Metadata-Version: 2.4
Name: openpkflow
Version: 2.6.0
Summary: Python-first toolkit for dissolution, NCA, PK/PD simulation, and pharmacometric reporting.
Project-URL: Homepage, https://github.com/priyamthakar/openpkflow
Project-URL: Repository, https://github.com/priyamthakar/openpkflow
Project-URL: Issues, https://github.com/priyamthakar/openpkflow/issues
Project-URL: Documentation, https://priyamthakar.github.io/openpkflow/
Author-email: Priyam Thakar <priyamthakar1@gmail.com>
License: MIT
License-File: LICENSE
Keywords: NCA,PKPD,bioequivalence,dissolution,formulation,pharmacokinetics,pharmacometrics
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering
Classifier: Typing :: Typed
Requires-Python: >=3.10
Requires-Dist: jinja2>=3.1
Requires-Dist: matplotlib>=3.7
Requires-Dist: numpy>=1.24
Requires-Dist: pandas>=2.0
Requires-Dist: pydantic>=2.0
Requires-Dist: scipy>=1.10
Requires-Dist: typer>=0.12
Provides-Extra: bayes
Requires-Dist: arviz>=0.16; extra == 'bayes'
Requires-Dist: cmdstanpy>=1.2; extra == 'bayes'
Requires-Dist: pymc>=5.0; extra == 'bayes'
Provides-Extra: dev
Requires-Dist: build>=1.2; extra == 'dev'
Requires-Dist: httpx2>=2.0; extra == 'dev'
Requires-Dist: hypothesis>=6.100; extra == 'dev'
Requires-Dist: mkdocs-material>=9.5; extra == 'dev'
Requires-Dist: mypy>=1.8; extra == 'dev'
Requires-Dist: pytest-benchmark>=4.0; extra == 'dev'
Requires-Dist: pytest-cov>=5.0; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.5; extra == 'dev'
Requires-Dist: twine>=5.0; extra == 'dev'
Provides-Extra: ml
Requires-Dist: scikit-learn>=1.4; extra == 'ml'
Requires-Dist: torch>=2.0; extra == 'ml'
Provides-Extra: reports
Requires-Dist: openpyxl>=3.1; extra == 'reports'
Requires-Dist: python-docx>=1.1; extra == 'reports'
Requires-Dist: reportlab>=4.0; extra == 'reports'
Description-Content-Type: text/markdown

# OpenPKFlow

<p align="center">
  <img src="docs/logo.png" alt="OpenPKFlow" width="640"/>
</p>

**A transparent, reproducible, open-source Python toolkit for dissolution, NCA, PK/PD simulation, and pharmacometric reporting. Every formula is cross-validated against published references and the output is regulatory-ready.**

[![CI](https://github.com/priyamthakar/openpkflow/actions/workflows/ci.yml/badge.svg)](https://github.com/priyamthakar/openpkflow/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/priyamthakar/openpkflow/branch/main/graph/badge.svg)](https://codecov.io/gh/priyamthakar/openpkflow)
[![PyPI version](https://img.shields.io/pypi/v/openpkflow)](https://pypi.org/project/openpkflow/)
[![Python](https://img.shields.io/pypi/pyversions/openpkflow)](https://pypi.org/project/openpkflow/)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Docs](https://img.shields.io/badge/docs-live-brightgreen)](https://priyamthakar.github.io/openpkflow/)

