Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery
- PMID: 33775449
- DOI: 10.1016/j.tibtech.2021.03.003
Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery
Abstract
For years, a discussion has persevered on the benefits and drawbacks of antibody discovery using animal immunization versus in vitro selection from non-animal-derived recombinant repertoires using display technologies. While it has been argued that using recombinant display libraries can reduce animal consumption, we hold that the number of animals used in immunization campaigns is dwarfed by the number sacrificed during preclinical studies. Thus, improving quality control of antibodies before entering in vivo studies will have a larger impact on animal consumption. Both animal immunization and recombinant repertoires present unique advantages for discovering antibodies that are fit for purpose. Furthermore, we anticipate that machine learning will play a significant role within discovery workflows, refining current antibody discovery practices.
Keywords: animal immunization; antibody discovery; artificial intelligence; display technologies; machine learning; monoclonal antibodies.
Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Conflict of interest statement
Declaration of Interests A.H.L. is a co-founder of Bactolife ApS, which utilizes nanobody technology (immunization of camelids coupled to phage display selection), VenomAid Diagnostics ApS, which utilizes hybridoma technology, and receives academic research funding for other projects involving phage display technology. A.K-V. is a cofounder of Maxion Therapeutics Ltd., which utilizes phage and mammalian display technologies. V.G. declares advisory board positions in aiNET GmbH and Enpicom B.V. S.M. and T.P.J. have no interests to declare.
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