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Evaluating Legislative Options

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

Abstract

As concluded in the previous chapter, the conventional innovation-based justifications of exclusive control over R&D results can hardly rationalise treating IPD as an excludable good. Rather, access to IPD and robust secondary analysis should be prioritised. This chapter contemplates legislative means of implementing access to IPD taking into account the pharmaceutical sector specificities. Its starts by revisiting the policy objectives and outlining the main aspects of the access regime. Next, three policy alternatives are examined: (i) no intervention whereby access and usage rights in IPD can be allocated on a contractual basis; (ii) creating a statutory right of access to IPD for research purposes; (iii) providing for an obligation on the trial sponsors to transfer IPD to a centralised repository whereby third-party access would be subject to terms and conditions implementing the necessary safeguards but not subject the authorisation by trial sponsors. The pros and cons of each option are evaluated relative to the policy goals.

Keywords

  • Access
  • Clinical trial data
  • Data sharing
  • EU database
  • EU clinical trials regulation
  • Individual patient-data
  • Innovation incentives
  • Obligation to submit data
  • REACH
  • Revision

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Notes

  1. 1.

    Reg 536/2014/EU, rec 85.

  2. 2.

    European Commission, COM/2014/0442 final.

  3. 3.

    European Commission, COM(2015) 192 final.

  4. 4.

    European Commission, COM(2017) 9 final.

  5. 5.

    European Commission, COM(2018) 232 final.

  6. 6.

    As discussed in Chap. 6 at 6.1.4.

  7. 7.

    See e.g. Institute of Medicine of the National Academies (2015), pp. 27–42; Miller et al. (2019); Hollis et al. (2016); Sudlow et al. (2016); Manamley et al. (2016); Fletcher et al. (2013).

  8. 8.

    As discussed in Chap. 7 at 7.1.1.1, clinical trials can be viewed as an inherently public good prone to the market failure of insufficient incentives.

  9. 9.

    Grootendorst et al. (2011), p. 681.

  10. 10.

    Kaul I (2013) Public goods: a positive analysis. Discussion draft, UNDP Office of Development Studies, p. 17 (emphasis added).

  11. 11.

    As discussed in Chap. 6 at 6.4.2.4.

  12. 12.

    Shapiro (1978); Reichman (2009); Rodwin (2012). See also Lexchin (2012), p. 258 (finding ‘no evidence that any measures that have been taken so far have stopped the biasing of clinical research’ and concluding that ‘[w]hat will be needed to curb and ultimately stop the bias […] is a paradigm change in the way that we treat the relationship between pharmaceutical companies and the conduct and reporting of clinical trials’).

  13. 13.

    Chapter 7 at 7.1.1.2.

  14. 14.

    See e.g. Rose (1986), p. 719 (noting that ‘a governmental body might be the most useful manager where many persons desire access to or control over a given property, but they are too numerous and their individual stakes too small to express their preferences in market transactions’); Smith (2005), p. 93.

  15. 15.

    Mandelkern Group on Better Regulation (13 Nor 2001) Final report, p. 15 (recommending that the ‘do-nothing’ option should be considered among policy alternatives).

  16. 16.

    As shown in Chap. 8.

  17. 17.

    Cornes and Sandler (1999), p. 10.

  18. 18.

    Shavell (2004), p. 108.

  19. 19.

    Coase (1960).

  20. 20.

    de Meza (2002), p. 280 (noting that Coase’s article ‘does enough to show that the impact of the law on resource allocation is by no means as straightforward as it seems and provides many clues as to how to approach the issue’). According to de Meza, Coase’s ‘The Problem of Social Cost’ ‘is cited even more frequently in law journals than in economics journals’. ibid.

  21. 21.

    Lemley (1997), p. 1048 ff; Merges (1994), p. 2664 ff; Rai (1999), p. 839 ff; Merges and Nelson (1990), pp. 876–877.

  22. 22.

    Coase (1960), p. 15.

  23. 23.

    Wallis and Dollery (1999), p. 18.

  24. 24.

    Coase (1960), p. 15.

  25. 25.

    Ibid pp. 15–16 (emphasis added).

  26. 26.

    Ibid.

  27. 27.

