Among the many software packages used in PM analysis, NONMEM is still accepted as the gold standard, although the user interface is not as good as other software and it has a steep learning curve.
Use the nlmixr function to run the appropriate code.One of most important steps in pharmacometric (PM) analysis is simulation of various scenarios. Once the data has been converted to the appropriate format, you can Therefore not supported by the conversion function.Īs an example, you can use a simulated rich 1-compartment dataset. Note that steady state doses are not supported by RxODE, and You may convert theseĭatasets to RxODE-compatible datasets with the nmDataConvertįunction. NONMEM use a different dataset description. This dataset has to have RxODE compatible events IDs. Once the model function has been created, you can use it combined withĪ dataset to estimate the parameters for a model given a dataset.
Using the model syntax for estimating a model
Solved system/RxODE), initial estimates as well as the code for the In general this gives you information about the model (what type of # it knows that this is a one-compartment model with first-order # to use by the parameters that are defined. # This function determines the type of PK solved system # the linCmt() function to use the solved system. # Instead of specifying the ODEs, you can use # 1-compartment model with first-order absorption in terms of Cl Options to the estimation routines can be specified using nlmeControl with 129 more rows, and 10 more variables: CWRES, eta.ka , Fit Data (object fit is a modified ame):. No correlations in between subject variability (BSV) matrixįull BSV covariance (fit$omega) or correlation (fit$omega.R diagonals=SDs)ĭistribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink Parameter Estimate SE %RSE Back-transformed(95%CI) BSV(CV%) Saem setup Likelihood Calculation covariance table nlmixr SAEM fit (ODE) OBJF calculated from FOCEi approximation. We can alternatively express the same model by ordinary differential equations (ODEs): fit with 129 more rows, and 7 more variables: ka, cl , We can try fitting a simple one-compartment PK model to this small dataset. Ggplot(theo_sd, aes(TIME, DV)) + geom_line(aes(group=ID), col="red") + scale_x_continuous("Time (h)") + scale_y_continuous("Concentration") + labs(title="Theophylline single-dose", subtitle="Concentration vs. Upton of the University of California, San Francisco: # Load libraries Single-dose theophylline dataset generously provided by Dr. Let's start with a very simple PK example, using the Nlmixr uses a unified interface for specifying and running