LabGenius Uses Machine Learning to ‘Transform’ Cancer Care

This presentation premiered data from LabGenius’ T Cell Engager (TCE) lead optimization platform that delivers highly potent and effective single domain antibodies with best-in-class killing selectivity.

Optimizing antibody therapeutics across multiple properties is particularly challenging for TCE. As a result, many existing immunotherapies have poor selective cell-killing profiles, resulting in on-target, off-tumor toxicity that can lead to treatment discontinuation.

On-target, off-tumor effects occur due to the presence of target antigens across multiple tissue types. This means that healthy tissue is also targeted.

LabGenius’ ML-driven platform can be used to simultaneously optimize potency, efficacy, tumor cell selectivity and developmental potential. In a proof-of-concept study, LabGenius’ platform was used to co-optimize VHH-based HER2xCD3 TCEs, resulting in the delivery of novel, highly selective, high-performance molecules with non-intuitive design capabilities.

The best-performing molecule exhibits greater than 10,000-fold killing selectivity, representing a greater than 400-fold improvement over the relevant clinical benchmark, lunimotamab (TCE currently in Phase 1 clinical trials).

LabGenius leverages this target- and format-agnostic platform technology across both its affiliated programs and its wholly-owned TCE pipeline.

Transforming cancer care ​

“Antibody-based immunotherapies, including TCE, have the potential to change the way we approach cancer care,” said Dr. Gino Van Heeke, Chief Scientific Officer of LabGenius.