Vision & Impact
A narrative on why regulatory-grade FAIR metadata aligned with FDA, EMA, and ICH S11 matters for preclinical science.
Vision
Metadatapp is building the FAIR metadata infrastructure that turns preclinical studies into reusable, regulator-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 regulatory alignment so evidence can move across teams, CROs, pharma, and regulators without being reworked or repeated.
Mission
Make preclinical metadata regulator-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
Regulatory interoperability: Evidence packages can be assembled faster and with fewer translation steps because metadata are structured and aligned to regulatory 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 regulator-ready evidence reduce duplicate studies as a consequence of higher-quality metadata, not as a standalone tactic.