PAWG Pilot Study on Quantification of SARS-CoV-2 Monoclonal Antibody - Part 1
Bibliographic record
Abstract
Main text Under the auspices of the Protein Analysis Working Group (PAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) a pilot study, CCQM-P216, was coordinated by the Chinese National Institute of Metrology (NIM), National Research Council of Canada (NRC) and the Bureau International des Poids et Mesures (BIPM). Eleven Metrology Institutes or Designated Institutes and the BIPM participated in the first phase of the pilot study (Part 1). The purpose of this pilot study was to develop measurement capabilities for larger proteins using a recombinant humanized IgG monoclonal antibody against Spike glycoprotein of SARS-CoV-2 (Anti-S IgG mAb) in solution. The first phase of the study was designed to employ established methods that had been previously studies by the CCQM Protein Analysis Working Group, involving the digestion of protein down to the peptide or amino acid level. The global coronavirus pandemic has also led to increased focus on antibody quantitation methods. IgG are among the immunoglobulins produced by the immune system to provide protection against SARS-CoV-2. Anti-SARS-CoV-2 IgG can therefore be detected in samples from affected patients. Antibody tests can show whether a person has been exposed to the SARS-CoV-2, and whether or not they potentially show lasting immunity to the disease. With the constant spread of the virus and the high pressure of re-opening economies, antibody testing plays a critical role in the fight against COVID-19 by helping healthcare professionals to identify individuals who have developed an immune response, either via vaccination or exposure to the virus. Many countries have launched large-scale antibody testing for COVID-19. The development of measurement standards for the antibody detection of SARS-CoV-2 is critically important to deal with the challenges of the COVID-19 pandemic. In this study, the SARS-CoV-2 monoclonal antibody is being used as a model system to build capacity in methods that can be used in antibody quantification. Amino acid reference values with corresponding expanded uncertainty of 36.10 ± 1.55 mg/kg, 38.75 ± 1.45 mg/kg, 18.46 ± 0.78 mg/kg, 16.20 ± 0.67 mg/kg and 30.61 ± 1.30 mg/kg have been established for leucine, valine, phenylalanine, isoleucine and proline, respectively. Agreement between nearly all laboratories was achieved for the amino acid analysis within 2 to 2.5 %, with one participant achieving markedly higher results due to a technical issue found in their procedure; this result was thus excluded from the reference value calculations. The relatively good agreement within a laboratory between different amino acids was not dissimilar to previous results for peptides or small proteins, indicating that factors such as hydrolysis conditions and calibration procedures could be the largest sources of variability. Peptide reference values with corresponding expanded uncertainty of 4.99 ± 0.28 mg/kg and 6.83 ± 0.65 mg/kg have been established for ALPAPIEK and GPSVFPLAPSSK, respectively. Not surprisingly due to prior knowledge from previous studies on peptide quantitation, agreement between laboratories for the peptide-based analysis was slightly poorer at 3 to 5 %, with one laboratory's result excluded for the peptide GPSVFPLAPSSK. Again, this level of agreement was not significantly poorer than that achieved in previous studies with smaller or less complex proteins. To reach the main text of this paper, click on Final Report .
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.002 |
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".