Relationships between surface tensiometry properties and fluorescence intensity of dark and light exposed monoclonal antibody Nivolumab/Opdivo® by using the contact angle method: A pilot study
Bibliographic record
Abstract
Monoclonal antibodies (mAbs) are a class of therapeutic proteins widely used for the treatment of different kinds of cancers and immune-mediated disorders. During their real-life, they encounter various stressors, such as light exposure, able to modify their physico-chemical properties both in their formulation and when diluted for patient administration. Several biochemical and biophysical analytical approaches are currently used to characterize the physico-chemical properties of mAbs, such as spectroscopic methods (i.e., UV absorption, fluorescence, near and far UV circular dichroism) for conformational studies, size exclusion chromatography, electrophoresis and dynamic light scattering for detecting aggregate formation, LC-MS for their chemical modifications. On these bases, our work is focused on the novel surface tension characterisation of one of these therapeutic mAbs, Nivolumab, in its formulation Opdivo® and after dilution and the relationship with classical fluorescence data. In particular, the mAb has been exposed to two different doses of simulated sunlight and the effect of the light stressor has been compared to the mAb kept in the dark. The application of Solid-like methodology, using the Rossi number as main surface tensiometry parameter, allowed us to demonstrate the close relationship between the physical, i.e., surface tension properties, and physico-chemical fluorescence emission of these big molecules.
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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".