---

## 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, maximum deviation, MSD (Mahalanobis Statistical Distance), model fitting (Weibull, Higuchi, first-order, zero-order, Korsmeyer-Peppas), model-dependent comparison via 90% CI
- **NCA:** AUClast, AUCinf, Cmax, Tmax, lambda_z, half-life, CL/F, Vz/F; three AUC methods, explicit BLQ handling, %AUCextrap flag, dose-normalised parameters, CDISC PP output; sparse NCA from 3-5 samples
- **Bayesian PK (v2.0.0):** MAP individual PK estimation (scipy, no extra deps) plus full posterior via PyMC (`[bayes]` extra); Bayesian 2x2 crossover BE with P(GMR in 80-125) decision quantity alongside frequentist 90% CI
- **Bioequivalence:** paired 2x2 TOST (80-125% FDA/EMA limits), GMR + 90% CI, intra-subject CV; research-grade replicate-design screening with CVwR and scaled-limit summaries
- **Report generation:** Markdown, HTML, PDF, Word
- **PK simulation:** 1- and 2-compartment models, oral/IV bolus/IV infusion, repeated dosing
- **Population PK diagnostics:** 4-panel GOF plots (OBS vs PRED, IWRES vs TIME/IPRED), simulation-based VPC with percentile bands, NONMEM-style dataset helpers
- **Population PK estimation (v2.3.0):** FOCE-I (scipy, zero extra deps) and SAEM (PyMC `[bayes]` extra) for 1- and 2-compartment oral/IV models; diagonal or full Omega block matrix; `PopPKResult` with `.summary()`, `.plot()` (6-panel), `.report()` (research-grade; FOCE-I sanity-checked against the `nlme` Theophylline reference)
- **Theory guide:** Full LaTeX formula derivations for every module (NCA, simulation, dissolution, IVIVC, BE, pop PK, Bayesian PK) for regulatory review support and teaching
- **ML surrogate (experimental):** torch MLP that approximates 1-cmt oral profiles

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

---

## Install

```bash
pip install openpkflow
```

For PDF and Word reports:

```bash
pip install openpkflow[reports]
```

For full Bayesian PK (PyMC MCMC):

```bash
pip install openpkflow[bayes]
```

---

## Quick start: dissolution similarity

```python
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

```python
from openpkflow.dissolution import DissolutionStudy

study = DissolutionStudy.from_csv("dissolution.csv")
# or load directly from Excel (requires pip install openpkflow[reports]):
# study = DissolutionStudy.from_excel("dissolution.xlsx", sheet_name="Data")

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

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

---

## Quick start: NCA

```python
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

```csv
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 x 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`.

---

## Quick start: PK simulation

```python
import numpy as np
from openpkflow.sim import simulate
from openpkflow.sim.models import OneCompartmentModel
from openpkflow.sim.dosing import DoseRegimen

model = OneCompartmentModel(route="oral", CL_F=5.0, Vz_F=50.0, ka=1.2)
regimen = DoseRegimen.from_repeated(amount=100.0, route="oral", tau=24.0, n_doses=3)
times = np.linspace(0, 72, 500)

result = simulate(model, regimen, times)
print(result.summary())
result.report("sim_report.html")
result.report("sim_report.pdf", format="pdf")   # requires [reports]
```

---

## Quick start: Bayesian individual PK (MAP)

```python
from openpkflow.bayes import map_individual_pk, PKPrior
import math

# Noiseless 1-cmt oral data (CL_F=5, Vz_F=50, ka=1.2, dose=100)
times = [0.5, 1.0, 2.0, 4.0, 8.0, 12.0]
concs = [1.23, 1.85, 1.97, 1.61, 0.89, 0.49]

result = map_individual_pk(times, concs, dose=100.0, route="oral", subject="S01")
print(result.summary())   # MAP estimates, SEs, diagnostics, disclaimer
result.report("map_pk_report.html")
```

For full posterior sampling (requires `pip install openpkflow[bayes]`):

```python
from openpkflow.bayes.bayes_pk import bayes_individual_pk

result = bayes_individual_pk(times, concs, dose=100.0, route="oral",
                              n_samples=1000, tune=1000, chains=2)
print(f"CL_F = {result.cl_mean:.3g}  [95% CrI: {result.cl_95ci[0]:.3g}, {result.cl_95ci[1]:.3g}]")
print(f"P(shrinkage) = {result.shrinkage_cl:.1%}")
```

