Data Intelligence for Opportunity Screening and Prioritization in Drug Discovery

Client Challenge

The traditional approach to periodically screening and prioritization requires significant effort and resources to:

• Develop predictive models for target prioritization that aligns with the global strategy of the company and considers the potential clinical and commercial value
• Continually monitor the latest advancements in scientific production to systematically review new opportunities over the time

Scouting and evaluating targets in this way demands considerable resources and requires recurrent efforts for each review. Additionally, failing in even one of these activities undermines the capacity of the R&D team to timely identify suitable opportunities and reduce the time to market to leverage those opportunities.

Our Approach

Our data analytics and technology experts have developed a customizable, actionable digital solution that support strategic decision-making. Utilizing our business strategists, scientists, and software developers, this tailored solution outlines the best fit for the company’s strategic priorities and capabilities.

To deliver comprehensive analytics that our client could immediately action on, we followed a multi-step process:

Step 1: Integrated biological evidence and drug data sources

• Identified over 5,000 biological targets and more than 1,000 clinical indications in the company’s top therapeutic areas
• Integrated drug and scientific evidence on biological target and indication from seven databases
• Cross-referenced information to create a knowledge base of more than 130,000 biological target-indication associations and 12,000 drugs

Step 2: Developed a prioritization model based on scientific and market evaluations

• Defined parameters that condensed the information into a few relevant metrics
• Developed scoring criteria and ranking rules for biological target-indication associations
• Defined default algorithm weights according to scientific relevance and underlying business potential of each criterium

Step 3: Provided expert support and indication prioritization

• Developed an intuitive, web-based tool that allowed users to explore data, tune model weights, and achieve different prioritization outcomes depending on assumptions
• Delivered training sessions to help identify the final prioritized list of associations
• Leveraged AI-assisted data mining to prioritize associations through the analysis of clinical, commercial, and health economics endpoints

Outcomes

The complete, customized solution allows our client to explore the full spectrum of R&D opportunities through the integration of multiple public and private data sources and an actionable prioritization algorithm. With our deep industry knowledge and therapeutic expertise, we can leverage the power of data to develop tailored digital solutions that reduce the burden of data mining and early opportunity assessment. 

The client can also rely on these additional benefits of our analytics:

• Reduction of human-error related risks in data analysis
• Reduction of time and financial effort required by manual data gathering and analysis
• Enhanced interdepartmental collaboration
• Optimized opportunity review process that utilizes continually updated data