlier detection of metabolic deviations, supporting more robust, proactive process control.
A validated analytical platform centered on digital SERS, with enhanced reproducibility, robustness, and operating‑range consistency demonstrated across complex media and upstream conditions.
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Johns Hopkins University
Boehringer Ingelheim
Federal Stakeholder: National Institute of Standards and Technology
Genentech, Inc.
