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 Region...

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Autores principales:	Alejandro Clavero-Álvarez, Tomas Di Mambro, Sergio Perez-Gaviro, Mauro Magnani, Pierpaolo Bruscolini
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Murine Sequences Manual Scoring Immunogenicity Experiments Complementarity-determining Regions (CDR) Human Framework Regions Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d7d0d04158114730870c611335bc2747
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spelling	oai:doaj.org-article:d7d0d04158114730870c611335bc27472021-12-02T15:08:29ZHumanization of Antibodies using a Statistical Inference Approach10.1038/s41598-018-32986-y2045-2322https://doaj.org/article/d7d0d04158114730870c611335bc27472018-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-32986-yhttps://doaj.org/toc/2045-2322Abstract 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.Alejandro Clavero-ÁlvarezTomas Di MambroSergio Perez-GaviroMauro MagnaniPierpaolo BruscoliniNature PortfolioarticleMurine SequencesManual ScoringImmunogenicity ExperimentsComplementarity-determining Regions (CDR)Human Framework RegionsMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-11 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Murine Sequences Manual Scoring Immunogenicity Experiments Complementarity-determining Regions (CDR) Human Framework Regions Medicine R Science Q
spellingShingle	Murine Sequences Manual Scoring Immunogenicity Experiments Complementarity-determining Regions (CDR) Human Framework Regions Medicine R Science Q Alejandro Clavero-Álvarez Tomas Di Mambro Sergio Perez-Gaviro Mauro Magnani Pierpaolo Bruscolini Humanization of Antibodies using a Statistical Inference Approach
description	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.
format	article
author	Alejandro Clavero-Álvarez Tomas Di Mambro Sergio Perez-Gaviro Mauro Magnani Pierpaolo Bruscolini
author_facet	Alejandro Clavero-Álvarez Tomas Di Mambro Sergio Perez-Gaviro Mauro Magnani Pierpaolo Bruscolini
author_sort	Alejandro Clavero-Álvarez
title	Humanization of Antibodies using a Statistical Inference Approach
title_short	Humanization of Antibodies using a Statistical Inference Approach
title_full	Humanization of Antibodies using a Statistical Inference Approach
title_fullStr	Humanization of Antibodies using a Statistical Inference Approach
title_full_unstemmed	Humanization of Antibodies using a Statistical Inference Approach
title_sort	humanization of antibodies using a statistical inference approach
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/d7d0d04158114730870c611335bc2747
work_keys_str_mv	AT alejandroclaveroalvarez humanizationofantibodiesusingastatisticalinferenceapproach AT tomasdimambro humanizationofantibodiesusingastatisticalinferenceapproach AT sergioperezgaviro humanizationofantibodiesusingastatisticalinferenceapproach AT mauromagnani humanizationofantibodiesusingastatisticalinferenceapproach AT pierpaolobruscolini humanizationofantibodiesusingastatisticalinferenceapproach
_version_	1718388152068997120

Humanization of Antibodies using a Statistical Inference Approach

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