HKeyBio Launches HKEY-AI-NAM-Bridge™ 1.0 to Close Evidence Gaps Between AI, NAMs, and In Vivo CRO Validation

• HKeyBio, a preclinical CRO specializing exclusively in autoimmune and allergic diseases with sites in Boston, Massachusetts and Suzhou, China, has launched HKEY-AI-NAM-Bridge™ 1.0 — an AI and New Approach Methodologies (NAMs) in vivo evidence bridging service designed to help drug development teams identify evidence gaps and design more targeted in vivo validation paths from existing AI predictions, in vitro assays, organ-on-chip data, cell models, omics, and preliminary PK data.

• The service delivers a structured evidence-bridging package including an existing evidence summary, evidence gap assessment, in vivo validation rationale, minimal animal study design, biomarker and PK/PD bridging plan, NHP necessity assessment, IND-enabling relevance review, and next-step recommendations — drawing on HKeyBio’s in vivo efficacy evaluation, NHP models, pathology, immune phenotyping, biomarker, and translational reporting capabilities.

HKeyBio, a preclinical CRO dedicated exclusively to autoimmune and allergic disease research, has launched HKEY-AI-NAM-Bridge™ 1.0, a structured evidence-bridging service that connects AI predictions, New Approach Methodologies, and in vivo validation data to support more targeted preclinical study design. Announced July 12, 2026, from Boston, Massachusetts and Suzhou, China, the service is designed for drug development teams that have accumulated AI prediction, in vitro, cell model, organ-on-chip, omics, or preliminary PK data and need to identify the most critical in vivo validation questions before committing to full preclinical programs.

HKEY-AI-NAM-Bridge™ 1.0 is built around a three-component analytical framework. The first component, evidence gap analysis, maps the specific gaps between a sponsor’s existing data and the disease-relevant in vivo evidence needed for IND progression — identifying common shortfalls such as in vitro activity without disease-model efficacy, AI binding predictions without confirmed target engagement, or mouse efficacy data without cross-species interpretation. The second component identifies the minimal set of critical in vivo questions a study must answer first, prioritizing scientific and regulatory value over study comprehensiveness. The third component, bridging endpoint design, connects pharmacodynamic endpoints, mechanism readouts, translational biomarkers, PK exposure measurements, PK/PD correlations, and basic safety observations into a coherent study architecture.

For autoimmune and allergy CRO sponsors, the service addresses a practical challenge that has grown alongside the proliferation of AI-based drug discovery and NAM technologies. As sponsors accumulate increasingly diverse early-stage datasets — spanning computational predictions, high-throughput in vitro screens, organoids, and multi-omics — the translation of these data into well-designed in vivo validation studies has become a distinct bottleneck. Without structured evidence mapping, sponsors risk either over-investing in broad animal studies that answer questions already addressed by existing data, or under-designing studies that miss the mechanistic or translational endpoints required for IND-enabling packages. HKEY-AI-NAM-Bridge™ 1.0 is positioned as the analytical step that sits between data generation and study execution.

HKeyBio operates with a disease-area focus spanning rheumatoid arthritis, systemic lupus erythematosus, inflammatory bowel disease, asthma, atopic dermatitis, and related autoimmune and allergic indications, supported by rodent disease models, NHP models, pathology services, immunology assays, PK/PD, and biomarker capabilities. The company’s dual-site presence in Boston and the Singapore Industrial Park in Suzhou supports both U.S. and Asia-based biopharma sponsors requiring English-language translational reporting and global regulatory alignment across their preclinical programs.

“A more responsible approach is to use AI, in vitro and NAM data to ask better questions, and then validate the critical evidence in disease-relevant in vivo models.”

Head of Translational Medicine, HKeyBio