Evidence map›Paper›PMID 36917814›Full record

ReviewAnnual review of chemical and biomolecular engineering2023

Outsmarting Pathogens with Antibody Engineering.

Ahlam N Qerqez, Rui P Silva, Jennifer A Maynard

Open access · hybridAbstract readReview
In one paragraph

Review in Annual review of chemical and biomolecular engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
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

3 authors at 1 institution in 1 country.

Ahlam N QerqezDepartment of Chemical Engineering, The University of Texas, Austin, Texas, USA; email: maynard@che.utexas.edu.
Rui P SilvaDepartment of Molecular Biosciences, The University of Texas, Austin, Texas, USA.
Jennifer A MaynardDepartment of Chemical Engineering, The University of Texas, Austin, Texas, USA; email: maynard@che.utexas.edu.
The University of Texas at Austin · US

Funding

Structure, function and antigenicity of B. pertussis virulence factorsR01AI155453 · NIAID · UNIVERSITY OF TEXAS AT AUSTIN · PI MAYNARD, JENNIFER A, MCLELLAN, JASON SCOTT · 2021 to 2025
$2.8M
NIAID NIH HHS R01 AI155453
6 · The paper itself

Abstract

There is growing interest in identifying antibodies that protect against infectious diseases, especially for high-risk individuals and pathogens for which no vaccine is yet available. However, pathogens that manifest as opportunistic or latent infections express complex arrays of virulence-associated proteins and are adept at avoiding immune responses. Some pathogens have developed strategies to selectively destroy antibodies, whereas others create decoy epitopes that trick the host immune system into generating antibodies that are at best nonprotective and at worst enhance pathogenesis. Antibody engineering strategies can thwart these efforts by accessing conserved neutralizing epitopes, generating Fc domains that resist capture or degradation and even accessing pathogens hidden inside cells. Design of pathogen-resistant antibodies can enhance protection and guide development of vaccine immunogens against these complex pathogens. Here, we discuss general strategies for design of antibodies resistant to specific pathogen defense mechanisms.

Indexed as

Antibodies, NeutralizingVaccinesAntigensEpitopesHumansAntibodies, NeutralizingAntigensEpitopesVaccinesantibody–drug conjugatebispecific antibodyFc engineeringimmune evasionpassive immunizationvaccines

Identifiers

PMID36917814
PMCPMC10330301
OpenAlexW4324131460

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.