ArticlePloS one2020
Prediction of immunogenicity for humanized and full human therapeutic antibodies.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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.
Who cites it
15 citing papers in PubMed, 21 citations in OpenAlex.
- Characterization of the VHH-Fc construct rimteravimab in healthy adults and patients hospitalized for mild-to-moderate COVID-19: Two Phase 1 randomized clinical trials.PLoS medicine · 2026Trial
- Genome-wide pharmacogenetics of anti-drug antibody response to bococizumab highlights key residues in HLA DRB1 and DQB1.Scientific reports · 2022Trial
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- Enhancing protein immunogenicity prediction via uncertainty weighted deep ensemble.Oxford open immunology · 2026Article
- Deep Mutational Scanning in Immunology: Techniques and Applications.Pathogens (Basel, Switzerland) · 2025Review
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Antibody Therapy for Patients with Lymphoid Malignancies: Past and Present.International journal of molecular sciences · 2025Review
- Pharmacovigilance study on the reporting frequency of atrial fibrillation with immune checkpoint inhibitors: insights from FDA Adverse Event Reporting System.Therapeutic advances in drug safety · 2025Article
- Mechanistic insights into resistance mechanisms to T cell engagers.Frontiers in immunology · 2025Review
- Plasticity of BioPhi-driven humanness optimization in ScFv-CD99 binding affinity validated through AlphaFold, HADDOCK, and MD simulations.Computational and structural biotechnology journal · 2025Article
- Exploring the Biological Activity of a Humanized Anti-CD99 ScFv and Antibody for Targeting T Cell Malignancies.Biomolecules · 2024Article
- AbImmPred: An immunogenicity prediction method for therapeutic antibodies using AntiBERTy-based sequence features.PloS one · 2024Article
- Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics.Molecular pharmaceutics · 2023Article
- Development and characterization of a camelid derived antibody targeting a linear epitope in the hinge domain of human PCSK9 protein.Scientific reports · 2022Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immunogenicity is an important concern for therapeutic antibodies during drug development. By analyzing co-crystal structures of idiotypic antibodies and their antibodies, we found that anti-idiotypic antibodies usually bind the Complementarity Determining Regions (CDR) of idiotypic antibodies. Sequence and structural features were identified for distinguishing immunogenic antibodies from non-immunogenic antibodies. For example, non-immunogenic antibodies have a significantly larger cavity volume at the CDR region and a more hydrophobic CDR-H3 loop than immunogenic antibodies. Antibodies containing no Gly at the turn of CDR-H2 loop are often immunogenic. We integrated these features together with a machine learning platform to Predict Immunogenicity for humanized and full human THerapeutic Antibodies (PITHA). This method achieved an accuracy of 83% in leave-one-out experiment for 29 therapeutic antibodies with available crystal structures. The accuracy decreased to 65% for 23 test antibodies with modeled structures, because their crystal structures were not available, and the prediction was made with modeled structures. The server of this method is accessible at http://mabmedicine.com/PITHA.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.