refua-clinical 0.7.1

PK/PD-driven virtual patient and clinical trial simulation toolkit for Refua.

refua-clinical

refua-clinical is a simulation package for PK/PD-driven clinical trial design in the Refua ecosystem. It generates copula-based virtual patients, runs adaptive multi-arm trial simulations, and produces a tailored protocol that can be re-simulated with modified inputs.

What it provides

• Population PK/PD simulation with inter-individual variability and covariate effects.
• Virtual patient generation using Gaussian copulas and configurable marginals.
• Estimand-aware analysis (treatment_policy, hypothetical, composite, while_on_treatment) for intercurrent-event handling.
• Clinical trial simulation with multi-arm randomization, Bayesian response-adaptive allocation, early stopping (success/futility), enrollment drift, and optional site/country heterogeneity.
• Dynamic external-control borrowing with commensurability-weighted discounting.
• Protocol recommendation engine that scores candidate designs (sample size + interim cadence) on simulated operating characteristics and expected cost.
• Multi-objective design optimization with Pareto front outputs.
• Value-of-information (VOI) analysis for expansion decisions.
• Transportability diagnostics (covariate shift, overlap score, risk level) for target-population extrapolation.
• Explainability/advice engine that outputs a narrative, interim decision-card summaries, and prioritized actionable recommendations.
• Re-run workflow from prior run artifacts with YAML/JSON or inline overrides.
• ADMET-aware simulation path with optional parameter adjustments from Refua ADMET profiles.
• Biologics mode with IV/SC route support, interval dosing, and optional TMDD-like clearance scaling.
• One-shot workup CLI to generate all major artifacts in one run.
• Optional integrations:
  • refua-data for covariate inference from materialized datasets.
  • refua-regulatory for audit/evidence bundles.

Install

cd refua-clinical
pip install -e .

With integrations:

pip install -e .[integrations]

With Refua ADMET support from SMILES:

pip install -e .[admet]

Check installed CLI version:

refua-clinical --version

CLI quickstart

Create a starter config:

refua-clinical init-config --output examples/default_config.yaml

Run simulation:

refua-clinical simulate \
  --config examples/default_config.yaml \
  --output artifacts/run.json

Run with ADMET-informed adjustments:

refua-clinical simulate \
  --config examples/default_config.yaml \
  --admet-json artifacts/admet_profile.json \
  --admet-adjustments \
  --output artifacts/run_admet.json

Run with richer Refua payload integration (affinity + confidence + properties + ADMET):

refua-clinical simulate \
  --config examples/default_config.yaml \
  --refua-json examples/refua_integration_payload.json \
  --refua-apply \
  --output artifacts/run_refua.json

Enforce canonical Refua handoff keys (reject legacy aliases/fallback-only payloads):

refua-clinical simulate \
  --config examples/default_config.yaml \
  --refua-json examples/refua_integration_payload.json \
  --refua-apply \
  --refua-strict-contract \
  --output artifacts/run_refua_strict.json

Run using a shared biologics preset from CLI:

refua-clinical simulate \
  --config examples/default_config.yaml \
  --modality-preset biologic-sc \
  --preset-dosing-interval-hours 336 \
  --preset-tmdd-strength 0.35 \
  --output artifacts/run_biologic.json

Generate an adjusted config from Refua payload without running simulation:

refua-clinical integrate-refua \
  --config examples/default_config.yaml \
  --refua-json examples/refua_integration_payload.json \
  --output-config artifacts/config_refua.yaml \
  --output-summary artifacts/refua_integration_summary.json

Generate tailored protocol:

refua-clinical protocol \
  --run artifacts/run.json \
  --output artifacts/protocol.json \
  --markdown artifacts/protocol.md

Run multi-objective optimization:

refua-clinical optimize \
  --run artifacts/run.json \
  --output artifacts/optimization.json \
  --markdown artifacts/optimization.md

Run value-of-information scenarios:

refua-clinical voi \
  --run artifacts/run.json \
  --extra-n 0 30 60 90 \
  --output artifacts/voi.json \
  --markdown artifacts/voi.md

Assess transportability between reference and target populations:

refua-clinical transportability \
  --reference data/reference_population.csv \
  --target data/target_population.csv \
  --output artifacts/transportability.json \
  --markdown artifacts/transportability.md

Generate explainable narrative and actionable advice:

refua-clinical advise \
  --run artifacts/run.json \
  --protocol artifacts/protocol.json \
  --include-sensitivity \
  --output-json artifacts/advice.json \
  --output-markdown artifacts/advice.md

ADMET-aware advice from SMILES (requires .[admet]):

refua-clinical advise \
  --run artifacts/run.json \
  --admet-smiles "CCO" \
  --output-json artifacts/advice_with_admet.json

Run everything all at once (simulate + protocol + optimize + VOI + advice):

refua-clinical workup \
  --config examples/default_config.yaml \
  --output-dir artifacts/full_workup \
  --include-sensitivity

Re-run with overrides:

refua-clinical rerun \
  --run artifacts/run.json \
  --set enrollment.total_n=240 \
  --set adaptive.interim_every=20 \
  --output artifacts/run_rerun.json

Infer virtual-patient covariates from refua-data:

refua-clinical from-data \
  --dataset-id chembl_activity_ki_human \
  --output examples/from_data.yaml

Create an evidence bundle with refua-regulatory:

refua-clinical evidence \
  --run artifacts/run.json \
  --output-dir artifacts/evidence/clinical_run_001

