Table 1.

Comparison of R‐based quantitative systems pharmacology modeling workflow features

Functionality	IQRtools	mrgsolve	RxODE
Standardized data set	Yes—flexible data set suitable for heterogeneous data	Yes—flexible data set suitable for heterogeneous data	No—no predefined data set structure
Data exploration	Yes—designated functions available	Yes—designated functions available	No—requires manual implementation
Translation to NLME software	Yes—IQR modeling project can be translated to Monolix, NONMEM, and NLMIXR; projects can be executed and results are postprocessed	No	Yes—works with NLMIXR
Integrated parameter estimation tool	Yes—fully integrated likelihood‐based parameter estimation tool	No—requires additional R packages (e.g., minqa, RcppDE, GenSA, etc.) and manual implementation of objective function	No—requires additional R packages (e.g., minqa, RcppDE, GenSA, etc.) and manual implementation of objective function
Model diagnostics	Yes—automatic simulations vs. the data and goodness of fit plots	Yes—available through model simulations based on data set‐derived event tables	No—requires manual simulations vs. experimental data
Model simulations	Yes	Yes	Yes—the fastest simulation tool
Local sensitivity analysis	Yes—requires additional programming	Yes—fully automated through designated functions	Yes—requires additional programming
Identifiability analysis	Yes—calculation of FIM and evaluation of profile likelihood	No	No
User interface	Yes—available as an add‐on	No	No

NLME, nonlinear mixed‐effects.