Making the Most of Drug Development Data
Improving time to market requires finding — and sharing — the right information at the right time. A benchmarking survey reveals that pharma and biopharma still have a long way to go.
By Ken Morris, Ph.D., associate head of Industrial and Physical Pharmacy, Purdue University, with Sam Venugopal, director, Life Science Business Operations, Conformia Software, Inc. and Michael Eckstut, vice president, Life Science Operations, Conformia Software, Inc.
To help make these costs more tangible, we looked at the results of a 2001 survey of 21 pharmaceutical companies. One of the outputs of the survey estimated that 25% of drug development costs were associated with the Cost of Goods Sold with 12% attributed to R&D expenditures.  Applying just a 25% reduction in the number of repeat experiments could cause a significant shift in the cost breakdown. Indeed, reducing the number of experiments repeated by just 25% and moving the costs out of COGS to the 12% R&D spending, could increase the R&D budget by as much as 50%!IT systems and user satisfaction
The survey also examined how satisfied participants are by their existing IT systems, particularly given the growing trend of repurposing existing commercial systems for the drug development function. Participants were asked to rate their current systems’ ability to capture information, assessing their ability to:
- Capture large amounts of data created from different sources and types throughout development;
- Enable traceability across the full/complete development life cycle and capture composite views at specific points in time critical to build process understanding or assess status;
- Identify and organize operating discontinuities typical within product development — for example, stop/start nature of campaigns, stop/restart development activities on a compound, changes in clinical or commercialization dates, or development priorities
Results, summarized in Figures 9 and 10, below, indicate dissatisfaction with existing systems.
Results indicate that companies are far from achieving the “desired state” required to manage information, and indicate improvement opportunities.Staying on the Critical Path
Are today’s drug development organizations ready, from an IT perspective, to reach the “desired state” of drug development? Survey responses (Figure 11), and an average rating of 2.2, indicate lack of alignment between existing IT systems and future goals.
Despite the obstacles and challenges they face, development professionals have a clear “wish list,” summarized below:
A DRUG DEVELOPMENT WISH LIST
- Get more complete information capture/reusability over the life of the product
- Better manage resource streams (equipment, materials, etc.) across the development lifecycle
- Harmonize business processes across sites/business partners
- Gain “near-real-time” information visibility across global silos
- Optimize data transfers between enterprise and its development partners
- Perform better risk-based decision-making to enable “fail fast”, “quick win”, and “quick kill”
- Capture context of Information as it moves downstream the development lifecycle
- Implement PAT techniques needed to generate process understanding capabilities
- Create and maintain correlation between key process parameters; establish understanding and rationale behind process revisions and associated behavior
- Demonstrate better control of development processes/improve Pilot Plant efficiency
- Reduce amount of time spent assembling IND and NDA materials
- Compare development activity across different stages/scale of development needed to develop optimal design space
- Provide the “whys” of development — full traceability
To achieve the operational efficiencies afforded by better information management and integration across the process development enterprise, companies must move towards a system that captures data in a systematic, structured and actionable fashion and eliminates information gaps allowing them to leverage data to make decisions and establish rationale.
The goal is converting information into knowledge — actionable data that will enable full traceability along the complete set of dimensions making up drug development.