CASE STUDY: Top 20 Pharma with a Focus on Rare Disease
Easy, comprehensive and data driven method to prioritize therapeutic area focus by mining the competitive landscape for rare disease groups, stratified based on molecular mechanisms.
Top 3 therapeutic focus areas arrived at 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.
An in-house person assigns disease classes.
Arbitrary, subjective and inconsistent - No one person can have in-depth knowledge about every disease
The analyst evaluates 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 allowing the analyst to build a view of the competitive landscape and identify white spaces to pursue
Resolve diseases across data sets and varied taxonomies.Create a connected knowledge graph with disease data such as targets, pathways, tissue expression and trials from across sources.Stratify diseases using Machine Learning. Identify biologically relevant entities using Natural language processing to map data from structured and unstructured sources.
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