Background: Quantitative Systems Pharmacology (QSP) gains more and more attention in the Pharmacometric Community. Models in this area are often far more complex than typical PK/PKPD models, traditionally used in Pharmacometrics. Modelers in this field are often faced with the fact that none of the typically used pharmacometric tools has been developed or optimized for this more complex type of modeling. Already available powerful tools might not be based on R (e.g., [2,3]), or might require extensive training (e.g. ).
Methods: The approach in this work was to use R Shiny to encapsulate the underlying complexity (data/model handling, simulation, objective function definition, parameter estimation, diagnostic plot generation) in a user-friendly graphical user interface. The underlying functionality has been developed in R, based on the principles used in [2,3,4]. As parameter estimation method a maximum-likelihood objective function is used, combined with a trust-region optimizer, and symbolically derived sensitivity equations. This allows generating the gradient and the Hessian of the objective function symbolically, rather than by numeric differentiation. To better explore the objective-function hyper-surface, a multi-start optimization algorithm was used, based on randomly sampled initial parameter guesses.
Results: An R Shiny application was created, allowing to perform parameter-estimation and model simulation tasks on complex Systems Pharmacology models. The interface allows to upload modeling datasets, graphical exploration of the data, model definition (including import of general SBML  models), simulation, manual parameter tuning. and parameter estimation. Additionally, diagnostics for the parameter estimation results are provided and a summary report in WORD DOCX format can be generated on the push of a button. The functionality of the R Shiny interface has been tested with several challenging parameter estimation problems.
Conclusions: The work resulted in a user-friendly Shiny interface for QSP modeling, giving access to a very powerful and robust parameter estimation method. This allows a user to focus on the tasks of main interest, namely the assessment of different candidate model structures and the evaluation and interpretation of estimated model parameters.
Disclaimer: The IQRsysPharm Shiny app has been developed mainly to be used on a local computer. Server side deployment has not been in scope, although some basic support has been added.
Availability: Please contact firstname.lastname@example.org for more information.
 Raue, Bioinformatics 31(21), 2015
 Schmidt, IQM Tools, 2016 (https://www.intiquan.com/iqm-tools)
 Kaschek, dMod, 2016 (https://cran.r-project.org/package=dMod)
 SBML (http://sbml.org)