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Nih dempwolf 20160408-v4

Presentation at joint NIH - NSF workshop on Science and Innovation Policy Research

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Nih dempwolf 20160408-v4

  1. 1. Modeling Drug and Medical Device Innovation as Temporal Sequences using EventFlow NIH and the Science of Science and Innovation Policy: A Joint NIH-NSF Workshop April 7 – 8 Bethesda Maryland C. Scott Dempwolf, PhD Assistant Research Professor University of Maryland – Morgan State Joint Center for Economic Development Ben Shneiderman, PhD Distinguished University Professor University of Maryland Institute for Advanced Computer Science (UMIACS) (and a few networks)
  2. 2. Pennsylvania Innovation Networks 1990 – 2007 Emergence of Philadelphia Biopharma cluster and Pittsburgh Nuclear Cluster Modeled with Pajek & KING 2010 ME: “It’s cool, but… How do I make it useful?” BEN: “You must use NodeXL” ME: “Obiwan Shneiderman, you are my Jedi Master”
  3. 3. Innovation A process of transforming knowledge and scientific research into a new product in the marketplace. Think of that process as a sequence of related activities Research Invention Proof Commercialization Product With this intended outcome
  4. 4. Innovation Each activity has inputs, outputs, associated documents and artifacts With this intended outcome
  5. 5. Innovation Each activity involves people and organizations producing intermediate outcomes Contributing to this intended innovation outcome
  6. 6. Innovation The people and organizations from each activity create an activity network
  7. 7. Activities become sequences through shared people and organizations, citations, and other linkages With this intended outcome
  8. 8. Innovation Ecosystems Innovation networks with embedded knowledge & resources along with intermediaries comprise Innovation Ecosystems. Activity networks combine to form innovation networks. The Regenerative Medicine cluster (ecosystem) in Howard County, MD Combining two activities: NSF# 1551041 and today’s presentation NSF# 1551041 activity network
  9. 9. Innovation Metrics Some are based on organizations & resources None are based on intended outcome Some are based on inputs Some are based on outputs Some are based on talent Some are comparative indexes Product Launch
  10. 10. Modeling Innovation Sequences with EventFlow We use newly developed EventFlow software to model innovation in drugs and medical devices from multiple datasets: • RePORTER_PATENTS_C_ALL • RePORTER_CLINICAL_STUDIES_C_ALL • CTTI AACT Database • FDA Orange Book (drugs) • Drugs@FDA • Pre-Market Approvals (PMA) (med devices) • SBIR/STTR (pending) • CrunchBase (pending) • NSF (pending) Supporting and core data sources • NIH RePORTER • PatentsView • USASpending • STARMETRICS http://hcil.umd.edu/eventflow/
  11. 11. A Quick Tour of EventFlow Each product (drug or medical device) is a record in EventFlow (34,331 records) Event categories: • Clinical Trials (commercialization activity) • FDA Approval (proxy for product launch) • Patents (invention) • Research Overview (Aggregation) Individual Timelines
  12. 12. Product-Based Innovation Metrics Temporal Metrics How long does innovation take? How many activities are involved? What types? In what sequence? How long does each take? Are there gaps? Is the sequence pattern common or rare?
  13. 13. How long does innovation take? (drugs) From: Patent application  FDA approval (26 products)
  14. 14. How long does innovation take? (drugs) From: Patent application  FDA approval (product launch) (884 drugs in the FDA Orange Book)
  15. 15. How long does innovation take? (med devices) From: Start of clinical trials  FDA approval (1,225 medical devices)
  16. 16. How long does innovation take? (med devices) FDA Approval during Clinical Trial FDA Approval after Clinical Trial
  17. 17. Illinois Battery Cluster 2010 – 2014 Modeled with NodeXL Bridge Broader applications of temporal metrics: the Illinois Battery Cluster Innovation Ecosystems research component Industry component Bridging component
  18. 18. Research Publication Invention Proof-of-Concept Commercialization Product Bridge The Innovation Ecosystem and the Valley of Death A network representation of the valley of death
