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IPD as a Research Resource: Exclusively Controlled or Readily Accessible?

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Part of the Munich Studies on Innovation and Competition book series (MSIC,volume 16)

Abstract

This chapter applies the above-outlined conceptual framework to examine whether and to what extent de facto exclusive control over IPD can be justified as a means of internalising R&D externalities. The analysis seeks to define and qualitatively weigh up innovation-related benefits and costs of exclusive control over vis-à-vis unrestricted access to IPD as a knowledge resource for research and innovation. The policy conclusion is drawn as to whether regulatory intervention by access measures can be justified on the grounds of promoting drug innovation.

Keywords

  • Access to data
  • Anticommons
  • Clinical trial data
  • Commons
  • Innovation incentives
  • Knowledge externalities
  • Knowledge spillovers
  • R&D externalities
  • Research tools
  • Underutilisation

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Notes

  1. 1.

    See Chap. 7 at Sect. 7.1.1.1.

  2. 2.

    Kwerel (1980), p. 506; Lemmens (2004), p. 647.

  3. 3.

    The purpose of enforcing the regulatory standards for drug efficacy and safety is to mitigate ‘harmful “externalities” generated by an industrialized market economy’ and the risk of misrepresentation of product information due to information asymmetry in the marketplace. Stewart (1981), p. 1260. See also Katz (2007), p. 11. The ‘market-for-lemons’ metaphor for the information asymmetry-induced market failure was introduced by George Akerlof. See Akerlof (1970), p. 488 (referring to ‘lemons’ as a phenomenon of the diminishing quality of goods due to the information asymmetry between buyers and sellers). See also Eisenberg (2007), p. 367 (referring to the ‘original function’ of the USFDA as ‘protecting the public from snake oil’).

  4. 4.

    Chapter 5 at Sect. 5.2.3.

  5. 5.

    Chapter 5 at Sect. 5.2.2.2.

  6. 6.

    WHO collaborating centre for drug statistics methodology, ATC, structure and principles. https://www.whocc.no/atc/structure_and_principles. Accessed 26 Mar 2021.

  7. 7.

    European Commission (8 Jul 2009) Pharmaceutical sector inquiry report. Final report, p. 22. https://ec.europa.eu/competition/sectors/pharmaceuticals/inquiry/staff_working_paper_part1.pdf. Accessed 26 Mar 2021.

  8. 8.

    The business model of generic companies is based on producing drugs identical or equivalent to successful originator treatments and their commercialisation upon the loss of exclusivity of the originator product. ibid p. 35.

  9. 9.

    On the strategies deployed by originator companies to delay the generic entry, see ibid p. 351 ff.

  10. 10.

    OECD and Eurostat (2005), p. 17.

  11. 11.

    For definitions and an overview of the literature on follow-on drugs, see e.g. Andrade et al. (2016), pp. 45–47.

  12. 12.

    DiMasi and Paquette (2004), p. 2; Ahn (2014), p. 56.

  13. 13.

    Petrova (2014), p. 67. Sometimes a follow-on drug ‘may even surpass the pioneer drug through enhanced effectiveness, greater convenience, or weaker side effects’. ibid pp. 34–35.

  14. 14.

    Some authors differentiate between ‘me-too’ and follow-on drugs: the former result from the parallel R&D by competing drug companies, while the latter are developed and launched by the same drug company that sponsored the pioneer drug. See Petrova (2014), p. 33 ff.

  15. 15.

    DiMasi and Paquette (2004) and DiMasi and Faden (2011).

  16. 16.

    See e.g. Petrova (2014), p. 24; Pharmaceutical sector inquiry report (n 7), p. 187.

  17. 17.

    Petrova (2014), p. 23.

  18. 18.

    In general terms, personalised medicine refers to targeted diagnostics and treatments based on an individual patient’s medical history and genetic profile. More specifically, it is defined as ‘a medical model using molecular profiling technologies for tailoring the right therapeutic strategy for the right person at the right time, and determine the predisposition to disease at the population level and to deliver timely and stratified prevention’. European Commission (2010) Stratification biomarkers in personalised medicine. Workshop report. http://ec.europa.eu/research/health/pdf/biomarkers-for-patient-stratification_en.pdf. Accessed 26 Mar 2021. See also Jain (2015), pp. 1–2.

  19. 19.

    Andrade et al. (2016), p. 48.

  20. 20.

    Pharmaceutical sector inquiry report (n 7), p. 49; Ahn (2014), p. 56.

  21. 21.

    This strategy corresponds to competition for the market—R&D efforts directed at developing new products that can either replace the existing ones or create a new market.

  22. 22.

    Reinganum (1981), p. 21 ff.

  23. 23.

    Drexl (2012), p. 507.

  24. 24.

    Schumpeter (1950), pp. 81–86. On competition in innovation and dynamic efficiency in high technology industries and markets, see e.g. Jones and Sufrin (2016), p. 13 ff; van den Bergh and Camesasca (2001), p. 36 ff.

  25. 25.

    See e.g. Kerber and Schwalbe (2008), para 1-8-071 (with further references); Fatur (2012), p. 71. For an overview of the theories of dynamic competition, see Elling and Lin (2001), pp. 16–44.

  26. 26.

    Cohen (2010), p. 156.

  27. 27.

    Kerber and Schwalbe (2008), paras 1-8-097, 1-8-207 (observing that there might be ‘only a weak relationship between the degree of concentration, R&D expenditure and innovation in a market’, which ‘means that no unambiguous conclusions can be drawn regarding the effects of changes in R&D activities on the rate of innovation’ (with further references)). See also Glader (2006), p. 85 (noting that, even though ‘scholars and policy makers have been sensitive to the possibility that a more permissive attitude could be beneficial to spur international competition and R&D incentives, the problem has been that the links between concentration and R&D or concentration and innovation have always been murky’). See also below (n 242).

  28. 28.

    As summarised by Carrier, ‘[a]fter a half-century of debate and innumerable studies, the overwhelming consensus is that there is no clear answer to the question [regarding the relationship between market structure and innovation]’. Carrier (2008), p. 396. See also Rapp (1995), p. 27 (stating that ‘[t]he leap of faith is to believe that there is a positive functional relationship between the rate of R&D expenditure (or the amount of R&D capacity) and the quantum of innovation produced by a firm’).

  29. 29.

    Henderson and Cockburn (1996), p. 36.

  30. 30.

    Orsenigo et al. (2006), p. 407. See also OECD (2013) The role of efficiency claims in antitrust proceedings. DAF/COMP(2012)23, p. 15 (noting that, in the pharmaceutical industry, ‘competition mainly occurs through races to innovate rather than through price setting, in a process known as Schumpeterian rivalry’); Gambardella (1995), p. 142 (concluding that ‘research and innovation are the most important determinants of competitive performance and growth among the largest US drug companies’); Cockburn and Henderson (1994), p. 483 (referring to competition in the pharmaceutical industry as ‘a prime example of the types of strategic racing behavior [whereby] firms invest heavily in research and development since successful research is a key contributor to commercial success’).

  31. 31.

    Charles River Associates (2004) Innovation in the pharmaceutical sector, p. 63. https://www.crai.com/insights-events/publications/innovation-pharmaceutical-sector/. Accessed 26 Mar 2021.

  32. 32.

    Bansal AK, Koradia V (2005) The role of reverse engineering in the development of generic formulations. Pharmaceutical Technology 28(9). http://www.pharmtech.com/role-reverse-engineering-development-generic-formulations. Accessed 26 Mar 2021.

  33. 33.

    Pharmaceutical sector inquiry report (n 7), p. 8.

  34. 34.

    Case T-44/13 R AbbVie v EMA [2013] ECLI:EU:T:2013:221, para 60 (emphasis added).

  35. 35.

    EMA (26 Jan 2017) CHMP assessment report. Amgevita. EMA/106922/2017, p. 9. Besides, several biosimilar products were authorised by the EMA in 2017–2018, for which Humira was designated as a reference medicine, including Imraldi, Hyrimoz, Hefiya, Cyltezo, Halimatoz, Hulio.

  36. 36.

    Amgen (15 Oct 2018) Amgen launches AMGEVITA™ (biosimilar adalimumab) in markets across Europe. https://www.amgen.com/media/news-releases/2018/10/amgen-launches-amgevita-biosimilar-adalimumab-in-markets-across-europe/. Accessed 26 Mar 2021. See also Davio K (15 Oct 2018) On the eve of Humira’s patent expiry, Europe prepares for biosimilar Adalimumab (stating that ‘October 16 marks European patent expiry for AbbVie’s blockbuster anti-tumor necrosis factor drug, adalimumab (Humira), and multiple competitors stand ready to launch their biosimilar products on, or shortly after, that date’). https://www.centerforbiosimilars.com/view/on-the-eve-of-humiras-patent-expiry-europe-prepares-for-biosimilar-adalimumab. Accessed 26 Mar 2021.

