MathWorks provides pharmacokinetics software tools for your entire pharmacokinetic/pharmacodynamic (PK/PD) workflow, from modeling to analysis, within a single graphical environment. The SimBiology® pharmacokinetics software environment provides point-and-click tools to make PK/PD modeling and analysis accessible, even if you have little to no programming experience. Built on MATLAB®, SimBiology pharmacokinetics software provides direct access to an industry-tested simulation solver suite and enables you to integrate PK/PD modeling with other functionality such as parallel computing, statistics, and optimization. The pharmacokinetics software also lets you experiment with new approaches, such as integrating PK models with mechanistic or physiologically based PK models.
You can import into SimBiology pharmacokinetics software PK and PD data of various sorts, including text files, Microsoft® Excel® files, and MATLAB files. You can also import data from other sources, such as databases or SAS files, using the MATLAB workspace as an intermediate. If a data file has common headers, such as ID or Time, SimBiology pharmacokinetics software automatically recognizes and stores the headers as the group and independent variables.
The external data panel in SimBiology gives you easy access to plotting tools that enable you to quickly visualize your data in various ways. The external data panel also includes features that let you exclude outliers or bad data points either by selecting rows in the data table or by specifying rules. Excluding a data point does not remove it from the data set permanently, but rather flags the data point to be ignored during analysis. You can also perform statistical analyses on the data, such as calculating the mean, area under the curve (AUC), area under the first moment curve (AUFMC), and mean residence time (MRT).
SimBiology pharmacokinetics software allows you to create a PK model by specifying your desired model options in the PK model wizard. Model options include number of compartments, dosing type, and elimination route. Alternatively, you can create a PK or PD model using the graphical, tabular, or programmatic interfaces.
With the Fit Parameters task in SimBiology pharmacokinetics software, you can interactively configure your parameter estimation task. You can specify parameters to estimate as well as parameters for which you want to calculate random effects. You can also identify key model variables such as the dosed and response variables.
SimBiology pharmacokinetics software generates two types of outputs to determine how well a parameter fit performed: a data panel summarizing the results, and diagnostic plots specified in the Fit Parameters task. The data panel includes fixed estimates for the parameters, a table of the random effects for each parameter for each subject, a summary of statistics on the fit, and the estimated covariance matrix. You can iterate on the PK/PD modeling workflow by using the goodness-of-fit statistics, such as Akaike and Bayesian information criteria (AIC and BIC, respectively), to decide the best model for your data.
The SimBiology pharmacokinetics software desktop offers several prepackaged diagnostic plot types, including the trellis plot, which plots both the observed and predicted time courses of drug concentration for each patient. Other plot types include observed versus predicted concentration values, box plots of the random effects calculated for each parameter, residual errors over time, and the distribution plots of residuals.
A SimBiology project stores all components used in the workflow—such as multiple models, data sets, reports, and analysis tasks—in one file, making it easy to manage and organize associated components. You can capture your work by automatically creating an HTML or XML report with the SimBiology report generator.
Because SimBiology is based on MATLAB, SimBiology users can use MATLAB functionality to extend the PK/PD modeling workflow. MATLAB capabilities such as parallel computing, optimization, statistics, and curve fitting can help you further analyze the results of your PK/PD modeling.