Pharma R&D and IT: Focus on Decision Support to maximize productivity and ROI
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Pharma R&D and IT: Focus on Decision Support to maximize productivity and ROI

April 29, 2019

Pharma R&D and IT: Focus on Decision Support to maximize productivity and ROI

It’s a well-accepted truism that drug discovery is hard (only 10% of potential new drugs make it to market), expensive ($2.6B and counting), and time consuming (12 years from bench to bedside). Biopharma companies continue to look for ways to address these threats to success and to make the process easier, cheaper, and quicker; their colleagues in the scientific software industry and academia have done an excellent job in identifying key challenges and that need to be overcome; and smart apps, artificial intelligence, and machine learning may be able to help.

Scientists and IT teams in drug discovery face these high-level challenges:

  • The science underlying drug discovery is becoming more complex
  • This increased complexity causes research data to be more varied and voluminous, and therefore harder to find, search, and analyze
  • Research is increasingly externalized and globalized, complicating data integration, communication, and collaboration
  • Biopharma portfolios contain increasing numbers of biologic entities, which bring a raft of new data types and data management challenges

At the same time, regulators and payors are increasingly focused on optimal outcomes evaluated based on cost, efficacy, and safety. And all this is against a backdrop of growing expectations for companion diagnostics, personalized medicine, and full-scale adoption of cloud technologies.

If smart software can help, where can it have the greatest impact? If data is hard to find, hard to analyze, and difficult to integrate, it is likely that scientists will waste time as data hunter/gatherers, make poor or misleading decisions based on incomplete data, and run up costs as they demand expensive IT systems to pull the data together.

Viable solutions to these challenges would require capturing data into a common digital platform, and structuring and indexing the data for analysis at the point of capture, so that the data can be turned into insights with visual analytics, artificial intelligence, and machine learning.

Drilling into the underpinning components of the drug discovery process, there are three primary areas of endeavor that might be targets for improvement: optimizing the research workflow; enhancing data capture and collaboration; and providing more powerful and incisive data analysis tools for better decision-making.

In a recent webinar “Is your scientific research platform helping you make the right decisions?” PerkinElmer asked attendees to indicate the most important of these areas to address, and their rankings were: workflow optimization 58%, better decision support 28%, and better data capture and collaboration 14%.

This viewpoint certainly reflects our experience in working with biopharma customers during the last 20 years, where they observed substantial efforts, expense, and time being spent on attempting to provide optimized, all-encompassing, end-to-end integrated research workflows, only for scientists to suffer continued disruptions to daily lab activities, and the eventual deployment of an already obsolete, hard-to-maintain system, with very little or no pay-back for all the effort.

Based on our extensive experience in providing research informatics solutions, backed up with customer evidence, is that a much greater and more immediate return on investment can be realized by focusing not on workflow optimization, but instead on improving data capture and collaboration, and better decision support.

These tools and applications can be layered on top of an existing workflow, with minimal disruption, while researchers gain immediate benefits from intuitive and efficient data capture, inclusive and open collaboration, and better informed and more timely decisions based on complete data sets analyzed with powerful and scientifically intelligent tools. With well-defined application programming interfaces, the underlying workflows can be iteratively refined over time after stabilizing capture and analysis, without adversely impacting the collaboration and analysis tools, so that scientists can continue, unimpeded, to hunt for the next new biotherapeutic.

In a subsequent post we will discuss how PerkinElmer’s Signals platform can help organizations deliver enhanced data capture, collaboration, and decision support tools to aid in easier, quicker, and cheaper drug discovery.

Contact us for more information, case studies or solution demos for decision support.


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