  37. 37.

    EMA (26 Jan 2017) CHMP Assessment Report. Amgevita. EMA/106922/2017, p. 36 ff.

  38. 38.

    ibid pp. 53, 70.

  39. 39.

    In that case, access to CSRs was requested by a university student. Case T-44/13 R AbbVie v EMA [2013] ECLI:EU:T:2013:221, para 20. AbbVie argued that, ‘even if access to the disputed reports were granted only to one student, the confidential information could be disclosed to anybody, including current or potential competitors’. ibid para 46.

  40. 40.

    The EMA publication policy provides for the reservations to mitigate competitive concerns regarding competitors’ use of the trial-related information and data in R&D. For instance, information regarding bioassays and analytical methods can be removed from a CSR before it can be disclosed. EMA publication policy 0070, p. 18.

  41. 41.

    For an analysis, see Chap. 5 at Sect. 5.4.

  42. 42.

    For an analysis, see Chap. 5 at Sect. 5.3.

  43. 43.

    As discussed in Chap. 3.

  44. 44.

    Pharmaceutical sector inquiry report (n 7), p. 9. The notion of R&D pole is often applied in the competition law analysis, e.g. in the assessment of mergers and horizontal co-operation agreements. See European Commission (14 Jan 2001) Communication from the Commission. Guidelines on the applicability of Article 101 of the Treaty on the Functioning of the European Union to horizontal co-operation agreements. OJ C 11, paras 119–122.

  45. 45.

    Cockburn and Henderson (1994), p. 490 ff (discussing the example of the discovery of an angiotensin-converting enzyme inhibitor). See also Merges and Nelson (1990), p. 908.

  46. 46.

    See e.g. Institute of Medicine of the National Academies (2015), p. 20 (noting that clinical trial data analysis can facilitate ‘the identification and validation of new drug targets [and] new indications for use’).

  47. 47.

    Case T-44/13 R AbbVie v EMA [2013] ECLI:EU:T:2013:221, para 60.

  48. 48.

    ibid.

  49. 49.

    Bonini et al. (2014), p. 2454 (emphasis added).

  50. 50.

    See Eisenberg (2011), pp. 468–469 (observing that ‘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’, which creates ‘redundancy in protection’).

  51. 51.

    Case T-44/13 R AbbVie v EMA [2013] ECLI:EU:T:2013:221, para 60 (emphasis added).

  52. 52.

    For a review of these arguments, see Chap. 5 at Sect. 5.1.

  53. 53.

    See Chap. 6 at Sect. 6.3.2.

  54. 54.

    ibid.

  55. 55.

    EMA publication policy 0070, p. 4 (emphasis added).

  56. 56.

    On the subgroup IPD analysis, see Chap. 3 at Sect. 3.2.2.4.

  57. 57.

    Response to the questionnaire by a chemist who works in a pharmaceutical company (on file with the author). The respondent also noted that such ‘correlation can be done only if the full [individual patient-level] data are available’ and that the clinical study reports and the respective publications do not contain such data.

  58. 58.

    See e.g. Institute of Medicine of the National Academies (2015), p. 20.

  59. 59.

    Case T-44/13 R AbbVie v EMA [2013] ECLI:EU:T:2013:221, para 60 (emphasis added). See also Eisenberg (2007), p. 383.

  60. 60.

    See e.g. Correa (2015), p. 44; Pharmaceutical sector inquiry report (n 7), p. 187 (observing that ‘clinical trials may reveal new medical uses’). The often-cited example of a new use discovered during clinical trials is the drug Viagra first tested to treat angina pectoris. Another example is minoxidil, initially developed to treat ulcers that demonstrated effectiveness as a vasodilator and was subsequently ‘re-purposed’ for the severe hypertension condition. Watkins et al. (1979). Later on, the antihypertensive agent minoxidil showed a side effect of hair growth during efficacy trials and was repositioned for treating alopecia. Clissold and Heel (1987).

  61. 61.

    For instance, thalidomide was approved as a treatment for morning sickness in pregnant women in the 1950s and repositioned to treat multiple myeloma in 2006. Thioguanine was initially developed to treat leukemia in children; it took 65 years before it was approved for inflammatory bowel disease. Simsek (2018), pp. 17–18.

  62. 62.

    Gauch (2009), p. 285 (defining data-mining as ‘searching a database and looking for every kind of a relationship between variables that have not been previously discovered’).

  63. 63.

    See Chap. 5 at Sect. 5.1.

  64. 64.

    Pharmaceutical sector inquiry report (n 7), p. 150 (stating that ‘[d]ifferent dosages or different forms of administration of the same prescription medicine have been considered as different products’).

  65. 65.

    See Case T-718/15 PTC Therapeutics International v EMA [2018] ECLI:EU:T:2018:66, para 92.

  66. 66.

    Dir 2001/83/EC, art 10(2)(b) (stipulating that ‘different salts, esters, ethers, isomers, mixtures of isomers, complexes or derivatives of an active substance shall be considered to be the same active substance, unless they differ significantly in properties with regard to safety and/or efficacy’; if so, ‘additional information providing proof of the safety and/or efficacy of the various salts, esters or derivatives of an authorised active substance must be supplied by the applicant’.) See also Dir 2001/83/EC, annex I, pt II(3).

  67. 67.

    Patents related to such improvements are also known as ‘second-generation’ or ‘secondary’ patents. See Pharmaceutical sector inquiry report (n 7), p. 381 (explaining that originator companies usually file for secondary patents (e.g. directed at the modifications, improvements, combinations with other molecules, etc.) in order to maintain the freedom to operate when conducting further research and improve first-generation products ‘without interference from competitors’).

  68. 68.

    Andrade et al. (2016), pp. 51–53.

  69. 69.

    Reichman (2009), p. 39. See also above (n 16) and the accompanying text.

  70. 70.

    Institute of Medicine of the National Academies (2015), p. 63.

  71. 71.

    See Scotchmer (2004), p. 147 (pointing out that the faster the improvements come, ‘the shorter is the market incumbency of each innovator’, and that the prospect of selling an innovative product ‘at a price established in competition with its successor […] might easily discourage investment’).

  72. 72.

    Brody (2016), p. 88. See also EMA (23 Jan 2014) EMA guideline on the investigation of subgroups in confirmatory clinical trials. EMA/CHMP/539146/2013, p. 9.

  73. 73.

    Moyé (2003), p. 119.

  74. 74.

    Senn (2007), p. 43.

  75. 75.

    ibid.

  76. 76.

    Browner et al. (2007), p. 61.

  77. 77.

    Kirwan (1997), pp. 822–825. See also Merson et al. (2016), p. 2414 (referring to ‘repositories of data without metadata, data dictionaries, or documentation needed for meaningful or correct reanalysis’ as ‘data dumpster’); Sydes et al. (2015) (pointing out that ‘data dredging is likely to provide some false positive findings and lead to over-interpretation’). But see Meinert (2012), p. 446 (finding that ‘ad hoc data analysis aimed at finding statistically significant differences among different groups, especially when leading to a presentation or publication heralding differences [is] real and important’).

  78. 78.

    Browner et al. (2007), p. 61.

  79. 79.

    Hughes (2011), p. 1239.

  80. 80.

    Above (n 66).

  81. 81.

    Studies show that pharmaceutical companies pursue the ‘best-in-class’ strategy aimed at developing drugs ‘with a particularly attractive clinical or economic profile’ improving the existing medicines, instead of the ‘first-in-class’ strategy. See Lanthier et al. (2013), pp. 1434–1436 (observing that the time-lag between the marketing of a first-in-class drug and the marketing of similar products has decreased significantly over time); Pharmaceutical sector inquiry report (n 7), p. 49 (stating that ‘incremental innovations constitute a parameter for competition between originator medicines in the same therapeutic class’); Andrade et al. (2016), p. 48; DiMasi and Paquette (2004), p. 12.

  82. 82.

    DiMasi and Faden (2011).

  83. 83.

    Within those classes, the first-in-class compound was approved in the US between 1960 and 2003 and 287 follow-on drugs were authorised in the US between 1960 and 2007.

  84. 84.

    ibid p. 25 (emphasis added).

  85. 85.

    ibid.

  86. 86.

    See Petrova (2014), p. 33 (explaining that, since competing drug companies ‘work in parallel on similar targets, often applying the same fundamental knowledge sourced from open science, the solutions they come up with may not be all that different’, and that ‘the vast majority of me-too drugs are not the product of brazen, deliberate imitation [as most] of them have been in clinical development prior to the approval of the pioneer drug’ (emphasis added)).

  87. 87.

    Pharmaceutical sector inquiry report (n 7), p. 514 (identifying over 1100 instances, where patents of one originator company could be infringed by the overlapping R&D programmes and/or patents of another originator company).

