The service addresses a persistent challenge in pharmaceutical research: the disconnect between high-throughput AI predictions, omics data, and organ-on-chip results versus the realities of disease-relevant animal models. Rather than positioning technology as a total replacement for in vivo research, HKeyBio utilizes these tools to pinpoint specific evidence gaps. This allows developers to move beyond generic screening and toward highly targeted validation paths.
HKeyBio Launches AI-NAM Bridge to Refine Preclinical Drug Testing
Boston and Suzhou-based firm HKeyBio has introduced HKEY-AI-NAM-Bridge™ 1.0, a service designed to bridge the chasm between computational AI predictions and traditional animal testing. By integrating new approach methodologies with in vivo validation, the platform aims to streamline drug development for autoimmune and allergic disease programs.
Technicians use the bridge to identify critical failures, such as discrepancies between binding affinity and functional downstream activity, or AI models that lack cross-species translational relevance. The process prioritizes a minimal set of experimental questions, focusing on essential pharmacodynamic endpoints, biomarkers, and pharmacokinetic correlations. According to the company's Head of Translational Medicine, the objective is to leverage AI and NAMs to ask better questions, ensuring that subsequent animal studies are both more interpretable and ethically justified.
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