## Quick start: Bayesian bioequivalence (requires `[bayes]`)

```python
import pandas as pd
from openpkflow.bayes.bayes_be import bayes_be

# Long-format 2x2 crossover data
data = pd.DataFrame({
    "subject":   ["S01","S01","S02","S02","S03","S03","S04","S04"],
    "sequence":  ["RT", "RT", "TR", "TR", "RT", "RT", "TR", "TR"],
    "period":    [1,    2,    1,    2,    1,    2,    1,    2   ],
    "treatment": ["R",  "T",  "T",  "R",  "R",  "T",  "T",  "R" ],
    "value":     [98.0, 103.0, 95.0, 91.0, 107.0, 112.0, 99.0, 94.0],
})

result = bayes_be(data, metric="AUC", n_samples=2000, tune=1000, chains=2)
print(f"P(BE) = {result.p_be:.3f}")
print(f"GMR = {result.gmr_mean:.4g}  [95% CrI: {result.gmr_95ci[0]:.4g}, {result.gmr_95ci[1]:.4g}]")
print(f"Frequentist 90% CI: [{result.freq_90ci[0]:.4g}, {result.freq_90ci[1]:.4g}]")
result.report("bayes_be_report.html")
```

---

## Quick start: bioequivalence

```python
import pandas as pd
from openpkflow.be import BEStudy

# Wide-format DataFrame: one row per subject, reference and test PK parameter values
be_df = pd.DataFrame({
    "subject":   ["S01", "S02", "S03", "S04", "S05", "S06"],
    "sequence":  ["RT",  "RT",  "RT",  "TR",  "TR",  "TR"],
    "reference": [100.2, 98.7, 105.1, 97.3, 102.8, 99.5],
    "test":      [95.1,  94.0,  99.8, 92.9,  97.4, 94.8],
})

study = BEStudy(be_df, parameter="AUCinf")
result = study.analyze()          # default: 80-125%, alpha=0.05
print(result.summary())
result.report("be_report.html")

# NTI products: pass narrower limits
result_nti = study.analyze(be_lower=0.90, be_upper=1.1111)
```

### From NCAStudy results (convenience)

```python
from openpkflow.be import BEStudy

# Run NCA separately on each formulation's PK data
# reference_nca_summary = NCAStudy.from_csv("ref_pk.csv", ...).analyze()
# test_nca_summary      = NCAStudy.from_csv("test_pk.csv", ...).analyze()

study = BEStudy.from_nca_results(
    reference_nca_summary, test_nca_summary, parameter="AUCinf"
)
result = study.analyze()
```

### Formal BE with BioEqPy

OpenPKFlow deliberately keeps `openpkflow.be` as a lightweight convenience layer.
For regulator-facing BE analysis with long-format crossover data, ANOVA source
tables, NTI, ABEL/RSABE, and validation fixtures, export a BioEqPy-ready table:

```python
from openpkflow.be import BEStudy
from bioeqpy import analyze

study = BEStudy(be_df, parameter="AUCinf")
bioeqpy_input = study.to_bioeqpy_dataframe()
formal_results = analyze(bioeqpy_input, parameters=["AUCinf"])
```

### CLI

```bash
openpkflow be compare be_data.csv --parameter AUCinf --report be_report.html
```

CSV format: `subject, sequence, reference, test`

---

## Quick start: population PK diagnostics

```python
import pandas as pd
from openpkflow.pop import GOFResult, simulate_vpc
from openpkflow.sim.models import OneCompartmentModel
from openpkflow.sim.dosing import DoseRegimen

# GOF: supply your own PRED/IPRED from NONMEM or nlmixr2
gof = GOFResult(
    dv=[5.2, 8.1, 6.4, 3.2],
    pred=[4.9, 7.8, 6.0, 3.0],
    ipred=[5.1, 8.0, 6.3, 3.1],
    time=[1.0, 2.0, 4.0, 8.0],
    id=["S1", "S1", "S1", "S1"],
    sigma=0.15,
    study_label="Phase 1 Study",
)
print(gof.summary())
gof.report("gof_report.html")

# Simulation-based VPC
model = OneCompartmentModel(route="oral", CL_F=5.0, Vz_F=50.0, ka=1.2)
regimen = DoseRegimen.from_repeated(amount=100.0, route="oral", tau=24.0, n_doses=1)
observed = pd.DataFrame({"TIME": [1, 2, 4, 8, 12], "DV": [5.1, 8.2, 6.5, 3.8, 2.1]})

vpc = simulate_vpc(model, regimen, observed, n_replicates=500, seed=42)
vpc.report("vpc_report.html")
```

