Abstract

Abstract

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 several hard-to-target antigens exist. Similarly, targeting predetermined epitopes and optimizing multiple biophysical traits simultaneously remains a challenge. I will discuss emerging technologies of computational antibody design, which enable the targeted design of antibodies for predetermined epitopes and the prediction and modulation of their developability potential, through the optimisation 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, which provide time and cost-saving benefits and increased precision. These approaches are becoming sufficiently mature to be highly competitive for some applications, thus offering novel opportunities to streamline antibody development.

Biography

Pietro Sormanni is a University Research Fellow supported by the Royal Society. He leads a research group at the University of Cambridge that sits at the interface between computation and in vitro experiments. Their research is primarily focused on the development and validation of innovative technologies for computational antibody design, aimed at transforming the ways antibodies are currently discovered and optimised. Through numerous collaborations and industrial partnerships, their 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 held a postdoctoral Borysiewicz Biomedical Sciences Fellowship from the University of Cambridge, obtained a PhD in Chemistry, and an MSc in Theoretical Physics.

Zoom Link: https://zoom.us/j/97037774493?pwd=Rlh3YXA5VHk5cEdGdlIwUzJWdzNhdz09

Meeting ID: 970 3777 4493

Passcode: kYZ4LEU6JU