Literature DB >> 34478133

The Therapeutic Antibody Profiler for Computational Developability Assessment.

Matthew I J Raybould1, Charlotte M Deane2.   

Abstract

The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is critical to industrial workflows, to avoid devoting resources to non-productive candidates. Here, we describe a high-throughput computational developability assessment tool, the Therapeutic Antibody Profiler (TAP), which assesses the physicochemical "druglikeness" of an antibody candidate. Input variable domain sequences are converted to three-dimensional structural models, and then five developability-linked molecular surface descriptors are calculated and compared to advanced-stage clinical therapeutics. Values at the extremes of/outside of the distributions seen in therapeutics imply an increased risk of developability issues. Therefore, TAP, starting only from sequence information, provides a route to rapidly identifying drug candidate antibodies that are likely to have poor developability. Our web application ( opig.stats.ox.ac.uk/webapps/tap ) profiles input antibody sequences against a continually updated reference set of clinical therapeutics.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Antibody drug discovery; Charge patches; Computational developability assessment; Druglikeness; Hydrophobic patches; Surface physicochemistry

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Year:  2022        PMID: 34478133     DOI: 10.1007/978-1-0716-1450-1_5

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  2 in total

1.  Engineering the expression of an anti-interleukin-13 antibody through rational design and mutagenesis.

Authors:  Bojana Popovic; Suzanne Gibson; Tarik Senussi; Sara Carmen; Sara Kidd; Tim Slidel; Ian Strickland; Xu Jianqing; Jennifer Spooner; Amanda Lewis; Nathan Hudson; Lorna Mackenzie; Jennifer Keen; Ben Kemp; Colin Hardman; Diane Hatton; Trevor Wilkinson; Tristan Vaughan; David Lowe
Journal:  Protein Eng Des Sel       Date:  2017-04-01       Impact factor: 1.650

2.  Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.

Authors:  Claire Marks; Jaroslaw Nowak; Stefan Klostermann; Guy Georges; James Dunbar; Jiye Shi; Sebastian Kelm; Charlotte M Deane
Journal:  Bioinformatics       Date:  2017-05-01       Impact factor: 6.937

  2 in total

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