Evidence map›Paper›PMID 36741337›Full record

ArticleCell reports. Physical science2023

A covalent opsonization approach to enhance synthetic immunity against viral escape variants.

Eden Kapcan, Anthony F Rullo

Open access · goldAbstract read
In one paragraph

Article in Cell reports. Physical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 32% of its field
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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
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

2 authors at 1 institution in 1 country.

Eden KapcanMcMaster Immunology Research Centre (MIRC), McMaster University, 1280 Main Street West, Hamilton, ON, Canada.
Anthony F RulloMcMaster Immunology Research Centre (MIRC), McMaster University, 1280 Main Street West, Hamilton, ON, Canada.
McMaster University Medical Centre · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The sensitivity of therapeutic antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral "escape" mutations has inspired efforts to develop treatment strategies that are still effective in the face of rapidly mutating viral surface proteins. Here, we demonstrate a chemical strategy that enforces viral opsonization by natural serum antibodies. This strategy uses chimeric molecules that we call covalent viral opsonizers, which covalently label viral surface proteins, with synthetic antibody-binding ligands. As a proof of concept, we develop covalent viral opsonizers that covalently label the spike protein on SARS-CoV-2 using a "mutation-proof" small-molecule-binding ligand for anti-dinitrophenyl serum antibodies. In model assays, we observe that covalent viral opsonizers can rapidly and selectively covalently label the receptor-binding domain of both native and mutant spike proteins, leading to antibody opsonization. Opsonization mediated by this strategy is able to efficiently block the key binding domain interactions, in contrast to non-covalent analogs. We also show that covalent viral opsonizers enact targeted anti-viral phagocytotic immune function. This strategy has potential general utility for the rapid deployment of anti-viral synthetic immunotherapeutics at the onset of a new pandemic to reinforce vaccination and antibody engineering efforts.

Indexed as

bifunctional chimeric moleculeschemical immunologycovalent drugscovalent proximity inductionCOVID-19SuFExsynthetic immunotherapy

Identifiers

PMID36741337
PMCPMC9885534
OpenAlexW4319294895

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.