FOOD AND DRUG LAW JOURNAL - FDLI

FOOD AND DRUG LAW JOURNAL

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Judy Rein

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Laurie Lenkel FDA – OC

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Robert Giddings Hutchison PLLC

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Joseph A. Page Georgetown University Law Center

________________________________

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Catherine Clements Express Scripts

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Nathan Cortez Southern Methodist University

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Ian Fearon British American Tobacco

James Flaherty Fresenius Medical Abraham Gitterman Arnold & Porter LLP Kimberly Gold Norton Rose Fulbright LLP John Johnson FDA Imports Alan Katz toXcel, LLC Sara Koblitz Fish & Richardson Valerie Madamba Blue Apron Alan Minsk Arnall Golden Gregory LLP Nicole Negowetti The Good Food Institute James O’Reilly University of Cincinnati

Francis Palumbo University of Maryland School of Pharmacy Sandra Retzky FDA – CTP Joan Rothenberg FDA - CFSAN Jodi Schipper FDA – CDER Christopher van Gundy Keller and Heckman James Woodlee Kleinfeld Kaplan & Becker LLP Emily Wright Pfizer Kimberly Yocum TC Heartland LLC Lowell Zeta Hogan Lovells Patricia Zettler Georgia State University Law School

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Eric N. Lindblom

FOOD AND DRUG LAW JOURNAL

VOLUME 71 NUMBER 4 2016

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544 Early Developments in the Regulation of Biologics Terry S. Coleman 608 FDA Regulation of Clinical Applications of CRISPR-CAS Gene Editing

Technology Evita V. Grant 634 E-cigarette Regulation and Harm Reduction: The Case of Hong Kong Shue Sing Churk 658 The Extent of Harm to the Victim as an Alternative Aggravating Factor

for the Conviction of Felony Fraud in the Context of Food-Safety Violations

Yi-Chen Su 673 Knowledge Sharing as a Social Dilemma in Pharmaceutical Innovation Daria Kim

FDLI

673

Knowledge Sharing as a Social Dilemma in Pharmaceutical Innovation

DARIA KIM*

I. ABSTRACT

This article addresses the problem of restricted access to industry-sponsored clinical trial data. In particular, it analyzes the intersection of the competing claims that mandatory disclosure of pharmaceutical test data impedes drug sponsors’ innovation incentives and that access facilitates new drug development. These claims are characterized in terms of public-good and common-resource dilemmas. The analysis finds confidentiality protection of primary research data plays an ambiguous role. While secrecy, as such, does not solve the public-good problem in pharmaceutical innovation (in the presence of regulatory instruments that protect the originator drug against generic competition), it is likely to exacerbate the common-resource problem, in view of data as a source of scientific knowledge. It is argued that the claim by the research-based industry that disclosure of clinical data impedes innovation incentives is misplaced and should not be leveraged against pro-access policies.

While much attention has been paid to the problem of appropriability of drug R&D investment, this analysis highlights another consequence resulting from the private provision of clinical trials, i.e., factual confidentiality and possession of data by drug sponsors. The associated social costs are qualified in terms of internalized knowledge externalities. Since, in a competitive environment, companies are unlikely to change the strategy of non-sharing of primary data, the resulting economic inefficiencies at the sector level call for regulatory intervention. To reconcile the competing policy objectives, it is proposed that the rules of access should be designed in such a way that third-party use of primary data does not interfere with protection against generic competition. At the same time, the long-term collective benefit can be maximized when the “cooperative choice” (i.e., when everyone shares data) becomes the “dominant strategy.” This can be achieved only when access is not subject to the authorization of the initial trial sponsors, and when primary data is aggregated, refined, and managed on the collective basis.

* Daria Kim 2016. Max Planck Institute for Innovation and Competition Law, research fellow (2012-

2013), Doktorandin (since 2015), MA, LL.M., Ph.D. candidate (University of Augsburg). The initial draft of the article was presented at the First European Workshop for Junior Researchers in Intellectual Property on June 10, 2016 at the Centre for IT and IP law (CiTiP), University of Leuven. I would like to thank Professors Christine Godt, Geertrui Van Overwalle and Alain Strowel, as well as the reviewers of the Food and Drug Law Journal for helpful feedback.

674 FOOD AND DRUG LAW JOURNAL VOL. 71

II. INTRODUCTION

As Albert Einstein famously noted, “We cannot solve problems with the same level of thinking that created them.”1 In social sciences, a social dilemma or a collective action problem is a deadlock situation that cannot be resolved unless the participants change their mind-sets. In public policymaking, such situations may require the regulator to intervene to prevent the worst outcome for those who are involved in, or affected by, socially “defecting” decision-making.

This article addresses the problem of restricted access to industry-sponsored, non-summary clinical trial data—an issue subject to long-standing, intense debate. In the EU, access to patient-level data has been implemented through the recent transparency policies of the European Medicines Agency (hereinafter, the EMA).2 According to the EMA Executive Director Guido Rasi, the 2015 publication policy “sets a new standard for transparency in public health and pharmaceutical research and development.”3 The EU Ombudsman appraised the new measures as “a paradigm shift.”4 In the U.S., the Health and Medicine Division the National Academy of Sciences (hereinafter, the

1 DENNIS CASTRO, THE ALBERT EINSTEIN HANDBOOK - EVERYTHING YOU NEED TO KNOW ABOUT

ALBERT EINSTEIN 14 (2016). 2 European Medicines Agency, European Medicines Agency Policy on Access to Documents

(Related to Medicinal Products for Human and Veterinary use) (Policy/0043) (EMA/110196/2006), 6–7 (Nov. 30, 2010), http://www.ema.europa.eu/docs/en_GB/document_library/Other/2010/11/WC500099473.pdf (last visited Aug. 26, 2016) [hereinafter EMA access policy]. European Medicines Agency, The European Medicines Agency Policy on Publication of Clinical Data for Medicinal Products for Human Use, EMA/240810/2013, 4 (Oct. 2, 2014), http://www.ema.europa.eu/docs/en_GB/document_library/Other/2014/10/WC500174796.pdf (last visited Aug. 26, 2016) [hereinafter EMA publication policy]. The adoption of the publication policy has stirred a heated debate: for an overview of the submissions, see European Medicines Agency, Publication and Access to Clinical Data: An Inclusive Development Process (2014), http://www.ema.europa.eu/ema/index.jsp?curl=pages/special_topics/general/general_content_000556.jsp (last visited Aug. 24, 2016). Some submissions alleged that disclosure of that data violates trade secrets protection and contradicts the obligation under Article 39 of the Agreement on Trade-Related Aspects of Intellectual Property Rights. Agreement on Trade-Related Aspects of Intellectual Property Rights, April 15, 1994, Marrakesh Agreement Establishing the World Trade Organization, Annex 1C, The Legal Texts: The Results of the Uruguay Round of Multilateral Trade Negotiations 320, 336–37 (1999), 1869 U.N.T.S. 299, 33 I.L.M. 1197 (1994) [hereinafter TRIPS Agreement]. See, e.g., European Medicines Agency, Overview of Comments Received on ‘Publication and Access to Clinical-Trial Data’ (EMA/240810/2013), EMA/344107/2014, 1, 86, http://www.ema.europa.eu/docs/en_GB/document_library/Overview_of_comments/2014/09/WC500174225.pdf. See also U.S. Chamber of Commerce & Global Intellectual Property Center, Heading in a Different Direction? The European Medicines Agency’s Policy on the Public Release of Clinical Trials Data 15, 27 (2014), http://www.theglobalipcenter.com/wp-content/uploads/2014/05/EMA-Study-COMPLETE.pdf (last visited Aug. 24, 2016) (concluding that the EMA’s initiative “is a stark contrast and break from preceding EMA practices” and “in a broader context . . . also contrasts starkly with existing international practices”).

3 European Medicines Agency, Publication of clinical reports: EMA adopts landmark policy to take effect on 1 January 2015, 1 (Oct. 2, 2014) EMA/601455/2014, http://www.ema.europa.eu/docs/en_GB/document_library/Press_release/2014/10/WC500174767.pdf (last visited Aug. 26, 2016).

4 EU Ombudsman, Case OI/3/2014/FOR, Decision on own-initiative inquiry OI/3/2014/FOR concerning the partial refusal of the European Medicines Agency to give public access to studies related to the approval of a medicinal product, Report, para 71 http://www.ombudsman.europa.eu/cases/decision.faces/en/68107/html.bookmark (last visited Aug. 26, 2016).

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Health and Medicine Division) has led public awareness initiatives regarding access to clinical data.5 At the international level, the World Health Organisation (hereinafter, the WHO) has promoted mandatory trial registration and results reporting. The WHO, in its recently updated official statement regarding clinical trial data disclosure, takes a somewhat cautious position: While recognizing the “benefit of sharing research data and the facilitation of research through greater access to primary datasets” as well as “support[ing] sharing of health research datasets whenever appropriate,” the WHO does not call for “sharing of primary data.”6

The availability of clinical trial primary outcomes for independent analysis as an imperative for evidence-based medicine, as well as a scientific norm, appears self-evident. Prior research developed persuasive arguments as to why the regulatory framework should support access to industry-sponsored clinical trial data as well as how to implement the change.7 The private sponsorship of clinical trials poses a

5 Formerly, the Institute of Medicine of the National Academy of Sciences. Two comprehensive

reports should be mentioned in particular, see THE INSTITUTE OF MEDICINE OF THE NATIONAL ACADEMIES, SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, MINIMIZING RISK (2015), http://www.nationalacademies.org/hmd/Reports/2015/Sharing-Clinical-Trial-Data.aspx (last visited Aug. 25, 2016) [hereinafter SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS]; CLINICAL DATA AS THE BASIC STAPLE OF HEALTH LEARNING: CREATING AND PROTECTING A PUBLIC GOOD (Claudia Grossmann, Alexander W. Goolsby, LeighAnne Olsen & Michael McGinnis eds., 2010) [hereinafter CLINICAL DATA AS THE BASIC STAPLE].

6 In 2015, the WHO updated its official statement on public disclosure of clinical trial results taking into account the results of the public consultations conducting in 2014-2015. The World Health Organization, WHO Statement on Public Disclosure of Clinical Trial Results, 3, http://www.who.int/ictrp/results/WHO_Statement_results_reporting_clinical_trials.pdf?ua=1 (last visited Aug. 26, 2016) (emphasis added).

7 Within the limits of this paper, it is impossible to provide an exhaustive account of works addressing the issue of clinical trial data disclosure. Scholars have argued for the fundamental change towards the treatment of clinical trial data as a public good. See Jerome H. Reichman, Rethinking the Role of Clinical Trial Data in International Intellectual Property Law: The Case for a Public Goods Approach, 13 MARQ. INTELL. PROP. L. REV. 49–52 (2009); Antony Taubman, Unfair Competition and the Financing of Public Knowledge Goods: The Problem of test data protection, 3 J. INTELL. PROP. L. & PRAC. 591 (2008); Tracy R. Lewis, Jerome H. Reichman & Anthony D. So, The Case for Public Funding and Public Oversight of Clinical Trials, 4 ECONOMISTS’ VOICE 1 (2007); W. A. Ray & C. M. Stein, Reform of Drug Regulation – Beyond an Independent Drug-Safety Board, 354 NEW ENG. J MED. 194, 196–99 (2006); Trudo Lemmens, Leopards in the Temple: Restoring Scientific Integrity to the Commercialized Research Scene, 32 J.L. MED. & ETHICS 641, 643–45 (2004); Uwe E. Reinhardt, An Information Infrastructure for the Pharmaceutical Market, 23 HEALTH AFF. 107, 109 (2004) (stressing non-excludable and non-rivalrous character of “information that facilitates the proper functioning of a healthcare market—such as that for drugs”); Marcia Angell, The Clinical Trials Business: Who Gains?, BUYING IN OR SELLING OUT? THE COMMERCIALIZATION OF THE AMERICAN RESEARCH 127–32 (D. G. Stein ed., 2004). Disclosure of pharmaceutical test data has also been viewed as the counterbalance for regulatory exclusivity of the originator drug. See Thomas O. McGarity & Sidney A. Shapiro, The Trade Secret Status of Health and Safety Testing Information: Reforming Agency Disclosure Policies, 93 HARV. L. REV. 837, 839 (1980) (proposing “to couple disclosure with generic ‘exclusive use periods’ which guarantee a data submitter that no one else can use its data to register a product for a specific number of years”); Rebecca S. Eisenberg, Data Secrecy in the Age of Regulatory Exclusivity, THE LAW AND THEORY OF TRADE SECRECY 467, 491 (Rochelle C. Dreyfuss & Katherine J. Strandburg eds., 2011) (concluding that the confidential treatment of test data submitted for new drug marketing authorization “has outlived its original justification [in the presence of] regulatory exclusivity [that] offer[] a better way to protect innovators from unfair competitive use of their data without the need for secrecy”, and proposing that “[r]egulatory exclusivity could follow the example of the patent system, providing innovators with the exclusive right to use submitted data for regulatory purposes for a period of time in exchange for disclosure” id. at 488); Mustafa Ünlü, It Is Time: Why FDA Should Start Disclosing Drug Trial Data, 16 MICH. TELECOMM. TECH. L. REV. 511, 545 (2010) (arguing that “a simple and direct Congressional mandate for full disclosure is the next best remedy” if FDA


fundamental conflict of interest that “institutional financial interests may unduly influence professional judgments [and] threaten the integrity of scientific investigations,”8 which, alone, can provide a plausible justification for regulatory intervention.9

This paper addresses a different conflict of interest that bears on trial sponsors’ decision-making as to whether or not to share clinical trial data for R&D purposes. Dynamic competition in the research-oriented pharmaceutical sector frames the discussion and focuses in on the value of primary research data as a source of scientific knowledge.

Clinical dossiers for drug marketing authorization comprise diverse materials.10 Yet, legal analysis of clinical trial data oftentimes treats dossiers as a single entity neither distinguishing types of data and information therein nor accounting for differing implications of their release.11 Subject to the present analysis is access to non-summary, patient-level (“raw”) data12 gathered in clinical trials conducted by the

cannot change its confidentiality policy, and that a proper balance can be achieved if “data exclusivity would prevent competitors and generic manufacturers from using data access as a means to gain regulatory approval while allowing research data to be mined for maximum benefit”); see also Trudo Lemmens & Candice Telfer, Access to Information and the Right to Health: The Human Rights Case for Clinical Trials Transparency, 38 AM. J.L. & MED. 63, 66 (2012) (arguing that “access to information about clinical trials . . . ought to be recognized as a fundamental component of the right to health” and for the role of human rights in countervailing the obligation under international agreements to protect test data against disclosure); Trudo Lemmens, Pharmaceutical Knowledge Governance: A Human Rights Perspective, 41 J.L. MED. & ETHICS 163, 164, 172 (2013) (highlighting the deficiencies of the system governing the production and distribution of drug safety and efficacy information from the human rights perspective) [hereinafter Knowledge Governance]; Janene Boyce, Disclosure of Clinical Trial Data: Why Exemption 4 of the Freedom of Information Act Should Be Restored, 4 DUKE L. & TECH. REV. 1, (2005) (proposing to remove the judicially imposed limitations on Exemption 4 of the Freedom of Information in the case of clinical trial data).

8 INSTITUTE OF MEDICINE, CONFLICT OF INTEREST IN MEDICAL RESEARCH, EDUCATION, AND PRACTICE, at 2 (Bernard Lo & Marilyn J. Field eds., 2009) available at http://www.ncbi.nlm.nih.gov/books/NBK22942/pdf/Bookshelf_NBK22942.pdf (last visited Aug. 26, 2016); see also Julio S G Montaner, Michael V O’Shaughnessy & Martin T Schechter, Industry-sponsored clinical research: a double-edged sword, 358 THE LANCET 1893, 1894 (2001); Joel Lexchin et al., Pharmaceutical Industry Sponsorship and Research Outcome and Quality: Systematic Review, 326 BRIT. MED. J. 1167, 1170 (2003).

9 Reichman, supra note 7, at 50–51 (proposing to establish an independent testing agency conducting clinical trials under the conditions of transparency that would allow to “remov[e] the power of the pharmaceutical industry over the design, conduct, analysis and reporting of clinical trials” based on the cost and benefit analysis of data disclosure”); Mark A. Rodwin, Independent Clinical Trials to Test Drug: The Neglected Reform, 6 ST. LOUIS U. J. HEALTH L. & POL’Y 113, 113 (2013) (elaborating on the idea of independent drug testing).

10 For the model specification of the structure and content of clinical study reports, see INT’L CONFERENCE ON HARMONISATION OF TECHNICAL REQUIREMENTS FOR REGISTRATION OF PHARM. FOR HUMAN USE, ICH HARMONISED TRIPARTITE GUIDELINE: STRUCTURE AND CONTENT OF CLINICAL STUDY REPORTS (1995), http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E3/E3_Guideline.pdf.