  88. 88.

    According to the report by the European Commission, the majority (75.8%) of the examined patent infringement cases had been litigated between the originator companies pursuing R&D programs in the same ATC3 class. Pharmaceutical sector inquiry report (n 7), pp. 410.

  89. 89.

    ibid pp. 184–185. See also Petrova (2014), p. 24.

  90. 90.

    Creating ‘patent clusters’ through secondary patents is reportedly one of the so-called ‘lifecycle management’ strategies often employed by research-based drug companies to protect exclusivity in the market. Pharmaceutical sector inquiry report (n 7), pp. 60, 173 ff.

  91. 91.

    ibid p. 381 ff.

  92. 92.

    Cockburn and Henderson (1994), p. 495. See also Pharmaceutical sector inquiry report (n 7), p. 57 (finding that the competitive environment (product differentiation) is one of the key determinants of R&D investment decision making).

  93. 93.

    If third-party research can benefit through the analysis of disclosed IPD, IPD disclosure can be viewed as a catalyst of R&D externalities.

  94. 94.

    Antonelli (2017), p. 97.

  95. 95.

    See e.g. Hall et al. (2010), p. 1065; Antonelli (2017), p. 5.

  96. 96.

    Jaffe (1998), p. 14.

  97. 97.

    Small-molecule drugs can be ‘reverse-engineered’ without accessing and re-analysing IPD related to the originator drug. Bansal AK, Koradia V (2005) The role of reverse engineering in the development of generic formulations. Pharmaceutical Technology 28(9). http://www.pharmtech.com/role-reverse-engineering-development-generic-formulations. Accessed 26 Mar 2021.

  98. 98.

    The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) (1998) ICH harmonised tripartite guideline. Statistical principles for clinical trials. E9, p. 5.

  99. 99.

    See e.g. Huque and Röhmel (2010), p. 3.

  100. 100.

    Bonini et al. (2014).

  101. 101.

    EMA publication policy 0070, pp. 19–20.

  102. 102.

    Academic researchers also consider exploratory biomarker studies as commercially-sensitive information that needs to be maintained confidential. See e.g. Multi-Regional Clinical Trials Center at Harvard University (2014) Overview of data disclosure initiatives: current and ongoing data transparency activities in the pharmaceutical industry. https://www.regulations.gov/comment/FDA-2013-N-0271-0031. Accessed 26 Mar 2021.

  103. 103.

    Bone (1998), pp. 270–271.

  104. 104.

    According to the EMA, for instance, disclosure of data on exploratory endpoints could allow ‘competitors to gain insights into additional future study plans and/or indications for the product’ and, in some situations, affect the patentability of future inventions. Hence, such information can qualify as CCI and be deleted. EMA publication policy 0070, pp. 19–20.

  105. 105.

    See e.g. Ben-Asher (2000), p. 279 (noting that if the additional knowledge is not discovered in the course of the initial or follow-on projects, ‘there is likely to be a welfare loss’).

  106. 106.

    Institute of Medicine of the National Academies (2015), p. 18 (emphasis added) (with further references).

  107. 107.

    Ostrom (2008), p. 5. See also King et al. (2016), p. 67 (defining an anticommons as a situation, where ‘[b]ehavior that is individually rational and maximizing […] results in outcomes that are collectively perverse and systematically suboptimal’).

  108. 108.

    Heller (1998).

  109. 109.

    King et al. (2016), p. 70.

  110. 110.

    ibid (further arguing that ‘there is no apparent necessity to insert the concept of legal property as a critical element’ of the anticommons definition because ‘separate, necessary yet complementary inputs [are the sufficient] preconditions to the anticommons problem, consistent with the non-cooperative game model’).

  111. 111.

    Heller and Eisenberg (1998), p. 698; Hess and Ostrom (2007), p. 11 (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’); Wang (2008), p. 253 (noting that the ‘core problem with the anticommons is underuse’); Frost and Morner (2010), p. 178 (noting that resources are ‘underused, because too many “knowledge empire builders” have the right to exclude’).

  112. 112.

    Hardin (1968).

  113. 113.

    See e.g. Murray and Stern (2007), p. 654; Heller and Eisenberg (1998), p. 698 (arguing that the anticommons problem in the case of patents for biotechnological research tools is ‘distinct from the routine underuse inherent in any well-functioning patent system’).

  114. 114.

    Heller and Eisenberg (1998), pp. 698–699.

  115. 115.

    ibid.

  116. 116.

    Major et al. (2016), Zhou (2015), Schulz et al. (2001) and Parisi et al. (2004).

  117. 117.

    Buchanan and Yoon (2000).

  118. 118.

    ibid p. 4.

  119. 119.

    Parisi et al. (2004), p. 183.

  120. 120.

    ibid.

  121. 121.

    ibid p. 184.

  122. 122.

    ibid.

  123. 123.

    Zhou (2015), p. 3.

  124. 124.

    ibid p. 14.

  125. 125.

    King et al. (2016), pp. 72–74. The authors also argue that

    Ronald Coase was not correct when he asserted, given rational and costless transactions, that the negotiated outcome will always lead to the Pareto efficient use of resources. Anticommons provides a direct counter-example. It is not merely that multiple owners complicate transactions, or even that they have extra incentive to exaggerate costs and provide misleading information. Rather, the logic of anticommons maximization itself entails equilibrium at a suboptimal location relative to what would have occurred had the property right been unified. ibid p. 72.

  126. 126.

    ibid p. 77.

  127. 127.

    ibid (emphasis added).

  128. 128.

    ibid p. 72 (emphasis added).

  129. 129.

    As argued by Walsh, Arora and Cohen in the context of patent rights for research tools, ‘[f]rom a social welfare perspective, nothing is wrong with restricted access to IP for the purpose of subsequent discovery as long as the patent holder is as able as potential downstream users to fully exploit the potential contribution of that tool or input to subsequent innovation and commercialization’. Walsh et al. (2003), pp. 290–291 (emphasis added). See also Scotchmer (2004), p. 161 ff; Scotchmer (1991), p. 32 ff.

  130. 130.

    Walsh et al. (2003), pp. 285–340; Walsh et al. (2005). These empirical studies are not reviewed here in detailed as they addressed the anticommons hypothesis specifically in the case of patented research tools and, therefore, might not be applicable to research data (for a critical view of the study methodology, see David (2003), p. 31 ff). However, one finding is worth highlighting: based on the survey among 414 biomedical researchers in universities, government and non-profit institutions, Walsh, Cho and Cohen found ‘little empirical basis for claims that restricted access to IP is currently impeding biomedical research’; at the same time, they submit that ‘there is evidence that access to material research inputs [such as research data not protected by IP rights] is restricted more often’. Walsh et al. (2005), p. 2003. According to the authors, the welfare effects of restricted access to such research materials are inconclusive as the restrictions can both impede scientific progress and benefit it (in particular, by reducing duplicative research and enhancing project diversity).

  131. 131.

    Murray and Stern (2007), p. 649 (emphasis added) (with further references). See also Long (2000), p. 239 (stating that the question of whether the broad scope of patent protection can be an efficient instrument of stimulating downstream innovation in the field of biomedical research as ‘an unsettled issue in need of more research’); Wang (2008), p. 253 ff.

  132. 132.

    Biomedical research tools refer to research resources such as cell lines, monoclonal antibodies, reagents, DNA libraries, clones, etc. See the US Department of Health and Human Services (23 Dec 1999) Principles and guidelines for recipients of NIH research grants and contracts. Fed. Reg. 64(246), p. 72092. https://grants.nih.gov/grants/intell-property_64FR72090.pdf. Accessed 26 Mar 2021.

  133. 133.

    On scientific data as research input, see Chap. 7 at Sects. 7.1.1.2 and 7.1.1.3.

  134. 134.

    Such control can be enabled through technical measures of protection and non-property forms of legal protection. See Chap. 4 at Sect. 4.2.

  135. 135.

    As discussed earlier, many data-sharing policies adopted by pharmaceutical companies contain a standard clause explicitly stating that data shall not be shared with actual or potential competitors. See Chap. 6 at Sect. 6.3.2. See also Mattioli (2017), p. 179.

  136. 136.

    King et al. (2016). On the game-theoretic models of anticommons, see above (nn 116–128) and the accompanying text.

  137. 137.

    Stacking licences present another anticommons mechanism, whereby ‘too many upstream patent owners [can] stack licenses on top of the future discoveries of downstream users’. Heller and Eisenberg (1998), p. 699 (further arguing that such agreements can provide the patent holder with a ‘continuing right to be present at the bargaining table as a research project moves downstream toward product development’). See also Wang (2008), p. 282 (noting that ‘[t]he sheer number of relevant patents and patentees in a research or commercialization project spawns the burden of reviewing patent claims and negotiating necessary licenses’).

  138. 138.

