Evidence map›Paper›PMID 40843674›Full record

ArticleAntibodies (Basel, Switzerland)2025

Immunogenicity Risk Assessment of Biotherapeutics Using an Ex Vivo B Cell Assay.

Kevin M Budge, Ross Blankenship, Patricia Brown-Augsburger, Lukasz K Chlewicki

Abstract read
In one paragraph

Article in Antibodies (Basel, Switzerland), 2025. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Article
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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

4 authors.

Kevin M BudgeEli Lilly and Company, Lilly Corporate Center Indianapolis, Indianapolis, IN 46285, USA.
Ross BlankenshipEli Lilly and Company, Lilly Corporate Center Indianapolis, Indianapolis, IN 46285, USA.ORCID 0009-0002-7375-5255
Patricia Brown-AugsburgerEli Lilly and Company, Lilly Corporate Center Indianapolis, Indianapolis, IN 46285, USA.
Lukasz K ChlewickiEli Lilly and Company, Lilly Corporate Center Indianapolis, Indianapolis, IN 46285, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesAnti-drug antibody (ADA) formation can impact the safety, pharmacokinetics, and/or efficacy of biotherapeutics, including monoclonal antibodies (mAbs). Current strategies for ADA/immunogenicity risk prediction of mAbs include in silico algorithms, T cell proliferation assays, MHC-associated peptide proteomics assays (MAPPs), and dendritic cell internalization assays. However, B cell-mediated responses are not assessed in these assays. B cells are professional antigen-presenting cells (APCs) and secrete antibodies toward immunogenic mAbs. Therefore, methods to determine B cell responses would be beneficial for immunogenicity risk prediction and may provide a more comprehensive assessment of risk.

methodsWe used a PBMC culture method with the addition of IL-4, IL-21, B cell activating factor (BAFF), and an anti-CD40 agonist mAb to support B cell survival and activation.

resultsB cells in this assay format become activated, proliferate, and secrete IgG. A panel of 51 antibodies with varying clinical immunogenicity rates were screened in this assay with IgG secretion used as a readout for immunogenicity risk. IgG secretion differed among test articles but did not correlate with the clinical immunogenicity rating.

conclusionsThis dataset highlights the challenges of developing a B cell assay for immunogenicity risk prediction and provides a framework for further refinement of a B cell-based assay for immunogenicity risk prediction of mAbs.

Indexed as

ADAantibodiesanti-drug antibodiesB cellsbiotherapeuticimmunogenicity

Identifiers

PMID40843674
PMCPMC12372046

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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.