install.packages("PKPDsim")
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 codefile
: similar to code
, but reads the code from a fileFor example, a 1-compartment oral PK model can be obtained using:
library("PKPDsim")
pk1 <- new_ode_model(model = "pk_1cmt_oral")
Run the new_ode_model()
function without arguments to see the currently available models:
new_ode_model()
The custom model needs to be specified as a string or text block:
pk1 <- new_ode_model(code = " dAdt[1] = -KA * A[1] dAdt[2] = +KA * A[1] -(CL/V) * A[2] ")
The input code should adhere to the follow rules:
dAdt
omputate[ ]
.
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.=
pow(base,exp)
.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:
pk1
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:
mod <- new_ode_model(code = " dAdt[1] = -(CL/V)*A[1]; ", dose = list(cmt = 1, bioav = "WT"), obs = list(cmt = 1, scale = "V"), covariates = list("WT" = new_covariate(value = 70)) )
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:
pk1 <- new_ode_model(code = " dAdt[1] = -KA * A[1] dAdt[2] = +KA * A[1] -(CL/V) * A[2] CONC = A[2] ", obs = list(variable = "CONC"), declare_variables = "CONC" )
Using the file=
argument, the model code is read from the specified files. This is just a convenience function, i.e. it allows you to separate models from R code more easily.
pk1 <- new_ode_model( file = "pk_1cmt_oral_nonlin_v1.txt", declare_variables = c("KEL", "CLi") )
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