    Chapter 9 at Sect. 9.3.2.4, subheading ‘Concerns Regarding ‘Stacking Licenses’’.

  139. 139.

    Buchanan and Yoon (2000), p. 4 (emphasis added). According to the authors, anticommons is ‘a useful metaphor for understanding how and why potential economic value may disappear into the “black hole” of resource underutilization, a wastage that may be quantitatively comparable to the overutilization wastage employed in the conventional commons logic’. ibid p. 2.

  140. 140.

    Käseberg (2011), p. 13 (observing that ‘it has been recognised […] that broad patent protection in cumulative innovation settings may impede follow-on innovation and lead to opportunity losses in terms of dynamic efficiency’).

  141. 141.

    Zhou (2015), p. 4.

  142. 142.

    David (1993), p. 55 (emphasis added).

  143. 143.

    Institute of Medicine of the National Academies (2015), p. 141 (with further references). See also Brandt-Rauf (2003), p. 66 (highlighting an analogous argument with regard to scientific data in general, namely, that the resistance to share such data can ‘slow the progress of science because scientists cannot easily build on the efforts of others or discover errors in completed work’).

  144. 144.

    See above (nn 129–131) and the accompanying text.

  145. 145.

    On the ‘access-incentives paradox’ concerning access to IPD, see Chap. 7 at Sect. 7.1.2.

  146. 146.

    Chapter 6 at Sect. 6.3.2.

  147. 147.

    Chapter 5 at Sects. 5.3 and 5.4.

  148. 148.

    See above (n 107) and the accompanying text.

  149. 149.

    Institute of Medicine of the National Academies (2015), p. 18 (emphasis added) (with further references).

  150. 150.

    ibid.

  151. 151.

    See e.g. Hall and Harhoff (2012), p. 554 (observing that the hypothesis that the overall effect of patents can be negative, especially in a setting, where innovation is cumulative, ‘is difficult to test because of the absence of a true counterfactual’); Heller (2011), p. 73 (noting that it is ‘hard to know how to quantify gridlock, in part because it involves testing a counterfactual: what cures would we have it people could work together more easily?’). Conversely, one could also question whether research tools would have been invented but for the incentives provided by patent law, which locks the debate in a circular argumentation. See Murray and Stern (2007), p. 684 (finding support for the ‘anticommons’ effect and also acknowledging that such evidence ‘captures only one aspect of the impact of IP on dual knowledge’, while patent rights might have ‘enhanced the incentives for (unobserved) research [and] led to more effective (or rapid) commercialization […], or allowed for cumulative innovation through patents and future patent citations’).

  152. 152.

    See Chap. 4 at Sect. 4.2.1.2, subheading ‘The Obligation to Protect Data Against Unauthorised Access as the Source of de facto Exclusive Control’.

  153. 153.

    Johnson (2005), pp. 42–43; Edquist (2005), p. 19; David (1993), p. 28 (arguing that scientific and technological knowledge is ‘cumulative and interactive [and] grows by increments, with each advance building on […] previous findings in complicated and often unpredictable ways’).

  154. 154.

    OECD (2017), p. 11 (defining wasteful spending as costs that could have been avoided by cheaper substitutes with identical or better benefits).

  155. 155.

    Pharmaceutical sector inquiry report (n 7), p. 395.

  156. 156.

    European Commission (21 Jul 2012) Commission recommendation of 17 July 2012 on access to and preservation of scientific information, 2012/417/EU. OJ L 194/39.

  157. 157.

    ibid rec 10.

  158. 158.

    ibid rec 6, 10.

  159. 159.

    EMA. Clinical data publication. http://www.ema.europa.eu/ema/?curl=pages/special_topics/general/general_content_000555.jsp. Accessed 26 Mar 2021.

  160. 160.

    Levin (1988), p. 427 (noting in the chemical and drug industries ‘innovations […] stand alone as isolated discoveries’).

  161. 161.

    Cefis et al. (2006), p. 164.

  162. 162.

    Garavaglia et al. (2006), p. 244.

  163. 163.

    See Orsenigo et al. (2006), p. 416 (emphasising that the role of science is ‘more direct and immediate in pharmaceuticals than in most other technologies’).

  164. 164.

    See e.g. British Pharmacological Society. Pharmacology skills for drug discovery, p. 4. https://thebiologist.rsb.org.uk/images/Pharmacology_Skills_for_Drug_Discovery.pdf. Accessed 26 Mar 2021; Achilladelis and Antonakis (2001), p. 571.

  165. 165.

    Henderson and Cockburn (1996).

  166. 166.

    Taniguchi et al. (2008), p. 63. On the types of unintended effects, see Kim et al. (2016), p. 399.

  167. 167.

    Khan (2014), p. 397 ff.

  168. 168.

    Walsh et al. (2003), p. 281.

  169. 169.

    Gøtzsche (2012), p. 237.

  170. 170.

    ibid.

  171. 171.

    Chapter 3 at Sect. 3.4.2.

  172. 172.

    EMA (19 Mar 2013) Concept paper on extrapolation of efficacy and safety in medicine development. EMA/129698/2012.

  173. 173.

    Research: increasing value, reducing waste. http://www.thelancet.com/series/research. Accessed 26 Mar 2021.

  174. 174.

    Chalmers and Glasziou (2009), p. 88.

  175. 175.

    ibid p. 87. See also Flohr and Weidinger (2016), p. 1930 (pointing out that an ‘unnecessarily large number of vehicle-controlled studies’ have been conducted in the field of atopic eczema (with further references)).

  176. 176.

    For an overview and discussion, see Kim and Hasford (2020).

  177. 177.

    Flohr and Weidinger (2016), p. 1931.

  178. 178.

    See Stoney and Johnson (2018), p. 251 (emphasising that understanding the state of scientific knowledge in a particular therapeutic area is key in trial design).

  179. 179.

    See Massaro (2009), p. 46 (noting that one must have

    a reasonable assumption […] to ensure an adequate sample size and sufficient power. Hopefully there are previous exploratory or pilot studies or previously published data that the researchers can use to make a reasonable assumption of the true effect size. If not, the researchers are left with no choice but to make a “best guess” at the effect size (emphasis added).

    See also The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) (1998) ICH harmonised tripartite guideline. Statistical principles for clinical trials. E9, p. 5 (recommending to use ‘a reliable and validated variable with which experience has been gained either in earlier studies or in published literature’ (emphasis added)); Stoney and Johnson (2018), p. 215; Cleophas et al. (2006), p. 1.

  180. 180.

    Tierney et al. (2015).

  181. 181.

    Gøtzsche (2012), p. 237 (observing that ‘[w]hen 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’).

  182. 182.

    Jaffe (1998), p. 11.

  183. 183.

    See Chap. 3 at Sect. 3.3.4.

  184. 184.

    See e.g. Gustafsson (2010), p. 941 (referring to situations where ‘commercial sponsors […] have lost interest supporting academic trialists in pursuing posttrial studies or even have attempted to discourage them from doing so, particularly if the original hypothesis was rejected’).

  185. 185.

    Case T-718/15 R PTC Therapeutics International v EMA [2016] ECLI:EU:T:2016:425, para 92 (emphasis added).

  186. 186.

    Nevitt et al. (2017).

  187. 187.

    Below at Sect. 8.3.5 in this chapter.

  188. 188.

    Mitscher (2002), p. 31.

  189. 189.

    See e.g. USGAO (2006), p. 87 (reporting the failure rates in human clinical trials due to the lack of safety or efficacy: 82% during the period between 1996 and 1999 and 91% during the period between 2000 and 2003).

  190. 190.

    Nightingale and Mahdi (2006), p. 81. See also National Research Council of the National Academies (2010), p. 126 (defining the process of treatment discovery as ‘an iterative process of studying a disease, hypothesizing and developing treatments, evaluating those treatments, and, for successful treatments, further refining the indication to account for lack of efficacy or toxicities (or both) in particular subgroups of patients’). Furthermore, the report points out that ‘the scientific development of a particular treatment indication is [as a rule] connected with that of other treatments, and thus it may be difficult to identify the exact process that led to the adoption of some treatment’. ibid.

  191. 191.

    Pammolli et al. (2011), p. 428. See also Pharmaceutical sector inquiry report (n 7), p. 395 (finding that the overlaps in drug R&D projects are common because most companies direct R&D efforts at the unmet needs).

  192. 192.

    See e.g. Dosi and Mazzucato (2006), p. 3; Garavaglia et al. (2006), p. 238. See generally Drews (2000).

  193. 193.

    OECD (2004), p. 44. See also Dosi and Mazzucato (2006), p. 3.

  194. 194.

    Khan (2014), p. 497.

  195. 195.

    ibid.

  196. 196.

    ibid.

  197. 197.

    ibid.

  198. 198.

    ibid.

  199. 199.

    ibid.

  200. 200.

