# `scaRabee`: An R-based Tool for Model Simulation and Optimization in Pharmacometrics

#### Sebastien Bihorel

# Introduction

`scaRabee` is a toolkit for modeling and simulation primarily intended for the field of pharmacometrics. It was initially a R port [1] of `Scarabee`, a Matlab-based application developed for the simulation and optimization of pharmacokinetic and/or pharmacodynamic models specified with algebraic equations, ordinary or delay differential equations [2]. It is now the only version supported and being developed. Since its first release, the `scaRabee` package has been improved and now contains functionality which was not present in the original Matlab version.

## Preliminary notice

This vignette constitutes a user manual for `scaRabee`. This manual assumes that the reader is familiar with the concepts of pharmacokinetic and pharmacodynamic modeling and the underlying statistical theories. It is not the objective of this manual to explain and review those methods and theories. Readers who are new to this field are invited to read the excellent introductory and more advanced books from Gabrielsson and Weiner [3], Bonate [4] or Ette and Williams [5].

The systems analyzed in `scaRabee` must be specified, for most parts, using the R language and all analyses should be executed within the R environment in interactive or batch mode. Presentations of the R language are out of the scope of this manual.

## What’s new?

Version 1.1-3 introduces minor changes to `scaRabee` related primarily to the update of the `neldermead` package. Dosing variables Dx and Rx can now be provided as derived variables.

## How to obtain scaRabee

`scaRabee` is available at the Comprehensive R Archive Network and also on gihtub. `scaRabee` is distributed under a GNU General Public License version 3. Please, review the terms of this license before using this package.

## Installation and dependencies

This package is available as source archive. You are invited to read Section 6.3 Installing packages from the R Installation and Administration manual [6] for more details on how to install a source package from a local .zip or .tar.gz file on your system. Model optimization in `scaRabee` (as described in Section ‘Types of analysis performed in `scaRabee`’) relies on functions from the `neldermead`, `optimsimplex`, and `optimbase` packages, which are also distributed from CRAN and github as compressed archives or source.

## Credits

`scaRabee` was written by Sebastien Bihorel, alumni of the Paris 5 - Rene Descartes University and of the State University of New York (SUNY) at Buffalo, upon suggestions and contributions from:

- Pawel Wiczling, alumni of SUNY at Buffalo, who shared codes at the basis of the first Matlab versions of the fitmle and fitmle.cov functions,
- John Harrold, Post-Doctoral Fellow at SUNY at Buffalo, who provided numerous advises during the creation of the Matlab version of`scaRabee`,
- Sihem Ait-Oudhia, alumni of the Paris 5 - Rene Descartes University and of SUNY at Buffalo, for her suggestions and support.

The `neldermead`, `optimsimplex`, and `optimbase` packages, used for parameter optimization in `scaRabee`, are R ports of the Scilab modules of the same names which were developed by Michael Baudin at INRIA (Institut National de Recherche en Informatique et en Automatique) and now at the Digiteo consortium. More information on Scilab can be found at www.scilab.org.

## Reporting bugs

We welcome bug reports, questions, and suggestions concerning any aspect of `scaRabee` functions, documentation, installation, anything… Please email them to sb.pmlab@gmail.com.

For bug reports, please include enough information to allow the maintainer to reproduce the problem. Generally speaking, that means:

- your version of `scaRabee` and the function(s) or manual involved.
- your version of R and package dependencies.
- hardware and operating system names and versions.
- the contents of any data and model files necessary to reproduce the bug.
- a description of the problem and samples of any erroneous output.
- any unusual options you gave to configure the problem.
- anything else that you think would be helpful.

Patches are also most welcome.

# Analysis types

`scaRabee` allows the optimization and simulation of non-linear systems at the population and subject levels but does not implement non-linear mixed effects modeling. For each subject included in an analysis, `scaRabee` allows users to split the analysis problem into subproblems (also referred to as treatments), while still defining a unique model. This feature is especially useful when data obtained from several dose levels or regimens are fitted simultaneously, because it avoids the duplication of algebraic or differential equations usually needed to accommodate the different dose levels or regimens.

`scaRabee` allows users to perform three types of analysis: simulations, estimations, and direct grid searches.

## Simulation

Simulation runs allow to generate detailed model predictions based upon initial parameter values provided by the user. `scaRabee` also produces default overlay plots of model predictions and actual observations.

## Estimation

Estimation runs allow to optimize model parameters based upon the observations, the structural model and a residual variability model. Model parameters are optimized by the method of the maximum likelihood, more precisely by the minimization of an objective function defined as the exact negative log likelihood of the observed data, given the model structure and a set of parameter values. The minimization algorithm is based upon the Nelder-Mead simplex method, as implemented by the `fminsearch` function from the `neldermead` package. The computation of the data likelihood and the covariance matrices of primary and secondary parameters are performed as described in the Adapt II software user’s manual written by D’Argenio and Schumitzky [7].

The analysis of population data can be performed by naive averaging, naive pooling, or by the standard two-stage approach [5]. Note that the standard two-stage approach is automated only since version 1.1-0.

## Direct grid search

Direct grid search runs allow to explore the search space of an optimization problem around the initial point \(x_0\) of parameter estimates. This `scaRabee` feature automatically creates a grid of search points selected around the initial point and evaluates the objective function at each one of these search points. The boundaries of the grid are set either by the lower and upper boundaries set by the users, or by a vector of factors \(\alpha\) applied to \(x_0\) as follows: \([x_{0}/\alpha,x_{0}\times\alpha]\). The number \(npts\) of points evaluated for each parameter (or dimension of the optimization problem) can also be defined. The total number of points in the grid is \(npts^{p_e}\), where \(p_e\) is the number of parameters to be estimated. At the end of the search, a table sorted by increasing value of the objective function is created.

This table also reports the feasibility of the objective function at each particular search point because the grid search is actually delegated to the `fmin.gridsearch` function from the `neldermead` package. This function is a wrapper around `optimbase.gridsearch` from the `optimbase` package, which assesses the feasibility of a cost function in addition to calculating its value at each particular search point. Because `fmin.gridsearch` does not accept constraints, the objective function should always be feasible. Additional information is available in `optimbase.gridsearch` and `?fmin.gridsearch`.

