Intrinsic fluorescence‐based <i>at situ</i> soft sensor for monitoring monoclonal antibody aggregation
Why this work is in the frame
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Bibliographic record
Abstract
Intrinsic fluorescence spectroscopy, in conjunction with partial least squares regression (PLSR), was investigated as a potential technique for online quality control and quantitative monitoring of Immunoglobulin G (IgG) aggregation that occurs following exposure to conditions that emulate those that can occur during protein downstream processing. Initially, the impact of three stress factors (temperature, pH, and protein concentration) on the degree of aggregation determined using size exclusion chromatography data, was investigated by performing a central composite designexperiment and applying a fitting response surface model. This investigation identified the influence of the factors as well as the operating regions with minimum propensity to induce protein aggregation. Spectral changes pertinent to the stressed samples were also investigated and found to corroborate the high sensitivity of the intrinsic fluorescence to conformational changes of the proteins under study. Ultimately, partial least squares regression was implemented to formulate two fluorescence-based soft sensors for quality control--product classification--and quantitative monitoring--concentration of monomer. The resulting regression models exhibited accurate prediction ability and good potential for in situ monitoring of monoclonal antibody downstream purification processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 it