Developing effective antibody therapeutics requires balancing innovation with efficiency—scientists must rapidly assess developability while minimizing bottlenecks in data access and analysis. Too often, researchers rely on fragmented tools or need to repeatedly request analyses from bioinformatics teams, leading to delays and inefficiencies. SignalsOne streamlines this process, enabling scientists to predict antibody liability sites and optimize candidates in real time—without waiting for pipeline results. This webinar will showcase Design-Make-Test- Decide as a framework for ADC discovery while emphasizing predictive antibody developability assessments, allowing you to do more with less.

In silico immunogenicity risk assessment is a key step in the development path for biologic therapeutics. Computational tools have been used to identify T cell epitopes from primary amino acid sequences and assess the immunogenic potential of therapeutic candidates for several decades. In silico modeling during discovery and preclinical development is recommended as T cell epitopes contained in biologic sequences may activate the immune system, enabling the development of anti-drug antibodies that can reduce drug efficacy and/or induce adverse events. 

This webinar will review an integrated web-based platform called ISPRI (Interactive Screening and Protein Reengineering Interface) which contains a multitude of tools for assessing immunogenic risk of biotherapeutics, such as identification of promiscuous T cell epitopes, and prediction of anti-drug antibody (ADA) responses. Novel artificial intelligence and machine learning (AI/ML) techniques have now been integrated into ISPRI, leading to improved performance. This presentation will focus on ISPRI’s new AI-based models which have led to a 6-fold increase in the correlation between predicted and observed rates of ADAs, while significantly reducing the rate of false negative (low predicted immunogenicity / high observed immunogenicity) by 85%.