pharmpy-core 1.8.0

Pharmacometric modeling

Pharmpy is an open-source software package for pharmacometric modeling. It has functionality ranging from reading and manipulating model files and datasets to full tools where subsequent results are collected and presented.

Features include:

• A model abstraction which splits a model into core components which Pharmpy understands and can manipulate: parameters, random variables, statements (including ODE system), dataset, and execution steps

• An abstraction for modelfit results which splits a parsed results into core components: e.g. OFV, parameter estimates, relative standard errors (RSEs), residuals, predictions

• Functions for manipulation of models and datasets in the modeling-module: e.g. change structural model, add time-after-dose column, add covariate effects

• Tools for automated model development: building various aspects (structural, iiv, iov, ruv, covariates, …) of PK, PKPD, TMDD and drug-metabolite models automatically

• Tools to aid model development in the tools-module: execution of models within Python/R scripts, bootstrap, comparison of estimation methods

• Simplify scripting of workflows. Makes it possible to run scripts including calls to long running tools multiple times without having to rerun already finished tool runs.

• Support for multiple estimation tools: parse NONMEM models, execute NONMEM, nlmixr2, and rxODE2 models, run all Pharmpy tools with NONMEM and some with nlmixr2

For more comprehensive information and documentation, see: https://pharmpy.github.io

Pharmpy can be used as a regular Python package, in R via the pharmr package, or via its built in command line interface.

Getting started

Installation

For installation in R, see pharmr.

Install the latest stable version from PyPI:

pip install pharmpy-core # or ‘pip3 install’ if that is your default python3 pip

Python Example

>>> from pharmpy.modeling import read_model
>>> from pharmpy.tools import load_example_modelfit_results
>>> model = load_example_model("pheno")
>>> model.parameters
            value  lower upper    fix
POP_CL   0.004693   0.00     ∞  False
POP_VC   1.009160   0.00     ∞  False
COVAPGR  0.100000  -0.99     ∞  False
IIV_CL   0.030963   0.00     ∞  False
IIV_VC   0.031128   0.00     ∞  False
SIGMA    0.013086   0.00     ∞  False
>>> res = load_example_modelfit_results("pheno")
>>> res.parameter_estimates
POP_CL     0.004696
POP_VC     0.984258
COVAPGR    0.158920
IIV_CL     0.029351
IIV_VC     0.027906
SIGMA      0.013241
Name: estimates, dtype: float64
>>>

CLI Example

# Get help
pharmpy -h

# Remove first ID from dataset and save new model using new dataset
pharmpy data filter run1.mod 'ID!=1'

# Run tool for selecting IIV structure
pharmpy run iivsearch run1.mod

User guide

There is also a user guide for getting started

Contact

This is the team behind Pharmpy

Please ask a question in an issue or contact one of the maintainers if you have any questions.

Contributing

If you interested in contributing to Pharmpy, you can find more information under Contribute.