# Getting started

All `scaRabee` analyses are typically conducted in analysis-specific folders and rely on the presence of a given list of files in this working directory. A typical `scaRabee` folder, as one created by the `scarabee.new` function, contains at least the following files:

- myanalysis.R: the master R script; this file is required to initiate the analysis. Section ‘Editing of the master scaRabee script’ describes which parts of the code must be edited.
- data.csv: the data file; this file is a comma-separated table containing the dosing and observation data to be used for model simulation or optimization. Section ‘Editing of the data file’ describes how this data must be specified.
- initials.csv: the parameter file; this file is a comma-separated table containing the names and values of the model parameters, used for model optimization or as inputs for model simulation. In the former case, the values provided for each parameters are used as initial estimates for the optimization. Section ‘Editing of the parameter file’ describes how these parameters must be specified.
- model.txt: the model file; this file is a text file in which the structural model, residual variability model, and secondary parameter computations are defined. Section ‘Editing of the model file’ describes the syntax that must be applied to edit this file.

While the names of these files correspond to the default assumed by the `scarabee.new` function, they can be modified at the user’s discretion. In this manual, the default files names are used for the sake of simplicity. Finally, please note that you can add any file needed or not for your analysis in your analysis folder. The Sections ‘Creation of a new analysis folder’ through ‘Execution of the master scaRabee script’ offer a step-by-step description of the analysis process.

## Creation of a new analysis folder

If you start a brand new data analysis, it is recommended that you open an interactive R session, and use the `scarabee.new` function to create a new analysis folder that will contain `myanalysis.R`, `data.csv`, `initials.csv`, and `model.txt`. It is recommended that you provide all arguments of `scarabee.new` to better set up the new folder:

- `name` controls the name of the analysis, which is used as a base name for the master R script file (in place of the default ‘myanalysis’) and is also inserted after the $ANALYSIS tag in the model file (see Section ‘Editing of the model file’),
- `path` defines the (absolute or relative) path to the directory to be created; if the `path` argument is NULL, then it is coerced to `name`, thus causing the (tentative) creation of a new folder named as the `name` argument in the current working directory,
- `type` defines whether the analysis is a simulation (default), an estimation, or a direct grid search run,
- `method` defines if the analysis is to be performed at the population (default) or subject level,
- `template` controls which template will be used for `model.txt`; templates are available for models defined with algebraic (‘explicit’), ordinary differential equations (‘ode,’ default), or delay differential equations (‘dde’).

Here is an example of `scaRabee` folder creation:

    require(scaRabee)
    scarabee.new(name='myanalysis',
                 path = 'some/target/directory/',
                 type = 'simulation',
                 method = 'population',
                 template = 'ode')

## Creation of new models in an on-going analysis

Alternative models for an on-going analysis might be created in three different ways:

- Create copies of the master R script and `model.txt` of interest in the current working directory. This method is not recommended but should work as long as the new master R script is updated appropriately and the string following the $ANALYSIS tag in the new model file is different from the one used in the original model.
- Create a brand new analysis folder using the method described in Section ‘Creation of a new analysis folder.’
- Copy an existing analysis folder to a different location, and make the appropriate deletion of analysis subfolders and report files.

Regardless of the chosen method, most analysis files require some form of modification, that are described in Section ‘Editing of the data file’ through ‘Editing of the master scaRabee script.’ Symbols and notations used in those sections as well as in the `scaRabee` function man pages are summarized below:

Symbol | Definition
--- | ---
\(p_{e}\) | Number of parameters to be estimated
\(p_{f}\) | Number of fixed parameters
\(p_{s}\) | Number of secondary parameters to be estimated
\(p_{d}\) | Number of derived parameters
\(c\) | Number of covariates in the dataset
\(n\) | Number of subjects in the dataset
\(k_{i}\) | Number of subproblems for the \(i^{th}\) subject
\(b_{ij}\) | Number of bolus events in the \(j^{th}\) subproblem for the \(i^{th}\) subject
\(f_{ij}\) | Number of infusion events in the \(j^{th}\) subproblem for the \(i^{th}\) subject
\(d_{ij}\) | Number of dosing events in the \(j^{th}\) subproblem for the \(i^{th}\) subject (\(b_{ij}\)+\(f_{ij}\))
\(m_{ij}\) | Number of observation times in the \(j^{th}\) subproblem for the \(i^{th}\) subject
\(s\) | Number of system states
\(o\) | Number of system outputs
\(l\) | Number of delays defined for a solution of a system of delayed differential equations

## Editing of the data file

The data file (named `data.csv` by default) contains the dosing information and endpoint measurements to be modeled or matched against a model simulation. It is required for any type of run. The `scaRabee` data files adopt similar structure and standards as those used in programs commonly used in pharmacometrics, such as NONMEM [8], S-SADAPT [9], or MONOLIX [10].

The data must consist of a time-ordered series of dosing and observations events specific to each subproblem (or treatment; see below) of each subject included in the dataset. Blocks of subject/treatment specific data must simply be stacked one after the other. The dataset must respect a tabulated, comma-separated value, format and can be edited in any text editor or spreadsheet. All `scaRabee` data files can be saved as any user-defined base name; however, the .csv extension is compulsory. The content of the data file must be a full rectangular table, with the following structure:

- All data variables must be stored in specific columns, each having a unique header. A series of variables with reserved names and expected content must be present, but users can add any number of custom (usually numerical) variables. The names and the meanings of the variables required in any `scaRabee` dataset are provided in the following listing, which also includes one useful but optional variable:

  - OMIT (optional) omission flag. Only the data records with the OMIT variable set to 0 are included in the analysis. The OMIT variable is coerced to integer numbers by `scaRabee`.

  - ID subject identifier. A sequence of unique integers starting at 1 is expected to distinguish the subjects in the dataset. The ID variable is coerced to integer numbers by `scaRabee`.

  - TRT subproblem identifier. This variable must contain integer numbers in **{increasing order from 1 to \(k_{i}\), the total number of subproblems for the \(i^{th}\) subject. If the user decides to define different subproblems for one or more individuals, all subproblems are evaluated separetely, but all contribute to the value of objective function for this(ese) individual(s). This feature typically allows users to define simpler systems when modeling different dose levels/regimens, as it avoids e.g. the duplication of the system equations to accomodate data collected at multiple dose levels, or the need for a system reset between to treatment period. Therefore, the TRT variable is indistinctly referred to as the subproblem or treatment variable in this manual. All records with a similar TRT value will be considered as part of the same subproblem. The TRT variable is coerced to integer numbers by `scaRabee`.

  - TIME independent variable. It represents the time since the first event; therefore, TIME should be 0 for the first (dosing or observation) record of each unique treatment of each subject. If this is not the case for at least one treatment for one subject, the dataset is processed by `scaRabee` and a new dataset including the calculated time since first event is saved to the working directory and used for the analysis.

