openpkflow 0.2.0

Python-first toolkit for dissolution, NCA, PK/PD simulation, and pharmacometric reporting.

OpenPKFlow

A transparent, reproducible, open-source Python workflow for dissolution, NCA, PK/PD simulation, and pharmacometric reporting.

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What it does

OpenPKFlow gives formulation scientists, PK/PD researchers, and CRO/CDMO teams a clean Python workflow for:

• Dissolution similarity: f1, f2, bootstrap f2, model fitting
• NCA: AUC, Cmax, Tmax, half-life, CL/F, Vz/F · planned v0.4.0
• PK simulation: 1- and 2-compartment models, oral/IV/infusion · planned v0.5.0
• Report generation: Markdown, HTML, PDF, Word · planned v0.3.0

It does not replace expert regulatory judgement or validated commercial platforms. It makes routine analysis faster, cleaner, and more reproducible.

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Install

pip install openpkflow

For report generation:

pip install openpkflow[reports]

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Quick start

from openpkflow.dissolution import f1, f2

reference = [20.0, 40.0, 60.0, 80.0, 90.0]
test      = [21.0, 39.0, 61.0, 79.0, 88.0]

print(f"f1 = {f1(reference, test):.2f}")
print(f"f2 = {f2(reference, test):.2f}")

From a CSV file

from openpkflow.dissolution import DissolutionStudy

study = DissolutionStudy.from_csv("dissolution.csv")

result = study.compare(reference="reference", test="test")
result.summary()
result.report("dissolution_report.html")

CSV format

formulation,batch,time,percent_released
reference,R1,5,18.2
reference,R1,10,31.4
reference,R1,15,47.9
test,T1,5,17.5
test,T1,10,30.1
test,T1,15,46.2

CLI

openpkflow version
openpkflow similarity --reference "20,40,60,80" --test "21,39,61,79"

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Current status

Module	Status
dissolution.f1 / dissolution.f2	Stable
Bootstrap f2	Stable
Dissolution CSV loader	Stable
HTML report with profile plot	Stable
CLI	Stable
Dissolution model fitting	Planned v0.2.0
Full PDF/Word reports	Planned v0.3.0
NCA	Planned v0.4.0
PK simulation	Planned v0.5.0
Population PK	Planned v0.6.0
Bayesian PK	Planned v0.8.0
ML / neural ODE	Planned v0.9.0

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Validation

All formula implementations are validated against published FDA/EMA guidance examples. Each test case cites its source: paper DOI, FDA guidance ID, or R-package vignette. See tests/ for details.

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Disclaimer

This software is for research and decision-support workflows. Final regulatory interpretation should be reviewed by qualified formulation, pharmacokinetic, and regulatory experts.

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Contributing

Issues and PRs welcome at https://github.com/priyamthakar/openpkflow/issues

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Citation

If you use OpenPKFlow in research, please cite:

Thakar, P. (2026). OpenPKFlow: Python-first pharmacometrics and dissolution toolkit.
https://github.com/priyamthakar/openpkflow

License

MIT · see LICENSE