Separation of monoclonal antibody alemtuzumab monomer and dimers using ultrafiltration
Bibliographic record
Abstract
This article examines the feasibility of using ultrafiltration to separate the monomer of the monoclonal antibody alemtuzumab (Campath or Campath-1H) from a mixture of dimer and higher-order oligomers (collectively called "dimers" here). Using parameter scanning ultrafiltration, we initially assessed the suitability of the following membranes: 100 kDa and 300 kDa polyethersulfone (PES) membranes, and a 100 kDa polyvinylidene fluoride (PVDF) membrane. A detailed study was then carried out to examine the effects of operating conditions (such as solution pH, ionic strength, stirring speed, and permeate flux) on the separation of the monomer from the dimers using 300 kDa PES and 100 kDa PVDF membranes. Results of the experiments carried out in the carrier phase ultrafiltration (CPUF) mode indicate that the size-based protein-protein separation critically depends on the membrane used as well as the system hydrodynamics. The separation of the monoclonal antibody monomer and dimers using 100 kDa PVDF membranes in the diafiltration mode was also examined. Experimental results demonstrate that under suitable conditions, it is feasible to obtain the alemtuzumab monomer with a purity of more than 93% and a yield of more than 85% (from a mixture of 75% monomer and 25% dimers, which is the typical composition obtained after affinity chromatography). Simulation study indicates that this could be further improved to a purity of more than 96% and a monomer yield of more than 96% by increasing the selectivity of separation or by employing a two-stage diafiltration process.
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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 | 0.000 |
| 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 | 0.001 |
| 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".