Advective hydrogel membrane chromatography for monoclonal antibody purification in bioprocessing
Bibliographic record
Abstract
Protein A chromatography is widely employed for the capture and purification of monoclonal antibodies (mAbs). Because of the high cost of protein A resins, there is a significant economic driving force to seek new downstream processing strategies. Membrane chromatography has emerged as a promising alternative to conventional resin based column chromatography. However, to date, the application has been limited to mostly ion exchange flow through (FT) mode. Recently, significant advances in Natrix hydrogel membrane has resulted in increased dynamic binding capacities for proteins, which makes membrane chromatography much more attractive for bind/elute operations. The dominantly advective mass transport property of the hydrogel membrane has also enabled Natrix membrane to be run at faster volumetric flow rates with high dynamic binding capacities. In this work, the potential of using Natrix weak cation exchange membrane as a mAb capture step is assessed. A series of cycle studies was also performed in the pilot scale device (> 30 cycles) with good reproducibility in terms of yield and product purities, suggesting potential for improved manufacturing flexibility and productivity. In addition, anion exchange (AEX) hydrogel membranes were also evaluated with multiple mAb programs in FT mode. Significantly higher binding capacity for impurities (support mAb loads up to 10Kg/L) and 40X faster processing speed were observed compared with traditional AEX column chromatography. A proposed protein A free mAb purification process platform could meet the demand of a downstream purification process with high purity, yield, and throughput.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".