SimBiology® software extends the MATLAB® computing environment for analyzing pharmacokinetic (PK) data using models. The software lets you do the following:
Create models — Use a model construction wizard. Alternatively, extend any model with pharmacodynamic (PD) model components, or build higher fidelity models. See Model for more information.
Fit data — Fit nonlinear, mixed-effects models to data, and estimate the fixed and random effects, or fit the data using nonlinear least squares. For more information, see Analyze Data Using Models.
Generate diagnostic plots — For more information, see Analyze Data Using Models.
The software lets you work with different model structures, thus letting you try multiple models to see which one produces the best results.
You can import tabular data into the SimBiology Model Analyzer or the MATLAB Workspace. The supported file types are .xls,
.csv, and .txt. You can specify that the data is
in a NONMEM® formatted file. The import process interprets the columns according to the
NONMEM definitions. For details, see Importing Data.
SimBiology provides an extensible modeling environment. You can do any of the following:
Create a PK model using a model construction wizard to specify the number of compartments, the route of administration, and the type of elimination.
Extend any model with pharmacodynamic (PD) model components, or build higher fidelity models.
Build or load your own SimBiology, or SBML model.
For more information, see What is a SimBiology Model?.
Perform both individual and population fits to grouped longitudinal data:
Individual fit — Fit data using nonlinear least-squares method, specify parameter transformations, estimate parameters, and calculate residuals and the estimated coefficient covariance matrix. For a command line workflow, see Fitting Workflow for sbiofit. For an app workflow, see Calculate NCA Parameters and Fit Model to PK/PD Data Using SimBiology Model Analyzer App.
Population fit — Fit data, specify parameter transformations, and estimate the fixed effects and the random sources of variation on parameters using nonlinear mixed-effects models. For a command line workflow, see Nonlinear Mixed-Effects Modeling Workflow.
Population fit using a stochastic algorithm — Fit data, specify parameter
transformations, and estimate the fixed effects and the random sources of variation on
parameters, using the Stochastic Approximation Expectation-Maximization (SAEM)
algorithm. SAEM is more robust with respect to starting values. This functionality
relaxes assumption of constant error variance. Specify nlmefitsa
as the estimation function name when you run sbiofitmixed.
In addition, you can turn on the ProgressPlot option to get the live feedback on the status of parameter estimation.
The following examples show how to estimate pharmacokinetic parameters at the command line.
For an app example, see Calculate NCA Parameters and Fit Model to PK/PD Data Using SimBiology Model Analyzer App.
Acknowledgements for data in the tobramycin.txt file are located in
the /matlab/toolbox/simbio/simbiodemos folder. Data set is provided by
Dr. Leon Aarons (laarons@fs1.pa.man.ac.uk).
The data in the tobramycin.txt file were downloaded from the Web site
of the Resource Facility for Population Kinetics
http://depts.washington.edu/rfpk/service/datasets/index.html (no longer
active). Funding source: NIH/NIBIB grant P41-EB01975.
The original data set was modified as follows:
Header comments were removed.
The file was converted to a tab-delimited format.
Missing values in the HT column were denoted with
"." instead of 100000000.000.
[1] Original Publication: Aarons L, Vozeh S, Wenk M, Weiss P, and Follath F. “Population pharmacokinetics of tobramycin.” Br J Clin Pharmacol. 1989 Sep;28(3):305–14.