Safety Data Sheet for 103497, 103498, 103499, 103500, 103501, 103502, 103503, 103504, 103700, 103701, 103702, 103703, 103704, 103705, 103706, 103707

This webinar will detail how scientists at Antiverse used an AI-based approach to generate diverse and functional antibodies against hard-to-drug targets, including G-protein coupled receptors, with increased diversity, specificity, and affinity. 

Antibody Therapeutic Development with An Integrated Platform Combines Immunized Libraries, Synthetic Libraries, AI/ML Guided Leads Optimization, and HT IgG Production at Twist Bioscience.  Antibody drugs are a promising and rapidly growing component of the therapeutic landscape. The success of antibody therapeutics is largely driven by its specificity and selectivity in contrast to the off-targets liabilities of small molecule drugs. To advance antibody drug discovery, Twist integrated multiple techniques and platforms including synthetic and immunized libraries, artificial intelligence and machine learning (AI/ML), and High Throughput IgG Production (HT IgG) to discover, optimize, produce, and characterize antibody drug candidates against a wide range of inflammatory and immune-oncology targets to assist in generating leads for downstream pre-clinical and clinical campaigns. Wnt signaling pathway has been known to play important regulatory roles in tumorigenesis. Dickkopf-1 (DKK1) is an endogenous protein known to over-express in tumor microenvironments and can block Wnt/β-catenin signaling by interrupting Wnt-FZD-LRP interactions. Previous studies have shown the anti-tumor effect of anti-DKK1 antibodies in multiple different cancers including esophagogastric cancer, lung cancer, prostate cancer, etc. Here, we would like to show how Twist uses our integrated platform to identify functional anti-DKK1 antibody drug candidates through screening of hundreds of hits enriched from phage display and AI/ML affinity maturation. This platform can also be easily expanded to any other “difficult to target” drug targets.