refua-preclinical 0.6.0

GLP tox/pharmacology planning, in vivo scheduling, and bioanalytical pipelines for Refua.

refua-preclinical

refua-preclinical adds operational preclinical R&D workflows to Refua:

• GLP tox/pharmacology study planning
• In vivo execution scheduling
• Bioanalytical ETL/QC/group summaries/NCA-like metrics

The package is designed for direct integration into refua-studio and refua-deploy.

What It Provides

• Typed study specs for repeat-dose tox/pharmacology programs.
• GLP readiness scoring/checklist (QA, protocol approval, CSV, chain-of-custody, archival).
• Calendar-ready in vivo schedules for dosing, observations, sampling, and necropsy.
• Bioanalytical pipeline:
  • row-level validation and QC flags
  • BLQ tracking vs LLOQ
  • grouped concentration summaries (mean/SD/CV)
  • AUC-last/Cmax/Tmax by arm/analyte
• CLI + Python API.

Install

cd refua-preclinical
pip install -e .

CLI Quickstart

Write starter config:

refua-preclinical init-config --output examples/default_study.json

Build a study plan:

refua-preclinical plan \
  --config examples/default_study.json \
  --output artifacts/plan.json \
  --markdown artifacts/plan.md

Build the in vivo schedule:

refua-preclinical schedule \
  --config examples/default_study.json \
  --output artifacts/schedule.json

Run bioanalysis from sample rows (JSON/CSV):

refua-preclinical bioanalysis \
  --config examples/default_study.json \
  --samples artifacts/samples.json \
  --lloq 1.0 \
  --output artifacts/bioanalysis.json

Run full workup:

refua-preclinical workup \
  --config examples/default_study.json \
  --samples artifacts/samples.json \
  --output artifacts/workup.json

Python API

from refua_preclinical import (
    build_in_vivo_schedule,
    build_study_plan,
    build_workup,
    default_study_spec,
)

study = default_study_spec()
plan = build_study_plan(study, seed=11)
schedule = build_in_vivo_schedule(study)
workup = build_workup(study)

Research Basis (Current as of March 2026)

The defaults/checks are intentionally aligned with recent primary guidance and standards.

1. FDA (April 10, 2025): plan to phase out animal testing requirements for some programs and increase NAM/model use. https://www.fda.gov/news-events/press-announcements/fda-announces-plan-phase-out-animal-testing-requirement-monoclonal-antibodies-and-other-drugs
2. EMA/ICH M10 (effective in EU from Jan 2023): bioanalytical method validation framework. https://www.ema.europa.eu/en/m10-bioanalytical-method-validation-scientific-guideline
3. FDA Study Data Technical Conformance Guide (December 2025): submission-facing data format expectations. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/study-data-technical-conformance-guide-technical-specifications-document
4. OECD GLP Advisory Document No. 24 (Nov 2024): GLP and IT security. https://www.oecd.org/en/publications/advisory-document-of-the-working-group-on-good-laboratory-practice-on-position-paper-on-good-laboratory-practice-and-it-security_90f42001-en.html
5. ARRIVE resources update (Nov 2024): Essential 10 reporting and study design hygiene. https://arriveguidelines.org/resources/author-and-reviewer-resource-centre
6. EMA/ICH S5(R3) (2023): reproductive/developmental toxicity modernization. https://www.ema.europa.eu/en/ich-s5-r3-guideline-detection-toxicity-reproduction-human-medicinal-products-scientific-guideline
7. NIH statement (July 7, 2025): prioritization of human-based research technologies. https://www.nih.gov/about-nih/who-we-are/nih-director/statements/nih-prioritize-human-based-research-technologies
8. CDISC standards development page (accessed 2026): ongoing SEND evolution workstreams. https://www.cdisc.org/standards/develop

Notes

• This package supports planning/operations and data processing; it does not establish efficacy.
• Regulatory expectations are jurisdiction- and program-dependent.