Overview and Features of SBPOP

The SBPOP extension package (SBPOP) for the Systems Biology Toolbox 2 adds functionality related to pharmacometrics and systems pharmacology applications. Functions are available that support the complete model building workflow for population PK models. Additionally, SBPOP contains interfaces to Monolix and NONMEM, powerful and flexible graphical analysis functions, and a user-friendly and powerful support for clinical trial simulations.  

Main Features SBPOP

  • Dosing descriptions, allowing to simulate dosing scenarios
  • Automatic generation of Monolix MLXTRAN models and full Monolix projects
  • Automatic generation of NONMEM control files
  • Run Monolix and NONMEM parameter estimation from within SBPOP
  • Automatic import of Monolix and NONMEM results, including sampling from estimated parameter distributions
  • Import, export, and conversion of clinical datasets
  • Powerful graphical analysis functions (including Trellis, Facet-grid, export of figures to relevant formats, etc.)
  • Powerful population PK workflow tools (Standard dataset specification, Data Exploration, Data Cleaning, Model building (base model, covariance model, covariate model, robustness assessment, ...))
  • Population PKPD support functions
  • Powerful support of complex clinical trial simulations

Dosing descriptions and model syntax

Dosing descriptions (SBPOPdosing objects) are used to define the type of administration of doses to a model. Possible dosing administrations are infusion, bolus, first and zero order absorption. Each dose administration needs to be given a name "INPUT*" where "*" is a numeric value. This name then can be used within an SBmodel to apply a certain dose input to a desired differential equation / compartment.
In an SBmodel, the INPUT* identifier is used to select the differential equation (or compartment) to which the dose should be added. The syntax is flexibel and powerful. Several dosings/inputs can be defined and applied to different compartments. Doses can also be split up between compartments.
The "OUTPUT*" definition of variables is used for identification of the model outputs that are to be considered for parameter estimation with the NLME tool of choice.

Automatic generation of NONMEM control files, MLXTRAN models and Monolix projects

The SBmodel syntax can automatically be converted to MLXTRAN models and NONMEM control files. This of course is limited to ODE based models, which is the focus of the whole SBPOP PACKAGE. The only things that need to be defined apriori are an SBmodel and an SBPOPdosing object.

Additionally, SBPOP allows to generate complete Monolix and NONMEM parameter estimation projects. The typically used settings can be manipulated directly from within SBPOP, without the need to start the Monolix GUI or to write Monolix project or NONMEM code manually. Obviously, this allows for powerful scripting of many different NLME estimation based analyses.

Graphical analysis functions

SBPOP contains many powerful functions for graphical data analysis. Unfortunately Mathworks has so far not seen the value in Trellis and Facet-Grid plots (other than in the sysbio toolbox), so these are now implemented in SBPOP and somehow tuned to look similar to R plots. Additionally, support for grouped histograms, etc., is available.

Population PK workflow

SBPOP contains extensive functionality targeting population PKPD analyses. At the backend Monolix and/or NONMEM are used to perform NLME parameter estimation.

Population PK analyses often are very time consuming. Standards enable these analyses to be performed with considerably increased efficiency. SBPOP assumes a certain data standard, covering not only for population PK, but also PKPD and time-to-event analyses. No need to request several different datasets from the programmers.

Functionality is included that supports a complete population PK model building workflow. The "Pareto principle" has been applied, meaning that this workflow is not required to be able to cover 100% of the possible population PK models, but if it covers 80% and does so efficiently, i.e., a lot of time is gained and can be put to better use.

Clinical trial simulations

The core of the clinical trial simulation functionality is the parameter sampling function. It samples population parameters from the uncertainty distributions and individual parameters from the variability distributions, directly from the Monolix and/or NONMEM results - easy to use and powerful. The user has full control over what exactly to sample. Different types of distributions of individual parameters (normal, log-normal, logit-normal, etc.) are taken into account and handled automatically, estimated correlations of fixed and random effects are also handled automatically. The sampling function is also able to handle user defined covariate settings automatically.

Once the parameters for a simulation can be easily sampled, the clinical trial simulation boils down to repeated simulations, which can be easily and efficiently implemented using the SBPOP package.


User's Reference SBPOP user's reference guide.





Publications, discussing results obtained with the help of the SBPOP PACKAGE, are asked to reference the most relevant of the following papers and additionally the link to this webpage:

  • Systems Biology Toolbox for MATLAB: A computational platform for research in Systems Biology, Bioinformatics, 22(4), 514-515, 2006, doi:10.1093/bioinformatics/bti799
  • SBaddon: high performance simulation for the Systems Biology Toolbox for MATLAB, Bioinformatics, 23(5), 646-647, 2007, doi:10.1093/bioinformatics/btl668
  • SBPOP Package: Efficient support for model based drug development from mechanistic models to complex trial simulation, PAGE meeting, Glasgow, UK [abstract]
  • Enhancing population pharmacokinetic modeling efficiency and quality using an integrated workflow, Journal of Pharmacokinetics and Pharmacodynamics, doi:10.1007/s10928-014-9370-4, 2014.
  • 16th March 2015: Many small improvements ... also SBML export now fully working again
  • 20th January 2015: Many improvements ... Windows 64bit support and compatibility with MATLAB versions >=R2014B
  • 28th July 2014: Happy to announce that our paper about efficient conduct of popPK anlalyses has been published (doi:10.1007/s10928-014-9370-4)
  • 3rd July 2014: Update to Revision 1361 (due to packaging bug in Rev 1352)
  • 18th June 2014: Update to Revision 1352 (popPK modeling workflow MONOLIX and NONMEM, "median" modeling support)
  • 25th March 2014: Update to Revision 1278
  • 7th May 2013: Revision 1172 had a minor bug due to packaging of the public version - main impact on running SBPDgui. Fixed now in Revision 1176
  • 2nd May 2013: SBTOOLBOX2 and SBPD are now integrated into the same package, called "SBPOP PACKAGE". The new combined package additionally includes "SBPOP", focusing on PK/PKPD/PBPK models, population modeling, nonlinear mixed effect parameter estimation, clinical trial simulation