Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery

For years, a discussion has persevered on the benefits and drawbacks of antibody discovery using animal immunization versus in vitro selection from nonanimal-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.

Automated summary

The debate on the benefits and drawbacks of antibody discovery through animal immunization versus in vitro selection from nonanimal-derived recombinant repertoires has persisted for years. While some argue that using recombinant display libraries can reduce animal consumption, the number of animals sacrificed during preclinical studies far exceeds those used in immunization campaigns. Therefore, improving quality control before in vivo studies will have a larger impact on animal consumption. Both animal immunization and recombinant repertoires offer unique advantages for discovering fit-for-purpose antibodies. Additionally, machine learning is expected to play a significant role in refining current antibody discovery practices.