11 See Erika Lietzan, A New Framework for Assessing Clinical Data Transparency Initiatives, 18 MARQ. INTELL. PROP. L. REV. 33, 37 (2014) (noting that “scholars in the intellectual property field devote very little attention” to the contents of drug applications). For non-legal analysis of potential benefits and risks of sharing different types of clinical trial data, see SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 79–93.

12 This article does not address issues related to privacy protection. For the purpose of this discussion, it is assumed that data can be de-identified or anonymized to protect privacy of trial participants. For instance, earlier this year, the EMA adopted new Guidance on the anonymization of clinical reports for the


private sector.13 This excludes information that may qualify as “competitive intelligence,”14 such as business strategy and know-how related to clinical development programs. Studies find that patient-level data, to a great extent, remains inaccessible for a broader research community.15 At the same time, such data presents utmost value for follow-on research, insofar as it may enhance pharmacological knowledge and inform subsequent drug development.16 Secondary analysis by other drug developers and researchers carries the potential to amplify knowledge externalities and improve R&D efficiency. The distinction between initial data generators and follow-on drug developers, however, is rather notional. Clinical data is produced and is then used as a knowledge input in drug R&D. Thereby, does the pharmaceutical industry play against itself by not sharing?

Rational choice theory posits that in interdependent situations “the maximisation of short-term self-interest yields outcomes leaving all participants worse off than feasible alternatives.”17 As Elinor Ostrom observed: “The paradox that individually rational

purpose of the implementation of the EMA 2015 publication policy. See European Medicines Agency, External Guidance on the Implementation of the European Medicines Agency Policy on the Publication of Clinical Data for Medicinal Products for Human use, EMA/90915/2016, at 32–43 (2 March 2016) EMA/90915/2016, http://www.ema.europa.eu/docs/en_GB/document_library/Regulatory_and_procedural_guideline/2016/03/WC500202621.pdf; see also SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 146 (noting that “successful re-identification attacks on properly de-identified or anonymized health or clinical data are rare”).

13 See European Medicines Agency, supra note 12, at 8 (defining clinical trial data as “characteristics or information, usually numerical, that are collected through observation [and] can also be used to describe statistics (i.e. aggregations or transformations of raw data)”).

14 Lietzan, supra note 11, at 69. For instance, during the EMA’s public consultations, Novo Nordisk submitted that clinical trial reports contain “detailed strategic and operational information revealing general company know-how about the efficient and competitive set-up of clinical studies, such as trial site performance” and “disclosure of such information could provide competitors with a roadmap to facilitate their own product development programs.” See the EMA, Overview of comments received on ‘Publication and access to clinical-trial data’ (EMA/240810/2013) EMA/354914/2014, at 70–71 (Oct. 2, 2014), http://www.ema.europa.eu/docs/en_GB/document_library/Overview_of_comments/2014/09/WC500174224.pdf. See also the submission by the German Pharmaceutical Industry Association (BPI), Overview of comments received on ‘Publication and access to clinical-trial data’ (EMA/240810/2013) EMA/349245/2014, 14, 27 (October 2 2014), http://www.ema.europa.eu/docs/en_GB/document_library/Overview_of_comments/2014/09/WC500174222.pdf.

15 Clinical reports submitted for drug marketing authorization provide by far more comprehensive records of trial findings as compared to summary-level results that can be disclosed in public registrars of clinical trials, scientific publications and patent specifications. See, e.g., Lemmens, Knowledge Governance, supra note 7, at 177 (concluding that “[m]any countries . . . have failed to implement adequate transparency measures through enforceable and stringent regulation”); Mark J. Scheineson & M. Lynn Sykes, Major New Initiatives Require Increased Disclosure of Clinical Trial Information, 60 FOOD & DRUG L.J. 525, 525 (2005) (noting that FDA “releases summaries and descriptions of . . . trials to the public, but not the complete underlying data” after the drug is approved for marketing); David Brailer, Clinical Data as the Basic Staple of the Learning Health System,, in CLINICAL DATA AS THE BASIC STAPLE, supra note 5, at 53 (“ . . . we do not have the ability to actually get information in a raw, useful, assembled analyzable format”). As for the EU, it remains to be seen how the centralized database initiative under Regulation (EU) No 536/2014 of the European Parliament of the Council of 16 April 2014 on clinical trials on medicinal products for human use will be implemented, especially, in light of the recent case law of the Court of Justice of the European Union on the EMA’s disclosure of clinical trial reports. See infra section IV. B.

16 See infra section IV. A. 17 Elinor Ostrom, A Behavioral Approach to the Rational Choice Theory of Collective Action, in

CHOICE, RULES AND COLLECTIVE ACTION: THE OSTROMS ON THE STUDY OF INSTITUTIONS AND GOVERNANCE 121, 122 (Filippo Sabetti & Paul Dragos Aligica eds., 2014).


strategies lead to collectively irrational outcomes seems to challenge a fundamental faith that rational human beings can achieve rational results.”18 Applied to clinical trial data, interdependency can be viewed insofar that more restrictive access to already gathered data causes greater inefficiencies due to duplicative research. One might not need to employ rational choice theory to understand the strategy of maximizing private gains behind the industry practice of restricting access to “raw” data. The longer the time and the greater investment for a competitor to develop a new product, the greater is the lead time—during which the originator firm can maximize commercial returns on its own R&D.19 At the same time, the greater the scope of the available data from previous trials, the more targeted and efficient follow-on R&D can become, facilitating the development of competitors’ products. The competitive environment motivates market players to pursue an individually rational strategy of restrictive access to data, if such a strategy helps delay new product competition or if access would otherwise strengthen competitors’ R&D capacity or market position. As Elinor Ostrom notes, “Models of complete rationality have been highly successful in predicting marginal behavior in competitive situations in which selective pressures screen out those who do not maximize external values, such as profits in a competitive market.”20

The policy question is whether society is better off in terms of innovative medicines under the regime that favors confidentiality protection over one that facilitates access. Whether or not clinical trial data is shared, new drugs are being developed—although with presumably different ratios of social costs to benefits. Under one regime, third parties’ access to and use of data is subject to the original trial sponsors’ authorization and can be granted to individual datasets on a case-by-case basis. Under the other regime, access to data is not at the original data holders’ discretion but may be granted by a drug authority, such as the case of the EMA.21 To resolve the dilemma, one needs a better understanding as to which way and to what extent mandatory disclosure of clinical data may affect the innovation incentives of drug sponsors, and how such access may facilitate new drug development.

This discussion does not intend to apply collective action theory to determine the extent to which industry clinical data non-sharing may be “socially defecting.”22 Quite clearly, social costs of clinical trial data secrecy can be appreciable. This article argues that confidentiality protection of non-summary clinical data in the presence of innovation incentives that protect originator drugs against generic competition is not

18 ELINOR OSTROM, GOVERNING THE COMMONS: THE EVOLUTION OF INSTITUTIONS FOR

COLLECTIVE ACTION 5 (2011) (emphasis added). 19 There might be other reasons as well, e.g., that the secondary analysis might compromise the

reputation of the originator drug, either justifiably or not. 20 Ostrom, supra note 17, at 123. 21 Pursuant to the EMA 2015 publication policy, access to clinical study reports submitted to the

EMA for EU-wide marketing authorization through the centralized procedure can be granted to third parties without the authorization of and remuneration to the original trial sponsors, under the condition that the released data is used for scientific, non-commercial research purposes and not for the purpose of supporting an application for drug approval. See the EMA publication policy, supra note 2, at 10–11, 14.

22 See Julie De Coninck, Behavioural Economics and Legal Research, in METHODOLOGIES OF LEGAL RESEARCH, 257, 272–73 (Mark Van Hoecke ed., 2011) (noting that “whether and how a behavioural account can be used for the purpose of legal analysis will arguably always, to some extent, remain a matter of ‘plausibility’ in the light of the existing empirical and theoretical knowledge, which is never truly complete and always indeterminate to some degree”).


redundant as has been considered prior.23 Rather, actual and legal secrecy provides an additional layer of protection that allows drug sponsors to maximize private returns by internalizing knowledge externalities.24 Social costs of such protection increase proportionally as data analytic techniques advance, which enhance opportunities to exploit research potential of aggregated data and facilitate new drug development. Whether confidentiality fosters any additional social benefits—especially given multiple regulatory instruments intended to solve the appropriability problem in drug R&D—is dubious.25 The question then arises as to whether a regime that supports restricted access to trial data yet neglects to account for a divergence between social costs and benefits can, in fact, qualify as a regulatory failure.

In focus of the analysis is the assertion of the research-based pharmaceutical industry that mandatory disclosure of test data impedes innovation incentives, which can explain why a legislator may hesitate to intervene with pro-access measures. The debate over access to clinical data features two conflicting propositions, which appear as a Hegelian paradox: access has been argued to impede innovation incentives26 and, at the same time, to facilitate new drug development and innovation in the pharmaceutical sector.27 Hence, must these positions be reconciled; and, if so, how would it be theoretically possible? To dissect these competing claims and to understand their interrelationship, the present analysis employs the concepts of public-good and common-resource dilemma. The claim of an impediment to innovation incentives corresponds to the public-good problem, when socially valuable goods can be undersupplied. The claim that access facilitates new drug development corresponds to the (reverse) common-resource dilemma, when the underuse of valuable resources can cause economic inefficiencies.

The discussion does not deal with the questions of test data ownership and the legal basis for its disclosure.28 Nor does it intend to propose specific rules of access, which

23 Eisenberg, supra note 7, at 468–69 (“Regulatory exclusivity creates another redundancy in

protection . . . with regulatory exclusivity to protect against free riders, it is difficult to justify the continuing treatment of data submitted in pursuit of regulatory approval as trade secret or confidential information belonging to the submitter.”).

24 See infra section V. B. 2. 25 See, e.g., McGarity & Shapiro, supra note 7, at 848–49 (“The case for disclosure is clear; the case

for nondisclosure is weakened by conflicting economic studies as well as the existence of alternative means for protecting research incentives.”).

26 See infra notes 32–33. 27 See infra section IV. C. 28 As far as the EMA transparency policies are concerned, the legality of disclosure of clinical trial

reports remains uncertain in light of the recent decisions of the Court of Justice of the European Union. See infra note 64-66. At the international level, considerable ambiguity persists regarding the minimum standard of protection of pharmaceutical test data under Article 39 of the TRIPS Agreement. It is unclear whether access to primary research data for drug R&D purposes violates the obligation of protection of undisclosed information under Article 39 of the TRIPS Agreement. To date, there has been no decision of a competent WTO body regarding the interpretation and application of this provision. See World Trade Organization, WTO Analytical Index: TRIPS available at https://www.wto.org/english/res_e/booksp_e/analytic_index_e/trips_03_e.htm#article39 (last visited Nov. 4, 2016). Notably, the World Intellectual Property Organization regards the disclosure of regulatory data for research purposes as an act of unfair competition. See World Intellectual Property Organization, Model Provisions on Protection Against Unfair Competition, Note 6.26 (1996) (“The act of disclosure of [test data] is . . . considered an act of unfair competition. The unauthorized disclosure may consist in publishing the information or in passing it on to others, for example for research purposes.” (emphasis added).).


is the subject matter of a larger research project.29 Instead, the present analysis addresses the principle by which the data sharing conflict of interest may be resolved. It proposes to apply the basic economic principles underlying intellectual property (IP) law, according to which legal protection promotes dynamic efficiency (technological progress, creativity and innovation) at the expense of time-limited static inefficiency, caused by restrained price competition (competition by imitation).30 Accordingly, regulatory instruments designed to facilitate pharmaceutical innovation may be justified to the extent that they support innovative activity by eliminating generic competition, but not if they inhibit the development of new medicines. This is particularly relevant to the pharmaceutical sector, wherein innovation is the main form of competition.31 Often the literature on access to pharmaceutical test data refers generally to competitive effects, without differentiation between competition by imitation, improvement and substitution. However, these forms of competition differ in principle, especially, in terms of welfare effects. Accounting for such differences, one can draw various conclusions regarding the legality and legitimacy of access policies, as well as the conception of the regulatory framework governing access to data. This paper argues that the research-based industry’s claim of hampered innovation incentives due to clinical data disclosure is misplaced and should not be leveraged against pro-access policies. While clinical trial data confidentiality alone, does not resolve the public-good dilemma in pharmaceutical innovation, it is likely to aggravate the resource dilemma.

The article is structured as follows. Part II analyzes the public-good dilemma in relation to clinical data production and critically evaluates the claim that third-party access to clinical data for R&D purposes impedes innovation incentives of originator companies. Part III examines the common-good dilemma related to clinical trial data in light of the value of data in drug R&D as a source of knowledge. Part IV explores the related policy dilemmas and raises the question whether drug sponsor should be allowed to internalize knowledge externalities generated by clinical research ‘under the guise’ of innovation incentives. Part V draws on the policy implications.

29 Max-Planck-Institute für Innovation und Wettbewerb, Weiteres Forschungsprojekt, The Role of

Competition Law in Regulating Access to Clinical Trial Data in the Context of Competition in Innovation in the Pharmaceutical Sector, http://www.ip.mpg.de/de/projekte/details/the-role-of-competition-law-in-regulating-access-to-clinical-trial-data-in-the-context-of-competition-in-innovation-in-the-pharmaceutical-sector.html (last visited Aug. 27, 2016).

30 See Josef Drexl, Intellectual Property in Competition: How to Promote Dynamic Competition as a Goal, in MORE COMMON GROUND FOR INTERNATIONAL COMPETITION LAW 210, 215–17 (Josef Drexl et al. eds., 2011); David W. Barnes, The Incentives/Access Tradeoff, 9 NW. J. TECH. & INTELL. PROP. 96, 97, 99 (2010) (analyzing the trade-off between more competitive prices and potential benefits from increased innovative activity); Josef Drexl, IMS-Health and Trinko – Antitrust Placebo for Consumers Instead of Sound Economics in Refusal-to-Deal Cases, 35 INT’L REV. INTELL. PROP. & COMPETITION L. 788, 790 (2004) (applying the concepts of competition by imitation and competition by substitution as analytical tools); MASSIMO MOTTA, COMPETITION POLICY: THEORY AND PRACTICE 55, 56–57 (2004); Joseph Farell & Michael Katz, The Effects of Antitrust and Intellectual Property Law on Compatibility and Innovation, 43 ANTITRUST BULL. 609, 612 (1998); William S. Comanor, The Political Economy of the Pharmaceutical Industry, 24 J. ECON. LIT. 1178, 1180 (1986) (noting that “at issue . . . is the appropriate level of returns from pharmaceutical innovation. [ . . . ] The essential question is the extent to which competitive forces should be promoted or retarded so that the most desirable rate and pattern of innovation are induced.”).

31 See infra note 128.


III. THE PUBLIC-GOOD DILEMMA: TO GENERATE OR TO GENERATE NOT CLINICAL TRIAL DATA

During public consultations on the EMA’s publication policy, originator pharmaceutical companies and associations claimed that disclosure of clinical dossiers submitted for drug marketing authorization—even if disclosed for non-commercial research purposes—impedes innovation incentives.32 The free-riding argument has been raised in a pending case at the Court of Justice of the European Union (CJEU). An applicant seeking a stay of the EMA’s decision to disclose clinical study reports alleged:

In the context of the competitive and innovative pharmaceutical industry, [provisions on public access to information] strike a balance between, on the one hand, the interests of transparency, legitimate public interest considerations, and the desirability of avoiding duplicative research, and, on the other hand, the need to give undertakings a proper incentive to invest in research and development without their having to fear that competitors will be able to “free-ride” on their innovation.33

In the U.S., the Food and Drug Administration (FDA) may deny third-party access to clinical reports pursuant to Exemption 4 under the Freedom and Information Act (FOIA) if disclosure causes “substantial competitive harm.” The courts have

32 See the German Association of Research-Based Pharmaceutical Companies, Overview of comments

received on ‘Publication and access to clinical-trial data’ (EMA/240810/2013) 23 (2014) (submitting that “[t]he implementation of the EMA’s policy would have a substantial detrimental impact on Germany as a center of research, as well as the research landscape in Europe: the publication of approval information jeopardizes research and development investments in the billions, and therefore threatens to undercut the research and development of new and innovative medicinal products for the promotion of public health”); id. at 25–26 (“publication destroys all protection of the intellectual property inherent in the study data, so that the incentive to conduct clinical trials in the interest of promoting public health is lost as well”); the Danish Association of the Pharmaceutical Industry, id. at 70–71, 74 (raising “concern that the . . . EMA Policy will . . . “[w]eaken incentives for companies to invest in biomedical research by disclosing companies‟ commercially confidential information (CCI), without due consideration of the competing interests that may or may not justify disclosure” and “[t]he clinical trial data in a MA dossier may contain commercially sensitive information. The protection of this information helps to maintain the incentive for companies to continue innovating and making the enormous investments needed in medical and scientific research. The EMA’s plans to release this data are therefore a threat to research and innovative medicine development”); the European Federation of Pharmaceutical Industry Associations (EFPIA), Overview of comments received on ‘Publication and access to clinical-trial data’ (EMA/240810/2013) 22, 31 (2014) (submitting that the EMA disclosure policy could “weaken incentives for companies to invest in biomedical research by disclosing companies’ commercially confidential information (CCI), without due consideration of the competing interests” and “Clinical trials data within the MA dossier may include commercially sensitive information, the protection of which helps incentivise companies to continue innovating and investing in medical and scientific research”); BioIndustry Association, id. at 12 (submitting that “It should not be ignored that allowing third parties access to CT data held by the Agency may negatively impact on the value, competitiveness, ownership of trade secrets and intellectual property rights of undertakings . . . ”); The EuropaBio, Overview of comments received on ‘Publication and access to clinical-trial data’ (EMA/240810/2013) 28 (2014) (asserting that “[p]remature disclosure of CCI [commercially confidential information], misappropriation of research know-how and the lack of a predictable EU intellectual property/trade secret protection framework are factors that can undermine the ability of EuropaBio members to operate and innovate, and ultimately ‘their value proposition’ for potential investors”) (emphasis added).

33 Case T-718/15R, PTC Therapeutics Int’l Ltd. v European Medicines Agency, Order of the President of the General Court, at ¶ 35 (July 20, 2016) (emphasis added).


interpreted this standard to support marketing authorization of a competing drug “without incurring the time, labor, risk, and expense involved in developing them independently,”34 this can be interpreted as indirectly protecting innovator companies’ incentives.

Since protection against free-riding on R&D investment is associated with public-good problems in innovation, this section addresses: (i) whether clinical research data is a public good; (ii) whether the public-good dilemma exists in relation to industry-sponsored clinical research and data; and, if so, (iii) what specifics should be taken into account by regulation.

A. Public-Good Characteristics of Clinical Trial Data Public goods are characterized by non-excludable35 and non-rivalrous36

consumption—technical innovation and information production being the classic examples.37 In the case of clinical trial data, third parties’ access and use can be excluded by virtue of actual and legal secrecy. According to Blumenthal data comprised in

large clinical databases have an aspect of a quasi-public good . . . . They are definitely excludable . . . . These data create opportunities for private gain. The data create competitive advantage by enabling organizations to learn from their experience, perhaps to achieve better outcomes than their rivals; for example, they may learn how to treat a certain disease better . . . . This is the equivalent of using trade secrecy for medical practice . . . .38

Knowledge gained via clinical trials can qualify as tacit so long as it remains within the company’s control. Once the findings of clinical trials are divulged, the original trial sponsors can no longer restrict their use,39 and re-analysis by multiple researchers.

34 Webb v. HHS, 696 F.2d 101, 103 (D.C. Cir. 1982). In Pub. Citizen, the court held that Exemption

4 under the FOIA intends to protect against disclosures that would allow competitors “to eliminate much of the time and effort that would otherwise be required to bring to market a [competing] product”. See Pub. Citizen Health Res. Grp. v. FDA, 185 F.3d 898, 905 (D.C. Cir. 1999). See also Gov’t Accountability Project v. HHS, 691 F. Supp. 2d 170, 175–76 (D.D.C. 2010) (finding that defendants “failed to meet their burden of showing a ‘a likelihood of substantial competitive injury’ [and] have proffered only vague and conclusory allegations in support of their claim that release of the information at issue will likely cause competitive harm”).

35 Paul A. Samuelson, The Pure Theory of Public Expenditure, 36 REV. ECON. & STAT. 387 (1954); Joseph E. Stiglitz, Knowledge as a Global Public Good, GLOBAL PUBLIC GOODS: INTERNATIONAL COOPERATION IN THE 21ST CENTURY 308 (I. Kaul, I. Grunberg & M. A. Stern eds.,1999).

36 Richard A. Musgrave, Provision for Social Goods, in PUBLIC ECONOMICS: AN ANALYSIS OF PUBLIC PRODUCTION AND CONSUMPTION AND THEIR RELATIONS TO THE PRIVATE SECTORS 124–45 (Julius Margolis & H. Guitton eds. 1969); RICHARD A. MUSGRAVE, THE THEORY OF PUBLIC FINANCE; A STUDY IN PUBLIC ECONOMY 43–44 (1959); Stiglitz, supra note 35, at 308.

37 Hertog, infra note 43, at 16; see also ROBERT P. MERGES, PETER S. MENELL & MARK A. LEMLEY, INTELLECTUAL PROPERTY IN THE NEW TECHNOLOGICAL AGE 10, 11, 12 (2003) (discussing the public goods characteristics of intellectual property); Stiglitz, supra note 35, at 308–09.

38 David Blumenthal, Characteristics of a Public Good and How They Are Applied to Healthcare Data, in CLINICAL DATA AS THE BASIC STAPLE 140, 142 (A.W. Goodby, L. Olsen & M. McGinnis eds., 2010) (emphasizing that in this sense, clinical trial findings are “fundamentally nonexcludable”).

39 Id.


Clinical trial data can thus be characterized as an impure,40 or quasi–public, good.41 Then, how does the public-good dilemma relate to its production, if at all?

B. The Public-Good Problem in Relation to Clinical Trial Data A public–good dilemma refers to a situation when “all those who benefit from the

provision of a public good . . . find it costly to contribute and would prefer others to pay for the good instead.”42 Hence, such goods can be “either not produced at all or not in the optimum quantities because of free-rider problems.”43 As mentioned earlier, innovation is classically a public good. The contention is that, if innovators cannot make returns on innovative activity because competitors may easily copy innovative products, society will, eventually, be left “innovationless.” In healthcare, this concern translates into an under-development and undersupply of innovative medicines. The public-good problem is especially acute for pharmaceutical innovation, due to the substantial investment required for new drug development and the relative ease to reverse-engineer and produce generic copies. These conditions explain why, compared to other sectors, pharmaceutical companies rely more on exclusive rights.44

C. Third Party Access to Clinical Data Impedes Innovation Incentives: Evaluating the Claim

To evaluate the claim of impediment to innovation incentives, one must first define the term. In general terms, innovation refers to a broad range of activities,45 and incentives embrace various factors bearing on drug sponsors’ decision-making to

40 See Inge Kaul et al., Defining Global Public Goods, in GLOBAL PUBLIC GOODS: INTERNATIONAL

COOPERATION IN THE 21ST CENTURY 2, 4 (Inge Kaul, Isabelle Grunberg & Mark A. Stern eds.,1999) (“Goods that only partly meet either or both of the defining criteria are called impure public goods. Because impure goods are more common than the pure type, we use the term “public good” to encompass both pure and impure public goods.”); Stiglitz, supra note 35, at 309–10 (“Some forms of knowledge . . . can be made excludable [through] trade secrets and “because the returns to some forms of knowledge can to some extent be appropriated . . . knowledge is often thought of as an impure public good.”) (emphasized in the original).

41 Blumenthal, supra note 38, at 141 (arguing that “[a]pplied biomedical research has aspects of a public good as well as aspects of a quasi-public good. Knowledge concerning research related to a particular drug or device can be appropriated up to a point. It is excludable within limits and it is rival within limits. Clearly one can keep this kind of information secret and benefit from it in a marketplace, and many medical device companies make their living without patenting by keeping secret how their devices are produced.”) (emphasis added).

42 Ostrom, supra note 17, at 122. 43 Johan den Hertog, Review of Economic Theories of Regulation, Tjalling C. Koopmans Research

Institute, Discussion Paper Series No. 10–18, at 16 (2010). 44 DOMINIQUE FORAY, ECONOMICS OF KNOWLEDGE 146 (2004) (arguing that “[p]atents are most

likely to foster innovation when the following conditions converge: high R&D costs; reverse engineering and other means of knowledge absorption that allow competitors for rapid and inexpensive imitation; and low costs of manufacturing the final product . . . These conditions are . . . typical of the pharmaceutical industry”); see also infra note 71, at 4, 14.

45 OSLO MANUAL, THE MEASUREMENT OF SCIENTIFIC AND TECHNOLOGICAL ACTIVITIES. PROPOSED GUIDELINES FOR COLLECTING AND INTERPRETING TECHNOLOGICAL INNOVATION DATA 39, 47 (2004) (defining innovation activity as “all those scientific, technological, organisational, financial and commercial steps which actually, or are intended to, lead to the implementation of innovations include[ing] R&D that is not directly related to the development of a specific innovation”).


invest in drug R&D.46 Relatedly, the trade secrets incentive theory holds that confidentiality protection motivates entities to generate new, valuable information47 and non-patentable inventions.48 Studies find that research-based pharmaceutical companies rely on trade secrets, in order to ensure R&D investment returns, equally—and sometimes to a greater extent—to patent protection.49 While this may hold true for certain types of information, the case for clinical trial data appears less convincing. If confidentiality protection indeed motivated drug sponsors to generate the optimal amount of clinical data, why would the regulator need to enforce the standards for safety and efficacy data in the first place?

1) Regulatory Standards as an Incentive for Clinical Trials Clinical trials are conducted as a part of product development; however, it is a

regulatory authority that sets the standards and enforces the scope of clinical development and data collection. The purpose of drug regulation is “to reduce harmful ‘externalities’ generated by an industrialized market economy”50—i.e., the risk of the misrepresentation of product information owed to marketplace information asymmetry. The concerns are that without the regulatory intervention pharmaceutical market can be a market for “snake oil”51 or “lemons,”52 and that firms “will do less

46 For definitions of innovation, see Charles Edquist, Systems of Innovation Approaches - Their

Emergence and Characteristics, in SYSTEMS OF INNOVATION TECHNOLOGIES, INSTITUTIONS AND ORGANIZATIONS 10–12 (Ch. Edquist ed., 2006).

47 Mark A. Lemley, The Surprising Virtues of Treating Trade Secrets as IP Rights, in THE LAW AND THEORY OF TRADE SECRECY: A HANDBOOK OF CONTEMPORARY RESEARCH 109, 122–23 (Rochelle C. Dreyfuss & Katherine J. Strandburg eds., 2011) (rationalizing trade secret protection as an incentive to invent); Michael Risch, Trade Secret Law and Information Development Incentives, in THE LAW AND THEORY OF TRADE SECRECY: A HANDBOOK OF CONTEMPORARY RESEARCH 152, 154 (Rochelle C. Dreyfuss & Katherine J. Strandburg eds., 2011).

48 David D. Friedman, William M. Landes & Richard A. Posner, Some Economics of Trade Secret Law, 5 J. ECON. PERSP. 61, 62 (1991) (analyzing this proposition). But see Robert G. Bone, A New Look at Trade Secret Law: Doctrine in Search of Justification, 86 CALIF. 241, 264–72 (1998) (rebutting the efficiency and incentive-to-create arguments for trade secret protection). ). It should be noted that, even though trade secrets are usually associated with non-patentable subject matter, in some circumstances, companies would still choose trade secret protection over patents in order to protect otherwise-patentable inventions.

49 Wesley M. Cohen et al., Protecting Their Intellectual Assets: Appropriability Conditions & Why U.S. Manufacturing Firms Patent (or Not), tables 1, 2 (Nat'l Bureau of Econ. Research, Working Paper 7552, 2000); see also Richard C. Levin et al., Appropriating the Returns from Industrial Research & Development, 18 BROOKINGS PAPERS ECON. ACTIVITY 783–832 (1987), https://www.brookings.edu/bpea-articles/appropriating-the-returns-from-industrial-research-and-development/.

50 Richard B. Stewart, Regulation, Innovation, and Administrative Law: A Conceptual Framework, 69 CALIF. 1256, 1260 (1981); see also McGarity & Shapiro, supra note 7, at 837 (stating that “market mechanisms, even as enhanced by a tort compensation system, do not adequately protect man and the environment from the risks posed by new products, chemicals, and technologies”).

51 Rebecca S. Eisenberg, The Role of FDA in Innovation Policy, 13 MICH. TELECOMM. TECH. L. REV. 345, 371 (2007).

52 Ariel Katz, Pharmaceutical Lemons: Innovation and Regulation in the Drug Industry, 14 MICH. TELECOMM. TECH. L. REV. 1, 11 (2007). The ‘market for lemons’ metaphor in relation to the market failure of information asymmetry between buyers and sellers was introduced by George Akerlof. See George A. Akerlof, The Markets for ‘Lemons’: Qualitative Uncertainty and the Market Mechanism, 84 Q. J. ECON. 488, 488 (1970) (examining the phenomenon of the degradation of the quality of goods being overtaken by low-quality goods – “lemons” – in the presence of information asymmetry between buyers and sellers when consumers cannot distinguish properly between products).


than the socially optimal amount [of testing if they] cannot appropriate the entire gain from [it].”53 Thus, by enforcing safety, efficacy and quality standards, as required for drug commercialization, the regulator addresses the market failures of insufficient clinical trials and drug testing as well as inaccurate information about the claimed therapeutic properties.54 In this regard, drug sponsors have little discretion to decide how many trials to conduct and how much data to gather.

2) Incentives as a Means to Appropriate Benefits of Investment into Innovation

More specifically, innovation incentives refer to the legal means of appropriating the value of innovative activities. The economic rationale of IP protection is associated with public-good market failure. Protection intends to restrain competitive imitation so that innovators earn supra-competitive profits to recover their R&D costs, which thereby can support continuing innovative activity. For instance, by excluding third parties’ use of a patented technology, patent protection can create market power and allow the patent holder to earn profits under restrained price competition. Does the same economic logic apply to clinical trial data confidentiality?

Pharmaceutical companies conduct trials not for their own sakes but in the anticipation of successful new drug development and commercialization. Data is not the final product but, rather, a by-product of R&D, while profits are earned in the downstream product market. The main business function of data in clinical dossiers is to support drug-marketing authorization. Clinical data is not utilized in a commercial sense thereafter.55 Clinical dossiers do not have a productive use comparable to patents or to technological know-how that can add value to the marketed product.

The effectiveness of legal protection, as an economic incentive, depends on how it correlates with profits. Profit-making implications of confidentiality can differ depending on the particular type of information, its economic value, and functions in business operations. To understand the effects of third-party use on incentives, one must analyze the relationship between confidentiality protection of clinical dossiers, market power, and competition. Two types of competition should be distinguished in this regard, competition between generic-originator companies and that among originator companies.

53 Evan R. Kwerel, Economic Welfare and the Production of Information by a Monopolist: The Case

of Drug Testing, 11 BELL J. ECON. 505, 506 (1980). 54 On market failures in the pharmaceutical industry, see Luigi Orsenigo, Giovanni Dosi & Mariana

Mazzucato, The Dynamics of Knowledge Accumulation, Regulation, and Appropriability in the Pharma-biotech Sector: Policy Issues, in KNOWLEDGE ACCUMULATION AND INDUSTRY EVOLUTION: THE CASE OF PHARMA-BIOTECH 402, 406–08 (Mariana Mazzucato & Giovanni Dosi eds., 2006).

55 In some situations, clinical data can be licensed out to a generic company in the markets where the originator company itself is not present, or shared under contractual terms for research purposes. However, in general, clinical data can hardly be viewed as constituting a market of its own in a sense that it is offered for sale and becomes subject to regular market transactions. Unlike in the case of biopharmaceutical collaboration, there is no structural division of labor between the entities conducting upstream research to develop an input (e.g. research tools) and drug companies using such inputs to further develop and commercialize medicines.


3) Implications of Access to Data for Generic-Originator Competition

Some jurisdictions approve a generic product on the basis of bioequivalence studies that demonstrate interchangeability with the innovator drug, exempting the generic applicant from conducting full-scale clinical trials to submit safety and efficacy data.56 This is also known as referential use of data, since a generic drug may be approved with reference to the originator’s data. In this case, generic companies do not obtain actual access the originators’ dossiers.

