`new_ode_model`

is the function that creates a new ODE
model that can be used in the `sim()`

command. It defines the
ODE system and sets some attributes for the model. The model can be
specified in three different ways:

`model`

: a string that references a model from the library included in`PKPDsim`

. Examples in the current library are e.g.`pk_1cmt_oral`

,`pk_2cmt_iv`

. To show the available models, run`new_ode_model()`

without any arguments.`code`

: using code specifying derivatives for ODE specified in*pseudo-R*code`file`

: similar to`code`

, but reads the code from a file

For example, a 1-compartment oral PK model can be obtained using:

Run the `new_ode_model()`

function without arguments to
see the currently available models:

```
## Error in new_ode_model(): Either a model name (from the PKPDsim library), ODE code, an R function, or a file containing code for the ODE system have to be supplied to this function. The following models are available:
## pk_1cmt_iv
## pk_1cmt_iv_auc
## pk_1cmt_iv_mm
## pk_2cmt_iv
## pk_2cmt_iv_auc
## pk_3cmt_iv
## pk_1cmt_oral
## pk_2cmt_oral
## pk_3cmt_oral
```

The custom model needs to be specified as a string or text block:

The input code should adhere to the follow rules:

- the derivatives in the ODE system are defined using
`dAdt`

omputate - array indices for the derivatives and compartments are indicated
with
`[ ]`

. Compartments indices can start at either 0 or 1. If the latter, all indices will be reduced by 1 in the translation to C++ code. - equations are defined using
`=`

- power functions need to be written out as
`pow(base,exp)`

. - force any numbers to be interpreted as
`real`

, to avoid them being interpreted as`integer`

. So if an equation involves the real number`3`

, it is usually safer to write this as`3.0`

in code.

The input code is translated into a C++ function. You can check that the model compiled correctly by typing the model name on the R command line, which prints the model information:

```
## ODE definition:
##
## dAdt[1] = -KA * A[1];
## dAdt[2] = +KA * A[1] -(CL/V) * A[2];
## ;
##
## Required parameters: KA, CL, V
## Covariates:
## Variables:
## Fixed parameters:
## Number of compartments: 2
## Observation variable:
## Observation scaling: 1
## Lag time: none
## IOV CV: {}
## IOV bins: 1
## Comments:
## -
```

If you’re interested, you can also output the actual C++ function
that is compiled by specifying the `cpp_show_code=TRUE`

argument to the `new_ode_model()`

function.

You can introduce new variables in your code, but you will have to
define them using `declare_variables`

argument too:

```
pk1 <- new_ode_model(code = "
KEL = CL/V
dAdt[1] = -KA * A[1]
dAdt[2] = +KA * A[1] -KEL * A[2]
", declare_variables = c("KEL"))
```

Also, when you want to use covariates in your ODE system (more info
on how to define covariates is in the *Covariates* vignette), you
will have to define them, both in the code and in the function call:

```
pk1 <- new_ode_model(code = "
CLi = WT/70
KEL = CLi/V
dAdt[1] = -KA * A[1]
dAdt[2] = +KA * A[1] -(CL*(WT/70)/V) * A[2]
", declare_variables = c("KEL", "CLi"), covariates = c("WT"))
```

One exception to the input code syntax is the definition of power
functions. `PKPDsim`

does not translate those from the
*pseudo-R* code to valid C++ syntax automatically. C/C++ does not
use the `^`

to indicate power functions, but uses the
`pow(value, base)`

function instead, so for example an
allometric PK model should be written as:

```
pk1 <- new_ode_model(code = "
CLi = CL * pow((WT/70), 0.75)
dAdt[1] = -KA * A[1]
dAdt[2] = +KA * A[1] -(CLi/V) * A[2]
", declare_variables = c("CLi"))
```

The default dosing compartment and bioavailability can be specified
using the `dose`

argument. By default, the dose will go into
compartment `1`

, with a bioavailability of `1`

.
The `bioav`

element in the list can be either a number or a
character string referring a parameter.

```
pk1 <- new_ode_model(code = "
dAdt[1] = -KA * A[1]
dAdt[2] = +KA * A[1] -(CL/V) * A[2]
",
dose = list(cmt = 1, bioav = "F1"),
parameters = list(KA = 1, CL = 5, V = 50, F1 = 0.7)
)
```

Bioavailability can also be used for dosing based on mg/kg, since
that is not supported in `new_regimen()`

. The way to
implement this is by scaling the dose by the “weight” covariate using
the bioavailability:

The observation compartment can be set by specifying a list to the
`obs`

argument, with either the elements `cmt`

and
`scale`

, or `variable`

.

```
pk1 <- new_ode_model(code = "
dAdt[1] = -KA * A[1]
dAdt[2] = +KA * A[1] -(CL/V) * A[2]
",
obs = list(cmt = 2, scale = "V")
)
```

The `scale`

can be either a parameter or a number, the
`cmt`

can only be a number.

*Note that the variables specified inside the differential
equation block are not available as scaling parameters. E.g. for
allometry you will have to redefine the scaled volume as
follows:*

```
pk1 <- new_ode_model(code = "
Vi = V * (WT/70)
dAdt[1] = -KA * A[1]
dAdt[2] = +KA * A[1] -(CL/Vi) * A[2]
",
obs = list(cmt = 2, scale = "V * (WT/70)")
)
```

Or define the observation using a variable: