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Record W4381886756 · doi:10.1088/0026-1394/60/1a/08016

PAWG pilot study on quantification of SARS-CoV-2 monoclonal antibody - Part 2

2023· article· en· W4381886756 on OpenAlexaboutno aff
Mi Wang, R D Josephs, Jeremy E. Melanson, Xin Dai, Y Wang, R Zhai, Zhi‐gang Chu, Xiaoming Fang, M-P Thibeault, Bradley B. Stocks, Juris Meija, Magali Bedu, Gustavo Martos, Steven Westwood, Robert Wielgosz, Merve Öztuğ, Evren Saban, T Kinumi, K Saikusa, P J Beltrão, Sandra Mara Naressi Scapin, Y B Sade

Bibliographic record

VenueMetrologia · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicAntibodyMonoclonal antibodySars virusVirologyVaccinationMedicine2019-20 coronavirus outbreakImmunologyDiseaseInfectious disease (medical specialty)Outbreak

Abstract

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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). 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 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 played 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. 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. A Final Report on the first round of CCQM-P216 PAWG Pilot Study on Quantification of SARS-CoV-2 Monoclonal Antibody - Part 1 focusing on the assessment of both mass fraction determinations of different AAs in the material and mass fraction determinations of proteotypic peptides belonging to the constant region of the mAb has already been published in Metrologia. The present Final Report on the second round of CCQM-P216 - Part 2 was designed to investigate optional methods for the characterization of Anti-S IgG mAb in solution for the assessment of size heterogeneity purity determinations, mass fraction measurements of SARS-CoV-2 monoclonal antibody in the material by UV-VIS spectrophotometry, mass fraction measurements of proteotypic peptides belonging to the variable region of the mAb and mass fraction measurements of monomeric mAb in the material. Six Metrology Institutes or Designated Institutes and the BIPM participated in the second phase of the pilot study (Part 2). Acceptable agreement between all laboratories was for the assessment of size heterogeneity of the anti-S IgG mAb material. For example, a reference value with its corresponding expanded uncertainties of (99.48 ± 0.49) % has been obtained for the monomer. Good between laboratories for UV-VIS spectrophotometric analysis at a pre-agreed wavelength of 280 nm was obtained with individual relative expanded uncertainties from 2.8 % to 15.5 % and a mass concentration reference value and corresponding expanded uncertainty of (500.8 ± 15.4) mg/L. Mass fraction assignments of the target proteotypic peptide YSPSFQGQVTISADK in the variable region of the anti-S IgG mAb material by NIM and BIPM resulted in a reference value and corresponding expanded uncertainty of (9.8 ± 2.0) mg/kg. Both NIM and BIPM have provided estimates for the mass fraction of monomeric mAb in the material with individual relative expanded uncertainties of 4.3 % and 4.1 %, respectively. A mass fraction reference value and corresponding expanded uncertainty of (411 ± 88) mg/kg was calculated by applying an 'excess-variance' approach for the monomeric mAb mass fraction. In addition, a post-hoc assessment of the monomeric mAb mass fraction from the subset of that produced both amino acid and proteotypic peptide-based mass fraction results and SEC measurements during the 1st and present study round provided more detailed information. A reference value and its expanded uncertainty for the mass fraction of monomeric mAb in the anti-S IgG mAb material was calculated to be (434.3 ± 11.9) mg/kg applying an 'excess-variance' approach. The level of agreement was not significantly poorer than that achieved in previous studies with smaller or less complex proteins. The two-stage pilot study was a big step forward to build and improve capacity within NMIs/DIs to undertake future key comparisons in the field of mass fraction determinations of antibodies and other large proteins. To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/ . The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.212
GPT teacher head0.439
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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