All Times EDT
Keywords: PKPD, Exposure-response, MIDD, Pharmacometrics
Modeling and simulation plays an essential role in the development of new treatments. Statistical modeling is a key foundation of model-based decisions ensuring that the best decisions are made based on available information. Pharmacometrics combines statistical models with a biological understanding of the disease to develop quantitative systems pharmacology (QSP), exposure-response (ER), and pharmacokinetic/pharmacodynamic (PK/PD) models. Typically these pharmacometrics models are nonlinear models defined by a system of differential equations. But statistics is at the foundation of all these models. Dose selection, dose recommendations, patient selection, probability of technical success for go/no-go decisions, and many other key decisions are informed by both statistical and pharmacometrics models. In this roundtable we will discuss some of the ways in which modeling and simulation play a key role in drug development as well as the ways in which statisticians and pharmacometricians work symbiotically. We will briefly cover some examples of statistical models used to develop pharmacometrics models. The ASA has an official interest group, joint with the International Society of Pharmacometrics (ISoP), that focuses on the interactions between statistics and pharmacometrics (SxP).
This roundtable will focus on: Examples of statistical models used for pharmacometrics modeling. Examples of the impacts of pharmacometrics modeling in drug development. How statisticians and pharmacometricians work together.