Instead of waiting for allergy patients to have symptoms on study days, Dr. Abelson’s team created a model to induce the allergic reaction in a controlled way. This 'Conjunctival Allergy Challenge' allowed for precise, predictable testing of new drugs, dramatically speeding up development.

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.

The push away from animal models is a technical necessity, not just an ethical one. Advanced therapeutics like T-cell engagers and multispecific antibodies depend on human-specific biological pathways. These mechanisms are not accurately reproduced in animal models, rendering them ineffective for testing these new drug classes.

Only 5% of investigational cancer drugs reach the market due to the gap between lab models and human biology. Dr. Saav Solanki highlights organoids, which use real patient tissue, as a key translational model to improve the predictive accuracy of preclinical research and increase the low success rate.

The process of testing drugs in humans—clinical development—is a massive, under-studied bottleneck, accounting for 70% of drug development costs. Despite its importance, there is surprisingly little public knowledge, academic research, or even basic documentation on how to improve this crucial stage.

Drug development can take a decade, a timeframe that misaligns with typical investor horizons and employee careers. Success requires navigating fluctuating capital market cycles and implementing strategies to retain key scientific talent for the long haul.

With clinical development cycles lasting 7-10 years, junior team members rarely see a project to completion. Their career incentive becomes pushing a drug to the next stage to demonstrate progress, rather than ensuring its ultimate success. This pathology leads to deferred problems and siloed knowledge.

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.

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.