Case Study: Top 20 Pharma with a Focus on Rare Disease
An easy, comprehensive and data driven method to prioritize therapeutic area focus by mining the competitive landscape for rare disease groups.
An in-house person assigned disease classes.
Arbitrary, subjective and inconsistent - No one person can have in-depth knowledge about every disease
The analyst evaluated the competitive landscape by manually collecting Clinical Trials data for each disease in the class.
Laborious, time consuming and error prone
A web based tool allowed the analyst to build a view of the competitive landscape and identify white spaces to pursue.
- Resolved diseases across data sets and varied taxonomies. - Created a connected knowledge graph with disease data such as targets, pathways, tissue expression and trials across sources. - Used machine learning to stratify diseases by molecular mechanisms. - Identified biologically relevant entities using Natural language processing to map data from structured and unstructured sources.
The analyst can now arrive at the Top 3 therapeutic focus areas in a few hours instead of weeks, freeing up time to focus on the merits of pursuing each area.
A rational data driven approach to prioritize therapeutic areas.