So now we can add sliders to the simulation and bring the simulation in line with the observations. And these are the observations, and in blue at the bottom here, you see the simulation. So now we only simulate the model, and we can add the data from the Theophyline data set to compare the simulation here with the observations. So we're going to simulate this model also for 25 hours. And the final time points of the observations in the data set are around 25 hours. This dose needs to go to dose central in our model, and the time units are in hour. So we can use the columns in the data set, in this case, theophyline.dose_total. And we can select a dose from the data set that we have. We then select our model, the one compartment model. And the program is called Simulate Model. So in order to simulate the model, we need to go back to the model analyzer and create a program. Because if it is zero, the dose will never appear in the central compartment. And we need to make the absorption rate one, or at least not zero, such that if we apply a dose to dose central, the rate from dose central to drug central is not zero. So currently, these parameters are all one, except for the absorption rate. ![]() And in order to do that, we can simulate the model and see whether we can bring the simulation results in line with the observations. So those are the three parameters that we will be estimating from the data.īefore we start estimating these three parameters, it can be helpful to understand what the initial estimates might be for these three parameters. So that is the absorption rate and the elimination rate, as well as the volume of the central compartment. We can now estimate the parameters for this model. You can see this is a simple model, where the dose is absorbed into the central compartment, and subsequently eliminated. The model we will want to add is a one compartment model with first-order dosing and linear clearance. We can edit one compartment model by going to the model builder and adding a model from the PK library. And so this implies that the one compartment model might fit this data well. You can see that there is an absorption phase, followed by a linear elimination phase, linear in the log scale. Here you see the data plotted in a semi-log y-scale. The first thing we want to understand is what kind of model we should be fitting. ![]() The data set contains 12 subjects, and for each subject there is a single oral dose followed by concentration measurements and it also has a co-variate column. In this video, we will be fitting a model to the Theophyline data sets that we imported in the previous video on importing data and performing non-compartmental analysis.
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