This document discusses optimal design and population modeling. It begins with an introduction to optimal design, noting that it allows parameters to be estimated without bias and with minimum variance. The advantages of optimal design are that it reduces experimentation costs by allowing statistical models to be estimated with fewer runs. It then describes different types of optimal designs such as A, C, D, and E optimality. The document next discusses population modeling, explaining that it is a tool for integrating data to aid drug development decisions. It notes the key components of population models are structural models, stochastic models, and covariate models. Structural models describe the response over time using algebraic or differential equations, while stochastic models describe variability and covariate models influence factors like dem