Some jurisdictions also enforce data exclusivity protection, which precludes referential use of data for a certain period of time, in order to protect returns on R&D investment.57 For example, the EMA transparency policies permit access to data for scientific, non-commercial research purposes but not for generic drug approval.58 Nevertheless, during public consultations, some submissions to the EMA argued that the new policy exposes drug sponsors to a risk that clinical reports, albeit accessed under the terms for non-commercial use, can be used for regulatory approval “in regions where the originator company does not have a marketing authorization, or where no stewardship or adequate protection for [confidentially commercial information] exists.”59 Should that happen, innovation incentives—those protecting against generic competition—can be hindered.60

4) Implications of Access to Data for Competition Among Originator Companies

Clinical data confidentiality can assist drug sponsors to retain competitive advantage by potentially restraining dynamic competition. The longer the time and the greater investment it takes a competitor to develop a new product, the longer is the lead time, during which an originator firm can maximize commercial returns on its R&D.61 As discussed later, access to data from prior trials can contribute to the

56 See MARKETING AUTHORIZATION OF PHARMACEUTICAL PRODUCTS WITH SPECIAL REFERENCE TO

MULTISOURCE (GENERIC) PRODUCTS: A MANUAL FOR NATIONAL MEDICINES REGULATORY AUTHORITIES (NMRAS) 4, WORLD HEALTH ORGANIZATION [WHO] (2011), http://apps.who.int/iris/bitstream/10665/44576/1/9789241501453_eng.pdf.

57 For an overview of data exclusivity regimes in 44 jurisdictions, see INT'L FED'N OF PHARM. MFRS AND ASS'NS (IFPMA), DATA EXCLUSIVITY: ENCOURAGING DEVELOPMENT OF NEW MEDICINES (2011), http://www.ifpma.org/wp-content/uploads/2016/01/IFPMA_2011_Data_Exclusivity__En_Web.pdf.

58 European Medicines Agency, supra note 2, Annex 1, ¶ 3; Annex 2, ¶ 3. 59 European Medicines Agency, Overview of comments received on ‘Publication and access to

clinical-trial data’ (EMA/240810/2013) 27, http://www.ema.europa.eu/docs/en_GB/document_library/Overview_of_comments/2014/09/WC500174225.pdf; see also Pfizer, supra note 2, at 85; European Medicines Agency, Overview of comments received on ‘Publication and access to clinical-trial data’ (EMA/240810/2013) 11 (Oct. 2, 2014), http://www.ema.europa.eu/docs/en_GB/document_library/Overview_of_comments/2014/09/WC500174222.pdf.

60 The risk that data, even if shared for research purposes, can be used for marketing approval of an identical drug, requires further investigation; in particular, whether such risk can be eliminated by technological or regulatory measures. If a jurisdiction approves a generic product based on the applicant’s own test data, it appears to be rather a matter of enforcement of the requirement that the applicant, in fact, registered and conducted its own clinical trials.

61 Assuming during that period the innovator drug would also be protected against generic competition by patent(s).


development of a new potential competitor drug.62 In this regard, disclosing clinical data to competitors runs counter to business logic.63

In the EU, there have been several recent cases in which pharmaceutical companies objected to disclosure of their clinical reports submitted to and held by the EMA.64 A detailed analysis of these cases exceed the scope of this paper; however, some arguments raised by the claimants are worth highlighting. For instance, in AbbVie v EMA, AbbVie argued that access to dossiers mandated by the EMA

would undermine the protection of [AbbVie’s] commercial interests [as] the applicants’ competitors could use the disputed reports to improve their competitive position with (actually or potentially) competing products in the highly competitive class of TFN antagonists. . . . [Clinical trial] reports therefore provide a very specific road map for a company wishing to develop a TNF antagonist for the therapeutic use in question, by enabling it to develop a similar ‘biologics/biosimilar’ strategy in order to produce a follow-on medicinal product or to add new therapeutic indications to an existing medicinal product. The reports also provide information . . . , which could reduce the development process for a medicinal product by two to three years.65

Similarly, InterMune, alleged that “damage [caused by access to its clinical reports] would arise . . . from the future use of the information [in] requested documents by the InterMune companies’ competitors—and specifically by Boehringer Ingelheim GmbH . . . – in order to develop a medicinal product which would compete with the medicinal product Esbriet . . . .”66

Both arguments reflect apprehension that access to data facilitates development of competing products. Competing, in this context, cannot be interpreted as referring to a generic copy. In fact, access to clinical dossiers is not necessary to reverse engineer the qualitative and quantitative formulation of the innovator drug.67 More likely, access can contribute to the development of a follow-on drug that represents an improvement over the originator drug.68 If the time lag between first-in-class drug

62 See infra section IV. C. 63 Brian Kelly, Technical and Operational Challenges, CLINICAL DATA AS THE BASIC STAPLE, supra

note 5, at 212–23 (noting that “ . . . . no one really shares data—for many reasons. One is that in many instances, not sharing data gives [biopharmaceutical companies] a competitive advantage. . . . you can actually run secondary analysis . . . . and derive insight, which then can translate into improvements in care and outcome.”).

64 Case C-390/13P(R), European Medicines Agency v. InterMune UK Ltd., Order of the Vice-President of the Court, ¶ 57 (Nov. 28, 2013); Case C-389/13P(R), European Medicines Agency v. AbbvVie Inc., Order of the Vice-President of the Court, ¶ 55 (Nov. 28, 2013); Case T-718/15 R, PTC Therapeutics Int'l Ltd. v. European Medicines Agency, Order of the President of the General Court (July 26, 2016).

65 Case T-44/13 R, AbbVie, Inc. v. European Medicines Agency, Order of the President of the General Court, ¶ 60 (Apr. 25, 2013) (emphasis added). Notably, in that case, access was petitioned a university science student in connection with the preparation of a master’s thesis.

66 Case C-390/13P(R), European Medicines Agency v. InterMune UK Ltd., Order of the Vice-President of the Court, ¶ 34 (Nov. 28, 2013).

67 Arvind K. Bansal & Vishal Koradia, The Role of Reverse Engineering in the Development of Generic Formulations, PHARM. TECH., Aug. 2005, http://www.pharmtech.com/role-reverse-engineering-development-generic-formulations.

68 See Joseph A. DiMasi & Cherie Paquette, The Economics of Follow-on Drug Research and Development: Trends in Entry Rates and the Timing of Development, 22 PHARMACOECONOMICS 1, 2 (2004)


marketing and the launch of an improved product is viewed as “a key driver of return on investment for R&D,” the reduction of this time “could discourage future investment in new product development.”69

As discussed earlier, the non-rivalrousness of data and information permits multiple researchers to use them for analysis in parallel R&D. What can be rivalrous is the market share of follow-on drugs developed on the basis of secondary data analysis. While third parties’ use of R&D data does not depreciate the inherent value of data, such use may subsequently diminish the competitive advantage of the data originator. Thus, business instinct would urge drug sponsors to preclude third-party access.

5) Implications for Regulation The foregoing discussion demonstrates that, in the context of industry-sponsored

clinical research, the public-good dilemma does not arise with regard to clinical trial data as such, but relates to drug innovation in general. Drug R&D—clinical trials, in particular—requires substantial investment.70 However, there are a number of regulatory incentives already in place to support pharmaceutical innovation and to alleviate the problem of investment appropriability in drug R&D such as patent

(defining a follow-on new drug as “a new drug entity with a similar chemical structure or the same mechanism of action as that of a drug already on the market . . . . [i.e.] a new entrant to a therapeutic class that had already been defined by a separate drug entity that was the first in the class (sometimes referred to as the breakthrough drug).”).

69 SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 63. 70 For recent cost estimates, see Joseph A. DiMasi et al., Innovation in the Pharmaceutical Industry:

New Estimates of R&D Costs, 47 J. HEALTH ECON. 20, 20 (2016) (detailing recent cost estimates). See RICHARD E. ROWBERG, CONG. RESEARCH SERV., RL30913, PHARMACEUTICAL RESEARCH AND DEVELOPMENT: A DESCRIPTION AND ANALYSIS OF THE PROCESS 1, 14–15 (2001) (estimating that phases I–III of clinical research can take up to ten years). For factors accounting for increasing expenditures in clinical research, see Joseph DiMasi et al., The Price of Innovation: New Estimates of Drug Development Costs, 22 J. HEALTH ECON. 151 (2003); RICHARD E. ROWBERG, CONG. RESEARCH SERV., RL30913, PHARMACEUTICAL RESEARCH AND DEVELOPMENT: A DESCRIPTION AND ANALYSIS OF THE PROCESS (2001) (estimating that phases I-III of clinical research can take up to ten years). One of the factors is the burden of compliance with the regulatory standards of clinical trials and drug authorization. See Denise T. Resnik, Clinical Research from the Industry Perspective, in PRINCIPLES & PRACTICE OF CLINICAL RESEARCH 391, 396 (John I. Gallin & Frederick P. Ognibene eds., 2007) (claiming that “regulations surrounding the development of new medicines and the clinical trials used to support claims of efficacy and safety become more encompassing”).


protection,71 patent term extension,72 sui generis data exclusivity protection,73 and different types of marketing exclusivities.74 For example, under the EU framework innovator drugs can benefit from marketing exclusivity, test data protection, and the extension of the Supplementary Protection Certificate.75 These regulatory instruments underlie the same innovation incentive logic: Protection allows drug originators to recover R&D costs through temporary restrictions on generic competition. In principle, these incentives target different subject matter: patent protection motivates to create, disclose and commercialize an invention,76 while sector-specific instruments such as test data exclusivity and drug marketing exclusivities motivate to invest in downstream product development. In practice, protection terms can overlap, and it may not be possible to differentiate the share of profits that correspond to a particular legal provision.77

71 Pharmaceutical innovation is generally regarded to be reliant on patent protection. See, e.g., Luigi

Orsenigo & Valerio Sterzi, Comparative Study of the Use of Patents in Different Industries 7 (Università Commerciale Luigi Bocconi KITeS Knowledge, Internationalization and Tech. Studies, Working Paper 33/2010, 2010) (arguing that “the role of patents is likely to be higher . . . where imitation is easier, i.e. when the ratio between the costs of imitation to the costs of innovation is lower (e.g. chemicals, pharmaceuticals, machinery).”); James Bessen & Michael J. Meurer, Lessons for Patent Policy from Empirical Research on Patent Litigation 10, 26–27 (B.U. School of Law, Working Paper Series, Law & Economics, Working Paper No. 05–22, 2005) (noting that “patent premium is greatest in the pharmaceutical industry”, and while “the patent system has widely varying effects across different industries . . . . [i]t provides critical incentives for research and development in the pharmaceutical and a few other industries.”); Richard C. Levin et al., Appropriating the Returns from Industrial Research and Development 796 (Brookings Institution, Brookings Papers on Economic Activity No. 3, 1987) (arguing that only in the drug industry “were product patents regarded by a majority of respondents as strictly more effective than other means of appropriation.”). See generally Edwin Mansfield, Patents and Innovation: An Empirical Study, 32 MGMT. SCI. 173, 175 (1986).

72 Notably, patent term extension is rationalized on the grounds of the effective patent term reduction due to the lengthy procedures of marketing authorization; in the case of pharmaceuticals, effective patent term can be substantially reduced due to clinical trials.

73 For the critical analysis of innovation rationale for data exclusivity protection reduction, see Reichman, supra note 7, at 43 (emphasizing the distinction between patent protection and data exclusivity, the latter “protect[ing] investment as such, not a technological achievement.”).

74 Marketing exclusivity provisions are jurisdiction-specific. For a comparative overview of the U.S. and the EU regulations, see Valerie Junod, Drug Marketing Exclusivity Under the United States and European Union Law, 59 FOOD & DRUG L. J. 479, 480 (2008). See also Trevor Cook, Data Protection and Market Exclusivities for Pharmaceuticals in the EU, 16 PHARMACEUTICALS POL'Y & L. 19, 20 (2014). For an overview of orphan drug exclusivities in the U.S., EU, Australia and Japan, see Regulatory Framework for Drugs for Rare Diseases, in RARE DISEASES AND ORPHAN PRODUCTS: ACCELERATING RESEARCH AND DEVELOPMENT 1, 89–90 (Marilyn J. Field & Thomas F. Boat eds., 2011).

75 For an overview of incentives for pediatric drug R&D under the EU framework, see European Medicines Agency, Report to the European Commission on companies and products that have benefited from any of the rewards and incentives in the Pediatric Regulation and on the companies that have failed to comply with any of the obligations in this Regulation, EMA/24516/2014 Corr.3 (Sept. 9, 2014).

76 Suzanne Scotchmer, Incentives to Innovate, in THE NEW PALGRAVE DICTIONARY OF ECONOMICS AND THE LAW 273, 273–76 (Peter Newman ed., 1998).

77 It is also important to emphasize that actual profits can depend on factors such as the national system for public drug procurement, insurance and reimbursement, as well as market size. See Suzanne Scotchmer, The Political Economy of Intellectual Property Treaties, 20 J. L. ECON & ORG. 415, 422 (2004) (speculating that “for some subject matter, protection in any one of the large markets, the United States, Europe, or Japan, is enough to compensate an inventor, regardless of where the inventor is domiciled.”).


As long as there is a prospect of successful new drug commercialization, drug sponsors will conduct clinical trials and generate data pursuant to regulatory standards of clinical research and data. The policy implication is that there is no need for additional incentives to generate data, in the presence of regulatory standards, on the one hand, and different types of IP protection, which protect the innovator drug against generic competition, on the other hand. So long as access to data for research purposes does not interfere with these incentives, access measures do not impede innovation in the sense of undermining the capacity of drug sponsors to earn time-limited supra-competitive profits in the market for the product, for which the initial clinical trials were conducted.

What, then, is the role that clinical data confidentiality plays in the regulatory framework? Is it rather a de facto state of affairs, not rationalized on economic grounds? The next section examines the functions of primary test data in drug R&D that suggest that regulatory incentives might be needed to encourage data sharing.

IV. THE COMMON-RESOURCE DILEMMA: TO SHARE OR NOT TO SHARE DATA

While the public-good dilemma is associated with the supply side of innovation, the common-resource dilemma arises in relation to access, dissemination, and utilization of innovative activity results. In the case of public goods, the socially suboptimal outcome is the underproduction of a socially valuable good; in the case of common goods, it is the overconsumption of a scarce resource. Goods that are rivalrous and non-excludable might be subject to Hardin’s “tragedy of commons,”78 if members do not agree on the rules of use and preservation.79 The reverse, the “tragedy of anti-commons,”80 can arise when the use of non-subtractable resources is excluded.81 While the concern of the classic ‘tragedy of commons’ is that the resource will be

78 Garrett Hardin, The Tragedy of the Commons, 162 SCIENCE 1243, 1243–48 (1968); see also David

Feeny et al., The Tragedy of the Commons: Twenty-Two Years Later, 18 HUM. ECOLOGY 1, 3 (1990) (providing an overview of the development of the concept before and after Hardin’s publication); Roy Gardner et al., Social Capital and Cooperation: Communication, Bounded Rationality, and Behavioral Heuristics, in SOCIAL DILEMMAS & COOPERATION 375, 375–411 (Ulrich Shultz, Wulf Albers & Ulrich Mueller eds., 1994).

79 As in the case of public goods, the common-good dilemma is characterized by the split between individual and collective rationality: while “[i]n the interest of the group, moderate usage of the resource is the most preferred solution . . . .in the interest of the individual, it is most rational to use the resource maximally.” Anisha Shankar & Charles Pavitt, 2 REV. COMM. 251, 257 (2002). See also Kimberly A. Wade-Benzoni et al., Egocentric Interpretations of Fairness in Asymmetric, Environmental Social Dilemmas: Explaining Harvesting Behavior & The Role of Communication, 67 ORG'L BEHAVIOR & HUM. DECISION PROCESSES 111, 111 (1996).

80 Michael A. Heller, The Tragedy of the Anticommons: Property in the Transition from Marx to Markets, 111 HARV. L. REV. 621, 622 (1998). The original term of “anticommons” is attributed to Frank Michelman. See Frank Michelman, Ethics, Economics, & the Law of Property, 39 TULSA L. REV. 663, 670 (2003).

81 See Jetta Frost & Michèle Morner, Overcoming Knowledge Dilemmas: Governing the Creation, Sharing & Use of Knowledge Resources, 2 INT’L J. STRATEGIC CHANGE MGMT 172, 178 (2010) (using the term of “withheld resources” to refer to information and knowledge that are strategically withheld as the holders of “idiosyncratic knowledge . . . do not want to relinquish their knowledge advantage to a third party, perhaps because they do not see any benefit from sharing these knowledge resources with other[s] . . . exchange or sharing of those resources . . . is perceived as a loss.”).


overused because there is no incentive for preservation,82 in the reverse version, inefficiency can result from the underuse of a resource, for example, “because governments give too many people rights to exclude others.”83 With physical resources, the policy objective is to preclude depletion; whereas for intangibles, the goal is to prevent undue restrictions on access and use. In biopharmaceutical innovation, the anticommons problem has been hypothesized in relation to patents on upstream research when “people underuse scarce resources because too many [patent] owners [in upstream research] can block each other . . . adding to the cost and slowing the pace of downstream biomedical innovation.”84 Similar concerns arise when artificial scarcity of another type of innovation resource—biomedical and pharmacological knowledge—is fostered by factual, legally enforceable secrecy.