  19. 19. Emerging Theory & Research Bridge What’s in the Bridge? • Working Hypothesis • Regions with denser, more connected bridging components will be characterized by faster innovation sequences and more innovation sequences leading to new products. Measured using new temporal metrics
  20. 20. Stem cell products group • Commercialization support • Acceleration • Attract complementary firms Delivery devices groups, ECM group • Facilitate collaboration • Niche market development • Attract complementary firms Regenerative Medicine & Nutraceuticals groups • Develop ‘Keystones’ • Promote local sourcing • Industry partnerships • FDI / Business expansion • Attraction - supply chain • University partnerships University groups (JHU, UMCP, UMB) • Leads for licensing (green ties) • Key labs (dense subgroups) • Opportunities for faculty spin outs • Accelerate student startups • Corporate Partnerships Targeted Economic Development Strategies At the Cluster Level Regenerative Medicine Cluster – Howard County, MD Innovation-Led Economic Development Drill-down to Company Profiles • Click to follow link Nascent / emerging Growth stage Infrastructure for maturing cluster ~Labs
  21. 21. Howard County, Maryland - Full Innovation Network Universities (JHU, UMCP, UMB, UMBC+) • Follow-up leads for licensing or other engagements (green ties) • Identify key labs (dense subgroups) and evaluate for expansion / enhancement • Identify opportunities for faculty spin outs • Identify / accelerate potential student startups that can be seeded in this cluster • Build long-term sponsored research relationships with keystone companies Main Innovation Clusters • Regenerative Medicine • Telecom / networks / cyber • Defense / Security / SBIR • Nutraceuticals • Research & Development Entrepreneurial Acceleration Opportunities • Commercialization, acceleration, entrepreneurial support for early stage companies located in the county • Assistance with market Connections to capital & cluster keystones Business Attraction Opportunities • Focus on early stage companies with innovation cluster growth potential; companies are located outside of the county but have a HoCo connection • Develop relationships and help them plan for move to HoCo for next growth stage • Connections to capital Keystones • Identify & cultivate keystones in each innovation cluster • Identify & cultivate capital networks around each innovation cluster Business Expansion & FDI Opportunities • Focus BRE on growth stage & mature companies in innovation clusters. • Develop keystones in the process. • Engage MD DOC in developing FDI. • Engage foreign-owned companies in innovation clusters to expand their presence in the cluster through FDI. Workforce Development • Develop industry partnerships (EARN) around innovation clusters • Work with universities & community colleges on talent pipeline Federal Strategy pending The ‘group-in-a-box’ layout organizes groups from largest to smallest. This also corresponds to a ‘strategy gradient’ for economic development. Research Component Entrepreneurial strategies Attraction strategies Research & Tech Transfer strategies Retention, Expansion & Workforce strategies Industry Component Bridging Component (partial)
  22. 22. A few Data Issues & Needs • Data cleaning & disambiguation • Data matching across datasets • RePORTER, Clinical Trials, FDA, SBIR • Matching on full project numbers (not core) • SBIR – More complete dates; Access to bibliographies for citation linkages • FDA, Clinical Trials – Basic information at the front-end • FDA – ability to roll up drug families i.e. Adderall 10mg, 15mg, 20mg…
  23. 23. Upcoming Events April 13, Wednesday 10am at NIH Porter Building 35A, Room 610, NIH Main Campus, Bethesda, MD Interactive Visual Discovery in Event Analytics: Electronic Health Records Ben Shneiderman datascience.nih.gov/community/datascience-at-nih/frontiers May 26, Thursday at University of Maryland Human-Computer Interaction Lab EventFlow Workshop hcil.umd.edu/eventflow/ hcil.umd.edu/eventflow-workshop-2016/`
  24. 24. Implications for Universities: visualizing labs and research partnerships Identify key labs (dense subgroups) and evaluate for expansion / enhancement Identify opportunities for faculty spin outs Identify / accelerate potential student startups that can be seeded in emerging clusters Link to Lab and researcher pages (click to follow) University of Maryland, College Park Research labs, research partnerships, and individual researchers

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