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

Journal: Bioinformatics (Oxford, England)
Published Date: Nov 18, 2021

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.

Authors

  • Claire Marks
    Department of Statistics, University of Oxford, Oxford, UK.
  • Alissa M Hummer
    Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
  • Mark Chin
    Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
  • Charlotte M Deane
    Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.

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