  - AMT amount variable. This variable is used to define dosing events in combination with the RATE, CMT, and TIME variables. For each dosing record, the value set for the AMT variable represents the dose administered at the TIME for the record and assigned to the system state defined in the CMT variable (see below). The content of the AMT variable is ignored for observation records.

  - RATE rate variable. This variable is used to define dosing events in combination with the AMT, CMT, and TIME variables. For each dosing record, the value set for the RATE variable reflects the rate at which the dose AMT is administered into the system state CMT (see below). The RATE variable can be set to:

    - 0 to indicate an instantaneous input into the system,
    - any value > 0 to define the rate of a zero-order input into the system,
    - -1 to request the estimation of the rate of a zero-order input into the system, and
    - -2 to request the estimation of the duration of a zero-order input into the system.
      The user cannot request the estimation of the duration for one record, and the estimation of the rate for another: -1 and -2 are mutually exclusive across the dataset.

  - CMT compartment variable. This variable represents the system state (i.e. a compartment in the standard representation of system in pharmacometrics) associated to a dosing record. The CMT variable is ignored for observation records. The CMT variable is coerced to integer numbers by `scaRabee`.

  - EVID event identifier. This variable is used to define the type of record/event. The EVID variable is set to:

    - 0 for observation records, and to
    - 1 for dosing records.
      The EVID variable is coerced to integer numbers by `scaRabee`.

  - DV dependent variable. This variable represents the observed value associated with the record. This value assigned to this variable is ignored for dosing records.

  - DVID dependent variable. This variable represents the model output (see Section ‘Editing of the model file’) associated to an observation record. Although DVID could be missing for dosing events and is ignored by `scaRabee`, if a value is provided, this value must be 0. The DVID variable is coerced to integer numbers by `scaRabee`.

  - MDV missing dependent variable. This variable must be set to 1 for dosing records and to 0 for observation records that are to be included in the analysis dataset. Observation records with a DVID value other than 0 are excluded. The DVID variable is coerced to integer numbers by `scaRabee`.

- Any other variable provided in the data file is considered as a covariate. The total number \(c\) of covariates are available for use in selected blocks of code in the model file (see Section ‘Editing of the model file’).

- Record values set to . or NA are considered missing information by `scaRabee`.

- All data files must contain at least the header line and two records per subproblem for each subject, indicating the beginning and the end of the observation intervals.

NONMEM users must be warned that several data standards and variables are not implemented in `scaRabee`, e.g. all records set with EVID of 2, 3, or 4 are ignored by `scaRabee`, and the CONT, ADDL, II, and SS variables are considered as covariates.

## Editing of the parameter file

The parameter file (named `initials.csv` by default) contains the information about the primary model parameters. Derived parameters, i.e. parameters that are needed for model computations but do not need to be estimated, can be specified in the $DERIVED or $OUTPUT blocks in the model section. Secondary parameters, i.e. parameters that are typically not needed for model computations but fro which precision statistics are required, can also be defined in the model file using the $SECONDARY block of code.

This parameter file is required in all types of runs, and can be edited in any text editor or spreadsheet. All parameter files must respect the comma-separated values format but can be saved under any user-defined name (the .csv extension is compulsory though). The content of parameter files must be provided as a full \((p_e + p_f + 1) \times{} 6\) rectangular table (where \(p_e\) and \(p_e\) are the numbers of fixed and estimated parameters), with the following structure:

- The first line must contain the headers of each column of your data table. This line is provided in the original `initials.csv` and should typically not be modified.
- There must be 6 columns, ordered as follows:
  Parameter, Type, Value, Fixed, Lower bound, Upper bound
  where Parameter, Type, and Value are the columns of parameter names, types and values, Fixed is the column indicating whether a given parameter should be estimated or fixed in an estimation analysis, and Lower bound and Upper bound are the columns defining the range of values that a given parameter could take.
- Each line must contain 6 elements separated by commas. There cannot be any missing data in this table.
- The Parameter column can contain numbers or strings of characters, representing the name of your model parameters (numbers will be handled as strings of characters).
- The Type column must contain single characters, indicating the type of each single parameter. There is four types of variables in Scarabee, so only four authorized characters:
  - P indicates that the parameter is a structural model parameter.
  - L indicates that the parameter is a delay. This category exists for the user convenience in the definition of model with delayed differential equations.
  - IC indicates that the parameter is used to define an initial condition of a differential equation. This category is a legacy of the original Scarabee Matlab code. It exists for the user convenience in the definition of model with delayed differential equations but is handled exactly the same way as parameters of type ‘P.’
  - V indicates that the parameter is used to specify the residual variability model.
- The Value column must contain real numbers, representing the values taken by the parameters.
- The Fixed column must contain either 0’s or 1’s, indicating whether a parameter should be fixed (1) or estimated (0) during an estimation analysis. This column has no impact on simulation runs.
- The Lower and Upper bounds must contain real numbers, representing the range of values that parameters can take. The optimization algorithm implemented in `scaRabee` forces all estimated parameters to remain within these defined ranges.
- All parameter files must contain at least a header line and one parameter definition line.

## Editing of the model file

The model file (named `model.txt` by default) is a text file in which users can specify the structural model, residual variability model, and secondary parameter computations. The model file is required for all types of analysis. It can be modified in any text editor and saved under any user-defined name.

The model file implements a tag-based syntax similar to the one used in NM-TRAN control streams [8], S-ADAPT-TRAN [11] or MONOLIX [10] model files. Tags are defined as strings of characters starting by the $ symbol followed by a keyword. At the exception of $ANALYSIS, each tag of the listing below marks the beginning of a block of R code defining one particular component of the evaluated system. Because of these tags, `scaRabee` model files cannot be interpreted directly by R; their content must first be parsed by `scaRabee`, before each block of R code could be evaluated at relevant stages of the analysis process. Within those blocks of code, users can call any R function that would be available in their work space.

Upon creation of a new analysis folder, the model file is pre-filled with the tags that are appropriate and required for the specified category of model. The complete list of tags required for each category of model is below

- explicit model:
  $ANALYSIS
  $OUTPUT
  $VARIANCE
  $SECONDARY
- ode model:
  $ANALYSIS
  $DEREIVED
  $IC
  $SCALE
  $ODE
  $OUTPUT
  $VARIANCE
  $SECONDARY
- dde model: $ANALYSIS
  $DEREIVED
  $IC
  $SCALE
  $LAGS
  $ODE
  $OUTPUT
  $VARIANCE
  $SECONDARY

As stated above, users can modify the newly created file in any text editor. Note that any tag keyword could be abbreviated to the first three letters of the keyword, except for $IC. When a analysis is started (see Section ‘Execution of the master scaRabee script’), the model file is read, parsed, and checked by `scaRabee`. If requirements are not met, the analysis stops and users are invited to check the content of the model file. Note that `scaRabee` determines the category of structural model by scanning the content of the file for the $ODE and $DDE tags: if the $ODE tag is detected, the model is assumed to be defined with ordinary differential equations; if the $DDE tag is detected, the model is assumed to be defined with delay differential equations; if both tags are not detected, the model is assumed to be defined with algebraic equations. The $ODE and $DDE tags cannot coexist within the same model file.

