Bringing In Silico Immunogenicity Prediction into the 21st Century with augmented intelligence

Many of the in-silico platforms used for immunogenicity risk assessment use algorithms and data which is over two decades old. Abzena have employed the latest technology in machine learning to develop a new in silico immunogenicity intelligence platform “iTope-AI” to help improve lead selection and safety assessment.

  • Reduce false negative / false positive rates using state-of-the-art Machine Learning risk assessment algorithms
  • Predicting peptide binding specificities across 46 major HLA-DR, DP and DQ isotypes
  • Filter out high risk clones or variants from a panel of biologics with higher confidence
  • Rank clones or variants with high sequence homology based on immunogenicity risk

Design deimmunised or humanised proteins with reduced risk of antigen presentation

Anais Manin
Senior Director Bioassays

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