Evidence map›Paper›PMID 28971356›Full record

ArticleThe AAPS journal2017

How Close Are We to Profiling Immunogenicity Risk Using In Silico Algorithms and In Vitro Methods?: an Industry Perspective.

Jochem Gokemeijer, Vibha Jawa, Shibani Mitra-Kaushik

Abstract read
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In one paragraph

Article in The AAPS journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Trial
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  6. Review
  7. Beyond Efficacy: Ensuring Safety in Peptide Therapeutics through Immunogenicity Assessment.Journal of peptide science : an official publication of the European Peptide Society · 2025
    Review
  8. Article
  9. Article
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  11. Article
  12. Review
  13. Article
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  18. 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

3 authors.

Jochem GokemeijerDiscovery Chemistry and Molecular Technologies, Bristol-Myers Squibb, Waltham, Massachusetts, USA.
Vibha JawaBiologics and Vaccine Formulations, Merck Sharp & Dohme Corp, 2000 Galloping Hill Road, K-15 H406, Kenilworth, New Jersey, 07033, USA.
Shibani Mitra-KaushikBiomarker and Clinical Bioanalysis, Sanofi, 1 The Mountain Road, Framingham, Massachusetts, 01701, USA. Shibani.Mitra-Kaushik@sanofi.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In silico HLA-binding algorithms and in vitro T cell-based assays as predictive tools for human immunogenicity risk have made inroads in the biotherapeutic drug discovery and development process. Currently, these tools are being used only for candidate selection or characterization and not for making a go/no-go decision for further development. A clear limitation for a broader implementation is the lack of correlation between the predicted T cell epitope content/immune reactivity potential of a biotherapeutic and the subsequent ADA-related clinical immunogenicity outcome. The current state of technologies and their pros and cons were discussed as a part of the 2016 AAPS National Biotechnology Conference in a themed session. A review of the advances in the area and the session talks along with the ensuing discussions are summarized in this commentary.

Indexed as

Biological TherapyDrug DiscoveryDrug IndustryAlgorithmsEpitopes, T-LymphocyteLymphocyte ActivationT-LymphocytesEpitopes, T-Lymphocytealgorithmanti-drug antibodyantigenicityimmunogenicityMAPPSpredictionT cell epitope

Identifiers

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.