Introduction

What is OpenDose-PopPK?

OpenDose-PopPK is an open-source Python library for Population Pharmacokinetic and Pharmacodynamic (PopPK/PD) modelling. It targets researchers and clinical pharmacologists who need reproducible, scriptable simulation and estimation workflows without depending on proprietary software (NONMEM, Monolix, Phoenix WinNonlin).

Key capabilities

• 1-compartment PK model — analytical first-order absorption/elimination with optional radioactive decay (nuclear-medicine dosimetry).

• 2-compartment PK model — numerical ODE solver for central/peripheral distribution and inter-compartmental flow.

• Emax Hill PD model — sigmoidal concentration–effect relationship with configurable Hill coefficient.

• Monte Carlo population simulation — inter-individual variability (IIV) via log-normal random effects; 90 % prediction intervals.

• Covariate modelling — weight, renal function (CrCl), age, and hepatic markers via the Power Model.

• MAP estimation — Bayesian individual fitting from sparse observed samples using scipy.optimize.

• DrugDatabase — loads and manages pharmacokinetic parameters from CSV datasets.

Design principles

Modularity — every component (PK, PD, covariate, simulator, estimator) is an independent class that can be replaced or extended.

Reproducibility — random seeds are explicit; all outputs are deterministic when seeded.

Scientific transparency — formulas are documented with TeX in Mathematical Notes and cross-linked to the companion paper.

Who should use it?

• Pharmacology / PKPD researchers prototyping new models in Python.

• Clinical teams validating dosing regimens via simulation before a trial.

• Students learning PopPK concepts with real, runnable code.

Citation

If you use OpenDose-PopPK in published work, please cite it — see Citation.