<scp>PlasmaCap EBA</scp>: An innovative method of isolating plasma proteins from human plasma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The growing demand for immunoglobulin (IG) requires development of improved plasma fractionation methods to provide higher yields in a cost effective, scalable manner without compromising product purity and efficacy. A novel protein extraction method, utilizing expanded bed adsorption (EBA) chromatography, has been developed. PlasmaCap IG (10% liquid formulation intravenous IG [IVIG]) is the first plasma-derived product manufactured using PlasmaCap EBA technology. MATERIALS AND METHODS: The PlasmaCap EBA platform consists of a series of consecutive columns which bind a target protein, or group of proteins, in their native state directly from cryo-poor plasma. EBA chromatography includes five key steps: (1) expand, (2) sanitize and equilibrate, (3) load, (4) wash and (5) elute. These steps are made possible using high-density tungsten-carbide agarose beads, suspended by upward flow. The PlasmaCap EBA process was evaluated during Evolve's clinical campaign for scalability, product quality and yield. RESULTS: PlasmaCap EBA technology can be predictably scaled by maintaining the minimum residence time and residence time distribution for EBA columns of different diameters. Scalability of the manufacturing process was demonstrated by the 50-fold volumetric increase from laboratory-scale lots to clinical-scale lots. The process is also associated with enhanced product purity, such as lower aggregates. The PlasmaCap EBA process is expected to have the same or better yield and purity at commercial scale production compared to the clinical campaign. CONCLUSION: The PlasmaCap EBA platform was used to successfully develop PlasmaCap IG (10% liquid formulation IVIG) with proven scalability, product quality and yield.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".