Molecular Pharmacoepidemiology

Description

This course is designed for researchers, healthcare professionals, and students seeking to understand how molecular and genetic data can be used to predict drug safety and efficacy and optimize personalized medicine approaches. Participants will explore how omics technologies and artificial intelligence (AI) methods are applied in pharmacoepidemiology to assess and predict medication responses.

Through lectures, case studies, and hands-on analysis, the course emphasizes leveraging multi-omics data and advanced analytical techniques to study variations in drug response, identify biomarkers, and advance the understanding of pharmacoepidemiology research.

Participants will engage with topics in molecular pharmacoepidemiology, including omics (e.g., genomics and metabolomics, gut microbiome), methods for causal inference in multi-omics studies, AI and advanced analytical techniques, and translating molecular pharmacoepidemiology such as pharmacogenomics research into clinical practice.

Learning Outcomes
By the end of the course, participants will:

• Gain the skills to utilize omics data in pharmacoepidemiological studies.
• Apply causal inference techniques to analyze multi-omics datasets.
• Implement AI methods to predict drug safety and efficacy.
• Understand the impact of genetic variability on drug response and its role in personalized medicine.
• Translate pharmacogenomic research into clinical applications to support safer, more effective treatments.

Learning will be facilitated through interactive lectures, computer-based practical sessions, and group discussions, with comprehensive background materials provided to reinforce theoretical concepts.

Target audience

This course is ideal for MS and PhD students, postdoctoral researchers, healthcare professionals (physicians, pharmacists, and practitioners) interested in pharmacogenomics, epidemiologists, and public health professionals focused on drug safety and efficacy who wish to leverage omics data for better health outcomes.

For the computer labs, we will use R statistical software. Some experience with this software is recommended. The course will be conducted in English.