Development of an ELISA-based device to quantify antibody adsorption directly on medical plastic surfaces
Bibliographic record
Abstract
Monoclonal antibodies (mAbs) encounter numerous interfaces during manufacturing, storage, and administration. While protein adsorption at the solid/liquid interface has been widely explored on model surfaces, a key challenge remains - the detection of very small amounts of adsorbed mAb directly on real medical surfaces. This study introduces a novel ELISA-based device, ELIBAG, a new tool for measuring mAb adsorption on medical bags. The efficacy of this device was highlighted by successfully confirming the adsorption of an IgG1 on two medical bag types: a polypropylene IV administration bag and a low-density polyethylene pharmaceutical manufacturing bag. We also investigated IgG1 adsorption on plastic model surfaces, revealing a similar range of mAb bulk concentration for surface saturation on both model and bag surfaces. This innovative device, characterized by its high-throughput and rapid approach, paves the way for extensive investigations into therapeutic proteins, such as mAbs, adsorption on a variety of medical or pharmaceutical surfaces, diverse adsorption conditions, and the influence of excipients employed in mAb formulation, which could enhance the knowledge of mAb interactions with plastic surfaces throughout their lifecycle.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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) | 0.000 | 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 teacher head, 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".