ArticleThe AAPS journal2025
Evaluating the Immunogenicity Risk of Protein Therapeutics by Augmenting T Cell Epitope Prediction with Clinical Factors.
Article in The AAPS journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed.
- Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.Frontiers in immunology · 2026Review
- The immunogenicity database collaborative: a standardized, publicly available database for clinical immunogenicity observations and insights.Frontiers in immunology · 2026Article
- Pharmacological considerations for next-generation protein therapeutics in cardiovascular disease.The Journal of pharmacology and experimental therapeutics · 2025Review
- Towards new approach methodologies for biological therapeutics: a novel model-informed metric to assess immunogenicity risk.Frontiers in immunology · 2025Article
- T cell assays for non-clinical immunogenicity risk assessment: best practices recommended by the European Immunogenicity Platform.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-based therapeutics may elicit undesired immune responses in a subset of patients, leading to the production of anti-drug antibodies (ADA). In some cases, ADAs have been reported to affect the pharmacokinetics, efficacy and/or safety of the drug. Accurate prediction of the ADA response can help drug developers identify the immunogenicity risk of the drug candidates, thereby allowing them to make the necessary modifications to mitigate the immunogenicity. In this study, we leveraged the rich clinical study data collected by Roche/Genentech to identify factors that impact drug immunogenicity. We focused on conventional monoclonal antibodies, but have included a variety of additional drug modalities in the analysis. We show that the clinical ADA incidences are associated with the mechanism of action of the drugs, the mechanism of action of comedications, the routes of drug administration and the diseases of the patient cohort. By combining these clinical factors with the in silico epitope prediction, we improved the prediction accuracy of drug immunogenicity in clinical trials (AUC of cross validation improved from 0.72 to 0.93).
Indexed as
Identifiers
39849284What Socratic holds
Registered trials
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.