Humanization of antibodies using a machine learning approach on large-scale repertoire data.

ABSTRACT:

Motivation

Monoclonal antibody (mAb) therapeutics are often produced from non-human sources (typically murine), and can therefore generate immunogenic responses in humans. Humanization procedures aim to produce antibody therapeutics that do not elicit an immune response and are safe for human use, without impacting efficacy. Humanization is normally carried out in a largely trial-and-error experimental process. We have built machine learning classifiers that can discriminate between human and non-human antibody variable domain sequences using the large amount of repertoire data now available.

Results

Our classifiers consistently outperform the current best-in-class model for distinguishing human from murine sequences, and our output scores exhibit a negative relationship wit

SUBMITTER: Marks C 

PROVIDER: S-EPMC8760955 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature