Iain M. Cockburn Boston University and NBER

1. Blurred Boundaries: Tensions Between Open Scientific Resources and Commercial Exploitation of Knowledge in Biomedical Research Iain M. Cockburn Boston University and NBER Advancing Knowledge and the Knowledge Economy Conference Washington DC, January 2005

2. The crisis in research productivity • More money, less drugs? • $318MM per drug in the late 80s • $806MM per drug in the late 90s

3. Crisis, what crisis? The long term view

4. Gathering storm? • Rising R&D spending (predicated on getting premium prices) … meets • Cost-containment pressures, worldwide

5. What drives research productivity? • Efficiency of firms • “Shots on goal x success probability” • Function of quality of inputs, research tools and techniques, PLUS incentives, organization of R&D, economies of scale & scope, resource allocation process… • Nature of knowledge exchange • Interactions among larger set of institutions (firms, universities, foundations, govt labs etc.) • Spillovers, externalities, knowledge flows

6. “Component” vs. “System” performance • Firm productivity driven by • Discovery technology (revolutionary new techniques and knowledge such as bioinformatics, microfluidics, UHTS etc.) • Efficiency of internal resource allocation • System productivity driven by • Knowledge flows among institutions (spillovers) • Division of effort among component institutions • Cost and efficiency of interactions and knowledge exchange

7. Why has R&D spending risen so much? • New opportunities generated by basic research • Re-tooling in response to innovation in “methods of invention” • Mining out of easy targets? • Slow and overly risk-averse drug approval process? • Inefficient production and exchange of knowledge under a new industry structure?

8. Good Old Days The Present Public sector science Public sector science Big Pharma Big Pharma Historical evolution of industry structure Biology Product biotechs Tool biotechs CROs Drugs

9. What caused vertical dis-integration? • New science (gene splicing, mabs) very closely connected to public sector and individual academics • Evolution of US patent law • Diamond v. Chakrabarty • Extension of IPRs into “upstream” science (tools, methods, mechanisms, genetic sequences) • Bayh-Dole Act legitimized commercial rights in federally funded research • Financial market deregulation/innovation • pension funds & venture capital • Genentech IPO

10. Blurred boundaries • “Bright line” between Open Science and commercial R&D shifted (and breached) • Universities now enthusiastic participants in the patent system • Big Pharma and new biotechs are major players in basic research • Patents (and secrecy?) increasingly used to obtain proprietary control over foundational research • Commercial entities using Open Science norms to manage creation and exchange of knowledge

11. Consequences • “Gold rush” to get proprietary rights over upstream science • University tech transfer • Large amounts of entry (so far markedly unprofitable for the average new firm) • License negotiations replace internal resource allocation decisions • Product developers now have to pay for what used to be “free” spillovers from upstream

12. Beneficial effects of the vertical struggle for rents • Entrepreneurial energy and high-powered incentives: speed, risk taking • Efficiency gains from specialization • Prevents incumbents “shelving” or delaying new technology • Filing patents forces disclosure • Resource allocation through market processes • Efficient pricing and unbundling of risk?

13. Things to worry about • Dissipative racing behavior, defensive duplicative investments, litigation • Research effort over-focused on commercially appealing areas (+ herding) • Poor resource allocation because of market failure and “wrong” prices • Asymmetric information • Bargaining disparities • Anti-commons (too many property rights) • Royalty stacks • Double (or triple) marginalization

14. Impact on not-for-profit science • “Mertonian” governance of science has been very effective at generating fundamental knowledge • Priority-based rewards, collaboration, publication • Investigator-initiated, peer-reviewed, taxpayer-funded resource allocation • Will “propertized” science be as fertile? • Market signals vs. curiosity + peer review • Exclusion vs. collaboration • Conflicts of interest

15. The Academy pushes back • Open Science rules and norms permeate creation and exchange of knowledge in for-profit research • Collaboration, publication, peer review used to manage R&D • Public sector science uses the public domain, open source, copyleft etc. to limit patents • e.g. HGP, bioinformatics

16. Bioinformatics • Digital biology • Databases, algorithms, linkages, integration • Very large scale projects & collaboration • Limited modularity? • Open Source, public domain software • Non-proprietary data (Genbank, SWISS-PROT) • Standard-setting

17. Benefits More productive commercial R&D (?) Limits to “bad” patents Large scale distributed research projects Costs For-profit science may turn to secrecy Relocate profits downstream Life gets tough for “tool” biotechs Dilemmas for university tech transfer Consequences of push back

18. So what? Some wild guesses • Drug development is probably going to get faster and better, but not cheaper • Savings from superior research tools, specialization, advantages of small firms will be offset by adverse effects of new industry structure • The big social bet on markets vs. hierarchies as ways of governing knowledge and allocating resources will probably pay off… • But persistently poor returns to biotech investment, and the “pipeline crisis” for Big Pharma signal potential problems

19. Policy implications • Watch out for the interaction of “distant” policy choices e.g. IP law, financial market regulation • Different modes of governing knowledge coexist and interact fruitfully, but create a complex industry structure that is hard to manage, potentially fragile • Take care of the goose that laid the golden eggs: further erosion of Open Science is surely a bad idea

20. Thank you