Development of Flow-NMR Spectroscopy for Real-Time Monitoring and Kinetics Studies of Biomolecules: Case Study of Liraglutide Oligomerization
Bibliographic record
Abstract
We report the development of a comprehensive flow-NMR methodology for mechanistic studies of biomolecules. This approach allows for systematic kinetic investigation via precise sample condition modulations. Traditionally utilized for reaction monitoring and kinetic studies of small molecules, the application of flow-NMR to larger biomolecules, such as peptide oligomers and proteins, has remained unexplored. Here, we present a pioneering study using flow-NMR to examine the pH-dependent oligomeric interconversion of liraglutide, a glucagon-like peptide 1 (GLP-1) receptor agonist known for its efficacy in managing type 2 diabetes and obesity. Liraglutide molecules are prone to forming distinct oligomers and even fibrils under certain conditions, influencing their stability, absorption, and bioavailability─factors critically important in pharmaceutical applications. The developed methodologies and suite of flow-NMR experiments collectively yield comprehensive insights into the interconversion process of liraglutide without resorting to combining multiple other techniques. It incorporates various 1D and pseudo-2D proton NMR experiments, including GUPPY-DOSY, a newly developed version of a flow-compatible DOSY experiment, to monitor critical parameters such as diffusion coefficients ( D ), transverse relaxation ( R 2 ), and structural similarity. The relative ease of setting up and executing this set of flow-NMR experiments offers a straightforward path to extending their application to the characterization of other complex systems, including therapeutic proteins and biologic drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".