Model libraries are useful to have consistent high-quality basic models that can be used as a model itself or as a building block for other models.
Compartment and parameter names should be all lower case when on their own and should use snakeCase when combined in some way.
Compartment and parameter names are selected to align with those used
by rxode2::linCmt()
which are described in the vignette:
vignette(“rxode2-model-types”, package = “rxode2”).
Compartment naming follows compartment names with the
linCmt()
with augmentation for other compartments:
depot
: The extravascular dosing compartment (for
example, the gut for oral dosing or subcutaneous space for subcutaneous
dosing)central
: The intravascular compartment used for
intravenous dosing or for typical pharmacokinetic (PK) model sampling of
the drugperipheral1
, peripheral2
: The first and
second peripheral compartments for 2- and 3-compartment PK modelseffect
: The compartment for effect compartment
modelsTo enable more consistent cross-model compatibility, the following conventions should be used unless there is a strong reason for an exception:
Cc
. Cc
should be used even when using a
linCmt()
model (in which case
Cc <- linCmt()
should be used and the residual error
should be applied to the Cc
parameter).PK models should use the following parameter naming conventions:
ka
: absorption ratecl
: clearanceq
: intercompartmental clearance (central
to and from peripheral1
compartments)q2
: second intercompartmental clearance
(central
to and from peripheral2
compartments)vc
: central volume of distributionvp
, vp2
: first and second peripheral
compartment volumesWhen micro-constants are used, they should use the following naming conventions:
kel
elimination rate (cl/vc
)k12
, k21
, k13
,
k31
: intercompartmental transit rates (q/vc
,
q/vp
, q2/vc
, and q2/vp2
,
respectively)Parameters are often estimated on a transformed scale. For instance, a natural logarithm transform is often used for parameters that must be positive, and a logit transform is often used when a parameter must remain within a specific range.
Transformed parameters should be prefixed with an indicator of the transformation. Preferred transformation prefixes are:
l
(lower case L): natural log transformlogit
: logit transformprobit
: probit transformGenerally, for any transform other than natural logarithm, include
the full name as a prefix. For example, natural logarithm-transformed
ka
would be lka
and logit-transformed
emax
would be logitemax
.
Random effects are estimates as part of a distribution varying by some grouping factor. The grouping factor is often a subject in a clinical trial. (For NONMEM users, random effects are often referred to as inter-individual variability.)
Random effect parameters should prefix the (transformed) parameter
name with eta
. For example, a random effect on
log-transformed clearance would be named etalcl
.
Different drug effects may be investigated during model building. And, multiple drug effect styles (linear, Emax, threshold, etc.) may be investigated by the user.
To enable simpler changes to drug effects and to minimize the chance of parameter name collisions when combining models, the following rules are strongly recommended:
drugEffect
followed by the name of the part of the model
that is most closely associated with the drug effect. For example, in
the Simeoni 2004 model, the drug effect is called
drugEffectCyclingCells
.Files in a model library should have the following characteristics:
The first line inside the function should have a description
assignment. That is
description <- "This is the description of the model"
right inside the function()
before the ini({})
block.
If the model has a literature reference associated with it, then
the second line of inside the function should have the reference, for
example,
reference <- "Richard Hooijmaijers, Matthew Fidler, William S. Denney (2022). nlmixr2lib: A Model Library for 'nlmixr2'. https://nlmixr2.github.io/nlmixr2lib/"
If it would be helpful to give the user some information about
the model on load, it can be added as meta-data as a
"message"
attribute to the model. Note that in that case,
you must give the function name as the last line of the model to ensure
that it is the returned value from evaluation of the file. (See
oncology_xenograft_simeoni_2004.R
for an example of adding
a message.)
If the model is to be combined with other models and it expects
certain objects will be defined a depends value should be specified. For
example if there is a tumor growth model that is driven by the drug
concentration in the central compartment, then the following could be
used: depends <"Cc"
It can also be helpful to specify the compartments where dosing
is expected. This can be done in the following manner:
dosing <- c("central", "depot")
Units used in the model can be specified using a list
units <- list(dosing= "mg/kg", time="hr")
To add more
fields to this list please discuss first in a GitHub issue.
The remainder of the file should be an nlmixr2 model in a
function with a typical ini()
and model()
block.
The name of the file should match the name of the model within the file.
If a function to modify, self-start, or otherwise help the user would
make sense, add it as a new file in the R/
directory with
the file name and function name updateModelName()
using the
word update followed by the model name in camelCase
(e.g. updateOncologyXenograftSimeoni2004
). If such a
function is added, please add it in the messages
described
above, as well. Update functions must be able to take in a function, an
rxUi object, or an nlmixr2fitCore object and should usually return an
rxUi object.
For examples, see the package installation directory.