A. Clinical Data as a Source of New Knowledge The peculiar feature of industry-sponsored clinical data, generated throughout

downstream drug development, is that it enhances the breadth of biomedical knowledge and presents value for upstream pre-clinical drug R&D phases.85 Clinical trials have been viewed as a main source of pharmacological knowledge.86 Research data gathered through clinical trials improves the understanding of “the relationship between the desired and adverse effects of a drug,” which is considered to be “a key guiding principle in drug development.”87 Such data also presents new evidence on

82 Hardin, supra note 78. 83 Michael A. Heller & Rebecca S. Eisenberg, Can Patents Deter Innovation? The Anticommons in

Biomedical Research, 280 SCIENCE 698, 698 (1998); see also Charlotte Hess & Elinor Ostrom, Introduction: An Overview of the Knowledge Commons, in UNDERSTANDING KNOWLEDGE AS A COMMONS: FROM THEORY TO PRACTICE 3, 11 (Charlotte Hess & Elinor Ostrom eds., 2007) (defining the “tragedy of the anticommons in the knowledge arena . . . . [as] the potential underuse of scarce scientific resources caused by excessive intellectual property rights and over patenting in biomedical research.”); Frost & Morner, supra note 81, at 178 (“Resources are underused, because too many ‘knowledge empire builders’ have the right to exclude.”).

84 Heller & Eisenberg, supra note 83, at 698–99 (emphasis added), 698 (speculating that “more intellectual property rights [for upstream research] may lead paradoxically to fewer useful products for improving human health.”).

85 Information, in general, is characterized by the dual nature as an input and output. See Kenneth Arrow, Economic Welfare and the Allocation of Resources for Invention, in THE RATE AND DIRECTION OF INVENTIVE ACTIVITY: ECONOMIC AND SOCIAL FACTORS 609, 618 (National Bureau of Economic Research ed., 1962), http://www.nber.org/chapters/c2144 (emphasizing that “[i]nformation is not only the product of inventive activity, it is also an input —in some sense, the major input apart from the talent of the inventor.”).

86 See Basil Achilladelis & Nicholas Antonakis, The Dynamics of Technological Innovation: The Case of the Pharmaceutical Industry, 30 RES. POL'Y 535, 574 (2001) (defining human pharmacology as the science of identification and evaluation of medicinal properties of chemicals). Topel (eds.) MEASURING THE GAINS FROM MEDICAL RESEARCH: AN ECONOMIC APPROACH, 170-171 (2003); in CLINICAL DATA AS THE BASIC STAPLE, supra note 5, at 14; ClinicalTrials.gov, Learn About Clinical Studies https://clinicaltrials.gov/ct2/about-studies/learn (last visited May 17, 2016); C. Fitz-Gibbon, What’s All This About ‘Evidence’? Learning and Skills Research 27-29 (2001) (emphasizing the potential of randomised controlled trial in generating scientific knowledge). See also NAT’L INST. OF HEALTH, Learn About Clinical Studies, https://clinicaltrials.gov/ct2/about-studies/learn (last visited May 17, 2016); C. Fitz-Gibbon, What’s All This About ‘Evidence’? 5 LEARNING AND SKILLS RESEARCH 1, 27–29 (2001) (emphasizing the potential of randomised controlled trial in generating scientific knowledge).

87 BRITISH PHARMACOLOGICAL SOC’Y, Pharmacology Skills for Drug Discovery, 1, 4, http://www.biochemistry.org/Portals/0/Education/Docs/Pharmacology%20Skills%20for%20Drug%20Discovery.pdf.


the pharmacological properties of tested molecules, the interaction between drug molecules and drug targets, and how such interaction influences disease processes.88

In science data plays an evidentiary role.89 However, it is important to draw a distinction between data, as primary evidence, and knowledge derived through data analysis.90 Clinical data can be the source of knowledge when it is systematized, analyzed, and validated through peer analysis.91 In this view, industry-sponsored clinical trials can be characterized as a knowledge production activity if primary trial evidence is accessible to the broader research community.

B. Non-sharing of Data as a Dominant Strategy of Drug Sponsors

Many pharmaceutical research companies adopt policies for clinical data sharing that, in principle, allow access by external request for a bona fide or legitimate research purpose.92 Often, policy terms explicitly state access shall be refused if there is a

88 Id. at 2; see also European Parliament & Council Regulation 536/2014, art. 2 § 2(1)(a), 2014 O.J.

(L 158/1) (defining clinical study as “any investigation in relation to humans intended . . . to discover or verify the clinical, pharmacological or other pharmacodynamic effects of one or more medicinal products”).

89 Harry M. Collin, The Meaning of Data: Open & Closed Evidential Cultures in the Search for Gravitational Waves, 104 AMERICAN J. SOCIOLOGY 293, 294 (1998). On access to empirical evidence as an imperative norm of science, see ROBERT K. MERTON, THE SOCIOLOGY OF SCIENCE: THEORETICAL & EMPIRICAL INVESTIGATIONS (1973) (elaborating the social norms of science that reflect the idea that research findings do not belong to an individual researcher but to the scientific community at large); see also David, supra note 132, at 19 (stating that the “progress of scientific and technological knowledge is a cumulative process [and] depends in the long-run on the rapid and widespread disclosure of new findings, so that they may be rapidly discarded if unreliable, or confirmed and brought into fruitful conjunction with other bodies of reliable knowledge.”); Blumenthal, supra note 38, at 140 (stipulating that “[t]he scientific method in itself requires broad dissemination of results to confirm their validity”).

90 The distinction between data, information and knowledge is attributed to Fritz Machlup. See Hess & Ostrom, supra note 83, at 8 (summarizing Machlup's distinction as data being “raw bits of information, information being organized data in context, and knowledge being the assimilation of the information and understanding of how to use it”). As summarized by Charlotte Hess and Elinor Ostrom, the distinction lies in data being “raw bits of information, information being organized data in context, and knowledge being the assimilation of the information and understanding of how to use it.” Hess & Ostrom, supra note 85/86, at 8 (discussing how Fritz Machlup introduced the distinction between data, information and knowledge; "data being raw bits of information, information being organized data in context, and knowledge being the assimilation of the information and understanding of how to use it.”); see also THOMAS H. DAVENPORT & LAURENCE PRUSAK, WORKING KNOWLEDGE 6 (1998) (stating that “[k]nowledge derives from information as information derives from data”); Max Boisot & Agustí Canals, Data, Information & Knowledge: Have We Got It Right?, 14 J. EVOLUTIONARY ECON. 43, 43–67 (2004) (analyzing the distinction between data, information, and knowledge from the perspective of economics of information, information theory and physics of information).

91 Dana Dalrymple, Scientific Knowledge as a Global Public Good: Contributions to Innovation & the Economy, THE ROLE OF SCIENTIFIC & TECHNICAL DATA AND INFORMATION IN THE PUBLIC DOMAIN: PROCEEDINGS OF A SYMPOSIUM 35, 35 (National Academy of Sciences ed., 2003) (stating that “knowledge in general is broader, less transitory, and more cumulative [than data and information]. It is derived from perception, learning, and discovery. Scientific knowledge, in particular, is organized in a systematic way and is testable and verifiable”) (emphasis added).

92 PHARMACEUTICAL RESEARCH & MANUFACTURERS OF AMERICA (PhRMA) & EUROPEAN FEDERATION OF PHARAMACEUTICAL INDUSTRIES & ASSOCIATIONS (EFPIA), Principles for Responsible Clinical Trial Data Sharing: Our Commitment to Patients and Researchers 1, 4 (2013), http://www.phrma.org/sites/default/files/pdf/PhRMAPrinciplesForResponsibleClinicalTrialDataSharing.pdf (stipulating the principle of enhancing data sharing with researchers, according to which “[b]iopharmaceutical companies commit to sharing upon request from qualified scientific and medical researchers patient-level clinical trial data, study-level clinical trial data, and protocols from clinical trials


potential conflict of interest93 or, actual or potential, “competitive risks.”94 A conflict of interest can be understood as the potential that knowledge gained from the data contributes to the development of new (including improved) products.95 Companies’ rules on data sharing can be restrictive as to potential patenting and commercialization of research outcomes, which might result from access to clinical datasets. For instance, Roche’s policy on data sharing used to state that the company would “prevent others from using [their] data to develop intellectual properties that interfere with [Roche’s] ability to develop and commercialize [its] products.”96 The similarity of data-sharing provisions suggests that companies adhere to a certain industry code based on the principle that it is “appropriate” to refuse requests for patient-level clinical data if access leads to a conflict of interests.97

Uncertainty is the key factor in the choice not to share.98 At the time of an access petition, it may be unclear what drug will eventually be developed based on the data. It is hard to pinpoint the correlation of drug A, the subject of the original clinical trials, to drug B, the development of which can be facilitated by knowledge derived from the primary data for drug A. The outcome would depend on other R&D inputs, including

in patients for medicines and indications approved in the United States (US) and the European Union (EU) as necessary for conducting legitimate research”).

93 The eligibility of external requests for data is usually determined by an internal or independent review panel based on the analysis of the research proposal of the petitioner for access and the assessment of a potential conflict of interests.

94 In general, the terms of data-sharing policies demonstrate similarity. For an overview of clinical data-sharing policies from twelve research-based pharmaceutical companies, see Harlan M. Krumhollz et al., Sea Change in Open Science and Data Sharing: Leadership by Industry, 7 CIRCULATION: CARDIOVASCULAR QUALITY & OUTCOMES 499, 499–504 (2014).

95 Supra notes 65–66. 96 Roche, Roche Global Policy on Sharing of Clinical Trials Data 1 (2013),

https://web.archive.org/web/20160623230707/http://roche-trials.com/pdf/RocheGlobalDataSharingPolicy.pdf

97 See PhRMA, supra note 92, at 4 (“Under commitment 1, companies will evaluate, among other things, whether the research proposed has a legitimate scientific or medical purpose, including whether there is any potential conflict of interest between the data requestor and the company . . . . While companies may enter into agreements to co-develop medical products, these data sharing Principles are not intended to allow free-riding or degradation of incentives for companies to invest in biomedical research. Accordingly, it would be appropriate under commitment 1 for companies to refuse to share proprietary information with their competitors.” (emphasis added)).

98 Under uncertainty, data holders might overestimate the contribution of their data to third parties’ drug development—a phenomenon known as a cognitive bias of overvaluing own assets; see, e.g., Heller & Eisenberg, supra note 86, at 701 (referring to this cognitive phenomenon to explain ”hold-ups” in licensing transactions for biotechnological patents—i.e., when the owners of patents for upstream biomedical research can “overvalue their discoveries” and “overestimate[] the likelihood that [the] patent will be the key” for downstream product development, and also distinguishing a related “attribution bias”—i.e. when “people systematically overvalue their assets and disparage the claims of their opponents when in competition with others.”). On uncertainty in decision-making in a common-resource dilemma, see, e.g., Ramzi Suleiman et al., Fixed Position and Property Rights in Sequential Resource Dilemmas under Uncertainty, 93 ACTA PSYCHOLOGICA 229, 230–31 (1996); Anders Biel & Tommy Gärling, The Role of Uncertainty in Resource Dilemmas, 15 J. ENVTL. PSYCHOL. 221, 221–33 (1995); David V. Budescu, Amnon Rapoport, & Ramzi Suleiman, Resource Dilemmas with Environmental Uncertainty and Asymmetric Players, 20 EUR. J. SOC. PSYCHOL., 475 (1990); David V. Budescu et al., Common Pool Resource Dilemmas under Uncertainty: Qualitative Test of Equilibrium Solutions, 10 GAMES & ECON. BEHAV. 171, 173–76 (1995).


the background knowledge of drug developers working on drug B.99 Potentially, access to drug A’s clinical dossier can lead to the development of drug B that will (i) be an improvement to drug A, (ii) feature a new medical use of the same compound that underlies the drug A,100 or (iii) treat a different condition and compete in a different therapeutic market.101 One can hardly identify which of these three patterns might prevail on average. In the improvement scenario, original and follow-on drugs can vary by degree of substitutability. The impact on competition and sales from drug A would depend on the therapeutic characteristics of drugs A and B, as well as the specific market conditions. Even if drug B represents a small improvement over drug A, the competitive impact can be substantial because “incremental improvements may elicit big therapeutic benefits.”102 The mere prospect of such an outcome can be enough to deny data access on the grounds of a potential conflict of interest.

There is an additional argument for why pharmaceutical companies might oppose full disclosure of trial data. When released into the public domain, trial findings add to the prior art and can affect the assessment of novelty and inventive step of patent applications for biopharmaceutical inventions.103 This can affect the interests of research-based companies in obtaining patent protection for future inventions.104

99 See Paul Nightingale & Surya Mahdi, The Evolution of Pharmaceutical Innovation, in

KNOWLEDGE ACCUMULATION & INDUSTRY EVOLUTION: THE CASE OF PHARMA-BIOTECH 73, 73 (Mariana Mazzucato & Giovanni Dosi eds., 2006) (emphasizing that “economically important interdependencies [within knowledge reproduction] are a consequence of knowledge having value only in context, and generate scale economies because information that is integrated into a coherent whole can have more economic value than the same information divided into its component parts and distributed between economic agents who fail to realize its full value.”).

100 This may occur due to additional observations that might be disclosed in clinical study reports (i.e. findings on secondary effects of tested drug candidates that had not been the primary endpoints of a trial). See Michael S. Lauer, Data Primarily Collected for New Insights, in CLINICAL DATA AS THE BASIC STAPLE, supra note 5, at 91 (viewing clinical trials as a “rich source[] of observational data, useful for exploring questions that go beyond their original hypotheses”) (emphasis added).

101 This might be possible because the correlation between the chemical structure and the pharmacological characteristics of a new compound accompanied with comprehensive efficacy and safety data can direct the researchers to alternative chemical structures and new drugs. Another possibility is that firm B learns about the adverse reactions from full reports and diverts its R&D resources to another project.

102 Reichman, supra note 7, at 39. 103 See Boards of Appeal of the European Patent Office, Decision of the Technical Board of Appeal

3.2.2 of 28 October 1998: Case T-0158/96, European Patent Office 9 (Oct. 28, 1998) (stating that if the person skilled in the art could “conclude with the required certainty” that the new therapeutic indication claimed by the applicant “or any other pharmacological effect, i.e., indisputably underlying such a therapeutic application, had already been shown or proven during [previous pre-clinical or clinical trials], the teaching of document (5) would have to be regarded as prejudicial to the novelty of the claimed subject-matter”); see also W. Nicholson Price II & Timo Minssen, Will clinical trial data disclosure reduce incentives to develop new uses of drugs?, 33 NATURE BIOTECHNOLOGY 685, 686 (2015) (arguing that “the broad increase to the ‘prior art’ and ‘common general knowledge’ created by clinical trial disclosure may render many related new drugs unpatentably obvious”).

104 See The German Association of Research-Based Pharmaceutical Companies, Overview of comments received on ‘Publication and access to clinical-trial data’ (EMA/240810/2013), EMA/349245/2014, at 17 (raising concern that “[i]f pre-clinical and/or clinical data are published by the authorities, subsequent patents can no longer be obtained if the therapeutic effect on animals has been published (e.g. in vivo or in vitro)”). It is important to emphasize that public disclosure of trial data can interfere with the patentability of subsequent inventions, but not with patent protection related to the same compound, which is usually obtained much earlier than clinical trials are conducted.


C. Sharing as a Cooperative Choice Proponents of broad access to industry-sponsored clinical research data have argued

that data “ha[s] the potential to be an unprecedented resource . . . to facilitate the development and testing of new hypotheses, to confirm or refute existing beliefs, and to advance knowledge in an effort to improve patient outcomes and public health.”105

The NAS Health and Medicine Division argued that sharing raw clinical data and analyzable datasets

enables additional scientific discoveries by allowing other investigators to carry out alternative or additional analyses to test the robustness of published findings, such as post hoc subgroup analyses or composite endpoints; to compare outcomes from other trials; to plan meta-analyses; and to carry out exploratory studies to generate new hypotheses.106

As envisaged by the EMA, access to clinical trial dossiers can “avoid[s] duplication of clinical trials, foster[s] innovation and encourage[s] development of new medicines.”107 One objective behind the EMA’s disclosure policy is “to enable the wider scientific community to make use of detailed and high quality clinical trial data to develop new knowledge in the interest of public health.”108 Similarly, the U.S. Centers for Disease Control and Prevention and the Agency for Toxic Substances and Disease Registry envisions that data sharing can “uncover new areas of research.”109 The WHO has also emphasized the importance of facilitating drug research through greater access to primary data.110 Further, according to the Multi-Regional Clinical Trials Center at Harvard University, “[a]llowing data that have already been collected to be used for secondary purposes can speed up scientific discoveries by relieving investigators of the burden of collecting new data. Such secondary research may lead to promising new treatments or a better understanding of disease through various ‘big data’ projects.”111

The efficiency argument has been reiterated during public consultations for the EMA’s publication policy.112 Pharmaceutical companies, too, recognize that the

105 Harlan M. Krumholz et al., Sea Change in Open Science and Data Sharing Leadership by Industry,

7 CIRCULATION: CARDIOVASCULAR QUALITY & OUTCOMES 499, 502 (2014). 106 SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 84 (emphasis added). 107 Clinical Data Publication, EUROPEAN MEDICINES AGENCY, http://www.ema.europa.eu/ema/

index.jsp?curl=pages/special_topics/general/general_content_000555.jsp (last visited Aug. 1, 2016). 108 EMA publication policy, supra note 2, at 4 (emphasis added). 109 CENTERS FOR DISEASE CONTROL AND PREVENTION & AGENCY FOR TOXIC SUBSTANCES AND

DISEASE REGISTRY, CDC/ATSDR Policy on releasing and sharing data 4 (2005), http://www.cdc.gov/maso/Policy/ReleasingData.pdf.

