Quantitative Detection of PEGylated Biomacromolecules in Biological Fluids by NMR
Bibliographic record
Abstract
The accumulation, biodistribution, and clearance profiles of therapeutic agents are key factors relevant to their efficacy. Determining these properties constitutes an ongoing experimental challenge. Many such therapeutics, including small molecules, peptides, proteins, tissue scaffolds, and drug delivery vehicles, are conjugated to poly(ethylene glycol) (PEG) as this improves their bioavailability and in vivo stability. We demonstrate here that (1)H NMR spectroscopy can be used to quantify PEGylated species in complex biological fluids directly, rapidly, and with minimal sample preparation. PEG bears a large number of spectroscopically equivalent protons exhibiting a narrow NMR line width while resonating at a (1)H NMR frequency distinct from most other biochemical signals. We demonstrate that PEG provides a robust signal allowing detection of concentrations as low as 10 μg/mL in blood. This PEG detection limit is lowered by another order of magnitude when background proton signals are minimized using (13)C-enriched PEG in combination with a double quantum filter to remove (1)H signals from non-(13)C-labeled species. Quantitative detection of PEG via these methods is shown in pig blood and goat serum as examples of complex biological fluids. More practically, we quantify the blood clearance of (13)C-PEG and PEGylated-BSA (bovine serum albumin) following their intravenous injection in live rats. Given the relative insensitivity of line width to PEG size, we anticipate that the biodistribution and clearance profiles of virtually any PEGylated biomacromolecule from biological fluid samples can be routinely measured by (1)H NMR without any filtering or treatment steps.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| 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 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".