Next-generation sequencing has transformed the rate of genetic biomarker discovery across large populations, but genomic information provides a largely static snapshot of disease risk. Given the dynamic nature of disease, there is a need to extend drug development programs to include the discovery of dynamic biomarkers – metabolites, lipids, and proteins – to complement and extend genomics data for greater understanding of disease mechanisms and drug response over time. We will share how these technologies are leveraged within a larger discovery infrastructure that includes comprehensive biocomputational analysis using AI/ML tools to derive actionable insights that can accelerate clinical development of therapies and diagnostics.
Learning Objectives:
Understand the limitations of genomic information as a static snapshot of disease risk and the necessity for dynamic biomarker discovery.
Explore how next-generation, high throughput mass spectrometry can overcome traditional bioanalytical constraints to enable multi-omics interrogation.
Learn about the integration of these technologies within a discovery infrastructure, including AI/ML tools, for comprehensive biocomputational analysis.
