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Mar 29, 2024
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PGPM 502 Genomic Data Science and PharmacometricsThis course covers genomic data science principles
that include: importing, restructuring,
sub-setting, and computational analysis of FDA
Adverse Events Reporting System data for
identification of genomic markers among most
commonly reported drugs, DNA microarray data
and/or RNA-Sequencing
datasets of patient responders and non-responders
to pharmacotherapy. The course also introduces
the application of predictive modeling algorithms
to
assess and predict drug responders,
non-responders, and patients at risk of
adverse-drug reactions. Students will be exposed
introduced to the Genome Analysis Toolkit for
analyzing Germline and Somatic Mutations in the
genome. In the second portion of the course, the
following pharmacometric principles will be
covered: Nonlinear-Mixed Effect Modeling (NLME)
of blood drug concentration data,
Physiologically-based Pharmacokinetic (PBPK)
modeling, and Bayesian
Dosing of Therapeutic Drug Monitoring (TDM) data.
Throughout the course, recent, relevant, and
foundational publications will accompany lecture
and guide discussions for clinical application in
personalizing medicine. Data presented in this
course will stem from published clinical studies
and simulated data examples that include, but not
limited to the following specialties: Oncology,
Psychiatry, Pediatrics, Rheumatology, Surgery,
Anesthesiology, and Family/Internal Medicine. Credit(s): 3
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