Humanization of Antibodies using a Statistical Inference Approach

Alejandro Clavero-Álvarez,Mauro Magnani,Tomas Di Mambro,Pierpaolo Bruscolini,Sergio Perez-Gaviro

doi:10.1038/s41598-018-32986-y

Alejandro Clavero-Álvarez, Mauro Magnani + Show 3 more

Open Access

https://doi.org/10.1038/s41598-018-32986-y

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Journal: Scientific Reports	Publication Date: Oct 4, 2018
Citations: 29	License type: open-access

Affiliation: Universidad de Zaragoza, University of Urbino

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.

Highlights

Antibody-based drugs have acquired an increasing importance in the last two decades, both for imaging and for therapeutic uses, especially to treat different types of cancer and autoimmune diseases
We have seen that its performance in distinguishing human from murine sequences is higher than the methods relying on just the sequence similarity, due to the fact that the statistical model, on which the MG-score is based, accounts for pair-correlations between residues at different positions
The proposed humanness score shows a correlation with the experimental immunogenicity of therapeutic antibodies, even if such correlation is far from perfect

Summary

Introduction

Antibody-based drugs have acquired an increasing importance in the last two decades, both for imaging and for therapeutic uses, especially to treat different types of cancer and autoimmune diseases. New antibodies are typically developed in animal models (most often, in mouse); the antibodies obtained by this way are usually not tolerated by humans, eliciting in vivo an immune response against the murine antibody They need to be “humanized”, substituting part of their sequences by the human ones, while preserving their specificity, affinity and stability. A different approach as been proposed by Seeliger[6], introducing a score function that accounts both for local preferences and for pair correlations between residues at different positions Such approach is reasonably capable to distinguish between human and mouse sequences, despite a relevant residual overlap between their distribution; the stochastic humanization process under such score function samples regions of low immunogenicity, even though the final basin of attraction of the trajectories presents an intermediate value of immunogenicity (as measured by the Epivax score)[7].

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