Non-human primate models are poor predictors of human immunogenicity. The industry has shifted to human-relevant ex vivo assays using whole blood or PBMCs. These tests can assess risks like complement activation upfront, enabling proactive protein engineering to improve a drug's safety profile.

Instead of testing a single drug candidate in cheap models before moving to expensive ones, Gordian's parallel testing platform makes it cost-effective to use clinically relevant large animals, like horses, at the very beginning of the discovery process. This flips the traditional R&D funnel on its head.

Contrary to the popular belief that antibody development is a bespoke craft, modern methods enable a reproducible, systematic engineering process. This allows for predictable creation of antibodies with specific properties, such as matching affinity for human and animal targets, a feat once considered a "flight of fancy."

To overcome on-target, off-tumor toxicity, LabGenius designs antibodies that act like biological computers. These molecules "sample" the density of target receptors on a cell's surface and are engineered to activate and kill only when a specific threshold is met, distinguishing high-expression cancer cells from low-expression healthy cells.

Modern, highly sensitive assays often detect high rates of anti-drug antibodies (ADAs). However, the critical question for drug developers isn't the ADA incidence rate itself, but whether that immune response actually impacts drug exposure, efficacy, or overall patient outcome.

Unlike using genetically identical mice, Gordian tests therapies in large, genetically varied animals. This variation mimics human patient diversity, helping identify drugs that are effective across different biological profiles and addressing patient heterogeneity, a primary cause of clinical trial failure.

The NIH will no longer award funding to new grant proposals that rely exclusively on animal models. This policy forces a shift towards New Approach Methodologies (NAMs), such as organoids and organ-on-chips, serving as a major catalyst for innovation and adoption in the preclinical testing space.

The bottleneck for AI in drug development isn't the sophistication of the models but the absence of large-scale, high-quality biological data sets. Without comprehensive data on how drugs interact within complex human systems, even the best AI models cannot make accurate predictions.

The company intentionally makes its early research "harder in the short term" by using complex, long-term animal models. This counterintuitive strategy is designed to generate highly predictive data early, thereby reducing the massive financial risk and high failure rate of the later-stage clinical trials.