Quantitative microcapillary electrophoresis immunoassay (mCE IA) for end-to-end analysis of pertactin within in-process samples and Quadracel® vaccine
Bibliographic record
Abstract
Protein concentration is an important attribute in the production of subunit or component-based vaccine antigens. Rigorous monitoring of protein concentration is required to identify potential areas for yield improvement. The current GMP method for quantitation is the plate-based ELISA which requires numerous hands-on steps and has low sensitivity in comparison to new microfluidic systems. To address this issue, a sensitive automated microCapillary Electrophoresis ImmunoAssay (mCE IA) method was developed to accurately separate and quantitate pertactin (PRN), an important antigen of the modern acellular Pertussis (aP) vaccine. PRN is reported to be a low-yielding antigen; thus, it is critical to observe its concentration throughout its manufacturing process. First, a primary antibody for PRN was identified to establish suitable immunoprobing conditions for detection of PRN over a wide linear dynamic range that spans 3 orders of magnitude. Next, the pre-adsorbed PRN Drug Substance (DS) was used as a reference standard to quantitate PRN samples against a calibration curve with adequate accuracy and precision. Four representative samples including three in-process steps and final adjuvanted drug product: Quadracel®, were examined to demonstrate the capability of mCE IA to quantitate PRN with high sensitivity and specificity. The matrices of the selected samples contain additional components (e.g. other proteins, growth factors, cell culture media, residual ammonium sulfate, and aluminum adjuvant) often making the quantitation of PRN challenging. The specificity and method linearity were demonstrated by spiking pre-adsorbed PRN DS into the four representative samples. In addition, it was shown that reportable concentrations of PRN for nine downstream process steps as analyzed by our method is comparable to concentrations obtained with ELISA. Most importantly, this study demonstrated that our method's quantitative accuracy is independent of matrix components, as each sample undergoes extensive dilution. This allows for seamless end-to-end analysis of PRN from fermenter harvest, through to complex downstream process samples to adjuvanted drug products. Finally, for the first time the developed and qualified mCE IA method was shown to quantify PRN throughout the entire manufacturing process to provide rapid feedback for process optimizations allowing for accurate yield and step-loss calculations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".