    Complementarity between confirmatory and exploratory analyses constitutes a general principle of scientific research. See e.g. David (2003), p. 19.

  201. 201.

    Mueller and Frenzel (2015), p. 73.

  202. 202.

    In this regard, patents for drug improvement have derived criticism for reducing the incentive to invest in the development of pioneering drug. See Hollis A (13 Dec 2004) Me-too drugs: is there a problem? p. 3. https://www.who.int/intellectualproperty/topics/ip/Me-tooDrugs_Hollis1.pdf. Accessed 26 Mar 2021.

  203. 203.

    Foray (2004), p. 169; Cockburn and Henderson (1994), p. 484 (noting that ‘free entry into R&D competition [can] result in overinvestment relative to both the private or social optima’). See also Chap. 7 at Sect. 7.2.3.3, subheading ‘The ‘Exhaustion’ Externality or the ‘Stepping-on-Toes’ Effect’.

  204. 204.

    Cockburn and Henderson (1994), p. 482; Pharmaceutical sector inquiry report (n 7), p. 57. As noted earlier, patents are generally viewed as a strong incentive in the drug industry. See Chap. 5 Sect. 5.2.4.

  205. 205.

    Cockburn and Henderson (1994), p. 486. See also Foray (2004), p. 171.

  206. 206.

    Cockburn and Henderson (1994), p. 484 (highlighting the finding of the literature on firms’ strategic interaction that ‘free entry into R&D competition [can] result in overinvestment relative to both the private or social optima’).

  207. 207.

    Linge (2008), p. 214.

  208. 208.

    See Chap. 7 at Sect. 7.2.3.3, subheading ‘The ‘Exhaustion’ Externality or the ‘Stepping-on-Toes’ Effect’.

  209. 209.

    Cornes and Sandler (1999), p. 8.

  210. 210.

    ibid.

  211. 211.

    See e.g. Nelson (2009), p. 10 (observing that ‘understandings won from any R&D effort have public good properties […] in the sense that use by one party does not reduce the stock of understanding that might be used by another’); Reichman (2009), p. 51 (arguing 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’).

  212. 212.

    David (2003), p. 20 (observing that the ‘re-use of the information will neither deplete it nor impose further costs’).

  213. 213.

    On intermediate non-rivalrous goods, see Chap. 7 at Sect. 7.1.1.2.

  214. 214.

    Walsh et al. (2003), p. 332 (referring to the ‘rival-in-use’ research tools as tools ‘primarily used to develop innovations that will compete with one another in the marketplace’).

  215. 215.

    ibid (referring to a receptor responsible for a particular disease as an example of ‘rivalrous’ research tools). Once one firm identifies a compound that blocks such receptor, ‘it undermines the ability of another to profit from [the] compound that blocks the same receptor’. In contrast, non-rivalrous research tools tend to be of the ‘general purpose’ nature, in the sense of being utilised across various therapeutic areas, e.g. genomics databases, DNA chips, recombinant DNA technology, etc. ibid p. 323.

  216. 216.

    Henderson and Cockburn (1996), p. 36. See Chap. 7 at Sect. 7.2.3.3, subheading ‘The ‘Exhaustion’ Externality or the ‘Stepping-on-Toes’ Effect’.

  217. 217.

    Denicolo and Franzoni (2012), p. 120 (emphasis added).

  218. 218.

    ibid (also referring to such situation as ‘a common pool problem in innovation races’).

  219. 219.

    The economic relevance of knowledge is arguably an integral characteristic of the rivalry of goods.

  220. 220.

    See Blass (2015), p. 455 (defining duplicative efforts as the research programs ‘focused on the same macromolecular target’). See also Ben-Asher (2000), p. 232 (noting, in the context of the assessment of drug mergers, that the ‘straightforward and wasteful duplication in medical research leading to welfare loss is unlikely’); Cockburn and Henderson (1994), p. 484 (observing that ‘competing [drug R&D] projects may well be complementary [and] similar research can lead to related but significantly different outcomes’); Carrier (2009), p. 305 ff.

  221. 221.

    Kerber (2010), p. 184 (with further references).

  222. 222.

    On the resource allocation to research in general, see Dasgupta and Maskin (1987), p. 582 (arguing that parallel research does not necessarily imply waste and, since ‘the outcome of any research project is uncertain, it is generally in society’s interest to hold a portfolio of active projects on any scientific or technological problem’). Concerning drug R&D, see Ben-Asher (2000), p. 317; Pammolli et al. (2011), p. 437 (arguing that ‘parallel R&D along similar trajectories […] should not necessarily be considered as wasteful duplication or imitation’).

  223. 223.

    As emphasised by Carrier,

    where the merging firms only have products in preclinical development, the staggering odds that either one would reach the market, let alone both, counsels the agencies not to challenge the merger. Nor does a merger between a firm with a product in advanced trials […] and one in preclinical development raise concern. Even if […] those two firms are ‘closest’ to the market, the improbability that the latter will ever reach the market reduces concern.

    Carrier (2009), pp. 305–306.

  224. 224.

    See Foray (2004), p. 57 (noting that efficiency gains arising from collaborative research include ‘sharing research costs and avoiding duplicative projects; the benefit to be harnessed from creating larger pools of knowledge, which in turn generate greater variances from which more promising avenues of research can be selected; and the economic gains to be generated from division of labor in research activities’. See Kerber and Schwalbe (2008), para 1-8-368 (observing that ‘it is not clear-cut that the reduction of parallel R&D tracks is always efficient’). They argue, in the context of merger assessment, that ‘in some situations, where the firms prior to a merger might be involved in a patent race, a reduction in R&D efforts is likely to be justified on the efficiency grounds, while in other situations, it might be more beneficial if the merging firms keep own research activities, especially, if this increases the probability of making a certain invention’). See also Link (2007), p. 135; OECD (2013) The role of efficiency claims in antitrust proceedings. DAF/COMP(2012)23, p. 15.

  225. 225.

    Gustafsson et al. (2010).

  226. 226.

    Above (n 215) and the accompanying text.

  227. 227.

    As discussed in Chap. 3, exploratory data analysis often requires aggregating datasets from multiple trials.

  228. 228.

    Gustafsson et al. (2010), p. 938. For an overview of the types of IPD analysis, see Chap. 3.

  229. 229.

    On the role of multiplicity and diversity of experimentation in evolutionary economics, see Chap. 7 at Sect. 7.2.3.3, subheading ‘Duplicative Research v Multiplicity and Diversity of Experimentation’.

  230. 230.

    On this issue, see Chap. 7 at Sect. 7.2.3.3, subheading ‘The (Controversial) Role of Patents as a Means to Coordinate Research Efforts’.

  231. 231.

    For a detailed analysis, see Chap. 9 at Sect. 9.3.1.

  232. 232.

    Tierney et al. (2015).

  233. 233.

    Jones et al. (2013). Given the potential of systematic reviews to optimise the design of subsequent trials, the authors proposed that special guidelines for applicants and funders should be developed ‘to optimise delivery of new studies informed by the most up-to-date evidence base and to minimise waste in research’).

  234. 234.

    Goudie (2010).

  235. 235.

    ibid p. 984.

  236. 236.

    Storz-Pfennig (2017).

  237. 237.

    ibid p. 61.

  238. 238.

    See Eisenberg (2011), p. 487 (suggesting that regulatory 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’).

  239. 239.

    Drahos and Braithwaite (2002), p. 13.

  240. 240.

    Ben-Asher (2000), p. 292. See generally Jones and Williams (2000).

  241. 241.

    Ben-Asher (2000), p. 232 (emphasis added). See also Carrier (2009), p. 298 (noting that more R&D ‘does not necessarily result in more innovation’ (with further references)); Scherer (1993), p. 111 (noting that ‘the conditions for determining the socially optimal R&D program are too complex to reach a confident judgment as to whether the market has overshot or undershot’); Henderson and Cockburn (1996), p. 55 (noting that ‘determining the size and shape of the optimal research portfolio requires solving a complex, nonlinear constrained optimization problem whose parameters are not fully known’); Cockburn and Henderson (1994), p. 487 (observing that economic models of competition by R&D investment ‘are fundamentally indeterminate: competitive industries may invest too much, too little or just about the right amount in research’); Rapp (1995), p. 33 (submitting that ‘[i]n fact, there is no functional relationship between the level of R&D expenditure and the level of innovation the market level’).

  242. 242.

    Dasgupta and David (1987), p. 532.

  243. 243.

    Hollis A (13 Dec 2004) Me-too drugs: is there a problem? p. 4 (noting that ‘[t]he more differentiated the me-too product is from the pioneer, the greater the likelihood that the social value of having more product diversity will compensate for the harm to the incentive for pioneering R&D’). https://www.who.int/intellectualproperty/topics/ip/Me-tooDrugs_Hollis1.pdf. Accessed 26 Mar 2021.