110 WHO, supra note 6, at 3 (“The benefit of sharing research data and the facilitation of research through greater access to primary datasets is a principle which WHO sees as important. . . . WHO will continue to engage with partners in support of an enabling environment to allow data sharing to maximize the value of health research data.”) (emphasis added).

111 Current and Ongoing Data Transparency Activities in the Pharmaceutical Industry: Brief of the Multi-Regional Clinical Trials Center at Harvard University to the Standing Senate Committee on Social Affairs, Science and Technology, MULTI-REGIONAL CLINICAL TRIALS (Oct. 1, 2014), http://mrctcenter.org/wp-content/uploads/2015/11/2014_10_1_harvard_mrct_brief_of_mrct_on_bill_c-17.pdf(emphasis added).

112 See, e.g., European Medicines Agency, Overview of comments received on ‘Publication and access to clinical-trial data’ (EMA/240810/2013): From Stakeholder 157 to Stakeholder 169 43 (“[E]ven

http://mrctcenter.org/wp-content/uploads/2015/11/2014_10_1_harvard_mrct_brief_of_mrct_on_bill_c-17.pdf

http://mrctcenter.org/wp-content/uploads/2015/11/2014_10_1_harvard_mrct_brief_of_mrct_on_bill_c-17.pdf


“sharing of clinical trial information enables the medical and scientific community to further develop the medical and scientific knowledge base.”113 Yet, the representatives of the research-based biopharmaceutical industry disagreed that the EMA’s intention “to make medicine development more efficient” by providing access to clinical dossiers is “a proper purpose under EU law.”114 Evidence from previous trials enables drug developers “to extrapolate likely effect of new drugs, doses and combinations,”115 “more quickly eliminate potentially troublesome compounds earlier in the drug development process . . . [and to] focus [R&D] efforts on more relevant targets,”116 as well as to contribute to “rational prediction of the likely ‘on-target’ effects of novel compounds [and] the definition of likely ‘off-target’ beneficial and adverse effects.”117

Overall, the greater is the scope of data from previous trials, the more informed, targeted, and efficient subsequent drug R&D becomes—if access is not restricted.118

D. The Interdependence between Individual Choices and Collective Results

Social dilemmas result from the interdependence between individual choices and collective results—or, private gains that come at the expense of collective loss.119 In other words, individuals’ self-interested behavior can be tolerated to a point, beyond which maximization of individual short-term benefits leads to minimization of

assuming pharmaceutical companies could gain a hypothetical benefit from reviewing the clinical trials information submitted by competitors during the regulatory review process, this increased understanding will confer its own public health benefit by making drug development more efficient.”).

113 EMD SERONO, SUMMARY OF EMD SERONO’S RESPONSIBLE DATA SHARING POLICY 1, http://www.emdserono.com/ms.country.us/en/images/Summary_of_EMD_Seronos_Responsible_Data_Sharing_Policy_US_tcm115_131043.pdf?Version= (emphasis added); see also European Medicines Agency, Overview of comments received on “Publication and access to clinical-trial data” (EMA/240810/2013): From Stakeholder 01 to Stakeholder 88 37 (“EFPIA considers that a more robust mechanism of data sharing should be put in place, and is committed to implement a system to receive and review research proposals and provide applicable data to help facilitate such scientific and medical research.”). 114 The European Federation of Pharmaceutical Industry Associations (EFPIA), Overview of comments received on ‘Publication and access to clinical-trial data’ (EMA/240810/2013), EMA/342115/2014, at 35 (emphasis added).

115 British Pharmacological Society, supra note 87, at 5. 116 Ünlü, supra note 7, at 542. 117 British Pharmacological Society, supra note 87, at 3. 118 Peter Smith, Registries and Care with Evidence Development, CLINICAL DATA AS THE BASIC

STAPLE, supra note 5, at 125, 129 (discussing particular examples when the impact of sharing aggregated clinical data “has been profound”). Notably, greater availability and dissemination of biomedical knowledge has been viewed as one of the factors contributing to the development of rational drug design and rational design of clinical trials; the transition from random, heuristic drug discovery to more “guided search.” See Mariana Mazzucato & Giovanni Dosi, Introduction, KNOWLEDGE ACCUMULATION AND INDUSTRY EVOLUTION. THE CASE OF PHARMA-BIOTECH 402, 3 (Mariana Mazzucato & Giovanni Dosi eds., 2006); Garavaglia, Malerba & Orsenigo, infra note 140, at 238; ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT (OECD), INNOVATION IN THE KNOWLEDGE ECONOMY: IMPLICATIONS FOR EDUCATION AND LEARNING 44 (2004) (pointing out the shift in drug discovery from large-scale random screening towards “a more science guided approach relying on knowledge of the biological basis of a disease to frame a research strategy”).

119 Shankar & Pavitt, supra note 79, at 254; see also Robyn M. Dawes, Social Dilemmas, 31 ANN. REV. PSYCHOL. 169, 169 (1980) (observing that while, in the short-term perspective, “each individual receives a higher payoff for a socially defecting choice . . . than for a socially cooperative choice,” in the long run, “individuals are better off if all cooperate than if all defect”).


collective long-term benefit and, eventually, can “leave all participants worse off than feasible alternatives.”120

Drug sponsors view non-sharing of clinical data as a means to retain competitive advantage. As discussed earlier, the longer time and the greater the investment it takes the competitor to develop a new product, the longer is the lead time, during which the originator firm maximizes commercial returns on R&D. The extension of lead-time due to non-sharing can be defined as individual benefit achieved by non-cooperative strategy. Alternatively, access to detailed patient-level evidence from earlier trials may potentially facilitate new drug development and enhance dynamic efficiency at sector level. Under these assumptions, the non-sharing strategy of pharmaceutical companies, in game theory terms, corresponds to the “prevalent choice” or “a dominant strategy” that can result in “a deficient equilibrium.”121

As the NAS Health and Medicine Division avers, clinical data sharing

could advance scientific discovery and improve clinical care by maximizing the knowledge gained from data collected in trials, stimulating new ideas for research, and avoiding unnecessarily duplicative trials. The ultimate goal of data sharing should be to increase scientific knowledge, leading to better therapies for patients. [ . . . ] Greater data sharing could enhance public well-being by accelerating the drug discovery and development process, reducing redundant research, and facilitating scientific innovation.122

The could language points to potential gains in innovation that result from the use of clinical trial data in third party R&D. In economic terms, such effects of confidentiality can be qualified as internalized positive externalities. The corresponding social costs can hardly be measured given the prospective and probabilistic nature of R&D results.

V. REGULATORY DILEMMAS

The foregoing discussion suggests there are plausible reasons for regulatory intervention into the data (non-)sharing practice of the pharmaceutical industry. The very nature of clinical data can be a decisive factor for intervention. Industry clinical data sharing practices affect not only the interests of drug companies but the well-being of society at large. The design of an appropriate regulatory framework alone may pose a policy—or “second-order”123—dilemma. In particular, two questions must

120 Tom Dedeurwaerdere, The Role of Law, Institutions and Governance in Facilitating Access to the

Scientific Research Commons: A Philosopher’s Perspective, GENE PATENTS AND COLLABORATIVE LICENSING MODELS; PATENT POOLS, CLEARINGHOUSES, OPEN SOURCE MODELS AND LIABILITY REGIMES 365, 368 (Geertrui Van Overwalle, ed. 2009).

121 Dawes, supra note 119, at 179. 122 SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 1, 4 (emphasis added). 123 Dedeurwaerdere, supra note 119, at 368–69 (distinguishing between the “primary” social

dilemmas and “second-order” policy dilemmas and defining the latter as regulatory instruments implemented to cope with the former, and emphasizing that it is “important to assess the relative costs and benefits of the different types of formal and informal institutional arrangements that can alleviate the collective action problems”).


be addressed at the policy-making level: one on policy priorities and objectives, given the competing interests at stake, and, the other, on the measures to achieve them.

A. The Dilemma over the Competing Policy Objectives The case of clinical research illustrates the problem studied in economics of

knowledge when knowledge production is costly at the same time that its rapid distribution maximizes social returns on drug R&D.124 For industry-sponsored research, this translates into the dilemma between the social objectives of providing an “ideal motivation to the private producer” to create knowledge and that of “ensuring efficient use of knowledge once it has been produced.”125

As argued above, the public-good problem does not arise as to the generation of clinical trial data as such but relates to the general public-good problem in pharmaceutical innovation. The adversity of confidentiality protection in this context is that while by itself it neither induces generation of data, nor solves the public-good problem in pharmaceutical innovation, it is likely to aggravate the resource dilemma. Accordingly, the regulatory objective should be formulated to create data-sharing incentives that do not interfere with other regulatory instruments. In particular, regulation should not render patent protection or regulatory exclusivities ineffective, both of which temporarily restrain generic competition as a means to appropriate returns on drug R&D.126

B. The Dilemma over the Alternative Means Arguments that access to clinical data facilitates new drug development can compel

the conclusion that data sharing maximizes the collective benefit. But is non-sharing indeed the defecting choice that leads to deficient equilibrium? What if such extended lead-time allows drug originators to re-invest more in drug R&D and to achieve better results? Admittedly, arguments on both sides appear speculative.

As observed by Blumenthal,

[i]n some cases making the information available would broadly benefit society, leading to the advancement of other knowledge. Keeping knowledge private causes a loss of efficiency, but we tolerate this loss for the gain that is created by the incentives for innovation resulting from the opportunity for economic gain.127

The research-based pharmaceutical sector is characterized by fierce competition in innovation, i.e., competition by effort and investment in R&D to be the first to identify a promising target and to develop a new drug.128 Whether or not clinical data is shared

124 Foray, supra note 44, at 116. 125 Id. 126 See supra note 75. 127 Blumenthal, supra note 38, at 141 (emphasis added). 128 Innovation has been viewed as the main form of competition in the pharmaceutical industry. See

Orsenigo, Dosi & Mazzucato, supra note 54, at 407 (stipulating that “[o]n the supply side, the pharmaceutical industry is inherently characterized by non-price competition. . . . producers are attributed (temporary) monopoly power through patent protection”). In this sense, the research-based pharmaceutical sector illustrates the Schumpeter’s view that innovation is driven not by price competition, but by “competition from the new commodity, the new technology . . . competition . . . which strikes not at the


among companies, new drugs are developed.129 Since dynamic efficiency prospects are at stake both under the regimes of sharing or non-sharing, it might not be clear whether, on balance, society is better-off in terms of development and supply of innovative medicines if data is subject to mandatory disclosure. This calls for a cost-benefit analysis of the two regimes.

The basic principle of regulatory intervention, which also informs IP law as an area of economic law, holds that policy goals should be achieved in the most effective and efficient way. Effectiveness and efficiency of a regulatory intervention should be assessed in light of policy objectives.130 A policy measure is (i) effective if its benefits exceed its costs expressed in discounted terms and (ii) efficient when the benefit-cost ratio is higher in comparison to the benefit-cost ratio of an alternative regulatory measure(s).131 Applying this proposition to clinical trial data, access shall be justified if net gains in dynamic efficiency achieved under the regime of access outweigh net gains in dynamic efficiency under the regime of confidentiality, and, furthermore, if such advantage is achieved with a more favorable cost-benefit ratio.

At the outset, it appears highly complex, if feasible at all, to identify the full range of welfare implications, i.e., all relevant social costs and benefits under each regime. The regulator would inevitably deal with indeterminacy. While it appears impossible to specify the entire welfare function under the scenarios of intervention and non-intervention, the remaining Part evaluates qualitatively how data sharing can affect costs and benefits relative to the regime that supports the dominant industry strategy of non-sharing.

margins of the profits . . . of the existing firms but at their foundations and their very lives.” See JOSEPH A. SCHUMPETER, CAPITALISM, SOCIALISM AND DEMOCRACY 84 (1950).

129 Studies show that pharmaceutical companies increasingly pursue so-called “best-in-class” strategy aimed at the development of a drug “with a particularly attractive clinical or economic profile” that would improve the existing therapy, as opposed to the “first-in-class” strategy aimed at winning the “race” of developing a “breakthrough drug.” See Joseph A. DiMasi & Cherie Paquette, The Economics of Follow-on Drug Research and Development: Trends in Entry Rates and the Timing of Development, 22 PHARMACOECONOMICS 1, 12 (2004); see also Michael Lanthier, Kathleen L. Miller, Clark Nardinelli, & Janet Woodcock, An improved approach to measuring drug innovation finds steady rates of first-in-class pharmaceuticals, 1987–2011, 32 HEALTH AFFAIRS 1433, 1434–36 (2013) (demonstrating that the time-lag between the marketing of a first-in-class drug and the marketing of similar products has been decreased significantly over time).

130 See, e.g., ORGANIZATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT (OECD), REGULATORY IMPACT ANALYSIS: A TOOL FOR POLICY COHERENCE (2009); EUROPEAN COMMISSION, COMPETITIVENESS OF THE EU MARKET AND INDUSTRY FOR PHARMACEUTICALS: WELFARE IMPLICATIONS OF REGULATION (2009); ORGANIZATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT, POLICY COHERENCE: VITAL FOR GLOBAL DEVELOPMENT 2 (2003) (defining “policy coherence” as “the systematic promotion of mutually reinforcing policy actions across government departments and agencies creating synergies towards achieving the agreed objectives”).

131 Id. at 97 (emphasizing the importance of the benefit-cost ratio as it allows alternatives to be ranked according to their efficiency); see also CARL V. PATTON & DAVID S. SAWICKI, BASIC METHODS OF POLICY ANALYSIS & PLANNING 227 (1986) (highlighting that when comparing the ratios of discounted benefits to discounted costs of alternative instruments, alternatives “that have the highest benefit-cost ratios are not necessarily those that deliver the highest net present value”).


1) Does the Sharing Regime Entail Additional Costs? Data may be used by multiple researchers simultaneously without depreciating

inherent value132 or adding amortization costs to the original data producer and holder.133 While marginal reproduction and re-use costs of existing knowledge, as a non-rivalrous resource, are nil,134 administrative costs arise in relation to aggregation, processing and formatting data as well as organization and management of data repositories.135 To a certain extent, such costs can be offset by the elimination of transaction costs associated with access petitions on an individual basis.136

2) Can the Sharing Regime Produce Additional Benefits?

a) Knowledge Externalities and Cumulativeness of Biomedical Research

From the research perspective, the main benefit of access can be viewed as generating knowledge externalities. New knowledge created through the transformative secondary use maximizes the value of the primary data.137 Positive externalities that are enhanced due to knowledge non-rivalrousness and cumulativeness justify why, in principle, clinical trial data should be treated as a public good.138

132 Paul David, The Economic Logic of “Open Science” and the Balance between Private Property

Rights and the Public Domain in Scientific Data and Information: A Primer, THE ROLE OF SCIENTIFIC AND TECHNICAL DATA AND INFORMATION IN THE PUBLIC DOMAIN: PROCEEDINGS OF A SYMPOSIUM, 19, 20 (National Academy of Sciences 2003) (“[R]e-use of the information will neither deplete it nor impose further costs.”); Reichman, supra note 7, at 51 (“At the outset, it seems clear that the information gleaned from the clinical testing of drugs and therapies is a public good in the sense that each individual citizen benefits from such information without reducing its value to others.”).

133 Id. (observing that although the cost of the first instance of use of new knowledge may be large, in that it includes the cost of its generation, further instances of its use impose at most a negligibly small incremental cost”).

134 See Harold Demsetz, The Private Production of Public Goods, 13 J.L. & ECON. 293, 295 (1970) (distinguishing the public-good characteristic as a “possib[ility] at no cost for additional persons to enjoy the same unit of a public good”).

