For the convenience of explanation, there was no inter-individual variability (all OMEGA values were fixed to 0) and residual variability was minimal (additive and proportional error were 0.001 and 0.01). The parameters were clearance (CL), central volume (Vc), inter-compartmental CL (Q), and peripheral volume (Vp), which were 10 L/hr, 30 L, 10 L/hr, and 50 L, respectively. In the first example, we assumed a 2-compartment IV bolus PK model result. More complex structural models such as the physiologically-based PK model, target-mediated drug disposition model, complex absorption models, and categorical data simulations will be covered in the next tutorial. The main focus of this manuscript is learning the components of simulation code and the model translation method of this software by working with simple models such as the 2-compartment pharmacokinetic (PK) model and turnover pharmacodynamic (PD) model, as well as dosage regimen adjustment and incorporation of interindividual variability. There are also some excellent tutorials available for PM model simulation using Berkeley Madonna and R.
In this tutorial, we will not introduce the detailed analysis procedure for each program, as our intended audience is those who have a basic understanding of modeling using NONMEM.
It is very important for the modeler to have various software options because the appropriate method can vary depending on who will see the PM analysis results. The purpose of this tutorial is to compare the characteristics of NONMEM, Berkeley Madonna, and R by simulating PM models. 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. One of most important steps in pharmacometric (PM) analysis is simulation of various scenarios.
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