### $ANALYSIS

The $ANALYSIS tag allows users to provide a name to the analysis, which is used to name the folder created to store the results of the analysis (see Section ‘Execution of the master scaRabee script’) and the analysis report files. The name extracted by `scaRabee` is the first word following the tag.

The $ANALYSIS tag must be present in all model files, regardless of the category of models.

### $DERIVED

The $DERIVED tag is specific to and required for structural models specified with ordinary or delay differential equations. It allows users to define derived parameters which could be called later within the $ODE or $DDE blocks of code. Within the $DERIVED block, users can call any primary parameter defined in the parameter file and any covariate name to define new objects. Only the new R objects created in the $DERIVED block will be considered as secondary parameters; in other words, all modifications of a primary parameter will be ignored. Furthermore, users can choose to leave this block of code empty, if no derived parameter is needed.

Although users could choose to define derived parameters within the $ODE or $DDE blocks, it is computationally more efficient to define them in the $DERIVED block, as this block of code is only evaluated once for each model evaluation, while the $ODE or $DDE blocks of code are evaluated up to several thousands of times.

Note that the $DERIVED tag is not required (and actually ignored) for models specified with algebraic equations, because derived parameters could be defined within the $OUTPUT block without loss of computation efficiency.

### $IC

The $IC tag is specific to and required for structural models specified with ordinary or delay differential equations. It allows to define the initial conditions of the system of differential equations. Users can call any primary or derived parameters within the $IC block.

`scaRabee` expects the creation of the `init` object, which must be a vector containing as many elements as there are states in the system of differential equations.

### $SCALE

The $SCALE tag is specific to and required for models specified with ordinary or delay differential equations. It allows users to scale any instantaneous or continuous inputs into the system. This is particularly useful when the dimensions of the inputs and the associated stated are different, which is the case when a dose of drug in mass (g) or amount (mol, IU) is assigned to a state modeled as a concentration (g/L, mol/L or IU/L). Users can call any primary or derived parameters within the $SCALE block.

`scaRabee` expects the creation of the `scale` object, which must be a scalar or a vector containing as many elements as there are states in the system of differential equations. Consequently, all inputs into a given system state will be scaled identically.

### $LAGS

The $LAGS tag is specific to and required for structural models specified with delay differential equations. It allows users to define the delays at each the system of differential equations should be evaluated. Users can call any primary parameter and any derived parameter to define delays within the $LAGS block.

All primary parameters of type ‘L’ and all new R objects created in the $LAGS block will be considered as delays. All modifications of a primary or derived parameter will be ignored, so users cannot directly set primary or derived parameters as systems delays. Except for the parameters of type ‘L,’ all delay parameters must be derived from previous parameter and be given new names.

Users must define at least one system delay (either as a primary parameter of type ‘L’ or as a new R object inside the $LAGS block) when the structural model is defined by delay differential equations.

### $ODE

The $ODE tag is specific to and required for structural models specified with ordinary differential equations. It allows users to define the system of differential equations. The parameters available to users within the $ODE block are:

- the primary parameters,
- the derived parameters,
- \(t\), the time of evaluations of the system,
- \(a_1\), …, \(a_s\), the values of the system states at time \(t\), where \(s\) is the total number of states, and
- any covariate name. However, by default, `scaRabee` does not interpolate the covariate data at time \(t\). Users might want to call the `approx` function for this purpose (see `?approx`).

`scaRabee` expects the creation of the `dadt` object, a \(1 \times s\) matrix of system states. Note that it is not necessary to include exogenous inputs (boluses and infusions) into the system of differential equations, this is automatically done by the code.

### $DDE

The $DDE tag is specific to and required for structural models specified with delay differential equations. It allows users to define the system of differential equations. The parameters available to users within the $ODE block are:

- the primary parameters,
- the derived parameters,
- \(t\), the time of evaluations of the system,
- \(a_1\), …, \(a_s\), the values of the system states at time \(t\), where \(s\) is the total number of states,
- \(alag.lag_1\), …, \(alag.lag_l\), the vector of system states at time \(t-lag_1\), …, \(t-lag_l\), where \(l\) is the total number of delays defined in the $LAGS block of code. To access to the value of a particular system state at a particular delay, users must subset the appropriate \(alag.lag_i\) vector: i.e. \(alag.past[3]\) would extract the value of the 3\(^{rd}\) system state at a delay named `past`, and
- any covariate name. However, by default, `scaRabee` does not interpolate the covariate data at time \(t\). Users might want to call the `approx` function for this purpose (see `?approx`).

`scaRabee` expects the creation of the `dadt` object, a \(1 \times s\) matrix of system states. Note that it is not necessary to include exogenous inputs (boluses and infusions) into the system of differential equations, this is automatically done by the code.

### $OUTPUT

The $OUTPUT tag must be present in all model files, regardless of the category of models. It allows users to defined the output(s) of the structural model.

In models defined with algebraic equations, the $OUTPUT block is the place where the derived parameters and the structural model should be defined. The parameters available to users within the $OUTPUT block are:

- the primary parameters,
- \(times\), the vector of unique times of observations (or simulated observations),}
- \(bolus\) and \(infusion\), the data frames of bolus and infusion dosing records extracted from the data file, and
- any covariate data. Note that it is only necessary to interpolate the covariate data for simulation or direct grid search runs, as covariate data should be available at any observation time in estimation runs.

In models defined with ordinary or delay differential equations, the $OUTPUT block is the place to define the model output using the predictions from the integration of the system of differential equations. The parameters available to users within the $OUTPUT block are:

- the primary parameters,
- the derived parameters,
- \(times\), the vector of unique times of observations (or simulated observations), and
- \(f\), the \(s \times{} m_{ij}\) matrix of system state predictions, where \(m_{ij}\) is the total number of observations in the \(j^{th}\) subproblem for the \(i^{th}\) subject.