Python API (Object-Oriented)

The primary API is the fluent object model centered on ClinicalStudy and ClinicalRun.

from refua_clinical import ClinicalStudy

study = (
    ClinicalStudy.default()
    .trial(
        trial_id="oncology-refua-oo-demo",
        indication="Oncology",
        phase="Phase II",
        replicates=96,
    )
    .set("enrollment.total_n", 220)
)

run = study.simulate()
protocol = run.recommend_protocol(replicates_per_candidate=30)
print(run.summary["power"], protocol.protocol["protocol_id"])

Biologics preset example:

from refua_clinical import ClinicalStudy

study = (
    ClinicalStudy.default()
    .trial(trial_id="mab-phase2", indication="Immunology", phase="Phase II", replicates=80)
    .modality_preset(
        preset="biologic-sc",
        dosing_interval_hours=336.0,
        tmdd_strength=0.35,
    )  # q14d
)
run = study.simulate()
print(run.summary["power"], run.config.pk_model.modality, run.config.pk_model.route)

Refua payload-to-clinical mapping API:

from refua_clinical import (
    ClinicalStudy,
)

payload = {
    "ligands": [
        {
            "ligand_id": "lead_a",
            "affinity": {"ic50": 42.0, "binding_probability": 0.79},
            "structure": {"confidence_score": 0.77},
            "admet": {"admet_score": 0.68, "adme_score": 0.70, "safety_score": 0.64},
            "rdkit": {"mol_wt": 340.0, "mol_log_p": 2.1, "qed": 0.73},
        }
    ],
    "target_properties": {"length": 850.0, "instability_index": 45.0},
}

study = (
    ClinicalStudy.default()
    .trial(trial_id="refua-bridge-demo", replicates=80)
    .refua_payload(
        payload,
        apply=True,
        max_candidate_arms=3,
        strict_contract=True,
    )
)
run = study.simulate()
workup = run.workup(
    replicates_per_candidate=30,
    voi_extra_n=[0, 30, 60],
    include_sensitivity=True,
)
print(run.summary["power"], workup.advice.report["summary"]["safety_event_rate"])

End-to-end notebook (Refua + refua-clinical)

Use examples/refua_api_to_clinical_e2e.ipynb for a full workflow that starts with a small-molecule SMILES and protein target in refua, computes molecule/target properties, and feeds those into refua-clinical simulation and protocol recommendation outputs.

From a monorepo checkout:

cd refua
pip install -e .[admet]
cd ../refua-clinical
pip install -e .[admet]
jupyter notebook examples/refua_api_to_clinical_e2e.ipynb

Minimal combined API sketch:

from refua import Protein, SM
from refua_clinical import (
    ClinicalStudy,
)

sm = SM("Cn1cnc2n(C)c(=O)n(C)c(=O)c12")  # small molecule
target = Protein("MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQQLAGKYTPEEIRNVLSTLQKAD", ids="A")

study = (
    ClinicalStudy.default()
    .trial(
        trial_id="refua-object-api-demo",
        indication="Target-program demo",
        replicates=72,
    )
    .admet_profile(sm.admet_profile(include_scoring=True), apply=True)
)

run = study.simulate()
protocol = run.recommend_protocol(replicates_per_candidate=30)
print(target.length(), run.summary["power"], protocol.protocol["protocol_id"])

Research-informed design choices

This package maps current literature and regulatory guidance into practical simulation defaults:

1. ICH M15 (Step 5, adopted November 24, 2025): https://www.ema.europa.eu/en/ich-m15-general-principles-model-informed-drug-development-step-5
2. ICH E9 (R1) estimands addendum: https://www.ema.europa.eu/en/ich-e9-statistical-principles-clinical-trials-scientific-guideline
3. FDA adaptive design guidance (2019): https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adaptive-design-clinical-trials-drugs-and-biologics-guidance-industry
4. FDA Bayesian methods guidance page (updated January 2026): https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-bayesian-methodology-clinical-trials-drug-and-biological-products
5. Copula virtual patients (CPT Pharmacometrics Syst Pharmacol, 2024): https://pubmed.ncbi.nlm.nih.gov/38853786/
6. Copula adult populations (J Pharmacokinet Pharmacodyn, 2024): https://pubmed.ncbi.nlm.nih.gov/38661766/
7. Dynamic borrowing framework (BMC Med Res Methodol, 2025): https://link.springer.com/article/10.1186/s12874-025-02691-2
8. Non-concurrent control time-trend methods (Biometrical Journal, 2025): https://pmc.ncbi.nlm.nih.gov/articles/PMC12458466/
9. Backfilling in adaptive platform trials (BMJ, 2024): https://pmc.ncbi.nlm.nih.gov/articles/PMC11698248/
10. Trial-to-target transportability with synthetic populations (BMJ Open, 2025): https://pmc.ncbi.nlm.nih.gov/articles/PMC12306361/
11. High-throughput Bayesian simulation tooling (BATSS, 2024): https://arxiv.org/abs/2410.02050

Test

cd refua-clinical
python -m pytest -q

Notes

• Intended for research design support and internal decision analysis.
• Not a substitute for clinical, biostatistical, ethics, or regulatory review.

Project docs

• Changelog: CHANGELOG.md
• Contributing guide: CONTRIBUTING.md
• Security policy: SECURITY.md