Prediction of immunogenicity for humanized and full human therapeutic antibodies

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. Sequ...

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Bibliographic Details
Published in:PloS one 2020-08, Vol.15 (8), p.e0238150-e0238150
Main Authors: Liang, Shide, Zhang, Chi
Format: Article
Language:English
Subjects:
Algorithms
Antibodies
Antigens
Biology and Life Sciences
Crystal structure
Drug development
FDA approval
Hydrophobicity
Identification and classification
Immune response
Immunogenicity
Immunologic research
Immunopharmacology
Learning algorithms
LSD
Lymphocytes
Lysergic acid diethylamide
Machine learning
Medicine and Health Sciences
Physical Sciences
Proteins
R&D
Research & development
Structure
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Summary: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
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0238150