Discovery, Characterization, and Generative Deep Learning-Based Humanization of Therapeutic Antibody Candidates
In Part I of this webinar, we will describe discovery of therapeutic, diagnostic, and tool antibody candidates against the Receptor for Advanced Glycation End products (RAGE) in mouse, rat, and rabbit hosts using the Beacon® Optofluidic instrument. Additionally, we will show how the Carterra LSA with HT-SPR technology was used to measure binding kinetics of the anti-RAGE antibodies – including multiple rabbit antibodies with sub 10 pM affinity – and to run epitope binning on candidates from each of the three host species in a single experiment. Finally, we will conclude part I by illustrating use of ENPICOM’s software platform for biologics discovery to select lead candidates by combining sequence diversity, wet-lab data, and in-silico generated developability profiles of all antibodies.
In Part II of this webinar, we will introduce a next generation, multi-species antibody humanization service powered by ENPICOM’s novel AIGX generative deep learning model. Through case study data on anti-RAGE antibodies, we will show that GenovacAI generates humanized antibody sequences at a much greater success rate – measured by percent germline identity and retention of binding affinity – in comparison to traditional tools, including CDR grafting and publicly available machine learning algorithms. The measurably improved functionality of GenovacAI effectively enables humanization of large numbers of candidate antibodies immediately post-discovery, resulting in reduced development costs and shortened timeline to IND filing.