PITHA: A Webtool to Predict Immunogenicity for Humanized and Fully Human Therapeutic Antibodies.

Abstract

Immunogenicity is an important concern to therapeutic antibodies during antibody design and development. Based on the co-crystal structures of idiotypic antibodies and their antibodies, one can see that anti-idiotypic antibodies usually bind the complementarity-determining regions (CDR) of idiotypic antibodies. Sequence and structural features, such as cavity volume at the CDR region and hydrophobicity of CDR-H3 loop region, were identified for distinguishing immunogenic antibodies from non-immunogenic antibodies. These features were integrated together with a machine learning platform to predict immunogenicity for humanized and fully human therapeutic antibodies (PITHA). This method achieved an accuracy of 83% in a leave-one-out experiment for 29 therapeutic antibodies with available crystal structures. The web server of this method is accessible at http://mabmedicine.com/PITHA or http://sysbio.unl.edu/PITHA . This method, as a step of computer-aided antibody design, helps evaluate the safety of new therapeutic antibody, which can save time and money during the therapeutic antibody development.