`scaRabee` expects the creation of the `y` object, which must be a \(o \times{} m_{ij}\) matrix, where \(o\) is the number of system outputs. For any type of run, the data records set with a DVID value of dvid will be matched against the \(dvid^{th}\) system output. Therefore, the maximum value of the DVID variable in the dataset must be \(o\).

### $VARIANCE

The $VARIANCE tag must be present in all model files, regardless of the category of structural model. The presence of a $VARIANCE tag is required for types of runs, except for simulations. The $VARIANCE block allows users to define the residual variability models associated with each structural model outputs. The parameters available to users within the $VARIANCE block are:

- the primary parameters,
- the derived parameters,
- \(y\), the \(o \times{} m_{ij}\) matrix of structural model predictions, and
- \(ntime\), a scalar which value is set to \(m_{ij}\).

`scaRabee` expects the creation of the `v` object, which must have exactly the same dimension as the `y` object created in the $OUTPUT block of code. `v` represents the matrix of variance associated with each model prediction. Typical residual variability models are (assuming \(o=1\)):

- additive variability model with variance 1

    v <- rbind(ones(1,ntime))

- additive variability model with estimated or fixed standard deviation, SD:

    v <- rbind((SD^2)*ones(1,ntime))

- coefficient of variation model with estimated of fixed standard deviation, CV:

    v <- rbind((CV^2)*(y[1,]^2))

- additive and constant coefficient of variation model with estimated or fixed standard deviations, SD and CV:

    v <- rbind((SD^2)*ones(1,ntime) + (CV^2)*(y[1,]^2))

### $SECONDARY

The $SECONDARY tag is optional for all model files, regardless of the category of structural model or run type. It allows users to define \(p_s\) secondary parameters for which associated statistics must be computed (typically precision and parametric confidence interval). The only parameters available to users within the $SECONDARY block are the primary parameters. Only the new R objects created in this block will be considered as secondary parameters; in other words, all modifications of a primary parameter will be ignored. Furthermore, users can choose to leave this block of code empty, if no secondary parameter should be computed.

## Editing of the master scaRabee script

The master `scaRabee` script (named `myanalysis.R` by default) is the R script that you must execute to perform any analysis. You must edit several lines location in a specific section of the file (from line 21 to line 57, or 60 if ‘dde’ was selected as a template when `scarabee.new` was called) to define the settings of your analysis. Any other line of this file should typically not be modified. Commented lines within the user-editable section explain what and how variable(s) should be defined.

- Line 25: users can choose to define a working directory by adding a valid path within the `setwd` function. This is optional but recommended if users work in an interactive R session. If provided, the path to the working directory must contain the files specified in the `files` list (see below).
- Lines 34-36: the `data`, `param`, and `model` levels of the `files` list are character variables defining the names of the files where your data, parameters, and model are respectively defined. The default content of these levels matches the name of corresponding files created by `scarabee.new`. Users can change those default names.
- Line 39: the `runtype` variable is a character variable, defining if the analysis is an estimation, a simulation, or a direct grid search. Any other character string than ‘estimation,’ ‘simulation,’ or ‘gridsearch’ will cause an early termination of the run and the display of an error message to the console or log file.
- Line 42: the `method` variable is a character variable, defining the scope of the analysis. It must be set to ‘subject’ or ‘population.’ Any other character string will cause an early termination of the run and the display of an error message to the console or log file.
- Line 44: the optimization algorithm is designed to return an infinite objective function value in case the computation of the objective function at a given point of the multi-dimensional search space returns an error message. This is meant to prevent R from stopping the optimization process. Unfortunately, this will also happen if an execution error occurs during the evaluation of the model or the residual variability functions. The `debug` variable allows users to shut down this feature, and identify potential syntax or variable dimension problems in your model or residual variability files. The `debug` variable is a logical that can only take TRUE or FALSE as value.
- Lines 49-50: `estim` is a list with two levels, `maxiter` and `maxfunc`, defining the maximum number of iterations and function evaluations during an estimation run. Both must be scalar integers. The default values are 500 and 5000, which should typically allow user’s problem to converge to a stable point of the search space.
- Lines 54-55: the `npts` and `alpha` are variables specific to direct grid search runs. `npts` must be an integer greater than 2 and defines the number of points that the grid should contain per dimension (i.e. variable model parameter). `alpha` must be a scalar or a vector of real numbers greater than 1, which give the factor(s) used to calculate the range of evaluation for each dimension of the search grid (see `?scarabee.gridsearch` for more details). If `alpha` is set to NULL, the lower and upper boundaries set in the parameter file are used to define the range of evaluation for each dimension of the grid.

## Execution of the master scaRabee script

Once all necessary files have been edited, the analysis can be performed by executing the master R script. This can be done in two ways:

- from an interactive R session: we recommend that you set the working directory as the path to the analysis folder both in the R session and in the master script (see Section ‘Editing of the master scaRabee script’). Then, type: `source('myanalysis.R')`.
  You will be asked whether or not you want to change the working directory, press ENTER if this is not the case. At the end of the run, press ENTER when prompted to display the different plots generated by `scaRabee`.
- from a shell or dos window: navigate to the directory containing the master R file of interest, then run the analysis by typing: `R CMD BATCH myanalysis.R`
  You may add any option you see fit.

In both modes, `scaRabee` creates a new folder in the working directory which name has the following structure: ..#
where is the string of character directly following the $ANALYSIS tag in the model file, is ‘est’ for estimation runs, ‘sim’ for simulation runs, ‘grid’ for direct grid search runs, and # is a two-digit integer.

At the exception of the .Rout file, all run outputs are stored in the newly created folder. Additionally, a subfolder called ‘run.config.files’ is created to backup all original analysis files (`data.csv`, `initials.csv`, `model.R`, and `myanalysis.R`).

In interactive mode, the run progression will be reported to the console, while it is stored to a log file in batch mode. Upon successful completion of the run, a termination message is reported and graphical outputs and ASCII text reports are produced. Most errors happening during the execution of the master R script should stop the run and prevent the creation or the finalization of the graphs and report files. Instead, an informative message should be displayed.

Simulation runs

Upon successful completion of the run, you should be able to see (in interactive mode) as many figures as the number of subject-subproblem combinations (see Section ‘Scope of analysis’ for more details about how the scope of analysis impacts this number). Those overlay figures represent the predicted changes in all selected outputs on top of the observed data. As stated above, all figures are stored in the newly created folder.

A file called `<myanalysis>.simulation.csv` file is also saved in the same folder. This file lists the values taken by the model outputs at >1001 points within the studied time interval (typically from the minimum dose event or observation time to the maximum observation time), for each subproblem of each subject (see Section ‘Scope of analysis’ for more details about the impact of the scope of analysis on this file).