    See de Meza (2002), p. 270 (noting that Coase’s ‘conclusion is not that market processes always make regulation unnecessary, but that as transaction costs are normally present, it is necessary to investigate case by case to find the best solution’ (emphasis added)). See also Shavell (2004), p. 108 (observing that the choice of the default legal rules matters as they can produce the socially advantageous outcome ‘directly, reducing the need for parties to bargain and to incur associated transaction costs’).

  28. 28.

    GDPR, art 89. See also Chap. 6 at 6.5.2.1.

  29. 29.

    In some cases, the originator company can license the marketing authorisation dossier to a generic manufacturer (‘authorised generics’).

  30. 30.

    As discussed in Chap. 6 at 6.3.2.

  31. 31.

    See e.g. Data use agreement, para 1.2. http://yoda.yale.edu/data-use-agreement. Accessed 26 Mar 2021. CSDR standard contract template for clinical trial data sharing (10 Apr 2017). https://www.clinicalstudydatarequest.com/Documents/CSDR%20DATA%20SHARING%20AGREEMENT%20Version%201%204.10.2017.pdf. Accessed 26 Mar 2021.

  32. 32.

    For instance, the CSDR initiative aspires to become ‘a leader in the data sharing community inspired to drive scientific innovation and improve medical care by facilitating access to patient-level data from clinical studies’. Our Mission. https://clinicalstudydatarequest.com/About/Mission.aspx. Accessed 26 Mar 2021. The recitals of the standard data-sharing agreement used by Johnson & Johnson, Janssen, Queen Mary University of London and SI-BONE state that access to data is provided ‘for the purpose of promoting Research which will be used to create or materially enhance generalizable scientific and/or medical knowledge to inform science and public health’. Data use agreement. http://yoda.yale.edu/data-use-agreement. Accessed 26 Mar 2021.

  33. 33.

    As discussed in detail in Chap. 5 at 5.1 and Chap. 6 at 6.3.2.

  34. 34.

    Covey (2014), p. 647.

  35. 35.

    See e.g. de Coninck (2011), p. 269 (observing that, ‘even though loss aversion is often presented as one of the most successful explanatory constructs within behavioural economics, it does not allow for predicting under what circumstances these effects are likely to occur or to vary’).

  36. 36.

    Chapter 3 surveys the potential benefits of secondary IPD analysis for medical research and drug R&D.

  37. 37.

    As discussed in Chap. 8 at 8.1.4.4.

  38. 38.

    Hoffman et al. (2002), p. 120.

  39. 39.

    Shavell (2004), p. 108; Polinsky and Shavell (2008), p. 22.

  40. 40.

    Ibid.

  41. 41.

    Mazzoleni and Nelson (1998), p. 280 (emphasis added).

  42. 42.

    Ibid. See also Reichman et al. (2016), p. 151 (observing that, where the prospect of deriving financial gain through using a research resource is unknown, concluding contracts over their use can entail ‘endless amounts of speculation resulting in even higher transaction costs for all the parties’).

  43. 43.

    As discussed in Chap. 3.

  44. 44.

    Cameron (2001), p. 32.

  45. 45.

    Pammolli et al. (2011), p. 429 (noting that ‘measuring research inputs and outputs for pharmaceuticals is difficult, as the innovation process builds on multiple and heterogeneous sources of knowledge, involves significant knowledge spillovers and lasts several years’).

  46. 46.

    Given that the information gained through data analysis and its value are context-dependent, assessing the value of data prior to its analysis might be ‘almost impossible’. OECD (2015), p. 186.

  47. 47.

    Niehans (2018), p. 13782.

  48. 48.

    Shavell (2004), p. 84.

  49. 49.

    Above (nn 22–27).

  50. 50.

    Heller (1998), p. 640.

  51. 51.

    Parisi et al. (2004), p. 184; Heller and Eisenberg (1998), p. 700 ff; Walsh et al. (2003), p. 314 ff (reporting that transaction costs related to licensing for patents on upstream research tools can be substantial).

  52. 52.

    Buchanan and Yoon (2000), p. 4.

  53. 53.

    On the costs of data de-identification, see Institute of Medicine of the National Academies (2015), p. 68.

  54. 54.

    Exploratory data analysis often requires data aggregation. See Stewart and Tierney (2002), p. 91 (noting that ‘[a] key but time-consuming aspect of IPD meta-analysis is contacting trialists and persuading them to participate and provide data’).

