The industry need addressed by this project is centered on improving efficiency and accuracy in bioprocessing metabolite analysis, which is critical for biopharmaceutical manufacturing. Specifically:
The goal of this project is to evaluate and validate Matterworks’ Pyxis metabolomics platform to improve bioprocessing consistency and efficiency.
Develop Pyxis as a proprietary consumable kit that fits seamlessly into current LCMS workflows and automated data processing enable the analysis of cell lysates and media
Explore using Pyxis for early detection and monitoring of glycosylation activity impacting glycoform profiles and process excursions
The implementation of Matterworks’ Pyxis™ platform enables real-time, absolute quantitation of critical bioprocessing metabolites with accuracy comparable to traditional LC-MS methods, while significantly reducing labor and expertise requirements. By automating analysis and cutting time by up to 70–80%, manufacturers can save approximately $150–$250 per sample in labor costs, accelerating metabolomic profiling across large bioreactor sample sets to optimize processes faster—translating into substantial financial benefits for large-scale biopharmaceutical manufacturing.
Demonstration of Pyxis™ Accuracy: Verified that the AI-powered Pyxis platform can replicate the accuracy of traditional LC-MS metabolite quantitation methods.
High-Throughput Metabolomic Profiling: Showed that metabolomic data can be rapidly obtained from large bioreactor sample sets, enabling real-time monitoring of critical metabolites.
Advancement of BRL: Increased Bioprocessing Readiness Level from 3 to 5, positioning Pyxis as an early-stage commercial product launched at ASMS.
Geremia, J., Henriques da Costa, A., Kassis, T., Reger, L., & Niemann, J., Direct comparison of rapid absolute quantification of metabolites – ML based Pyxis and traditions LC-MS methods, NIIMBL National Meeting, Washington, DC, June 25, 2024.
Henriques da Costa, A., Campbell, J. M., Pollard, D., Niemann, J., Reger, L., Lauterbach, J., Kassis, T., Pruyne, J., Ferro, L., Howland, J., Geremia, J., Hooper, S., & Delmar, M. C., Direct comparison of a machine learning-based tool for rapid absolute quantification of metabolites in biological samples with traditional isotopologue analysis, American Society for Mass Spectrometry 72nd Conference, Anaheim, CA, June 6, 2024.
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Matterworks, Inc.
Eli Lilly and Company
Sartorius Stedim