Computational Antibody Discovery Symposium: Introduction, Pietro Sormanni (University of Cambridge).

Computational Antibody Discovery: State of the Art, June 22, 2023,
Computational Antibody Discovery Symposium: Introduction - Pietro Sormanni

Antibodies play a crucial role as reagents in research and diagnostics, and are a key class of therapeutics. However, current technologies for antibody discovery and optimization are still subject to limitations. Established screening procedures are laborious, and targeting predetermined epitopes and optimizing multiple biophysical traits simultaneously remains a challenge. In this presentation, we will discuss emerging computational antibody design methods, which enable the targeted design of antibodies for pre-selected epitopes and the prediction and modulation of their developability potential through optimization of multiple biophysical properties. Overall, it is increasingly possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with in silico (third generation) approaches, with potential time and cost-saving benefits. These approaches are becoming sufficiently mature to be highly competitive for some applications, thus offering novel opportunities to streamline antibody development.

Pietro Sormanni is a University Research Fellow supported by the Royal Society and leads a research group at the University of Cambridge that sits at the interface between computation and in vitro experiments. His research is primarily focused on the development of innovative technologies for computational antibody design, aimed at transforming the ways antibodies are currently discovered and optimised. Through numerous collaborations and industry partnerships, Pietro’s work has demonstrated the potential for computational approaches to complement established procedures and streamline antibody development, offering novel, time- and cost-effective alternatives. Prior to his current position, Pietro was a Borysiewicz Biomedical Sciences Fellow at the University of Cambridge, and holds a PhD in Chemistry and an MSc in Theoretical Physics.

Agenda
Introduction - Janice Reichert (The Antibody Society); Konrad Krawczyk (Natural Antibody); Andrew Buchanan (AstraZeneca)
Speaker 1) Pietro Sormanni (University of Cambridge). Third-generation approaches of antibody discovery and optimization
2) Tzvika Hartman (Biolojic Design). AI-driven design of smart therapeutics
3) Victor Greiff (University of Oslo). Computational developability profiling of antibody repertoire data
4) Sandeep Kumar (Boehringer Ingelheim). Biopharmaceutical Informatics: Syncretic use of computation and experimentation in discovery and development of biotherapeutics
5) Ben Holland (Antiverse). Machine learning-based design of antibodies against difficult targets

Panel discussion: Panelists provided their opinions and insights on these questions:
1. What properties are more urgent to be able to design in silico - binding specificity, developability or something else?
2. What performance should computational antibody design achieve to improve upon established protocols?
3. What are the biggest hurdles for computational antibody discovery to achieve its full potential (models, data or something else)?
4. How could industry and academia complement each other to solve the problem of computationally designing antibodies?
5. What role does big tech/biopharma have to play in development and adoption of computational antibody design paradigms?

Concluding remarks (Andrew Buchanan, AstraZeneca)