Abstract: The EXIS™ platform at Prellis Biologics accelerates antibody discovery by integrating natural immune processes with advanced AI to navigate the complexity of the human immune repertoire. Using two-photon laser bioprinting, we generate 3D organoids that recapitulate the complexity of immune responses in vitro. Antibodies identified through this platform are characterized using Carterra LSA^XT, supporting a portfolio of fully human therapeutic candidates across multiple disease target areas. Discovery targets span diverse structural formats, including peptides, monomers, multimers, nanodiscs, and virus-like particles. We present optimized screening and analysis strategies that enable efficient characterization and pilot lead selection across these multiple formats.

Abstract: Surface plasmon resonance (SPR) has long been the gold standard for characterizing molecular interactions in drug discovery, but throughput limitations have historically constrained its use in early-stage screening campaigns where large numbers of compounds or biologics candidates require rapid triage. With high sensitivity, high throughput biosensors, Carterra’s biosensors enable SPR screening and characterization at a new scale. The Ultra is the newest in the line of one on many biosensors, building on the one-on-many array-based architecture of Carterra’s LSA platforms which has been broadly adopted in biologics discovery, now with the sensitivity and sample handling features to enable small molecule and fragment assays. Carterra Vega™, Carterra’s newest platform, moves from the one-on-many format to a new architecture with 48 needles and 48 parallel flow cells each with two ligands and a reference. Paired with an optional plate handling robot, Vega can process up to 20,000 analyte compounds in a day against two targets. A 384-well-plate of analytes can be injected in as little as 35 minutes. This enables new approaches like dose responses in primary fragment screens and kinetic characterization of thousands of compounds a day. This talk will highlight the range and scale of assays now possible on these platforms.

Abstract: Generative artificial intelligence has advanced antibody discovery, yet de novo design of therapeutic antibodies against targets with “zero-prior” epitopes remains a fundamental challenge. We define “zero-prior” epitopes as target sites lacking structural data from any reported antibody-antigen or protein-protein complex involving the target. Here we present Origin-1, a generative AI platform that overcomes this by integrating epitope-conditioned all-atom structure generation, paired complementarity determining region sequence design, and a specialized co-folding-based scoring protocol to select antibody designs predicted to be high-confidence, specific binders with favorable developability. We evaluated Origin-1 on a panel of ten targets selected to have no available protein–protein complex structures and minimal homology (≤60% sequence identity) to proteins with known complexes, creating stringent design conditions. In fewer than one hundred design attempts per target, we identified developable, specific antibodies, validated across multiple biophysical and developability assays, for four targets: COL6A3, AZGP1, CHI3L2, and IL36RA, with functional inhibition demonstrated for IL36RA. Cryogenic electron microscopy confirmed the atomic accuracy of our designs, revealing complexes that closely matched the computational models with high structural fidelity (3.0-3.1 Å resolution; 0.73-0.83 DockQ). Furthermore, we employed AI-guided affinity maturation to optimize a de novo antibody against IL36RA into a functional antagonist with 104 nM potency. This design was further improved to 590 pM affinity through a second round of computational affinity maturation. These results demonstrate a framework for targeting epitopes without structural precedent, expanding the programmable therapeutic antibody landscape.