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Discovery to Commercialization of a Drug: The IT Holy Grail and Enabler of the Supply Chain

Discovery to Commercialization of a Drug: The IT Holy Grail and Enabler of the Supply Chain. David Wiggin, Program Director, Healthcare and Life Sciences, Teradata. Great innovations & discoveries have been the result of. Accidents Penicillin – Sir Alexander Fleming, 1928

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Discovery to Commercialization of a Drug: The IT Holy Grail and Enabler of the Supply Chain

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  1. Discovery to Commercialization of a Drug: The IT Holy Grail and Enabler of the Supply Chain David Wiggin, Program Director, Healthcare and Life Sciences, Teradata

  2. Great innovations & discoveries have been the result of • Accidents • Penicillin – Sir Alexander Fleming, 1928 • Persistence / hard work / brute force • Light bulb – Thomas Edison, 1879 • A brilliant mind • Theory of Relativity – Einstein, 1915 We’re intrigued by the notion of ‘the next big thing’!

  3. One from recent memory… …but it was fiction! The year was 1989 The field was electrochemistry The discovery was almost as good as world peace - an abundant, safe source of energy! …Cold Fusion

  4. Today • We’re not here to talk about the discoveries themselves • I’d like to propose that we think about the largest untapped resource at your organization; you have it in great abundance and it holds the answers to the next big thing • The paradox is that it’s everywhere, but we are all powerless to use it • The ‘it’ here is data • The next great discovery from your organization will be the result of analyzing data

  5. A thought experiment…what if • You could capture all the data from your enterprise, a project cradle to grave (early research projects, research, development, clinical trials through post-market analysis) • Keep it, regardless of the kind of data (Mass Spec, genomics, machine data, web data,…) • Integrate it (tie it together) so it’s ready for analysis • Access/analyze it using the most powerful analytics tools • On a platform that is flexible, fast, scalable & affordable

  6. Hype Cycle for Life Sciences

  7. For example, Biotech Manufacturing Process Analytics Demand Driven Supply Network Procurement R1 R2 R3 R4 R5 R 6 R 10 R 11 R 12 R 13 R 14 R 7 R 8 R 9

  8. Data Sources for Biotech Manufacturing Process Analytics Demand Driven Supply Network Procurement R1 R2 R3 R4 R5 R 6 R 10 R 11 R 12 R 13 R 14 R 7 R 8 R 9

  9. Health Economics & Outcomes ResearchIntegrated Discovery and Intelligence Environment Pattern Analysis Cluster Analysis Text Analysis RWE Data Partners End Users Employer Data Brand Teams Strategic and Operational Intelligence Practice Data Capture, Store, Refine Output HEOR Integrated Repository Data Aggregators Rx Data Input Managed Markets Claims Data LAB LAB LAB LAB Research Networks LAB HIE R&D Payer Data Clinical Data EMR Data

  10. How to get started • “To Succeed with Big Data, Start Small” Bill Franks • Select simple analytics that won’t take much time or data to run • Capture data in ‘one-off’ fashion • Limit data volume, e.g. 1 month data instead of 5 years • A successful prototype paves the way for investing in larger effort • Start with a sketch, not a full blueprint • Choose technologies that can grow with you and help you deliver results

  11. BONUS: Proteomics using MPP database to greatly improve protein identification

  12. Benefits of Streaming Mass Spec data to MPP platform Speeding overall data processing time Improving the selection of proteins by peak matching over a broader range of scans Provision of full traceability of identified proteins to the data that formed the m/z peak Facilitates rapid cross-experiment analysis on a common repository of trace information, built as a by-product of the analysis

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