The early stages of drug development are inherently high-risk; molecule screening, target evaluation and lead refinement can take years and cost millions of dollars – often have little to show for the effort. Indication expansion and drug repurposing can open new avenues of possibility, after careful evaluation of the relationships between the drug, its targets and the new disease context. These connections can be uncovered by mining curated, causal knowledge graphs, which inform smarter target identification.

Learn how to evaluate targets and drugs in this oncology-focused webinar, which examines BRAF as a target in multiple myeloma. Also, we’ll evaluate different drugs in clinical trials with an analysis that covers the GOT-IT assessment blocks for drug evaluation. These blocks, developed by the GOT-IT (Guidelines On Target Assessment for Innovative Therapeutics) working group, are part of a target assessment framework that supports robust, reproducible data.

We’ll cover how to:

• Apply the GOT-IT framework to target evaluation and indication expansion

• Uncover causal relationships between existing drugs and new diseases with our curated knowledge graphs

• Make informed decisions based on concrete data, including toxicity, adverse events and competing drugs in clinical trials

Speakers:

Sathiya Manivannan, PhD

Scientific Consulting Expert (Multiomics), QIAGEN Digital Insights

Sathiya Manivannan, PhD, delivers tailored solutions for pharmaceutical and biotech companies. His work includes large-scale single-cell and spatial transcriptomics analyses, multi-omics database generation, and the development of graph knowledge bases and AI/ML-driven solutions that accelerate therapeutic target discovery and optimize drug development.

Ruth Stoney, PhD

Senior Field Application Scientist, QIAGEN Digital Insights

Ruth Stoney, PhD, designs data science projects for customers using ‘omics datasets and insights mined from the QDI knowledge graph. She collaborates with academics and biopharma data scientists on biological discovery, AI usage and more.