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Article Blog Life Sciences October 29, 2018 2 minute read

Cost Considerations: Big Data Analytics for Clinical Trials in Biotech

This week concludes Saama’s Clinical Trial Excellence for Tomorrow and Beyond blog series, each installment of which has been authored by one of our six new Clinical Board of Advisor members. In this final post, Jay Kaminski, Chief Operating Officer at Celularity and former Senior Vice President of Global Clinical Research & Development Operations at Celgene Corporation, explores the challenges and considerations that are unique to biotechnology clinical trials, and how data analytics can help address those issues.

Biotech and pharma share many of the same challenges when it comes to clinical trials, including common bottlenecks involving patient identification and recruitment, investigator and site start-up, protocol design and trial feasibility assessments. Biotech and pharma also share another distinction – access to unprecedented amounts of data to inform clinical development.

Thanks to breakthroughs in areas like genomics and DNA sequencing, the amount and availability of Big Data in biotechnology has proliferated over past decades. As a technology and industry that is based on biology and uses living materials and/or processes on a cellular or biomolecular level to create new products and therapies that improve human health, biotechnology is unique – as is the quality and quantity of data the industry generates and can access.

With the exponential growth of data, the biotech industry has both incredible opportunity and responsibility to discover and develop novel products and therapies to combat rare and debilitating diseases. Concurrently, the industry faces intense challenges as companies strive to effectively manage and utilize the petabytes of data available through a wide variety of research platforms.

Data analytics solutions are invaluable in helping the biotechnology industry make sense of and appropriately leverage these enormous stockpiles of data. AI-enabled platforms allow biotech companies to develop predictive models that sort through and unleash the potential of massive amounts of data in virtual libraries to inform and direct clinical trials. Failure to take advantage of big data analytics for clinical trials aligns biotech companies with the Dark Ages of clinical development, rather than catapulting the industry to its frontier.

Today there are over 250 biotech products and therapies available to combat and prevent deadly diseases.[i] For the industry to continue to harness the incredible power of nature’s biology toolkit, it is more critical than ever before that biotechnology companies leverage state-of-the-art analytics solutions to activate and deploy the full value chain of the data at their fingertips for more efficient, effective and successful clinical development.

This concludes Saama’s Clinical Trial Excellence for Tomorrow and Beyond blog series. Please refer to previous posts from John Fox (10/15/18), Scot Harper (10/8/18), Stephen Cunningham (10/1/18), Opinder Bawa (9/24/18) and Jonathan Zung (9/17/18) for additional information.


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