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Required and Recommended Software for Pharmacokinetic Modeling How SimBiology Supports Pharmacokinetic Modeling Using the Command Line Versus the SimBiology Desktop |

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.

MATLAB | Provides a command-line interface and an integrated software environment. For instructions, see the MATLAB installation documentation for your platform. If you have installed MATLAB and
want to check which other MathWorks |

| Provides fitting tools including functions used to analyze nonlinear mixed effects. |

C Compiler | Required to prepare the model for accelerating simulations. For list of supported compilers, see Supported and Compatible Compilers. |

Optimization Toolbox™ | Optimization Toolbox extends
the MATLAB technical computing environment with tools and widely
used algorithms for standard and large-scale optimization. These algorithms
solve constrained and unconstrained, continuous and discrete problems.
If the Optimization Toolbox product is installed, you can specify
additional methods for likelihood maximization. If you do not have
this product, SimBiology uses |

You can import tabular data into the SimBiology desktop
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.

From the SimBiology desktop, you can filter the raw data
to suppress outliers, visualize data using common plots (such as `plot`, `semilog`, `scatter`,
or `stairs`), and perform basic statistical analysis.
You also can use functions to process and visualize the data at the
command line.

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 on building SimBiology models, see What is a 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.

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.

You can use the following methods to estimate the fixed effects:

`LME`— Linear mixed-effects approximation`RELME`— Restricted LME approximation`FO`— First-order estimate`FOCE`— First-order conditional estimate

For more information about each of these methods, see

`nlmefit`in the Statistics Toolbox documentation.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.

For more information, see

`nlmefitsa`in the Statistics Toolbox documentation.

In addition, you can generate diagnostic plots that show:

The predicted time courses and observations for an individual or the population

Observed versus predicted values

Residuals versus time, group, or predictions

Distribution of the residuals

A box-plot for random effects or parameter estimates from individual fitting

SimBiology extends MATLAB and lets you access pharmacokinetic modeling functionality at the command line and in the graphical SimBiology desktop.

Use the command line to write and save scripts for batch processing and to automate your workflow.

Use the SimBiology desktop to interactively change and iterate through the model workflow. The SimBiology desktop lets you encapsulate models, data, tasks, task settings, and diagnostic plots into one convenient package, namely a SimBiology project.

Furthermore, if you are using the SimBiology desktop and want to learn about using the command line, the MATLAB code capture feature in the desktop lets you see the commands and export files for further scripting in the MATLAB editor.

For an example showing pharmacokinetic modeling functionality at the command line, see Modeling the Population Pharmacokinetics of Phenobarbital in NeonatesModeling the Population Pharmacokinetics of Phenobarbital in Neonates.

Acknowledgements for data in the `tobramycin.txt` file
in the `/matlab/toolbox/simbio/simbiodemos` folder:

[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.

Data set 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`.

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