Engineering multispecific antibodies with complete killing selectivity through the closed-loop integration of machine learning and high-throughput experimentation

On-target, off-tumor toxicities remain a major barrier for T-cell engagers in solid tumors. We present EVA^TM, a closed-loop design platform integrating high-throughput functional assays with multi-objective Bayesian optimization to explore combinatorial T-cell engager (TCE) spaces. In a HER2×CD3 case study, iterative design-build-test-learn cycles traversed 44,160 designs defined by valency, topology, affinity and spacing. Compared with a Sobol baseline, EVA achieved 14-fold enrichment of potent, tumor-selective candidates. Multiple architectures reached sub-10 pM potency on HER2-high cells, near-complete efficacy, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>≥</mml:mo></mml:math>10,000-fold selectivity over HER2-low models, consistent with avidity gating. EVA™ recovered diverse high-performing topologies and generalized to a second target, supporting density-gated avidity as a design principle and providing an operational template for rapid, data-efficient optimization.