135 For the specification of administrative costs entailed by sharing clinical trial data, see SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 68 (listing among other costs categories de-identification of data, redaction of documents, setting up databases, maintaining websites/portals, payments for steering committees and administrators); see also Catrin Tudur Smith et al., Sharing Individual Participant Data from Clinical Trials: An Opinion Survey Regarding the Establishment of a Central Repository, 9 PUB. LIBR. SCI. 1, 4–5 (2014) (exploring the issues that need to be resolved to implement the repository model of data management).

136 See SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 141 (“The key argument in favor of open access is that removing barriers for those who seek access to data and not placing limitations on how data can be used will promote transparency, reproducibility, and more rapid advancement of new knowledge and discovery.”).

137 Barry Bozeman & Juan D. Rogers, A Churn Model of Scientific Knowledge Value: Internet Researchers as a Knowledge Value Collective, 31 RES. POL’Y 769, 774–80 (2002) (arguing that the value of scientific and technical knowledge resides in its use and is created through the transformation of knowledge).

138 See Foray, supra note 44, at 113–14 (“[U]ncontrollability, nonrivalry, and cumulativeness threesome . . . accounts for the importance of social returns to research and innovation, and . . . makes these activities an essential basis for growth.”); see also Blumenthal, supra note 39, at 141 (defining healthcare data as a quasi-public good due to its externalities—“a positive or negative effect on the third party [who] are not captured in the market transactions”); Reichman, supra note, at 51 (“At the outset, it


Conventionally, innovation in the pharmaceutical industry has been characterized by high serendipity and low cumulativeness, so that “knowledge and experience acquired in the search for and development of a new drug do not usually entail advantages in the discovery and introduction of further drugs belonging to the same [therapeutic category]” and “that innovation in one [therapeutic] market . . . does not entail higher probabilities of success in another one.”139 Even for new drug discovery and development within the same therapeutic category, “knowledge and experience acquired in previous experience in the search and development were not seen as entailing significant advantages.”140 Such view of cumulativeness can be attributed to the fragmented structure of the pharmaceutical market (e.g., sedatives do not compete with laxatives). Thus, one would assume that a firm’s innovative capacity and experience in one therapeutic area would be of limited relevance to conduct research in another therapeutic area.

However, by focusing on the product market, one misses the broader view of innovation as a multi-stage and cumulative process,141 and the crucial role of knowledge accumulation.142 Pharmaceutical innovation is science-driven and cumulative: discovery and development of new molecules and targets are guided by previous research.143 Comprehensive efficacy and safety data can generate new knowledge and guide follow-on researchers to alternative chemical structures. In general, this reflects the view that new endeavors build upon previous achievements,

seems clear that the information gleaned from the clinical testing of drugs and therapies is a public good in the sense that each individual citizen benefits from such information without reducing its value to others.”); Hess & Ostrom, supra note 83, at 8 (referring to the cumulative effect of knowledge and ideas as a public good).

139 Elena Cefis, Matteo Ciccarelli & Luigi Orsenigo, Heterogeneity and firm Growth in the Pharmaceutical Industry, KNOWLEDGE ACCUMULATION AND INDUSTRY EVOLUTION. THE CASE OF PHARMA-BIOTECH 163, 164 (Mariana Mazzucato & Giovanni Dosi eds., 2006); see also Franco Malerba & Luigi Orsenigo, Innovation and Market Structure in the Dynamics of the Pharmaceutical Industry and Biotechnology: Towards a History-Friendly Model, 11 INDUSTRIAL & CORPORATE CHANGE 667, 671–72 (2002) (pointing out “the absence of any form of cumulativeness in the search and development process” in the pharmaceutical industry).

140 Christian Garavaglia, Franco Malerba & Luigi Orsenigo, Entry, Market Structure, and Innovation in a “History-Friendly” Model of the Evolution of the Pharmaceutical Industry, KNOWLEDGE ACCUMULATION, supra note 148/151, at 244.

141 Bjorn Johnson, Systems of Innovation: Overview and Basic Concepts, INSTITUTIONS AND ORGANIZATIONS IN SYSTEMS OF INNOVATION 36, 42–43 (Charles Edquist ed., 2006).

142 Charles Edquist, System of Innovation Approaches: Their Emergence and Characteristics, in INSTITUTIONS AND ORGANIZATIONS IN SYSTEMS OF INNOVATION 1, 19 (Charles Edquist ed., 2006); see also Nirmal Sengupta, Neglected Sources of Irrigation, ECONOMIC STUDIES OF INDIGENOUS AND TRADITIONAL KNOWLEDGE 121, 176 (Nirmal Sengupta ed. 2007) (“[I]nnovation is cumulative and interactive, each advance builds on an earlier innovation or existing stock of knowledge. Knowledge evolves and acquires new utility through cumulation and interaction.”); Paul A. David, Intellectual Property Institutions and the Panda’s Thumb: Patents, Copyrights, and Trade Secrets in Economic Theory and History, GLOBAL DIMENSIONS OF INTELLECTUAL PROPERTY RIGHTS IN SCIENCE AND TECHNOLOGY 19, 28 (Mitchel B. Wallerstein, Mary Ellen Mogee, & Roberta A. Schoen eds., 1993) (arguing that scientific and technological knowledge is “cumulative and interactive [by] nature. . . . [It] grows by increments, with each advance building on . . . previous findings in complicated and often unpredictable ways”).

143 British Pharmacological Society, supra note 87, at 4; Basil Achilladelis & Nicholas Antonakis, The Dynamics of Technological Innovation: The Case of the Pharmaceutical Industry, 30 RES. POL’Y 535, 550 (2001). See Orsenigo, Dosi & Mazzucato, supra note 54, at 416 (emphasizing that the role of science is “more direct and immediate in pharmaceuticals than in most other technologies”).


or stand on the shoulders of predecessors.144 The use of data as a knowledge input corresponds to the type of cumulativeness, when multiple results of earlier innovative activities constitute an input for the creation of new-generation innovation.145

b) Elimination of Uncertainty in Drug R&D and Duplicative Research

Relatedly and equally important, access to research data can eliminate costs of duplicative trials. Drug R&D is highly serendipitous and often compared to a “lottery game.”146 Clinical trials, as the term itself suggests, are a trial-and-error process with persistent high failure risk until late stages.147 Few compounds become marketable products through the “iterative cycles of testing, understanding, modifying, and retesting potential solutions.”148 Information from previous trials failures is especially valuable as it can reduce the likelihood of reaching the same dead-end at a late stage of drug development and, thus, improve the overall success rate of drug research.149

Quite obviously, duplicative research that can “slow the progress of science because scientists cannot easily build on the efforts of others or discover errors in completed work.”150 As concluded by the NAS Health and Medicine Division,

144 Literature on cumulative innovation oftentimes employs the metaphor of “standing on the

shoulders of giants.” See, e.g., Rebecca S. Eisenberg, Patents and the Progress of Science: Exclusive Rights and Experimental Use, 56 U. CHI. L. REV. 1017, 1055–56 (1989) (invoking Newton’s epigram in the context of the discussion of experimental use exception from patent protection that “promotes scientific progress by permitting other scientists to use prior discoveries in subsequent research”; arguing that “[i]t may be that most if not all new discoveries build upon prior discoveries, and that scientists therefore need to use prior discoveries in order to advance the state of scientific knowledge”); see generally Suzanne Scotchmer, Standing on the Shoulders of Giants: Cumulative Research and the Patent Law, 5 J. ECON. PERSP. 29 (1991).

145 See SUZANNE SCOTCHMER, INNOVATION AND INCENTIVES 123–32 (2005) (distinguishing between three types of cumulativeness: (i) when a single first-generation innovation can yield multiple second-generation innovations; (ii) when multiple first-generation innovations constitute an input for the creation of second-generation innovation, whereby the final product may embody some of such inputs; (iii) “a quality ladder”—i.e. when multiple innovators—“sequential improver”—engage into competition by creating “successfully better products” that improve existing innovative products).

146 JOHN SUTTON, TECHNOLOGY AND MARKET STRUCTURE. THEORY AND HISTORY 197, 228 (2001). On random screening, see R. M. Henderson, L. Orsenigo, & G. Pisano, The Pharmaceutical Industry and the Revolution in Molecular Biology: Exploring the interactions between scientific, institutional, and organizational change, SOURCES OF INDUSTRIAL LEADERSHIP: STUDIES OF SEVEN INDUSTRIES 267–311 (D. C. Mowery & R. R. Nelson eds., 1999).

147 On the rate of failures in drug R&D, see GAO REPORT, U.S. GOV’T ACCOUNTABILITY OFFICE, NEW DRUG DEVELOPMENT: SCIENCE, BUSINESS, REGULATORY, AND INTELLECTUAL PROPERTY ISSUES CITED AS HAMPERING DRUG DEVELOPMENT EFFORTS 25, 25 (2006) (finding that failure rates in human clinical trials based on lack of safety or efficacy were eighty-two percent in the 1996-99 period and ninety-one percent in the 2000–2003 period).

148 Nightingale & Mahdi, supra note 99, at 81. 149 Peter C. Gøtzsche, Strengthening and Opening up Health Research by Sharing Our Raw Data,

236, 237 CIRC CARDIOVASC QUAL OUTCOMES (2012) (deliberating that “When failures with previous drugs or devices are kept secret, expensive development programs for similar drugs or devices can continue for years after they would have been stopped if the data had been known”).

150 Sherry Brandt-Rauf, Biomedical Research, THE ROLE OF SCIENTIFIC AND TECHNICAL DATA AND INFORMATION IN THE PUBLIC DOMAIN: PROCEEDINGS OF A SYMPOSIUM 65, 66 (National Academy of Sciences, 2003).


confidentiality of clinical data “serve[s] only to slow the rate of scientific discovery and advancement.”151

To apprehend social costs of research data secrecy, one can invoke one of the rationales underlying the patent system, i.e., to avoid wasteful duplication of research efforts that hinders scientific and technological progress.152 Although technological and scientific information have different economic characteristics,153 the disclosure theory of the patent system can rationalize access to scientific data.154 Social costs of scientific knowledge secrecy are likely to be greater than those costs owed to the confidentiality of technological information.155 While technical knowledge can be revealed once the product is commercialized, the underlying research data cannot be reverse engineered to enable cumulativeness of research.156 Confidentiality protection of research data can thus eliminate the very availability of the source of technological information and scientific knowledge.157 If disclosure of technological information

151 Id.; supra note 5/5, at 112 (with further referenced); see also Gøtzsche, supra note 149/152, at 237

(arguing that “[a]n incomplete knowledge base . . . leads to redundant research” and “[m]uch research could be performed, at almost no cost, on existing data, making it unnecessary to collect new data”); NUNO PIRES DE CARVALHO, THE TRIPS REGIME OF PATENTS AND TEST DATA 570-71 (2014) (stating that “exclusive protection of test data leads to waste of scarce resources, because it requires competitors to repeat the same test on a product that is known [and] leads to the re-invention of the wheel”).

152 See, e.g., WILLIAM M. LANDES & RICHARD A. POSNER, THE ECONOMIC STRUCTURE OF INTELLECTUAL PROPERTY LAW, ECONOMICS OF PATENT LAW, 304 (2003) (“Invention is a matter of adding to the stock of useful knowledge and so of reducing uncertainty.”); Brian Kahin, Patents and Diversity in Innovation, 13 MICH. TELECOMM. & TECH. L REV. 389, 397 (2007). In the case of clinical trials, there is a persuasive ethical argument against duplicative research as the unjustified risk exposure of human participants. See Bryan C. Mercurio, TRIPS-Plus Provisions in FTAs: Recent Trends, in REGIONAL TRADE AGREEMENTS AND THE WTO LEGAL SYSTEM 215, 227 (Lorand Bartels & Frederico Ortino eds., 2006).

153 See generally Partha Dasgupta & Paul A. David, Information Disclosure and the Economics of Science and Technology, in ARROW AND THE ASCENT OF MODERN ECONOMIC THEORY 519 (G. R. Feiwel ed., 1987).

154 On disclosure theory of patent law, see Roberto Mazzoleni & Richard R. Nelson, The Benefits and Costs of Strong Patent Protection: A Contribution to the Current Debate, 27 RES. POL’Y 273, 273 (1998); Mazzoleni & Nelson, Patents Induce Disclosure and Wide Use of Inventions 278 (arguing that “patents encourage disclosure and, more generally, provide a vehicle for a quick and wide diffusion of the technical information underlying new inventions”).

155 Confidentiality protection of data can also entail higher social costs compared to sui generis protection for scientific databases that does not eliminate the accessibility of data. Scholars have continuously questioned whether IP is an adequate mode of governance of scientific data. See, e.g., Jerome H. Reichman & Paul F. Uhlir, A Contractually Reconstructed Research Commons for Scientific Data in a Highly Protectionist Intellectual Property Environment, 66 LAW & CONTEMP. PROBS. 315, 411 (2003) (arguing that “exclusive property right might a priori constitute the wrong kind of solution for a legal regime that aims to protect investment in large-scale aggregates of data as such”). See generally National Academy of Sciences, THE ROLE OF SCIENTIFIC AND TECHNICAL DATA AND INFORMATION IN THE PUBLIC DOMAIN: PROCEEDINGS OF A SYMPOSIUM (2003).

156 See Deepa Varadarajan, Trade Secret Fair Use, 83 FORDHAM L. REV. 1401, 1420 (2014) (pointing out the failure of trade secret law to address cumulative innovation concerns and arguing that “trade secret law does not have limiting doctrines akin to those of patent and copyright—i.e., doctrines sufficiently attuned to cumulative innovation”).

157 Dasgupta & David, supra note 153, at 535 (emphasizing the economic value of scientific knowledge: “The existing pool of knowledge is an essential input in the production of new knowledge. That is why technology draws so heavily upon the infrastructure provided by science. If, to take an extreme example, science were to close down, each enterprise in technology would, roughly speaking, have to rely on its private knowledge pool. This would dampen technological progress enormously, as technological enterprises would then, for the most part, be conducting duplicative research. The public-good-producing


was justified on the efficiency grounds, the economic impetus for research data disclosure would be even stronger.

c) Enabling Confirmatory Secondary Analysis

Primary data can be transformed into “cumulative and verifiable” knowledge158 if independent analysis can confirm the robustness of the original findings and conclusions. Access to “the full analyzable data set allows other investigators to reproduce the original analyses and carry out alternative, scientifically valid analyses of the primary study aim.”159

Notwithstanding the mandatory requirements of clinical trial registration and subsequent publication of trial outcomes, trial results may never be published160 or may otherwise be published in an incomplete format.161 Confirmatory analysis plays a crucial role in evidence-based medicine.162 Two recent reports by the National Academy of Sciences emphasize the importance of depositing primary clinical trial data as well as methods necessary for the replication and interpretation of the results.163 As the Institute of Medicine summarizes,

aspect of science is, of course, recognized to be of considerable importance to the technological community . . . ”).

158 Dalrymple, supra note 91, at 42; see also SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 92 (arguing that the verification and replication of investigators’ claims is essential [for] science [and] findings . . . should [be] subject . . . to scrutiny. Allowing timely verification and reproduction serves the public good by preventing other researchers or clinicians from building upon findings whose validity cannot be established and by preventing patients from receiving recommendations for clinical care that are based on invalid information”).

159 SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 99; see, e.g., John P.A. Ioannidis, Why Most Clinical Research Is Not Useful, 13 PUB. LIBR. SCI. MED. 1, 1 (2016) (addressing the issue of reproducibility in clinical research); Christine Laine et al., Reproducible Research: Moving toward research the public can really trust, 146 ANNALS INTERNAL MED. 450, 450–53 (2007).

160 Studies find that vast amount of clinical trial data is not publicly available. See, e.g., Peter Doshi & Tom Jefferson, Clinical study reports of randomised controlled trials: An Exploratory Review of Previously Confidential Industry Reports, 3 BMJ OPEN 1, 1 (2013) (referring to industry-generated clinical data as “a mostly hidden and untapped source of detailed and exhaustive data”); Peter C. Gøtzsche, Strengthening and Opening Up Health Research by Sharing Our Raw Data, 5 CIRCULATION: CARDIOVASCULAR QUALITY & OUTCOMES 236, 236–37 (2012); Peter Doshi et al., Raw Data from Clinical Trials: Within Reach?, 34 TRENDS PHARMA. SCI. 645, 645–47 (2013).

161 See, e.g., Andrew P. Prayle, Compliance with mandatory reporting of clinical trial results on ClinicalTrials.gov: cross sectional study, 344 BIOMEDICAL J. 1,1 (2012). Michael S. Lauer, Data Primarily Collected for New Insights, CLINICAL DATA AS THE BASIC STAPLE, supra note 5, at 98–99 (discussing specific examples of incomplete and inaccurate publication of trial results).

