Standardised quantitative assays for anti-SARS-CoV-2 immune response used in vaccine clinical trials by the CEPI Centralized Laboratory Network: a qualification analysis
Bibliographic record
Abstract
BACKGROUND: Accurate quantitation of immune markers is crucial for ensuring reliable assessment of vaccine efficacy against infectious diseases. This study was designed to confirm standardised performance of SARS-CoV-2 assays used to evaluate COVID-19 vaccine candidates at the initial seven laboratories (in North America, Europe, and Asia) of the Coalition for Epidemic Preparedness Innovations (CEPI) Centralized Laboratory Network (CLN). METHODS: Three ELISAs (pre-spike protein, receptor binding domain, and nucleocapsid), a microneutralisation assay (MNA), a pseudotyped virus-based neutralisation assay (PNA), and an IFN-γ T-cell ELISpot assay were developed, validated or qualified, and transferred to participating laboratories. Immune responses were measured in ELISA laboratory units (ELU) for ELISA, 50% neuralisation dilution (ND50) for MNA, 50% neutralisation titre (NT50) for PNA, and spot-forming units for the ELISpot assay. Replicate assay results of well characterised panels and controls of blood samples from individuals with or without SARS-CoV-2 infection were evaluated by geometric mean ratios, standard deviation, linear regression, and Spearman correlation analysis for consistency, accuracy, and linearity of quantitative measurements across all laboratories. FINDINGS: High reproducibility of results across all laboratories was demonstrated, with interlaboratory precision of 4·1-7·7% coefficient of variation for all three ELISAs, 3·8-19·5% for PNA, and 17·1-24·1% for MNA, over a linear range of 11-30 760 ELU per mL for the three ELISAs, 14-7876 NT50 per mL for PNA, and 21-25 587 ND50 per mL for MNA. The MNA was also adapted for detection of neutralising antibodies against the major SARS-CoV-2 variants of concern. The results of PNA and MNA (r=0·864) and of ELISA and PNA (r=0·928) were highly correlated. The IFN-γ ELISpot interlaboratory variability was 15·9-49·9% coefficient of variation. Sensitivity and specificity were close to 100% for all assays. INTERPRETATION: The CEPI CLN provides accurate quantitation of anti-SARS-CoV-2 immune response across laboratories to allow direct comparisons of different vaccine formulations in different geographical areas. Lessons learned from this programme will serve as a model for faster responses to future pandemic threats and roll-out of effective vaccines. FUNDING: CEPI.
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 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 | 0.073 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".