Comparative Analysis of One-Dimensional Protein Fingerprint by Line Shape Enhancement and Two-Dimensional <sup>1</sup>H,<sup>13</sup>C Methyl NMR Methods for Characterization of the Higher Order Structure of IgG1 Monoclonal Antibodies
Bibliographic record
Abstract
The use of NMR spectroscopy has emerged as a premier tool to characterize the higher order structure of protein therapeutics and in particular IgG1 monoclonal antibodies (mAbs). Due to their large size, traditional 1 H– 15 N correlation experiments have proven exceedingly difficult to implement on mAbs, and a number of alternative techniques have been proposed, including the one-dimensional (1D) 1 H protein fingerprint by line shape enhancement (PROFILE) method and the two-dimensional (2D) 1 H– 13 C methyl correlation-based approach. Both 1D and 2D approaches have relative strengths and weaknesses, related to the inherent sensitivity and resolution of the respective methods. To further increase the utility of NMR to the biopharmaceutical community, harmonized criteria for decision making in employing 1D and 2D approaches for mAb characterization are warranted. To this end, we have conducted an interlaboratory comparative study of the 1D PROFILE and 2D methyl methods on several mAbs samples to determine the degree to which each method is suited to detect spectral difference between the samples and the degree to which results from each correlate with one another. Results from the study demonstrate both methods provide statistical data highly comparable to one another and that each method is capable of complementing the limitations commonly associated with the other, thus providing a better overall picture of higher order structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".