Estimation runs

Upon successful completion of the run, a figure summarizing the changes in the objective function and the estimated parameter values as a function of the iteration number is created for each subject and stored in the newly created folder. A overlay figure of model predictions and observed data, and a figure showing 4 goodness-of-fit plots (predictions vs observations, weighted residuals vs time, weighted residuals vs observations, weighted residuals vs predictions) for each subproblem of each subject are also created and stored in the same folder (see Section ‘Scope of analysis’ for more details about the impact of the scope of analysis on these plots). Starting on `scaRabee` version 1.1-0, those figures are not displayed on screen when the analysis is run in interactive mode.

A file called `<myanalysis>.report.txt` file is also saved in the same folder and provides, for each subject in the analysis, a summary of the estimation run, a summary table of final parameter estimates associated with precision statistics expressed as a coefficient of variation and a confidence intervals (calculated as described in [7]), the matrices of covariance and correlation for primary parameters, plus a summary table of computed secondary parameters associated with coefficient of variation and confidence intervals (calculated as described in [7]).

Moreover, a file called `<myanalysis>.iterations.csv` is saved in the folder and provides, in a tabulated format, the values of objective function and estimated parameters obtained at all iterations for each subject.

A file called `<myanalysis>.predictions.csv` is also saved in the folder and provides the values of observations, predictions, residuals, variances, and weighted residuals for each non-missing observation time, stacked by subject, subproblem, and model output.

Finally, a file called `<myanalysis>.estimates.csv` is also saved in the folder and summarizes the final parameter estimates for each subject included in the analysis. This file could be helpful to calculate statistics of distribution of the different parameters in the analysis population.

Direct grid search runs

Direct grid search runs include two main steps: the actual grid search, followed by a simulation step that is based upon the combination of parameter values that provided the lowest objective function value during the grid search. Direct grid search runs coerce the scope of the analysis to the population, even though the `method` variable in the master `scaRabee` script is set to ‘subject.’ Therefore, the computation of the objective function during the grid search and the model predictions obtained during the simulation step are performed at the population level (see Section ‘Scope of analysis’ for more details about the impact of the scope of analysis)

The progression of the grid search step is reported on the console in interactive mode or in the log file in batch mode. Upon completion of this step, no graph is created. Instead, a regular simulation run starts and results in the creation of the standard diagnostic plots mentionned above.

The same files created by standard simulation runs are generated by a direct grid search run in the newly created folder. Furthermore, the results of the grid search are reported in a text file called `<myanalysis>.report.txt` that is also saved in the newly created folder.

# Scope of analysis

`scaRabee` analysis can be conducted at the subject or population level. Users can set this scope of analysis by modifying the `method` variable in the master `scaRabee` script, as described in Section ‘Editing of the master scaRabee script.’

When `method` is set to ‘subject,’ `scaRabee` processes and stratifies the content of the data file assuming that all dosing and observation records with specific ID values were obtained from different individuals. In this case, estimation runs optimize the model parameter separately for each individual, starting at the same search point provided by the initial parameter estimates. This corresponds to the standard two-stage approach, when the data file actually contains data from multiple subjects (i.e. multiple unique ID variable values can be found in the data file), or to the naive pooling approach, when the data file only contains data from a single individual (i.e. the ID variable is set to 1 for all records) [5]. Simulation runs performed at the subject level evaluate the model for each subproblem/treatment of each subject using the same initial parameter estimates. Grid search runs are not performed at the individual level, as the `method} variable is coerced to ‘population’ for this type of analysis.

When `method` is set to ‘population,’ `scaRabee` processes the content of the data file assuming that all observation records were obtained from a single individual. The dosing history is extracted from the dosing records with an ID variable set to 1. In this case, estimation runs optimize the model parameter on the global data, starting at the search point provided by the initial parameter estimates. This corresponds to the naive pooling approach [5]. Simulation runs performed at the population level evaluate the model for all detected subproblems/treatments found in the dataset, using the initial parameter estimates. Finally, all grid search runs are performed at the population level.

# Design information

## Solvers of differential equations

Structural models defined using systems of differential equations require those systems to be integrated before model outputs could be generated. This step of integration is performed using solvers of differential equations, which are the `lsoda` solver from the `deSolve` package for systems of ordinary differential equations and the `dde` solver from the `PBSddesolve` package for systems of delay differential equations. Users are invited to refer to the documentation of those packages for more information.

## Implementation of dosing history for model defined with differential equations

Instantaneous (i.e. bolus) and zero-order (i.e. infusion) inputs are automatically allocated to the appropriate system state by the functions evaluating the systems of differential equations (see the source code of `ode.model` and `dde.model` for more details). General rules for the implementation of dosing history are provided below.

Input scaling

All bolus and infusion input amounts (provided in the AMT variable) must be scaled by users. Input scaling is implemented in the R code provided in the $SCALE block of the model file as explained in Section ‘Editing of the model file’). Scaling is particularly useful when the dimensions of the inputs and the associated stated are different, which is the case when a dose of drug in mass (g) or amount (mol, IU) is assigned to a state modeled as a concentration (g/L, mol/L or IU/L).

Bolus inputs

The `lsoda` solver used for models defined with ordinary differential equations does not include any handler of discontinuities. Because bolus inputs represents discontinuous events, their implementation require the integration of the system of differential equations to be performed by steps. When bolus inputs are detected in the data file, `scaRabee` splits the global integration interval into several continuous integration intervals based upon the dose event times. The initial conditions of the system are updated for each integration interval by adding the scaled bolus amount (AMT) specified in the data file to the value of the state (CMT) at the end of the previous interval (or the specified initial conditions in the case of the first interval). Therefore, all model predictions made at the time of a bolus assume that this bolus has entered the system. Users are thus advised to set the time of pre-dose samples slightly before the time of the boluses, to ensure that those samples are handled as pre-dose and not post-dose samples.

Infusion inputs

The reduction from multiple data files to a single one introduced in the version 1.1-0 of `scaRabee` resulted in major modifications in the automated processing and assignment of infusions to system states.

Previous versions of `scaRabee` required infusions to be ‘constructed’ by multiple records in a dosing-specific input files. Input rates were then linearly interpolated between two consecutive time points, allowing for an infusion rate to change over time. In version 1.1-0 of `scaRabee`, infusions are documented as single dosing records in the data file, providing the time of infusion start, the amount and rate of dosing. The rate is assumed to be constant for the whole duration of the infusion. If RATE>0 for the dosing record, the duration is calculated as RATE/AMT. If RATE=-1, the rate of infusion is estimated and the duration is calculated as R#/AMT, where R# is a derived parameter expected to be defined in the $DERIVED block (# represents the value of the CMT variable set for the dosing record). If RATE=-2, the duration of infusion is estimated and the rate is calculated as AMT/D#, where D# is a derived parameter expected to be defined in the $DERIVED block (# represents the value of the CMT variable set for the dosing record).

