openpkflow 0.9.0

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 — Weibull, Higuchi, first-order, zero-order, Korsmeyer-Peppas
• NCA: AUClast, AUCinf, Cmax, Tmax, lambda_z, half-life, CL/F, Vz/F — three AUC methods, explicit BLQ handling
• Report generation: Markdown, HTML, PDF, Word
• PK simulation: 1- and 2-compartment models, oral/IV/infusion — planned v0.5.0

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 PDF and Word reports:

pip install openpkflow[reports]

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Quick start: dissolution similarity

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")
result.report("dissolution_report.pdf", format="pdf")   # requires [reports]

CSV format: formulation,batch,time,percent_released

CLI

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

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Quick start: NCA

from openpkflow.nca import NCAStudy

study = NCAStudy.from_csv(
    "pk_data.csv",
    auc_method="linear_up_log_down",   # required: "linear", "log", or "linear_up_log_down"
    blq_method="none",                  # required: "none", "drop", "zero", "half_lloq", "lloq"
)
summary = study.analyze()
print(summary.summary())               # tabular ASCII output

# Per-subject results
result = summary.results[0]
print(f"Subject: {result.subject}")
print(f"AUClast: {result.AUClast:.2f} h*mg/L")
print(f"Cmax:    {result.Cmax:.2f} mg/L")
print(f"Tmax:    {result.Tmax:.2f} h")
print(f"t1/2:    {result.half_life:.2f} h")
print(f"CL/F:    {result.CL_F:.2f} L/h")

# Reports
result.report("nca_subject1.html")
summary.report("nca_summary.html")

NCA CSV format

subject,time,conc,dose,route
1,0.0,0.0,320.0,oral
1,0.5,4.2,320.0,oral
1,1.0,8.1,320.0,oral
1,2.0,6.8,320.0,oral
1,4.0,3.5,320.0,oral
1,8.0,1.7,320.0,oral
1,12.0,0.9,320.0,oral
1,24.0,0.2,320.0,oral

Required columns: subject, time, conc, dose, route. Dose units must match concentration × time — mg when conc is mg/L and time is h. Route values: "oral", "iv_bolus", "iv_infusion".

Oral route yields apparent clearance and volume: CL_F, Vz_F. IV routes yield absolute clearance and volume: CL, Vz.

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Current status

Module	Status
Dissolution f1 / f2	Stable
Bootstrap f2	Stable
Dissolution CSV loader	Stable
Dissolution model fitting (5 models, AICc)	Stable
HTML, Markdown, PDF, Word reports	Stable
NCA (AUC, lambda_z, CL/F, reports)	Stable — v0.4.1
PK simulation (1/2-comp, oral/IV)	Planned v0.5.0
Population PK diagnostics	Planned v0.6.0
Bayesian PK (PyMC, CmdStanPy)	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. NCA results are validated against the R nlme Theoph reference dataset. 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