QSP Modeling in Shiny

Case Study

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QSP Modeling in Shiny

The Challenge

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. Another challenge in this field is that communication of modeling results is not an easy task.

We needed an effective communication tool that allowed us to evaluate different mechanistic hypotheses in an interactive way, both data driven and mechanism driven. The desired tool should allow to be used during team meetings and provide a visually appealing representation of model, simulations, and estimation results.

The Solution

Our approach 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 and is made available as the IntiQuan R Tools.

We took care to incorporate the currently most powerful parameter estimation method [1,2] into this Shiny app. This has several benefits: of course we want to be able to quickly home in onto the global optimum but we also want to do that in a speed that allows us to use the tool interactively during meetings.

We also ensured that the Shiny App is compatible with the Systems Biology Markup Language (SBML) [3], as many QSP models are available in online databases in this format.

Finally, the tool should be able to produce a Microsoft Word report with the modeling and analysis results to allow reproducibility.

The Benefit

The work resulted in a user-friendly Shiny interface for QSP modeling, giving access to a very powerful and robust parameter estimation method.
The Shiny App has been used successfully in interactive meetings, allowing everyone to contribute ideas about potential mechanisms and see their impact. In addition, the App has been proven useful for modelers who do want to get a quick overview over model behavior in relation to observed data without the need to write lengthy and potentially cryptic R code. Basically, the tool allows the 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.

The QSP Shiny Interface is available online and for download

References

[1] Raue, Bioinformatics 31(21), 2015
[2] Kaschek, dMod, 2016 (https://cran.r-project.org/package=dMod)
[3] SBML (http://sbml.org)

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