  244. 244.

    Spence (1984), pp. 101–122.

  245. 245.

    See Chap. 7 at Sect. 7.2.3.1. For a discussion in the context of drug R&D, see Cockburn and Henderson (1994), pp. 482 ff.

References

  • Achilladelis B, Antonakis N (2001) The dynamics of technological innovation: the case of the pharmaceutical industry. Res Policy 30(4):535–588

    CrossRef  Google Scholar 

  • Ahn H (2014) Second generation patents in pharmaceutical innovation. Nomos, Baden-Baden

    CrossRef  Google Scholar 

  • Akerlof GA (1970) The markets for ‘lemons’: qualitative uncertainty and the market mechanism. Q J Econ 84(3):488–500

    CrossRef  Google Scholar 

  • Andrade LF, Sermet C, Pichetti S (2016) Entry time effects and follow-on drug competition. Eur J Health Econ 17:45–60. https://doi.org/10.1007/s10198-014-0654-9

    CrossRef  Google Scholar 

  • Antonelli C (2017) Endogenous innovation. The economics of an emergent system property. Edward Elgar Publishing, Cheltenham

    Google Scholar 

  • Ben-Asher D (2000) In need of treatment? Merger control, pharmaceutical innovation, and consumer welfare. J Leg Med 21(3):271–349. https://doi.org/10.1080/01947640050174813

    CrossRef  Google Scholar 

  • Blass B (2015) Basic principles of drug discovery and development. Elsevier, Amsterdam

    Google Scholar 

  • Bone RG (1998) A new look at trade secret law: doctrine in search of justification. Calif Law Rev 86(2):241–313

    CrossRef  Google Scholar 

  • Bonini S et al (2014) Transparency and the European Medicines Agency--sharing of clinical trial data. N Engl J Med 371(26):2452–2455. https://doi.org/10.1056/NEJMp1409464

    CrossRef  Google Scholar 

  • Brandt-Rauf S (2003) Biomedical research. In: Esanu JM, Uhlir PF (eds) The role of scientific and technical data and information in the public domain: proceedings of a symposium. The National Academies Press, Washington DC, pp 65–72

    Google Scholar 

  • Brody T (2016) Clinical trials: study design, endpoints and biomarkers, drug safety, and FDA and ICH Guidelines, 2nd edn. Elsevier, Amsterdam

    Google Scholar 

  • Browner WS, Newman TB, Hulley SB (2007) Getting ready to estimate sample size: hypotheses and underlying principles. In: Hulley SB et al (eds) Designing clinical research, 3rd edn. Wolters Kluwer Health, Philadelphia, pp 51–64

    Google Scholar 

  • Buchanan JM, Yoon YJ (2000) Symmetric tragedies: commons and anticommons. J Law Econ 43:1–13

    CrossRef  Google Scholar 

  • Carrier MA (2008) Two puzzles resolved: of the Schumpeter–Arrow stalemate and pharmaceutical innovation markets. Iowa Law Rev 93:393

    Google Scholar 

  • Carrier MA (2009) Innovation for the 21st century. OUP, Oxford

    CrossRef  Google Scholar 

  • Cefis E, Ciccarelli M, Orsenigo L (2006) Heterogeneity and firm growth in the pharmaceutical industry. In: Mazzucato M, Dosi G (eds) Knowledge accumulation and industry evolution: the case of pharma-biotech. CUP, Cambridge, pp 163–207

    CrossRef  Google Scholar 

  • Chalmers I, Glasziou P (2009) Avoidable waste in the production and reporting of research evidence. Lancet 374(9683):86–89. https://doi.org/10.1016/S0140-6736(09)60329-9

    CrossRef  Google Scholar 

  • Cleophas TJ, Zwinderman AH, Cleophas TF (2006) Statistics applied to clinical trials, 3rd edn. Springer, Dordrecht

    CrossRef  Google Scholar 

  • Clissold SP, Heel RC (1987) Topical minoxidil. A preliminary review of its pharmacodynamic properties and therapeutic efficacy in alopecia areata and alopecia androgenetica. Drugs 33(2):107–122. https://doi.org/10.2165/00003495-198733020-00002

    CrossRef  Google Scholar 

  • Cockburn I, Henderson R (1994) Racing to invest? The dynamics of competition in ethical drug discovery. J Econ Manag Strategy 3(3):481

    CrossRef  Google Scholar 

  • Cohen WM (2010) Fifty years of empirical studies of innovative activity and performance. In: Hall BH, Rosenberg N (eds) Handbook of the economics of innovation, vol 1. Elsevier, Amsterdam, pp 129–213

    CrossRef  Google Scholar 

  • Cornes R, Sandler T (1999) The theory of externalities, public goods, and club goods. CUP, Cambridge

    Google Scholar 

  • Correa CM (2015) Guidelines for pharmaceutical patent examination: examining pharmaceutical patents from a public health perspective. UNDP, Geneva

    Google Scholar 

  • Dasgupta P, David PA (1987) Information disclosure and the economics of science and technology. In: Feiwel GR (ed) Arrow and the ascent of modern economic theory. Palgrave Macmillian, London, pp 519–542

    CrossRef  Google Scholar 

  • Dasgupta P, Maskin E (1987) The simple economics of research portfolios. Econ J 97(387):581–595

    CrossRef  Google Scholar 

  • David P (2003) The economic logic of ‘open science’ and the balance between private property rights and the public domain in scientific data and information: a primer. In: Esanu JM, Uhlir PF (eds) The role of scientific and technical data and information in the public domain: proceedings of a symposium. The National Academies Press, Washington DC, pp 19–34

    Google Scholar 

  • David PA (1993) Intellectual property institutions and the panda’s thumb: patents, copyrights, and trade secrets in economic theory and history. In: Wallerstein MB, Mogee ME, Schoen RA (eds) Global dimensions of intellectual property rights in science and technology. National Academy Press, Washington DC, pp 19–61

    Google Scholar 

  • Denicolo V, Franzoni LA (2012) Weak intellectual property rights, research spillovers, and the incentive to innovate. Am Law Econ Rev 14(1):111–140

    CrossRef  Google Scholar 

  • DiMasi JA, Faden LB (2011) Competitiveness in follow-on drug R&D: a race or imitation? Nat Rev Drug Discov 10(1):23–27. https://doi.org/10.1038/nrd3296

    CrossRef  Google Scholar 

  • DiMasi JA, Paquette C (2004) The economics of follow-on drug research and development: trends in entry rates and the timing of development. Pharmaeconomics 22(2):1–14

    CrossRef  Google Scholar 

  • Dosi G, Mazzucato M (2006) Introduction. In: Dosi G, Mazzucato M (eds) Knowledge accumulation and industry evolution. The case of pharma-biotech. CUP, Cambridge, New York, pp 1–18

    Google Scholar 

  • Drahos P, Braithwaite J (2002) Information feudalism. Who owns the knowledge economy? Earthscan Publications, London

    Google Scholar 

  • Drews J (2000) Drug discovery: a historical perspective. Science 17(287):1960–1964

    CrossRef  Google Scholar 

  • Drexl J (2012) Anti-competitive stumbling stones on the way to a cleaner world: protecting competition in innovation without a market. J Compet Law Econ 8(3):507–542. https://doi.org/10.1093/joclec/nhs019

    CrossRef  Google Scholar 

  • Edquist C (2005) Systems of innovation approaches – their emergence and characteristics. In: Edquist C (ed) Systems of innovation. Technologies, institutions and organizations. Routledge, London, pp 1–35

    Google Scholar 

  • Eisenberg RS (2007) The role of the FDA in innovation policy. Mich Telecomm Tech Law Rev 13:345–388

    Google Scholar 

  • Eisenberg RS (2011) Secrecy in the age of regulatory exclusivity. In: Dreyfuss RC, Strandburg KJ (eds) The law and theory of trade secrecy. Edward Elgar Publishing, Cheltenham, pp 467–491

    Google Scholar 

  • Elling J, Lin D (2001) A taxonomy of dynamic competition theories. In: Elling J (ed) Dynamic competition and public policy. CUP, Cambridge, pp 16–44

    CrossRef  Google Scholar 

  • Fatur A (2012) EU competition law and the information and communication technology network industries: economic versus legal concepts in pursuit of (consumer) welfare. Hart, Oxford

    Google Scholar 

  • Flohr C, Weidinger S (2016) Research waste in atopic eczema trials-just the tip of the iceberg. J Invest Dermatol 136(10):1930–1933. https://doi.org/10.1016/j.jid.2016.06.614

    CrossRef  Google Scholar 

  • Foray D (2004) Economics of knowledge. MIT Press, Cambridge

    CrossRef  Google Scholar 

  • Frost J, Morner M (2010) Overcoming knowledge dilemmas: governing the creation, sharing and use of knowledge resources. Int J Strateg Change Manag 2(2/3):172–199

