pH Transitions in Ion‐Exchange Systems: Role in the Development of a Cation‐Exchange Process for a Recombinant Protein
Bibliographic record
Abstract
Unexpected transient changes in effluent pH can occur during ion-exchange chromatography. Such changes can occur even if a column that is equilibrated with a buffer receives another solution in the same buffer and of the same pH but of a different salt concentration. An attempt is made to understand the basis for this phenomenon and apply it to the process purification of a recombinant protein on a strong cation-exchange resin. Incomplete column equilibration was eliminated as a possible cause of these effects. Various buffering species and various salt ions were studied at different solution concentrations to investigate pH transitions on strong cation-exchange resins. A further comparison was made between cation-exchange resins with different backbone chemistries. On the basis of these studies, a mechanism is proposed for these phenomena based on competitive equilibria between ions from the buffer salts and H(+)/OH(-) ions. In addition to the equilibria between these ions and the functional groups on the resins, charged groups on the resin backbone were also found to contribute to transient pH changes. The results from this study were applied to the cation-exchange step for a recombinant protein that was sensitive to pH excursions to help maintain activity of the protein during the purification 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".