  55. 55.

    See e.g. Heller and Eisenberg (1998), p. 699 (stating that ‘the use of RTLAs [effectively] gives each upstream patent owner a continuing right to be present at the bargaining table as a research project moves downstream toward product development’); Mueller (2001), p. 57 (observing that ‘[t]he royalty stacking problem in biotechnology […] has escalated in severity’).

  56. 56.

    Heller and Eisenberg (1998), p. 699.

  57. 57.

    Ibid.

  58. 58.

    CSDR standard contract template for clinical trial data sharing (10 Apr 2017), para 1.7. https://www.clinicalstudydatarequest.com/Documents/CSDR%20DATA%20SHARING%20AGREEMENT%20Version%201%204.10.2017.pdf. Accessed 26 Mar 2021.

  59. 59.

    Ibid para 4.2 (emphasis added).

  60. 60.

    Nadel (2003), p. 216.

  61. 61.

    Heller and Eisenberg (1998), p. 701.

  62. 62.

    Heller (1998), p. 625. Further, he observes that ‘[e]ven in a world without transaction costs, people would not necessarily bargain to put the anticommons resource to a unique use’. ibid pp. 673–674.

  63. 63.

    See Chap. 6 at 6.3.2.

  64. 64.

    Arrow (1962), pp. 614–616.

  65. 65.

    Advice to the European Medicines Agency from the clinical trial advisory group on good analysis practice (CTAG4)—final advice (20 Mar 2013) https://www.ema.europa.eu/documents/other/ctag4-advice-european-medicines-agency-clinical-trial-advisory-group-good-analysis-practice-final_en.pdf. Accessed 26 Mar 2021. See also EFSPI (25 Apr 2013) European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) position on European Medicines Agency (EMA) access to clinical trial data initiative, p. 6 (pointing out that IPD analysis requires ‘advanced statistical expertise’). https://www.efspi.org/documents/publications/efspipositiononema250413.pdf. Accessed 26 Mar 2021. See also Skovlund (2009), p. 260 (noting that ‘[s]tatistics is essential to the design of clinical trials and the interpretation of results’).

  66. 66.

    See CIOMS (2005), p. 49 (wondering: ‘How should access by the public to such complicated data be arranged? How can they, or even healthcare professionals, evaluate the quality of the study and interpret the statistics provided?’).

  67. 67.

    Reg 536/2014/EU, art 37(4).

  68. 68.

    Annex A to this study.

  69. 69.

    See Case C-513/16 EMA v PTC Therapeutics International [2018] ECLI:EU:C:2017:148, para 139 (holding that the President of the General Court did not err in law […] in finding that the public interest in transparency was sufficiently satisfied […] by the publication of the summary of the characteristics of Translarna, the patient information leaflet and the [European Public Assessment Report]’ (emphasis added)).

  70. 70.

    Nevitt et al. (2017).

  71. 71.

    Institute of Medicine of the National Academies (2015), p. 6; Yang et al. (2009), p. 151.

  72. 72.

    Strom et al. (2016); Navar et al. (2016), p. 1284.

  73. 73.

    Koenig (2015), p. 17.

  74. 74.

    See Institute of Medicine of the National Academies (2015), p. 33.

  75. 75.

    Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC (30 Dec 2006) OJ L 396 [hereinafter REACH Regulation].

  76. 76.

    Data sharing obligations under the REACH Regulation do not apply to substances used in medicinal products for human or veterinary use and regulated under Directive 2001/82/EC and Regulation 726/2004/EC. REACH Reg, art 2(5)(a).

  77. 77.

    REACH Reg, art 26(1).

  78. 78.

    REACH Reg, rec 49, art 27(1)(a).

  79. 79.

    REACH Reg, rec 50, 51, arts 27 and 30(2). Furthermore, payment procedures are detailed under Commission Implementing Regulation 2016/9/EU of 5 January 2016 on joint submission of data and data-sharing in accordance with Regulation (EC) No 1907/2006 of the European Parliament and of the Council concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) and the Commission Regulation (EC) No 340/2008 of 16 April 2008 on the fees and charges payable to the European Chemicals Agency pursuant to the REACH Regulation.