    CrossRef  Google Scholar 

  • Gambardella A (1995) Science and innovation: the US pharmaceutical industry during the 1980’s. CUP, Cambridge

    CrossRef  Google Scholar 

  • Garavaglia C, Malerba F, Orsenigo L (2006) Entry, market structure, and innovation in a ‘history-friendly’ model of the evolution of the pharmaceutical industry. In: Mazzucato M, Dosi G (eds) Knowledge accumulation and industry evolution: the case of pharma-biotech. CUP, Cambridge, pp 234–265

    CrossRef  Google Scholar 

  • Gauch RR (2009) It’s great! Oops, no it isn’t: why clinical research can’t guarantee the right medical answers. Springer, Dordrecht

    Google Scholar 

  • Glader M (2006) Innovation markets and competition analysis. EU competition law and US antitrust law. Edward Elgar, Cheltenham

    CrossRef  Google Scholar 

  • Gøtzsche PC (2012) Strengthening and opening up health research by sharing our raw data. Circ Cardiovasc Qual Outcomes 5(2):236–237. https://doi.org/10.1161/CIRCOUTCOMES.112.965277

    CrossRef  Google Scholar 

  • Goudie AC (2010) Empirical assessment suggests that existing evidence could be used more fully in designing randomized controlled trials. J Clin Epidemiol 63(9):983–991. https://doi.org/10.1016/j.jclinepi.2010.01.022

    CrossRef  Google Scholar 

  • Gustafsson F et al (2010) Maximizing scientific knowledge from randomized clinical trials. Am Heart J 159(6):937–943. https://doi.org/10.1016/j.ahj.2010.03.002

    CrossRef  Google Scholar 

  • Hall BH, Harhoff D (2012) Recent research on the economics of patents. Annu Rev Econ 4:541–565

    CrossRef  Google Scholar 

  • Hall BH, Mairesse J, Mohnen P (2010) Measuring the returns to R&D. In: Hall BH, Rosenberg N (eds) Handbook of the economics of innovation, vol 2. Elsevier, Amsterdam, pp 1034–1082

    Google Scholar 

  • Hardin G (1968) The tragedy of the commons. Science 162:1243–1248

    Google Scholar 

  • Heller MA (1998) The tragedy of the anticommons: property in the transition from Marx to markets. Harv Law Rev 111(3):621–688

    CrossRef  Google Scholar 

  • Heller MA (2011) The anticommons lexicon. In: Ayotte K, Smith HE (eds) Research handbook on the economics of property law. Edward Elgar Publishing, Cheltenham, pp 57–57

    Google Scholar 

  • Heller MA, Eisenberg RS (1998) Can patents deter innovation? The anticommons in biomedical research. Science 280(5364):698–701. https://doi.org/10.1126/science.280.5364.698

    CrossRef  Google Scholar 

  • Henderson R, Cockburn I (1996) Scale, scope, and spillovers: the determinants of research productivity in drug discovery. RAND J Econ 27(1):32–59

    CrossRef  Google Scholar 

  • Hess C, Ostrom E (2007) Introduction: an overview of the knowledge commons. In: Hess C, Ostrom E (eds) Understanding knowledge as a commons: from theory to practice. MIT Press, Cambridge, pp 3–26

    Google Scholar 

  • Hughes JP et al (2011) Principles of early drug discovery. Br J Pharmacol 162(6):1239–1249. https://doi.org/10.1111/j.1476-5381.2010.01127.x

    CrossRef  Google Scholar 

  • Huque M, Röhmel J (2010) Multiplicity problems in clinical trials: a regulatory perspective. In: Dmitrienko A, Tamhane AC, Bretz F (eds) Multiple testing problems in pharmaceutical statistics. Taylor & Francis Group, Boca Raton, pp 1–34

    Google Scholar 

  • Institute of Medicine of the National Academies (2015) Sharing clinical trial data: maximizing benefits, minimizing risk. The National Academies Press, Washington DC

    Google Scholar 

  • Jaffe AB (1998) The importance of ‘spillovers’ in the policy mission of the advanced technology program. J Technol Transf 23(2):11–19

    CrossRef  Google Scholar 

  • Jain KK (2015) Textbook personalized medicine, 2nd edn. Humana Press, Springer, New York, Heidelberg

    CrossRef  Google Scholar 

  • Johnson B (2005) Systems of innovation: overview and basic concepts. In: Edquist C (ed) Systems of innovation. Technologies, institutions and organizations. Routledge, London, pp 36–49

    Google Scholar 

  • Jones A, Sufrin B (2016) EU competition law: text, cases, and materials, 6th edn. OUP, Oxford

    Google Scholar 

  • Jones AP et al (2013) The use of systematic reviews in the planning, design and conduct of randomised trials: a retrospective cohort of NIHR HTA funded trials. BMC Med Res Methodol 13:50. https://doi.org/10.1186/1471-2288-13-50

    CrossRef  Google Scholar 

  • Jones CI, Williams JC (2000) Too much of a good thing? The economics of investment in R&D. J Econ Growth 5:65

    CrossRef  Google Scholar 

  • Käseberg T (2011) Intellectual property, antitrust and cumulative innovation in the EU and the US. Hart Publishing, Oxford

    Google Scholar 

  • Katz A (2007) Pharmaceutical lemons: innovation and regulation in the drug industry. Mich Telecomm Tech Law Rev 14:1–43

    Google Scholar 

  • Kerber W (2010) Competition, innovation and maintaining diversity through competition law. In: Drexl J, Kerber W, Podszun R (eds) Competition policy and the economic approach: foundations and limitations. Edward Elgar, Cheltenham, pp 173–201

    Google Scholar 

  • Kerber W, Schwalbe U (2008) Economic foundations of competition law. In: Hirsch G, Montag F, Säcker FJ (eds) Competition law: European Community practice and procedure. Article-by-article commentary of the EC competition law. Sweet & Maxwell, London, pp 202–392

    Google Scholar 

  • Khan SA (2014) Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis. Bioinformatics 30(17):i497–i504. https://doi.org/10.1093/bioinformatics/btu456

    CrossRef  Google Scholar 

  • Kim D et al (2016) Predicting unintended effects of drugs based on off-target tissue effects. Biochem Biophys Res Commun 469(3):399–404. https://doi.org/10.1016/j.bbrc.2015.11.095

    CrossRef  Google Scholar 

  • Kim D, Hasford J (2020) Redundant trials can be prevented, if the EU clinical trial regulation is applied duly. BMC Med Ethics 21:107. https://doi.org/10.1186/s12910-020-00536-9

    CrossRef  Google Scholar 

  • King RF, Major I, Marian CG (2016) Confusions in the anticommons. J Polit Law 9(7):64–79

    CrossRef  Google Scholar 

  • Kirwan JR (1997) Making original data from clinical studies available for alternative analysis. J Rheumatol 24(5):822–825

    Google Scholar 

  • Kwerel ER (1980) Economic welfare and the production of information by a monopolist: the case of drug testing. Bell J Econ 11(2):505–518

    CrossRef  Google Scholar 

  • Lanthier M et al (2013) An improved approach to measuring drug innovation finds steady rates of first-in-class pharmaceuticals, 1987-2011. Health Aff (Millwood) 32(8):1433–1439. https://doi.org/10.1377/hlthaff.2012.0541

    CrossRef  Google Scholar 

  • Lemmens T (2004) Leopards in the temple: restoring scientific integrity to the commercialized research scene. J Law Med Ethics 32(4):641–657. https://doi.org/10.1111/j.1748-720X.2004.tb01969.x

    CrossRef  Google Scholar 

  • Levin RC (1988) Appropriability, R&D spending, and technological performance. Am Econ Rev 78(2):424–428

    Google Scholar 

  • Linge G (2008) Competition policy, innovation, and diversity. Tectum-Verlag, Marburg

    Google Scholar 

  • Link AN (2007) Public policy and entrepreneurship. In: Audretsch DB, Grilo I, Thurik AR (eds) Handbook of research on entrepreneurship policy. Edward Elgar, Cheltenham, pp 131–139

    Google Scholar 

  • Long C (2000) Patents and cumulative innovation. Wash Univ J Law Policy 2:229–246

    Google Scholar 

  • Major I, King RF, Marian CG (2016) Anticommons, the Coase Theorem, and the problem of bundling inefficiency. Int J Commons 10:244–264

    CrossRef  Google Scholar 

  • Massaro J (2009) Experimental design. In: Robertson D, Williams GH (eds) Clinical and translational science: principles of human research: principles of human research. Elsevier, Amsterdam, pp 41–58

    Google Scholar 

  • Mattioli M (2017) The data-pooling problem. Berkley Technol Law J 32(2):179–236

    Google Scholar 

  • Meinert CL (2012) Clinical trials: design, conduct and analysis, 2nd edn. OUP, Oxford, New York

