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Niagara Falls, NY
This workshop is full. Please register for our waitlist to get notified if a seat becomes available.
This introductory population PK training workshop has been designed to provide the necessary information to successfully implement population pharmacokinetic methodology in a drug development program and to provide a foundational understanding of the basics of NONMEM coding and interpretation of NONMEM output. The material is structured to impart both the theoretical and practical aspects of the population approach and is versatile so that participants with diverse backgrounds and areas of expertise may benefit. No prior experience with NONMEM is assumed or required. Examples of the use of population PK studies in drug development programs will be presented to provide specific details of various implementations and better illustrate essential aspects of population PK methods. Participants will gain an appreciation for the essentials of accurate and sufficient data collection and learn how to proactively plan in order to maximize study effectiveness. Throughout the workshop, the presenters will provide examples from their experience to inform best practices for implementation and avoiding problems. Emphasis will be placed on compliance with the FDA’s Guidance for Industry on Population PK and the EMA’s Guideline on Reporting the Results of Population PK Analyses.
The workshop content will be provided as a combination of live lectures, review of data, code, and modeling results, plus hands-on individual and small group exercises. Participants will be able to practice coding control streams, running various models, and evaluating the results. A thorough examination of an example dataset, from development of the structural and statistical models through covariate analysis will be covered. To ease the learning curve and ensure that participants are up and running with NONMEM very quickly, the KIWI™ Pharmacometric Communication Platform will be used in conjunction with NONMEM. KIWI is useful in facilitating code writing, finding errors, comparing output from different models, and generating point-and-click model diagnostics.