Physiologically-based pharmacokinetic (PBPK) and quantitative systems pharmacology/toxicology (QSP/T) modeling are crucial tools in drug development and regulatory decision-making. The Open Systems Pharmacology Suite (OSPS) is a powerful open-source software suite that offers PBPK and QSP/T modeling capabilities. The core software tools PK-Sim and MoBi enable model creation and simulation results generation in a graphical user interface (GUI) based environment. However, the GUI-based tools have limited functionalities when it comes to automated set up of simulation scenarios and reproducible systematic results analysis.
To address this gap, a landscape of R packages has been developed exclusively for the OSPS to streamline the process of PBPK and QSP/T model development. In this article, we present an overview of available packages and the developed workflow for controlled and reproducible modeling and simulation activities.
Methods:
The OSPS R landscape includes the following R packages: ospsuite, ospsuite.utils, tlf, ospsuite.parameteridentification, ospsuite.reportingengine, and esqlabsR. The core ospsuite package is written in R and utilizes the rClrpackage to communicate with .NET Core functionalities of the OSPS tools PK-Sim and MoBi. Simulations created with OSPS are exported to *.pkml file format and can be loaded in R. Parameter values and initial conditions can be changed, simulations run using the CVODES solver (same as PK-Sim/MoBi), and results processed. The esqlabsRpackage contains additional functions that allow to define simulation scenarios and output graphics using non-code Excel-based definitions.
Results:
We demonstrate an example workflow which is representative of a PBPK modeling project. It includes creating a model in PK-Sim/MoBi, exporting a ready model into a *.pkml file, loading this file into the R environment with the ospsuite package, changing the model parameters, running the simulation, comparing the simulation against the observed data with the tlfpackage, estimating model parameters with the ospsuite.parameteridentification package, and generating a report with the ospsuite.reportingengine package.
The esqlabsR package offers a variety of functions to facilitate reproducible and quality-controlled workflows. The workflow that we explored is based around a single PKML model file. The scenarios comprising the workflow and the figures to be generated are defined with a no-code approach using Excel files. The reports are generated in R Markdown format for easier reproducibility and traceability.
Conclusions:
The OSPS R landscape provides a set of powerful tools for controlled and reproducible modeling and simulation activities. The use of R packages allows for an automated set up of simulation scenarios and reproducible results analysis. The proposed workflow provides an efficient and effective way to manage modeling and simulation projects. The use of esqlabsR package further enhances the reproducibility and quality control of modeling and simulation activities.
Sergei Vavilov, Pavel Balazki, Stephan Schaller, Juri Solodenko, Michael Sevestre
https://www.page-meeting.org/default.asp?abstract=10669
Objectives:
Physiologically-based pharmacokinetic (PBPK) and quantitative systems pharmacology/toxicology (QSP/T) modeling are crucial tools in drug development and regulatory decision-making. The Open Systems Pharmacology Suite (OSPS) is a powerful open-source software suite that offers PBPK and QSP/T modeling capabilities. The core software tools PK-Sim and MoBi enable model creation and simulation results generation in a graphical user interface (GUI) based environment. However, the GUI-based tools have limited functionalities when it comes to automated set up of simulation scenarios and reproducible systematic results analysis.
To address this gap, a landscape of R packages has been developed exclusively for the OSPS to streamline the process of PBPK and QSP/T model development. In this article, we present an overview of available packages and the developed workflow for controlled and reproducible modeling and simulation activities.
Methods:
The OSPS R landscape includes the following R packages:
ospsuite,ospsuite.utils,tlf,ospsuite.parameteridentification,ospsuite.reportingengine, andesqlabsR. The coreospsuitepackage is written in R and utilizes therClrpackage to communicate with .NET Core functionalities of the OSPS tools PK-Sim and MoBi. Simulations created with OSPS are exported to *.pkml file format and can be loaded in R. Parameter values and initial conditions can be changed, simulations run using the CVODES solver (same as PK-Sim/MoBi), and results processed. TheesqlabsRpackage contains additional functions that allow to define simulation scenarios and output graphics using non-code Excel-based definitions.Results:
We demonstrate an example workflow which is representative of a PBPK modeling project. It includes creating a model in PK-Sim/MoBi, exporting a ready model into a *.pkml file, loading this file into the R environment with the ospsuite package, changing the model parameters, running the simulation, comparing the simulation against the observed data with the
tlfpackage, estimating model parameters with theospsuite.parameteridentificationpackage, and generating a report with theospsuite.reportingenginepackage.The
esqlabsRpackage offers a variety of functions to facilitate reproducible and quality-controlled workflows. The workflow that we explored is based around a single PKML model file. The scenarios comprising the workflow and the figures to be generated are defined with a no-code approach using Excel files. The reports are generated in R Markdown format for easier reproducibility and traceability.Conclusions:
The OSPS R landscape provides a set of powerful tools for controlled and reproducible modeling and simulation activities. The use of R packages allows for an automated set up of simulation scenarios and reproducible results analysis. The proposed workflow provides an efficient and effective way to manage modeling and simulation projects. The use of
esqlabsRpackage further enhances the reproducibility and quality control of modeling and simulation activities.