The following example illustrates the automated dosing allocation in `scaRabee`. Let’s assume that the system is specified by two ordinary differential equations, both fixed to zero, and that the data in provided as follows in the dataset:

OMIT | ID | TRT | TIME | AMT | RATE | CMT | EVID | DV | DVID | MDV
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0
0 | 1 | 0 | 0 | 1000 | 100 | 1 | 1 | 0 | 0 | 1
0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0
0 | 1 | 0 | 15 | 100 | 0 | 2 | 1 | 0 | 0 | 1
0 | 1 | 0 | 20 | 10000 | 50 | 1 | 1 | 0 | 0 | 1
0 | 1 | 0 | 20 | 250 | 0 | 1 | 1 | 0 | 0 | 1
0 | 1 | 0 | 45 | 0 | 0 | 1 | 0 | 0 | 1 | 0
0 | 1 | 0 | 45 | 0 | 0 | 2 | 0 | 0 | 1 | 0

State 1 receives 1 bolus dose at time 20 and 2 infusions: the first, between 0 and 10, has a constant rate of 100, and a second starts at time 20 and does not stop before the last observation. State 2 only receives a bolus dose at time 15. The following graphs show the changes in the infusion rate entering both states (top graphs), as well as the accumulation of the drug in both states (bottom graphs).

## Implementation of dosing history for model defined with algebraic equations

Dosing history cannot be automatically assigned to a model defined with algebraic equations. However, users can use dosing information in the $OUTPUT block by calling the `bolus` and `infusion` variable, which each contain the TIME, CMT, AMT, RATE variable extracted from the \(d_{ij}\) dosing records identified as instantaneous (RATE=0) or zero-order (RATE$$0) inputs. Relevant data extraction would need to be performed by user-specific code.

It might also be convenient to carry dosing information in a covariate (i.e. DOSE) which could be used in a explicit solution of a specific pharmacokinetic model.

As such, it might have occurred to users familiar with NONMEM that the implementation of models defined with algebraic equations in `scaRabee` is not too different from what NONMEM allows via to the $ERROR record.

# Analysis examples

This section is designed to illustrate some selected features of `scaRabee`. Eight examples are available as demos using calls such as the following (replacing `ex` by `example1` to `example8`):

    demo(ex, package = 'scaRabee', echo = FALSE)

Running these examples will create analysis folders in your working directory. We recommend that you review their content after their creation.

## Example 1: Simulation of a model defined with algebraic equations at the population level

A simple PK/PD model defined with algebraic equations is simulated at the population in this example . The PK model describes the drug concentration \(C_p\) using a one-compartment model with linear elimination after a single 2h infusion. The response \(E\) is related to \(C_p\) by a direct effect Imax model:

\[ \begin{array}{l} C_p(t) = \frac{infusion \, rate}{CL} \cdot{} \bigg( 1-H(t-2) \cdot{} \Big( 1-e^{-\frac{CL}{V_c} \cdot{}(t-2)} \Big) - e^{-\frac{CL}{V_c}\cdot{}t} \bigg)\\ E(t)= E_0\cdot\Big(1-\frac{I_{max}\cdot{}C_p(t)}{IC_{50}+C_p(t)}\Big) \end{array} \]

where \(H\) is the Heaviside function, \(CL\) the elimination clearance, \(V_c\) the volume of distribution, \(E_0\) the baseline response, \(I_{max}\) the maximum inhibition factor, and \(IC_{50}\) the half-inhibitory concentration.

Note that because this is a simulation, there is no need for a $VARIANCE block.

## Example 2: Simulation of inputs into a model defined with ordinary differential equations

This example uses a model defined by a system of 2 ordinary differential equations to illustrate how inputs are automatically assigned and scaled to system states. Because both states have null initial conditions and gradients, the output of the model represents the cumulative scaled amount of drug assigned to each state based upon the information provided in the dataset.

## Example 3: Simulation of a model defined with ordinary differential equations at the population level

A target-mediated disposition model for interferon-$$1a pharmacokinetics in monkey was described by Mager and colleagues [12]. This model is simulated at the population level in example 3 using the following system of differential equations:

\[ \begin{array}{l l l} & SC & IV\\ \frac{dA_D}{dt}=-k_a\cdot{}A_D & A_D(0)=F\cdot{}D_{SC} & A_D(0)=0\\ \frac{dA_L}{dt}=k_a\cdot{}A_D - k_{a2}\cdot{}A_L & A_L(0)=0 & A_L(0)=0\\ \frac{dA_P}{dt}=k_{a2}\cdot{}A_L + k_{tp}\cdot{}A_T + k_{off}\cdot{}DR - (k_{on}/V_c)\cdot{}A_{P}\cdot{}R - & A_{P}(0)=0 & A_{P}(0)=D_{IV}\\ \quad{} \quad{} (k_{pt}+k_{loss})\cdot{}A_P & & \\ \frac{dA_T}{dt}= k_{pt}\cdot{}A_P - k_{tp}\cdot{}A_T & A_{T}(0)=0 & A_{T}(0)=0\\ \frac{dDR}{dt}=(k_{on}/V_c)\cdot{}A_{P}\cdot{}R - (k_{off}+k_{int})\cdot{}DR & DR(0)=0 & DR(0)=0\\ R=R_{max}-DR & &\\ \end{array} \]

where \(A_D\), \(A_L\), \(A_P\), and \(A_T\) are the amounts of drug in the subcutaneous depot, lymph, central, and peripheral compartments and \(DR\) is the concentration of drug-receptor complex. The noteworthy features of this example are:

- how dose information is extracted from the vector of covariate DOSE to define the scaling bioavailability factor \(F\) in the $DERIVED block,
- how the TRT variable is used in the dataset to define the different dosing regimens (3 different dose levels administered by single sub-cutaneous or intravenous dosing), and to avoid the duplication of the model equations, and
- how the output of the system of differential equations is subset and transformed to just extract the predicted concentration in the central compartment in the $OUTPUT block.