  80. 80.

    REACH Reg, rec 51.

  81. 81.

    REACH Reg, art 27 (3).

  82. 82.

    Dir 2001/83/EC, rec 10; Dir 87/21/EEC, rec 4. See also Case C-368/96 The Queen v The Licensing Authority [1998] ECLI:EU:C:1998:583, paras 69–71.

  83. 83.

    ‘Unjustified’ is an important qualifier as the concept of research reproducibility presupposes repetition of an experiment. In many cases, the distinction between wastefully duplicative research and research directed at clarifying genuine uncertainties might not be clear. Kim and Hasford (2020).

  84. 84.

    REACH Reg, rec 49, art 25(1); Dir 2010/63/EU, rec 31 (stating that animal welfare ‘should be given the highest priority in the context of animal keeping, breeding and use’).

  85. 85.

    See e.g. OECD (12 May 1981) Final decision of the Council amending the decision concerning the mutual acceptance of data in the assessment of chemicals. C(81)30; OECD (10 Feb 1989) Final decision-recommendation on compliance with principles of good laboratory practice. C(89)87.

  86. 86.

    Dir 2010/63/EU, rec 13, 16, 42; arts 4, 13. Moreover, the compliance with the principles of good laboratory practice established by the OECD when testing chemical products is regulated under Directive 2004/10/EC of the European Parliament and of the Council of 11 February 2004 on the harmonisation of laws, regulations and administrative provisions relating to the application of the principles of good laboratory practice and the verification of their applications for tests on chemical substances. The principle of replacement and reduction of animal testing is reiterated under Regulation (EU) 2017/746 of the European Parliament and of the Council of 5 April 2017 on in vitro diagnostic medical devices (rec 74) and Regulation (EC) No 1107/2009 of the European Parliament and of the Council of 21 October 2009 concerning the placing of plant protection products on the market (rec 40, arts 61–62).

  87. 87.

    On the abridged procedure for drug marketing authorisation, see Chap. 4 at 4.2.4.1.

  88. 88.

    See above (n 69) and the accompanying text.

  89. 89.

    By providing for the right to obtain fair compensation from test data users, the legislator intends to ‘strengthen the competitiveness of Community industry’ and ‘respect the legitimate property rights of those generating testing data’. REACH Regulation, rec 51-52.

  90. 90.

    See Chap. 5 at 5.4.1.2.

  91. 91.

    Above (n 148).

  92. 92.

    REACH Reg, rec 49; art 26(3); Reg 1107/2009/EC, rec 40.

  93. 93.

    On the referential use of test data for the generic drug approval, see Chap. 4 at 4.2.4.1.

  94. 94.

    The dominance of the public interest in avoiding repetitive testing makes the right of access under the REACH Regulation unique and, thus, not generally applicable to industrial data. For a discussion of the relevance of the REACH data-sharing model for data-driven innovation, see Drexl (2017), paras 176–180.

  95. 95.

    See Chap. 4 at 4.2.4.1.

  96. 96.

    REACH Reg, arts 25 and 27.

  97. 97.

    In this regard, the statistics on the data requests under the EMA publication policy is quite curious. See EMA (16 Jul 2018) Clinical data publication (Policy 0070) report Oct 2016-Oct 2017. EMA/630246/2017, p. 1 (reporting that during the first year of implementing the EMA’s publication policy, the released data comprised over 80,000 document downloads for non-commercial research purposes).

  98. 98.

    Foray (2004), p. 51 (with further references).

  99. 99.

    As discussed in Chap. 3.

  100. 100.

    See above at Sect. 9.3.2.2.

  101. 101.

    Above at Sect. 9.3.2.

  102. 102.

    REACH Reg, art 27(2).

  103. 103.

    See e.g. Institute of Medicine of the National Academies (2015), pp. 164–165; Kelly (2010), p. 212 ff; Multi-Regional Clinical Trials Center at Harvard University (2014) Overview of data disclosure initiatives: current and ongoing data transparency activities in the pharmaceutical industry (proposing a ‘learned intermediary’ model of data-sharing). https://www.regulations.gov/comment/FDA-2013-N-0271-0031. Accessed 26 Mar 2021.

  104. 104.

    See Chap. 3 at 3.2.2.4.

