Evidence map›Paper›PMID 35330908›Full record

ArticleFrontiers in immunology2022

Neutralisation Hierarchy of SARS-CoV-2 Variants of Concern Using Standardised, Quantitative Neutralisation Assays Reveals a Correlation With Disease Severity; Towards Deciphering Protective Antibody Thresholds.

Diego Cantoni, Martin Mayora-Neto, Angalee Nadesalingam, David A Wells, George W Carnell, Luis Ohlendorf, Matteo Ferrari, Phil Palmer, Andrew C Y Chan, Peter Smith and 9 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Diego CantoniViral Pseudotype Unit, Medway School of Pharmacy, Universities of Kent & Greenwich, Chatham, United Kingdom.
Martin Mayora-NetoViral Pseudotype Unit, Medway School of Pharmacy, Universities of Kent & Greenwich, Chatham, United Kingdom.
Angalee NadesalingamLaboratory of Viral Zoonotics, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
David A WellsLaboratory of Viral Zoonotics, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
George W CarnellLaboratory of Viral Zoonotics, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
Luis OhlendorfLaboratory of Viral Zoonotics, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
Matteo FerrariDIOSynVax, University of Cambridge, Cambridge, United Kingdom.
Phil PalmerLaboratory of Viral Zoonotics, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
Andrew C Y ChanLaboratory of Viral Zoonotics, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
Peter SmithLaboratory of Viral Zoonotics, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
Emma M BentleyInstitute of Clinical Microbiology and Hygiene, University Hospital Regensburg, Regensburg, Germany.
Sebastian EinhauserInstitute of Medical Microbiology and Hygiene, University of Regensburg, Regensburg, Germany.
Ralf WagnerInstitute of Clinical Microbiology and Hygiene, University Hospital Regensburg, Regensburg, Germany.
Mark PageDivision of Virology, National Institute for Biological Standards and Control, Potters Bar, United Kingdom.
Gianmarco RaddiRoyal Papworth Hospital NHS Foundation Trust, Cambridge, United Kingdom.
Helen BaxendaleRoyal Papworth Hospital NHS Foundation Trust, Cambridge, United Kingdom.
Javier Castillo-OlivaresLaboratory of Viral Zoonotics, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
Jonathan HeeneyLaboratory of Viral Zoonotics, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
Nigel TempertonViral Pseudotype Unit, Medway School of Pharmacy, Universities of Kent & Greenwich, Chatham, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rise of SARS-CoV-2 variants has made the pursuit to define correlates of protection more troublesome, despite the availability of the World Health Organisation (WHO) International Standard for anti-SARS-CoV-2 Immunoglobulin sera, a key reagent used to standardise laboratory findings into an international unitage. Using pseudotyped virus, we examine the capacity of convalescent sera, from a well-defined cohort of healthcare workers (HCW) and Patients infected during the first wave from a national critical care centre in the UK to neutralise B.1.1.298, variants of interest (VOI) B.1.617.1 (Kappa), and four VOCs, B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma) and B.1.617.2 (Delta), including the B.1.617.2 K417N, informally known as Delta Plus. We utilised the WHO International Standard for anti-SARS-CoV-2 Immunoglobulin to report neutralisation antibody levels in International Units per mL. Our data demonstrate a significant reduction in the ability of first wave convalescent sera to neutralise the VOCs. Patients and HCWs with more severe COVID-19 were found to have higher antibody titres and to neutralise the VOCs more effectively than individuals with milder symptoms. Using an estimated threshold for 50% protection, 54 IU/mL, we found most asymptomatic and mild cases did not produce titres above this threshold.

Indexed as

COVID-19SARS-CoV-2Antibodies, ViralCOVID-19 SerotherapyHumansImmunization, PassiveSeverity of Illness IndexAntibodies, Viral20/136correlates of protection (CoP)COVID-19disease severityinternational standardIU/mLvariants of concern

Identifiers

PMID35330908
PMCPMC8940306

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.