• Knowledge Discovery Powers Agile R&D

  • Name

Defining R&D Direction

CASE STUDY: Top 20 Pharma with a Focus on Rare Disease

  • The Goal

    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.

  • The Benefit

    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.

  • The Challenges

    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

  • The Solution

    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.