Evidence map›Paper›PMID 38261155›Full record

ReviewBioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy2024

Reducing Immunogenicity by Design: Approaches to Minimize Immunogenicity of Monoclonal Antibodies.

Chantal T Harris, Sivan Cohen

Abstract readReview
In one paragraph

Review in BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing 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

64 citing papers in PubMed.

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  15. Antibody-drug conjugates in gastric cancer: clinical advances and resistance mechanisms.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026
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4 more citing papers are in PubMed but not listed here.

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.

Chantal T HarrisDepartment of BioAnalytical Sciences, Genentech Inc., South San Francisco, CA, 94080-4990, USA.ORCID http://orcid.org/0000-0002-4797-0966
Sivan CohenDepartment of BioAnalytical Sciences, Genentech Inc., South San Francisco, CA, 94080-4990, USA. cohen.sivan@gene.com.ORCID http://orcid.org/0000-0001-6213-6200

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monoclonal antibodies (mAbs) have transformed therapeutic strategies for various diseases. Their high specificity to target antigens makes them ideal therapeutic agents for certain diseases. However, a challenge to their application in clinical practice is their potential risk to induce unwanted immune response, termed immunogenicity. This challenge drives the continued efforts to deimmunize these protein therapeutics while maintaining their pharmacokinetic properties and therapeutic efficacy. Because mAbs hold a central position in therapeutic strategies against an array of diseases, the importance of conducting comprehensive immunogenicity risk assessment during the drug development process cannot be overstated. Such assessment necessitates the employment of in silico, in vitro, and in vivo strategies to evaluate the immunogenicity risk of mAbs. Understanding the intricacies of the mechanisms that drive mAb immunogenicity is crucial to improving their therapeutic efficacy and safety and developing the most effective strategies to determine and mitigate their immunogenic risk. This review highlights recent advances in immunogenicity prediction strategies, with a focus on protein engineering strategies used throughout development to reduce immunogenicity.

Indexed as

Antibodies, MonoclonalProtein EngineeringHumansAntibodies, Monoclonal

Identifiers

PMID38261155
PMCPMC10912315

What OpenQuestion holds

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LicenceCC BY-NC
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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.