Commercial immunoassays for detection of anti-SARS-CoV-2 spike and RBD antibodies: urgent call for validation against new and highly mutated variants
Bibliographic record
Abstract
Abstract Measuring the level of protection conferred by anti-SARS-CoV-2 (trimeric) spike or RBD (receptor binding domain) antibodies (especially total and IgG) is a suitable and reliable approach for predicting biological protection against the risk of infection and severe coronavirus disease 2019 (COVID-19) illness. Nonetheless, SARS-CoV-2 has undergone a broad process of recombination since the identification of the prototype lineage in 2019, introducing a huge number of mutations in its genome and generating a vast array of variants of interest (VoI) and concern (VoC). Many of such variants developed several mutations in spike protein and RBD, with the new Omicron (B.1.1.529) clade displaying over 30 changes, 15 of which concentrated in the RBD. Besides their impact on virus biology, as well as on the risk of detection failure with some molecular techniques (i.e., S gene dropout), recent evidence suggests that these mutations may also jeopardize the reliability of currently available commercial immunoassays for detecting anti-SARS-CoV-2 antibodies. The antigen (either spike or RBD) and epitopes of the prototype SARS-CoV-2 coated in some immunoassays may no longer reflect the sequence of circulating variants. On the other hand, anti-SARS-CoV-2 antibodies elicited by highly mutated SARS-CoV-2 variants may no longer be efficiently recognized by the currently available commercial immunoassays. Therefore, beside the compelling need to regularly re-evaluate and revalidate all commercially available immunoassays against live virus neutralization assays based on emerging VoCs or VoIs, diagnostic companies may also consider to redevelop their methods, replacing former SARS-CoV-2 antigens and epitopes with those of the new variants.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".