## Example 4: Simulation of a model defined with delay differential equations at the population level

This example features a 2-compartment model with linear inter-compartment distribution but with a delayed entry of the drug into the peripheral compartment. The system can described by the following equations:

\[ \begin{array}{l l} \frac{dA_P}{dt}=-(k_e+k_{pt})\cdot{}A_P(t) + k_{tp}\cdot{}A_T(t) & A_P(0)=0\\ \frac{dA_T}{dt}= k_{pt}\cdot{}A_P(t-xyz) - k_{tp}\cdot{}A_T & A_T(0)=0 \end{array} \]

where \(A_P\) and \(A_T\) are the amounts of drug in the central and peripheral compartments and \(xyz\) is the delay of entry into the peripheral compartment.

This system is simulated at the population level assuming repeated bolus administrations is the central compartment. The noteworthy features of this example are:

- how derived rate constants are computed in the $DERIVED block using the clearance and volume parameters defined in the parameter file,
- how the delay \(xyz\) is directly available in the system of delay differential equations, because it was defined as a ‘L’ parameter in the parameter file, and
- how a variance model is defined in the $VARIANCE block but not used for the simulation (this could be useful, if the same model is then used in an estimation analysis).

## Example 5: Estimation of a model defined with algebraic equations at the

population level

This example estimates the parameters of the Example 1 model using the observations provided in the Example 1 dataset and the naive pooling approach. The model file was however modified to include the variance models of the concentrations and responses. Note that the concentrations were initially log-transformed in the dataset to fit the original data with log residual variability model. Consistently, the predicted \(C_P\) concentrations are log-transformed before assigned as the first raw of \(y\).

## Example 6: Simulation of a model defined with ordinary differential equations at the subject level

In this example, a precursor turn-over model is simulated at the subject level. The rate of transformation of the precursor \(P\) into response \(R\) is inhibited by the drug concentration \(C_p\). The changes in drug concentration, precursor and response are described by the following equations:

\[ \begin{array}{l l} \frac{dC_P}{dt}=-k_e\cdot{}C_P(t) & C_P(0)=D/V_c\\ \frac{dP}{dt}= k_{in}- k_t\cdot{}(1-\frac{I_{max}\cdot{}C_p}{IC_{50}+C_p})\cdot{}P & P(0)=R_0\\ \frac{dR}{dt}= k_t\cdot{}(1-\frac{I_{max}\cdot{}C_p}{IC_{50}+C_p})\cdot{}P - k_{out}\cdot{}R & R(0)=R_0 \end{array} \]

## Example 7: Estimation of a model defined with algebraic equations at the subject level

WARNING: this example can be time consuming.

This example estimates the parameters of the Example 1 model using the observations provided in the Example 1 dataset and the standard two-stage approach. The model file was however modified to include the variance models of the concentrations and responses. Note that the concentrations were initially log-transformed in the dataset to fit the original data with log residual variability model. Consistently, the predicted \(C_P\) concentrations are log-transformed before assigned as the first raw of \(y\).

## Example 8: Direct grid search for a model defined with delay differential equations

WARNING: this example can be time consuming.

This example illustrates how direct grid search can be performed using a life-span model for paclitaxel (\(C_P\)) on leukocytes counts in cancer patient [13].

Normalized leukocyte counts (\(R_\%\)) collected in one patient were digitized and a direct grid search run is performed to improve the estimates roughly chosen for the PD parameters (\(C_P\) is assumed to be accurately described by the parameter estimates obtained for a 3-compartment model). The paclitaxel effect is modeled with the following delay differential equation:

\[ \begin{matrix} \frac{dR_\%}{dt}=k_{in\%} \cdot{} \Big( e^{-\int_{t-T_P-T_M}^{t-T_M} f\big(C_P(z)\big)dz} - e^{-\int_{t-T_P-T_M-T_R}^{t-T_M-T_R} f\big(C_P(z)\big)dz} \Big)\\ f\big(C_P\big)=\frac{K_{max} \cdot{} C_P}{KC_{50} + C_P} \end{matrix} \]

The grid is formed by combining 3 grid points per variable parameters (\(T_P\), \(T_M\), \(K_{max}\) and \(KC_{50}\)) and by setting the scaling factor to 2 for all parameters. \(T_R\) was fixed as described in [13]. The best solutions found by direct grid search is finally compared to the reported estimates.

# Network of `scaRabee` functions

Map of the functions distributed with scaRabee (1/2, click to view at full scale)

Map of the functions distributed with scaRabee (2/2, click to view at full scale)

# References

1. R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2009.
2. Bihorel S. Scarabee user’s manual. Buffalo, NY, USA; 2009.
3. Gabrielsson J, Weiner D. Pharmacokinetic/pharmacodynamic data analysis: Concepts and applications. 4th ed. ApoteKarsocieteten; 2007.
4. Bonate PL. Pharmacokinetic pharmacodynamic modeling and simulation. New York, NY: Springer; 2006.
5. (ed.) Ette EI, (ed.) Williams PJ. Pharmacometrics: The science of quantitative pharmacology. Hoboken, NJ: John Wiley & Sons. xix, 1205 p.; 2007.
6. R Development Core Team. R installation and administration. R Foundation for Statistical Computing; 2010.
7. Schumitzky A, D’Argenio DZ. ADAPT II users’guide: Pharmacokinetic/pharmacodyanmic system analysis software. Los Angeles, CA, USA: Biomedical Simulations Resource; 1997.
8. Stuart Beal, Lewis B. Sheiner, Alison Boeckmann, and Robert J. Bauer. NONMEM user’s guides. Ellicott City, MD, USA: Icon Development Solutions; 2009.
9. Bauer RJ. S-ADAPT/MCPEM user’s guide (version 1.56). Software for pharmacokinetic, pharmacodynamic and population data analysis. Berkeley, CA, USA; 2008.
10. Kuhn E, Lavielle M. Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis. 2005;49:1020–38.
11. Jürgen Bulitta, Ayhan Bingölbali, and Cornelia B. Landersdorfer. Development and evaluation of a new efficiency tool (SADAPT-TRAN) for model creation, debugging, evaluation, and automated plotting using parallelized S-ADAPT, Perl and R. PAGE 19 (2010) Abstr 1917. Available at www.page-meeting.org/?abstract=1917;
12. Donald E. Mager, Berend Neuteboom, Constantinos Efthymiopoulos, Alain Munafo, and William J. Jusko. Receptor-mediated pharmacokinetics and pharmacodynamics of interferon-beta1a in monkeys. J Pharmacol Exp Ther. 2003;306:262–70.
13. Wojciech Krzyzanski and William J. Jusko. Multiple-pool cell lifespan model of hematologic effects of anticancer agents. J Pharmacokinet Pharmacodyn. 2002;29:311–37.