Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery
Andreas H. Laustsen, Victor Greiff, Aneesh Karatt-Vellatt, Serge Muyldermans, Timothy P. Jenkins
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.