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.

Gordian Biotechnology embeds unique genetic "barcodes" into hundreds of different gene therapies. This transforms gene therapy from a treatment modality into a high-throughput screening tool, allowing them to test many potential drugs simultaneously inside a single living animal and trace which ones worked.

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.

By first targeting T-cell lymphoma, Corvus gathers crucial safety and biologic effect data in humans. This knowledge about the drug's impact on T-cells directly informs and de-risks subsequent trials in autoimmune diseases like atopic dermatitis, creating a capital-efficient development path.

While complex gene editing may be challenging in vivo, Colonia's platform presents a novel opportunity: targeting different immune cell types (e.g., T-cells and NK cells) with distinct payloads in a single treatment. This could create synergistic, multi-pronged attacks on tumors, a paradigm distinct from current ex vivo methods which focus on engineering a single cell type.

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.

Step Pharma's confidence in their drug's clean safety profile originated from studying a human population with a natural mutation in the CTPS1 gene. This real-world genetic data de-risked their therapeutic approach from the outset, guiding development towards a highly selective and safe inhibitor.