Unlike traditional therapies, the safety of multi-specific antibodies cannot be optimized later via dose adjustments. Critical safety profiles are determined at the initial design stage, and early flaws can prevent a molecule from ever reaching therapeutically effective doses.
Machine learning's application in multi-specific antibody design is hampered by a lack of public data. Companies must invest heavily in generating their own large-scale, proprietary datasets to train effective models, creating a significant barrier to entry and a competitive advantage.
In multi-specific antibody design, small structural modifications—like altering a linker length or binder position—can cause large, unpredictable shifts in potency, selectivity, and safety. This extreme sensitivity makes traditional, intuition-led engineering unreliable and necessitates data-intensive approaches.
A key advantage of LabGenius's AI platform is its unbiased approach, which proposes multi-specific antibody designs that traditional engineers might dismiss as too complex or unmanufacturable. By testing these counter-intuitive candidates, the platform identifies high-performing molecules that would otherwise be overlooked.
For solid tumors, the critical design hurdle for T-cell engagers is achieving selectivity. Most target antigens are also expressed at low levels on healthy cells, so molecules must be engineered to attack tumors with high antigen expression while sparing healthy tissue to avoid on-target, off-tumor toxicity.