knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

Initial estimates for a compartmental population PK model can be obtained using
`babelmixr2`

with the `"pknca"`

estimation method. Also, the central
compartment scaling factor can be auto-generated based on units for dosing,
concentration measurement, desired volume of distribution units, and time.

You do not need to perform NCA analysis by hand; the `"pknca"`

estimation method
will perform NCA analysis using the PKNCA
package automatically.

The methods used for converting NCA calculations to parameter estimates are
described in the help for `nlmixr2Est.pknca()`

.

Initial model setup is the same as for any other `nlmixr2`

model. You must load
the `babelmixr2`

library so that the `nlmixr()`

function recognizes
`est = "pknca"`

.

library(babelmixr2) one.compartment <- function() { ini({ tka <- log(1.57); label("Ka (1/hr)") tcl <- log(2.72); label("Cl (L/hr)") tv <- log(31.5); label("V (L)") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label("additive residual error (mg/L)") }) # and a model block with the error specification and model specification model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) vc <- exp(tv + eta.v) d/dt(depot) <- -ka * depot d/dt(center) <- ka * depot - cl / vc * center cp <- center / vc cp ~ add(add.sd) }) }

To use PKNCA to get initial estimates, use `est = "pknca"`

instead of one of the
other `nlmixr2`

estimation methods.

For unit conversions, provide the units to the `control = pkncaControl()`

argument. Unit conversions are only supported when the units can be
automatically converted; mass/volume can be converted to any other mass/volume
ratio, but mass to molar or molar to mass cannot because there is not a single
mass-to-molar conversion factor.

prepared <- nlmixr2( one.compartment, data = theo_sd, est = "pknca", control = pkncaControl(concu = "ng/mL", doseu = "mg", timeu = "hr", volumeu = "L") )

Now, you have the prepared model with updated initial estimates and the NCA
results embedded. You can see the new model and the PKNCA estimates by looking
at the `prepared$ui`

(the model as interpreted by rxode2) and `prepared$nca`

(the PKNCAresults object).

Note that in the new model, the fixed effect initial estimates are changed from their original values. The residual error and between-subject variability are unchanged.

prepared$ui knitr::knit_print( summary(prepared$nca) )

From the updated model, you can perform estimation on the new model object, as
with any other model that has been created for `nlmixr2`

:

fit <- nlmixr(prepared, data = theo_sd, est = "focei", control = list(print = 0)) fit

To get the initial estimate, `babelmixr2`

automatically converts your modeling
dataset to the format the is needed for PKNCA, and the NCA is automatically
performed using all the data.

In some cases (e.g. studies with sparse data), NCA may not be feasible. In those cases, you can provide a different dataset to PKNCA compared to the full modeling dataset. Usually, the simplest method is to provide single-dose, dense-sampling, dose-ranging data (i.e. the single-ascending dose portion of the first-in-human study) to be estimated.

To do this, give your data to PKNCA using the `ncaData`

argument to
`pkncaControl()`

as follows:

# Choose a subset of the full dataset for NCA dNCA <- theo_sd[theo_sd$ID <= 6, ] preparedNcaData <- nlmixr2( one.compartment, data = theo_sd, est = "pknca", control = pkncaControl(concu = "ng/mL", doseu = "mg", timeu = "hr", volumeu = "L", ncaData = dNCA) ) preparedNcaData$ui

The initial estimates are now based on NCA calculated from the `dNCA`

dataset
rather than the full `theo_sd`

dataset.

If you already have NCA results calculated by PKNCA with the required parameters
("tmax", "cmax.dn", and "cllast"), you can provide those instead using the
`pkncaControl(ncaResults)`

argument.

To update the initial estimates, the model must have parameters in the `model()`

block with the names that are expected by `est = "pknca"`

. The expected names
are:

`ka`

`vc`

`cl`

`vp`

`vp2`

`q`

`q2`

Any of those parameter names that are found in the `model()`

block will be
automatically traced back to the initial conditions (`ini()`

block), and the
parameter values will be updated. If the parameter is estimated on the log
scale, the updated parameter value will automatically be converted to the log
scale.

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