---

## Feature comparison

| Capability | OpenPKFlow | PKNCA (R) | WinNonlin | Pharmpy |
|---|---|---|---|---|
| Dissolution f1 / f2 | :white_check_mark: | :x: | :white_check_mark: | :x: |
| Bootstrap f2 | :white_check_mark: | :x: | :x: | :x: |
| Dissolution model fitting (5 models + AICc) | :white_check_mark: | :x: | :x: | :x: |
| MSD / max deviation / model-dependent comparison | :white_check_mark: | :x: | :white_check_mark: | :x: |
| NCA (AUClast, AUCinf, CL/F, lambda_z), cross-validated vs Phoenix WinNonlin | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: |
| C0 back-extrapolation for IV bolus (matches WinNonlin within 2%) | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: |
| %AUCextrap flag, dose-normalised params | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: |
| CDISC PP output (SDTM, PPTESTCD codes) | :white_check_mark: | :x: | :white_check_mark: | :x: |
| Bioequivalence convenience (paired 2x2 TOST) | :white_check_mark: | :x: | :white_check_mark: | :x: |
| PK simulation (1/2-cmt, oral/IV) | :white_check_mark: | :x: | :white_check_mark: | :white_check_mark: |
| Population PK diagnostics (GOF, VPC) | :white_check_mark: | :x: | :x: | :white_check_mark: |
| Multi-format reports (HTML, PDF, DOCX) | :white_check_mark: | :x: | :white_check_mark: | :x: |
| Open-source and free | :white_check_mark: | :white_check_mark: | :x: | :white_check_mark: |
| Python-native API | :white_check_mark: | :x: | :x: | :white_check_mark: |
| Regulatory reference validation (citations) | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: |
| IVIVC (Level A) | :white_check_mark: (v1.2.0) | :x: | :white_check_mark: | :x: |
| Multi-media dissolution | :white_check_mark: (v1.4.0) | :x: | :white_check_mark: | :x: |
| Sparse-sampling NCA | :white_check_mark: (v1.5.0) | :white_check_mark: | :x: | :x: |
| Steady-state NCA + urinary excretion | :white_check_mark: (v1.3.0) | :white_check_mark: | :white_check_mark: | :x: |
| MAP individual PK (scipy, no extra deps) | :white_check_mark: (v2.0.0) | :x: | :white_check_mark: | :x: |
| Full Bayesian PK + Bayesian BE (PyMC) | :white_check_mark: (v2.0.0) | :x: | :x: | :x: |
| Population PK estimation: FOCE-I + SAEM (1/2-cmt, full Omega) | :white_check_mark: (v2.3.0)\* | :x: | :x: | :x: |
| Replicate BE screening (CVwR/scaled-limit summaries) | :white_check_mark: (v2.4.0)\*\* | :x: | :white_check_mark: | :x: |
| Study pipeline (dissolution + NCA + BE orchestration) | :white_check_mark: (v2.6.0) | :x: | :x: | :x: |
| SUPAC-IR screening + alcohol dose-dumping f2 | :white_check_mark: (v2.6.0)\*\*\* | :x: | :x: | :x: |
| IVIVC Level B/C helpers (MDT/MRT) | :white_check_mark: (v2.6.0) | :x: | :x: | :x: |
| Transit-compartment oral absorption + SS metrics | :white_check_mark: (v2.6.0) | :x: | :x: | :x: |
| Formal BE ANOVA / validated RSABE decision | :x: | :x: | :white_check_mark: | :x: |

\* Research-grade; FOCE-I typical values are sanity-checked against `nlme` Theophylline reference values. See [HANDOFF.md](HANDOFF.md).
\*\* Research-grade screening only; not a validated FDA/EMA RSABE submission engine.
\*\*\* Screening helper with documented thresholds; not full SUPAC guidance automation.

## Roadmap

Post-1.0.0: IVIVC Level A, multi-media, SS/urine NCA, sparse NCA, Bayesian PK/BE,
FOCE-I/SAEM (frozen), replicate BE screening (v2.4), web app (v2.5), study pipeline +
SUPAC/alcohol + IVIVC B/C + transit (v2.6). See [ROADMAP.md](ROADMAP.md) and
[HANDOFF.md](HANDOFF.md) for current PR/release state.

