refua-regulatory is the Refua regulatory workflow and audit package.
It helps teams follow drug regulation processes by turning campaign decisions and execution outputs into verifiable evidence bundles with end-to-end lineage across plans, tool calls, models, datasets, and generated artifacts.
- Campaign decision extraction from campaign run outputs.
- Structured decision records (
decisions.jsonl) with deterministic decision IDs. - Model and data provenance capture per campaign decision.
- Lineage graph materialization (
lineage.json) for traceability. - Evidence bundle packaging with checksums (
checksums.sha256). - Integrity verification for audit handoff and compliance workflows.
- Regulatory checklist evaluation with strict and manual-review gates.
- Automatic comprehensive checklist generation during bundle build.
- Structured outputs that support internal regulatory readiness reviews before agency submission.
cd refua-regulatory
pip install -e .refua-regulatory --helprefua-regulatory build \
--campaign-run artifacts/kras_campaign_run.json \
--output-dir artifacts/evidence/kras_run_001 \
--data-manifest ~/.cache/refua-data/_meta/parquet/chembl_activity_ki_human/latest/manifest.json \
--extra-artifact artifacts/candidate_run.jsonOutput bundle layout:
evidence/
manifest.json
decisions.jsonl
lineage.json
checksums.sha256
artifacts/
campaign_run.json
data_manifests/
extras/
checklists/
drug_discovery_comprehensive.json
drug_discovery_comprehensive.md
By default, build auto-generates the drug_discovery_comprehensive checklist.
Build-time checklist controls:
refua-regulatory build \
--campaign-run artifacts/kras_campaign_run.json \
--output-dir artifacts/evidence/kras_run_001 \
--checklist-template core \
--checklist-template fda_cder_ai_ml \
--checklist-strictDisable checklist generation:
refua-regulatory build \
--campaign-run artifacts/kras_campaign_run.json \
--output-dir artifacts/evidence/kras_run_001 \
--no-checklistrefua-regulatory verify --bundle-dir artifacts/evidence/kras_run_001JSON mode:
refua-regulatory verify --bundle-dir artifacts/evidence/kras_run_001 --jsonrefua-regulatory summary --bundle-dir artifacts/evidence/kras_run_001Core automated checklist:
refua-regulatory checklist \
--bundle-dir artifacts/evidence/kras_run_001 \
--template drug_discovery_comprehensive \
--strictFDA/CDER-oriented template (includes manual-review items):
refua-regulatory checklist \
--bundle-dir artifacts/evidence/kras_run_001 \
--template fda_cder_ai_ml \
--output-json artifacts/evidence/kras_run_001/checklist.json \
--output-markdown artifacts/evidence/kras_run_001/checklist.mdfrom pathlib import Path
from refua_regulatory import build_evidence_bundle, verify_evidence_bundle
manifest = build_evidence_bundle(
campaign_run_path=Path("artifacts/kras_campaign_run.json"),
output_dir=Path("artifacts/evidence/kras_run_001"),
)
verification = verify_evidence_bundle(Path("artifacts/evidence/kras_run_001"))
print(manifest["bundle_id"], verification.ok)- Data provenance is loaded from explicit manifest paths (
--data-manifest). refua-dataparquet manifests are parsed directly for dataset IDs, versions, source URLs, and SHA256 checksums.- Model provenance is inferred from executed tool outputs and optional CLI overrides (
--model-name,--model-version).
cd refua-regulatory
python -m pytest -q- This package records reproducibility evidence; it does not claim biological efficacy.
- Regulatory submission requirements vary by jurisdiction and program type.