A universal buffer system for native LC-MS analysis of antibody-based therapeutics
Bibliographic record
Abstract
Abstract Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful analytical technique for analyzing biological macromolecules. A long-standing challenge has been applying LC-MS at physiological pH under native conditions using volatile buffers. The predominant “buffer” used, ammonium acetate (AmAc, p K a 4.75 for acetic acid and 9.25 for ammonium), does not offer sufficient buffering capacity in the physiological pH range of 7.0–7.4. To address this, we evaluated a set of fluorinated ethylamines, 2-fluoroethylamine (MFEA, p K a 8.9), 2,2-difluoroethylamine (DFEA, p K a 7.2), and 2,2,2-trifluoroethylamine (TFEA, p K a 5.5), that together provide buffering across the 4.5-9.8 pH range. We show that protein separations on strong cation- and anion-exchange resins in these volatile mobile phases perform comparably to traditional non-volatile buffers, with similar elution profiles and analyte elution ranking, albeit with slightly broader peaks. Using fully volatile gradients of pH or ionic strength, we chromatographically resolved charge variants of protein analytes such as mAbs and bovine serum albumin. For many of the eluting LC peaks, we obtained high-resolution mass spectra capable of resolving glycoforms of antibodies. Hydrophobic interaction chromatography (HIC) in volatile mobile phases preserved native separation order and further resolved drug-to-antibody ratio (DAR) species of the antibody-drug conjugate brentuximab-vedotin. For each chromatography modality we further compare innovator and biosimilar antibodies, demonstrating the reproducibility of results in the proposed volatile compounds. Together, our results establish fluorinated ethylamines, in combination with ammonium acetate, as a universal volatile buffer system for native LC-MS, broadly applicable across major chromatographic modalities while maintaining compatibility with mass spectrometry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".