---

## Current status

| Module | Status |
|---|---|
| Dissolution f1 / f2 | Stable |
| MSD / max deviation / model-dependent comparison | Stable |
| Bootstrap f2 | Stable |
| Dissolution CSV loader | Stable |
| Dissolution model fitting (AICc ranking) | Stable |
| SUPAC-IR screening + alcohol dose-dumping f2 | Stable screening (v2.6.0) |
| IVIVC Level A | Stable (v1.2.0) |
| IVIVC Level B/C helpers (MDT/MRT) | Stable (v2.6.0) |
| Multi-media dissolution | Stable (v1.4.0) |
| Study pipeline (`openpkflow study run`) | Stable (v2.6.0; tag pending) |
| HTML, Markdown, PDF, Word reports | Stable |
| NCA (incl. steady-state, urinary, sparse, CDISC PP) | Stable |
| PK simulation (1/2-comp + transit oral + SS metrics) | Stable (v2.6.0) |
| Population PK diagnostics (GOF, VPC) | Stable |
| FOCE-I / SAEM pop PK estimation\* | Stable research-grade; **frozen** for extension |
| Covariate modeling | Removed (v2.3.0) |
| MAP / Bayesian PK + Bayesian BE | Stable (v2.0.0) |
| Bioequivalence TOST + power/n + replicate screening\*\* | Stable |
| Web app (`api/` + `webapp/`) | Stable (v2.5+; v2.6 polish) |
| ML surrogate (torch MLP, EXPERIMENTAL) | Prototype (v0.9.0) |

\* Research-grade; FOCE-I checked against `nlme` Theophylline reference. See [HANDOFF.md](HANDOFF.md).
\*\* Replicate BE is research-grade screening only.

---

## By the numbers

| Stat | Value |
|---|---|
| Lines of source code (`src/`) | ~23,900 |
| Lines of tests (`tests/`) | ~17,200 |
| Total Python files | 162 (80 src + 82 tests) |
| Tests | 1,334 |
| HTML report templates | 12 |
| Bundled example datasets | 4 |
| Git commits | 139 |

---

## 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: four-way cross-validation against Phoenix WinNonlin:**
NCA results are cross-validated against Phoenix WinNonlin (Certara), PKNCA 0.12.1, and NonCompart 0.8.0
on the standard R `nlme::Theoph` (12-subject oral theophylline) and `nlme::Indometh` (6-subject IV bolus
indomethacin) datasets. Key validated parameters: AUClast, AUCinf, CL/F, Vz/F, lambda_z, half-life.
C0 back-extrapolation for IV bolus data (WinNonlin's approach: OLS regression on the first 2 points,
linear trapezoid area added from t=0 to t_first) is implemented in `c0_back_extrapolated()` and verified
to match WinNonlin reference values within 2% for all 6 Indometh subjects.

See [VALIDATION.md](VALIDATION.md) for the full regulatory test traceability matrix.

---

## Disclaimer

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

---

## Documentation

- **[Theory Guide](https://priyamthakar.github.io/openpkflow/theory/)** - Full LaTeX formula derivations for every module: NCA, simulation, dissolution, IVIVC, BE, pop PK, Bayesian PK. Designed for regulatory review support and teaching.
- **[Migration Guide](https://priyamthakar.github.io/openpkflow/migration-cheatsheet/)** - Coming from WinNonlin, NONMEM, or R? Quick-reference mapping for every parameter and function.
- **[Tutorials](https://priyamthakar.github.io/openpkflow/)** - Step-by-step worked examples for all 7 modules.
- **[Validation Matrix](https://priyamthakar.github.io/openpkflow/reference/validation/)** - Every test mapped to its FDA/EMA/ICH guidance section or published DOI.
- **[API Reference](https://priyamthakar.github.io/openpkflow/reference/)** - Full function and class reference for all 9 modules.

---

## Contributing

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

---

## 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](LICENSE)
