The shift we are enabling
- FAIR metadata as infrastructure, not an afterthought
- Evidence that travels across CROs, sponsors, and reviewers
- Fewer repeats through reuse-ready studies
Vision & Impact
A narrative on why research-grade FAIR metadata aligned with FDA, EMA, and ICH S11 matters for preclinical science.
Why it matters now
- Compliance expectations are rising (SEND, IND, IMPD)
- AI and analytics need structured, comparable metadata
- 3Rs pressure demands reuse, not repetition
Vision
Metadatapp is building the FAIR metadata infrastructure that turns preclinical studies into reusable, audit-ready evidence — accelerating drug development while reducing animal use.
This is not a data warehouse. It is an interoperability layer that preserves context, provenance, and standards alignment so evidence can move across teams, CROs, pharma, and reviewers without being reworked or repeated.
Mission
Make preclinical metadata share-ready by default, with standards-first structure and traceability that supports SEND, IND, and IMPD workflows and aligns with FDA, EMA, and ICH S11 expectations.
We connect the systems researchers already use (ELN, LIMS, animal management, analytics) and keep the metadata coherent, validated, and auditable from study design to submission.
Impact
Standards interoperability: Evidence packages can be assembled faster and with fewer translation steps because metadata are structured and aligned to standards expectations.
Scientific reuse: Studies become reusable assets, enabling cross-cohort comparison, longitudinal insight, and AI-ready analytics without re-running experiments.
Systemic reduction of animal use: Better reuse and traceable evidence reduce duplicate studies as a consequence of higher-quality metadata, not as a standalone tactic.