Real-time monitoring of bioreactions, using in-line, low-field, benchtop NMR, to facilitate adaptive control in biotherapeutic manufacturing
This project aims to use real-time NMR to monitor bioreactions in biomanufacturing, enabling adaptive control for improved production of biotherapeutics like antibodies and cell therapies.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: February 1, 2025 | May 1, 2025 | August 1, 2025 | November 1, 2025
Funding Amounts: $82,764 stipend plus $3,000 travel allowance; typical appointment duration 2 years.
Summary: Postdoctoral fellowship to develop real-time, in-line low-field benchtop NMR monitoring of bioreactions for adaptive control in biotherapeutic manufacturing.
Key Information: Open to U.S. citizens with a doctoral degree earned within the last 5 years; research conducted onsite at NIST in Gaithersburg, MD.
Description
This postdoctoral fellowship opportunity at the National Institute of Standards and Technology (NIST) focuses on advancing biomanufacturing of biotherapeutics such as monoclonal antibodies (mAbs) and personalized live-cell therapies like CAR-T. The project aims to use low-field, benchtop nuclear magnetic resonance (NMR) spectroscopy as an in-line, real-time detector to monitor bioreactions within bioreactors.
Bioreactors are sensitive to minor variations in materials or conditions that can rapidly affect the safety, efficacy, and yield of biotherapeutic products. Real-time chemical characterization of the bioreaction is critical to ensure quality. Since the cellular growth medium reflects the state of the bioreaction, NMR-based metabolomics can non-destructively quantify metabolites consumed and secreted during the process, enabling detailed metabolic flux analysis.
The research will develop a sterile closed-loop system to safely exclude cells while directing bioreactor media through an in-line NMR sample cell. It will evaluate production modalities ranging from mAb production by CHO cells to clinically prepared reference cell lines simulating CAR-T therapies. Computational methods will be developed to analyze spectral data and create dynamic models to facilitate adaptive control of biomanufacturing reactions.
Key research areas include biomanufacturing, NMR metabolomics, spectral analysis, machine learning, and biotherapeutic production.