  105. 105.

    On the importance of totality of evidence in meta-analysis, see e.g. Dias et al. (2018), p. 3; Council of Europe (2012), para 6.C.20.2.

  106. 106.

    Gustafsson et al. (2010), p. 939 ff; Institute of Medicine of the National Academies (2015), p. 212; Lauer (2010), p. 91.

  107. 107.

    European Commission, COM(2018) 233 final 8; European Commission, SWD(2018) 126 final 9.

  108. 108.

    Ibid p. 33.

  109. 109.

    European Commission, SWD(2018) 126 final p. 26.

  110. 110.

    Ibid pp. 9, 39 ff.

  111. 111.

    See e.g. Geifman et al. (2015) (noting that the quality and usability of data need to be ‘adequately addressed’ and that ‘the collaborative establishment of data standards and processes for data sharing and acquisition would greatly accelerate the progress of research based on this rich data source’); Yang et al. (2009), p. 151.

  112. 112.

    See e.g. Institute of Medicine of the National Academies (2015), p. 68.

  113. 113.

    Institute of Medicine of the National Academies (2015), p. 15 (observing that the existing data-sharing platforms ‘are not consistently discoverable, searchable, and interoperable’). See also Advice to the European Medicines Agency from the Clinical Trial Advisory Group on Clinical Trial Data Formats (CTAG2)—Final advice to EMA (30 Apr 2013). http://www.ema.europa.eu/docs/en_GB/document_library/Other/2013/04/WC500142850.pdf. Accessed 26 Mar 2021.

  114. 114.

    Above (n 7) and the accompanying text.

  115. 115.

    On the importance of secondary analysis of such data, see Chap. 3 at 3.3.4.

  116. 116.

    Reg 536/2014/EU, art 37(4), first indent.

  117. 117.

    See e.g. Nevitt et al. (2017) (pointing out the problem of ‘lost data’ in academic trials).

  118. 118.

    Reg 536/2014/EU, art 81.

  119. 119.

    See Chap. 6 at 6.5.2.1.

  120. 120.

    See European Data Protection Board (23 Jan 2019) Opinion 3/2019 concerning the Questions and Answers on the interplay between the Clinical Trials Regulation (CTR) and the General Data Protection Regulation (GDPR) (art. 70.1.b)), pp. 8–9 (clarifying the legal basis for the processing of personal data of clinical trial participants for the primary and secondary uses).

  121. 121.

    GDPR, rec 159.

  122. 122.

    As discussed in Chap. 8 at 8.1.4.4.3. Under the EMA publication policy 0070, data collected on exploratory objectives of a study can qualify as CCI and be deleted from CSRs. See EMA publication policy 0070, p. 19.

  123. 123.

    Institute of Medicine of the National Academies (2015), pp. 164–165; Kelly (2010), p. 212 ff; 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.

  124. 124.

    Such proposal is in line with the view of the European Ombudsman that ‘[the] only […] legitimate justification for redacting information from a clinical study report could relate to the ongoing development of new treatments or of new medicines’. European Ombudsman (8 June 2016) 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, para 72.

  125. 125.

    For an overview of the economic literature on this topic, see Rockett (2010), p. 339 ff.

  126. 126.

    Scotchmer (2004), p. 127.

  127. 127.

    Ibid.

  128. 128.

    Ibid.

  129. 129.

    Ibid.

  130. 130.

    Above at Sect. 9.3.2.1 in this chapter.

  131. 131.

    Above at Sect. 9.4.1) in this chapter.

  132. 132.

    Institute of Medicine of the National Academies (2015), pp. 131, 143–144; EFSPI (n 65), pp. 3–4; Rathi et al. (2012).

  133. 133.

    See e.g. Fletcher et al. (2013), p. 335 (explaining that ‘there could be many reasons for the results not completely matching the results generated by the owners of the data’; ‘the data sets will generally have complex data structures which a requester may not fully understand which could lead to an incorrect re-analysis; specific variables may be unavailable due to anonymising the data sets; and the requester will not have access to the computer software/code used to generate the analyses’).

  134. 134.

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

  135. 135.

    EFSPI (n 65), p. 7.

  136. 136.

    Ibid.

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Kim, D. (2021). Evaluating Legislative Options. 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_9

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