Humanization of Antibodies using a Statistical Inference Approach.

Abstract:

:Antibody humanization is a key step in the preclinical phase of the development of therapeutic antibodies, originally developed and tested in non-human models (most typically, in mouse). The standard technique of Complementarity-Determining Regions (CDR) grafting into human Framework Regions of germline sequences has some important drawbacks, in that the resulting sequences often need further back-mutations to ensure functionality and/or stability. Here we propose a new method to characterize the statistical distribution of the sequences of the variable regions of human antibodies, that takes into account phenotypical correlations between pairs of residues, both within and between chains. We define a "humanness score" of a sequence, comparing its performance in distinguishing human from murine sequences, with that of some alternative scores in the literature. We also compare the score with the experimental immunogenicity of clinically used antibodies. Finally, we use the humanness score as an optimization function and perform a search in the sequence space, starting from different murine sequences and keeping the CDR regions unchanged. Our results show that our humanness score outperforms other methods in sequence classification, and the optimization protocol is able to generate humanized sequences that are recognized as human by standard homology modelling tools.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Clavero-Álvarez A,Di Mambro T,Perez-Gaviro S,Magnani M,Bruscolini P

doi

10.1038/s41598-018-32986-y

subject

Has Abstract

pub_date

2018-10-04 00:00:00

pages

14820

issue

1

issn

2045-2322

pii

10.1038/s41598-018-32986-y

journal_volume

8

pub_type

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