We utilized our AI-driven workflow to enhance 3 antibodies with known developability risks. All 3 resulted in variants with substantial improvement. Fewer than 15 variants of each were expressed and tested to achieve the above results.
Case 1: Aggregation reduction of Bevacizumab (Formulated in 50 mM PB buffer, incubated at 52 °C)
Case 2: Improvement of both Tm & Tagg for Eldelumab
Case 3: Viscosity reduction for a highly-viscous antibody
IL-15 is an immunostimulatory cytokine that has shown therapeutic potential in immuno-oncology. IL-15 (often used in complex with its proprietary receptor IL-15Rα, aka receptor-linker-IL-15 or RLI) is prone to aggregation, which poses a major hindrance for therapeutic development.
We enhanced the developability of WT RLI to obtain two candidates with potential as best-in-class IL-15 super agonists (XtalPi RLIs). They achieved similar levels of binding and potency vs. WT RLI & a clinical benchmark (N-803), but exhibited significantly optimized purity, yield and stability.