The objective of this course is to introduce new and advanced features of population pharmacokinetics in NONMEM 7.4.
The objective of this course is to introduce advanced concepts of population pharmacokinetics in NONMEM. The course is targeted to individuals interested in solving population PK/PD problems using the new stochastic methods. The course is for those who are familiar with classic NONMEM methods (first order (FO)/first order conditional estimation (FOCE)/Laplace), and are familiar with constructing control stream files. Most of the course is instructional, with several hands-on examples for attendee participation.
A thorough discussion of stochastic approximation expectation maximization (SAEM), Importance sampling (IMP), and Markov chain Monte Carlo Bayesian analysis (Gibbs BAYES sampling and new in NONMEM7.4: Hamiltonian no-U turn sampling) will be presented, along with hands-on exercises. In addition, the FAST algorithm for FOCE analysis, new in NONMEM 7.4, will be introduced.
After attending this course, the participant will know how to use advanced stochastic methods for analyzing population PK/PD data; structure the models into a MU-referencing format that will greatly increase the efficiency of the analyses; apply prior information from previous analyses to the present data; create population mixture models; create models for categorical data; use new abbreviated code features for easier modeling of inter-occasion variability; model additional mixed effects levels for grouping individuals, such as inter-clinical site variability; use the DO loop feature in abbreviated code, such as for handling multiple bolus doses in transit compartment models that use the analytical absorption function.
Additional topics covered are: parallel computing and dynamic memory allocation for efficient memory usage; symbolic referencing to thetas, etas, and sigmas; Monte Carlo search algorithms to improve FOCE estimation; built-in individual weighted residuals; bootstrap tools for simulation; and greater control in average eta shrinkage calculations; and automatic protection against numerical exceptions using NONMEM7.4’s new $ABBR PROTECT feature.Download Agenda
Robert J. Bauer, PhD.
Vice President, Pharmacometrics, Research and Development (R&D), ICON plc
Robert Bauer (Robert.Bauer@iconplc.com) holds a PhD. in Pharmacology. He heads the Pharmacometrics R&D department at ICON Plc, Gaithersburg, MD, where he and his group maintain the population analysis software NONMEM®, incorporating leading edge algorithms in pharmacometrics. Dr. Bauer’s focus is on developing and implementing mathematical and statistical algorithms for biomedical problems.
Brian M. Sadler, PhD.
Sr. Scientific Director, Pharmacokinetics, Pharmacodynamics, Modeling & Simulation, ICON plc
Brian Sadler, Ph.D., Senior Scientific Director, Pharmacokinetics, Pharmacodynamics, Modeling and Simulation, joined ICON in 2010 and brings 37 years of relevant experience from the perspectives of his previous positions as a PKDM scientist at RTI International, a project pharmacokineticist at GlaxoSmithKline, an instructor and domain expert at a Pharsight, and as the principal and owner of his own pharmacometric consulting company. Dr. Sadler earned his Ph.D. in Pharmacology and B.A. in Biology from the University of Missouri - Columbia.
- $500 for industry
- $250 for academia
- $100 for students