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Salle S1-125
2940, chemin de la Polytechnique
Montréal (QC) Canada  H3T 1J7

Robert Bies (Département de pharmacologie clinique, Indiana University) est l’invité du professeur Fahima Nekka, dans le cadre des Conférences de prestige de l’axe « Pharmacométrie et pharmacothérapie » de la Faculté de pharmacie.  Ci-dessous, une courte biographie du conférencier :

Dr. Bies is an Associate Professor in the Division of Clinical Pharmacology and Director of the Disease Modeling Program for the Clinical and Translational Sciences Institute at Indiana University. Before that, he served as an Assistant Professor of Pharmaceutical Sciences and Psychiatry in the Schools of Pharmacy and Medicine at the University of Pittsburgh. His research interests encompass disease modelling, pharmacometrics and quantitative pharmacology. This research comprises two primary tracks, applied and methodological. The applied stream has included disease and drug models in oncology, psychiatry, neuropathic pain, traumatic brain injury and stroke. The methodological side has focused on issues of nonlinear dynamics in physiological/pharmacological systems as well as model search and optimization.

Dr. Bies specialized in the study of psychiatric disorders and the disposition of psychotropics as well as methodologic issues surrounding Bayesian hierarchical approaches. He has served on special study sections for NCI and NIA and is a member of the American Association of Pharmaceutical Scientists, American College of Clinical Pharmacology and American Society for Clinical Pharmacology and Therapeutics. Dr. Bies serves as executive editor for the British Journal of Clinical Pharmacology and is on the editorial boards of the Journal of Clinical Pharmacology and the Journal of Pharmacokinetics and Pharmacodynamics. He is a Scientific Advisor for the Metrum Institute as well as the Leiden Amsterdam Center for Drug Research quadrennial In-Vivo Measurement and Modelling of Drug Effects meeting.

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Challenges in model search and optimization for non linear mixed effects models
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