Effect of Polyethylene Glycol Conjugation on Conformational and Colloidal Stability of a Monoclonal Antibody Antigen-Binding Fragment (Fab′)
Bibliographic record
Abstract
We have investigated the effects of site specific "hinge" polyethylene glycol conjugation (PEGylation) on thermal, pH, and colloidal stability of a monoclonal antibody antigen-binding fragment (Fab') using a variety of biophysical techniques. The results obtained by circular dichroism (CD), ultraviolet (UV) absorbance, and fluorescence spectroscopy suggested that the physical stability of the Fab' is maximized at pH 6-7 with no apparent differences due to PEGylation. Temperature-induced aggregation experiments revealed that PEGylation was able to increase the transition temperature, as well as prevent the formation of visible and subvisible aggregates. Statistical comparison of the three-index empirical phase diagram (EPD) revealed significant differences in thermal and pH stability signatures between Fab' and PEG-Fab'. Upon mechanical stress, micro-flow imaging (MFI) and measurement of the optical density at 360 nm showed that the PEG-Fab' had significantly higher resistance to surface-induced aggregation compared to the Fab'. Analysis of the interaction parameter, kD, indicated repulsive intermolecular forces for PEG-Fab' and attractive forces for Fab'. In conclusion, PEGylation appears to protect Fab' against thermal and mechanical stress-induced aggregation, likely due to a steric hindrance mechanism.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".