Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and pharmacodynamics. It involves creating mathematical and statistical models, typically defined by integrated, matrix, or partial differential equations, to describe the behavior of a drug. These models are then fitted to the data using least squares, Bayesian, and maximum likelihood techniques, resulting in mean parameter estimates and their variability for individual or population analyses.

Despite their inability to fully explain the underlying mechanisms, compartmental models can reveal important correlations between covariates and parameters, providing insights for further studies and deeper mechanistic understanding. They offer advantages in studying special populations and partitioning variability into interindividual, intraindividual, interoccasion, and residual sources. Various compartmental analyses exist, including individual analysis, population pharmacokinetic modeling, and nonlinear mixed-effect modeling. Nonlinear regression is central to compartmental analyses, relying on equations whose partial derivatives involve other model parameters, unlike linear regression, which fits data with a straight line defined by a slope and intercept. Mechanistic compartmental models are crucial in understanding pharmacokinetics and pharmacodynamics, informing further research, and providing valuable insights into drug behavior.