162 Databases for genomic information presents another example, when access to the underlying primary data and the underlying interpretive algorithms is imperative for the replicability of the analysis, and when treating data as a proprietary asset impedes the cumulativeness of research and the advancement of health innovation. See, e.g., Robert Cook-Deegan et al., The Next Controversy in Genetic Testing Clinical Data as Trade Secrets?, 21 EUROPEAN J. HUM. GENETICS 585, 585 (2013) (addressing the case of the proprietary database of Myriad Genetics containing unique data on the clinical significance of genetic variations obtained from conducting genetic tests that apply patented testing technologies).

163 EVOLUTION OF TRANSLATIONAL OMICS: LESSONS LEARNED AND THE PATH FORWARD (Christine M. Micheel, Sharly J. Nass & Gilbert S. Omenn eds., 2012); NATIONAL RESEARCH COUNCIL: SHARING PUBLICATION-RELATED DATA AND MATERIALS: RESPONSIBILITIES OF AUTHORSHIP IN THE LIFE SCIENCES (2003).


Allowing timely verification and reproduction serves the public good by preventing other researchers or clinicians from building on findings whose validity cannot be established and by preventing patients from receiving recommendations for clinical care that are based on invalid information.164

d) Enabling Exploratory Research

Patient-level data, whether raw or cleaned (i.e. formatted and organized into analyzable datasets), arguably presents scientific value and may be highly relevant to subsequent drug R&D.165 As envisaged by the Health and Medicine Division, sharing non-summary data can “further scientific discovery through additional secondary analyses, as well as the conduct of exploratory research to generate hypotheses for additional studies.”166

Innovation in general, and drug innovation in particular, is a non-linear feedback-loop process that “involves the effective integration of a wide range of elements of knowledge and activities, which are not ordered in a linear way and which may not be easily separated.”167 It is hardly possible to know ex ante which piece of knowledge is missing in the R&D puzzle that might trigger discovery of a new drug molecule or target. As vividly portrayed by Graham Cameron, research and discovery is akin to an “unplanned journey through the information space,” in which one can even claim the “right to roam.”168

The substantial value of clinical data for research subsists not so much in access to individual datasets but to when they are aggregated and integrated with other types of health data.169 Refined data, aggregated in large volumes, enables exploratory research, i.e., open-ended analysis aimed to generate new hypotheses and extrapolate likely effects of potential drug candidates. Data aggregation allows to achieve

164 SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 114. 165 Id. at 80. 166 Id. at 81 (referring to primary data as data including participant characteristics, primary outcome,

pre-specified secondary and tertiary outcomes, adverse event data, and exploratory data). 167 Basil Achilladelis & Nicholas Antonakis, The Dynamics of Technological Innovation: The Case

of the Pharmaceutical Industry, 30 RES. POL’Y 535, 536 (2001) (arguing that “[f]rom its establishment to this day, [the pharmaceutical industry] has maintained a close and fruitful two-way relation with academic research institutions in chemistry, pharmacology, the life sciences and medicine”, that it “thrived on a two-way vigorous relation with chemistry, the life sciences and medicine”, and that “gradual accumulation of knowledge . . . at certain moments in time makes possible the understanding of a group of previously incomprehensible phenomena”); Id. at 550.

168 Graham Cameron, Scientific Data, The Electronic Era, Intellectual Property, IPR (INTELLECTUAL PROPERTY RIGHTS) ASPECTS OF INTERNET COLLABORATIONS, 31, 32 (European Commission, 2001) (arguing that “Creative discovery comes from unlikely journeys through the information space; No go zones restrict the right to roam”). See also Paul A. David, Digital Technologies, Research Collaborations and the Extension of Protection for Intellectual Property in Science: Will Building ‘Good Fences’ Really Make ‘Good Neighbors?,, IPR (INTELLECTUAL PROPERTY RIGHTS) ASPECTS OF INTERNET COLLABORATIONS 58 (European Commission, 2001) (evoking Cameron’s metaphor when defining a scientific database defined “as an information space constituting a dynamic collective research tool”).

169 See, e.g., AUSTRALIAN HEALTH ETHICS COMMITTEE, ESSENTIALLY YOURS: THE PROTECTION OF HUMAN GENETIC INFORMATION IN AUSTRALIA (ALRC REPORT 96) 472 (2003) (“Much of the research value of human genetic research databases is derived from linkages created between clinical, personal and genetic information. Examining these linkages is an important tool in identifying the genetic causes of disease and in other forms of human genetic research.”). See generally Lauer, supra note 161; Philip R. O. Payne, Biomedical Knowledge Integration, 8 PUB. LIBR. SCI. COMPUTATIONAL BIOLOGY 1, 1 (2012).


economies of scale and scope in drug R&D,170 carry out meta-analysis, derive statistically significant findings, apply advanced data analytics techniques to optimize R&D processes,171 as well as facilitate the development of personalized medicine.172 In this sense, clinical data repositories resemble the concept of research commons,173 which, in the life sciences context, has been analyzed and fragmentally implemented, e.g., with regard to genomic data,174 gene patents,175 and microbial organisms,176 as well as proposed in relation to clinical data.177 As discussed prior, clinical data is not knowledge, but can be its source, or a tributary to research commons. To enable exploratory analysis, it is necessary to ensure, in the first place, that new streams of data constantly replenish this resource.178 This cannot be achieved under a regime

170 SHARING CLINICAL TRIAL DATA: MAXIMIZING BENEFITS, supra note 5, at 131 (proposing that, in

order to achieve economies of scale that “all databases of clinical trial results [are] housed in a single global federated system [with] a uniform technical approach to implementing access control”).

171 Nicolas P. Terry, Legal Issues Related to Data Access, Pooling, and Use, in CLINICAL DATA AS THE BASIC STAPLE, supra note 5, at 162; Kelly, supra note 63, at 212; see PETR BERKA, JAN RAUCH & DJAMEL ABDELKADER ZIGHED, DATA MINING AND MEDICAL KNOWLEDGE MANAGEMENT: CASES AND APPLICATIONS (2009); see also Eisenberg, supra note 7, at 487–88 (“Applying modern bioinformatics techniques to aggregations of these data could greatly increase what is learned from the data . . . Combining data from multiple studies can minimize problems of statistical insufficiency and provide information about side-effects and toxicities that are too rare to give rise to statistically significant observations in any given study that is limited to a few thousand patients. Drug developers could use meta-analysis of pooled data to eliminate more quickly those products that are likely to fail in clinical trials and to target their efforts on more promising candidates . . . ” (internal references omitted)); L.A Stewart & M.K.B Parmar, Meta-analysis of the Literature or of Individual Patient Data: Is There a Difference?, 341 THE LANCET 418–22 (1993), (finding empirical evidence on the difference between meta-analysis of the literature and meta-analysis of individual patient data by comparing the two approaches using randomized trials); F. Koenig et al., Sharing Clinical Trial Data on Patient Level: Opportunities and Challenges, 57 BIOMETRICAL J. 1, 8–26 (2015).

172 See, e.g., Ünlü, supra note 7, at 537–43; MCKINSEY GLOBAL INSTITUTE, BIG DATA: THE NEXT FRONTIER FOR INNOVATION, COMPETITION, AND PRODUCTIVITY 49 (2011).

173 Dedeurwaerdere, supra note 120, at 366 (defining a research commons as consisting of scientific data, information, materials and research tools).

174 See, e.g., Geertrui Van Overwalle, Governing Genomic Data: Plea for an “Open Commons,” GOVERNING KNOWLEDGE COMMONS 137 (Brett M. Frischmann, Michael J. Madison & Katherine J. Strandburg eds., 2014); Dedeurwaerdere, supra note 119, at 376–77 (drawing on the examples of managing genomic data in the “research commons fashion”).

175 See, e.g., GENE PATENTS AND COLLABORATIVE LICENSING MODELS. PATENT POOLS, CLEARINGHOUSES, OPEN SOURCE MODELS AND LIABILITY REGIMES (Geertrui Van Overwalle ed., 2009).

176 See, e.g., JEROME H. REICHMAN, DUKE UNIVERSITY SCHOOL OF LAW; PAUL F. UHLIR, NATIONAL ACADEMY OF SCIENCES; TOM DEDEURWAERDERE, GOVERNING DIGITALLY INTEGRATED GENETIC RESOURCES, DATA, AND LITERATURE (2016).

177 See, e.g., William Crown, Characteristics of the Marketplace for Medical Care Data, in CLINICAL DATA AS THE BASIC STAPLE, supra note 5, at 143; CLINICAL DATA AS THE BASIC STAPLE, supra note 5, at 1 (highlighting the potential of clinical data as “most central [resource] to healthcare progress” (with further references)); Kathrine J. Strandburg, Brett M. Frischmann & Can Cui, The Rare Deceases Clinical Research Network and the Urea Cycle Disorders Consortium as Nested Knowledge Commons, GOVERNING KNOWLEDGE COMMONS 155–208 (Brett M. Frischmann, Michael J. Madison & Katherine J. Strandburg eds., 2014).

178 Charlotte Hess & Elinor Ostrom, Ideas, Artifacts, and Facilities: Information as a Common-pool Resource, 66 LAW & CONTEMPORARY PROBLEMS 111, 121 (2003) (pointing out the distinction between common resource systems and “a flow of resource units or benefits from these systems,” the former referring to “the stock or facility [that] generates a flow of resource units or benefits over time.”).


where access is permitted to individual datasets on a case-by-case basis, either on voluntary contractual terms or through regulation of public access to information.179

Both confirmatory and exploratory analyses are important for scientific progress and in line with the ethos of open science. As David comments,

The core of th[e] rationale [for open science] is the greater efficacy of open inquiry and complete disclosure as a basis for the cooperative, cumulative generation of predictably reliable additions to the stock of knowledge. In brief, openness abets rapid validation of findings, and reduces excess duplication of research efforts. Wide sharing of information . . . enlarges the domain of complementarity among additions to the stock of reliable knowledge, and promotes beneficial spill-overs among distinct research programs.180

On balance, it seems that the regime of data sharing can produce a higher cost-benefit ratio compared to that of non-sharing. Public-good characteristics of knowledge, especially its cumulativeness, can “enhance the strength of positive externalities and thus widen[] the difference between the private and social returns [on knowledge production that] may be so substantial that remunerating the inventor accordingly is unthinkable.”181 Yet, to what extent should the regulatory framework allow the innovator to internalize dynamic benefits to incentivize further innovative activity?

C. The Need for a Principle of Normative Delineation of Legal Protection as an Innovation Incentive

Clinical research data confidentiality—legal or factual—can provide an additional layer of protection against competition in the market and allow drug sponsors to maximize private returns by internalizing knowledge externalities. If undisclosed information is viewed as a subset of IP,182 the question is whether such an effect is compatible with the rationale of the IP system. In principle, IP protection intends to promote competition by substitution at the expense of the temporary limitation on competition by imitation.183 The goal is not to maximize the innovators’ reward but to solve a particular social problem, i.e., the (risk of) market failure related to public goods.184 The full internalization of benefits might not be necessary, or even possible, for that purpose. Protection targets free-riding imitators but does not render any

179 As discussed above, under data-sharing policies, access on the case-by-case basis is limited. Theoretically, compulsory access to data can also be imposed as a duty to deal under competition law; however, it is unlikely to be petitioned in practice.

180 David, supra note 132, at 22 (emphasis added). 181 Foray, supra note 44, at 114 (emphasis added). See Zvi Griliches, The Search for R&D Spillovers,

in R&D AND PRODUCTIVITY: THE ECONOMETRIC EVIDENCE 251, 252 (National Bureau of Economic Research, 1998) (observing that the “more difficult to measure and the possibly more interesting and pervasive aspect of R&D externalities is the impact of the discovered ideas or compounds on the productivity of the research endeavors of others. This is a nonpecuniary externality which is not embodied in a particular service or product, though it might be conveyed by a printed article or a news release. It has the classic aspect of a nonrivalrous good and it is usually very hard to appropriate more than a tiny fraction of its social returns.”) (emphasis added).

182 Article 39 of the TRIPS Agreement. 183 See supra note 30. 184 See Lemley, infra note 189, at 1049.


unauthorized act of benefiting from earlier innovation unlawful. For instance, the experimental use exception in patent law suggests that legal protection is inherently limited to account for innovation’s cumulativeness. Moreover, economic analysis of patent law suggests it is a misconception that the maximization of legal protection can yield a proportionate increase in innovation.185

To solve the public-good problem in innovation, the IP system attempts to balance interests of creators with those of information users.186 The latter includes final consumers as well as follow-on innovators. As discussed earlier, there is no distinction between data generators as initial innovators and data users as follow-on drug developers. The latter do not merely use others’ data but combine it with their own investment and research efforts. Drug R&D processes integrate multiple knowledge inputs, which makes the question of structuring the relationship between data holders and users highly complex.187 In practical terms, it is neither feasible to bargain ex ante over the missing fractions of knowledge nor to determine ex post what share of profits generated by the new drug should be passed to the trial sponsors of earlier drugs. However, when considering an adequate remuneration system, one may also consider to what extent the development of the latter is owed to the contribution of pre-existing knowledge.188

Due to the public good nature of innovation, it is impossible for innovators to appropriate the full value of returns on innovative activity.189 Thus, “inventors must expect to receive less than the social returns of their invention.”190 Positive knowledge externalities can be internalized by factual and legal exclusivity of data and thus maximize protection against competition. It appears then a matter of principle whether protection, as a means to promote innovation, should allow drug sponsors to earn

185 See The United Nations Conference on Trade and Development, Using Intellectual Property

Rights to Stimulate Pharmaceutical Production in Developing Countries: Reference Guide, UNCTAD/DIAE/PCB/2009/19, 8–9 (2011) (concluding that “based on a number of recent studies, the conclusion has emerged that stronger patent protection does not necessarily lead to more innovation”); William D. Nordhaus, An Economic Theory of Technological Change, 59 The American Economic Review, Papers and Proceedings of the Eighty-first Annual Meeting of the American Economic Association 18, 18 (1969) (observing that “[i]t is more accurate to state that the growth of inputs cannot explain most of the growth of output”).

186 Article 7 of the TRIPS Agreement. 187 Clinical data cannot be compared to such identifiable and delineable upstream innovation inputs

as, e.g., patents for research tools. Therefore, licensing can hardly present a viable solution for ‘structuring’ relationship between data holders and users.

188 See Stiglitz, supra note 41, at 315 (“How much of the returns to the innovation should be credited to this use of the global commons? . . . Every innovation makes use of previously accumulated knowledge – it draws on the global commons of pre-existing knowledge.”).

189 See Mark A. Lemley, Property, Intellectual Property, and Free Riding, 83 TEX L. REV. 1031, 1046 (2005) (arguing that the “assumption that intellectual property owners should be entitled to capture the full social surplus of their invention runs counter to our economic intuitions in every other segment of the economy. We do not permit producers to capture the full social value of their output. Nor do we permit the owners even of real property to internalize the full positive externalities associated with their property.” (emphasis added)). See, e.g., Ian Ayres & Paul Klemperer, Limiting Patentees’ Market Power Without Reducing Innovation Incentives: The Perverse Benefits of Uncertainty and Non-Injunctive Remedies, 97 MICH. L. REV. 985, 987–89 (1999) (“Even if this were possible, however, it would not be desirable to allow inventors to appropriate the full social value of their inventions”).

190 Foray, supra note 44, at 114.


profits under restrained generic competition or, in addition, at the expense of potentially delayed new drug development.

VI. CONCLUSION

In light of the foregoing analysis, confidentiality protection of primary clinical trial data cannot be justified on innovation grounds. While secrecy as such does not resolve the public-good problem in pharmaceutical innovation, it is likely to exacerbate the common-resource problem. Hence, the research-based pharmaceutical industry’s claim that access to primary data—in the presence of regulatory instruments that protect the originator drug against generic competition—impedes innovation incentives should not be leveraged against pro-access policies.

In the competitive market environment, and under conditions of uncertainty, companies are unlikely to change their perspectives on voluntary sharing. Hence, the robust secondary use of primary data can be achieved through regulation when third-party access is not at the discretion of trial sponsors. There is no argument that pharmaceutical companies conduct drug R&D at great expense and high failure risk, and that it is in society’s interest to protect companies’ capacity to earn adequate returns on R&D investment. To what extent the regulatory framework should allow the innovator to internalize dynamic benefits yielded by the innovative activity requires further deliberation. This article proposes that regulation should adhere to the principle that protection should be confined to competition by imitation. This implies that rules of access should be designed in such a way that third-party use of data should not interfere with protection against generic competition. At the same time, the long-term collective benefit can be maximized when the “cooperative choice”191—i.e., when everyone shares data—becomes the dominant strategy. This can be achieved only when primary data are aggregated and managed on a collective basis, and when access is not subject to the authorization of the initial trial sponsors.

191 Shankar & Pavitt, supra note 79, at 254.

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