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."

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 FDA is eliminating mandatory animal testing because it's often misleading—90% of drugs passing animal studies fail in humans. The agency is embracing modern alternatives like computational modeling and organ-on-a-chip technology to get faster, more accurate safety data.

A significant, often overlooked, hurdle in drug development is that therapeutic antibodies bind differently to animal targets than human ones. This discrepancy can force excessively high doses in animal studies, leading to toxicity issues and causing promising drugs to fail before ever reaching human trials.

Dr. Radvanyi explains that immune agonist drugs often fail because accelerating a biological pathway is inherently less controllable than inhibiting one. This is analogous to genetic knockouts being more straightforward than over-expression models, presenting a core challenge in drug development beyond just finding the right target.