A model is a set of mathematical equations that describe observations. In some cases, the observations are plasma concentrations. In other cases, it is intraocular pressure or enzyme inhibition. The “parameters” of the model are derived from the observations.
A simulation is when you use a model to predict something. Instead of estimating parameters from observed data, you take the model and a set of parameters to simulate some data.
The use of modeling and simulation (M&S) in drug development has evolved from being a research nicety to a regulatory necessity. Today, modeling and simulation is leveraged to some extent, across most development programs to understand and optimize key decisions related to safety, efficacy, dosing, special populations, and others. As advances in both computing power and our understanding of biological sciences increases, the value of modeling and simulation grows.
You can perform modeling and simulation of patient data when you are looking at a discovery model, or you can use post-marketing data to build new clinical models.