Accelerating perfusion process optimization by scanning non‐steady‐state responses
Bibliographic record
Abstract
Perfusion processes provide consistent culture conditions, high productivity and low product residence times. However, process development can be slow due to the 1 week or more required to reach each steady state. The objective of this work was to accelerate process development in perfusion cultures by scanning non-steady-state transient responses to qualitatively predict steady-state performance. The method was tested using a shift in temperature every 3 days, scanned down by steps of 2 degrees C from 37 degrees C to 31 degrees C, then scanned up to 37 degrees C. Higher t-PA concentrations were predicted at lower temperatures, confirmed by subsequent pseudo-steady-state results. In most cases, transient values on the 3rd day were in close concordance with pseudo-steady-state values. To further accelerate process development, transient scanning was applied to small-scale, non-instrumented cultures. Similar results were obtained, although quantitative t-PA values were 15-30 times lower than in high cell density perfusion cultures. The method was further explored by investigating 1 day transient shifts in temperature where more variability was observed, suggesting that the cells were still adapting to the new environment. Nonetheless, the overall response again qualitatively predicted the pseudo-steady-state temperature response. Use of transient scanning in conjunction with pseudo-steady-state verification and refinement of optimal results could reduce process development time to a third or less of comparable steady-state-based optimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".