In nlmixr2/babelmixr: Use 'nlmixr2' to Interact with Open Source and Commercial Software

knitr::opts_chunk$set(
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Introduction

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 example

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

Give PKNCA a different dataset or a completed NCA analysis

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.

Model requirements

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.

nlmixr2/babelmixr documentation built on Nov. 21, 2024, 3:16 a.m.