    Google Scholar 

  • Merges RP, Nelson RR (1990) On the complex economics of patent scope. Columbia Law Rev 90(4):839–916

    CrossRef  Google Scholar 

  • Merson L, Gaye O, Guerin PJ (2016) Avoiding data dumpsters - toward equitable and useful data sharing. N Engl J Med 374(25):2414–2415. https://doi.org/10.1056/NEJMp1605148

    CrossRef  Google Scholar 

  • Mitscher LA (2002) Drug design and discovery: an overview. In: Krogsgaard-Larsen P, Liljefors T, Madsen U (eds) Textbook of drug design and discovery, 3rd edn. CRC Press, Boca Raton, pp 1–36

    Google Scholar 

  • Moyé LA (2003) Multiple analyses in clinical trials. Fundamentals for investigators. Springer, New York

    CrossRef  Google Scholar 

  • Mueller MT, Frenzel A (2015) Competitive pricing within pharmaceutical classes: evidence on ‘follow-on’ drugs in Germany 1993-2008. Eur J Health Econ 16(1):73–82. https://doi.org/10.1007/s10198-013-0555-3

    CrossRef  Google Scholar 

  • Murray F, Stern S (2007) Do formal intellectual property rights hinder the free flow of scientific knowledge? An empirical test of the anti-commons hypothesis. J Econ Behav Organ 63:648–687. https://doi.org/10.1016/j.jebo.2006.05.017

    CrossRef  Google Scholar 

  • National Research Council of the National Academies (2010) The prevention and treatment of missing data in clinical trials. National Academies Press, Washington DC

    Google Scholar 

  • Nelson RR (2009) Building effective ëinnovation systemsí versus dealing with ëmarket failuresí as ways of thinking about technology policy. In: Foray D (ed) The new economics of technology policy. Edward Elgar Publishing, Cheltenham, pp 7–16

    Google Scholar 

  • Nevitt SJ et al (2017) Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic review. BMJ 357:j1390. https://doi.org/10.1136/bmj.j1390

    CrossRef  Google Scholar 

  • Nightingale P, Mahdi S (2006) The evolution of pharmaceutical innovation. In: Mazzucato M, Dosi G (eds) Knowledge accumulation and industry evolution: the case of pharma-biotech. CUP, Cambridge, pp 73–111

    CrossRef  Google Scholar 

  • OECD (2004) Innovation in the knowledge economy. Implications for education and learning. OECD Publishing, Paris

    CrossRef  Google Scholar 

  • OECD (2017) Tackling wasteful spending on health. OECD Publishing

    CrossRef  Google Scholar 

  • OECD, Eurostat (2005) Oslo Manual. Guidelines for collecting and interpreting innovation data, 3rd edn. OECD Publishing, Paris

    CrossRef  Google Scholar 

  • Orsenigo L, Dosi G, Mazzucato M (2006) The dynamics of knowledge accumulation, regulation, and appropriability in the pharma-biotech sector: policy issues. In: Mazzucato M, Dosi G (eds) Knowledge accumulation and industry evolution: the case of pharma-biotech. CUP, Cambridge, pp 402–431

    CrossRef  Google Scholar 

  • Ostrom E (2008) Governing the commons: the evolution of institutions for collective action. CUP, Cambridge

    Google Scholar 

  • Pammolli F, Magazzini L, Riccaboni M (2011) The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discov 10(6):428–438. https://doi.org/10.1038/nrd3405

    CrossRef  Google Scholar 

  • Parisi F, Schultz N, Depoorter B (2004) Simultaneous and sequential anticommons. Eur J Econ 17:175–190

    CrossRef  Google Scholar 

  • Petrova E (2014) Innovation in the pharmaceutical industry: the process of drug discovery and development. In: Ding M, Eliashberg J, Stremersch S (eds) Innovation and marketing in the pharmaceutical industry. Springer, New York, pp 19–81

    CrossRef  Google Scholar 

  • Rapp RT (1995) The misapplication of the innovation market approach to merger analysis. Antitrust Law J 64(1):19–47

    Google Scholar 

  • Reichman JH (2009) Rethinking the role of clinical trial data in international intellectual property law: the case for a public goods approach. Marquette Intellect Prop Law Rev 13(1):1–68

    Google Scholar 

  • Reinganum JF (1981) Dynamic games of innovation. J Econ Theory 25(1):21–41

    CrossRef  Google Scholar 

  • Scherer FM (1993) Prices, profits and technological progress in the pharmaceutical industry. J Econ Perspect 7(3):97–115

    CrossRef  Google Scholar 

  • Schulz N, Parisi F, Depoorter B (2001) Fragmentation in property: towards a general model. J Inst Theor Econ 158:594–613

    CrossRef  Google Scholar 

  • Schumpeter JA (1950) Capitalism, socialism and democracy. Harper, New York

    Google Scholar 

  • Scotchmer S (1991) Standing on the shoulders of giants: cumulative research and the patent law. J Econ Perspect 5(1):29–41

    CrossRef  Google Scholar 

  • Scotchmer S (2004) Innovation and incentives. MIT Press, Cambridge

    Google Scholar 

  • Senn S (2007) Statistical issues in drug development, 2nd edn. John Wiley & Sons, Hoboken

    CrossRef  Google Scholar 

  • Simsek M (2018) Finding hidden treasures in old drugs: the challenges and importance of licensing generics. Drug Discov Today 23(1):17–21. https://doi.org/10.1016/j.drudis.2017.08.008

    CrossRef  Google Scholar 

  • Spence M (1984) Cost reduction, competition, and industry performance. Econometric Soc 52(1):101–122

    CrossRef  Google Scholar 

  • Stewart RB (1981) Regulation, innovation, and administrative law: a conceptual framework. Calif Law Rev 69(5):1256–1377

    CrossRef  Google Scholar 

  • Stoney CM, Johnson LL (2018) Design of clinical trials and studies. In: Gallin JI, Ognibene FP, Johnson LL (eds) Principles and practice of clinical research, 4th edn. Academic Press, London, pp 250–268

    Google Scholar 

  • Storz-Pfennig P (2017) Potentially unnecessary and wasteful clinical trial research detected in cumulative meta-epidemiological and trial sequential analysis. J Clin Epidemiol 82:61–70. https://doi.org/10.1016/j.jclinepi.2016.11.003

    CrossRef  Google Scholar 

  • Sydes MR et al (2015) Sharing data from clinical trials: the rationale for a controlled access approach. Trials 16:104. https://doi.org/10.1186/s13063-015-0604-6

    CrossRef  Google Scholar 

  • Taniguchi CM et al (2008) Drug toxicity. In: Golan DE et al (eds) Principles of pharmacology: the pathophysiologic basis of drug therapy, 2nd edn. Wolters Kluwer, Lippincott Williams and Wilkins, Baltimore, pp 63–74

    Google Scholar 

  • Tierney JF et al (2015) How individual participant data meta-analyses have influenced trial design, conduct, and analysis. J Clin Epideiol 68(11):1325–1335. https://doi.org/10.1016/j.jclinepi.2015.05.024

    CrossRef  Google Scholar 

  • USGAO (2006) New drug development: science, business, regulatory, and intellectual property issues cited as hampering drug development efforts. GAO, Washington DC

    Google Scholar 

  • van den Bergh R, Camesasca PD (2001) European competition law and economics: a comparative perspective. Intersentia, Antwerpen

    Google Scholar 

  • Walsh JP, Arora A, Cohen WM (2003) Effects of research tool patents and licensing on biomedical innovation. In: Cohen WM, Merrill SA (eds) Patents in the knowledge-based economy. National Academies Press, Washington DC, pp 285–340

    Google Scholar 

  • Walsh JP, Cho C, Cohen WM (2005) Science and law. View from the bench: patents and material transfers. Science 309(5743):2002–2003. https://doi.org/10.1126/science.1115813

    CrossRef  Google Scholar 

  • Wang RL (2008) Biomedical upstream patenting and scientific research: the case for compulsory licenses bearing research-through royalties. Yale J Law Technol 10(7):251–330

    Google Scholar 

  • Watkins J et al (1979) Reduction of beta-blocking drugs in hypertensive patients treated with minoxidil. BMJ 1(6175):1400. https://doi.org/10.1136/bmj.1.6175.1400

    CrossRef  Google Scholar 

  • Zhou Y (2015) The tragedy of the anticommons in knowledge. Rev Radic Polit Econ 48(1):1–18. https://doi.org/10.1177/0486613415586992

    CrossRef  Google Scholar 

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Kim, D. (2021). IPD as a Research Resource: Exclusively Controlled or Readily Accessible?. In: Access to Non-Summary Clinical Trial Data for Research Purposes Under EU Law. Munich Studies on Innovation and Competition, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-030-86778-2_8

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